The authors demonstrate that scRNA-seq sample pooling followed by genetics-based separation of individuals is an effective means to identify individual samples in a variety of commonly studied species. Single-cell sequencing (sc-seq) provides a species agnostic tool to study cellular processes. However, these technologies are expensive and require sufficient cell quantities and biological replicates to avoid artifactual results. An option to address these problems is pooling cells from multiple individuals into one sc-seq library. In humans, genotype-based computational separation  of pooled sc-seq samples is common. This approach would be instrumental for studying non-isogenic model organisms. We set out to determine whether genotype-based demultiplexing could be more broadly applied among species ranging from zebrafish to non-human primates. Using such non-isogenic species, we benchmark genotype-based demultiplexing of pooled sc-seq datasets against various ground truths. We demonstrate that genotype-based demultiplexing of pooled sc-seq samples can be used with confidence in several non-isogenic model organisms and uncover limitations of this method. Importantly, the only genomic resource required for this approach is sc-seq data and a de novo transcriptome. The incorporation of pooling into sc-seq study designs will decrease cost while simultaneously increasing the reproducibility and experimental options in non-isogenic model organisms. Over the last decade, single-cell RNA sequencing (scRNA-seq) has exploded in popularity as a species agnostic tool for studying gene expression at the level of individual cells . The bigIn fields working with low cell numbers, like developmental biology, pooling of samples from multiple animals with no sample labeling method, or intention of demultiplexing has become a standard practice. This approach lacks advantages of true replicates because there is no way to assess the data for representation of all samples or variation between samples. The inability to demux pooled samples thus lacks the ability to account for replicate variation and perform replicate strengthened differential expression analysis . BecauseMethods for analyzing pooled data and for enabling the demultiplexing  of pooled scRNA-seq samples are varied in concept and accuracy and have been recently reviewed . To presIn contrast to these experimental demultiplexing approaches, computational methods have been developed to demultiplex pooled human samples without any labeling regimen using the natural genetic differences between individuals. These approaches detect genetic differences between samples at sites of single-nucleotide polymorphisms (SNPs) and implement demultiplexing based on differential distributions of these SNPs between samples. SNP-based approaches have been benchmarked and shown to be highly effective at separating human samples . RelativIn this project, we set out to learn whether SNP-based demultiplexers work in an array of non-human species. We benchmarked available SNP-based demultiplexing programs and found that most are highly accurate on model organism datasets. We then selected the demultiplexing tool with the broadest potential usability, souporcell , to moreWe first explored the performance of SNP-based demultiplexing methods when applied to a published zebrafish dataset. Two useful resources for applying SNP-based demultiplexing are available for zebrafish, a high-quality genome , and a chttps://github.com/statgen/popscle; After processing each zebrafish scRNA-seq sample individually, we performed in silico pooling of three samples . This syComparison of compute requirements for SNP demuxers.Table S1. To further investigate the demultiplexing accuracy of souporcell, SNP-based cell assignments were assessed for correlations with ground truth animal origin. We found a strong agreement between souporcell assignments and ground truth animal identities . We thenThe presence of doublets in single-cell RNA sequencing is a major confounder. A doublet is a droplet represented by a single-cell barcode that contains more than one cell. In these in silico pools, true doublets can be identified with absolute certainty because we have origin information. When comparing SNP-based demultiplexing results to ground truth, “confirmed doublets” are cells that were assigned doublets by both the ground truth and demuxer. Furthermore, “contested doublet” refers to cells in which the experimentally derived ground truth and SNP-based demuxer result disagree about a potential doublet. We thus investigated the doublet detection capacity of souporcell for heterotypic true doublets . Homotypic true doublets created during synthetic pooling were removed, as souporcell relies on intergenotypic doublet detection. We found that souporcell missed almost half of the synthetic heterotypic true doublets in the pooled dataset . The relWe next decided to in silico benchmark these SNP demultiplexers on another potentially inbred population, the African green monkey. The African green monkey is a pre-clinically relevant species, with a published genome  and SNP Finally, we also assessed results of SNP-based demultiplexing of synthetically pooled single-nuclei data from axolotl. The axolotl is an example of the type of organism for which scRNA-seq has enabled cell level study of regeneration and immunology for the first time . A genom6 total SNPs and indels  . In comparison to the inbred mouse strains, the SNP density of the successfully demultiplexed datasets was higher at 0.34/kb (axolotl), 0.86/kb (African green monkey), and 3.57/kb (zebrafish). Although an exact quantitative analysis of this possible genetic cutoff would be useful, these results imply that a range of 0.2–0.34 SNPs/kb may be the minimum required within a sc-seq dataset for SNP-based demultiplexing.Xenopus laevis scRNA-seq data containing eight experimentally pooled samples from three Xenopus transgenic lines that each overexpress a different fluorescent gene Xenopus ly genome .Xenopus transgenic line of origin, the low number of fluorescent gene counts left many cells without sufficient data to make an assignment prediction  but no available common SNPs VCF file. We first set out to assess souporcell demux assignments on pooled splenocytes from three transgenic Pleurodeles newts, which express different fluorescent proteins under the same ubiquitous promoter (CAG)  . We desier (CAG) .Xenopus analysis, we selected only cells that had sufficient read depth and fluorescent gene detection for benchmarking . The heterogeneity of sample representation in different cell clusters highlights the critical need for demultiplexing of pooled scRNA-seq data (As performed in the hmarking . We founhmarking . The fluhmarking  and S8H.hmarking . Furtherseq data . WithoutPleurodeles scRNA-seq data (Xenopus data). Similar to the Xenopus dataset, we attribute these fluorescence-based “negative” assignments to the low capture of fluorescent gene reads . It is more likely that these large pool experiments occur in non-human samples. Thus, to aid future validations, we provide examples and modified memory-efficient scripts to pool samples and determine accuracy which will aid laboratories’ working in any species to conduct their own benchmarking. In addition, this memory-efficient script allows for pooling without a VCF file, which will be critical for all researchers interested in benchmarking SNP demuxers in organisms without VCF files available. Once upper limits become established, another option to increase throughput would be layered multiplexing, for example, labeling 10 individuals with a CMO, another 10 with a second CMO, and so on. This could be paired with a second SNP-based demultiplexing step and could substantially expand sample throughput.It will also be important to define the upper limit for the number of distinct pooled samples followed by SNP-based demuxing for each organism. In line with this, we obtained assignments when performing SNP-based demultiplexing on a pool of 30 zebrafish samples. However, without a benchmarking assessment from a ground truth derived from a distinct technology, it is uncertain if these results can be trusted. We therefore propose that using SNP-based demultiplexers on large pools needs to be further validated. This can be performed in silico as more single-replicate scRNA-seq datasets become published. Until then, the developers of souporcell indicate that 21 pooled human samples can be demultiplexed and speculate that this could work in up to 40 . Multiple independent programs designed specifically for doublet detection using transcriptomic instead of genotypic information are available for scRNA-seq data including DoubletDetection , we modified the default souporcell pipeline to enable remapping of reads  studies will greatly expand single-cell–based discoveries. This will facilitate work in well-known and lesser studied species by lowering the financial and technical hurdles of producing adequately powered single-cell experiments. We predict that both species agnostic and cross-species comparative studies are going to be increasingly fruitful in uncovering biological insights and the application of SNP-based demultiplexing with minimal genomic resources is critical for future research.P. waltl and N. viridescens at Karolinska Institutet and were performed according to local and European ethical permits. N. viridescens and P. waltl were raised in-house. All animals were maintained under standard conditions of 12-h light/12-h darkness at 18–24°C , 20 ml of Yokuchi Bitamin Multivitamin and 10 ml of calcium supplementation  into 100 Liters of water). For Pleurodeles and Notophthalmus in the Cellplex experiment, animals were housed in the water as described but modified to only have sea salt, Ektozon, and calcium solution.All experiments were carried out in post-metamorphic  18–24°C . Before P. waltl and processed as individual samples in parallel. All animals were post-metamorphic newts from established transgenic lines close to sexual maturity: one female tgTol2(CAG:Nucbow CAG:Cytbow)Simon     were 85%, 98.4%, and 91.6% in eBFP, eGFP, and mCherry animals, respectively. FACS was used to sort for fluorescent-positive cells (Spleens were harvested from three separate  length) , one mal11.1 cm) , and fem10.8 cm) . Forcepsve cells . The sam5 cells of GFP+, 4 × 105 of mCherry+, and 3.15 × 105 BFP+ were isolated in individual 1.5 ml Eppendorf tubes. GFP and mCherry expression were high, but BFP expression was dim. 500 μl of each solution was then added to an individual 1.5 ml Eppendorf tube for the fluorescent pool sample   were excN. viridescens (4.45 g and 10.6 cm snout-to-tail) and one male N. viridescens (3.55 g and 10.2 cm) were collected, pooled into one tube, and then processed as described in “fluorescence-pooling experiment” excluding FACS cell sorting. The only modification to the above described processing is that cells were kept at room temperature throughout and that cells were resuspended in 0.7X PBS with 0.04% ultrapure BSA.Spleens from one female P. waltl, spleens were removed from animals as described in “fluorescent-pooling experiment” from one adult tgSceI(CAG:loxP-GFP-loxP-Cherry)Simon female (23.5 g and 16.1 cm snout-to-tail length), one male tgSceI(CAG:loxP-GFP-loxP-Cherry)Simon (13.95 g and 15.7 cm) animal. Pleurodeles were processed as individual samples. After the spleen was thoroughly mashed through the pre-wetted 70-μm nylon filter and the filter being washed with 10 ml of 0.7X PBS, the cells were centrifuged at 300g for 5 min. Splenocytes were resuspended cells in 1 ml of sterile filtered 1X ACK (http://cshprotocols.cshlp.org/content/2014/11/pdb.rec083295.short) to lyse red blood cells. After one minute of lysis, cells were diluted with 10 ml of 0.7X PBS and filtered through a 70-μm nylon mesh filter and centrifuged at 300g for 5 min. Cells were then resuspended in 0.7X PBS with 0.04% ultrapure BSA.For Notophthalmus sample and the individual Pleurodeles samples were then taken through the 10x Genomics 3′ Cellplex–labeling protocol  the only modifications being the use of 0.7X PBS + 0.04% BSA for all wash and resuspension steps. Samples were stained with CM304 , CMO305 , and CMO306 . Samples were manually counted and pooled at equal ratios immediately before loading onto the 10x Genomics Chromium Controller targeting 9,000 cells in total.The pooled Chromium single-cell 3′ kit v3 (10x Genomics) was used according to the manufacturer’s instructions.P. waltl de novo transcriptome from the study of https://figshare.com/articles/dataset/Trinity_Pwal_v2_fasta_gz/7106033/1 and unzipped. The Trinity  were downloaded from SRA using prefetch followed by fasterq-dump with flags split-files and include-technical. Files were renamed to 10x FASTQ format  and then aligned using Cell Ranger v7.0.0 count to GRCz11 with the corresponding gtf for GRCz11 filtered via Cell Ranger mkgtf for protein_coding genes.FASTQ files from a previously published study  (SRA acchttps://research.nhgri.nih.gov/manuscripts/Burgess/zebrafish/downloads/NHGRI-1/danRer11/danRer11Tracks/NHGRI1.danRer11.variant.vcf.gz and https://research.nhgri.nih.gov/manuscripts/Burgess/zebrafish/downloads/NHGRI-1/danRer11/danRer11Tracks/NHGRI1.danRer11.variant.vcf.gz.tbi (Preprint).To merge bams in silico, a VCF file and tbi index were downloaded from f.gz.tbi  and subsf.gz.tbi Preprintexec Demuxafy.sif bcftools filter --include “MAF ≥ 0.05” -Oz --output NHGRI1.maf0.05.danRer11.variant.vcf.gz NHGRI1.danRer11.variant.vcf.gzsingularity exec Demuxafy.sif bcftools sort -Oz NHGRI1.maf0.05.danRer11.variant.vcf.gz -o sorted.NHGRI1.maf0.05.danRer11.variant.vcf.gzsingularity The chromosomes between the vcf and gtf did not match so bcftools annotate --rename-chrs was used to change chromosome names in the VCF using a tab-separated file named chr.conv.txt with the format: chr1 1, chr2 2, etc.exec Demuxafy.sif bcftools annotate --rename-chrs chr.conv.txt sorted.NHGRI1.maf0.05.danRer11.variant.vcf.gz | singularity exec Demuxafy.sif bgzip > rename.sorted.NHGRI1.maf0.05.danRer11.variant.vcf.gzsingularity The sample-specific bam outputs were then merged using Vireo’s synth_pool.py script as follows:python synth_pool.py -s sample1_genome_bam.bam,sample2_genome_bam.bam,sample3_possorted_genome_bam.bam -b sample1/outs/filtered_feature_bc_matrix/barcodes.tsv,sample2/outs/filtered_feature_bc_matrix/barcodes.tsv,sample3/outs/filtered_feature_bc_matrix/barcodes.tsv -d 0.1 -o three_mixed_zf -p 1 -r NHGRI1.maf0.05.danRer11.variant.vcf.gz --randomSEED 50 --nCELL 500.Souporcell was run using souporcell_pipeline.py with inputs: merged BAM output from synth_pool.py, the output barcodes_pool.tsv from synth_pool.py, the genome fasta (Danio_rerio.GRCz11.dna.primary_assembly.fa), N = 3, and vcf file NHGRI1.maf0.05.danRer11.variant.vcf.gz.exec Demuxafy.sif souporcell_pipeline.py -i pooled.bam -b barcodes_pool.tsv -f Danio_rerio.GRCz11.dna.primary_assembly.fa -t 20 -o output -k 3 --common_variants NHGRI1.maf0.05.danRer11.variant.vcf.singularity We found that Freemuxlet failed when using the downloaded VCF file but was successful when inputting the VCF and minimap.bam generated via souporcell when running souporcell without a VCF. This implies that Freemuxlet may be able to be fed a sample-derived VCF . An example of a VCF that worked:exec Demuxafy.sif freebayes -f Danio_rerio.GRCz11.dna.primary_assembly.fa -iXu -C 2 -q 20 -n 3 -E 1 -m 30 -g 100,000 souporcell_minimap_tagged_sorted.bam > zf.freebayes.vcfsingularity exec Demuxafy.sif popscle dsc-pileup --sam pooled.bam--group-list barcodes_pool.tsv --vcf zf.freebayes.vcf --out $FREEMUXLET_OUTDIR/pileupsingularity exec Demuxafy.sif popscle freemuxlet --plp $FREEMUXLET_OUTDIR/pileup --out $FREEMUXLET_OUTDIR/freemuxlet --group-list barcodes_pool.tsv --nsample 3singularity exec Demuxafy.sif bash Freemuxlet_summary.sh $FREEMUXLET_OUTDIR/freemuxlet.clust1.samples.gz > $FREEMUXLET_OUTDIR/freemuxlet_summary.tsvsingularity Then Freemuxlet:“{print $1}” chromosomes.txt | paste -s -d, > chr.list.vireo.txtsingularity exec Demuxafy.sif samtools idxstats pooled.bam > chromosomes.txtawk exec Demuxafy.sif cellsnp-lite -s pooled.bam -b barcodes_pool.tsv -O $OUT_DIR -p 20 --chrom “$(<${DemuxSoupDir}chr.list.vireo.txt)” --minMAF 0.1 --minCOUNT 100 --gzipsingularity exec${DemuxSoupDir}Demuxafy.sif vireo -c $OUT_DIR -o $OUT_DIR -N $N.singularity Vireo:exec Demuxafy.sif samtools view -b -S -q 10 -F 3844 pooled.bam > $SCSPLIT_OUTDIR/filtered_bam.bamsingularity exec Demuxafy.sif samtools rmdupsingularity $SCSPLIT_OUTDIR/filtered_bam.bam$SCSPLIT_OUTDIR/filtered_bam_dedup.bamexec Demuxafy.sif samtools sort -osingularity $SCSPLIT_OUTDIR/filtered_bam_dedup_sorted.bam$SCSPLIT_OUTDIR/filtered_bam_dedup.bamexec Demuxafy.sif samtools indexsingularity $SCSPLIT_OUTDIR/filtered_bam_dedup_sorted.bamexec Demuxafy.sif freebayes -fsingularity Danio_rerio.GRCz11.dna.primary_assembly.fa -iXu -C 2 -q 1$SCSPLIT_OUTDIR/filtered_bam_dedup_sorted.bam >$SCSPLIT_OUTDIR/freebayes_var.vcfexec Demuxafy.sif vcftools --gzvcfsingularity $SCSPLIT_OUTDIR/freebayes_var.vcf --minQ 30 --recode --recode-INFO-all --out $SCSPLIT_OUTDIR/freebayes_var_qual30exec Demuxafy.sif scSplit count -csingularity NHGRI1.maf0.05.danRer11.variant.vcf -v$SCSPLIT_OUTDIR/freebayes_var_qual30.recode.vcf -i$SCSPLIT_OUTDIR/filtered_bam_dedup_sorted.bam -b barcodes_pool.tsv -r$SCSPLIT_OUTDIR/ref_filtered.csv -a $SCSPLIT_OUTDIR/alt_filtered.csv -o $SCSPLIT_OUTDIRexec Demuxafy.sif scSplit run -rsingularity $SCSPLIT_OUTDIR/ref_filtered.csv -a $SCSPLIT_OUTDIR/alt_filtered.csv -n 3 -o $SCSPLIT_OUTDIRexec Demuxafy.sif scSplit genotype -rsingularity $SCSPLIT_OUTDIR/ref_filtered.csv -a $SCSPLIT_OUTDIR/alt_filtered.csv -p $SCSPLIT_OUTDIR/scSplit_P_s_c.csv -o $SCSPLIT_OUTDIRexec Demuxafy.sif bash scSplit_summary.shsingularity $SCSPLIT_OUTDIR/scSplit_result.csv.scSplit:SRR12067711–SRR12067712) using prefetch followed by fasterq-dump with flags split-files and include-technical. Files were renamed to 10x FASTQ format  and then aligned using Cell Ranger v7.0.0 count to GRCz11 with the corresponding gtf for GRCz11 filtered via Cell Ranger mkgtf for protein_coding genes. The output possorted_genome_bam.bam and filtered barcodes.tsv file were then used to run souporcell:A previously published dataset of 30 pooled zebrafish embryos  was downexec Demuxafy.sif souporcell_pipeline.py -i possorted_genome_bam.bam -b barcodes.tsv -f Danio_rerio.GRCz11.dna.primary_assembly.fa -t $THREADS -o $SOUPORCELL_OUTDIR -k 30 --common_variants NHGRI1.maf0.05.danRer11.variant.vcfsingularity https://github.com/RegenImm-Lab/SNPdemuxPaper.The clusters.tsv file output from souporcell was then used to evaluate cluster distribution of cells. Code for this is the basis for https://sra-pub-src-2.s3.amazonaws.com/SRR13600554/107606_Xen_Pool_BL7_10_14dpa.bam.1) from a previously published publicly available dataset , three blastemas of three siblings , and three samples from three siblings  were download directly. SAMtools (https://ftp.ncbi.nlm.nih.gov/geo/samples/GSM5057nnn/GSM5057661/suppl/GSM5057661_107606_Xen_Pool_BL7_10_14dpa_barcodes.tsv.gz), X. laevis genome FASTA (https://sra-pub-src-2.s3.amazonaws.com/SRR13600553/Xenbase_v9.2.fa.1), and N = 8. Note: this specific reference includes the plasmid sequences necessary for mapping to fluorescent sequences.The Cell Ranger BAM file ( dataset  of fluorSAMtools  was usedE-MTAB-11662) labeled “reseq” and from samples D_1, L_1, and M_1, three individual animals all run on individual wells on a 10x Genomics chip were downloaded. To make a Cell Ranger reference, the axolotl genome (AmexG_v6.0-DD) was downloaded from https://www.axolotl-omics.org/dl/AmexG_v6.0-DD.fa.gz along with a gtf (AmexT_v47-AmexG_v6.0-DD.gtf) https://www.axolotl-omics.org/dl/AmexT_v47-AmexG_v6.0-DD.gtf.gz, which required the removal of white space  for use with Cell Ranger (v7.0.0) mkref. Cell Ranger count was run on each library individually, resulting in three position–sorted BAM files from samples D_1, L_1, and M_1. BAM files were merged using synth_pool.py from Vireo  which was filtered using BCFtools v1.11.FASTQ files from a previously published  axolotl om Vireo  using th“MAF ≥ 0.05” -Oz --output ddMale.common_maf0.05.vcf.gz ddMale_to_AmexGv6.vcf and then sorted:bcftools filter --include bcftools sort -Oz ddMale.common_maf0.05.vcf -o sorted.ddMale.common_maf0.05.vcf.gzBarcodes.tsv files were obtained from filtered outputs of Cell Ranger count for each library. Doublet rate (-d) was set to 0.1 and --randomSEED 50.python synth_pool.py -s /D_1/outs/possorted_genome_bam.bam,/L_1/outs/possorted_genome_bam.bam,/M_1/outs/possorted_genome_bam.bam /D_1/filtered_feature_bc_matrix/barcodes.tsv,/L_1/outs/filtered_feature_bc_matrix/barcodes.tsv,/M_1/outs/filtered_feature_bc_matrix/barcodes.tsv -d 0.1 -o pooled_bam -p 1 -r sorted.ddMale.common_maf0.05.vcf.gz --randomSEED 50.Note: we only expected the troublet portion of souporcell to be capable of detecting heterotypic doublets, so for downstream analysis of this synthetically pooled data, we removed all homotypic doublets.The pooled BAM was indexed using SAMtools index-c which made a .csi index and was renamed to have a .bai file extension for use in souporcell. Souporcell was run using souporcell_pipeline.py with inputs: merged BAM output from synth_pool.py, the output barcodes_pool.tsv from synth_pool.py, the genome fasta (AmexG_v6.0-DD.fa), N = 3, VCF file ddMale.common_maf0.05.vcf.gz, and --skip_remap SKIP_REMAP.exec Demuxafy.sif souporcell_pipeline.py -i pooled.bam -b barcodes_pool.tsv -f AmexG_v6.0-DD.fa -t 20 -o output -k 3 --skip_remap SKIP_REMAP --common_variants ddMale.common_maf0.05.vcfsingularity SRR12507774–SRR12507781), AGM3_Mediastinal Lymph Node (SRR12507790–SRR12507797), AGM5_Mediastinal Lymph Node (SRR12507806–SRR12507813), AGM7_Mediastinal Lymph Node (SRR12507822–SRR12507829), and AGM9_Mediastinal Lymph Node (SRR12507846–SRR12507853). C. aethiops has a robust VCF file available (European Variation Archive: PRJEB7923) that needs to be used in conjunction with genome assembly Chlorocebus_sabeus 1.1 (GCA_000409795.2). This assembly did not have an annotation file available, and we generated a gtf file for this GenBank assembly using minimap2   to create BAMs with these high quality cells. BAMs were subsequently merged using Vireo:Because of low reads/cell in these libraries, we selected barcodes using Seurat with between 1,000 and 2,000 features. These filtered barcode files were used with 10x subset-bam ” -Obarcodes_pool.tsv --chrom $VIREO_OUTDIR -p 20 --minMAF 0.1 --minCOUNT 100 --gzipexec Demuxafy.sif vireo -c $VIREO_OUTDIR -o $VIREO_OUTDIR -N 5.singularity And then cellsnp-lite and Vireo were run:exec Demuxafy.sif samtools view -b -S -q 10 -F 3844 pooled.bam > $SCSPLIT_OUTDIR/filtered_bam.bamsingularity exec Demuxafy.sif samtools rmdupsingularity $SCSPLIT_OUTDIR/filtered_bam.bam$SCSPLIT_OUTDIR/filtered_bam_dedup.bamexec Demuxafy.sif samtools sort -osingularity $SCSPLIT_OUTDIR/filtered_bam_dedup_sorted.bam$SCSPLIT_OUTDIR/filtered_bam_dedup.bamexec Demuxafy.sif samtools index $SCSPLIT_OUTDIR/filtered_bam_dedup_sorted.bamsingularity exec Demuxafy.sif freebayes -fsingularity $SCSPLIT_OUTDIR/filtered_bam_dedup_sorted.bam >GCF_000409795.2_Chlorocebus_sabeus_1.1_cds_from_genomic.fna -iXu -C 2 -q 1 $SCSPLIT_OUTDIR/freebayes_var.vcfexec Demuxafy.sif vcftools --gzvcfsingularity $SCSPLIT_OUTDIR/freebayes_var.vcf --minQ 30 --recode --recode-INFO-all --out $SCSPLIT_OUTDIR/freebayes_var_qual30exec Demuxafy.sif scSplit count -csingularity $SCSPLIT_OUTDIR/freebayes_var.vcf -v$SCSPLIT_OUTDIR/freebayes_var_qual30.recode.vcf -i$SCSPLIT_OUTDIR/filtered_bam_dedup_sorted.bam -b barcodes_pool.tsv -r$SCSPLIT_OUTDIR/ref_filtered.csv -a $SCSPLIT_OUTDIR/alt_filtered.csv -o $SCSPLIT_OUTDIRexec Demuxafy.sif scSplit run -rsingularity $SCSPLIT_OUTDIR/ref_filtered.csv -a $SCSPLIT_OUTDIR/alt_filtered.csv -n $N -o $SCSPLIT_OUTDIRexec Demuxafy.sif scSplit genotype -rsingularity $SCSPLIT_OUTDIR/ref_filtered.csv -a $SCSPLIT_OUTDIR/alt_filtered.csv -p $SCSPLIT_OUTDIR/scSplit_P_s_c.csv -o $SCSPLIT_OUTDIRexec Demuxafy.sif bash scSplit_summary.shsingularity $SCSPLIT_OUTDIR/scSplit_result.csvscSplit:Pleurodeles SuperTranscriptome FASTA and gtf files to produce a Cell Ranger compatible reference. Cell Ranger 7.0.0 count command was then used to map and count reads over the transcriptome for the three transgenic animal scRNA-seq dataset.Cell Ranger 7.0.0 mkref command was used with the above listed Preprint) singularity image (image version 1.0.3). The remapping and variant calling stages of souporcell were run externally because of problems with timeouts on the remapping process with the large salamander transcriptome, and issues with the souporcell internal freebayes command failing. The VCF from freebayes was then used in souporcell pipeline with the --skip_remap SKIP_REMAP and--common_variants ${VCF}. Full scripts used for souporcell processes are included below. Summary of souporcell run details can be found in Souporcell -related export“${DemuxSoupDir},${MappingAnalysisDir},${FastaDir}”SINGULARITY_BIND = exec${DemuxSoupDir}Demuxafy.sif renamer.py --bam $BAM --barcodes $BARCODES --out ${OutputName}.fqsingularity exec${DemuxSoupDir}Demuxafy.sif minimap2 -ax splice -I 9 G -t 20 -G50k -k 11 -K 50M -w 15 --sr -A2 -B8 -O12,32 -E2,1 -r200 -p.5 -N20 -f1000,5000 -n2 -m20 -s40 -g2000 -2K50m --secondary = no ${FASTA}${OutputName}.fq > minimap.samsingularity exec${DemuxSoupDir}Demuxafy.sif retag.py --sam minimap.sam --out minimap_tagged.bamsingularity exec${DemuxSoupDir}Demuxafy.sif samtools sort minimap_tagged.bam > minimap_tagged_sorted.bamsingularity exec${DemuxSoupDir}Demuxafy.sif samtools index minimap_tagged_sorted.bamsingularity #freebayes run:exec${DemuxSoupDir}Demuxafy.sif freebayes -f ${FASTA} -iXu -C 2 -q 20 -n 3 -E 1 -m 30 --min-coverage 6 minimap_tagged_sorted.bam > free.vcfsingularity #souporcell run.${CurrentAnalysisDir}free.vcfVCF = exec${DemuxSoupDir}Demuxafy.sif souporcell_pipeline.py -i ${CurrentAnalysisDir}minimap_tagged_sorted.bam -b ${BARCODES} -f ${FASTA} -t 20 -o ${OutputName} -k $N --skip_remap SKIP_REMAP --common_variants ${VCF}singularity Pleurodeles and Notophthalmus dataset was then mapped to this dual species index using Cell Ranger 7.0.0 count command. In addition, the Cell Ranger 7.0.0 multi-command was used to assess multiplexing Cellplex information for all cells in the same dataset. For the Cell Ranger multi-command, the following flags were used: . Souporcell demultiplexing was run identically to above on the Pleurodeles only samples but with N = 4, and the relevant FASTQ and dual species reference transcriptome FASTA files.A dual species Cell Ranger 7.0.0 reference was made using the SuperTranscriptomes and corresponding gtf files (described above) from the two species using Cell Ranger mkref command. The two species, four animal, pooled scRNA-seq from SRR15502048, SRR15502052, and SRR15502056 (https://sra-pub-src-1.s3.amazonaws.com/SRR20079758/WT3_possorted_genome_bam.bam.1wget https://sra-pub-src-1.s3.amazonaws.com/SRR20079759/WT2_possorted_genome_bam.bam.1wget https://sra-pub-src-2.s3.amazonaws.com/SRR20079760/WT1_possorted_genome_bam.bam.1.wget C57BL/6 data were downloaded from 15502056 . For DBA15502056  possortehttps://cf.10xgenomics.com/supp/cell-exp/refdata-gex-mm10-2020-A.tar.gz) and Cell Ranger 7.0.0 count command was then used to map and count reads from each strain and dataset.bamtofastq v1.4.1 was used to generate FASTQ files for subsequent mapping. A Cell Ranger reference was obtained from the 10x Genomics website . To enable more widespread use, we now stabilized memory use throughout the pooling and added an option (--noregionFile) which can pool in the absence of a VCF file. This means that species that do not possess a VCF file can still do the in silico ground truth benchmarking we performed in this study. A pull request has been initiated to propagate the changes to Vireo’s main GitHub repository (https://github.com/single-cell-genetics/vireo/pull/81). The modified version was used to pool the mouse data below. Note: if the pull request is accepted then calling the synth_pool.py script from Vireo’s GitHub repository will in fact be this modified script with new options added. This script can produce identical pooled BAM files to the original.To pool bams, we modified the original synth_pool.py script from Vireo’s GitHub repository which was memory intensive and only allowed for pooling in the presence of a VCF file *100. For Vireo, the “final donor size” numbers were used and percentage assigned calculated by ((donor0+donor1+donor2)/unassigned)*100.Vireo and souporcell were run as per the previous species and assignment percentages for souporcell were assessed via awk -F Preprint) singularity container (image version 1.0.3). SNP numbers in each VCF were counted using: grep “##” VCFname | wc -l.A summary figure reviews the computational details used to run souporcell on the above datasets . OverallPleurodeles and dual species datasets, the first two steps of the souporcell pipeline were run separately and then the output from these was introduced back into the souporcell pipeline for completion . Seurat was used to import and analyze single-cell gene expression data for all datasets and to analyze the multiplexing capture data for the dual-species Cellplex (CMO)-labeled dataset.A two part analysis in R and then Python was used to evaluate the efficacy of souporcell demultiplexing for each dataset. Scripts in R (version 4.1.2) primarily using Seurat (version 4.1.0)  were usePreprint) and upset plots , and included R version 4.1.2 (2021-11-01).Session info including package numbers for R analyses are embedded in the GitHub page  by a demultiplexing method. Cell ID value for each bin was then plotted against average total mapped reads. For datasets including fluorescent transgenic lines, binned data are also colored by the average number of summed mapped fluorescent reads per cell.Bar plots: filtered datasets were subset by the animal or animal group assignment from each demultiplex method being used to benchmark souporcell results. Within those subsets, the total cell quantity of cells assigned to each identity by souporcell was plotted (left plots). Alternatively, within each benchmarking demux result subset, the percentage of cells assigned to each identity by souporcell was calculated by dividing by total cells assigned to that identity by the benchmarking demuxer and multiplied by 100.VCF files were generated from 10x BAMs and the species-specific reference using VCFtools v1.11 with:$GENOME -b bamlist --threads 10 | bcftools call -m -Oz -f GQ --threads 10 -o allsites.vcfbcftools mpileup -f SNP density per kilobase was then calculated using VCFtools v 0.1.15:vcftools --SNPdensity 1000 --gzvcf allsites.vcf.gz“{ total + = $4; count++ } END { print total/count }” out.snpdenThis outputs an out.snpden file, and average SNP density across all sites was calculated using: awk E-MTAB-12186 for three animal pooled Pleurodeles splenocyte scRNA-seq and ArrayExpress accession E-MTAB-12182 for four animal pooled Pleurodeles and Notophthalmus splenocyte scRNA-seq. Code used to analyze the data are present in the Materials and Methods section, in linked Colab notebooks, or via GitHub (https://github.com/RegenImm-Lab/SNPdemuxPaper) All other data used in the study were from previously published works of which accessions are noted in the Materials and Methods section.Data are available on ArrayExpress with accession


Diabetes is a severe challenge to global public health since it is a leading cause of morbidity, mortality, and rising healthcare costs. 3.0 million Ethiopians, or 4.7% of the population, had diabetes in 2021. Studies on the chronic complications of diabetes in Ethiopia have not been conducted in lower-level healthcare facilities, so the findings from tertiary hospitals do not accurately reflect the issues with chronic diabetes in general hospitals. In addition, there is a lack of information and little research on the complications of chronic diabetes in Ethiopia. The objective of this study was to assess the degree of chronic diabetes complications and associated factors among diabetic patients presenting to general hospitals in the Tigray area in northern Ethiopia.As part of a multi-centre cross-sectional study, 1,158 type 2 diabetes (T2D) patients from 10 general hospitals in the Tigray region were randomly chosen. An interviewer-administered questionnaire, a record review, and an SPSS version 20 analysis were used to collect the data. All continuous data were presented as mean standard deviation (SD), while categorical data were identified by frequencies. Using a multivariable logistic regression model, the factors associated with chronic diabetes complications among T2D diabetic patients were found, and linked factors were declared at p 0.05.Fifty-four of people with diabetes have chronic problems. Hypertension (27%) eye illness, renal disease (19.1%), and hypertension (27%) eye disease were the most common long-term effects of diabetes. Patients with chronic diabetes complications were more likely to be older than 60, taking insulin and an OHGA  , having diabetes for more than five years, taking more than four tablets per day , and having high systolic and diastolic blood pressure. Patients with government employment , antiplatelet drug use , and medication for treating dyslipidemia , all had a decreased chance of developing a chronic diabetes problem.At least one chronic diabetic complication was present in more than half of the patients in this study. Chronic diabetes problems were related to patients’ characteristics like age, occupation, diabetes treatment plan, anti-platelet, anti-dyslipidemia medicine, duration of diabetes, high Systolic BP, high Diastolic BP, and pill burden. To avoid complications from occurring, diabetes care professionals and stakeholders must collaborate to establish appropriate methods, especially for individuals who are more likely to experience diabetic complications. Hyperglycemia is a metabolic condition associated with diabetes mellitus . There aDiabetes impacts people’s functional skills and quality of life, leading to severe morbidity and premature mortality, and is one of the top public health concerns in the globe . It negaFor instance, studies on the prevalence of chronic diabetic complications revealed that 96% of patients had hypertension, 46% had peripheral neuropathy, 30% had neuropathy, and 7% had neuropathy encountered impotence  and the Information on the prevalence of diabetes-related complications is essential to change diabetes management policies and practices to effectively control the disease. However, studies focusing on such topics are rare in Ethiopia ,20,21 anAlthough very few studies on chronic diabetes complications have been conducted in different parts of the country, there has not been a recent comprehensive study of outpatients in general hospitals. On the other hand, because those studies were conducted in tertiary hospitals, they were unable to provide a precise picture of diabetes complications at a lower level, such as in general hospitals. In addition, those studies were carried out among high-risk individuals, the study population of type 1 and 2 diabetes, assessing only acute complications, microvascular or macrovascular complications, and in some studies data were collected through document review only. Furthermore, it is essential to investigate diabetes complications and their contributing factors regularly to pot evolving patterns and formulate diabetes management strategies. Therefore, this study aimed to identify chronic complications related to diabetes and associated factors in general hospitals in the Tigray region of Northern Ethiopia.A multi-centre cross-sectional study was conducted in the Tigray region from September 2019 to January 2020. Tigray is one of the ten regional states of Ethiopia. The Ethiopian health care system is organized into three-tier: primary, secondary and tertiary levels of care. The primary level of care is provided at a primary hospital, health centre and health post. The Primary Health care Unit (PHCU) is composed of a health centre (HC) and five satellite health posts (HP). These facilities provide service to approximately 25,000 people. A primary hospital provides inpatient and ambulatory service to an average of 100,000 Population and has an inpatient capacity of 25–50 beds. A general hospital provides inpatient and ambulatory services to an average of 1,000,000 people. A tertiary hospital serves an average of five million people. It serves as a referral to the general hospital .In 2019/2020, in the Tigray region, there were 2 referral hospitals, 14 general hospitals, 24 primary hospitals, 230 health centres and 741 health posts. There were more than 310 ambulances and a well-established referral system. There were over 750 private health facilities in the private sector, ranging from drug vendors and clinics to general and specialized hospitals. There were more than 25,000 health workforce ranging from health extension workers to specialists and sub-specialists . This stAll type 2 diabetic (T2D) patients admitted to the study hospitals and those who attended diabetic clinics during the data collection period participated in the study. To be involved in the study participants had to be adult.All adult patients aged more than 18 years who were diagnosed to have T2D and had follow-up visits in the study hospitals for ≥1 year. The study did not include patients.All Patients who were pregnant and critically ill.20.535(1–0.535)/(0.03)2. 10% of the initial sample size was added for non-response rate and the final sample size was 1,168. Proportional allocation of participants was employed to allocate the sample size among the selected general hospitals based on caseload (The sample size (n) was estimated using a single population proportion formula proposed by Cochran  with an caseload .Ten out of fourteen public general hospitals were selected through a simple random sampling technique, and all general hospitals were not included because of budget constraints. Participants were selected using a systematic random sampling method whereby the first patient was selected randomly from the first three by a lottery method, and the next patient was selected every three intervals until the required sample was attained.The data were collected using a pre-tested, interviewer-administered questionnaire that was developed based on relevant literature ,21,31,32The T2D patients attending 10 hospitals during the data collection period were approached by the data collectors, verified for eligibility and then, after informed consent was obtained, data were collected. Data was collected by ten BSc nurses who had either MPH or MSc with multilingual abilities and were supervised by the first and third authors. The type 2 diabetic patients were identified by examining their diagnosis as reported in the medical record. Moreover, clinical and chronic diabetic complications data were extracted from patients’ medical records. The diagnosis of chronic diabetic complication was then confirmed by the physician.To assure data quality, training and orientation of the study were done for the data collectors and supervisors and the questionnaire was pre-tested and checked for its validity and reliability. The pretest of the questionnaire involved 2% of the sample size and it was carried out in Quiha general hospital two weeks before the actual data collection. The questionnaire was revised based on the pre-test results. The questionnaires were checked for completeness and consistency on the daily bases. The data was entered and cleaned, entered in the SPSS version 20 analyses were performed using. A binary logistic regression analysis model was used to identify factors associated with chronic diabetes complications. The Homer-Lemehow goodness-of-fit test was used to check the model fitness and the assumption of a P-value >0.05 was considered a good model fit. Independent variables with p < 0.20 during the bivariate analysis were then included in the multivariable logistic regression for further analysis to control confounding factors. Multicollinearity between independent variables was checked by using the tolerance test and variance inflation factor (VIF). P < 0.05 was considered the cut-off point for reporting an independent variable that shows a statistically significant association with the dependent variable in multivariate analysis. The strength of the association of factors with chronic diabetes complications was demonstrated by computing the adjusted odd ratio (AOR) and its 95% confidence interval (CI).Socio-demographic: age, sex, marital status, religion, ethnicity, educational status, occupation, residence, monthly income (UD), family history of diabetes and BMI.Clinical: diabetes treatment regimen, anti-platelet drug , anti-dyslipidemia drug, glucometer, duration of diabetes, Fasting Blood Glucose (FBG), Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Pill burden, and high health care cost.Behavioural: adherence to a diabetic diet, saturated fat consumption, vegetable consumption, adherence to diabetic medication, self-blood glucose test, smoking, alcohol consumption, physical activity, and diabetes education.Chronic diabetes complications statusThe data about chronic diabetes complications were extracted from the patient’s chart/medical record. Only chronic complications that developed after the diagnosis of T2D and could be attributed to diabetes were considered in this study.Hypertension: was defined as systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg, and/or patient on antihypertensive therapy was taken as the hypertensive patient ,36.Coronary artery disease (CAD): The diagnosis criteria for CAD were either a patient with typical anginal pain or equivalent symptoms or an abnormal resting ECG or an asymptomatic patient with the abnormal stress test, either by ECG or echo or a nuclear perfusion imaging test .Peripheral vascular disease: The presence of intermittent claudication and/or an ABI  value (< 0.9) in any limb was recorded as peripheral vascular disease .Neuropathy: was diagnosed if a change was found in two or more of the three items hypesthesia or anaesthesia in lower and upper limbs when the patient’s lower limp was evaluated .Eye diseases: Eye diseases such as cataracts, glaucoma and diabetic retinopathy were identified based on the report of the ophthalmologist or optometrist from the dilated eye (fundus photography for retinopathy), and comprehensive eye examination, which was recorded on the patient’s chart .Chronic Kidney disease: was diagnosed based on the presence of urinary albumin, and/or an abnormally high level of serum creatinine (low glomerular filtration rate) .Foot problem: The diagnosis of foot problem was made through foot examination for any abnormalities  or all patients were asked about a history of foot ulcer, neuro ischemic foot, or amputation .Follow a healthy diet: consuming vegetables, beans and peas, fruit, whole grain, nut, and seeds, seafood, low-fat milk and milk product, and a moderate amount of lean meat, poultry, and egg ,42.High fat/oil consumption: eat or consume more than 10% of calories from saturated fat, which means more than 20 grams of saturated fat per day ,44.Alcohol consumption: Adult with diabetes who drinks alcohol should do so in moderation (no more than one drink per day for adult women and no more than two drinks per day for adult men). One drink is defined as 12 oz/355ml of beer, 5 oz /148ml glass of wine, or 1.5 oz/44ml of distilled spirit .Physical activity: At least 150 minutes per week of aerobic exercise, plus at least two seasons per week of resistance exercise, are recommended .Ethical approval to conduct the study was obtained from the Institutional Review Board (IRB) of Mekelle University (Ref No. ERC 1370/2019). The study received approval from the Tigray regional health bureau and permission from the Medical Directors of the 10 involved hospitals. The study was conducted following the declaration of Helsinki. Study participants were recruited voluntarily after they were informed about the study and that they can withdraw from the study at any stage thereafter they signed the consent form. All data were kept in a safe and secure place anonymously to ensure confidentiality and only the researchers had access to the data.Overall, a total of 1,168 diabetic individuals were eligible for the study, but only 1,158 of the participant’s questionnaires were fit for final analysis, which makes the response rate 99.14 per cent. Thirty-four per cent of the participants had an age greater than 60 years, with a mean age of 55.9 (D± 11.9) years. Most of the participants were male (54%), Married (67%), Orthodox Christian (88%), Tigrian ethnicity (96.3%), and urban resident (72.3%). Majority of the participants: 50.5% had no formal education, 80.3% had no family history of diabetes, 29.7% were unemployed, 7.9% had a monthly income of $34.26-$171.16 and 76.5% had a BMI of <25 kg/m2 .Of the total patients included in this study, 78.9%, 11.7%, and 16.8% were taking oral hypoglycemic agent (OHGA), anti-coagulant drug, and anti-dyslipidemia drug respectively. The mean duration of diabetes was 6.3 (D ± 4.6) years and 54.6% of participants had diabetes duration of < 5 years. Of all participants, 10.1% had a glucometer at home, 60.7% had FBG of >130.00 mg/dl, 25.8% had SBP of >149.00 mmHg and 9.0% had DBP of > 90.00 mmHg. From the total participants, 42.7% were taking >4 pills per day, 86.4% had a high health care cost, 69.7% consumed a high amount of saturated fat (>20 g per day), and 68.7% were taking less than the recommended amount of vegetable (<4 serving per week). Of all participants, 90.6% and 50.4% adhered to diabetes medication and a healthy diet. Of the total participants, nine out of ten test their blood glucose once per month, 6.2% ever smoke tobacco product, 12.4% consume more than moderate amount of alcohol (≥3 drink per day). Moreover, of the total participants, 61.3% were active physically, and 76.5% were attending diabetes education at the time of follow visit .Overall, 54% of participants  suffered from at least one chronic complication of diabetes. Among all participants who had the complication, 10.5% had a single complication, 16.9% lived with two and 26.8% with more than two types of complications respectively.Of all types of macrovascular complications, hypertension 27%  was the most common but cerebrovascular disease 4.31%  was the least common type. Out of all participants who had cerebrovascular complications, 0.3% had TIA and 4% had a history of stroke .The most common type of microvascular complication was ocular disease 22.62% , followed by kidney disease 19.17%  and peripheral neuropathy11%  respectively. Patients with renal complications consisted of 9% with microalbuminuria,4.0% with macroalbuminuria and 6% with high levels of serum creatinine. Ocular complications included cataracts, retinopathy, diabetes blindness and glaucoma, with magnitudes of 5%, 9%, 1% and 8% respectively. Of the total participant who had a foot disease, 4% had a diabetes-related foot ulcer, 1% had a foot amputation, 3.6% had ischemic pain, 1% had gangrene and 3% had an infection .The Homer-Lemehow goodness-of-fit test was done, and its result showed P = 0.0063, which was considered a good model fit. The odds ratio was calculated for factors found to be associated with chronic diabetes complications among type 2 diabetic patients. After considering all assumptions of binary logistic regression and the p-value (≤ 0.05) in the bivariate analysis, fifteen variables were identified as candidates for analysis in the multivariable model. In the multivariable logistic regression analysis, nine variables were found to be factors associated with chronic diabetes complications at a 5% level of significance.The odds of a chronic diabetes complication was higher among patients age > 60 years ) than their counterpart, who took insulin and OHA had a higher chance of developing the complication  than patients taking insulin injection only, with diabetes duration of ≥ 5 years, were at higher risk to develop the complication  than patients with shorter diabetes duration and who took ≥4 pills a day had a greater risk to develop chronic diabetes complication  than their counterpart . SimilarThis study aimed to assess the magnitude of chronic diabetes complications and associated factors among diabetic patients attending the general hospitals in the Tigray region, Northern Ethiopia. The overall magnitude of chronic diabetes complications in this study was 54.0% . This is consistent with studies from Bahir Dar, Northwest Ethiopia (54%), and China (52%) ,21. The However, our study showed that 34%, 45% and 77% of the study participants were >60 years of age, with ≥ 5 years of diabetes duration and normal BMI respectively. However, the result of this study is higher than a study in Gurage Zone, Southwest Ethiopia (46%).  The probIn this study, 27.28%  participants had diabetes-related hypertension which is in line with a study done in Jimma, Ethiopia(25%) . This isIn this study, peripheral vascular disease was seen among 9.13%  participants, which is higher than the finding from a survey conducted in Sri Lanka (4.7%) ,58. The Coronary artery disease (CAD)occurred among 3.28%  participants in this study, which is lower than studies done in Sri Lanka (11%), Saudi Arabia (23%), Bangladesh (26%), India (8%), Nepal (23%), and Iraq (15%) ,60,62–64Of the total participants in this study, 4.31%  had a stroke, which is higher than studies done in Libya (1.9%), Saudi Arabia (0.19%), Nepal (1%) and Iraq (0.7%) ,51,54,56Peripheral neuropathy was seen among 11%  participants; this finding is almost similar to that of studies conducted in India (11%) and West Ethiopia (10%) ,67. The This result is higher than the result of Saudi Arabia (1.4%) and Iraq (6.5%) ,56. The However, it is lower than the result of Sri Lanka (63%), Southwest Ethiopia (15%), Nepal (15%), Bangladesh (28%), India (19%), Tanzania (29%), and Egypt (22%) ,62,68,69In this study the magnitude of diabetes nephropathy was 19.17% this figure is in line with the study conducted in Ethiopia . Unlike In this study, retinopathy was seen in 9% (95% CI: 7.25–10.53) of participants, which is in line with studies conducted in Indonesia (7%), Tunisia (8.1%), Iraq (7.5%), and Ethiopia (10%) ,64,65,69However this result is lower than research findings done in Ethiopia (26%), Sri Lanka (26%), SSA (15%), Nepal (29%), Korea (38%), India (15.4%), Bangladesh (38%), Libya (31%), Tanzania (50%), Sudan (14%), and Egypt 21%) Reviewers' comments:Reviewer's Responses to Questions  Comments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?  The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: PartlyReviewer #2: Yes**********  2. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: YesReviewer #2: Yes**********  3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.  The Reviewer #1: YesReviewer #2: Yes**********  4. Is the manuscript presented in an intelligible fashion and written in standard English?  PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: NoReviewer #2: No**********  5. Review Comments to the Author  Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: This article can be made simpler for the scientific community with some modifications and clarifications as below.Minor comments1. Consistently use either “Tigray” or “Tigrai” in the title, author affiliation, study area, and elsewhere.2. Change “north Ethiopia / northern Ethiopia” to “Northern Ethiopia” throughout the manuscript3. Instead of saying "an institutional-based study," say “a multi-center cross-sectional study." since the study was conducted in ten public general hospitals.4. It is better to say 54% rather than 54.0%, and 27% rather than 27.0%.5. Throughout the manuscript, apply comma for numbers with more than two digits, like instead of saying 60 000–100 000 people, better to say 60,000-100,000 people.6. Remove double full stops throughout the manuscript7. Since it is an international journal, better to change the income status that you mentioned in Ethiopian birr (ETB) to US dollar using the exchange rate at the date of data collection.8. I recommend you to avoid writing formulas for sample size calculations, it would be better to make it in sentence format the approach you applied for calculating the sample size than writing detailed mathematical equations.9. Remove the Hosmer-Lemeshow goodness-of-fit test result you mentioned in the method section and put it at the result section (particularly at the multivariable regression table), At the method section you have to mention what you have did not what you found.10. Add your response rate in the result section, as your study is a cross-sectional study11. I appreciate writing of the findings of the measure of effect (AOR with its 95% CI), make it consistent in the result section of the manuscript (you mentioned ), re-write it as you have rightly described in the abstract section.12. Avoid P-value if used 95% CI instead of writing like (), remove the P-value and re-write it as , and remove the hyphen (-), rather use comma for writing the 95% CI.13. Check the reference section, the majority of the citations are appropriate, but there are some references citrated in appropriately, check them again using software’s or manually14. Make sure whether your questionnaire has three or four parts, there are inconsistencies in their parts mentioned in the data collection tool and measurement part.Major comments1. Rationale of the study: You mentioned that the previous studies had a critical methodological limitation, which hindered the scientific community to make any conclusion or judgment? How do you know whether the sample size was small, if appropriately calculated even 10 sample size can be enough? There are more than ten similar articles published elsewhere in the country, including in the region, what was the added value of your study? I would suggest you to re-write it again, considering convincible scientific argument.2. Did you assess the risk factors? What you assessed was factors associated with chronic diabetic complications, do you think that we can interchangeably use risk factors and factors associated? Can we assess risk factors using simple, classical cross-sectional study design? I recommend you to consistently use the term factors associated, not risk factors in your manuscript.3. Sample size calculation: It is appreciable that you have used 3% margin of error to maximize the sample size, and your response rate was 94.6%, but after adding 10% non-response rate, the final sample size should be 1,178 not 1,061.4. Rewrite incomplete sentences, as there are many incomplete sentences, check the spelling and grammar issues, please check again the whole manuscript for spelling and grammar issues (use of present or past tense), I am not comfortable with the write-up.Reviewer #2: PLOS ONEPONE-D-22-12568Research ArticleMagnitude of Chronic diabetes complication and its associated factors among adultswith type 2 diabetes in Tigray region, northern EthiopiaBy: kalayou kidanu Berhe,Mekelle University College of Health SciencesMekelle, Tigray ETHIOPIADr Hussein Ismail, Reviewer report to PLOS one December 20221. There is a lot of English language errors that need to be corrected, I believe the manuscript needs a professional proofreading before publications. A lot of errors are identified as in the title Chronic .2. Moreover, there are a lot of abbreviation errors, that need to be corrected, e.g., in the abstract Bsc nurses, OHA, …etc. The manuscript has a lot of abbreviation errors as well, e.g., IDF in the abstract.3. Title: Magnitude of Chronic diabetes complication and its associated factors among adultswith type 2 diabetes in Tigray region, northern Ethiopia.The authors stated that they studied 10 general hospitals out of 13 hospitals, and they did not included any referral or primary care centers. So, the selection is based on general hospitals only, there fore it should be mentioned in the title.My suggestion for the title:Magnitude of chronic diabetes complication and its associated factors among diabetic patients attendingthe general hospitals in Tigray region, northern Ethiopia.4. METHODS4.1. Why did not the author include all the 13 general hospitals? I was surprised of taking 10 hospitals and leaving 3 hospitals. Please explain.4.2. Sampling:The methods of sampling were explained efficiently. Although, the author in included p=0.535, (p=proportion of chronic diabetes complications) please include the refence you used that stated the complications proportion as 0.535. Sampling is a step the author did before the research; I am surprised that this proportion used in the sampling was 0.535 is the same as the magnitude of diabetes complications which was the main finding of this study. Please explain.4.3. Regarding the operational definitions, the definition of hypertension the author used was BP> 140/90, I checked the reference used and it was outdated. Reference number 30.30. Muxfeldt ES NAdR, Salles GF, Bloch KV. Demographic and clinical characteristics of hypertensive patients in the internal medicine outpatient clinic of a university hospital in Rio de Janeiro. Sao Paulo Med J Child Adolesc Behav. 2004;122:87-93.My suggestion: please update all the operational definitions according to the updated guidelines or manuscript. Regarding hypertension, you may use the American heart association guidelines 2017 or the European Society of cardiology (ESC) guideline 2018. I recommend the ESC because it agrees with the level of 140/90 that you chose.Moreover, according to guidelines: No caffeine, no smoking, no eating for at least 2 h before measurement. The author stated BP was measured after30 minutes after hot drink as coffee. Please, explain and what is the refence you used?5. RESULTS5.1. The author stated in the results• • in which 42.7%, 10.0% and 13.0% had chronic diabetes complicationrespectively.• had DBP of > 90.00 mmHg in which 6.0%, 32.2% , 19.7% and 6.6%participants had at least one chronic diabetes complication respectively.Suggestion:• Please specify each complication associated with these numbers.• Please apply this notion along the whole paper.5.2. Tables: The authors need to put all the abbreviations in the footnote related to each table. Some abbreviations are missing in the footnotes.6. DISCUSSIONThe discussion is well written.7. CONCLUSIONIt highlights the main findings and supported by the study results.8. ReferenceThe authors included a lot of outdated refences. As the refences included:• Reference 10: 1996• Reference 22: 1965Suggestion: updating the refences accordingly.Regarding recent refences: Only one reference (number 35) was published 2020.**********  6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.  Yes: Zenawi Hagos Gufue, Adigrat University, EthiopiaReviewer #1: Yes: Hussein M. Ismail, MD CardiologyReviewer #2: **********https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  11 Apr 2023RESPONSE TO REVIEWERS_______________________________________________________________________________________________________Response to Reviewer #1I. Minor comments 1. “Tigray” is used consistently instead of “Tigrai” in the title, author affiliation, study area, and elsewhere in the manuscript ……………………………………………………………………………………………….2. “north Ethiopia / northern Ethiopia” was changed to “ Northern Ethiopia”…………………………3. "an institutional-based study," was replaced by “a multi-center cross-sectional study”…………….4. It was corrected as 54% from 54.0% and as 27% from 27.0% and other similar issues corrected throughout the document as per your suggestion .................................................................................................5. Comma was used for numbers with more than two digits ………... 6. Double full stops were removed………………………………………………………………………...7. The mentioned Ethiopian birr (ETB) in the Monthly income status section was changed in to US dollar based on average exchange rate of 2019…………………………………………………………(Page 9)8. The formula for sample size calculation was removed and rewrote in sentence format as per your recommendation and checked using StatCalc for population survey via Epi Info 7.0 software………..9. The result of Hosmer-Lemeshow goodness-of-fit test was removed from the method section and placed at the result section (Factors associated with chronic diabetes complication & table3 foot note), ……….(Page11)10. Result of response rate is included/added in the result section of the manuscript………………….........(Page 8)11. The multi variable analysis result was re-wrote as per your recommendation……………………...12. In Abstract & result section P-value was removed and re-wrote as , and the hyphen (-) was removed instead comma was use for writing the 95% CI……..............................13. In appropriately cited references were checked & corrected via endnote software ...………………(Page 18-21)14. The questionnaire has four parts, then corrected as “ it has four parts”…………………….……….....(Page 5)_______________________________________________________________________________________________II. Major comments1. Rationale of the study: revised and additional scientific arguments are included ………………2. The term “Risk factor” was replaced by “factors associated” and used consistently throughout the manuscript ………………………………………………………………………………………………3. Sample size calculation: …………………………………………………………………………...The required sample size (n) was estimated manually using a single population proportion formula and cheeked using STATCALC for population survey via Epi-info version 7 software with assumptions of 95% CI (z = 1:96), d=0.03, and P=0.535.Therefore the initial sample size was 1061.882 ( ni = (Z1-α/2)2p (1–p)/d2= (1.96)20.535(1-0.535)/(0.03)2 ). However, a refusal rate of 10% (1061.882*0.1) =106.1882 was added and gives a final sample size of 1,168.0702 , Accordingly10 questioners were excluded because of gross incompleteness and 1,158 participants’ questioner were fit for final analysis which makes response rate of 99.14 % ……………………………… 4. Incomplete sentences were re-wrote, spelling and grammar issues were checked and corrected… __________________________________________________________________________________________________________________________________________________________________________________Response to Reviewer #2: Title & abstract 1. Gross English language Proofreading was done to correct errors of Spelling, grammar, punctuation and statement construction throughout the manuscript…………………………………………………. 2. To avoid confusion with other similar abbreviations the expanded form of the abbreviations were included e.g. IDF , Bsc is corrected as BSc and other abbreviations errors corrected accordingly …………………………………………………………………………………….. 3. Research Title was modified as “Magnitude of chronic diabetes complication and its associated factors among diabetic patients attending the general hospitals in Tigray region, northern Ethiopia” based on your suggestion …………………………………………………………………………………………………………(page-1)________________________________________________________________________________________________4. Method and materials 4.1 Study Area:: the study was done at 10 general hospitals out of all 14  general hospitals, all general hospitals were not included because of the four hospitals were exclude randomly due to budget constraint / logistic issue…………………………………………………...(Page4) 4.2 Sampling: A reference (ref.no 21) for P=0.535, (p=proportion of chronic diabetes complications) was included and this figure is used to calculate the sample size which was done in 2015 which was before our study conducted (2019/20) and taken from a study done at FelegeHiwot referral hospital, Bahardar, Amhara region, Northwest Ethiopia…………………………………………………………………..(Page5)As you mention, by chance the chronic diabetes complication proportion of the study done at FelegeHiwot referral hospital (P=0.535) which we use for sample size calculation is similar to our finding (P=0.54). This could occur because of similarity in socio-demographic characteristics, poor glycemic control as a result of poor adherence to diabetes self-management recommendations……………………… Therefore, the finding of this study But both studies have difference in many things such as sample size , study facility , study area (Amhara region vs. Tigray region) and study period (2015 Vs. 2019/20)……………………………………………………………………………….... (Page4-6)________________________________________________________________________________________________4.3 Operational definition: 4.3.1 Definition of Hypertension: The reference was updated and replaced by the reference that you recommend “European Society of cardiology (ESC) guideline 2018” …………. 4.3.2 Definition of other chronic diabetes complications: all definition/ diagnostic criteria of other chronic diabetes complications  were updated according to the updated guidelines or manuscript based on your suggestion……………………………………….4.3.3 Data collection and measurement(BP): the timing of BP measurement related to coffee consumption was revised as 1-2 hours  and reference also included ………………………………………................................................................... (Page 6) ___________________________________________________________________________________________5. Result 5.1 Socio-demographic, Clinical and behavioral characteristics: In this section we try to explain the findings based on the cross-tabulation analysis results but as mentioned in your review • In which 29.4%, 34.0%, 47.1% and 52.4% participants had chronic diabetes complication respectively”• In which 42.7%, 10.0% and 13.0% had chronic diabetes complication respectively.• In which 6.0%, 32.2%, 19.7% and 6.6% participants had at least one chronic diabetes complication respectively.Those are findings of overall chronic diabetes complication in terms of socio-demographic, clinical and behavioral characteristics in the cross-tabulation analysis result  but we observed such way of explanation may result in confusion for the reader, so to make it clear and simple we prefer to omit/ remove all cross-tabulation findings of chronic diabetes complication from the text explanation of socio-demographic, clinical and behavioral characteristics section but readers can get those findings from the Table 1 & 2 …………………………………………………………………………………...(Page8-11)5.2 Tables: all the abbreviations related to each table were placed in the footnote…… 6. Discussion: Except English language Proofreading, revision was not done in this section because you mentioned as “The discussion is well written”7. Conclusion: Revision was not done because you stated as “It highlights the main findings and supported by the study results”8. Reference: Reference 10: was replaced with updated reference, Reference 22: was replaced with updated reference. Accordingly all references were updated as per your suggestion ………(Page 18-21)_______________________________________________________________________________________________AttachmentResponse to reviewers.docxSubmitted filename: Click here for additional data file. 16 May 2023
 PONE-D-22-12568R1

 Magnitude of chronic diabetes complications and its associated factors among diabetic patients attending the general hospitals in Tigray region, Northern Ethiopia

 PLOS ONEDear Dr. Berhe,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.Please submit your revised manuscript by Jun 30 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at
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For instructions see: We look forward to receiving your revised manuscript.Kind regards,Manal S. Fawzy, Ph.D., M.D.Academic EditorPLOS ONEJournal Requirements:Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.[Note: HTML markup is below. Please do not edit.]Reviewers' comments:Reviewer's Responses to Questions  Comments to the Author  1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.Reviewer #2: All comments have been addressedReviewer #3: All comments have been addressed**********  2. Is the manuscript technically sound, and do the data support the conclusions?  The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #2: YesReviewer #3: Yes**********  3. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #2: YesReviewer #3: Yes**********  4. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.  The Reviewer #2: YesReviewer #3: Yes**********  5. Is the manuscript presented in an intelligible fashion and written in standard English?  PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #2: NoReviewer #3: Yes**********  6. Review Comments to the Author  Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #2: First, the manuscript looks much better than the first version, thanks for the authors.Although, I do not think it is ready for publication.The most important is to revise the enligh language again, and the author should submit an offcial proofreading certifcate for the manuscript, if the Editorial Board advise on this regard, it will be very helpful. As I still see lot of English and Grammer mistakes, and the writing way is not quite professional.1. The abstarct/conclusion:Conclusion: In this study, the magnitude of chronic diabetes complication was higher because more than halfof the study participants had at least one complication.I donot understand this statement, the magitude is high than...what?Also, revise the conculsion2. The exclusion and inclusion criteria:The author put bothe the criteria togther, which is confusing. Please specifiy what are the inclusion criteria? and what are the eclusion criteria?3. Diagnosis of chronic diabetes complication:3.1. I suggest using this statment in stead of yoursCoronary artery disease (CAD): The diagnosis criteria for CAD were either a patient with typical anginal pai or equivalentsymptoms and an abnormal resting ECG or an asymptomatic patient with abnormal stress test, either byECG or echo or a nuclear perfusion imaging test .3.2. Peripheral vascuar disease:The peripheral vascular disease defintion, it is advised to limit the ABI to less than 0.9 only, and delete more than 1.33.3. Neuropathyloss of sensitivity is mis nomer, replace with hyposthesia or anasthesia in lower and upper limbsspelling: limp ---> limb.4. Tables:4.1. Yes column: yes is wrongly written. Plz, correct.4.2: The abbreveiations shoud be consistent: if you use SBP for systolic blood pressure, you have to use DBP for diastolic blood pressue, plz be consistent along the whole manuscriptReviewer #3: I thank the authors to conduct this interesting study at the local setting.All comments have addressed. No further comment required.**********  7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.  Yes: Hussein M IsmailReviewer #2: Yes: Mohammed Abdu SeidReviewer #3: **********https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  17 Jun 2023RESPONSE TO REVIEWERSResponse to Reviewer #21. Abstract 1.1 Conclusion: revision was made based on your comment and it is revised as “In this study, more than half participants had at least one chronic diabetes complication”…………………………………....(Page 2)2. Method 2.1 The exclusion and inclusion criteria: the inclusion criteria and exclusion criteria are separately written under eligibility criteria to avoid confusion………………………………………………………...(Page5) 2.2 Diagnosis of chronic diabetes complication2.2.1 Coronary artery disease (CAD): corrected as per your suggestion………………………....... was deleted from the definition, ABI to less than 0.9 only is used ………………………………………………………..(Page7)2.2.3 Neuropathy: misnomer of loss of sensitivity in the definition replaced with “ hyposthesia or anesthesia” in lower and upper limbs & the work limp is corrected as limb……………………...(Page7)3. Results 3.1 The wrongly written the word yes in the column is corrected throughout the tables………3.2 All abbreviations  were written consistently throughout the document ………...(Page 10-17) 4. Conclusion: corrected as “In this study, more than half participants had at least one chronic diabetes complication”…………………………………………………………………………………….........(Page15)zainab.karim4@gmail.com) and Prof. Lilian T. Mselle (email: nakutz@yahoo.com ) from MUHAS, Tanzania. Uploaded as Supporting Information file 1 ……………………………………………………………………………………………………….5. Language editing: Our Manuscript was copyedit for language usage, spelling, and grammar by Zainabu Karim Mohamed from MUHAS, Tanzania :The authors have adequately addressed the concerns raised by the reviewers. Thank youReviewers' comments: 17 Aug 2023PONE-D-22-12568R2 The magnitude of chronic diabetes complications and its associated factors among diabetic patients attending the general hospitals in Tigray region, Northern Ethiopia Dear Dr. Berhe:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staffon behalf ofProfessor Manal S. Fawzy Academic EditorPLOS ONE


Current trends indicate that 63 countries are not on track to achieve the 2030 Sustainable Development Goals (SDG) target of a neonatal mortality rate ≤12 per 1000 live births, with 55 needing to double the annual rate of decline in neonatal mortality to do so . 2017. Available: http://bvsms.saude.gov.br/bvs/publicacoes/atencao_humanizada_metodo_canguru_manual_3ed.pdf. Accessed: 14 March 2023. [in Brasilian].


An 82-year-old man was admitted for progressively worsening jaundice. Magnetic resonance cholangiopancreatography (MRCP) showed a stenosis of 2 cm in length in the common bile duct, with dilatation of the biliary system above . EndoscVideo 1 Laser ablation under intraductal cholangioscopic guidance is performed for a cholangiocarcinoma.In recent decades, endoscopic radiofrequency ablation has been introduced into clinical use to palliate unresectable cholangiocarcinomaEndoscopy_UCTN_Code_TTT_1AR_2AF


Interpretation of noncoding genomic variants is one of the most important challenges in human genetics. Machine learning methods have emerged recently as a powerful tool to solve this problem. State-of-the-art approaches allow prediction of transcriptional and epigenetic effects caused by noncoding mutations. However, these approaches require specific experimental data for training and cannot generalize across cell types where required features were not experimentally measured. We show here that available epigenetic characteristics of human cell types are extremely sparse, limiting those approaches that rely on specific epigenetic input. We propose a new neural network architecture, DeepCT, which can learn complex interconnections of epigenetic features and infer unmeasured data from any available input. Furthermore, we show that DeepCT can learn cell type–specific properties, build biologically meaningful vector representations of cell types, and utilize these representations to generate cell type–specific predictions of the effects of noncoding variations in the human genome. Second, some loci become epigenetically bookmarked at the stage of progenitor cells, making these loci unresponsive to trans-factors expressed later in development. See  positions in the sequence and at the boInput gradient for each position in the sequence . Since the training dataset mostly contains sequences with a 200-bp intersection, 1 sample contains a target window of another sample. Therefore, there is a consistent increase of importance in the 200-bp intervals. There is also a noticeable drop of gradients centered around the  positions in the sequence and at both ends of the input sequence, which reflects the training strategy. We would expect this effect to be alleviated by sampling training data with less consistency in sample overlap size.AP: average precision; bp: base pair; CNN: convolutional neural network; kbp: kilbase pair; MPI: mean feature positional information; SNV: single-nucleotide variant.giad015_GIGA-D-22-00103_Original_SubmissionClick here for additional data file.giad015_GIGA-D-22-00103_Revision_1Click here for additional data file.giad015_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giad015_Reviewer_1_Report_Original_SubmissionFangfang Yan -- 6/6/2022 ReviewedClick here for additional data file.giad015_Reviewer_2_Report_Original_SubmissionYuwen Liu -- 6/27/2022 ReviewedClick here for additional data file.giad015_Reviewer_2_Report_Revision_1Yuwen Liu -- 12/20/2022 ReviewedClick here for additional data file.giad015_Reviewer_3_Report_Original_SubmissionBorbala Mifsud -- 7/4/2022 ReviewedClick here for additional data file.giad015_Supplemental_FileClick here for additional data file.