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This study filled some information gaps on the mineral constituents of C. cibarius foraged in Poland and in Yunnan, China. Provided were for first time and discussed data on As, Li, V, Tl, and U in chanterelles from Poland and on Ba, Co, Cr, Ni, Rb, and Sr in chanterelles from China. The results obtained suggest a strong influence of the collection site on the occurrence of As, Ba, Cr, Li, Mn, Pb, Rb, Sr, U, and V in fruit bodies. These elements were found at higher values in Cantharellus mushrooms from polymetallic soils of Yunnan in China, but lower values were measured for Cs, Mn, and Rb, when compared to fruit bodies from Poland.

review

The human microbiome represents a diverse ecosystem of microbes housed in the human body. Microbial cells outnumber the cells in the human body by a factor of 10 and microbial genes out number human genes by a factor of over 100 (1–3). There is a particular focus on the enteric (gut) microbiota since it represents about 99% of the human microbiome (4). The importance of the enteric microbiome in relation to human health and disease has been recognized since it appears to influence the immune system (5), metabolic processes (6), gene expression (7, 8), the nervous system (9, 10), and behavior (9, 10). Disruption of the enteric microbiome has been implicated in a wide range of human diseases including depression and anxiety (11), gastrointestinal disorders (12), inflammatory airway disease (13), diabetes (14–16), obesity (17, 18), atopic disease (5), and neurodegenerative conditions (19). The enteric microbiome may be particularly important early in life around the time of birth as it has been linked to early brain development and behavior (9, 10, 20) and disruption and/or treatments (i.e. early antibiotics) early in life can influence the development of childhood diseases, particularly atopic disease (9, 10).

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The mechanism in which the enteric microbiome modulates particular effects on the host is not completely clear, although several mediators are potential vehicles for such influence. Such mediators include lipopolysaccharides, peptidoglycans, short-chain fatty acids (SCFAs), neurotransmitters and gaseous molecules (21–23). We are particularly interested in SCFAs because of their role as both mediators of physiology and mitochondrial fuels. SCFA are particularly intriguing as they are derived as a consequence of fermenting carbohydrates and some proteins, and also present naturally or as an additive in many foods, in particular wheat and dairy. Thus, dietary variations can have a larger influence on their production (19, 24, 25). Of the SCFAs, propionic acid (PPA) has been of key interest because it has several links to autism spectrum disorder (ASD), a disorder which affects as many as ~2% of children in the United States. What is intriguing about ASD is that the etiology is largely unknown but is strongly influenced by both genetic and environmental factors (26, 27).

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The enteric microbiome is a major environmental factor that may contribute to the etiology of ASD (2, 9, 10, 28). First, several factors which may have a direct effect on health through disruption of the microbiome are associated with increased risk of developing ASD, including dietary alteration, environmental exposures that disrupt enteric microbiome bacteria content and diversity, being born by C-section delivery which reduces maternal transfer of enteric and vaginal bacteria, increased antibiotic use which can destroy key bacteria in the enteric microbiome, formula feeding and early hospitalization (2, 9, 28). Second, specific bacteria, such as Clostridia spp., a major SCFA producer, have been repeatedly reported to be overrepresented in the ASD microbiome (29, 30). Third, exposure to PPA has been demonstrated in several animal models to result in the development of ASD-like behaviors and physiological changes to the brain similar to those found in ASD are seen in adult rats acutely exposed to PPA (24, 25, 31) and in juvenile rats systematically exposed to PPA pre- and postnatally (32–34).

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Although the mechanism by which PPA influences host function is still unclear, data from the animal model of PPA induced ASD demonstrates neuroinflammation and electrophysiological disturbances as well as disruptions in lipid, mitochondrial and redox metabolism (24, 25, 31). We have performed a series of studies to demonstrate that changes in mitochondrial metabolism similar to those found in the animal model exposed to PPA are also found in humans. For example, we found that the unique pattern of biomarkers of mitochondrial dysfunction found in the PPA rodent model was also found in a subset of children with ASD (28, 35, 36). We also demonstrated that PPA modulates mitochondrial respiration in lymphoblastoid cell lines (LCLs) derived from children with ASD differently than LCLs derived from age and gender matched typically developing control LCLs (37).

other

Each group of LCLs were cultured with PPA 1 mM for 24 or 48 h or left untreated (0 mM). This concentration was selected because it provided optimal metabolic activation in our previous studies (37). The sodium propionate was buffered with sodium bicarbonate in the culture medium to prevent changes in pH which could cause changes in influx of PPA (47). As PPA is mostly disassociated at physiological pH, the effects of the PPA treatment are most likely a combination of both PPA and propionate.

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Total RNA samples from each LCL group were pooled together and after DNase treatment and purified using RNeasy Mini Kit (Qiagen Sciences, MD, USA) as described in our previous studies (48). The cDNA synthesis and microarray analyses were performed at Keck Affymetrix GeneChip Resource at Yale, New Haven, CT, USA (NIH Neuroscience Microarray Consortium) as previously described (48).

other

PPA also could induce changes in host physiology through modulation of the immune system. The animal models of PPA induced ASD behavior demonstrates neuroinflammation but inflammatory mediators induced by PPA in human ASD cells has not been investigated. In this study, we investigate whether PPA can differentially regulate immune genes using our LCL model of ASD. We have developed a cell line model of ASD in which LCLs derived from individuals with autistic disorder (AD) are classified into two groups: those with normal mitochondrial function (AD-N) and those with atypical mitochondrial function (AD-A) (38–40). The AD-A LCLs have respiratory rates approximately twice that of control and AD-N LCLs and are very sensitive to in vitro increases in reactive oxygen species (ROS) (38–40). We recently demonstrated that this atypical increase in mitochondrial function characteristic of AD-A LCLs was associated with more severe repetitive behaviors in the children from which these LCLs were derived (40). In this way, we believe that the AD-A LCLs may represent a more severe ASD phenotype. Given the connection between metabolism and immune system (41), we hypothesize that the AD-A LCLs will demonstrate a greater activation of immune genes with PPA exposure as compared to the control and AD-N LCLs.

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Lymphoblastoid cell lines were derived from white males diagnosed with AD chosen from pedigrees with at least other 1 affected male sibling (i.e., multiplex family) [mean (SD) age 7.3 (3.5) years]. These LCLs were obtained from the Autism Genetic Resource Exchange (Los Angeles, CA, USA) or the National Institutes of Mental Health (Bethesda, MD, USA) center for collaborative genomic studies on mental disorders. In our previous studies (37, 39, 40, 42–44), these LCLs where categorized into two different types of AD LCLs; ones with atypical mitochondrial respiration (AD-A) and those with normal respiration (AD-N). These metabolic groupings have been shown to be consistent and repeatable in our previous studies (37, 39, 40, 42–44). Eight pairs of AD-N and AD-A LCLs were age and gender matched to control LCLs. The sample size chosen was based on our previous studies. Control (CNT) LCLs were derived from healthy white male donors with no documented behavioral or neurological disorder and with no first degree relative suffering from any medical disorder that might involve mitochondrial dysfunction [mean (SD) age 7.5 (3.3) years]. CNT LCLs were obtained from Coriell Cell Repository (Camden, NJ, USA). Due to low availability of CNT LCLs which fit our criteria, a single CNT LCL line was paired with two AD LCL lines in one case (see Table 1). Also two AD-A LCLs were paired twice with AD-N LCLs. On average, cells were studied at passage 12, with a maximum passage of 15. Genomic stability is very high at this low passage number (45, 46). Cells were maintained in RPMI 1640 culture medium with 15% FBS and 1% penicillin/streptomycin (Invitrogen, Grand Island, NY, USA) in a humidified incubator at 37°C with 5% CO2.

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Analysis of variance was conducted between the exposure conditions and different cell types. Genes showing expression of at least ≥2.0-fold were exported for functional annotation to several pathway analysis packages including Ingenuity Pathway Analysis (IPA) and Panther software. For the initial comparison of the effect of PPA for each exposure time on a particular LCL type, the statistical significance of the comparison was not considered as there was only an N of 1 for each example. When the ASD LCL types were compared to controls, the two PPA exposure times were combined and the genes selected not only showed a difference in expression of at least ≥2.0-fold but also a p < 0.05.

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The CNT LCLs demonstrated no upregulation or downregulation of known genes with 24 h PPA exposure and only one gene upregulated and downregulated with 48 h PPA exposure. Only the downregulated gene was associated with immune function. Panther analysis demonstrated no overrepresentation of immune genes associated with PPA exposure in CNT LCLs.

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Exposure of AD-N LCLs to PPA for 24 h demonstrated no upregulated genes and downregulation of several immune genes including two major histocompatibility complex genes. Exposure of AD-N LCLs to PPA for 48 h demonstrated upregulation of two microRNA genes not known to be involved in immune function and downregulation of the gene for complement C4B. Panther analysis demonstrated overrepresentation of genes associated with major histocompatibility complex antigen with 24 h PPA exposure in AD-N LCLs (see Table 2).

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Exposure of AD-A LCLs to PPA for 24 or 48 h demonstrated upregulation of several genes related to immune function, particularly several genes associated with immunoglobulin production and one gene related to activation of proinflammatory caspases. Downregulation of the gene for complement C4B was found for 24 h exposure and no genes were downregulated for 48 h exposure. Panther analysis demonstrated overrepresentation of many immune processes and proteins as result of PPA exposure to AD-A LCLs for 24 and 48 h, demonstrating that PPA did significantly activate immune processes for AD-A LCLs (Table 2).

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To better understand how PPA exposure affects ASD LCLs differently than control LCLs, gene expression was compared between CNT LCLs and each ASD LCL group independently. Both the 24- and 48-h PPA exposure data was combined since the previous analysis demonstrated little difference between the changes in gene expression with these two different exposure durations. Table S2 in Supplementary Material outlines the genes that were upregulated or downregulated with PPA exposure for each ASD LCL group as compared to CNT LCLs. Table 3 demonstrates the biological processes identified by the differential gene expression for AD-N and AD-A LCLs as compared to CNT LCLs. The major processes identified are also represented in Figure 1. Biological process was the only Panther analysis used as it was the most robust for representing the difference in pathway activation.

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This analysis suggests that both the AD-N and AD-A LCLs demonstrate change in immune genes as compared to CNT LCLs. Both AD-A and AD-N LCLs demonstrate an upregulation in genes associated with immunoglobulin production and adaptive immune responses without any downregulation in genes involved in these processes. AD-A LCLs demonstrate both upregulation and downregulation of genes involved in a wider variety of immune responses as compared to AD-N LCLs, including phagocytosis, complement system activation, B cell regulation, and B cell receptors. This suggests that AD-A LCLs may have a wider network of immune genes activated as compared to AD-N LCLs as well as CNT LCLs.

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Table 4 represents the top canonical pathways (p < 0.01) identified by IPA for the comparison between the AD-A and CNT LCLs. As we see, many of these processes are involved in immune activation and immune disorders. IPA also identified the top upstream regulators as RUNX3, ONECUT1, SNAI2, STAT5A, and TCF7. Interestingly, as will be discussed below, these genes are regulatory of both developmental and immune processes.

other

In this study, we examined the effect of PPA, a SCFA produced by enteric bacteria that are overrepresented in the ASD gut, on transformed B cells (LCLs) derived from children with ASD as well as controls. We examined two types of LCLs derived from children with ASD, those with mitochondrial dysfunction (AD-A) and those found to have mitochondrial function similar to controls (AD-N). We hypothesized that PPA would activate immune pathways in ASD LCLs since the PPA animal model of ASD demonstrates neuroinflammation and immune activation, including increased GFAP immunoreactivity in the hippocampus, increased activation of microglia, and increased interleukin (IL)-6 (24, 25, 31). We further hypothesized that the AD-A LCLs would have a greater enhancement of immune pathways since this is a more severe ASD phenotype and since optimal mitochondrial function is required for appropriate immune function and response (41).

other

Exposure to PPA for either 24 or 48 h resulted in upregulation in genes associated with immune system activation in AD-A LCLs, particularly genes involved in immunoglobulin production. This effect was not seen in CNT or AD-N LCLs. In fact, there was a decrease in major histocompatibility complex antigen genes in AD-N LCLs exposed to PPA for 24 h. We then compared the effect of PPA on ASD LCLs as compared to the effect of PPA on CNT LCLs. We found that both the AD-N and AD-A LCLs demonstrated changes in gene expression as compared to the control LCLs with a significant change in genes related to immune pathways almost exclusively. Although the AD-N LCLs demonstrated activation of immune pathways, the AD-A LCLs demonstrated a wider range of genes and processes involved in immune pathways. In addition, IPA analysis of AD-A LCL gene expression changes identified canonical pathways almost exclusively related to immune function.

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Several of the genes identified by the IPA analysis are involved in regulation of the immune system and may be linked to ASD. Several genes are linked to regulation of T cells. TCF7 is a T lymphocyte-specific enhancer of the CD3-Epsilon T cell antigen receptor complex. Interestingly TCF7 expression may be regulated by beta-catenin (49). This is intriguing since beta-catenin has been shown to be dysregulated in an animal model of ASD (50). STAT5 is induced in response to T cell activation with cytokines, most notably IL-2, and is believed to be involved in the effect of IL-2 in the immune response and may be involved in the suppression of IL-3 production. This is interesting as IL-2 is produced by neurons and astrocytes, is important in brain development and normal brain physiology and has been implicated in neurodegenerative disease, cognitive dysfunction and has been linked to ASD (51). RUNX3 is also important in immune system function as well as neuronal development. RUNX3 is essential during thymopoiesis where it modulates the development of CD8 T cells, thus having an important role in immune system development through lineage specification (52). Interestingly, RUNX3 is involved in the TNF-beta signaling cascade (53), a cytokine whose dysregulation has been correlated with ASD severity (51). RUNX3 appears to have an important role in the development of proprioceptive afferent neurons in mice, resulting in ataxia (54), a neurological finding that is not uncommon in ASD. Other genes identified are related to B cell function. SNAI2 is an evolutionarily conserved zinc finger transcription factor which plays an important role in prenatal fetal development, most notably the development of neural crest-derived cells and adipocytes (55). SNAI2 is also involved in regulation of B cells and can promote the aberrant survival and malignant transformation of mammalian pro-B cells otherwise slated for apoptotic death (56) and has antiapoptotic effects (57).

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In conclusion, ASD is being recognized as having a very strong immune component to its etiology (58). Several models of ASD demonstrate immune dysregulation, including prenatal exposure to immune challenges (59, 60). In fact two animal models have been developed to parallel prenatal exposure to autoantibodies (61), including fetal brain antibodies (62) and antibodies to the folate transporter (63, 64). The microbiome is being recognized as important in the etiology of neurodevelopmental disorders (9, 10), potentially through modulation of the immune system (65) through enteric metabolites (65) including SCFAs like PPA (24, 25, 31). It is important to note the effects of SCFA on gene expression and inflammation are complex, and include histone deacetylase activity, activation of free fatty acid G-coupled receptor and mitochondrial inflammatory signaling cascades, which may or may not be mutually reinforcing. Furthermore, we do not yet know if the effects found in our LCL model also occur in patients, as many effects of SCFA, in particular PPA and butyrate, are dose and tissue dependent, and have different effects at key developmental time periods (9, 10, 24, 31, 48, 66, 67). Nonetheless, this study provides insight into the mechanism in which the microbiome may influence the immune system to result in disease and demonstrates the predisposition of certain cells to be sensitive to microbiome metabolites. It also may lead to further reevaluation of the widespread use of PPA in agriculture and the food industry (24, 31). Certainly, further research is needed in this area to better define the role of the microbiome and microbial metabolites in immune modulation and disease.

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The conception and design of the work was agreed upon by all authors as was the drafting and final approval of the manuscript. BN, SR, and SB were involved in laboratory analysis. SB was involved in data analysis. RF, DM, SB, SR, and SB were involved in interpretation of data.

clinical case

Zygomycosis refers to infectious disease variants caused by Mucorales fungi species . It may occur in progressive underlying risk profile patients such as immunosuppression, diabetic ketoacidosis, malignancies, extreme malnutrition, any conditions led to iron overload, burns, and trauma. Zygomycosis has a life-threatening nature with multi-organ involvements such as cutaneous, pulmonary, and even neurologic invasions . Thus, proper management and treatment of this multi-infection needs early detection of underlying risk factors along with medical and even surgical aggressive therapies. However, misdiagnosing identifiable risk factors is now a major problematic issue to improve disease progression and outcome [3, 4]. Moreover, because of identified different routes of infection such as inhalation of conidia, gastrointestinal ingestion (among malnourished patients), non-sterile tape and contaminated wooden splints, traumatic inoculation, and even via natural disasters [5–9], the problem is compounded for disease control and treatment. Descriptions of zygomycosis appear to be increasing, perhaps given higher numbers of persons at risk . Epidemiologically, the infection by zygomycosis has a worldwide distribution with no known gender, age, or interracial preferences; however some institutional studies have shown a gender tendency with a male-to-female ratio of 3:1 . Regarding disease prognosis, the presence of some serious comorbidities and needing aggressive coordinated treatment strategies may potentially affect prognosis, however despite all efforts to identify disease risk factors as well as to control disease progress, zygomycosis may carry a mortality rate of 50-85% mostly due to its serious consequences including rhinocerebral and pulmonary events [12–14]. Unfortunately, no comprehensive reports have been published on epidemiological status of mucormycosis infections and its outcome in our population from 2007, Hence, the current study came to address epidemiological characteristics as well as clinical outcome of patients with mucormycosis infection referred to a referral hospital in Iran.

other

This retrospective study was performed at the Rasoul-e-Akram hospital, an 800-bed tertiary care teaching hospital in Tehran, Iran. The pathology recorded charts were reviewed to identify all cases of zygomycosis from patients admitted between April 2007 and March 2014. Patients were included in the study if they met the criteria for proven invasive Rhinocerebral zygomycosis based on the revised definitions of invasive fungal disease of the European Organization for Research and Treatment of Cancer/Mycosis Study Group (EORTC/MSG) . In total, 91 patients were identified that among them, 16 were excluded because of discharge against medical advice. The cases that were diagnosed on an outpatient basis or on the day of surgery and were not admitted to either hospital were also excluded. Data was collected on patients´ demographics, underlying conditions, concomitant immunosuppressive medications, laboratory data, radiologic findings, clinical features, antifungal treatment, surgical procedures, and outcomes. A diagnosis of Rhinocerebral zygomycosis was based on histopathological demonstration of broad, ribbon-like, wide-angled branching, non-septate hyphae even in the absence of positive cultures, and accompanying tissue invasion by fungal hyphae [16, 17]. The zygomycosis genus was determined by morphological examination of conidia, hyphae, and whole colonies. The study endpoint was first to assess in-hospital mortality and length of hospital stay and second was to determine main predictors of mortality and prolonged hospital stay in the patients. Results were presented as mean ± standard deviation (SD) for quantitative variables and were summarized by frequency (percentage) for categorical variables. Continuous variables were compared using t test or Mann-Whitney U test whenever the data did not appear to have normal distribution or when the assumption of equal variances was violated across the study groups. Categorical variables were, on the other hand, compared using chi-square test. The multivariable regression models were applied to determine indicators of disease outcome. For the statistical analysis, the statistical software SPSS version 16.0 for windows (SPSS Inc., Chicago, IL) was used. P values of 0.05 or less were considered statistically significant.

other

We saw an early mortality of 35% for affected patients with Rhinocerebral zygomycosis that seems to be considerably high despite performing curative management in our center. Naturally, the early and long-term mortality following Rhinocerebral zygomycosis is strongly associated with the presence of underlying comorbidities. It has been shown that the overall mortality of pulmonary mucormycosis is approximately 50 to 70% [32, 33]. Cutaneous and subcutaneous disease may lead to necrotizing fasciitis, which has a mortality approaching 80% [34, 35]. The mortality associated with dissemination to the brain approaches 100% . In this regard, managing disease by proper antifungal agents leads to considerably decrease in mortality. It has been indicated that in cases with rhinocerebral mucormycosis, the mortality is about 70% in cases treated with antifungal agents alone versus 14% in cases treated with antifungal agents plus surgery [37, 38]. Similarly, in a combined series of rhinocerebral, cutaneous, and pulmonary mucormycosis, 65% of patients treated with surgery plus antifungal agents survived the infection, compared to none of the patients treated with antifungal agents alone . Thus, the death rate is directly associated with whether the patients were managed medically or surgically. In our study, regardless of the type of comorbidity or treatment approach, high early mortality was reported following Rhinocerebral zygomycosis infection. However, as shown in our multivariable analysis, administrating antifungal treatment by high dose amphotericin, not only reduced in-hospital mortality, but also shortened hospital stay. However, it seems that the rate obtained for early mortality is notably high requiring deep assessment of its causes among our population.

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Two underlying conditions including history of cancer and presence of neutropenia can adversely affect outcome of Rhinocerebral zygomycosis as shown in our analysis. It has been clearly shown that the overall mortality rate for Rhinocerebral zygomycosis remains higher than 50%, and it approaches 100% among patients with persistent neutropenia [32, 40]. In fact, the neutrophils are critical for inhibiting fungal spore proliferation and thus decrease in neutrophil count can predispose the patients to progressive Rhinocerebral zygomycosis. The risky role of neutropenia is more prominent in patients with malignancies especially hematological cancers with immunosuppressive patterns.

study

In summary, comparing our results with data previously reported in the literature shows similarities in demographic distribution of Rhinocerebral zygomycosis in our population. Among common comorbidities, the presence of diabetes mellitus is closely associated with the presence of this infection because more than two-third of affected patients with this infection are diabetics. Sinus involvement is very common in those with Rhinocerebral zygomycosis even in early stages of disease leading to high mortality and morbidity in the patients needing invasive surgical treatments. Besides female gender, advanced age, and presence of neutropenia (an indicator for immunosuppression) act as a major risk factor for increasing early mortality, the use of antifungal treatment such as high dose amphotericin can prevent both mortality and prolonged hospital stay. The cancer patients may need longer hospital stay because of needing comprehensive in-hospital treatments.

study

Although decades of research have found negative consequences of HIV/AIDS (e.g. Israelski et al., 2007; King, 1993; Leserman, 2008; Safren et al., 2003; Rzeszutek et al., 2012, 2015), other studies have highlighted also the positive consequences of HIV infection, i.e. the occurrence of posttraumatic growth (PTG) (e.g. Milam, 2004, 2006; Murphy & Hevey, 2013; Sherr et al., 2011). There are several terms used to describe positive changes following traumatic events, such as benefit finding (Danoff-Burg and Revenson, 2005), stress-related growth (Siegel et al., 2005), thriving/flourishing (Sirois and Hirsch, 2013) or adversarial growth (McBride et al., 2009). In this study, however, we focused on posttraumatic growth’s definition by Tedeschi and Callhoun (1996, 2004), according to which PTG is defined as the set of positive changes in relations with others, self-perception and existential beliefs, in the form of greater appreciation of life and openness to spirituality, which can result from attempts at dealing with a traumatic or highly stressful life event. Particularly, people after these kinds of adverse life events may establish more satisfying relations with other people, start to recognize their strength in achieving new life goals and change their basic life values, which manifests by a shattering of their prior worldview.

other

In total, 64 patients with Rhinocerebral zygomycosis were assessed. The mean age of the patients was 46.07 ± 22.59 years (ranged 4 to 87 years) from which 51.6% were female. The mean age of affected men was similar to diseased women (45.89 ± 26.36 years versus 46.24 ± 18.80 years, p = 0.950). The overall prevalence of malignant conditions was 29.7%, 67.2% were diabetic, and 26.6% were hypertensive. The average dose of amphotericin in administered patients was 2135.64 ± 1870.91 mg. Different sinuses were infected in 73.4%, 26.6% underwent surgical procedures once and 56.2% underwent these procedures more than once, and 17.2% were controlled medically (Figure 1). Functional endoscopic sinus surgery (FESS) was also done on 53.1% of patients. Eye enucleating was considered for 18.8% of cases. Regarding diagnostic approaches, pathological assessment was planned for 85.9% and 25.0% were also assessed by endoscopy. Extensive debridement was carried out in 40.6% as a surgical procedure. The mean WBC count of the patients was 7947.60 ± 5446.86 cells per microliter (µl) of blood that neutropenia (<1500 cell/ µl) was revealed in 12.5%. In total, in-hospital mortality rate was 35.9%. The mean length of hospital stay was 26.94 ± 24.70 days that prolonged hospital stay (> 14 days) was found in 60.9% of the patients. According to the Multivariable logistic regression analysis (Table 1), the main predictors of in-hospital mortality included female gender (OR = 5.263, P = 0.002), advanced age (OR = 1.063, P = 0.001), the presence of sinus infection (OR = 4.836, P = 0.018), and neutropenia (OR = 31.250, P = 0.003), while higher dosages of amphotericin deoxycholate administered had a protective role in preventing early mortality (OR = 0.825, P = 0.025). In a similar Multivariate model (Table 2), history of cancer could predict prolonged hospital stay (OR = 8.413, P = 0.043), whereas using higher dose of amphotericin could lead to shortening length of hospital stay (OR = 0.998, P < 0.001).

review

As the first results and regarding demographic characteristics of our patients who suffered Rhinocerebral zygomycosis, we showed the prevalence of disease in an age wide spectrum from childhood to old adulthood. Also, men and women were similarly affected by this infection. Among underlying risk profiles, high prevalence of diabetes mellitus was revealed among patients that was shown in about two-third of them. Epidemiological studies showed high prevalence of diabetes mellitus in the patients in both developed and developing nations, however some clear differences were also revealed in the epidemiological aspects of disease between the nations that in developed countries, the disease remains uncommon and mostly occur in patients with diabetes mellitus and hematological malignancies, while in developing countries, zygormycosis has a sporadic pattern closely related to uncontrolled diabetes or trauma [18–20]. It seems that the relationship between high incidence rate of zygomycosis and diabetes is mainly influenced by uncontrolled situation of diabetes or occurring ketoacidosis as a serious predisposing factor for this infection. In total, reviewing the literature demonstrated the risky role of diabetes mellitus in 36% to 88% of patients [20–25]. In contrast, controlling diabetes, proper management of ketoacidosis and the use of statins as a main treatment protocols for metabolic syndrome could effectively reduce the incidence of zygomycosis [26–28]. Sinuses involvement is a common clinical manifestations affecting disease poor prognosis. This complication commonly appears as rhinocerebral mucormycosis, that is manifested by either sinusitis or periorbital cellulitis . If untreated, sinusitis can spread from the sinuses and extend into the neighboring tissues such as orbit or orbital muscular complex leading to potential visual complications or may extend into the mouth and produce painful, necrotic ulcerations of the hard palate . Another serious complication can be the spreading infection from sinus posteriorly to central nervous system and leading to cerebral vascular invasion and cerebral infarction, a serious cause for increasing disease-related mortality . In our observation, 73.4% of patients suffered sinuses infections that mostly underwent invasive surgical procedure to prevent its progression. More importantly, the presence of sinusitis has been identified as an important predicting factor for early mortality in these patients leading to4.8 times more risk for mortality.

study

Regarding people living with HIV (PLWH) and PTG, Milam (2004) in a longitudinal study (n1 = 835; n2 = 435) observed that 59% of HIV infected individuals reported some positive changes in the form of particular PTG dimensions, and these positive changes were negatively related to the level of depression and the intensity of substance use. Other advantages associated with PTG among PLWH were also found, including increased adherence to treatment and improvement of the immune system (Milam, 2006), as well as better psychological well-being and lower level of hospitalizations (Siegel et al., 2005). However, the majority of previous studies on PTG in PLWH were conducted in a cross-sectional framework and concentrated mainly on documenting particular PTG dimensions in this patient group and relating them to sociodemographic data, health status or HIV-related stigma (Murphy & Hevey, 2013; Sherr et al., 2011). Thus, knowledge about the psychological factors that might promote or hinder PTG in this sample is relatively scarce. In this study, we investigated the intensity of PTG and its association with the level of social support, stress coping strategies and resilience among a sample of PLWH in a 1 year longitudinal study.

other

According to Tedeschi and Calhoun (2004), social support received from close family and friends etc. (see, “supportive others”, Tedeschi & Calhoun, 2004, p. 8) helps people after traumatic events to express negative emotions and fosters cognitive processing (i.e. it mobilizes ruminative activity regarding the trauma, which is crucial in facilitating PTG). The degree of perceived support and the need for support, displayed in the intensity of support seeking, can also facilitate the use of more adaptive stress coping strategies (Tedeschi & Calhoun, 2004). Nevertheless, studies on the link between social support and PTG, especially in the aftermath of chronic illness, are inconclusive. Several authors have observed that social support may enhance PTG among cancer patients (Karanci & Erkam, 2007), rheumatoid arthritis patients (Dirik & Karanci, 2008) or stem cell transplant survivors (Nenova, 2013). However, Sheik (2004) found no association between social support and PTG among cardiac patients. In regard to PLWH, Cieślak et al. (2009) studied HIV infected survivors of Hurricane Katrina and found that received social support was positively linked only to one PTG subscale: relating to others. In addition, Wei et al. (2016) observed that perceived support mediated the link between stigma and PTG among children affected by the HIV/AIDS of their parents. Nevertheless, there is no consensus about the causal role of social support in PTG, especially among PLWH.

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The way the individual copes with the traumatic event is very important to triggering PTG (Tedeschi & Calhoun, 2004). The most common stress coping strategies that are important for PTG are meaning-focused coping strategies, especially positive reappraisal (also referred to as positive re-evaluation, see Measures), which has been shown to be a significant PTG predictor among cancer patients (Sears et al., 2003) and HIV/AIDS population (Siegel & Shrimshaw, 2005). This kind of coping means making sense of one’s life after trauma and integrating it with existing cognitive schemas about the self and the world. One of the meaning-focused coping strategies that are important for PTG is religious coping. In fact, according to a meta-analysis conducted by Prati and Pietrantoni (2009), of the many stress coping strategies, positive reappraisal and religious coping have the largest effect on PTG. Conversely, avoidance coping strategies, such as substance use hinder the probability of growth after trauma (Helgeson et al., 2006). This latter stress coping strategy is frequently used among PLWH to reduce HIV-related distress and may be related to perceived dissatisfaction with social support and low treatment adherence (Power et al., 2004). It is also worth mentioning that the effectiveness of stress coping depends not only on the characteristics of the traumatic event but also on the personal traits of the individual and the social environment (Morris et al., 2005).

other

The term resilience may be defined either as a process of successful adaptation to trauma and adversity (Bonano, 2004) or a personality trait, which refers to the degree of emotional stability after experiencing very stressful of traumatic events (Block & Kremen, 1996). In this study we concentrated on resilience as a personality trait. The link between resilience and PTG is ambiguous. Some authors observed that resilience is positively related to PTG (Bensimon, 2012; Westphal and Bonanno, 2007). Conversely, Tedeschi and Calhoun (2004) underlined that aforementioned variables may be negatively associated, as resilience acts only as a buffer that protects an individual from the negative consequences of trauma and adversity, but it does not promote PTG. In other word, whereas a resilient person usually recovers from a traumatic event without psychological disturbances, PTG means unexpected transformation, displayed in a level of functioning that is higher than before the trauma. Regarding PLWH, only Murphy and Hevey (2013) have investigated the role of resilience in PTG among this patient group, finding a positive link between these two variables in their cross-sectional study.

other

It was expected a positive relationship between the levels of received support in the first assessment and the intensity of PTG in the follow-up assessment, while controlling for the level of PTG in the first assessment. In addition, it was expected that perceived support and need for support will mediate the link between received support, stress coping and the global PTG score.

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Coping mechanisms2.It was expected a positive relationship between meaning-focused coping strategies (positive re-evaluation, return to religion), and a negative relationship between avoidance coping strategies (substance use) in the first assessment and the intensity of PTG in the follow-up assessment, while controlling for the level of PTG in the first assessment.

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It was expected a positive relationship between meaning-focused coping strategies (positive re-evaluation, return to religion), and a negative relationship between avoidance coping strategies (substance use) in the first assessment and the intensity of PTG in the follow-up assessment, while controlling for the level of PTG in the first assessment.

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A preliminary model was created, in which we expected a positive relationship between received support, resilience measured in the first and the follow-up assessment, return to religion and positive re-evaluation as meaning-focused coping strategies and the global PTG score (explained variable). Conversely, a negative relationship between substance use as an avoidance coping strategy and the global PTG score was expected. It was also hypothesised that perceived social support and need for support would act as mediators between received support, stress coping strategies (return to religion, positive re-evaluation, substance use) and the global PTG score. It was also expected that perceived social support would mediate the relationship between received support and substance use. The hypothesised model is depicted in Fig. 1.Fig. 1Hypothethised Path Diagram of the Relationship Between Social Support Dimensions, Stress Coping Strategies, Resilience as a Trait and the Intensity of the Global Posttraumatic Growth Score in HIV + Patients (n = 73)

review

This study investigated the level of PTG, as the explained variable, and its association with the levels of social support dimensions, the intensity of stress coping strategies and the level of resilience, defined as a personality trait, among a sample of HIV- infected patients in a one-year longitudinal study. Age, HIV infection duration and the presence of posttraumatic stress symptoms (PTSS) were also controlled in the study sample. Three hypotheses were formulated according to the longitudinal study framework (Cole & Maxwell, 2003):

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Social supportIt was expected a positive relationship between the levels of received support in the first assessment and the intensity of PTG in the follow-up assessment, while controlling for the level of PTG in the first assessment. In addition, it was expected that perceived support and need for support will mediate the link between received support, stress coping and the global PTG score.

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After the informed consent was obtained, the participants completed a paper-and-pencil version of the inventories and participated in the study voluntary, as there was no remuneration for the participation. The research questionnaires were distributed by the patients themselves in paper form by the authors of this study and professional interviewers to the patients in the Hospital of Infectious Diseases in Warsaw. The eligibility criteria were 18 years of age or older, a confirmed medical diagnosis of HIV infected and receiving care from the hospital where the study was conducted. The exclusion criteria encompassed HIV-related cognitive disorders, which were screened by psychiatrists working in the hospital, where the study was conducted. The research project was accepted by the Senate Ethics Committee of the University of Finance and Management in Warsaw.

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The first assessment was conducted between June and July 2015 in the Hospital of Infectious Diseases in Warsaw. From the 290 patients with a clinical diagnosis of HIV infection, eligible for the study, 110 patients (38%) were recruited for the first assessment, i.e. those patients agreed not only to fill the questionnaires, but also agreed to leave their contact details (i.e. telephone number and/or e-mail address) so the authors of this study could contact them for a one-year follow-up, and additionally indicated in the Posttraumatic Growth Inventory (see, Measures) that the diagnosis of HIV infection was a traumatic event for them. 140 patients (48%) refused to leave their contact details, 22 patients (8%) refused to fill the questionnaires and 18 patients (6%) completed the questionnaires with a level of missing data exceeding 50% of the study questionnaires, which precluded including them into the statistical analysis. Specifically, in the first assessment there were 105 men and 5 women from 19 to 76 years of age (M = 39.45; SD = 11.86). The duration of HIV infection among patients in the baseline assessment varied between 1 and 28 years (M = 7.19; SD = 6.99). The second assessment was conducted between June and July 2016, and out of the 110 patients who left contact details 1 year earlier, 73 patients agreed to participate in the second assessment; further statistical analysis was conducted on this sample. There were 68 men and 5 women from 19 to 76 years of age (M = 38.77; SD = 12.61). The duration of HIV infection among patients in the second assessment varied between 1 and 28 years (M = 6.42; SD = 6.63). The total response rate was 66% (73/110). There was no missing data in the final sample of 73 participants. There was no missing observations estimation method applied.

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Participants who refused to take part in the follow-up assessment did not differ from the sample that agreed to take part in the follow-up study with respect to age (t (108) = −.85, p > .05) or HIV infection duration (t (108) = −1.63, p > .05). However, the individuals who refused to participate in the follow-up assessment had significantly higher levels of global PTSS than those who agreed to participate in the follow-up study (t (108) = −8.09, p < .001).

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To measure the intensity of posttraumatic growth, the Posttraumatic Growth Inventory was used (PTGI; Tedeschi & Calhoun, 1996) in a Polish adaptation by Ogińska-Bulik and Juczyński (2010). It is important to underline that although the original PTGI comprises 5 specific domains of PTG (“relating to others”, “new possibilities”, “personal strength”, “spiritual change, and appreciation of life”), the Polish adaptation of PTGI measures only four domains of posttraumatic growth. Exploratory and confirmatory factor analysis revealed a four-factor structure for the PTG, including changes of perception of oneself (“perceiving new possibilities, feeling of personal strength”), changes in relationships with others (“feelings of greater connection with other people, increase in empathy, altruism”), greater appreciation for life (“changes in life philosophy and current life goals, greater appreciation for every day”), and spiritual changes (“better understanding of spiritual issues, increase in religiousness”) (Ogińska-Bulik & Juczyński, 2010). In the PTGI, participants have to rate 21 positive statements that describe various changes resulting from traumatic or highly stressful events, which are highlighted at the beginning of the inventory. Participants were instructed to focus on their HIV infection and as the example of traumatic experience. Global PTG score is obtained when one calculates all items of the inventory. The Cronbach’s α for the whole scale in the current study was .85 and for the four subscales varied between .83 and .85.

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Social support was measured by the Berlin Social Support Scales (BSSS), in a Polish adaptation of Łuszczyńska et al. (2006). The BSSS are comprised of scales used to evaluate different dimensions of social support. The BSSS measures different components of social support and in this study following scales were used: perceived support (the extent to which help from others is available), need for support (the extent to which support in stressful situations is important to the participant), and received support (the real quantity of support received from others). The Cronbach’s α reliability coefficients for all scales in the current study were satisfactory as well, fluctuating between .84 and .85.

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Stress coping strategies were evaluated by Carver’s Mini-COPE Inventory in the Polish adaptation of Ogińska-Bulik and Juczyński (2009). The Mini-COPE Inventory measures dispositional stress coping, defined as the typical pattern of reactions and feelings under high stress for a particular person. This inventory consists of 28 items, which form 14 subscales describing several stress coping strategies, including problem-focused coping (“active coping, planning, seeking instrumental support”), emotion-focused coping (“seeking emotional support”, “acceptance”, “sense of humour”), meaning-focused coping strategies (“positive re-evaluation, return to religion”) and avoidance coping strategies (“self-distraction, denial, venting, substance use, behavioral disengagement, and self-blame”). Cronbach’s α for the Mini-COPE in the current ranged from .79 to .87.

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The level of resilience as a personality trait was assessed with the Resiliency Assessment Scale (SPP-25), constructed by Ogińska-Bulik and Juczyński (2008). This scale consists of 25 items and provides a general resilience score and scores on five subscales describing particular aspects of resilience: “persistence and determination in action”; “openness to new experiences and a sense of humour”; “personal skills to cope and tolerance of negative emotions”; “tolerance of failure and viewing life as a challenge”; “an optimistic attitude towards life and the ability to mobilize in difficult situations”. Respondents rate the answers on a 5-point Likert-type scale. Cronbach’s α for the whole scale in the current study was .84, and for the five subscales varies between .84 and .85.

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To measure the level of posttraumatic stress symptoms as a control variable in the studied patient group, the PTSD Factorial Inventory (PTSD-F; Strelau et al., 2002) was used. This inventory contains 30 items, which are divided into three scales: intrusion/arousal (recurrent thoughts relating to the traumatic event and causing arousal; 15 items), avoidance/numbing (avoidance of trauma-related stimuli and weakened response to these stimuli; 15 items), and a global trauma score (all 30 items). Patients are asked to report how often in the past several months they experienced a given thought, behaviour, or emotion related to the traumatic event—participants were instructed to focus on their HIV infection. The PTSD-F has satisfactory psychometric properties for the current study: assessed with Cronbach’s α, the reliabilities for the intrusion/arousal scale, the avoidance/numbing scale, and the global trauma score were .85, .85, and .86, respectively.

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Analytic plan consisted of three stages. Each variable was measured twice. Firstly, possible differences between two assessments were examined. T test for dependent variables was used when distribution of the variables did not deviate from the normal distribution and Wilcoxon signed-rank test was employed when distribution of the variables differed from the normal distribution. Because the sample size was relatively small we did not apply correction for multiple comparisons, i.e. Bonferroni correction, but computed Cohen’s d effect size for each comparison. The use of Bonferroni correction is criticized for loss of statistical power, which is especially important when the sample size is small. In this case, reporting of effect size measures is recommended (see, Nakagawa, 2004) and we followed those guidelines.

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Second stage of the analysis was devoted to the selection of control variables. It was assumed that besides the post-traumatic growth level in the first assessment, participants’ age, HIV infection duration and the global posttraumatic stress symptoms score should be controlled for as well, but only if they are significantly related to the explained variable, which was the post-traumatic growth level in the second assessment. Multiple regression analysis with the use of the entry method was applied to make an appropriate decision (Kutner et al., 2004).

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The final stage of the analysis was concerned with the posttraumatic growth process. Hypothetical model explaining which variables and in what order led to the increase in post-traumatic growth level in the second assessment with respect to the first assessment was created. The model contained associations between variables that were mentioned in the three formulated hypotheses: relationship between the levels of received support and the intensity of PTG (hypothesis 1.), relationship between meaning-focused coping strategies, avoidance coping strategies and the intensity of PTG (hypothesis 2.) and association between the intensity of resilience and the intensity of PTG (hypothesis 3.). Model, in which received support leads to the global PTG score in the follow-up assessment among participants, while controlling for the level of the global PTG score in the first assessment was verified. The global PTG score in the follow-up assessment was defined as an explained variable, and received support, measured the first time, was defined as an exogenous variable. Path analysis based on the maximum likelihood method was applied to verify and modify the assumed model. Appropriate modifications were made on the basis of modification indices with the threshold level of 4. IBM SPSS 24 and IBM AMOS 24 statistical package was used to conduct the statistical analysis (IBM Corp. Released, 2016).

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First, for descriptive reasons, means and standard deviations of two assessments with respect to variables in the PTGI, BSSS, Mini-COPE, SPP-25 and PTSD-F were calculated, using a t test for dependent samples and a Wilcoxon signed-rank test when the normal distribution was not achieved.

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Model coefficients for regression analysis predicting global PTG score in the follow-up assessment (explained variable), while controlling for the PTG score in the first assessment, in respect to age, HIV infection duration and global posttraumatic stress symptoms in the first assessment among HIV + Participants (n = 73)

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The last step of the statistical analysis was devoted to the creation of a path model. The AMOS graphics program was used to create a path diagram displaying process, in which received support leads to the global PTG score in the follow-up assessment among participants, while controlling for the level of the global PTG score in the first assessment. This study was based on the maximum likelihood method. The global PTG score in the follow-up assessment was defined as an explained variable, and received support, measured the first time, was defined as an exogenous variable. Preliminary, a model was created, in which a positive relationship was expected between received support, resilience measured in the first and follow-up assessments, return to religion and positive re-evaluation—as meaning-focused stress coping strategies—and the global PTG score (explained variable). Conversely, a negative relationship between substance use as an avoidance coping strategy and the global PTG score was expected. It was also hypothesised that perceived social support and need for support would act as mediators between received support, stress coping strategies (return to religion, positive re-evaluation, substance use) and the global PTG score. It was also thought that perceived social support would mediate the relationship between received support and substance use. The hypothesised model was depicted in Fig. 1 in the conceptual framework section.

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As can be seen in Table 1, a significant increase in the levels of particular PTG dimensions (see, changes of perception of oneself, greater appreciation for life) as well as in the global PTG score among the participants were observed. Achieved effect sizes ranged from Cohen’s d = −.22 to Cohen’s d = −.26. In addition, a significant increase in the intensity of return to religion (d = −.55) and behavioral disengagement (d = −.27), and a significant decrease in the level of active coping (d = .29) and positive re-evaluation (d = .25) as a stress coping strategy were observed. Finally, no differences in the levels of social support dimensions, resilience and PTSS among the participants between the two assessments were observed. Further analyses were performed only for the global PTG score, the general resilience score and the global PTSS score, as particular subscales in the Polish version of the PTGI inventory, the SPP-25 questionnaire and the PTSD-F questionnaire are highly intercorrelated (see, Ogińska-Bulik & Juczyński, 2008, 2010; Strelau et al., 2002).Table 1Means and standard deviations comparisons for two assessments of the variables in PTGI, the BSSS, the MINI-Cope, SPP-25 and the PTSD-F inventory in HIV + Sample (n = 73)VariablesTime 1 M (SD)Time 2 M (SD)Dependent-sample t test valueCohen’s d PTGIChanges of perception of oneself22.27 (12.30)25.15 (10.56)−2.15*−.25Changes in relations with others16.89 (9.12)19.07 (8.73)−1.54(a)−.22Greater appreciation for life8.45 (4.50)9.53 (3.58)−.2.07*−.24Spiritual changes3.29 (2.82)3.74 (2.96)−1.17(a)−.14Global posttraumatic growth score50.90 (25.92)57.49 (22.60)−2.26*−.26BSSSPerceived support18.59 (5.50)19.10 (4.60)−.06(a)−.08Need for support7.12 (2.83)7.52 (2.24)−.95(a)−.14Actually received support32.73 (10.39)31.02 (9.29)−1.36(a).13MINI-COPEActive coping4.52 (1.29)3.99 (1.59)−2.35*(a).29Planning4.37 (1.25)4.04 (1.53)−1.30(a).18Seeking instrumental support3.67 (1.76)3.96 (1.42)−1.08(a).08Seeking emotional support3.67 (1.76)3.52 (1.84)−.57(a)−.15Positive re-evaluation4.05 (1.35)3.60 (1.43)−2.09*(a).25Acceptance4.51 (1.30)4.25 (1.37)−1.26(a).14Sense of humour2.76 (1.31)2.95 (1.44)−.90(a)−.10Return to religion1.68 (1.99)2.93 (1.98)−4.30***(a)−.55Self-distraction3.38 (1.61)3.33 (1.44)−.46(a).03Denial1.84 (1.77)2.03 (1.72)−.70(a)−.09Venting3.15 (1.42)3.23 (1.38)−.13(a)−.05Substance use1.71 (1.90)2.22 (2.07)−1.53(a)−.20Behavioral disengagement1.68 (1.53)2.19 (1.41)−2.54*(a)−.27Self-blame3.14 (1.59)3.25 (1.51)−.40(a)−.06SPP-25 scalesPersistence and determination in action14.04 (3.76)14.16 (3.70)−.26−.03Openness to new experiences and a sense of humour16.11 (3.36)15.40 (3.56)−1.63(a).17Personal skills to cope and tolerance of negative emotions14.12 (3.93)14.55 (3.92)−1.29(a)−.09Tolerance of failure and view life as a challenge14.74 (3.65)14.79 (3.70)−.49(a)−.01Optimistic attitude towards life and the ability to mobilize in difficult situations13.39 (3.84)14.04 (4.15)−1.23−.14General resilience score72.41 (16.53)72.95 (17.66)−.24−.03PTSD-FIntrusion/arousal14.45 (10.31)13.43 (9.21)−.47(a).08Avoidance/numbing12.63 (10.23)11.96 (9.15)−.24(a).06Global posttraumatic stress score27.08 (19.27)25.40 (17.81)−.39(a).07 Note Time 1—first assessment; Time 2—follow-up assessment; (a) Z = value for Wilcoxon signed-rank test; * p < .05; *** p < .001

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Second, to examine whether the control variables (i.e. age, HIV infection duration and the global posttraumatic stress symptoms score) in the first assessment might be related to the global PTG score in the follow-up assessment (the explained variable), while controlling for the level of the global PTG score in the first assessment among participants, a multiple regression analysis via the entry method has been conducted (Kutner et al., 2004). In each step, the statistical significance of the increment in the explained variance was measured based on the F-change indicator, and the final model presents a semi-partial correlation for each independent variable. The results are presented in Table 2.Table 2Model coefficients for regression analysis predicting global PTG score in the follow-up assessment (explained variable), while controlling for the PTG score in the first assessment, in respect to age, HIV infection duration and global posttraumatic stress symptoms in the first assessment among HIV + Participants (n = 73)Model F F Δ R R 2 PredictorSemi-partial correlationGlobal posttraumatic growthScore (T1)21.12(a)***–.48.23Global posttraumatic growthScore (T1).48***Global posttraumatic growthScore (T1)10.70(b)***.45.48.23Global posttraumatic growth Score (T1).47***+ Age (T1)Age (T1)−.07Global posttraumatic growthScore (T1)7.06(c)***.08.48.24Global posttraumatic growthScore (T1).47***Age (T1)Age (T1)−.05+ HIV infection duration (T1)HIV infection duration (T1)−.03Global posttraumatic growth5.85(d)***1.92.51.26Global posttraumatic growth.48***Score (T1)Score (T1)Age (T1)Age (T1)−.05HIV infection duration (T1)HIV infection duration (T1)−.02+ Global posttraumatic stressGlobal posttraumatic stress symptoms−.15Symptoms score (T1)Score (T1) Note Explained variable: Global Posttraumatic Growth Score in the Follow-up- Assessment (T2); T1—First Assessment(a) df = 1,71, *** p < .001(b) df = 2,70, *** p < .001(c) df = 3,69, *** p < .001(d) df = 4,68, *** p < .001

clinical case

Contrary to preliminary expectations, the following paths were not significant: between substance use and the global PTG score (explained variable), between positive re-evaluation and the global PTG score (explained variable) and between positive re-evaluation and return to religion. Therefore, these paths were removed from the model, and in order to additionally increase the model fit, the regression path between return to religion and the global PTG score was changed into a covariance. A covariance between received support and resilience from the first assessment was also added. The final model is presented in Fig. 2.Fig. 2Final path diagram of the relationship between social support dimensions, stress coping strategies, resilience as a trait and the intensity of the global posttraumatic growth score in HIV + Patients (n = 73). *p < .05; **p < .01; ***p < .001

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The model contains relations between explanatory variables. Effect size was interpreted following Cohen’s guidelines (1988). The magnitude of positive correlation between general resilience and received support was small. The magnitude of positive correlation between general resilience and global posttraumatic growth score was high. Received support explained 27.2% of perceived support’s variance (strong effect size). Perceived support explained 23.4% (medium effect size) of need for support’s variance and 6.0% of substance use’s variance (weak effect size). The model contains three tested predictors of the explained variable (received support and general resilience from the first and second assessment) and one controlled variable (the global posttraumatic growth score in the first assessment). Achieved statistical power was .78. Finally, the values of goodness of fit indices suggested that the model fit was very good (chi 2 = 23.14; df = 23; p > .05; CFI = .999; RMSEA = .009).

review

It was found that received support was positively related to the global PTG score in the follow-up assessment (beta = .38) as well as to the level of resilience in the first assessment (r = .25). It was also observed that perceived support and need for support mediated only the link between received support and stress coping strategies (return to religion). It was also found that perceived support mediated the relationship between received support and substance use, i.e. perceived support led to lower level of substance use. Finally, resilience, in the first assessment, was positively related to the global PTG score in the first assessment (r = .50). Similarly, resilience in the second assessment was also positively related to PTG in the follow-up assessment (beta = .37). However, resilience in the first assessment was negatively related to the global PTG score in the second assessment (beta = −.38), which means that participants with a higher level of resilience in the first assessment experienced a lower increase in PTG between the two assessments. Comparing the hypothetical model with the final model we would like to highlight that positive re-evaluation was supposed to be important coping strategy for PTG, but the results did not confirm its role. We did not find evidence that substance use inhibits PTG. It was supposed that return to religion leads to PTG, but it was found that these two variables correlated negatively with each other. PTG in the first assessment correlated with general resilience in the first and in the second assessment.

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The first hypothesis was supported, as received social support was directly, positively related to the global PTG score at the one-year follow-up. Received social support has been shown to be a PTG-promoting factor in the HIV infected sample (Cieślak et al., 2009), as well as in many non-HIV samples (Nenova 2013). In particular, Cieślak et al. (2009) found that received social support was positively associated with one PTG subscale (i.e. relating to others) and these authors argued that they examined the role of received support only 2 months prior to the study, so perhaps the significance of received support for the global PTG score appears over a longer period of time. Likewise, also in our study received social support was directly, positively associated with PTG score at the one-year follow-up. Finally, the model offered evidence that perceived support and need for support mediated the relationship between received support and stress coping. According to Tedeschi and Calhoun (2004), the degree of perceived support and the need for support, which are displayed in the intensity of support seeking, can facilitate the use of more adaptive stress coping strategies. In particular, Peterson et al. (2011) observed that social support may enhance various stress coping strategies, thus improving well-being among PLWH. Specifically, perceived support led to a lower level of substance use among participants, which corresponds with the findings of Rothman et al. (2008), who observed that drug use were related to dissatisfaction in perceived social support among PLWH.

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This result corresponds with those of other studies indicating that resilience facilitates the probability of PTG in various populations after traumatic events (Bensimon, 2012). Murphy and Hevey (2013) showed that resilience was positively associated only with outcomes in the domains of personal strength and appreciation of life. According to Walsch (2007), resilient people are capable of rebounding from traumatic or highly stressful events and adapt to change due to changes in cognitive schemas, which are similar to those observed in PTG. In addition, resilience is closely related to other personality factors, which are positively related to PTG, such as sense of coherence, self-efficacy or optimism (Bensimon, 2012). The role of this latter variable (i.e. optimism in PTG promotion among PLWH) was proven by Milam (2006) in a longitudinal study.

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No significant relationship between the level of PTSS and the global level of PTG was found among participants. Previous research has not reached a consensus on the link between PTG and PTSS. While Frazier et al. (2001) observed a negative relationship between PTG and PTSS. Conversely, Tedeschi and Calhoun (1996) found that higher level of PTSS is inevitable to facilitate growth after trauma. The positive link between PTG and PTSS was observed in HIV infected sample (Cieślak et al., 2009). In particular, Rzeszutek et al. (2016) in a cross-sectional study found a positive association between PTG and PTSS, but only among HIV infected women. In addition, Kleim and Ehlers (2009) wrote about curvilinear relationship between this constructs PTG and PTSD. Furthermore, there are studies highlighting the lack of a significant association between PTG and PTSS (see, Salsman et al., 2009), which was proven in this study.

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The second hypothesis was not supported. On one hand, a negative link between return to religion as a meaning-focused coping strategy (covariance) was observed and, at the same time, no association between positive re-evaluation as a meaning-focused coping strategy and the global PTG score among was found the study sample. Additionally, there were no direct links between avoidance coping (substance use) and the global PTG score among participants. The negative relationship between return to religion and the global PTG score was an intriguing result and can be explained in two ways. First, some authors highlighted that in certain situations religious coping may not only be unrelated to PTG but also hinder growth after trauma, when a person lays the blame and responsibility for his or her disease on God (or some other force majeure), which strengthens passivity and contributes to giving up on medical treatment (Pargament, 2007). Wanyama et al. (2007) showed that religious beliefs about HIV may cause fatalistic attitudes and resignation from treatment. Furthermore, Zou et al. (2009) observed that moral connotations usually associated with HIV infection can turn the religious community into a stigmatizing atmosphere for PLWH, which can lead them to withdraw from such a community. Conversely, the aforementioned result may be understood to indicate that receiving support in PLWH may have two, separate, positive consequences: an increase in the level of PTG or an increase in the intensity of return to religion, mediated by perceived support and need for support (see Table 1; Fig. 2). Perhaps those PLWH who engage in religious coping may not experience growth while those who experience growth are not interested in searching for relief in religion. Nevertheless, this latter explanation requires further study. In addition, the lack of association between positive-re-evaluation and PTG among the study sample proved that this coping strategy may not necessarily be important for PTG promotion, especially taking into account that the level of positive re-evaluation decreased over 1 year among participants. Even so, this needs further investigation. Finally, the lack of a direct relationship between substance use as an avoidance coping strategy and the global PTG score can likely be attributed to perceived support, which was negatively related to this stress coping strategy among participants.

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The last hypothesis was supported, as resilience as a personality trait, in the first assessment, was positively related to the global PTG score in the first assessment. Likewise, the level of resilience in the second assessment was also positively related to the global PTG score in the second assessment. Interestingly, resilience level in the first assessment was negatively related to the global PTG score in the second assessment, while the global PTG score in the first assessment was controlled for, which means that participants with a higher level of resilience in the first assessment experienced a smaller increase in PTG between the two assessments. Moreover, this is not due to a ceiling effect, because there was only one participant with the highest possible PTG score in the second assessment, which is 105 points.

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There was also no significant relationship between the participants’ age and HIV infection duration and the global PTG score among participants. Studies on the link between age and PTG are equivocal. Some authors found a higher intensity of PTG among younger people (Helgeson et al., 2006). Conversely, other studies showed that older people, facing the imminence of death, can have a greater sense of meaning and openness to spiritual issues (Karanci & Erkam, 2007). Similarly, inconsistent findings can be found in the literature regarding the link between the amount of time since a traumatic event and PTG. Frazier et al. (2001) observed a negative correlation between PTG and time since a sexual assault. Conversely, Park and Fenster (2004) found that the longer the period after a cancer diagnosis, the higher the intensity of PTG. Furthermore, Prati and Pietrantoni (2009) in a meta-analysis, underlined that the time since trauma is not a significant moderator of the link between personal (optimism, stress coping) and social resources (social support) and PTG in many samples after trauma. The lack of an association between HIV infection duration and PTG in the study sample could indicate that HIV disease stage is not related to growth. Substantial progress in antiretroviral therapy has led to a decrease in HIV-related mortality in the last decade, and many authors now perceive HIV infection more as a chronic rather than terminal illness (Deeks et al., 2013). Siegel and Shrimshaw (2005) underlined that the most critical moment for PLWH is the moment of being diagnosed with HIV, which may result in modifications in the individual’s current beliefs and cognitive schemas, comprising the core elements of PTG. Nevertheless, participants had various lengths of HIV infection, which may also explain the lack of association between HIV infection duration and PTG in this study.

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Finally, it is worth mentioning that this study may be important for Polish HIV infected individuals, as each year the number of new HIV infections in Poland increases by 13–14% (Supreme Audit Office, 2015). In addition, HIV education and prevention in Poland remain at a relatively poor level. Particularly, the majority of the funds from the National Programme for Preventing HIV Infections and Combating AIDS is spent on treatment, and not on prevention and education, which is responsible for that increasing number recently infected individuals in Poland do not know about their HIV-positive status. Furthermore, high levels of HIV-related stigma and discrimination may be still observed in Poland and the access to mental health care for HIV/AIDS population is rather scarce (Skonieczna, 2013). In the light of aforementioned factors, continuing research on psychological aspects of HIV/AIDS in Poland, including research on PTG is fully justified.

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It is vital to mention the limitations of this study. There was only one follow-up assessment, and the follow-up cohort of participants was relatively low, so the size of the statistical effects is not very high (see Table 1). Perhaps more follow-up assessment s would answer the question of whether the relationships between the study variable are, for example, curvilinear. In addition, other social support scales have not been examined (e.g. those who provided social support) that may be associated with PTG in participants. In the study sample, there was also a significant underrepresentation of HIV infected women, so no gender differences could be found. Another limitation is that data were collected from a convenience sample that suffered from substantial loss to follow-up. In addition, as was stated previously, participants had various durations of HIV infection, which may have influenced the shape and magnitude of some relationships, such as the association between HIV infection duration and PTG or the lack of relationship between PTG and PTSD symptoms. Finally, demographic data have not been investigated thoroughly (e.g. education level, employment, religious affiliation, sexual orientation) or HIV-related variables (e.g. HIV transmission, treatment).

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Despite these limitations, the current study provides new insights into the personal and social predictors of posttraumatic growth in a sample of people living with HIV in Poland. Clinicians and researchers need to focus on potentially positive consequences of HIV infection, i.e. PTG. As PTG is related to several health-related benefits in this patient group (Milam, 2004, 2006) and the substantial amount of variation of HIV infection progression is still relatively poorly understood, further exploration of this topic is necessary.

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Computed tomography (CT) and positron emission tomography (PET) † are imaging modalities of different qualities which are complementary. The development of PET/CT scanners has allowed for both modalities to be acquired in a single session with the significant advantage that their spatial coordinates are inherently linked. In addition to providing important diagnostic information, the CT component of PET/CT also provides information necessary for attenuation correction (CTAC) of the emission data during PET image reconstruction. This step is essential for the PET image data to be accurate for quantitative applications such as standardized update value (SUV) calculation.

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Both PET and PET/CT imaging have multiple clinical applications. In radiotherapy, PET is frequently used to assist physicians in defining target volumes to be irradiated.( 1 ) Since its inception, numerous researchers have evaluated the potential for PET and PET/CT to be employed in the treatment planning process for lung cancer, with a number of studies focusing specifically on the impact of PET on target volume definition.( 2 – 5 ) Understanding the efficacy of PET in this context is hindered by the lack of a suitable ground truth, and the ideal methodology for quantitative use of PET or PET/CT in these processes remains a subject of debate.( 6 , 7 ) It has been shown, however, that interobserver variability is reduced when PET/CT is used for lung cancer radiotherapy target definition.( 2 ) In any case, a quantitative application of PET images clearly requires that the image data possess quantitative accuracy.

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While applications of PET to lung cancer are well established, this anatomical site in particular presents a well‐known challenge due to internal respiratory motion. Motion produces two distinct challenges for PET/CT. First, it blurs PET emission data and reduces SUVs. In addition, the fast acquisition times of CT produce images which represent a specific moment within the respiratory cycle, while the much slower acquisition time of PET produces an image that represents the average of many respiratory cycles. This mismatch in phases can produce artifacts and affect SUV values.( 8 – 12 ) This is particularly true when the region of interest is at a boundary between tissue types of different densities, most significantly, at the lung/diaphragm interface.( 11 , 13 , 14 ) It has been reported that using CT data acquired at deep inspiration can produce severe artifacts,( 15 ) and also that CT protocols using normal expiration breath‐hold or partial breath‐hold acquisition can reduce artifacts in this region.( 15 , 16 ) Others have proposed that errors due to attenuation correction can be reduced by using a Cine‐CT to produce an averaged (or max) image dataset for correction purposes.( 17 – 20 )

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Gated PET (4D PET) can be used to reduce motion artifacts due to the blurring of the emission data and the reconstructed images. However, a 4D PET study may or may not be corrected for attenuation by an analogously acquired phase matched 4D CT scan (4D CTAC). Figure 1 illustrates the imaging artifact that can occur when a 4D PET image is corrected using CTAC data which is out of phase of the 4D PET data. While it has been reported that it is feasible to minimize motion artifacts by using 4D PET corrected by 4D CT in patients in a clinical setting,( 21 – 23 ) the addition of a 4D CT adds considerable complexity, as well as increased patient dose. As a consequence, clinical centers may avoid these complications by using 4D PET imaging protocols with attenuation correction based on 3D CT. A number of studies have compared different methods for 4D PET attenuation correction in patients( 21 ) and phantoms,( 24 – 26 ) as well as simulated data based on 3D PET and 4D CT in patients.( 13 ) As expected, these studies conclude generally that 4D PET reduces motion artifacts, and that 4D PET corrected by phase matched 4D CT is the most quantitatively accurate approach. However, 4D CT may not always be appropriate or available in the clinical setting due to scanner capabilities and/or the additional patient dose and complexity it adds to the scan. For this reason, it is important to fully understand the relative benefits of different attenuation correction methods.

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Illustration of the type of artifact that can occur from using a single CT image for attenuation correction when motion is present. Both PET images are reconstructed from the same raw data of the 0% phase bin of a 4D gated PET acquisition. The PET image on the left has been corrected for attenuation using CT 0% phase CT data (phase‐matched), while the image on the right has been corrected using CT data from the 50% phase (phase‐mismatched) resulting in overcorrection.

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The purpose of this study is to evaluate the performance of 4D PET imaging, and examine the choice of attenuation correction method using a phantom designed to represent the lung–diaphragm interface. While it is well‐known that this interface is a source of attenuation correction errors, we know of no other report that has employed a phantom specifically designed to model this anatomical region. In particular, we sought to quantify motion artifacts that occur during the attenuation correction portion of PET image reconstruction.

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Phantom studies were specifically designed to evaluate 4D PET images of targets near a density interface using three different methods for attenuation correction: a single 3D CT (3D CTAC), an averaged 4D CT (CINE CTAC), and a fully phase matched 4D CT (4D CTAC).

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The phantom was designed to approximate the geometry of the anatomical region between the lung and diaphragm. A watertight plastic box was used which consisted of two sections: a low density section to represent lung and a water‐filled section to approximate liver. The “lung” side was filled with a mixture of water and Styrofoam packing material (‘peanuts’), as shown in Fig. 2. CT images of the phantom show an average HU value of −630 on the ‘lung’ side and close to zero on the ‘liver’ side, being representative of the relative densities at the lung/ diaphragm interface. A small amount of 18F‐FDG (~300 μCi) was added to both sections to represent background activity. The tumor was represented by an 8 mL sphere phantom (Hollow Sphere Set, Model ECT/HS/SET6, Data Spectrum Corporation, Hillsborough, NC) loaded with 18F‐FDG which could be placed on either side of the border. The phantom was situated on a small platform with wheels, and the entire assembly was connected to a commercially available dynamic phantom (Dynamic Thorax Phantom, Computerized Imaging Reference Systems (CIRS), Norfolk, VA) which was driven by a computerized motion controller. Figure 3 shows a photo of the phantom in position for scanning.

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A GE PET/CT scanner (Discovery DVCT‐PET/CT, GE Medical Systems, Milwaukee, WI) was used for these studies. The PET scanner has a 15 cm axial field of view which is combined with a 64 slice CT scanner. The Varian RPM motion tracking system (Real‐time Position Management System, Varian Medical Systems, Palo Alto, CA) is used for motion tracking based on an IR reflective maker. Clinically this marker is normally placed on the anterior surface of the patient's abdomen to allow respiratory motion monitoring via an optical camera. For this phantom study, the marker box was placed on a small platform which moves in the AP direction synchronously with the phantom's SI motion.

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CT scans for attenuation correction were acquired using a standard protocol: a 0.5 s rotation time, 32×1.25 mm scan acquisition reconstructed to 3.75 mm thick slices with a 500 mm axial field of view. Tube potential of 140 kVp and current of 75 mAs were used. Traditional 3D PET/ CT studies were acquired both with and without the phantom in motion. 4D PET scans were acquired with the phantom in motion while the RPM system simultaneously recorded the motion of the IR reflective marker. The motion trace and images from the CINE scan were then transferred to a GE workstation (Advantage 4D, Advantage Windows, GE Medical Systems, Milwaukee, WI) for reconstruction of a phase binned 4D image dataset using the Advantage 4D application. These data were then transferred back to the PET/CT console to be used for attenuation correction of PET emission data.

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10 μCi of 18F‐FDG, a concentration of (1.25 μCi/cc), was loaded into the sphere phantom “target”, as well as a lower concentration (300 μCi−0.16 μCi/cc) into both phantom compartments to provide an approximate target to background ratio of 8:1. A series of scans was performed with the target vial on the “liver” side of the phantom and then repeated with the vial placed on the “lung” side. The vial was reloaded with 18F‐FDG prior to the second series.

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Data was acquired with the phantom moving according to two different motion patterns, a simple sinusoid sin(x) as well as sin4(x). The simple sinusoid sin(x) provides a baseline for motion effects but may not be a realistic model of actual breathing patterns. The motion pattern sin4(x), as suggested by Lujan et al. and Seppenwoolde et al.,( 27 , 28 ) more closely represents an actual realistic breathing motion than the simple sinusoid. For both motion patterns an amplitude of 2 cm over a period of 4.5 sec was used with the direction of motion towards and away from the gantry, corresponding to the superior/inferior direction in a patient.

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Three basic types of imaging studies were performed: 3D Static: To set a baseline for comparison, a traditional 3D PET/CT scan was first acquired without phantom motion. The PET portion was performed using a 2 min acquisition time.3D Dynamic: With the phantom in motion, a 3D PET/CT scan was acquired using the same parameters as for 3D Static, for both phantom motion patterns sin(x) and sin4(x).4D Dynamic: Next, a gated study was acquired consisting of a 4D CT scan and a 20 min PET acquisition. 4D CT and 4D PET scans were acquired with the phantom in motion while the RPM system simultaneously recorded the motion of the IR reflective marker. CT data was processed into a 10‐bin 4D image set using GE Advantage 4D software.

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4D Dynamic: Next, a gated study was acquired consisting of a 4D CT scan and a 20 min PET acquisition. 4D CT and 4D PET scans were acquired with the phantom in motion while the RPM system simultaneously recorded the motion of the IR reflective marker. CT data was processed into a 10‐bin 4D image set using GE Advantage 4D software.

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The 3D Static and 3D Dynamic studies were both acquired using a 2 min acquisition time in order to match the average acquisition time per bin of the 4D studies (10 bins over 20 minutes acquisition). Since a 4 min acquisition time is more typical for clinical 3D PET, both 3D Static and 3D Dynamic studies were repeated using a 4 min acquisition. A comparison of the 2 min vs. 4 min acquisition images revealed only a small effect of the increased acquisition time (a difference of ~2% or less for maximum and average activity) for our phantom. Thus, for our subsequent analysis comparing 3D to 4D image data, the 2 min acquisition images were used exclusively.

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PET images were reconstructed, based on an existing clinical protocol, using a 128 × 128 matrix and an iterative ordered subsets expectation maximization (OSEM) algorithm (2 iterations, 30 subsets), yielding a volume of 47 sections with thickness and spacing of 3.27 mm and an in‐plane pixel size of 4.69×4.69 mm.

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Examples of both the 3D Static and 3D Dynamic scans were reconstructed using a single 3D CTAC dataset. This represented the current typical practice and was a basis for comparison. For the 3D Dynamic study, an ungated PET scan of a moving target was corrected with a 3D CTAC that was started at a random breathing phase. Since this example was meant to represent clinical practice, we did not make any effort to determine or control the phase. The result, therefore, represented one example from a random distribution. We did not feel that it was necessary to explore the range of possible outcomes for the 3D Dynamic case as this was achieved by extracting specific extremes of motion from the 4D studies.

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A single 3D CTAC image set was used for attenuation correction of the 4D PET datasets. This was closest to the current clinical practice, in which the fast CT acquisition speed will capture the patient (or phantom) at a random phase within the respiratory cycle. To determine the range of possible outcomes from using a single 3D CTAC image set, the 4D PET reconstruction was also performed with single phases picked from a 4D CT dataset, capturing the extremes of motion. These are described in more detail below. 3D‐CTAC‐00: the 0% phase CT images extracted from the 4D CT; this is analogous to using a breath hold ‘inhale’ CT scan3D‐CTAC‐50: the 50% phase CT images extracted from the 4D CT; this is analogous to a breath‐hold ‘exhale’ CT scan

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To reduce artifacts associated with using a single “snap‐shot” CT scan, a CINE CTAC scan was used for attenuation correction. Using CINE mode, the scanner acquires multiple images at each couch position. When used for attenuation correction, the images are combined by the console reconstruction software to generate a correction based on the average density at each image location.

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A computer program was developed, using the Java programming language, to read DICOM image data files and calculate quantitative measures for comparison of the different images. Values for activity/mL for each voxel were corrected for decay using the 3D Static image study as the basis for comparison. Two specific quantities are presented for analysis. Normalized Recovery Coefficient (NRC): The maximum value of activity/mL, represented as a percentage relative to the maximum value found for the corresponding 3D Static image acquisition. The presence of motion reduces this value.( 24 ) A 4D imaging technique can be evaluated in terms of its ability to increase NRC towards the levels observed when no motion is present.

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Normalized Recovery Coefficient (NRC): The maximum value of activity/mL, represented as a percentage relative to the maximum value found for the corresponding 3D Static image acquisition. The presence of motion reduces this value.( 24 ) A 4D imaging technique can be evaluated in terms of its ability to increase NRC towards the levels observed when no motion is present.

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While the recovery coefficient is commonly used in the analysis of PET images, it represents a single voxel (the max) and is disproportionately affected by image noise. To address these deficiencies we devised a quantity more directly related to the ability of the specific correction method to quantify the true volume of the target vial. We call this new quantity the Fixed Activity Volume (FAV).

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The FAV was determined by first making an array ‘list’ of the voxels in the image set sorted in descending order by activity/mL. From this list, the total amount of activity was found representing the known volume of the vial (8 mL) for the 3D STATIC image set. With this total activity value as a reference (corrected for decay), the amount of volume, in mL, needed to find the same amount of total activity in the dynamic image studies was calculated. The presence of motion spreads the activity over a large area and increases the FAV. The 4D imaging techniques can be compared in terms of how well they lower FAV values towards those observed without motion. We present FAV values as a percentage of the known volume (8 mL) of the target vial.

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Values for FAV and NRC were calculated for each of the 3D Static, 3D Dynamic, and the 4D Dynamic studies reconstructed with the methods of attenuation correction described above. For the 3D cases, only one value per image set was produced. For the 4D studies, a value was determined for each phase of the image dataset, producing a range of possible values over the motion cycle.

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Summary of the results comparing different methods for attenuation correction in terms of fixed activity volume (FAV). FAV values are normalized to the known volume of the sphere phantom (8 ml). The average, standard deviation, minimum, maximum, and range were determined over all the phases of a 10‐bin 4D PET image dataset reconstructed using the attenuation correction method in question. Key values are highlighted to illustrate the differences in the range of values for the different attenuation correction methods.

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In addition, the results are shown in graphs which compare the various attenuation correction methods in terms of the average values for comparison, as well as the range of values from each of the 10 phases of the motion cycle. Values representing the 3D Static and 3D Dynamic cases are shown for comparison purposes as flat lines. The graphs shown in Fig. 4 compare the range of outcomes of FAV for different correction methods for the case of the vial on the corresponding ‘lung’ and ‘liver’ sides of the 4D phantom when a motion pattern of sin(x) is used. The heading “3D CTAC RANGE” combines data from the extreme phases, 0% and 50%. This illustrates the full range of outcomes that can be produced when a single 3D CTAC image set of an essentially random phase is used. Figure 5 shows the results for FAV for the phantom motion pattern of sin4(x). The values for normalized recovery coefficient for sin(x) motion are shown in Fig. 6, while values for sin4(x) motion are shown in Fig. 7.

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Results for NRC and FAV are summarized in Tables 1 and 2, respectively. Values are tabulated for the average, standard deviation, minimum, maximum, and range of values for FAV and NRC. For each quantity (FAV or NRC), values are shown for data collected with the vial on the ‘lung side’ and on the ‘liver side’ using motion patterns sin(x) and sin4(x). Both FAV and NRC are normalized so that 100% represents the baseline: the 3D Static' dataset. For the values of FAV, 100% also represents the known volume of the target vial (8 mL).

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Summary of the results comparing the different attenuation correction methods in terms of normalized recovery coefficient (NRC). These values are normalized such that 100% represents the baseline recovery for the 3D Static dataset. The average, standard deviation, minimum, maximum, and range were determined over all the phases of a 10‐bin 4D PET image dataset reconstructed using the attenuation correction method in question. Key values are highlighted to illustrate the differences in the range of values for the different attenuation correction methods.

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Graphs comparing the different methods for attenuation correction based on the volume of voxels determined from PET images needed to recover the known amount of activity (Fixed Activity Volume). For each method, the value for FAV was calculated for each of the 10 phases of the 4D PET image sets and the results are shown as a range of values. The upper graph shows the results for the target vial on the ‘lung’ side of the phantom, and the lower graph shows the results for the vial on the ‘liver’ side. The phantom motion was according to sin(x).

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Graphs comparing the different methods for attenuation correction based on the volume of voxels determined from PET images needed to recover the known amount of activity (Fixed Activity Volume). For each method, the value for FAV was calculated for each of the 10 phases of the 4D PET image set and the results are shown as a range of values. The upper graph shows the results for the target vial on the ‘lung’ side of the phantom and the lower graph shows the results for the vial on the ‘liver’ side. The phantom motion was according to sin(x) raised to the 4th power.

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Graphs comparing the different methods for attenuation correction based on the normalized recovery coefficient. For each method, the value for NRC was calculated for each of the 10 phases of the 4D PET image set and the results are shown as a range of values. The upper graph shows the results for the target vial on the ‘lung’ side of the phantom and the lower graph shows the results for the vial on the ‘liver’ side. The phantom motion was according to sin(x).

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Graphs comparing the different methods for attenuation correction based on the normalized recovery coefficient. For each method, the value for NRC was calculated for each of the 10 phases of the 4D PET image set and the results are shown as a range of values. The upper graph shows the results for the target vial on the ‘lung’ side of the phantom and the lower graph shows the results for the vial on the ‘liver’ side. The phantom motion was according to sin(x) raised to the 4th power.

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What is immediately apparent from the results is that the use of a 3D CTAC, with no control over the specific phase it represents, can produce a wide range of results when motion is present. Both NRC and FAV metrics show that the 4D correction methods produce a clear reduction in the range of variation, reducing the effects of motion when compared to 3D CTAC. Between the two 4D techniques, 4D CTAC was generally more effective than 4D CINE at reducing motion effects, particularly when the target was placed on the low density (lung) side of the phantom. With the target on the “liver” side, the difference between 4D CINE and 4D CTAC was reduced. In particular, for the “liver” side studies, the 4D CTAC method produced a somewhat larger amount of variation in NRC than the 4D CINE method when the sin(x) motion pattern was used. This was not the case for the sin4(x) pattern, however, for which the 4D CTAC did have an advantage over 4D CINE for scans on the “liver” side.

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