Atrial fibrillation (AF) was more frequent in non-survivors (p < 0.0001), alongside a longer QTc interval (p = 0.0002), a lower Tp-e/QTc ratio (p = 0.0003), and right ventricular strain (p = 0.013). Remdesivir administration was associated with bradycardia development (p = 0.0005) but no increase in mortality rates. In a Cox regression model, AF (aHR 3.02 (95% CI 1.03–8.81); p = 0.042), QTc interval above 451 ms (aHR 3.24 (95% CI 1.09–9.62); p = 0.033), and right ventricular strain (aHR 2.94 (95% CI 1.01–8.55); p = 0.047) were associated with higher 28-day mortality risk. Conclusions: QTc interval > 451 ms, right ventricular strain, and AF are associated with higher mortality risk in SARS-CoV-2 hospitalized patients. ECG recording and its appropriate analysis offers a simple, quick, non-expensive, and validated approach in the emergency setting to guide COVID-19 patients’ stratification.Background: Electrocardiogram (ECG) offers a valuable resource easily available in the emergency setting. Objective: Aim of the study was to describe ECG alterations on emergency department (ED) presentation or that developed during hospitalization in SARS-CoV-2-infected patients and their association with 28-day mortality. Methods: A retrospective, single-center study including hospitalized patients with SARS-CoV-2 was conducted. ECG was recorded on ED admission to determine: heart rhythm, rate, and cycle; atrio-ventricular and intra-ventricular conduction; right ventricular strain; and ventricular repolarization. A specialized cardiologist blinded for the outcomes performed all 12-lead ECG analyses and their interpretation. Results: 190 patients were included, with a total of 24 deaths (12.6%). Age ( Since the beginning of the SARS-CoV-2 global emergency in December 2019, more than 300 million cases and 5 million deaths have been recorded worldwide, and these numbers keep rising [Multimorbidity, including past cardiovascular or pulmonary disease history and older age above all, have been previously associated with severity of infection and mortality . On the 1Q3T3 sign or inferior leads T wave inversion, which reflects the associated lung involvement and is already linked to higher disease burden [Electrocardiographic abnormalities have been observed in 99% of elderly and critically ill patients infected with SARS-CoV-2 . These ie burden .Despite the bulky amount of data, a comprehensive analysis of ECG parameters on emergency presentation in COVID-19 patients is missing, as either attention is focused on specific ECG abnormalities, or solid evidence on alterations is still lacking. ECG recording represents the first step of the cardiological assessment and can prove essential for patients’ risk stratification in the ongoing emergency frame, being a handy, inexpensive, and widely available tool.Therefore, this study aims to describe the prevalence and type of electrocardiographic alterations at emergency department (E.D.) arrival in subsequently hospitalized SARS-CoV-2-infected patients and to investigate the possible association between ECG parameters and 28-day mortality after adjusting for variables, including age, sex, comorbidities, and laboratory findings that could influence the endpoint.A monocentric, retrospective study was conducted at Azienda Ospedaliero Universitaria Policlinico Umberto I, a tertiary care hospital with 1235 beds, the seat of “Sapienza” University of Rome Medical School, between March 2020 and January 2021.Patients over 18 years of age with SARS-CoV-2 infection confirmed by rapid antigen or molecular  nasopharyngeal swab test subsequently admitted from E.D. to Infectious Diseases COVID-19 hospital wards in the abovementioned period were initially included in the analysis, for a total of 531 patients. Underage or discharged patients, patients with no laboratory proven infection, or those admitted to wards other than infectious diseases ward (I.D.)  as well as patients receiving drugs potentially elongating the QT as well as >48 h of azithromycin or hydroxychloroquine were excluded. In addition, patients were excluded whether data were incomplete for study purpose or E.D. recorded standard twelve-lead ECGs were missing. Accordingly, the proposed criteria led to 341 excluded and 190 included patients .2/FiO2 ratio; potential ICU stay during hospitalization; in-hospital and 28-day mortality, length of hospital stay, and therapy administered against SARS-CoV-2. Patients’ data were anonymously recorded from medical reports into an electronic spreadsheet for the following statistical analysis. These consisted of demographics; comorbidities included in the Charlson Comorbidity Index (CCI); plus systemic hypertension, AF, and asthma; vital signs recorded in E.D., including relative bradycardia (defined as copresence of body temperature ≥ 38.3 °C and heart rate (HR) < 90 bpm) [1Q3T3 or T3 alone pattern), and ventricular repolarization .All 12-lead ECG analyses and their interpretation were performed by a specialized cardiologist (M.C.G.) who was blinded for the outcomes. The following parameters were retrieved: heart rhythm, heart rate (expressed as bpm), and heart cycle , expressed as ms), atrio-ventricular and intra-ventricular conduction parameters , left posterior hemi-block (LPH), right or left bundle branch block (RBBB or LBBB)), morphological evaluation with particular emphasis on right ventricular strain . The need for informed consent was waived since all data were retrospectively extracted.Cardiovascular events during hospitalization were defined as the onset of new ischemic/embolic events, such as pulmonary thrombo-embolism by lung CT scan, acute cerebral ischemia, acute limb ischemia, or the development of myocardial infarction, Takotsubo syndrome, myocarditis.Heart rhythm disorders included the new onset of atrial fibrillation, supraventricular tachycardia, bradycardia, pairs of ventricular premature beats, and ventricular tachycardia.Relative bradycardia was defined as heart rate < 90 bpm and concomitant fever (tympanic temperature ≥ 38.3 °C) [Daytime bradycardia was defined as mean heart rate < 60 bpm recorded three times a day.1Q3T3 pattern  or negative T wave alone in leads V1–V3 or II, III, or aVF with or without ST depression [Right ventricular strain was defined as the presence of Spression .The QT interval was defined as the interval from the onset of the QRS complex to the end of the T wave and expressed as ms. Corrected QT interval (QTc) was calculated according to Bazett formula: QTc = QT√ . In the QT dispersion, which reflects regional differences in myocardial refractoriness and predict cardiac dysrhythmias , was expFurthermore, regional differences in myocardial refractoriness were calculated also by means of Tp-e interval  . Measure® software, v. 15 (StataCorp); charts were generated using Microsoft Office® and Graphpad Prism®. Continuous data are expressed as median and interquartile range (IQR) values and categorical variables as numbers and percentage values. Categorical variables were compared using χ2-test or Fisher’s exact test and continuous variables using Student’s t-test or Mann–Whitney U test as appropriate.Statistical analysis was performed using STATA2/FiO2, D-dimer, CRP, and lymphocytes count, expression of respiratory failure and inflammation during COVID-19, respectively, were performed. According to the reference values available at the laboratory of our hospital, abnormal levels of troponin corresponded to levels > 0.014 μg/L.In the subgroup of patients with troponin levels available, Spearman correlation analyses between troponin levels and PaOlogrank test. For all the statistical analyses, p < 0.05 was considered significant.Multivariate Cox regression models were used to determine the hazard ratios (HR) for mortality within 28 days from admission of the included variables accounting for covariables. Statistically and clinically relevant variables on univariate analysis were evaluated for the determination of the final multivariate model. To find the optimal cut-off of the QTc value associated with the highest sensitivity and specificity in the prediction of 28-day outcome, the Youden’s Index was used. The resulting value was further inserted in the final model. Twenty-eight-day survival curves were plotted using the Kaplan–Meier method and compared using the p < 0.0001), while no significant difference was observed regarding sex. No deaths were recorded within 48 h from E.D. arrival; therefore, this variable was not included in the study. Patients hospitalized during the first pandemic wave in Italy (February–June 2020) were evenly distributed among the two compared subgroups (89/166 (53.6%) vs. 10/24 (41.7%); p = 0.273). This reduces possible bias in different diagnostic and therapeutic management of SARS-CoV-2 infection over time.Overall, 190 patients were included in the study, with 83/190 (44%) females . Median p < 0.0001). Besides hypertension, myocardial infarction (MI) was the most common cardiovascular comorbidity , followed by AF  and chronic heart failure (CHF) , which was more prevalent in the deceased subgroup, where AF was recorded in 7/24 patients , MI in 6 , and CHF in 3   patients). Calculated CCI was significantly higher in non-survivors (median (IQR), 8 (8–10)) when compared to survivors (3 (1–6); = 0.160) .p = 0.002), whereas dyspnea was the most prevalent among deceased although not statistically significant (16/24 (66.7%) vs. 85/166 (51.2%) patients; p = 0.164). Moreover, in the latter subgroup, median time between symptoms onset and E.D. presentation was significantly shorter than in survivors (median (IQR), 1.8 (0–5.5) vs. 6 (2–9) days; p = 0.039). Relative bradycardia was registered in 22/190 patients (11.6%) and, though more frequent among survivors (21/166 patients (12.6%)), resulted as not statistically relevant (p = 0.225).Fever was the most prevalent symptom in both the overall population (149/190 patients (78.4%)) and among survivors (136/166 (82%) vs. 13/24 (54.2%) patients; 2/FiO2 ratio on triage arterial blood gas was lower in non-survivors (median (IQR), 302 (243–367) vs. 357 (314–424); p = 0.0007), together with high white blood cells count (7180 (5070–8820) vs. 5755 (4502–7790) cells/µL; p = 0.009) and neutrophils count (5465 (3647–7662) vs. 4150 (3110–5995) cells/µL; p = 0.0038), anemia (median Hb (IQR) 11.3 (10.2–14.5) vs. 13.8 (12.7–14.9) g/dL; p = 0.0004), high D-dimer (1995 (1012–3198) vs. 777 (429–1469) U/L; p = 0.0012) and LDH (330 (268–447) vs. 275 (213–349) U/L; p = 0.0001), and low serum albumin (32 (30–35) vs. 38 (35–41) g/L; p < 0.0001). Regarding serum troponin T, the test was available on admission in 125/190 patients only and was higher in non-survivors (0.031 (0.021–0.04) vs. 0.012 (0.007–0.0245) μg/L; p = 0.084). Turning to laboratory tests, PaOp < 0.0001) and paroxysmal supraventricular complexes (PSVC) in 20.8% (5/24 (20.8%) vs. 9/166 (5.4%) patients; p = 0.0193). Mean RR interval was reduced, albeit not significantly, in non-survivors (median (IQR), 637.5 (570–762) vs. 767 (664–875) ms; p = 0.006) as well as its standard deviation (18 (11–30) vs. 20 (13.4–35) ms; p = 0.414). Regarding ventricular conduction parameters, the only significant difference between the subgroups was observed for the higher prevalence of left anterior hemiblock (9/24 (37.5%) vs. 28 (19.8%) patients; p = 0.0258) in non-survivors, who also recorded a higher occurrence of right ventricular strain as S1Q3T3 pattern (7/24 (29.1%) vs. 18/166 (10.8%) patients; p = 0.013) or as single-components inverted T wave in DIII (T3) (15/24 (62.5%) vs. 45/166 (27.1%) patients; p < 0.0001) and prominent S wave in DI (S1) (9/24 (37.5%) vs. 30/166 (18%) patients; p = 0.034). Median QTc interval duration was longer in non-survivors (436.8 (435–487) vs. 428 (402–447) ms; p = 0.0002), resulting in lower Tp-e/QTc ratio (0.2 (0.158–0.198) vs. 0.22 (0.211–0.233); p = 0.0003). Prolonged QTc was observed in 55 (28.9%) subjects, higher in non-survivors than survivors (39 (23.49%) vs. 16 (66.6%), p < 0.0001). Following Youden’s index, the optimal cut-off of QTc differentiating 28-day survivors from non-survivors was 451 ms ).p < 0.0001) as new onset in-hospital AF although not relevant (3/24 (12.5%) vs. 8/166 (4.8%) patients; p = 0.1477). On the other hand, daytime bradycardia was more frequent in survivors (2/24 (8.3%) vs. 28/166 (16.9%); p = 0.284).p = 0.0045) and with systemic corticosteroids (17/24 (70.8%) vs. 72/166 (43.4%), p = 0.045), while administration of macrolides, HCQ, and prophylactic dose LMWH (4.000 UI/24 h s.q.) was uniform within the two subgroups, as shown in In the non-survivor subgroup, a higher rate of patients was treated with therapeutic dose LMWH (100 U/kg/12 h s.q.) (10/24 (41.7%) vs. 33/166 (19.9%) patients; p = 0.021 and 0.036, respectively) but not with right ventricular strain (p = 0.94) and tended to be associated with abnormalities at admission ECG (p = 0.08).Troponin levels at hospital admission was available in 125/190 (65.8%) subjects. Abnormal levels of troponin (>0.014 μg/L) were associated with AF and QTc , whereas a negative correlation was observed with PaO2/FiO2  and lymphocyte count   .logrank test analysis with p-values. Among electrocardiographic findings, AF on E.D. admission recording (p < 0.0001) or developed during hospitalization (p = 0.0409) or considered together as cumulative AF (p < 0.0001) were associated with lower 28-day survival rates for right heart strain (p = 0.0093) and QTc value > 451 ms (p < 0.0001). Relative bradycardia was not significantly different between survivors and deceased (p = 0.3148). In addition, age > 65 y (p = 0.0002), CRP over 4 mg/dL (p = 0.0023), D-dimer over 850 U/L (p = 0.0035), and serum albumin below 35 g/L (p < 0.0001) on E.D. admission laboratory tests were associated with lower 28-day survival rates.p < 0.05) and clinically relevant variables on univariate analysis were evaluated for the determination of hazard ratios (HRs) for 28-day mortality. Following Youden’s index results, QTc value > 451 ms was considered in the final model. AF detection on E.D. arrival ECG or its in-hospital development (HR 3.02 (95% CI 1.03–8.81); p = 0.042), QTc > 451 ms (HR 3.24 995% CI 1.09–9.62); p = 0.033), and right ventricular strain (HR 2.94 (95% CI 1.01–8.55); p = 0.047) were associated with higher 28-day mortality risk after adjustment for age, sex, cardiac and pulmonary comorbidities , and laboratory tests  that proved clinically pertinent and could potentially influence the outcome  or inverted T wave on ECG recordings. This has already been described as a negative prognostic factor in patients with non-COVID19-related PTE [3 sign alone, indeed, was included in the right ventricular strain analysis as a strain mark and a recognized negative prognostic factor [2 ≤ 95% and RR > 20 bpm. Concerning laboratory tests on E.D. admission, low PaO2/Fio2 ratio, anemia, low serum albumin, leukocytosis with neutrophilia, elevated LDH, D-dimer, and CRP were prevalent in non-survivors, as previously described [Furthermore, hypoxic stress and lung damage, its related pulmonary hypertension and right ventricular heart strain, in addition to the high rates of pulmonary thromboembolism (PTE)  registerated PTE  and in oc factor . Our finc factor , who linescribed . Though escribed . As for escribed .A total of 15.8% of patients developed sinus bradycardia during hospitalization. This was not linked to higher mortality rates, as previously described , even thAutonomic dysfunction is shown throughout the lower heart rate variability registered in non-survivors as RR interval, which reflects the sympathovagal balance interacting with IL-6 and the ongoing pro-inflammatory boost ,33. This2/FiO2 (expression of respiratory failure), D-dimer, CRP, and lymphocytes (expression of inflammation during infection). Furthermore, abnormal troponin levels were associated with AF and QTc and, overall, tended to be associated with abnormal ECG on hospital admission. Taken together, these findings suggest that the possible myocardial damage, expressed by the troponin values, may be directly related to the infection itself rather than to a previous cardiac problem. Nevertheless, although troponin represents a marker of myocardial injury, it may be unreliable when considered alone, and therefore, it should be analyzed only when combined with additional clinical and laboratory parameters.Statistically significant correlations were found between troponin values and specific parameters of COVID-19, such as PaOThis study shows several limitations. Primarily, its retrospective design does not allow a confident generalizability. Despite controlling for demographic and clinical data, there could be other confounding variables not included in multivariate analysis, and the omission of radiologic parameters does not grant a strict clinical assessment of right heart strain and conceivable underlying pulmonary thromboembolism. Moreover, the temporal nexus between ECG findings and their development is missing, as we only analyzed the E.D. ECG, and certain abnormalities could have developed prior to SARS-CoV-2 infection; nevertheless, having these ECG abnormalities was associated with a worse prognosis. The potential association between ECG findings and serum troponin T levels could not be assessed, because of the few available TnT, as a former non-routine test on E.D. admission; nevertheless, only a statistical trend over significance was observed, and therefore, this variable was further excluded from the final model.Lastly, in consideration of the test duration and emergency situation experienced by our country during the study period, we did not have the possibility to routinely perform cardiac MRI to identify myocardial alterations in hospitalized patients and to comprehensively collect the frequency of post-COVID-19 cardiovascular sequelae. Nevertheless, the aim of the present study was to investigate the electrocardiographic features on hospital admission and in the emergency setting and not the cardiovascular sequelae, which, in our opinion, deserve per se additional investigations.To conclude, our study was conducted in an emergency, namely the first and second pandemic waves in Italy, prior to the development and approval of the anti-SARS-CoV-2 management strategies currently used. This supports the goal of investigate the untainted interaction of SARS-CoV-2 with heart rate and rhythm and, therefore, ECG, avoiding additional confounders such as new antiviral drugs and prophylactic treatments or the role of virus variants.This study demonstrates the association between older age, AF, QTc, and right ventricular strain recorded on E.D. admission ECG and higher mortality risk after adjusting for cardiopulmonary comorbidities and disease severity markers. These results endorse the role of the ongoing cardiovascular alterations in SARS-CoV-2 infection, likely related to the direct and indirect action of virus and cytokine storm on cardiomyocytes and unbiased by therapeutic strategies. ECG recording and its appropriate analysis offers a simple, quick, non-expensive, and validated approach in the emergency setting to guide COVID-19 patients’ stratification in the ongoing pandemic frame, assisted by clinical and laboratory assessment.


The somehow prophetic 2007 publication reviews “natural disasters, climate change and mental health considerations for rural Australia” (2) and pinpoints central aspects of today’s debate, namely anxiety and depression, vulnerability and resilience. In addition to problems of rural areas (2), the impact of urbanicity (3) will be discussed as well as the role of air pollution on psychiatric disorders (4). (1) UN Environment Programme. https://www.unenvironment.org/explore-topics/climate-change/about-climate-change Dec 22nd, 2020. (2) Morissey SA, Reser JP. Aust J Rural Health. 2007 Apr;15(2):120-5. doi: 10.1111/j.1440-1584.2007.00865.x. (3) Krabbendam L et al. Psychol Med. 2020 Mar 11:1-12. doi: 10.1017/S0033291720000355. (4) Kim SY et al. Sci Total Environ. 2020 Dec 8;757:143960. doi: 10.1016/j.scitotenv.2020.143960.According to the UN Environment Programme “climate change is one of the most pervasive and threatening issues of our time”. “In many places, temperature changes and sea-level rise are already putting ecosystems under stress and affecting human well-being” (1). The presentation wants to give an overview on how climate change can affect mental health. A search was performed on PubMed for the combination of “climate change” and “mental health”. 281 publications were identified, the first being from 2007 (the only one in that year). In 2020, until Dec 22No significant relationships.


During the COVID-19 pandemic, cancer patients are regarded as a highly vulnerable population. Given the unavoidable bias and unmeasured confounders in observational studies, the causal effects of cancers on COVID-19 outcomes are largely unknown. In the study, we tried to evaluate the causal effects of cancers on COVID-19 outcomes using the Mendelian randomization (MR) approach. No strong evidence was observed to support a causal role of cancer in COVID-19 development. Previous observational correlations between cancers and COVID-19 outcomes were likely confounded. Large and well-conducted epidemiological studies are required to determine whether cancers causally contribute to increased risk of COVID-19.p-values for the casual associations were all statistically insignificant: overall cancer , lung cancer , breast cancer , endometrial cancer , prostate cancer , thyroid cancer , ovarian cancer , melanoma , small bowel cancer , colorectal cancer , oropharyngeal cancer , lymphoma  and cervical cancer . Sensitivity analyses and multivariable MR analyses yielded similar results. In conclusion, cancers might have no causal effect on increasing COVID-19 risk. Further large-scale population studies are needed to validate our findings.Observational studies have shown increased COVID-19 risk among cancer patients, but the causality has not been proven yet. Mendelian randomization analysis can use the genetic variants, independently of confounders, to obtain causal estimates which are considerably less confounded. We aimed to investigate the causal associations of cancers with COVID-19 outcomes using the MR analysis. The inverse-variance weighted (IVW) method was employed as the primary analysis. Sensitivity analyses and multivariable MR analyses were conducted. Notably, IVW analysis of univariable MR revealed that overall cancer and twelve site-specific cancers had no causal association with COVID-19 severity, hospitalization or susceptibility. The corresponding  Coronavirus disease 2019 (COVID-19) is a global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,4. ThereCancer patients are a vulnerable population during the COVID-19 pandemic ,6. CanceMendelian randomization, an epidemiological method, has been widely applied to assess the potential causal association between exposure and outcome ,15. Acco2, age × gender, principal components and study-specific covariates by the original GWAS researchers. The COVID-19 outcomes included 1,683,768 participants  for susceptibility, 1,887,658 participants  for hospitalization, and 1,388,342 participants  for severity, respectively. The uninfected individuals served as the controls. All cases were confirmed by laboratory, self-reported, or physician diagnosis. The severe cases were defined as patients who died or required respiratory support with COVID-19 infection [The summary statistics in the genome-wide association studies (GWASs) for COVID-19 were sourced from the COVID-19 Host Genetics Initiative V5 , which eThe summary statistics of the GWASs for cancers were obtained from the UK biobank , InternaCovariates for multivariable MR analyses were included: BMI  , educatiAt the beginning of our study design, 24 site-specific cancers were considered. Overall cancer and 12 site-specific cancers were included, but another 12 types of cancer were not included due to insufficient SNPs . In p < 5 × 10−8). To ensure independence, SNPs were restricted by low linkage disequilibrium  using clumping [Appropriate SNPs used as IVs must be robustly associated with cancers ; otherwise, a random-effect model was applied. In addition, we used the “leave-one-out” validation to determine whether a single SNP had a significant independent influence on the MR estimation.In the univariable MR analysis, the IVW analysis was chosen as the primary approach to estimate the causal effects of cancers on COVID-19 outcomes ,38. We ap-value using the Bonferroni correction  was used. 0.0033 < p < 0.05 was regarded as suggestive evidence for a potential association. The β  and its SE (standard error) were calculated to reflect effect sizes. All statistical analyses were conducted in R v4.0.1  with the packages “TwoSampleMR” and “MRPRESSO” [We applied the random-effect IVW method to assess the causal effects of cancers on COVID-19 outcomes for the multivariable MR analyses, after controlling BMI, educational attainment, intelligence, smoking and alcohol consumption. Given the number of cancers and COVID-19 outcomes considered, a two-sided RPRESSO” ,43.p = 0.07), ovarian cancer (p < 0.001), melanoma (p = 0.01) and cervical cancer (p = 0.08) (p = 0.34), lung cancer (p = 0.60), squamous cell lung cancer (p = 0.66), breast cancer (p = 0.43), ER+ breast cancer (p = 0.79), ER− breast cancer (p = 0.66), endometrial cancer (p = 0.79), prostate cancer (p = 0.54), thyroid cancer (p = 0.70), ovarian cancer (p = 0.62), melanoma (p = 0.79), small bowel cancer (p = 0.09), colorectal cancer (p = 0.85), oropharyngeal cancer (p = 0.31), lymphoma (p = 0.51) or cervical cancer (p = 0.25) on the COVID-19 severity  were included for COVID-19 severity. Severe COVID-19 cases were defined as patients who died or required respiratory support with COVID-19 infection. The effects of each SNP in cancers on COVID-19 severity can be found in  = 0.08) . Hence, severity .p = 0.03) and ovarian cancer (p = 0.01) . Although horizontal pleiotropy was observed in melanoma (p = 0.02), it showed no significant outlier. We conducted the “leave-one-out” analysis and found no potential SNP significantly biasing the results  . After r results . Taken t results . Results results .p = 0.06), ovarian cancer (p < 0.001) and cervical cancer (p = 0.04) (p = 0.42), lung cancer (p = 0.37), squamous cell lung cancer (p = 0.66), breast cancer (p = 0.74), ER+ breast cancer (p = 0.51), ER− breast cancer (p = 0.93), endometrial cancer (p = 0.24), prostate cancer (p = 0.17), thyroid cancer (p = 0.80), ovarian cancer (p = 0.96), melanoma (p = 0.45), small bowel cancer (p = 0.08), colorectal cancer (p = 0.79) or cervical cancer (p = 0.32) on COVID-19 hospitalization .  = 0.04) . The ranlization .p < 0.001; p = 0.78). The “leave-one-out” analysis showed no outliers (p = 0.04), estimates in the three analyses  and prostate cancer (p = 0.046) when adjusting for education attainment. A significant association was also found in small bowel cancer (p = 0.047) when adjusting for smoking. However, the associations of overall cancer, prostate cancer and small bowel cancer with COVID-19 hospitalization could not be replicated when intelligence , income  and alcohol consumption  were adjusted , prostate cancer (p = 0.06), thyroid cancer (p = 0.06), ovarian cancer (p < 0.001), melanoma (p = 0.05) and cervical cancer (p < 0.001) (p = 0.69), lung cancer (p = 0.96), squamous cell lung cancer (p = 0.08), breast cancer (p = 0.43), ER+ breast cancer (p = 0.30), ER− breast cancer (p = 0.18), endometrial cancer (p = 0.83), prostate cancer (p = 0.58), thyroid cancer (p = 0.28), ovarian cancer (p = 0.93), melanoma (p = 0.82), small bowel cancer (p = 0.19), colorectal cancer (p = 0.30), oropharyngeal cancer (p = 0.80), lymphoma (p = 0.37) or cervical cancer (p = 0.68) on COVID-19 susceptibility  were included for COVID-19 susceptibility. < 0.001) . Thus, wtibility .p = 0.02) and ovarian cancer (p < 0.001) . The “leave-one-out” plot showed one potential instrumental outlier (rs6983267) for colorectal cancer  . After rl cancer . Howeverl cancer  supporteDuring the COVID-19 pandemic, healthcare resources are extremely scarce, and there is an urgent need to allocate healthcare resources rationally . IdentifMR leverages the random allocation of genetic variants at conception, independently of confounders, to identify the causal effects that are substantially less confounded and not vulnerable to reverse causation ,15. We uAlthough many studies have generally shown positive correlations of cancers with the risk of COVID-19 ,45,46,47Risk factors may be correlated with COVID-19 outcomes, but not as a causal association. Previous MR studies have shown that many traditional risk factors have no causal association with COVID-19 outcomes, such as decreased lung function, chronic obstructive pulmonary disease, blood pressure, type 2 diabetes, chronic kidney disease, coronary artery disease, stroke and nonalcoholic fatty liver disease ,53,54,55MR design is less confounding than observational study, but limitations of this MR study need to be acknowledged. First, some types of cancer—such as stomach cancer, pancreatic cancer, liver cancer and brain cancer—were not included in the study because of insufficient SNPs . PotentiOverall, we used MR analysis to evaluate the causal effects of overall cancer and twelve site-specific cancers on COVID-19 severity, hospitalization and susceptibility. Results of the MR study did not suggest strong evidence to support the causal associations of any examined cancer with COVID-19 outcomes. Previous observational correlations of cancers with COVID-19 outcomes were likely confounded. More large-scale epidemiological studies are needed to validate our findings.


Health impact of monoclonal gammopathy of undetermined significance (MGUS) and monoclonal B-cell lymphocytosis (MBL): findings from a UK population-based cohort. BMJ Open 2021;11:e041296. doi: 10.1136/bmjopen-2020-041296Lamb MJ, Smith A, Painter D, This article was previously published with an error in table 1. MGUS control numbers and percentages by gender were transposed and should read: males 11308 (51.6) and females 10620 (48.4).


Families with children may be at higher risk for influenza infection. Community transmission can suffer from underreporting as testing is often not performed. We studied the epidemiology of influenza in households with school-aged children using home-based sample collection.We conducted a remote household study surveilling respiratory viruses from November 2019-June 2021, in King County, Washington (WA), USA. Households with school-aged children were enrolled, mailed home specimen collection kits, and asked to self-assess for weekly acute respiratory illness (ARI) using remote survey platforms. Participants with ARI symptoms were prompted to complete serial illness surveys and self-collect/parent collect mid-turbinate nasal swabs. Samples were sent to a University of Washington study laboratory for RT-PCR influenza testing. Influenza rates were compared to WA Department of Health (DOH) reporting.A total of 1861 ARI events were reported among 992 adults and 869 children in 470 households; 75 influenza cases were detected (36 influenza A and 39 influenza B). The study participant median age was 32 years (0-84), 10 years (1-49) for influenza A, and 11 years (3-49) for influenza B cases. Overall 13% of households had an influenza case, of which 13 (22%) reported >1 case. A total of 81% of participants reported receipt of one dose of the 2019-2020 influenza vaccine, including 91% of influenza A and 90% of influenza B cases, and 84% received the 2020-2021 influenza vaccine. Like WA DOH, we observed a wave of influenza B cases followed by influenza A in 2019-2020. During influenza season 2020-2021, WA DOH reported 9 positive influenza tests and none observed in our study. Commonly, influenza case-patients reported were fever, cough, rhinorrhea, and fatigue. GI symptoms were more common in children than adults. Of the cases, 92% of influenza A and 78% of influenza B occurred in children.D0-Day of reported onset, D7-7 days after reported illness onset. No participants >49 years were positive for influenza. D0: 30 participants responded and of respondents, 13% <5 years, 47% 5-12 years, 3% 13-17 years, and 37% 18-49 years. D7: 31 participants responded and of respondents 13% <5 years, 48% 5-11 years, 3% 12-17 years, and 36% 18-49 years.D0-Day of reported onset, D7-7 days after reported illness onset. No participants >49 years were positive for influenza. D0: 28 participants responded and of respondents, 4% <5 years, 57% 5-12 years, 14% 13-17 years, and 25% 18-49 years. D7: 28 participants responded and of respondents, 4% <5 years, 57% 5-11 years, 18% 12-17 years, and 21% 18-49 years.Influenza illness in 2019-2020 was initially influenza B, and subsequently replaced by influenza A. Most cases were in children and adolescents, despite at least one dose of influenza vaccine. Symptoms were widely distributed and similar between influenza A and B. Influenza incidence in our cohort declined to zero with the rise of SARS-CoV-2 cases and widespread mitigation efforts.Janet A. Englund, MD, AstraZeneca: Advisor/Consultant|AstraZeneca: Grant/Research Support|GlaxoSmithKline: Grant/Research Support|Meissa Vaccines: Advisor/Consultant|Merck: Grant/Research Support|Pfizer: Grant/Research Support|Sanofi Pasteur: Advisor/Consultant Helen Y. Chu, MD, MPH, Cepheid: Reagents|Ellume: Advisor/Consultant|Gates Ventures: Grant/Research Support|Merck: Advisor/Consultant|Pfizer: Advisor/Consultant.


T790M mutation, the major mechanism of acquired resistance to first-generation EGFR TKI. However, resistance to AZD9291 arises eventually and EGFRC797S mutation was reported to be a major resistance mechanism. Thus, it is highly valuable to develop novel EGFR fourth-generation inhibitors targeting C797S mutation to override the acquired resistance. In this study, we identified HCD3514 as a novel EGFR fourth-generation inhibitors targeting C797S triple mutation. It strongly inhibited EGFRL858R/T790M/C797S and EGFR19del/T790M/C797S mutations with IC50 values of 1.0 and 2.0 nM, respectively. HCD3514 dose-dependently inhibited the activation of EGFR in both engineered BaF3 cells and tumor cells harboring EGFRC797S triple mutant and thus effectively suppressed the proliferation of the cells. Moreover, HCD3514 caused a dose-dependent increase of apoptosis in C797S triple mutant cells accompanied by increased levels of cleaved caspase-3 and cleaved PARP. Furthermore, HCD3514 induced tumor growth inhibition in EGFR19del/T790M/C797S xenograft model as a single oral agent by decreasing the activation of EGFR. In addition to EGFRC797S triple mutations, HCD3514 also potently and selectively inhibited EGFRT790M double mutations (L858R/T790M and 19del/T790M). Collectively, HCD3514 is a highly selective and potent EGFR inhibitor against EGFRC797S triple mutations as well as EGFRT790M double mutations and is confirmed potently anti-tumor activity in preclinical models.Osimertinib (AZD9291), a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI), has significantly improved the survival of non-small cell lung cancer (NSCLC) patients with EGFR WT), resulting in severe side effects Lung cancer is a malignant tumor with high incidence and mortality throughout the world, being responsible for almost one-quarter of all cancer-related deaths T790M/C797S resistance mutation has gained much attention.Consequently, the urgent need for highly selective third-generation EGFR TKIs that target both activating mutations and T790M resistance mutation leads to the development of osimertinib (AZD9291). AZD9291 exhibits remarkable efficacy in treating patients of T790M mutation-positive NSCLC in vitro and in vivo, but quite similar to that with EAI045 treatment, its antitumor efficacy is limited as a single agent T790M/C797S inhibitors showing potential antitumor activity of AZD9291-resistant triple mutant EGFR in vitro and in vivo, including TQB3804, LS-106 T790M. Typical examples include BI-4020 Currently, encouraging achievements have been made in the development of the fourth-generation EGFR TKIs, which could be summarized into two categories based on their discovery strategies. 1) Exploiting allosteric binding pocket that is remote from the location of C797S mutation. For example, EAI045, an allosteric inhibitor, is reported as the first fourth-generation EGFR inhibitor overcoming T790M and C797S resistance mutation T790M inhibitor AZD9291, we identified a potent fourth-generation EGFR inhibitor, HCD3514, which is effective as a single agent to overcome EGFR triple mutation (EGFRL858R/T790M/C797S and EGFR19del/T790M/C797S) and shows promising antitumor activity both in vitro and in vivo.In this study, through structural hybridization of brigatinib with EGFR2.BaF3 cells and PC-9 cells were obtained from Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH (DSMZ) and the European Collection of Authenticated Cell Cultures (ECACC), respectively. MRC-9 cells and GSE-1 cells were purchased from the American Type Culture Collection (ATCC). All the cells were maintained in RPMI-1640 medium (Gibco) or EMEM medium (Gibco) supplemented with 10% FBS (Gibco) at 37 °C in 5% COL858R/T790M/C797S, EGFR19del/T790M/C797S, EGFRL858R/T790M or EGFR19del/T790M and cells stably expressing these mutants were subsequently selected in medium supplemented with 1 μg/mL puromycin (Sigma-Aldrich). PC-9-OR cells harboring EGFR19del/T790M/C797S mutation were constructed by CRISPR/Cas9 genome-editing technology to simultaneously knock-in the T790M and C797S mutations into PC-9 cells harboring EGFR19delBaF3 cells were transduced with retroviruses harboring genes encoding EGFR1H NMR and 13C NMR spectra was recorded on a Bruker AV-400 spectrometer at 400 MHz and Bruker AV-600 spectrometer at 151 MHz, respectively, in CDCl3. Coupling constants (J) are expressed in hertz (Hz). Chemical shifts (δ) of NMR are reported in parts per million (ppm) units relative to internal control (TMS). The first-order peak patterns are indicated as s (singlet), d (doublet), t (triplet), q (quadruplet). Complex non-first order signals are indicated as m (multiplet). The high-resolution ESI-MS results were recorded on an Applied Biosystems Q-STAR Elite ESI LC-MS/MS mass spectrometer. The purity of compound was determined by reverse-phase high-performance liquid chromatography (HPLC) analysis using an Agilent 1260 system (G1310B Iso pump and G1365D MWD VL detector) with an YMC Triart C18 reversed-phase column  at 254 nm. Elution was MeOH in water, and flow rate was 1.0 mL/min.1H NMR  δ 8.43 , 8.41 , 8.35 , 7.96 , 7.43 , 7.33 , 7.24 , 7.06 , 6.87 , 4.58 - 4.42 , 3.60 - 3.53 , 3.41 , 2.79 - 2.63 , 2.62 - 2.43 , 2.43 - 2.36 , 2.34 , 1.99 - 1.90 , 1.79 - 1.67 , 1.28 , 1.26 , 1.24 .13C NMR  δ 158.43, 158.22, 156.93, 150.71, 139.15, 134.79, 133.85, 130.39, 128.78, 125.49, 124.13, 123.55, 118.64, 117.55, 116.60, 113.01, 112.83, 108.99, 70.38, 61.96, 55.46 (2C), 50.81 (2C), 49.13, 48.94, 46.05, 28.80 (2C), 22.18 (2C), 8.14, 1.05. HRMS (m/z): [M+H] + calculated for C33H42ClN7O3S, 652.2831; found, 652.2814. HPLC purity: 98.1%. Brigatinib (cat. no. S8229) and AZD9291 (cat. no. S7297) were purchased from Selleck Chemicals.Compound HCD3514 was designed and synthesized by Ke Ding laboratory. L858R/T790M/C797S: cat. no. E10-12VG, EGFR19del/T790M/C797S: cat. no. E10-12UG, EGFRL858R/T790M: cat. no. E10-122DG, EGFR19del/T790M: cat. no. E10-122KG) or Eurofins Scientific (EGFRWT: cat. no. 14-531M). ELISA was used to evaluate the inhibitory activity of test compounds against EGFR, and the experiment was performed as described previously The recombinant EGFR proteins of the kinase domain were purchased from SignalChem Lifesciences (EGFRL858R/T790M/C797S was selected as the template to elucidate the binding mode of HCD3514. Protein structure was downloaded from Protein Data Bank (PDB 6LUD). The EGFR enzyme was defined as a receptor and the docking grid was centered on the ligand binding location. Docking simulations were performed in standard precision (SP) mode. Other parameters were set as default. After accomplishment of the molecular docking procedure, eight docking poses were scored and selected based on calculated energy.The molecular docking procedure was performed within Glide 7.9 software BaF3 cells, PC-9-OR cells, MRC-9 cells and GSE-1 cells were plated in 96-well plates in the corresponding medium and incubated overnight, followed by exposing to medium containing serial dilutions of compounds . After 72 h of drug treatment, cell counting kit-8 (CCK8) or sulforhodamine B (SRB) was added to each well and then measured by SoftMax Pro software at the absorbance of 450 nm or 560 nm Western blotting was performed as described previously L858R/T790M/C797S cells, BaF3-EGFR19del/T790M/C797S cells and PC-9-OR cells were seeded in 6-wells plates overnight, and exposed to indicated concentrations of compounds HCD3514 or brigatinib for 48 h. Then cells were collected and washed with PBS and measured by Annexin V-FITC Apoptosis detection kit . Signals were measured using FACS Calibur flow cytometer, and FlowJo software was used to analyze the data.BaF3-EGFR19del/T790M/C797S cells (2 × 106) were injected subcutaneously into BALB/c nude mice (Shanghai institute of medicine). After animals were randomly assigned to groups, tumor-bearing mice were orally administered vehicle, test compound HCD3514 or AZD9291 at the indicated doses once a day and tumor size were measured once per week. Tumor volume was calculated as length × width2 × 0.5 (mm3) and the tumor growth inhibition (TGI) = [1-RTV (treated) / RTV (control)] × 100%. Mice were sacrificed and tumors were harvested for Western blot analysis. All animal study was approved by the Institutional Animal Care and Use Committee of the Shanghai Institute of Materia Medica and strictly performed according to the institutional ethical guidelines on animal care.BaF3-EGFRP < 0.05, **P < 0.01, ***P < 0.001.Data were presented as mean ± Standard Deviation (SD) and were analyzed by GraphPad Prism 8.0 software. Statistical analysis of the difference between vehicle and compound treated groups was compared by a two-tailed Student's t-test. Statistical significance was defined as *T790M/C797S, we incorporated important pharmacophore of brigatinib to the 4-indolyl-2-phenylaminopyrimidine scaffold of EGFRT790M inhibitor AZD9291 and developed a novel series of potent and mutant selective EGFRT790M/C797S inhibitor. Following extensive medicinal chemistry optimization by adding H-bond receptor of the indole ring and hydrophobic moiety on the benzene ring, we identified HCD3514 as a selective EGFRT790M/C797S inhibitor  and EGFR19del/T790M/C797S (IC50 = 2.0 ± 0.3 nM) with a more than 78-fold selectivity over EGFRWT (IC50 = 156.0 ± 6.9 nM). By comparison, HCD3514 possessed more potent kinase inhibitory activity against EGFR triple mutations than brigatinib  and more potent than AZD9291 (IC50 = 6.30 μM)  and gastric mucosal (GSE-1) cell lines. As shown in Supplementary M Figure A, indicain vitro anti-tumor activity of HCD3514 in EGFRT790M/C797S triple mutant cells through suppression of EGFR phosphorylation, leading to the inhibition of cellular proliferation.Thus, these results demonstrated the L858R/T790M/C797S cells, HCD3514 triggered significant apoptosis with apoptosis rates of 72.39% and 98.27% at the concentration of 1 μM and 3 μM, respectively  and cysteinyl aspartate specific proteinase-3 (caspase-3) play an important role in cellular processes, including apoptosis To gain deeper insight into the potential mechanism of apoptosis induction in EGFRin vivo in BaF3-EGFR19del/T790M/C797S xenograft mouse model. BALB/c nude mice bearing established BaF3-EGFR19del/T790M/C797S mouse xenograft tumors were daily oral treatment with HCD3514 (50 and 75 mg/kg), AZD9291 (25 mg/kg) or vehicle control. Consequently, HCD3514 resulted in an obvious growth inhibition of BaF3-EGFR19del/T790M/C797S mouse xenograft tumors in a dose-dependently manner with tumor growth inhibition (TGI) values of 22.53% and 37.73% at the dosage of 50 mg/kg and 75 mg/kg, respectively, while AZD9291 treatment did not show significant reduction of tumor growth in the xenograft model compared to the vehicle treated mice , as suggested by the molecular docking experiment showed in Figure L858R/T790M and EGFR19del/T790M mutations with IC50 values of 4.9 and 8.9 nM in biochemical, respectively. Moreover, HCD3514 showed antiproliferative activities in BaF3-EGFRL858R/T790M and BaF3-EGFR19del/T790M cells with IC50 of 0.66 μM and 0.52 μM, respectively, although the IC50 values of HCD3514 were higher than that of AZD9291. Furthermore, Western blot analysis revealed that HCD3514 dose-dependently suppressed the EGFR phosphorylation in both BaF3-EGFRL858R/T790M cells and BaF3-EGFR19del/T790M cells. Besides, we also evaluated the in vitro anti-tumor activity of HCD3514 in NCI-H1975 cells harboring EGFRL858R/T790M. HCD3514 also displayed an excellent antiproliferative potency in NCI-H1975 cells with IC50 of 0.49 μM and the expression of phospho-EGFR was down-regulated after treatment with HCD3514 in a dose-dependent manner. Thus, HCD3514 could target EGFRT790M double mutations as well as EGFRT790M/C797S triple mutations.In addition, we checked whether HCD3514 inhibits EGFR-double mutations  exploiting allosteric binding pocket by allosteric inhibitors. 2) developing ATP-competitive inhibitors by structural modification of ALK/EGFR inhibitor brigatinib or the third-generation EGFR TKIs.Oncogenic mutations in EGFR are prevalent in NSCLC. Though third-generation EGFR inhibitor AZD9291 has achieved a great clinical benefit in NSCLC patients, the EGFRT790M inhibitor AZD9291, we identified a new fourth-generation EGFR triple mutant-selective inhibitor HCD3514, which possessed a promising antitumor activity both in vitro and in vivo. Compared to AZD9291, molecular docking of HCD3514 identified an additional hydrogen bond interaction towards Lys745, which was proposed to compensate for the loss of covalent bound due to the C797S point mutation and ultimately enhanced the inhibitory efficacy of HCD3514 on EGFRC797S mutations. HCD3514 displayed a single digit-nanomolar kinase inhibitory activity against triple mutant EGFR, including EGFRL858R/T790M/C797S and EGFR19del/T790M/C797S, which showed superior biochemical activity than brigatinib. Importantly, HCD3514 exhibited a sufficient EGFRWT sparing window, suggesting its low toxicity. In addition, HCD3514 simultaneously inhibited the activities of both mutant forms of EGFR at the cellular level. It showed that both engineered BaF3 cells harboring EGFRL858R/T790M/C797S or EGFR19del/T790M/C797S were sensitive to HCD3514, as well as the PC-9-OR cells (EGFR19del/T790M/C797S expressing PC-9 lung cancer cells), with enhanced inhibition the phosphorylation of EGFR. Besides, cleaved PARP and cleaved caspase-3 levels were showed to increase in three C797S mutant cells, which was consistent with apoptosis induction in a concentration-dependent manner. Furthermore, unlike EAI045 or brigatinib which showed limited antitumor effect without combined with anti-EGFR antibody C797S triple mutations but also EGFRT790M double mutations.In this study, through structural hybridization of brigatinib with EGFRin vitro and in vivo and we also provided a novel chemical scaffold and a new lead compound that worthy of further investigation.In summary, our preclinical data demonstrated that HCD3514 was effective in overcoming both triple mutant and double mutant forms of EGFR-dependent resistance mechanisms Supplementary figures.Click here for additional data file.


Assessing serological inflammation is difficult in tocilizumab (TCZ)-treated rheumatoid arthritis (RA) patients, as standard inflammation parameters, like erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP), are influenced by interleukin-6-receptor inhibition. Calprotectin in the serum, also named S100A8/S100A9, might be a more useful inflammation parameter in TCZ-treated patients.Sixty-nine RA patients taking TCZ were included. Serum-calprotectin levels were assessed, as well as ESR, CRP, need for a change in disease-modifying anti-rheumatic drugs due to RA activity (= active RA), and the RA clinical disease activity score (CDAI). Forty-five RA patients taking tumor-necrosis factor-inhibitors (TNFi) were investigated for the same parameters.P < 0.001). A calprotectin cut-off value of 1916.5 ng/ml resulted in a sensitivity and specificity of 80.0 %, respectively, for the detection of RA disease activity. Calprotectin values correlated with CDAI-scores . ESR and CRP were less suitable to detect RA activity in TCZ-treated patients. Also TNFi-treated patients with active RA had higher calprotectin values compared to not active RA . The calprotectin value with the best sensitivity and specificity for detecting RA activity was 3690.5 ng/ml among TNFi-treated patients.TCZ-treated patients with active RA had higher calprotectin values than not active RA patients (4155.5 [inter quartile range 1865.3–6068.3] vs 1040.0 [676.0–1638.0] ng/ml, Calprotectin in the serum can be a useful inflammation parameter despite TCZ-treatment.The online version contains supplementary material available at 10.1186/s13075-022-02887-7. Inflammation parameters in the serum are a useful tool to monitor disease activity in inflammatory rheumatic diseases. In rheumatoid arthritis (RA), mostly erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) are routinely assessed. CRP and acute phase protein synthesis in the liver is regulated by interleukin-6 (IL-6) , 2. TociCalprotectin, a heterodimer of the two calcium-binding proteins S100A8 and S100A9 , is a major cytosolic protein in monocytes and neutrophils, secreted during infections, malignancy, and inflammation , 6. CalpThe measurement of calprotectin in the serum as inflammation parameter in RA (without IL-6 receptor inhibitors) has been investigated and showed a correlation with radiographic damage scores and standard inflammation parameters . It alsoFew data is available for the use of calprotectin in RA with TCZ-treatment. Only one study of 33 patients is published , suggestOne hundred fourteen patients, fulfilling the 2010 ACR/EULAR classification criteria for rheumatoid arthritis , were inThe CDAI, comprising the sum of 28 swollen joints, 28 tender joints, patient global, and physician global assessment , was assSerum calprotectin was collected in serum-gel vials  and measured with EliA™ Calprotectin 2 wells  with a Phadia™ 250 device . For each measurement cycle, a commercial negative and positive control (Thermo Fisher Scientific) was analyzed. Serum samples were centrifuged within 1h after blood collection and stored between 2 and 8°C until measurement. Stable calprotectin values can be measured with this assay within 7 days .ESR (reference range in the first hour 3–8 mm) and CRP (reference < 0.5 mg/dl) were measured in the central laboratory of the University Hospital of Würzburg. CRP was detected by immunoturbidimetry .U tests were performed for continuous variables and Fisher’s exact tests for categorical variables. Spearman’s tests were used to calculate correlations. Receiver-operator characteristic (ROC) curves were calculated to detect cut-off values with the best sensitivity and specificity. SPSS Statistics v 28.0  was used for statistical analysis. For data collection Excel  was used. Figures were grouped by using Photoshop . Two-tailed P-values less than 0.05 were considered significant.Shapiro-Wilk tests were used to test for normal distribution. As normal distribution was mostly absent, medians with interquartile ranges (IQR) were shown. To test for differences between unpaired groups, Mann-Whitney Inflammation parameters were obtained from 69 RA patients. All patients received TCZ for at least 3 months. Forty-eight of 69 (69.6 %) were female; the median age was 64.0 years (range 33–89). Fifty-four of 69 (78.3 %) were rheumatoid factor (RF)-positive; 51/69 (73.9 %) were anti-citrullinated protein antibody (ACPA)-positive. All ACPA-positive patients were RF-positive. Fifty of 69 (72.5 %) received TCZ subcutaneously and 19/69 (27.5 %) intravenously. No concomitant DMARD was present in 43/69 (62.3 %) patients. Thirteen of 69 (18.8 %) received prednisolone , 12/69 (17.4 %) methotrexate (MTX), and 1/69 (1.4 %) lefunomide (LEF). The median activity parameters of the TCZ-treated patients were as follows: DAS28-ESR 1.5 (IQR 1.1–2.2), DSA28-CRP 1.7 (1.2–2.2), SDAI 5.0 (2.0–8.0), and CDAI 5.0 (2.0–8.0). The median inflammation parameters of the TCZ-treated patients were as follows: ESR in 1h 4.0 (2.0–6.0) mm, CRP 0.0 (0.0–0.1) mg/dl, and calprotectin 1072.0 (686.5–1916.5) ng/ml. Patients’ characteristics are summarized in Table n = 115) compared to the active patients (n = 10) (1040.0 (676.0–1638.0) ng/ml vs 4155.5 [1865.3–6068.3] ng/ml, P < 0.001)  one group of patients, who continued their DMARD regimen (i.e. tocilizumab + concomitant medication) (not active), and (2) patients, who needed a change or escalation of their DMARDs due to inflammatory activity of RA (active). One hundred twenty-five calprotectin values were included in this study as from some patients more than one time point was measured. The calprotectin value in the serum was significantly lower in the not active RA patients ( one grouP < 0.001)   had a lower median calprotectin value compared to patients with CDAI-low activity (n = 67) . Patients with CDAI-low activity had lower median calprotectin levels than patients with CDAI-moderate activity (n = 17) . Patients with CDAI-moderate disease activity had lower median calprotectin levels than patients with CDAI-high activity (n = 2)   Fig. a and sigr = 0.301; P < 0.001), with DAS28-CRP , and with SDAI   with IL-6-dependent composite scores was detected: with DAS28-ESR  were included in this study. Patients’ characteristics including sex, age, RF/ACPA-status, concomitant DMARDs, median activity scores, and median inflammation parameters are embedded in Table n = 43) had a lower serum-calprotectin (1845.0 [832.0–2569.0] ng/ml) than active RA patients (n = 10) receiving a change of their TNFi due to inflammatory activity of RA    Fig. d.r = 0.401; P = 0.001)  had a median calprotectin value of 1694.0 [773.5–2983.0] ng/ml, which was not significantly different from patients with CDAI-low activity (n = 26) . Patients with CDAI-low activity had lower median calprotectin levels than patients with CDAI-moderate activity (n = 5) . Patients with CDAI-moderate disease activity had numerically but not significantly lower median calprotectin levels than patients with CDAI-high activity (n = 2)  , with DAS28-CRP , and with SDAI   Fig. d. Patien Fig. d. P = 0.021, Fig. P < 0.001, Fig. In contrast to TCZ-treated patients, in TNFi-treated RA patients ESR and CRP showed differences between not active and active patients    and also found significant correlations between calprotectin values and RA-activity composite scores , which iDisease activity scores, including DAS28-ESR and DAS28-CRP, which contain serological inflammation parameters for their calculation (to a relevant extent), were lower in our TCZ-treated group than in the TNFi-group. In prior studies, RA patients receiving TCZ-treatment had higher DAS28-ESR remission rates than CDAI or SDAI remission rates, which was supposed to be due to the high weight of the ESR in the DAS28-ESR score . TherefoIt was formerly shown that RA patients with detectable TCZ trough levels (after intravenous application) did not have lower calprotectin values than patients with undetectable TCZ trough levels, in contrast to the CRP values, which were lower when TCZ was detectable . This suThe CDAI score is usually assessed directly after each patient’s visit without having the inflammatory parameters available at that time. This was also the case in our study: The CDAI scores were assessed without knowing the respective calprotectin values (neither ESR nor CRP). Thus, the decision of the investigator on the CDAI score was not biased by the knowledge of the inflammatory parameters.Concerns over the pre-analytical factors of serum-calprotectin values have been raised, but recently it was demonstrated that serum-calprotectin is stable for 1 week when kept refrigerated . This siOur study is limited due to its retrospective design and single-center approach. Additionally, future longitudinal studies are needed to evaluate calprotectin as follow-up parameter and its usefulness in detecting treatment response. Most of our patients had a low disease activity with a median CDAI value of 5, so that high inflammation parameters were only included in our study to a limited extent.Additional file 1: Supplemental Figure S1 Correlation of interleukin-6-dependend composite scores including DAS28-ESR , DAS28-CRP  and SDAI  with calprotectin in tocilizumab (TCZ)-treated RA patients (a-c) and in tumor necrosis factor alpha-inhibitor (TNFi)-treated RA (d-f) patients.


Salix species, including willow trees, are distributed in the temperate regions of Asian countries, including South Korea. Willow trees are used to treat pain and inflammatory diseases. Due to the medicinal properties of willow trees, pharmacological studies of other Salix spp. have gained attention; however, only a few studies have investigated the phytochemicals of these species. As part of our ongoing natural product research to identify bioactive phytochemicals and elucidate their chemical structures from natural resources, we investigated the marker compounds from indigenous Korean Salix species, namely, Salix triandra, S. chaenomeloides, S. gracilistyla, S. koriyanagi, S. koreensis, S. pseudolasiogyne, S. caprea, and S. rorida. The ethanolic extract of each Salix sp. was investigated using high-performance liquid chromatography combined with thin-layer chromatography and liquid chromatography–mass spectrometry-based analysis, and marker compounds of each Salix sp. were isolated. The chemical structures of the marker compounds (1–8), 3-(4-hydroxyphenyl)propyl β-D-glucopyranoside (1), 2-O-acetylsalicin (2), 1-O-p-coumaroyl glucoside (3), picein (4), isograndidentatin B (5), 2′-O-acetylsalicortin (6), dihydromyricetin (7), and salicin (8) were elucidated via nuclear magnetic resonance spectroscopy and high-resolution liquid chromatography–mass spectrometry using ultrahigh-performance liquid chromatography coupled with a G6545B Q-TOF MS system with a dual electrospray ionization source. The identified marker compounds 1–8 were examined for their antimicrobial effects against plant pathogenic fungi and bacteria. Dihydromyricetin (7) exhibited antibacterial activity against Staphylococcus aureus, inducing 32.4% inhibition at a final concentration of 125 μg/mL with an MIC50 value of 250 μg/mL. Overall, this study isolated the marker compounds of S. triandra, S. chaenomeloides, S. gracilistyla, S. koriyanagi, S. koreensis, S. pseudolasiogyne, S. caprea, and S. rorida and identified the anti-Staphylococcus aureus bacterial compound dihydromyricetin. Salix comprises approximately 500 species of deciduous trees and shrubs distributed in the temperate regions of Asian countries, including South Korea, and willow trees are the most representative plants of this genus  anthracene-induced phorbol ester-promoted skin carcinogenesis [S. caprea. Finally, S. rorida is a deciduous tree and a willow species native to Japan, northern China, Korea, and Russia. A recent phytochemical study revealed salicin as the major constituent of S. rorida and identified other constituents, including (+)-catechin, naringenin, salipurposide, aromadendrin, isosalipurposide, aromadendrin-7-O-β-D-glucopyranoside, and taxifolin-7-O-β-D-glucopyranoside, via liquid chromatography–mass spectrometry (LC/MS) analysis [S. rorida has not yet been elucidated.l (DPPH) . Despiteivatives ,11, whicinkiller . A pharmrophages . In additivities . Howeverl crafts . S. koripriority , and fewin Korea . S. koreth decay . A pharmer cells . S. kore effects ,21,22. Dnd fever  Recent p effects ,25. Prevextracts ,24. S. cSalix species S. triandra, S. chaenomeloides, S. gracilistyla, S. koriyanagi, S. koreensis, S. pseudolasiogyne, S. caprea, and S. rorida. Ethanolic extracts of these Salix spp. were investigated using high-performance liquid chromatography (HPLC) combined with thin-layer chromatography (TLC) and LC/MS-based analysis, followed by the isolation of each marker compound of Salix spp. The chemical structures of marker compounds (1–8) were elucidated via nuclear magnetic resonance (NMR) spectroscopy and electrospray ionization (ESI) LC/MS analyses. Finally, the identified marker compounds 1–8 were tested for their antimicrobial effects. Herein, we report the isolation and structural characterization of marker compounds 1–8 as well as their bioactivity with respect to their antimicrobial effects.As part of our ongoing research projects to identify bioactive natural products and elucidate their chemical structures from natural resources ,30,31,32Salix species  were collected from Chungcheongnam-do, Chungcheongbuk-do, and Gangwon-do (South Korea), thoroughly dried, and mounted. Plant specimens  to obtain EtOH crude extracts.The twigs of eight pecimens  were madSalix species  facilitated the determination of the marker compounds for these species based on the amount of the component present in each extract. Phytochemical investigation of the crude EtOH extracts was performed via TLC and LC/MS-based analysis using column chromatography and HPLC to isolate the marker compounds (1) was isolated from S. triandra, 2-O-acetylsalicin (2) from S. chaenomeloides, 1-O-p-coumaroyl glucoside (3) from S. gracilistyla, picein (4) from S. koriyanagi, isograndidentatin B (5) from S. koreensis, 2′-O-acetylsalicortin (6) from S. pseudolasiogyne, dihydromyricetin (7) from S. caprea, and salicin (8) from S. rorida propyl β-D-glucopyranoside (1) [O-acetylsalicin (2) [O-p-coumaroyl glucoside (3) [4) [5) [O-acetylsalicortin (6) [7) [8) [The marker compounds of side (1) , 2-O-aceicin (2) , 1-O-p-cside (3) , picein  (3) [4) , isogran [4) [5) , 2′-O-acrtin (6) , dihydro (6) [7) , and sal [7) [8)  . Next, the marker compounds were tested for their antibacterial activity against the Gram-positive bacterium Staphylococcus aureus (HG003) and Gram-negative bacterium Escherichia coli (MG1655). Among the isolates, only dihydromyricetin (7) exhibited antibacterial activity against S. aureus, inducing 32.4% inhibition at a final concentration of 125 μg/mL with an MIC50 value of 250 μg/mL (1) and 1-O-p-coumaroyl glucoside (3) exhibited very weak activity against S. aureus, inducing only 1.9% and 1.3% inhibition, respectively . Twigs of S. chaenomeloides, S. koriyanagi, and S. caprea were collected from Inje-gun and those of S. rorida from Hwacheon-gun . The twigs of S. gracilistyla and S. koreensis were collected from Jecheon-si . Each material was authenticated by the author J.N.Yu. Voucher specimens of the materials  were deposited at the Hongcheon Institute of Medicinal Herbs, Hongcheon-gun, South Korea.In June 2021, twigs of S. triandra twigs (1.6 kg) were dried at 35–45 °C in a plant-drying oven for one week, pulverized, and extracted with 80% ethanol (10 L) via sonication for 90 min three times at room temperature. The filtered ethanol extract was evaporated in vacuo to obtain the crude ethanol extract (86.6 g). The extract (10.1 g) was dissolved in MeOH (100 mL) and applied to a reverse-phase (RP) Sep-Pak column with 100% MeOH to remove the wax, lipids, and fatty acids, and the resultant residue was concentrated using an evaporator to obtain the crude extract (5.2 g). The crude extract (1.0 g) was separated via preparative RP-HPLC  to obtain four fractions (P1–P4). Fraction P2 (303.3 mg) was isolated via semipreparative RP-HPLC using 30% MeOH to obtain marker compound 1 .S. chaenomeloides twigs (1.8 kg) were dried at 35–45 °C in a plant-drying oven for one week, pulverized, and extracted with 80% ethanol (10 L) via sonication for 90 min three times at room temperature. The filtered ethanol extract was evaporated in vacuo to obtain the crude ethanol extract (144.6 g). The extract (10.3 g) was dissolved in MeOH (100 mL) and applied to an RP Sep-Pak column with 100% MeOH to remove the wax, lipids, and fatty acids, and the resultant residue was concentrated using an evaporator to obtain the crude extract (7.2 g). The crude extract (1.0 g) was separated via preparative RP-HPLC  to obtain five fractions (P1–P5). Fraction P4 (68.2 mg) was isolated via semipreparative RP-HPLC using 20% MeOH to obtain marker compound 2 .S. gracilistyla twigs (1.0 kg) were dried at 35–45 °C in a plant-drying oven for one week, pulverized, and extracted with 80% ethanol (10 L) via sonication for 90 min three times at room temperature. The filtered ethanol extract was evaporated in vacuo to obtain the crude ethanol extract (163 g). The extract (11 g) was dissolved in MeOH (100 mL) and applied to an RP Sep-Pak column with 100% MeOH to remove the wax, lipids, and fatty acids, and the resultant residue was concentrated using an evaporator to obtain the crude extract (5.6 g). The crude extract (1.0 g) was separated via preparative RP-HPLC  to obtain five fractions (P1–P5). Fraction P4 (468 mg) was isolated via semipreparative RP-HPLC using 23% MeOH to obtain marker compound 3 .S. koriyanagi twigs (2.0 kg) were dried at 35–45 °C in a plant-drying oven for one week, pulverized, and extracted with 80% ethanol (10 L) via sonication for 90 min three times at room temperature. The filtered ethanol extract was evaporated in vacuo to obtain the crude ethanol extract (167 g). The extract (10 g) was dissolved in MeOH (100 mL) and applied to an RP Sep-Pak column with 100% MeOH to remove the wax, lipids, and fatty acids, and the resultant residue was concentrated using an evaporator to obtain the crude extract (6.5 g). The crude extract (6.5 g) was subjected to HP-20 column chromatography, with eluting solvents of distilled water, MeOH, and acetone, to obtain three fractions (P1–P3). Fraction P1 (2.6 g) was separated via preparative RP-HPLC  to obtain five fractions (P11–P15). Fraction P13 (245 mg) was isolated via semipreparative RP-HPLC using 15% MeOH to obtain marker compound 4 .S. koreensis twigs (2.3 kg) were dried at 35–45 °C in a plant-drying oven for one week, pulverized, and extracted with 80% ethanol (10 L) via sonication for 90 min three times at room temperature. The filtered ethanol extract was evaporated in vacuo to obtain the crude ethanol extract (101 g). The extract (9.1 g) was dissolved in MeOH (100 mL) and applied to an RP Sep-Pak column using 100% MeOH to remove the wax, lipids, and fatty acids, and the resultant residue was concentrated using an evaporator to obtain the crude extract (7.9 g). The crude extract (7.9 g) was subjected to HP-20 column chromatography, with the eluting solvents distilled water, MeOH, and acetone to obtain three fractions (P1–P3). Fraction P3 (582 mg) was separated via preparative RP-HPLC  to obtain four fractions (P31–P34). Fraction P33 (582 mg) was isolated via semipreparative RP-HPLC using 43% MeOH to obtain marker compound 5 .S. pseudolasiogyne twigs (1.5 kg) were dried at 35–45 °C in a plant-drying oven for one week, pulverized, and extracted with 80% ethanol (10 L) via sonication for 90 min three times at room temperature. The filtered ethanol extract was evaporated in vacuo to obtain the crude ethanol extract (123.6 g). The extract (9.1 g) was dissolved in MeOH (100 mL) and applied to an RP Sep-Pak column using 100% MeOH to remove the wax, lipids, and fatty acids, and the resultant residue was concentrated using an evaporator to obtain the crude extract (4.0 g). The crude extract (4.0 g) was separated via preparative RP-HPLC  to obtain four fractions (P1–P4). Fraction P4 (306 mg) was isolated via semipreparative RP-HPLC using 39% MeOH to obtain marker compound 6 .S. caprea twigs (1.5 kg) were dried at 35–45℃ in a plant-drying oven for one week, pulverized, and extracted with 80% ethanol (10 L) via sonication for 90 min three times at room temperature. The filtered ethanol extract was evaporated in vacuo to obtain the crude ethanol extract (110.8 g). The crude extract (10.3 g) was separated using medium-pressure liquid chromatography (MPLC)  to yield seven fractions (P1–P7). Fraction P4 (507 mg) was isolated via semipreparative RP-HPLC using 27% MeOH to obtain marker compound 7 .S. rorida twigs (1.5 kg) were dried at 35–45 °C in a plant-drying oven for one week, pulverized, and extracted with 80% ethanol (10 L) via sonication for 90 min three times at room temperature. The filtered ethanol extract was evaporated in vacuo to obtain the crude ethanol extract (143.3 g). The extract (10.5 g) was dissolved in MeOH (100 mL) and applied to an RP Sep-Pak column with 100% MeOH to remove the wax, lipids, and fatty acids, and the resultant residue was concentrated using an evaporator to obtain the crude extract (7.8 g). The crude extract (7.8 g) was separated by MPLC  to obtain four fractions (P1–P4). Fraction P2 (218 mg) was isolated via semipreparative RP-HPLC using 15% MeOH to obtain marker compound 8 .B. cinerea (KACC40965), C. destructans (KACC 41077), F. solani (KACC 44891), F. asiaticum (KACC 46429), and R. solani (KACC 48921), were used for the in vitro antifungal activity assay. Fungi were cultivated on 25 mL of malt extract agar  at 25 °C for 14 d in the dark. Marker compounds (1–8) were evaluated for their antifungal activities in a 96-well microplate [6 cells/mL. The absorbance at 600 nm was measured every 12 h for 96 h. The experiments were performed in triplicate. The percentage of growth inhibition (GI%) was estimated using the following formula: GI% = 100 (Acontrol − Atest sample)/(Acontrol).Five plant pathogenic fungi provided by the Korean Agricultural Culture Collection . m KACC 469, and R.S. aureus (HG033) and the Gram-negative bacterium E. coli (MG1655) were used. Staphylococcus aureus strains were maintained aerobically in tryptic soy broth  or TSB 1.5% agar at 30 °C with shaking at 250 rpm. E. coli strains were maintained aerobically in lysogeny broth  or LB 1.5% agar at 37 °C with shaking at 250 rpm. The qualitative antibacterial activities of the marker compounds (1–8) were examined using the disk diffusion assay and MIC values [1–8). The marker compounds were prepared in DMSO (1%) at 100 μg/mL . The assay was performed with LB and TSB plates. Sterile beads were used to inoculate the surface of each plate to ensure homogeneous bacterial growth. Sample pellet disks were placed on the surface of the agar plates at equal distances. The inhibition was measured using a ruler after 24 h incubation at 30 and 37 °C. MIC assay was performed in a 96-well plate to examine the antibacterial effects of three marker compounds . Following serial dilution of the compounds  in 150 μL of Mueller–Hinton broth, the culture was inoculated at an optical density (OD) of 0.002. After incubation at 30 and 37 °C for 24 h with shaking, bacterial growth was measured at OD600 using a spectrophotometer (BioTek Synergy HTX).For the antibacterial activity assay, the Gram-positive bacterium C values . The disSalix species  were phytochemically investigated, followed by the isolation of their marker compounds: 3-(4-hydroxyphenyl)propyl β-D-glucopyranoside (1) from S. triandra, 2-O-acetylsalicin (2) from S. chaenomeloides, 1-O-p-coumaroyl glucoside (3) from S. gracilistyla, picein (4) from S. koriyanagi, isograndidentatin B (5) from S. koreensis, 2′-O-acetylsalicortin (6) from S. pseudolasiogyne, dihydromyricetin (7) from S. caprea, and salicin (8) from S. rorida. The chemical structures of these compounds were also determined via NMR spectroscopy and HR-ESI-MS analysis. Antimicrobial tests of the marker compounds (1–8) using plant pathogenic fungi and bacteria revealed that dihydromyricetin (7) exhibited antibacterial activity against S. aureus, inducing 32.4% inhibition at a final concentration of 125 μg/mL, with an MIC50 value of 250 μg/mL.In the present study, indigenous Korean


Ubiquitin-conjugating enzyme E2 T (UBE2T) is a potential oncogene. However, Pan-cancer analyses of the functional, prognostic and predictive implications of this gene are lacking.We first analyzed UBE2T across 33 tumor types in The Cancer Genome Atlas (TCGA) project. We investigated the expression level of UBE2T and its effect on prognosis using the TCGA database. The correlation between UBE2T and cell cycle in pan-cancer was investigated using the single-cell sequencing data in Cancer Single-cell State Atlas (CancerSEA) database. The Weighted Gene Co-expression Network analysis (WGCNA), Univariate Cox and Least absolute shrinkage and selection operator (LASSO) Cox regression models, and receiver operating characteristic (ROC) were applied to assess the prognostic impact of UBE2T-related cell cycle genes (UrCCGs). Furthermore, the consensus clustering (CC) method was adopted to divide TCGA-lung adenocarcinoma (LUAD) patients into subgroups based on UrCCGs. Prognosis, molecular characteristics, and the immune panorama of subgroups were analyzed using Single-sample Gene Set Enrichment Analysis (ssGSEA). Results derived from TCGA-LUAD patients were validated in International Cancer Genome Consortium (ICGC)-LUAD data.1 year = 0.720, AUC3 year = 0.700, AUC5 year = 0.630). The CC method classified the TCGA-LUAD cohort into 4 UrCCG subtypes (G1–G4). Kaplan–Meier survival analysis demonstrated that G2 and G4 subtypes had worse survival than G3 . A comprehensive analysis of immune infiltrates, immune checkpoints, and immunogenic cell death modulators unveiled different immune landscapes for the four subtypes. High immunophenoscore in G3 and G4 tumors suggested that these two subtypes were immunologically “hot,” tending to respond to immunotherapy compared to G2 subtypes (p < 0.001).UBE2T is highly expressed and is a prognostic risk factor in most tumors. CancerSEA database analysis revealed that UBE2T was positively associated with the cell cycle in various cancers. The risk signature of UrCCGs can reliably predict the prognosis of LUAD (AUCUBE2T is a critical oncogene in many cancers. Moreover, UrCCG classified the LUAD cohort into four subgroups with significantly different survival, molecular features, immune infiltrates, and immunotherapy responses. UBE2T may be a therapeutic target and predictor of prognosis and immunotherapy sensitivity.The online version contains supplementary material available at 10.1186/s12931-022-02226-z. To date, targeted therapy has become the first-line therapy for cancer patients harboring corresponding oncogenic driver mutations. However, drug resistance frequently occurs, resulting from de novo drug-resistant mutations, compensatory activation of collateral signaling pathways, or growth dominance of cells with preexistent drug-resistant genetic alterations . In the UBE2T gene is located in chromosome 1q32.1, encoding a protein product composed of 197 amino acids , we used a web-based analysis tool, the Gene Expression Profiling Interactive Analysis (GEPIA) (http://gepia.cancer-pku.cn/) [https://www.genome.gov/Funded-Programs-Projects/Genotype-Tissue-Expression-Project). The GEPIA included the re-computed TCGA and GTEx data that were processed from corresponding raw RNA-Seq datasets by the UCSC Xena project based on a uniform pipeline [http://ualcan.path.uab.edu/analysis-prot.html) [https://www.proteinatlas.org/) was used to explore the subcellular distribution of UBE2T in tumors [We downloaded the RNA-Seq expression data (FPKM format) of 33 different tumors from the TCGA database (pku.cn/) , to comppipeline . Batch eot.html) , 19 (Addot.html) , 19. Then tumors .Clinical data were fetched from the TCGA, including overall survival (OS), disease-specific survival (DSS), progression-free interval (PFI), and disease-free interval (DFI) survival data. Samples without survival information were excluded.  (http://biocc.hrbmu.edu.cn/CancerSEA/) database to examine the association of UBE2T with 14 primary tumor-related cellular activities, such as cell cycle, proliferation, and angiogenesis [UBE2T (FDR < 0.05 and correlation > 0.3). The correlations between UBE2T and scores of functional states were visualized using the “ggplot2” package. Our previous publication demonstrated that UBE2T promoted autophagy in LUAD; therefore, we further investigated the functional relevance of UBE2T in LUAD at the single-cell level. We chose EXP0066 (GSE69405) and EXP0067 (GSE85534) . The correlation between gene expression and immune infiltration was estimated by the Pearson correlation test  with the same settings  method to identify unsupervised intrinsic groups with similar biological characteristics . This mehttps://bioinformatics.mdanderson.org/estimate/disease.html) [The stromal score, immune score, and tumor purity were compared among different UrCCG subtypes by the ESTIMATE  method (se.html) . Moreovehttps://tcia.at/home). The IPS is calculated based on four significant categories of tumor immunogenicity determinants, including effector cells, immunosuppressive cells, major histocompatibility complex (MHC) molecules (antigen processing), and checkpoints/immunomodulators. The IPS, ranging from 0 to 10, was calculated based on the z-score for the expression of related genes . For all analyses. A two-tailed p < 0.05 was regarded as statistical significance if not noted.https://portal.gdc.cancer.gov/) , comprising gene expression in normal tissues from GETX (https://www.genome.gov/Funded-Programs-Projects/Genotype-Tissue-Expression-Project). Overall, UBE2T expression was significantly upregulated in various cancers. Additionally, GEPIA-based analysis showed that UBE2T was differentially expressed in different stages in adrenocortical carcinoma (ACC), invasive breast carcinoma (BRCA), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), ovarian serous cystadenocarcinoma (OV), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), LUAD, kidney renal clear cell carcinoma (KIRC), and thyroid carcinoma (THCA)  confirmed that UBE2T protein levels were significantly upregulated in LUAD, uterine corpus endometrial carcinoma (UCEC), renal cell carcinoma (RCC), OV, and BRCA  revealed both cytoplasmic and nuclear distribution of UBE2T  Fig. A. For inv/) Fig. B, we comE2T Fig. E.Fig. 1TWe investigated the clinical relevance of UBE2T in the different tumor types using Kaplan–Meier survival analysis Fig. : Fig. S5http://biocc.hrbmu.edu.cn/CancerSEA/) [We next investigated whether UBE2T regulates some fundamental cancer-associated cellular processes by mining the CancerSEA database (cerSEA/) . UBE2T whttp://timer.comp-genomics.org/) revealed the association of the eight genes with critical immune cell infiltration . Compared with the G3 group, G2 and G4 groups had higher enrichment scores on tumor-related signaling pathways, including cell cycle, autophagy, mismatch repair, and MTORC1 signaling, but lower scores on the B cell receptor and p53 signaling pathway  [Solid tumor tissue includes malignant cells, normal epithelial, stromal cells, immune cells, and vascular cells. The ssGSEA was performed on LUAD samples to analyze the differences in immune cell infiltration and tumor purity among different UrCCG subtypes by ESTIMATE (se.html) . G3 and Given the importance of immune checkpoints (ICPs)  and immuhttps://tcia.at/home) to determine the sensitivity to immune checkpoint inhibitors for the four subgroups, the most comprehensive immune determinant to date [Finally, we used immunophenoscore (IPS) for LUAD patients from The Cancer Immunome Atlas  genes have been reported –40. ProgFurthermore, based on the differential expression patterns of UrCCGs, CC analysis divided the TCGA-LUAD cohorts into G1 to G4 subtypes, with significantly different survival. G3 subtype patients had better survival than other subtypes. The four subtypes exhibited different molecular, cellular, and clinical characteristics. Consistently, the G3 subtype with better survival had lower expression levels of UBE2T than the G2 and G4 subtypes. G3 subtype patients predominated in clinical stage I and M0 subgroups, while the fraction of G2 and G2 subtypes increased with advanced stage and metastatic status. The subtypes were also associated with tumor biomarkers such as NSE, KRT19, and CA125. Overall, the survival status predicted by UrCCGs agrees with the traditional TNM stage, tumor metastasis status, and tumor biomarkers.Regarding the molecular and cellular characteristics, compared with the G3 group, G2 and G4 groups had higher enrichment scores on tumor-related signaling pathways, including cell cycle, regulation of autophagy, mismatch repair, MTORC1 signaling, MYC-targets V2, unfolded protein response, nucleotide expression repair, PI3K/AKT/mTOR, reactive oxygen species pathway, but lower scores on B cell receptor pathway and p53 pathway. EMT played an essential role in tumor metastasis . With a We analyzed the relationship between different subtypes and immunity. Intriguingly, tumors with G3 and G4 subtypes had higher levels of immune cell infiltration than tumors with G2 subtype. First, ESTIMATE analysis indicated that patients with G3 and G4 subtypes had higher stromal-score and immune-score but lower tumor purity than G2 type. The ssGSEA was adopted to calculate stromal score and immune score for each tumor sample with “stromal gene signature” and “immune gene signature,” respectively . Second,Moreover, with a published algorithm , we compIn conclusion, UBE2T is a critical oncogene. Moreover, UBE2T-related cell cycle genes could separate the LUAD cohort into four subgroups with significant differences in survival, immune cell infiltration, and immunotherapy sensitivity. Our findings demonstrated that UBE2T could be a therapeutic target, as well as a predictor of survival and immunotherapy in cancer treatment.https://portal.gdc.cancer.gov/. Accessed 20 Nov 2021.1. The TCGA database. https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/. Accessed 11 Nov 2021.2. The Gencode (GENCODE.v32). http://gepia.cancer-pku.cn/. Accessed 15 Nov 2021.3. The Gene Expression Profiling Interactive Analysis. https://www.genome.gov/Funded-Programs-Projects/Genotype-Tissue-Expression-Project. Accessed 11 Sep 2021.4. The Genotype-Tissue Expression database. http://ualcan.path.uab.edu/analysis-prot.html. Accessed 10 Nov 2021.5. The UALCAN portal. https://www.proteinatlas.org/. Accessed 23 Oct 2021.6. The Human Protein Atlas. http://biocc.hrbmu.edu.cn/CancerSEA/. Accessed 5 Aug 2021.7. The Cancer Single-cell State Atlas. http://timer.comp-genomics.org/. Accessed 27 Nov 2021.8. The tumor immune estimation resource database. https://dcc.icgc.org/. Accessed 7 Nov 2021.9. The ICGC cohort. https://tcia.at/home. Accessed 21 May 2021.10. The Cancer Immunome Atlas. Additional file 1: Figure S1. The flowchart of the study.Additional file 2: Figure S2. Association between UBE2T gene expression and overall survival (OS) of 33 different types of tumors in TCGA database. A-K. Significant association between UBE2T and OS of ACC (A), BRCA (B), KIRC (C), KIRP (D), LGG (E), LIHC (F), LUAD (G), MESO (H), OV (I), STAD (J), and THYM (K).Additional file 3: Figure S3. Association between UBE2T gene expression and disease-specific survival (DSS) of 33 different types of tumors in TCGA database. A-J. The significant association between UBE2T and DSS of ACC (A), COAD (B), KICH (C), KIRC (D), KIRP (E), LGG (F), LUAD (G), MESO (H), OV (I), and PCPG (J).Additional file 4: Figure S4. Association between UBE2T gene expression and disease free interval (DFI) of 33 different types of tumors in TCGA database. A-J. Significant association between UBE2T and DFI of ACC (A), BRCA (B), KIRP (C), LIHC (D), LUAD (E), MESO (F), PAAD (G), PRAD (H), STAD (I), and THCA (J).Additional file 5: Figure S5. Association between UBE2T gene expression and progression-free interval (PFI) of 33 different types of tumors in TCGA database. A-K. The significant association between UBE2T and OS of ACC (A), KICH (B), KIRC (C), KIRP (D), LGG (E), LIHC (F), LUAD (G), MESO (H), PARD (I), STAD (J), and UVM (K).Additional file 6: Figure S6. Validation of discrepancies in tumor immune environment among the four UrCCG subtypes in ICGC-LUAD cohorts. A-C. Differences in the stromal score (A), immune score (B), and tumor purity (C) . D. Heatmap of 28 types of infiltrating immune cells. E. The fractions of 28 infiltrating immune cells were compared among G1-G4 subtypes.Additional file 7. Detailed methods for the construction of risk signature with UrCCGs, discovery and validation of the subtypes by the UBE2T-related cell cycle genes, and investigation of different tumor-associated cellular and molecular states and immunotherapy sensitivity among the four UrCCG subtypes.


Staphylococcus aureus NCTC 6571-UB, a strain that was derived from S. aureus NCTC 6571. This strain was selected for sequencing in order to provide information on the genome dynamics and the acquired resistance genes for penicillin G, trimethoprim, and sulfamethoxazole resistance.We report the draft genome sequence of the laboratory strain  Staphylococcus aureus NCTC 6571 Oxford strain is a reference strain for penicillin sensitivity bioassays that was first deposited in the National Collection of Type Cultures (NCTC) by N. G. Heatley in 1943 .on assay  on Oxoidon assay . Therefode novo assembled using SPAdes v3.7 (The genomic DNA (gDNA) of NCTC 6571-UB was extracted from a 37°C overnight TS broth culture. Cells were lysed in Tris buffer (10 mM Tris-HCl pH 8.0) containing lysozyme. RNase A (0.1 mg/mL), proteinase K (0.1 mg/mL), and SDS (0.5% [vol/vol]) were subsequently added. gDNA was purified using SPRI beads  and sequenced by MicrobesNG . Multiple gDNA libraries were prepared using the Nextera XT library preparation kit , quantified using the Kapa Biosystems library quantification kit for Illumina, pooled, and sequenced with an Illumina NovaSeq sequencer (250-bp paired-end read setting). The raw data were quality filtered using Trimmomatic v0.36 , with a total of 2,661 coding DNA sequences, 58 tRNA genes, and 9 rRNA genes. The average coverage of the draft genome was 30×, and BUSCO analysis revealed 99.8% completeness. NCTC 6571-UB shared 99.86% 16S rRNA gene sequence similarity with the type strain S. aureus subsp. aureus DSM 20231 GenBank accession number AMYL01000007, but their genomes exhibited 97.5% average nucleotide identity (ANI) (in silico DNA-DNA hybridization (DDH) (accessed at https://ggdc.dsmz.de/ggdc.php#) .The sequencing resulted in 1,805,525 raw reads. The assembled draft genome of NCTC 6571-UB had a total length of 2,809,965 bp, with a G+C content of 32.7%, and it consisted of 65 contigs (ty (ANI)  and 76.8NCTC 6571-UB harbored a prophage-associated metallo-β-lactamase superfamily domain protein (locus tag M3M53_RS02030), which could confer penicillin resistance . ComparePRJNA835436, with BioSample and Sequence Read Archive (SRA) accession numbers SAMN28102073 and SRR19138527, respectively. The whole-genome sequence is available in GenBank under the accession number JAMFMC000000000.1.The whole-genome sequencing project was deposited in GenBank under the BioProject accession number