Patients with pouchitis presented the highest alterations in species, metabolic pathways, and enzymes, which was correlated with intestinal inflammation. Ruminococcus gnavus strains encoding mucin-degrading glycoside hydrolases were highly enriched in pouchitis. Butyrate and secondary bile acid biosynthesis pathways were decreased in IBD phenotypes and were especially low in pouchitis. Pathways such as amino acid biosynthesis and degradation of aromatic compounds and sugars, encoded by members of the Enterobacteriaceae, were enriched in pouch and CD but not in UC. We developed microbial feature-based classifiers that can distinguish between patients with a normal pouch and pouchitis and identified species and genes that were predictive of pouchitis. We propose that the noninflamed pouch is already dysbiotic and microbially is similar to CD. Our study reveals microbial functions that outline the pathogenesis of pouchitis and suggests bacterial groups and functions that could be targeted for intervention to attenuate small intestinal inflammation present in pouchitis and CD.Crohn's disease (CD), ulcerative colitis (UC), and pouchitis are multifactorial and chronic inflammatory bowel diseases (IBD). Pouchitis develops in former UC patients after proctocolectomy and ileal-pouch-anal anastomosis and is characterized by inflammation of the previously normal small intestine comprising the pouch. The extent to which microbial functional alteration (dysbiosis) in pouchitis resembles that of CD or UC has not been investigated, and the pathogenesis of pouchitis remains unknown. We collected 208 fecal metagenomes from 69 patients with a pouch  and compared them to publicly available metagenomes of patients with CD (IMPORTANCE Crohn's disease (CD), ulcerative colitis (UC), and pouchitis are chronic inflammatory conditions of the bowel. Pouchitis develops in former UC patients after proctocolectomy and ileal-pouch-anal anastomosis and is characterized by inflammation of the previously normal small intestine comprising the pouch. The extent to which microbial dysbiosis in patients with pouchitis resembles that of CD or UC and the pathogenesis of pouchitis remains unclear. We investigated the functions in the gut microbiomes of these patients using metagenomics. We found that the noninflamed pouch is already dysbiotic and microbially is similar to CD. Our study reveals microbial functions with a potential role in pouchitis pathogenesis such as depletion in butyrate and secondary bile acid synthesis and enrichment of amino acid synthesis and degradation of aromatic compounds, encoded by members of the Enterobacteriaceae. We developed microbial feature-based classifiers that can distinguish between patients with a normal pouch and pouchitis and identified species and genes that were predictive of pouchitis. We suggest species and functions that could be targeted for intervention to attenuate small intestinal inflammation present in pouchitis and CD. Inflammatory bowel diseases (IBD), including Crohn's disease (CD), ulcerative colitis (UC), and pouchitis, are chronic, relapsing, and remitting inflammatory conditions of the intestine. CD mainly affects the small intestine and colon, while UC is localized to the colon. The etiology of IBD is multifactorial, involving genetic predisposition , environApproximately 25% of patients with complicated UC may undergo total large bowel resection. Reconstruction of intestinal continuity is achieved by the creation of a reservoir (“pouch”) from the healthy small intestine which is connected to the anus  . Up to 7Enterobacteriaceae are increased, while beneficial obligate anaerobes, such as Faecalibacterium prausnitzii and Eubacterium rectale, are decreased  and those with pouchitis . We also used 20 additional patients with a pouch from our center  and 15 pouch patients from an American cohort , and 60% were males , sheet 1n cohort  as a valn cohort , sheet 210.1128/mSystems.00984-20.10DATA SET S1but, buk, ato, and 4hbt). (Sheet 6) Secondary bile acid producers based on the baiA-baiI operon. (Sheet 7) Linear mixed-effect model of enzymes profile. (Sheet 8) Linear mixed-effect model of metabolic pathways. (Sheet 9) Species feature importance scores. (Sheet 10) Pathway feature importance scores. (Sheet 11) Enzyme feature importance scores. (Sheet 12) All Kruskal-Wallis tests pairwise P value results. Download Data Set S1, XLSX file, 0.9 MB.(Sheet 1) Clinical metadata for patients with a pouch (discovery cohort). (Sheet 2) Clinical metadata for patients with a pouch . (Sheet 3) Clinical metadata for PRISM-DEEP-NLIBD cohorts. (Sheet 4) Linear mixed-effect model of species composition. (Sheet 5) Butyrate producers’ taxonomic composition based on the four terminal genes for butyrate biosynthesis , those with UC (n = 76), and healthy subjects (n = 56) were obtained from previously published data  of different disease phenotypes based on the first axis of microbiome variation, shifting away from the healthy subjects, in the following order: UC, CD, normal pouch, and pouchitis (P < 0.001) than all other phenotypes.The microbial community composition of disease phenotypes and that of healthy subjects  were well separated according to profiles of species, genes, and pathways (oA) were ouchitis  to C. Thouchitis , no sign10.1128/mSystems.00984-20.1FIG S1R2 values) independently of the other variables. *, P < 0.05; **, P < 0.01; ***, P < 0.001. Download FIG S1, TIF file, 1.4 MB.Microbiome variation  based on Bray-Curtis dissimilarity for (A) species, (B) pathway, and (C) enzyme compositional profiles of the fecal metagenomes. (D) Clinical variables explaining the variation in microbiome species, pathways and enzymes, analyzed with PERMANOVA. Percentages indicate variance explained by each variable , while patient age and gender (the latter was available only for the pouch cohort) explained only 0.39% to 0.71% and 0.42% to 0.52%, respectively. Antibiotic use (1 month or 6 months off antibiotics) accounted for only 0.45% to 0.62% of the variation. Fecal calprotectin, a biomarker for intestinal inflammation, explained a low yet significant degree of microbial variation . Consistent with previous studies of patients with UC and CD  among the longitudinal samples of patients with a pouch cohort. The cohort from which the samples were derived had a considerable effect as well, explaining 8.7% to 11.2% of the variation. Finally, in patients with a pouch, pouch phenotype and disease activity contributed 4.2% to 4.8% and 1.4% to 1.9%, respectively, to the microbiome variation  in species, metabolic pathway, and enzymes  . A weaker negative correlation was detected between calprotectin and Shannon diversity . These data suggest that patients with a normal pouch already show signs of inflammation and dysbiosis that may parallel those observed in CD and that these are intensified in patients with pouchitis. As patients with a pouch had the highest level of calprotectin  Fecal calprotectin (log transformed) in patients with IBD and healthy controls. (B to E) Effect of intestinal inflammation  and pouchitis on the variation in microbial composition based on (B) species, (C) pathways, (D) enzymes, and (E) Shannon diversity in pouch patients. Pouch samples were divided into three groups based on calprotectin levels . *, Copyright © 2021 Dubinsky et al.2021Dubinsky et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the To rule out the possibility that the observed differences between the pouch patients’ and nonpouch patients’ microbiomes are inflated due to center or country effects, we obtained and analyzed 15 publicly available pouch metagenomes  from an to D, th10.1128/mSystems.00984-20.3FIG S3but), (F) butyrate kinase (buk), (G) butyryl-CoA:acetoacetate CoA transferase (ato), and (H) butyryl-CoA:4-hydroxybutyrate CoA transferase (4hbt) (I) Secondary bile acid (DCA and LCA) formation (baiA-baiI operon). The samples labeled “American Pouch Cohort” are the pouch metagenomes from the American cohort , while patients with UC had lower mucin degradation potential than the other phenotypes (P < 0.01). These results suggest that the mucin degradation potential of the microbiome does not increase in IBD compared to healthy subjects and even decreases in UC patients.We specifically analyzed glycoside hydrolase (GH) genes that are involved in the breakdown and utilization of mucin-derived glycans and are encoded in the genomes of mucolytic bacteria . Accordienotypes  , and the y axis shows the log10-scaled total community gene abundance . Seven such enzymes were identified and quantified: (A) exo-alpha-sialidase, (B) α-l-fucosidase, (C) β-N-acetylhexosaminidase, (D) α-N-acetylgalactosaminidase, (E) β-galactosidase, (F) α-N-acetylglucosaminidase, and (G) protein O-GlcNAcase. The contributions of the top genera are proportionally scaled within the total. See the legend at the bottom of the figure for the color codes. “Other” encompasses contributions from additional lower-abundance genera. The labels over the bars represent the clinical phenotypes: HL, non-IBD; UC, ulcerative colitis; CD, Crohn's disease; NP, normal pouch; and P, pouchitis. Download FIG S4, JPG file, 2.8 MB.Bacterial community contribution of glycoside hydrolase enzymes that are involved in the breakdown and utilization of mucin-derived glycans by bacterial genera. Each stacked bar on the Copyright © 2021 Dubinsky et al.2021Dubinsky et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the R. gnavus in mucin degradation, we focused on its enzymes that are considered specific for host mucin utilization: GH29 (α-l-fucosidase), GH95 (glycoside hydrolase family 95), and GH33 . The former two can cleave fucose from host mucin glycans, while the latter can cleave terminal sialic acid from sialylated mucins. It was shown that the ability of R. gnavus to utilize host mucins is strain dependent and is attributed to a specific GH33 sialidase and to GH29 and GH95 fucosidases (Ruminococcus gnavus ATCC 29149 (JCM6515) and quantified the abundance of these genes and the fraction of R. gnavus strains that possess them in the metagenomes by read mapping (R. gnavus strains encoding GH29 and GH95 fucosidases varied extensively across disease phenotypes (P < 0.01). No significant difference was observed between patients with a normal pouch (11.3% and 10.1% [median]) and CD (18.3% and 13% [median]) for GH29 and GH95, respectively. Less than 6% of R. gnavus strains in UC patients and in healthy subjects encoded these fucosidases. Interestingly, GH33 sialidase was encoded by a significantly smaller proportion of R. gnavus strains , which was comparable to values for UC patients and healthy subjects . Patients with CD had the highest abundance of R. gnavus strains encoding GH33 sialidase .To better understand the specific role of osidases . Thus, w mapping  to D. Thenotypes  and C bu strains . PatientR. gnavus strains encoding fucosidases in patients with a normal pouch. Altogether, our findings demonstrate that as a consequence of strong dysbiosis in pouchitis, the overall bacterial community mucin degradation potential is lower, but there is a substantially higher number of mucin-degrading R. gnavus strains that might be proinflammatory. Mucin degradation in the normal pouch is similar to that of CD in terms of both microbial community- and R. gnavus-associated potential.A study by Ruseler-van Embden et al.  establisFirmicutes, Ruminococcaceae and Lachnospiraceae . Specifically, according to the but gene (P = 3.5 × 10−6). No significant difference in butyrate synthesis genes between CD  and UC  was observed. Analysis of the buk homologs (P < 0.01). Patients with pouchitis had the lowest levels . The lysine (ato) and 4-aminobutyrate (4hbt) pathways were lower in patients with a pouch , suggesting that the 4-aminobutyrate and lysine pathways for butyrate synthesis are exceptionally low in these patients. Similar trends in all four terminal genes were observed in pouch patients from the American cohort . This suggests that either a reduction in butyrate producers predisposes to intestinal inflammation or butyrate-producing bacteria are more sensitive to intestinal inflammation.The taxonomic affiliation of butyrate-producing bacteria based on the similarity of the identified acterium . Surpris10.1128/mSystems.00984-20.5FIG S5but (butyryl-CoA:acetate CoA transferase) and buk (butyrate kinase) genes to known butyrate producers. Coprococcus comes, Subdoligranulum variabile, and Clostridium perfringens were quantified based on buk, and the rest of the species were based on but. For a complete list of all the identified butyrate producers, see FIG S5, TIF file, 1.6 MB.Major butyrate producers in the fecal metagenomes of patients with IBD and healthy controls. The relative fraction and taxonomic affiliation of the most abundant butyrate-producing bacteria based on the similarity of the identified Copyright © 2021 Dubinsky et al.2021Dubinsky et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00984-20.6FIG S6but, buk, ato, and 4hbt), secondary bile acid formation genes (baiA-baiI operon), and fecal calprotectin levels in patients with IBD and healthy controls. Spearman correlation for (A to D) four main butyrate synthesis pathways, as in but, buk, ato, and 4hbt), and (E) secondary bile acid (DCA and LCA) formation (baiA-baiI operon). Lines are linear regression fitted with a 95% confidence interval. Fecal calprotectin measurements were available for 358 of 428 samples. Download FIG S6, TIF file, 1.5 MB.Spearman correlation between the abundance of butyrate synthesis genes  that had dietary information   and specifically with intake of fiber from fruit , vegetables , and potatoes . Higher fruit consumption was previously associated with increased abundance of known butyrate-producing species and lower recurrence of pouchitis  . The abuouchitis . Nutritiouchitis . Our resbut gene content, reduced butyrate synthesis was identified in patients with active and inactive CD but only in patients with active UC  gene clusters present in bacterial genomes from Ruminococcaceae and to a lesser extent from Lachnospiraceae and Peptostreptococcaceae  of total intestinal bacteria (The secondary bile acids deoxycholic acid (DCA) and lithocholic acid (LCA) are formed in the colon from primary bile acid metabolism by the microbiota via a multistep 7α-dehydroxylation reaction. Secondary bile acids (DCA and LCA) have extensive effects on host metabolism and play both negative and positive roles in health and disease . The lev effects , 45. Enzoccaceae . The baibacteria .bai gene cluster in the metagenomes of IBD and healthy subjects using an extensive and rigorously curated database for genes related to secondary bile acid formation (P < 0.05). In line with the highest dysbiosis in patients with pouchitis, the bai gene cluster abundance was lowest in this group  but higher in patients with a normal pouch (P = 0.002). UC and CD patients had comparable levels of bai gene abundance , and both were significantly higher than in patients with a pouch . A similar pattern in the bai gene cluster was observed in pouch patients from the American cohort . They belonged to three families of Firmicutes: Lachnospiraceae (Clostridium scindens), Peptostreptococcaceae (Clostridium sordellii and Proteocatella sphenisci), and Ruminococcaceae . For a complete list of the identified secondary bile acid producers, see bai gene cluster was significantly negatively correlated with fecal calprotectin , implying that DCA- and LCA-producing bacteria are associated with lower levels of intestinal inflammation.Only a few dominant strains in the analyzed metagenomes were identified to carry the bai clusters in our metagenomes are concordant with those identified in metagenomes of a healthy population , followed by patients with a normal pouch , and was significantly lower in the other phenotypes.The combined contribution of these seven oxidative stress response enzymes (combined oxidative stress response score)  was the 10.1128/mSystems.00984-20.7FIG S7P < 0.05; **, P < 0.01; ***, P < 0.001; Kruskal-Wallis, applying Dunn’s multiple correction test. For a list of all pairwise P values, see FIG S7, TIF file, 0.4 MB.Combined oxidative stress response genes in the microbiome. The combined contribution of seven oxidative stress response enzymes in metagenomes of patients with IBD and healthy subjects. *, Copyright © 2021 Dubinsky et al.2021Dubinsky et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the E. coli and to a lesser degree Klebsiella and few species of Streptococcus and Lactobacillus. In the latter, E. coli contribution was less pronounced and replaced by that of obligate anaerobes such as Bacteroides species, several Clostridiales species, A. muciniphila, Bifidobacterium longum, and Prevotella copri. A similar trend of increased facultative anaerobe abundance was previously observed in patients with CD or UC compared to healthy individuals , and the y axis shows the log10-scaled total community gene abundance . Seven such enzymes were identified and quantified: (A) glutamate-cysteine ligase, (B) glutathione synthase, (C) glutathione-disulfide reductase, (D) superoxide dismutase, (E) peroxiredoxin, (F) peptide-methionine sulfoxide reductase, and (G) catalase. The contributions of the top genera are proportionally scaled within the total. “Other” encompasses contributions from additional lower-abundance genera. See the legend at the bottom of the figure for the color codes. The labels over the bars represent the clinical phenotypes: HL, non-IBD; UC, ulcerative colitis; CD, Crohn's disease; NP, normal pouch; and P, pouchitis. Download FIG S8, JPG file, 2.9 MB.Bacterial community contribution of enzymes related to protection against oxidative stress in the metagenomes of patients with IBD and healthy subjects. Each stacked bar on the Copyright © 2021 Dubinsky et al.2021Dubinsky et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Our next goal was to use an untargeted approach to broadly analyze the functional potential of the microbiome and to identify metabolic pathways (MetaCyc annotations) that might be associated with IBD and with pouch phenotypes in particular. Such hypothesis-free analysis can reveal new functional pathways that may relate to the course of the disease and dysbiosis. We used HUMAnN2 to annotate the metagenomes for metabolic pathways and compared them across disease phenotypes using linear modeling , sheet 8l-lysine (PWY-5097 and PWY-2942) and l-ornithine (ARGININE-SYN4-PWY) were less abundant  in patients with a pouch compared to CD and UC patients. However, the abundances of other amino acid biosynthesis pathways were significantly enriched (FDR P < 0.1) in patients with a pouch and to some extent in those with CD: l-alanine (PWY0-1061), phenylalanine (PWY-6628), l-ornithine as an intermediate for l-arginine (GLUTORN-PWY), and l-arginine (ARGSYNBSUB-PWY). These pathways were encoded predominantly by members of the Enterobacteriaceae , Enterococcus  and Streptococcus species, which were enriched in CD and in pouch phenotypes in particular  in patients with a normal pouch  and pouchitis  and to a lesser degree in CD patients . Moreover, the pathway for degradation of the aromatic organic compound phenylpropanoid (PWY-6690), which can be derived from the putrefaction of proteins, was significantly higher in patients with a normal pouch , pouchitis , and CD . We identified two more pathways for aromatic compound degradation, 3-phenylpropanoate PWY0-1277) and phenylacetate (PWY0-321), that followed a similar pattern of higher abundance in pouch and to lower extent in CD. These phenolic compounds are important intermediates in bacterial degradation of aromatic molecules. Lastly, galactarate sugar degradation (GALACTARDEG-PWY) was increased in patients with a normal pouch , pouchitis , and CD . The degradation pathways we identified were encoded by is sugar . Our res with CD , 12.77 and phn = 35 for normal pouch, n = 34 for pouchitis). To improve the model’s predictive power, we used the feature importance scores that the algorithm assigns to each feature used during training; therefore, features with the highest scores are the most informative for correct classification. A GBT classifier trained on the 50 top-scoring features (species) achieved a mean area under the curve (AUC) of 0.841 ± 0.105  to train classifiers and evaluated its performance using 5-fold cross-validation. We designed our models based on the averaging of longitudinal data per subject, a classifier approach that was shown to be superior to the use of a single sample per subject . Our pou ± 0.105 . Slightl ± 0.105 . Further ± 0.105  in distiNext, we validated our model on 20 pouch patients from our center (not part of the discovery cohort) and on an American cohort of 15 patients with a pouch . The spe10.1128/mSystems.00984-20.9FIG S9FIG S9, TIF file, 0.8 MB.Classification models testing on a validation cohort of 20 pouch patients from our center (independent from the discovery cohort) and on an American cohort of 15 patients with a pouch .  between normal pouch and pouchitis (Ruminococcaceae (Ruminococcus obeum and Ruminococcus flavefaciens) and Lachnospiraceae (Dorea formicigenerans and Dorea longicatena) taxa and beneficial species such as F. prausnitzii, E. rectale, and Roseburia inulinivorans were more abundant in the normal pouch. Taxa that were enriched in the pouchitis group and might be inflammation associated were E. coli, an unclassified Providencia species, and Yersinia sp.  and an unclassified Acinetobacter sp. (Moraxellaceae). Interestingly, Providencia species have been implicated in diarrhea, urinary tract infections, and sepsis , while R. gnavus and A. muciniphila were higher in pouchitis .We analyzed the top-ranking features from each model in order to identify which species and functions are the most discriminatory between the pouch phenotypes and thus might serve as potential predictors of pouchitis. Among the 50 species with the highest importance score, 15 were significantly different . In addition, the pathway for biosynthesis of the lipid A (NAGLIPASYN-PWY) component of lipopolysaccharide (LPS) was significantly enriched in pouchitis. Lipopolysaccharide is a potent activator of innate immunity via Toll-like receptor 4, and patients with CD and UC, especially active ones, have been found to have increased serum levels of LPS . We also explored the enzymes that were highly ranked by the model (P < 0.05). Levels of genes for two putative aminopeptidases (EC 3.4.11.1 and EC 3.4.11.10) as well as a gene for rhamnose sugar degradation (l-rhamnose mutarotase) were increased in pouchitis. Additional enzymes that were higher in pouchitis included adenosylmethionine decarboxylase, which is involved in the biosynthesis of spermidine, a polyamine that may play a role in survival and virulence of human bacterial pathogens , which detoxifies hydrogen peroxide, was significantly higher in pouchitis, indicating the presence of bacteria able to sustain higher levels of oxidative stress (facultative aerobes). In the normal pouch, genes related to vitamin B12 biosynthesis , starch, sucrose, and galactose metabolism (EC 2.4.1.4 and EC 3.2.1.10), sorbitol sugar degradation (EC 1.1.1.14), and homocysteine biosynthesis (EC 2.5.1.49) were enriched.Metabolic pathways with a high-ranking score in differentiating patients with a pouch  and in facultative aerobes that may benefit from intestinal inflammation. In contrast, normal pouch often has a broader diversity in metabolic capacities that accompanies the higher species diversity .R. gnavus strains encoding fucosidases in patients with pouchitis, was seen. Patients with a pouch that later develop pouchitis are often considered a model for de novo inflammation in CD . The study was approved by the local institutional review board (0298–17) and the National Institutes of Health (NCT01266538). Demographic and clinical data were obtained during clinic visits. After providing written informed consent, patients were followed prospectively, and clinic visits, including fecal sample collection, were scheduled every 3 months or when patients had an episode of pouchitis. Fecal samples were collected in sterile cups and immediately frozen at −80°C until processing. Fecal calprotectin levels were measured. Pouch disease behavior (phenotype) was defined as normal pouch, acute/recurrent-acute pouchitis, chronic pouchitis, or Crohn’s-like disease of the pouch (CLDP); see “Pouch disease behavior (pouch phenotype)” below for a detailed description. Disease activity was defined based on global physician assessment and on the clinical subscore of the pouchitis disease activity index .n = 35; 103 fecal samples) and pouchitis  : normal pouch (samples) , sheet 1samples)  (Data Sesamples) , sheet 2We excluded samples collected during or within 1 month of antibiotic treatment. Of the pouch samples in our data set, 84% were from patients who had been off antibiotics for 180 days or longer and the rest were from patients who had been off antibiotics for 1 to 6 months , sheet 1Nonpouch IBD (patients with UC and CD) and healthy-adult metagenomes were obtained from previously published, publicly available data  and are Pouch disease behavior was defined as normal, acute/recurrent-acute pouchitis, chronic pouchitis, or CLDP, as previously defined . Brieflyn = 36). Intake of food groups and nutrients was assessed using food frequency questionnaires (FFQs) adapted to the Israeli population and administered in the MABAT Israeli nutrition and public health governmental study. For each of the 106 food items, a commonly used portion size was specified. Patients were asked how often on average they consumed each food item during the 6-month period prior to each visit, and daily intake of nutrients was calculated by multiplying the frequency of each food item by the nutrient content based on the Israeli TZAMERET food database and the United States Department of Agriculture food database  and butyrate kinase (buk). In the 4-aminobutyrate pathway, the terminal enzyme is butyryl-CoA:4-hydroxybutyrate CoA transferase (4hbt), and in the lysine pathway, the terminal enzyme is butyryl-CoA:acetoacetate CoA transferase (ato). For the analysis of microbiome-derived secondary bile acids, we used the database from reference bai gene cluster (baiA-baiI operon), which consists of baiA, baiB, baiCD, baiE, baiF, baiG, baiH, and baiI genes, obtained from all genomes available in PATRIC.For the identification and quantification of butyrate synthesis pathways, an extensive database from reference −5 . For butyrate synthesis genes, only reads with sequence similarity matches of ≥90% to a reference and an amino acid alignment length of ≥25 were retained and counted. For bacterial secondary bile acid production genes, the sequence similarity cutoff was lowered to ≥70% due to a less comprehensive reference database. Read counts for each of the key functions analyzed (butyrate and secondary bile acids genes) were normalized to RPKM units (reads per kilobase per million mapped reads), i.e., normalized according to the corresponding median gene length and total-sum scaled according to the number of reads in each metagenome. The key functional genes were assigned to bacterial taxa according to the taxonomic identity of the respective gene entries in the database.Quality-filtered metagenomic reads were used as a query in a translated nucleotide search (BLASTX using DIAMOND v0.9.24.125 ) againstin vitro activity or transcriptomic assays (O-GlcNAcase (EC 3.2.1.169), exo-alpha-sialidase (EC 3.2.1.18), β-galactosidase (EC 3.2.1.23), α-N-acetylgalactosaminidase (EC 3.2.1.49), α-N-acetylglucosaminidase (EC 3.2.1.50), α-l-fucosidase (EC 3.2.1.51), and β-N-acetylhexosaminidase (EC 3.2.1.52).For the analysis of glycoside hydrolases (GH) that are involved in mucin degradation, only GH that were functionally characterized by c assays  were conRuminococcus gnavus, GH29 (α-l-fucosidase), GH95 (glycoside hydrolase family 95), and GH33  protein sequences of Ruminococcus gnavus ATCC 29149 strain JCM6515 were downloaded from the Carbohydrate-Active enZYmes (CAZY) database  of the same R. gnavus strain were downloaded as well. These GH and the housekeeping genes were used to build a reference database for a similarity search, as mentioned above. For GH, sequence similarity matches of ≥90% and ≥25 amino acids, alignment length was used, and for housekeeping genes, sequence similarity matches were increased to ≥97% to count only closely related strains. Read counts were normalized by the corresponding gene length, and the percentage of R. gnavus strains encoding GH29, GH95, and GH33 were calculated by dividing each GH by the mean abundance of gyrA, recA, and rplB. As gyrA recruited the highest number of reads even after gene length normalization, we used only gyrA for the final calculation. Samples with fewer than 5 gyrA reads of R. gnavus were removed from the analysis.To analyze the specific GH of database . Three sThe Kruskal-Wallis H test with Dunn’s test for multiple-comparison correction (pairwise tests between phenotypes) was performed using kruskal.test and dunn.test functions in the R package dunn.test. Alpha-diversity (within samples) was calculated with the Shannon diversity index using the diversity function in the R package vegan. Beta-diversity (between samples) was calculated with Bray-Curtis dissimilarity (the vegdist function in the R package vegan) and visualized with the principal-coordinate analysis function cmdscale  in R. Spearman rank correlation between the first axis of microbiome variation, Shannon diversity, and key functional genes and fecal calprotectin was performed using the cor.test function in R.To calculate the proportion of variance explained by each tested variable on Bray-Curtis dissimilarity , permutational multivariate analysis of variance (PERMANOVA) was performed using the adonis function in the R package vegan. The variables included disease phenotype, age, antibiotic use, fecal calprotectin, gender, disease activity, pouch phenotype, and intersubject variation (the last four variables were available only for the cohort of patients with a pouch and were fitted separately). The variance explained by each variable was calculated independently of other variables to avoid issues related to variable ordering.http://huttenhower.sph.harvard.edu/maaslin) were used. Bacterial taxonomy and gene relative abundance data were log-transformed, and additive smoothing of minimum nonzero values was applied to zero values on a per-sample basis. The transformed abundances were modeled as a function of IBD phenotype  while adjusting for age and antibiotic use (fixed effects). For antibiotic use, a time window of 1 month (recent use) or 6 months off antibiotics was used. To account for the longitudinal data set from pouch patients (repeated measurements), individual patients from whom a set of samples were derived were specified as a random effect (patient ID). To account for potential confounders due to the combination of several cohorts of patients, the cohort from which each sample was derived was specified as another random effect (cohort ID). All reported P values were adjusted for multiple-hypothesis testing using the Benjamini-Hochberg method. For differential abundance analysis of key bacterial genes  in IBD phenotypes, a linear mixed-effects model was used with the same fixed and random effects as described above, with the lmer function from the R package lme4.To test for differently abundant microbiome features  in different IBD phenotypes, generalized linear mixed-effects models from the R package MaAsLin2 v0.99.12 . The classifiers were trained and evaluated on the discovery cohort by using 5-fold cross-validation (randomly repeated 100 times) with StratifiedKFold function (scikit-learn package), which preserves the same distribution of classes in each fold as in the complete training set. The classifiers were tested on the validation cohort, which consisted of 20 pouch patients from our center (not part of the discovery cohort), and on an American cohort of 15 patients with a pouch . The machine-learning algorithm of gradient boosting trees (GBT) was used, as implemented in the Python package XGBoost (eXtreme Gradient Boosting ) with th a pouch . The modTo improve the model’s predictive power (considering only the most informative features) and reduce complexity (reducing the feature space by removing uninformative and correlated features), feature importance scores were computed using the internal XGBoost function (feature_importances_), which assigns a score to each feature used in the decision tree during model training. The average feature importance scores for each model  were calculated by considering the mean score of each feature for each fold across the repeated (×100) 5-fold cross-validation.PRJNA524170. Metagenomes from patients with UC, patients with CD, and healthy controls were obtained from PRJNA400072. Additional pouch metagenomes from the American cohort were obtained from PRJNA600269.This study used our previously published sequence data from PRJNA637365. Analysis scripts with code for the machine learning classifiers are available in the GitHub repository (https://github.com/VadimDu/pouchitis_classifiers).All the metagenomic sequence data generated and used in this study (patients with a pouch) have been deposited in NCBI SRA and are available under BioProject number


High-need high-risk (HNHR) veterans are medically complex and at the highest risk of hospitalization and long-term institutionalization. Technology can mitigate challenges these veterans have in accessing healthcare. Willingness to use technology as well as access and ability to use technology were assessed in this study. At the time of the survey, 2543 Miami VAHS veterans were listed as HNHR. 634 veterans ultimately completed the questionnaire, and 602 answered the “willingness to use video-visits” question. Of the 602 respondents, 327 (54.3%) reported they were willing for video-visits with the VA, while 275 (45.6%) were not. Those who were willing were significantly younger (P<0.001), with higher educational qualifications (P=0.002), and more health literate than those not willing (P<0.001). They were more also capable of using the Internet, more likely to use email and be enrolled in the VA’s patient portal, My HealtheVet (P<0.001). However, of the veterans who were willing, 248 (75.8%) had a device with video-capable technology. Those with video-capable technology were younger (P=0.004), more health literate (P=0.01), and less likely to be Black or African American (P=0.007). They were more capable of using the Internet, more likely to use email, and be enrolled in My HealtheVet than those without (P<0.001). Half of the respondents were willing for video-visits but a quarter of those willing lacked requisite technology, thereby making only about 41.2% of the respondents willing and video-capable. To minimize the digital divide, especially during the ongoing COVID-19 pandemic, targeted measures need to address these disparities in this vulnerable population.


Past research has clearly demonstrated interrelations between drinking and health. However, little research has investigated this from a lifespan-development perspective, which is the objective of the current study. Our hypotheses predicted results consistent with the familiar “J-shaped curve” of drinking effects on health, including that health problems would be (1) lower in moderate drinkers than abstainers and (2) higher in excessive drinkers than moderate drinkers. We also hypothesized that these protective effects of moderate drinking would increase with age across the lifespan. The current study used two waves of data from a large U.S.-representative sample. Analyses used 3*3 between-persons ANCOVAs that tested a three-level Wave-1 drinking-group factor and a three-level Wave-1 age-group factor. Of particular importance were the drinking-group-by-age interactions. Various Wave-2 health outcomes were predicted in different ANCOVAs, and each ANCOVA controlled for Wave-1 levels of the Wave-2 health outcome. Across nearly all health outcomes, young adults did not show significant differences between abstainers and moderate drinkers, whereas midlife and older adults consistently showed better health for moderate drinking versus abstainers. This suggests that protective effects of moderate drinking apply more-so to midlife and older adults than young adults. Surprisingly, excessive drinkers generally did not show poorer health than moderate drinkers, except for mixed evidence for such effects only among older adults. Thus, only older adults showed patterns entirely consistent with our hypothesized “J-shaped curve.” A next analytic step we will conduct in advance of this poster presentation will assess if alternative excessive-drinking operationalizations more consistently signal health problems.


Saturation artifacts in optical coherence tomography (OCT) occur when received signal exceeds the dynamic range of spectrometer. Saturation artifact shows a streaking pattern and could impact the quality of OCT images, leading to inaccurate medical diagnosis. In this paper, we automatically localize saturation artifacts and propose an artifact correction method via inpainting. We adopt a dictionary-based sparse representation scheme for inpainting. Experimental results demonstrate that, in both case of synthetic artifacts and real artifacts, our method outperforms interpolation method and Euler’s elastica method in both qualitative and quantitative results. The generic dictionary offers similar image quality when applied to tissue samples which are excluded from dictionary training. This method may have the potential to be widely used in a variety of OCT images for the localization and inpainting of the saturation artifacts.  Th. Thet aldetector . BecauseSparse representation is related to the compressed sensing (CS) theory; if a signal is considered sparse or compressive, it can be faithfully reconstructed by exploiting a small amount of measurements, which is much less than the one suggested by Shannon’s sampling theorem . We can In this paper, we propose a novel method to localize and correct saturation artifacts in SD-OCT images. Specifically, we formulate the artifact removal problem as an image inpainting problem and adopt sparse representation framework to solve it. We first localize the saturation on A-line level and generate a mask which indicates the saturated regions. In particular, we train a generalized dictionary which includes OCT images from distinct types of samples. We devise a patch-based approach to perform image inpainting in the saturated region using dictionary-based sparse representation. The feasibility is demonstrated with synthetic artifacts and real artifacts. We further demonstrate that our design can be generalized to the scenario when the testing tissue type was not involved in dictionary training. Our experiment indicates that the proposed method outperforms cubic spline interpolation (SI) and Euler’s elastica method both qualitatively and quantitatively.2.2.1The proposed method consists of four major modules as The input noisy image is first fed into a saturation artifact detector. The original noisy image aligned with detected artifact mask is divided to overlapping patches. The patches with saturation will be processed via a dictionary-based inpainting process to predict the actual pixel value of corrupted region. We train a dictionary using external image set with high signal-to-noise-ratio (SNR). The output image is aggregated by weighted summation.2.2In SD-OCT, CCD, an array of photodetectors, measures interference signals in a range of specific frequency domain , 19]. T. T19]. T2.2.1We localize the saturation on A-line level by detecting the number of received charges on each spectrum point. If there is any spectrum point which achieves the full well capacity in an A-line, we label the corresponding A-line as being saturated. We observe that, in an unsaturated A-line , the max2.2.2After we examine all A-lines in a B-scan, we output a column-wise mask indicating all saturated A-lines. As shown in 2.3N high SNR images obtained by external averaged images as training data in sparse representation. The N external averaged images are selected evenly from various types of samples, such as coronary and onion. To make this dictionary applicable to OCT images with different size, we opt to divide OCT images into patches and use a single patch as a unit for dictionary learning and processing. We extract overlapping patches a × b from training images. We convert each patch to a column vector ab < q, where q represents the number of dictionary atoms) by using K-SVD and batch-OMP algorithm [α‖0 is the l0 norm, defined as the number of non-zero elements in vector α, ε is specific representation error, a constant for all patches, which describes the termination condition. As indicated in a × b.We build a generalized dictionary for inpainting. It is an overcomplete dictionary by using lgorithm  to solve2.4a × b. Each patch Yi is then converted to a vector form Similar to images for dictionary learning, the input image is decomposed into overlapping patches n pixels saturated in a patch, we first remove the saturated pixels from vector n saturated pixels are also removed from the dictionary p represents the sparsity level. We use K-SVD and batch-OMP algorithm to solve this sparse representation problem. As an inpainting process, we use the sparse coefficient and original dictionary to reconstruct the entire patch which has a size of ab × 1 by calculating The process of filling the saturated pixels is to solve a sparse-coding problem where the sparse coefficient 2.5Overlapping patches are extracted prior to sparse representation, therefore every pixel has multiple estimated values from multiple patches. One potential drawback is edge effect, which happens if all patches are simply restored back. The transition across the edge of each patch harms homogeneity of whole image as the magnitude difference is distinct across the edge . Besides3.3.1μm and a lateral resolution of 4 μm, all in air. Coronary image is with a size of 1024 × 1500 pixels, corresponding to a space of 1.98 × 3 mm2. Onion image is with a size of 750 × 1024 pixels, corresponding to a space of 1.98 × 1.5 mm2. Finger image is with a size of 1024× 400 pixels, corresponding to a space of 1.98 × 4 mm2. Autopsy specimens of heart vessel are collected and imaged through the same protocol in [To evaluate the performance of our artifact removal method, we use three OCT datasets, including OCT volumes from human coronary, onion, and human finger respectively. Samples are imaged via SDOCT system  with an axial resolution of 3 tocol in . All ima3.23.2.1by 8 as patch size. The threshold for saturation detection in The parameters in the following manuscripts are set based on our experiments and suggestions in . We empi3.2.2x × y, which represents x pairs of y consecutive saturated A-lines. To include a wide range of saturation levels, we setup the patterns from sparse saturation (30 × 2) to dense saturation (10 × 6).We use the peak-signal-to-noise (PSNR) to evaluate the intensity difference and structural similarity index (SSIM) to quantify the perceptual difference between reference and output image. The reference image is obtained by averaging consecutive images (N = 5). The comparison methods are SI and Euler’s elastica method [N1=20) and onion (N2=20), which are not seen in training dictionary. The comparison of PSNR and SSIM within saturated region is shown in We first generate synthetic artifact mask to quantitatively evaluate the inpainting performance. To evaluate the performance of the proposed method and the comparison methods, we generate random masks as detected saturation and overlayed the masks with experimental images. Each random mask contains 60 saturated A-lines. For each experimental image, we apply 5 different patterns of a method . The syn3.2.3The image with real artifacts is processed with the artifact detection in 3.2.4To evaluate the feasibility of the proposed method to other tissues, we adopt finger tissue, the images of which are not seen in training data, and generated random masks as detected saturation to quantitatively measure the performance. The comparison of PSNR in 4.In this paper, we propose a novel method using dictionary-based sparse representation to correct the saturation artifacts in OCT image. The experiment on synthetic artifacts and real artifacts demonstrates that the proposed method effectively corrects saturation artifacts. We are able to localize the saturation artifacts on A-line level and correct the saturation artifacts from sparse to dense with high quality. Furthermore, we adopt a general dictionary so that it could be utilized to inpaint tissue samples which are not initially included in training data.An important application of this work is to better the accuracy of segmentation. In clinic diagnosis, accurate estimation of tissue boundaries is crucial to determine the quantitative parameter for treatment . With thThere are two major limitations of our study. First, the performance is limited by memory. If the whole patch is corrupted, there is not enough unsaturated pixel to recover corrupted pixel information in 5.We demonstrate the feasibility of localizing and inpainting saturation artifacts in OCT images. We propose an artifact correction method via inpainting. The generic dictionary can be utilized to decompose images of tissue sample which is not included in training datasets. We have successfully localized saturated A-lines and achieved a better performance than SI and Euler’s elastica in inpainting saturated regions. Our method produces OCT images with high visual quality as a result of inpainting based on dictionary-based sparse representation scheme.


DPY19L2 and sequencing to detect mutations of SPATA16 and PICK1. SDF was evaluated using the TUNEL assay. In Group G, 10 patients had a complete form of globozoospermia, whereas 8 patients had a partial form. Molecular analysis revealed deletion of DPY19L2 in six of the patients, all of them with complete globozoospermia, while no mutations were found in the examined exons of PICK1 and SPATA16. TUNEL analysis showed a higher SDF% in Group G. Our findings confirm DPY19L2 defects as the most frequent genetic alteration in Italian patients contributing to globozoospermic phenotypes. Furthermore, spermatozoa with acrosomal defects could also display high levels of SDF as a possible consequence of abnormally remodeled chromatin. The possible effect on offspring of chromatin structure abnormalities and altered DNA integrity should be carefully evaluated by clinicians, especially regarding the feasibility and safety of artificial reproductive techniques, which represent the only treatment that allows these patients to conceive.This study aims to evaluate genetic contribution and sperm DNA fragmentation (SDF) in a cohort of 18 unrelated globozoospermic Italian men (Group G). Semen samples were assessed according to the WHO 2010 Laboratory Manual and compared with 31 fertile controls. We focused our genetic analysis on the exons of the main globozoospermia-associated genes, performing qualitative PCR to assess deletion of  Infertility, defined by the World Health Organization (WHO) as the failure to achieve a pregnancy after 12 months or more of regular unprotected sexual intercourse, is a major concern in public health. Current estimates indicate that up to 15% of couples of reproductive age may be affected, with an underlying complex multifactorial etiology, resulting from the interaction of both genetic and extrinsic factors [A rare (incidence < 0.1%) genetic cause of infertility is globozoospermia. The peculiar appearance of globozoospermic ejaculates is that of a monomorphic teratozoospermia, characterized by round-headed spermatozoa lacking an acrosome . The absGlobozoospermic spermatozoa can be round headed, completely lacking acrosomal structures and enzymes, and unable to fertilize oocytes (type 1 globozoospermia), or show a reduced acrosome often in association with other morphological abnormalities, but still capable of fertilizing (type 2 globozoospermia) ,8. AdditIt has been reported that round-headed spermatozoa possess a lower amount of protamines  than normal spermatozoa ,11 and tMost studies investigating the genetic background of globozoospermia included North-African or Middle Eastern infertile men. In contrast to Western countries, in these geographical areas globozoospermia shows a greater incidence; in particular, a possible higher rate of consanguineous marriages may increase the expression of this autosomal recessive trait .PICK1 (protein interacting with C kinase 1) gene is located on chromosome 22q13.1. It encodes for a membrane protein paramount for protein and vesicle trafficking. Deletion of this gene leads to round-headed spermatozoa and oligozoospermia. Liu et al. [PICK1 gene in a Chinese family. The family members affected by this homozygous missense mutation showed infertility caused by the absence of the acrosome.u et al.  discoverSPATA16 (spermatogenesis associated 16) gene is located on chromosome 3q26.31. The encoded protein is involved in acrosome biogenesis during proacrosomal vesicle transport. A homozygous mutation in this spermatogenesis-specific gene was identified in a consanguineous family [s family .DPY19L2 (dpy-19-like 2 (C. elegans)) gene is located on chromosome 12q14.2 and it is considered the main gene involved in the etiopathogenesis of human globozoospermia [DPY19L2 have normal or subnormal sperm concentration, indicating that this gene plays a role in spermiogenesis but not in germ cell proliferation or meiosis [DPY19L2 mutation [ospermia ,21. It eospermia . Patient meiosis ,22,23. Fmutation ,20,24,25Several mutations were identified and described for their strict association with globozoospermia in humans . The maiNonetheless, globozoospermia represents a heterogeneous disorder and determinants of the phenotype–genotype correlation remain unclear. In Italy, this severe form of teratozoospermia has been explored only in a small caseload of unrelated men .SPATA16, PICK1 and DPY19L2) in 18 unrelated Italian men. To further assess a putative correlation with impaired chromatin integrity, we also evaluated sperm DNA fragmentation (SDF) in acrosomeless spermatozoa in our cohort of globozoospermic patients and compared it to fertile controls.Therefore, the aim of our study was to investigate the genetic contribution of the main globozoospermia-associated genes  and, as controls, 31 normozoospermic Caucasian men (Group N), attending the Laboratory of Seminology - “Loredana Gandini” Sperm Bank, Department of Experimental Medicine at “Sapienza” University of Rome, for semen analysis as part of an andrological work-up for preconceptional screening.Semen samples were collected by masturbation after 3–5 days of abstinence. All samples were allowed to liquefy at 37 °C for 60 min and were then assessed according to the World Health Organization (WHO) Laboratory Manual .The following variables were taken into consideration: ejaculate volume (mL), sperm concentration (106 per mL), total sperm number (106 per ejaculate), progressive motility (%), and morphology .®, Madison, WI, USA) according to the manufacturer’s instructions. Extracted DNA was quantified by NanoDrop ND-2000  and underwent molecular analysis.Total DNA was extracted from sperm using the MasterPureTM DNA Purification Kit ). Extracted DNA was amplified with specific primers . Purification of samples was carried on with ®PureLink PCR purification kit . Amplification reaction occurred in a volume of 13 µL containing 1 µL of ®Big Dye , 2 µL of buffer ®Big Dye Terminator , 1 µL of each primer, 18 ng of DNA and the amount of DNAse free water  needed to reach the final reaction volume. Raw data from the capillary electrophoresis were analyzed by Sequencing Analysis . Subsequently, multiple alignment of the sequences versus the template was carried out to identify point mutations using the software Geneious Prime 2020.2 .PICK1: we investigated exon 13 by sequencing as described above.Genetic analysis was performed as follows:Sperm DNA fragmentation (SDF) was evaluated using the TdT-mediated dUDP nick-end labelling (TUNEL) assay  ,28.After cytological and morphological examination of sperm parameters, the samples were washed twice in phosphate-buffered saline and then cytocentrifuged . Samples were then analyzed using a fluorescence microscope , counting at least 500 cells .p value less than 0.05 was considered statistically significant. All computations were carried out with the Statistical Package for the Social Sciences (SPSS) 25.0 .Continuous variables are presented as mean ± SD and the normality of distributions was checked using the Kolmogorov–Smirnov test; the student’s t or Mann–Whitney U test was used to compare sperm parameters between the two groups. Categorical variables are presented as counts and percentages, and comparisons were performed by Fisher’s exact test. The presence of statistically significant correlations among the considered variables was evaluated using Spearman’s rank correlation test. The probability values are 2-sided; a p = 0.001) revealed a significantly lower total sperm number, progressive motility and higher percentage of abnormal forms in Group G . In particular, one patient showed deletion in exon 11, one patient in exon 22 and four patients in exons 10, 12 and 22.Group G molecular analysis, carried out by qualitative PCR, revealed a deletion in the DPY19L2 displayed complete globozoospermia , as shown in DPY19L2 deletion.A statistically significant difference in SDF was found between Groups G and N  and evaluated putative variants of these genes in a cohort of 18 unrelated men, including 10 patients affected by complete globozoospermia and eight patients affected by partial globozoospermia.In our study we considered only the main altered exons reported in literature of the three globozoospermia-associated genes  between two highly similar 28 kb low copy repeats (LCRs) flanking the gene [DPY19L2 gene in complete globozoospermia, thus indicating that this gene is necessary in men for sperm head elongation and acrosome formation [Among the globozoospermia-associated genes analyzed in the literature, ormation ,49.DPY19L2 deletion by detecting exons 1, 10, 11, 12, 20 and 22. Analyses carried out with qualitative PCR showed that six out of eighteen patients (33.3%) displayed deletions in DPY19L2, including one in exon 11, another one in exon 22 and the remaining four in exons 10, 12 and 22. All six patients carrying deletions in the DPY19L2 gene showed complete globozoospermia. Our findings agree with those in previous literature, suggesting that DPY19L2 defects could contribute to this severe form of teratozoospermia [In the present study, we evaluated ospermia ,52,53,54DPY19L2 mutations, together with those of the two other globozoospermia-associated genes SPATA16 and PICK1, more than half of the cases carried DPY19L2 mutations, with a rather lower frequency of SPATA16 or PICK1 mutations. These data highlight the role of DPY19L2 mutations as a major cause of globozoospermia. Moreover, by comparing reported genetic data from globozoospermic patients from Western countries with reported genetic data from those from China, the frequency of DPY19L2 variants in Chinese patients seems to be higher than that in patients from Western countries.Analyzing the reported percentage of To further investigate the chromatin integrity of the acrosomeless spermatozoa, we performed the TUNEL assay on semen samples of our caseload. In Group G the SDF was significantly higher than in the control group, suggesting that spermatozoa with acrosomal defects could also exhibit high levels of sperm DNA damage. On the other hand, no differences were found between complete and partial globozoospermic samples and between deleted and not deleted globozoospermic samples.A significantly higher level of SDF in globozoospermic patients was also reported in previous literature . HoweverPRM1 and PRM2 genes could result in reduced protamine expression associated with abnormal sperm morphology [DNA fragmentation could be a consequence of abnormal chromatin remodeling, which characterizes globozoospermic samples. To support this hypothesis, some authors described abnormal chromatin condensation in globozoospermia, with a high heterogeneity in the degree of maturity  due to arphology . Sperm cThe putative correlation between DNA damage and acrosomal deficiencies should be evaluated when patients affected by this severe form of teratozoospermia are enrolled in ICSI programs, which represent the only treatment for this type of male infertility . The higThe spread of assisted reproductive technologies since the last decade of the twentieth century allowed many globozoospermic subjects to access ICSI. The first reports indicated a greater incidence of fertilization failures compared to the “general population” . Round-hDPY19L2) it might be important to seek genetic counselling to prevent this form of infertility in future progeny [Oocyte activation is a complex network of intracellular interactions induced by oscillations in cytoplasmic calcium concentration that round-headed acrosomeless spermatozoa appear to be unable to trigger after ICSI. Therefore, AOA may potentially solve globozoospermia-induced inability to fertilize. Recreating the intracellular calcium rise, ooplasm/nuclear reactions of fertilized oocytes are artificially triggered and, thus, embryogenesis may proceed ,70. Desp progeny .In conclusion, available reports are highly heterogeneous, and a thorough efficacy/safety assessment is difficult to perform and, therefore, this procedure still cannot be routinely considered .DPY19L2 appears to play a pivotal role in contributing to globozoospermic phenotypes in patients from different ethnic and geographical origins. Furthermore, globozoospermic ejaculates seem to be characterized by a high level of sperm DNA fragmentation as a putative consequence of abnormal chromatin remodeling with an aberrant histones/protamines ratio [Globozoospermia is a rare genetic cause of infertility whose phenotype–genotype correlation still remains unclear. Among the genes mainly involved, es ratio ,10,40.Although ICSI represents the only treatment that allows these patients to conceive , the fer


BackgroundSmartphone technology is rapidly evolving and advancing, with many of them offering health applications being used for oximetry purposes, including the Samsung Health/S Health application. Measuring oxygen saturation is one of the important indications to monitor patients with COVID-19, as well as other health conditions. These applications can be used for measuring oxygen saturation to provide a convenient solution for clinical decisions.MethodsOxygen saturation measurements were collected using the Samsung Health application for Samsung Galaxy smartphone with a sensor and camera flash and a low-cost portable digital display ) finger pulse oximeter. Intra-session reliability was established to determine the consistency between the measures. Intra-class correlation coefficients (ICCs) were calculated with 95% confidence intervals (CIs) reported for both methods. The Bland-Altman plot was used to compare the level of agreement between the two measurement methods.Results125 = 4.407, p < 0.001), and on average, pulse oximeter measurement was 0.510 points higher than Samsung Health application measurement (95% CI = 0.281-0.740). The pulse oximeter and Samsung Health application scores were moderately correlated (r = 0.462). The results of the intra-session reliability test produced an acceptable ICC value of 0.557, indicating moderate reliability and consistent results for the measurement of oxygen saturation with both methods. The Bland-Altman plot showed a consistently equal distribution of data points scattered above and below zero.There was a statistically significant average difference between pulse oximeter and Samsung Health application measurements (tConclusionSmartphone health applications can be used with moderate reliability to measure oxygen saturation. The assessment of a patient with respiratory problems usually includes the measurement of blood oxygen saturation . The total study duration was about six months.Demographic and physical data of patients were collected, including age, gender, height, weight, and body mass index (BMI). The data for oxygen saturation was collected using a smartphone  application for Samsung Galaxy phone, which uses the sensor and the camera flash to measure oxygen saturation when the index finger is held to the camera for 10-30 seconds, and a low-cost portable digital display ) finger pulse oximeter. We anticipated this unique feature of Samsung to be of great value especially in the wake of COVID-19 where minimal physical contact is recommended with patients. Thus, we embarked upon determining its reliability to provide an alternative and convenient method of quantifying oxygen saturation in patients. Oxygen saturation was measured repeatedly thrice using both methods. To keep the measurements uniform, each participant was comfortably seated for at least 10 minutes before taking measurements with the index finger on the right hand.Data analysisData were analyzed anonymously with complete privacy and confidentiality. Descriptive analysis was performed for qualitative variables using percentages. The difference in measurement between both methods was computed as a new variable as follows:Difference of Measurement = Portable Oximeter Measurement - Samsung Health MeasurementThe mean score of both methods was also calculated as a new variable. These were calculated to construct the Bland-Altman plot for comparing the agreement level between the two methods of measuring oxygen saturation. The systematic differences between the means of the two methods  were analyzed using a paired t-test. Means and standard deviations (SD) and p-value (set at 0.05) were reported for both measurements.Intra-session reliability (the immediate test-retest reliability based on the mean of three measurements) was established by recording three consecutive measurements with each method to determine the consistency between the measures. Intra-class correlation coefficients (ICCs), using a two-way mixed-effects model (because we chose our subjects randomly and measuring methods were fixed) and absolute agreement (to obtain consistent results), were calculated with the 95% confidence intervals (CIs) reported for both methods. The ICC estimates and their 95% CI were interpreted as follows: poor reliability = 95% CI of ICC value < 0.5, moderate reliability = 95% CI of ICC value between 0.5 and 0.75, and good to excellent reliability = 95% CI of ICC value between 0.75 and 0.9.The Bland-Altman plot was used to compare the level of agreement between the two measurement methods . The mean of difference (bias) was calculated, and the upper and lower limits of agreement (LOAs) were constructed against the means of both measurements to define the 95% confidence intervals of agreement as follows:Upper LOA = (SD × 1.96) + Mean of Difference = (1.300 × 1.96) + 0.510 = 3.058Lower LOA = Mean of Difference - (SD × 1.96) = 0.510 - (1.300 × 1.96) = -2.038The limits of agreement were defined a priori using the one-sample t-test to obtain the mean and SD of the difference between the two measurements. On the plot, the X-axis represented the mean of the two measurements, and the Y-axis showed the difference between the two measurements. If 95% of data points were scattered all over the place, above and below zero (limit of agreement on the plot), it was assumed that both methods were equally consistent in their measurements without bias. Moreover, a linear regression analysis was performed to evaluate any proportional bias between the two measuring methods with a p-value set at 0.05. If the beta value was closer to zero, it was assumed that there was no proportional bias.Pearson correlations (-1 = perfectly negative correlation to +1 = perfectly positive correlation) with a p-value of 0.05 of both methods were also reported. The strength of the relationship between the two methods was assumed as follows: small/weak correlation: r = 0.1-<0.3, medium/moderate correlation: r = 0.3-0.5, and large/strong correlation: r = >0.5. 125 = 4.407, p < 0.001), and on average, pulse oximeter measurement was 0.510 points higher than Samsung Health application measurement (95% CI = 0.281-0.740). The pulse oximeter and Samsung Health application scores were moderately correlated (r = 0.462).The demographic characteristics of the participants are shown in Table The reliability data represented by intra-class correlation coefficient (ICC) for oxygen saturation measured with a pulse oximeter and Samsung Health application are presented in Table The Bland-Altman plot was constructed to show the level of agreement between the two measurement methods for oxygen saturation, which showed that both methods were equally consistent in their measurements of oxygen saturation as the plot showed consistently equal distribution of data points scattered above and below zero, suggesting that there is no consistent bias of one measurement method over the other Figure . FurtherThis study was conducted to determine the reliability and consistency of smartphone applications  compared with a portable finger pulse oximeter in measuring oxygen saturation in healthy adult individuals. The purpose behind this idea was that in unpredictable situations, such as the COVID-19 pandemic, when in-person contact is not encouraged, and in other situations when it is difficult to measure oxygen saturation with a finger pulse oximeter or when they are not available due to shortage, smartphone applications can be reliably used for oxygen saturation monitoring. Smartphone applications are also an easy and convenient method of measuring oxygen saturation in home settings in situations when people are monitored at home with oxygen saturation measurements.2 measurements using iPhone application; however, they conducted the study on real patients in clinical environments in contrast with our study that was conducted on healthy volunteers [Our findings suggested a moderate level of reliability between a portable finger pulse oximeter and the Samsung Health application for measuring oxygen saturation in healthy individuals. The reliability data suggests moderately consistent results for measuring oxygen saturation with the Samsung Health application and the portable finger pulse oximeter. The two measurement methods also demonstrated a good level of agreement for measuring oxygen saturation and positive correlation. Hence, we can suggest that these smartphone applications for measuring oxygen saturation can produce valid results, which can be used for clinical decisions in the same manner portable finger pulse oximeters are used. Jordan et al. also found a moderate agreement for SpOlunteers . In situlunteers . We alsolunteers -12. Othelunteers ,13,14. Alunteers -17. ThisThe Samsung Health/S Heath application can be reliably used for measuring oxygen saturation as moderately consistent results and a good level of agreement were shown for measuring oxygen saturation with the Samsung Health application and the portable finger pulse oximeter. In situations such as the COVID-19 pandemic where in-person contact is not encouraged and when it is important to monitor the oxygen saturation of patients, smartphone applications can be used to make clinical decisions based on their oximetry results.


COVID-19 is still spreading rapidly around the world. In this context, how to accurately predict the turning point, duration and final scale of the epidemic in different countries, regions or cities is key to enabling decision makers and public health departments to formulate intervention measures and deploy resources.Based on COVID-19 surveillance data and human mobility data, this study predicts the epidemic trends of national and state regional administrative units in the United States from July 27, 2020, to January 22, 2021, by constructing a SIRD model considering the factors of “lockdown” and “riot”.(1) The spread of the epidemic in the USA has the characteristics of geographical proximity. (2) During the lockdown period, there was a strong correlation between the number of COVID-19 infected cases and residents’ activities in recreational areas such as parks. (3) The turning point (the point of time in which active infected cases peak) of the early epidemic in the USA was predicted to occur in September. (4) Among the 10 states experiencing the most severe epidemic, New York, New Jersey, Massachusetts, Texas, Illinois, Pennsylvania and California are all predicted to meet the turning point in a concentrated period from July to September, while the turning point in Georgia is forecast to occur in December. No turning points in Florida and Arizona were foreseen for the forecast period, with the number of infected cases still set to be growing rapidly.The model was found accurately to predict the future trend of the epidemic and can be applied to other countries. It is worth noting that in the early stage there is no vaccine or approved pharmaceutical intervention for this disease, making the fight against the pandemic reliant on non-pharmaceutical interventions. Therefore, reducing mobility, focusing on personal protection and increasing social distance remain still the most effective measures to date. With the continuous spread of the COVID-19 epidemic, many countries or regions have been forced to take a series of temporary response measures, such as lockdown, suspending business, suspending schools and restricting the movement of people, incurring significant disruptions to the normal operations of social order  = . The parameter β is the infection rate, i.e., the probability per unit of time that a susceptible individual will contract the disease when entering into contact with an infected person. The parameters γ and δ respectively denote the recovery and death rates.In the formula, the initial condition is set at t0 time, β corresponds to the infection rate in each period [When modeling the epidemic trend of COVID-19, it is necessary to consider the influence of human mobility and social distancing on the infection rate. In line with the current development stage of COVID-19 in the USA, this study also adds in the “lockdown” and “riot” factors. The current epidemic situation of COVID-19 in the USA is divided into three stages . The influence of human mobility at different stages of the spread of COVID-19 was subsequently observed based on these divisions, where h period . As showβ0 represents the initial infection rate, and τβ represents the decay period; t is the time node, and tlockdown is the time node of the lockdown; triot is the time point of the riot, and friot indicates the spread index caused by the riot.In this formula, In order to improve the accuracy of the prediction results, the SIRD model was used to fit the COVID-19 epidemic situation forecast data and the actual data. Regarding ordinary differential equations similar to the SIRD model, this study used a using a bounded trust region (TRF) algorithm to perform non-linear least-squares regression using Python, and determined a 95% confidence interval to optimize the parameters of the ordinary differential equations. In order to verify the accuracy of the model, this study employed the MSLE (Mean Squared Log Error). This index corresponds to the expectation of the square logarithm (quadratic) difference and is applicable to the type of exponential growth examined here. The specific formula is as follows:n represents the number of observations; Yture indicates actual epidemic data and Ypredict indicates forecast epidemic data.Here, MSLE represents the root mean square logarithmic error between the actual and predicted data of the epidemic, and Based on the collected data pertaining to cumulative infected cases of COVID-19 in the USA, the spatial and temporal evolution trends of COVID-19 from January 22 to July 26, 2020, were drawn using Python . During this period, the epidemic in the USA actually entered the stage of large-scale outbreak and, in a sense, acquired a state of semi-free transmission (Second stage).Since the USA government adopted measures such as “lockdown”, “home isolation” and “shelter” in March 2020, the number of residents’ home activities increased, while other outbound activities showed a significant downward trend. Unfortunately, due to citizens’ generally weak awareness of prevention and control, the effectiveness of the latter measures was limited. On the one hand, with the improvement of testing ability, increasing numbers of people were diagnosed as positive. On the other hand, American citizens’ pursuit of “individuality” and “yearning for freedom”, not wanting to be “bound”, makes them resistant to the policy of maintaining a social distance. In addition, the government encouraged “restart” and “return to work and production” as soon as possible, and even permitted open bars and other places of mass entertainment to remain open. Around the same time, with the warming of the weather, residents’ outdoor activities increased significantly in May, and the number of stays in the resident began to decrease. For example, during the lockdown, many residents still engaged in recreational activities without wearing masks. They thought the epidemic was just a common flu, and that news reports had exaggerated the spread and death rates of the epidemic (https://www.sohu.com/subject/320511). Since the beginning of June, as the “riots” have continued to escalate (https://www.sohu.com), large-scale protests have been held in 22 states and 140 cities in the USA, leading to the USA’s second outbreak of COVID-19 (Third stage) .On May 25, a black man in Minnesota was suffocated to death by the violent law enforcement of white police, which shocked the USA. On that day, demonstrations began in Minnesota and gradually developed into riots (p < 0.01). Therefore, when forecasting and modeling the USA’s COVID-19 epidemic trend, the impact of social policy, social distancing and riot outbreak factors on the infection rate should be taken into account.Figure β in the study is described by an exponential function. The third stage encompasses the large-scale demonstrations triggered by the “riots” in the USA, which makes the contact rate rise sharply over a short period of time and marks the second growth stage; thus, here, increasing riot factors are considered in order to adjust the infection rate β. The transmission index friot caused by the riots is assumed to have a great impact on the spread of the epidemic in the USA. After repeated verification of the model, the value of friot is: 1 ≤ friot ≤ 3. On this basis, the friot in formula , on which to focus the analysis  algorithm was used to perform a non-linear least-squares simulation to simulate the epidemic trend of different states. This enabled us to obtain the fitting parameters of different regions and their 95% confidence intervals (CI), based on which we could subsequently diagnose and test the SIRD model in each region Table .In order to effectively contain the spread of the COVID-19 epidemic, the states of the USA have taken a series of emergency prevention and control measures. However, as the infected, recovered and death cases in each state remain in a dynamic process, significant differences have also emerged in the epidemic prevention and control measures taken by the different states, and the conditions of local medical and health facilities. All of these mean that if the unified threshold parameters  are used in the construction of SIRD model, this is bound to increase the cumulative error to some extent, thus affecting the accuracy of the SRD model’s prediction results.β0 value, decay period τβ value, recovery rate γ value and death rate δ value of the USA were 0.2965, 18.2532, 0.0085 and 0.0014, respectively.In view of this, the parameters adopted in this study were not fixed, but adaptive dynamic parameters in the process of epidemic trend prediction. According to Table γ value of 0.0135. The recovery rates in Texas, New York and Georgia are 0.0022, 0.0013 and 0.0012, respectively. The recovery rates in Arizona and Pennsylvania are in the medium level, with γ values of 0.0008 and 0.0003, respectively. Florida and New Jersey have the lowest recovery rates. In terms of the death rate, Texas can be seen to have the highest value, with a δ of 0.0469. New Jersey, California and Georgia also have high death rates, with respective δ values of 0.0022, 0.0021 and 0.0020. The death rate rates in Massachusetts and Pennsylvania are at the medium level, with a δ value of 0.0013 for both. Florida, New York and Illinois are at low levels, with δ values between 0.0006–0.0009. Arizona has the lowest death rate, with a δ value of only 0.0001.Among the 10 regions seriously affected by the COVID-19 epidemic in the USA, California can be seen to have the highest recovery rate, with a By observing the MSLE error between the predicted and actual active infected cases change curve, it can be seen that Florida and Georgia have the largest MSLE error, while New York, Massachusetts, Pennsylvania, Illinois, New Jersey and Arizona have relatively small MSLE errors. The key factor causing the deviation between the simulated value and the actual value may be due to the repeatability and lag of data reporting, resulting in a substantial increase or decrease in the number of cases within a certain day.There are spatio-temporal differences in the COVID-19 epidemic across the USA. In terms of temporal changes, the current spread process of the epidemic in the USA can be divided into early outbreaks, large-scale outbreaks and slow recession stages, with the implication that the epidemic will not end in a short period of time. In terms of spatial distribution, the worst-hit areas are mainly located in the northeastern USA and areas along the Great Lakes, with sporadic distribution in the southwest, southeast and south of the country.The epidemic situation in various states of the USA shows similar characteristics of phased changes, while the epidemic trajectory in other areas shows certain peculiarities. The development of the epidemic in the 10 most seriously affected regions can be divided into three stages: early outbreak, large-scale outbreak and slow recession; however, the turning point of the epidemic will be different in different regions.There is a strong correlation between the number of infected COVID-19 cases and the activities of residents visiting parks. Due to the impact of riots, the frequency of residents visiting parks greatly increased the infection rate of the epidemic, resulting in varying degrees of postponement of the turning points in the USA as a whole, and also in specific states.The turning points of the epidemic in the USA at the national and state levels show a high degree of consistency. Specifically, the turning point of the epidemic in the USA as a whole is predicted to occur on September and among the 10 states with experiencing the most severe epidemic, New York, New Jersey, Massachusetts, Texas, Illinois, Pennsylvania and California are all to meet this point in a concentrated period from July to September.Based on COVID-19 epidemic data and human mobility data in the USA from January 22 to July 26, this study uses the improved SIRD model to predict early epidemic trends in national and state regional administration units in the USA from July 27, 2020, to January 22, 2021. The conclusions are as follows.Based on the above conclusions, it is suggested that the USA government should adopt the lessons learned from the epidemic prevention and control measures applied in some areas, scientifically divide risk prevention and control areas, implement hierarchical management and control, and promote the resumption of work and production in an orderly manner, so as to avoid the negative impact of excessive epidemic prevention on society and people’s livelihood. At the same time, measures such as quarantine, isolation and psychological counseling should be taken to encourage people to maintain social distancing and avoid group activities. With regard to individual residents, it is recommended that they enhance their awareness of relevant precautions, avoid taking public transportation, and avoid going to closed places where crowds gather. In addition, when symptoms such as fever, cough and vomiting occur, they should self-isolate and report their symptoms, and pay attention to developing good personal hygiene habits, such as more ventilation, washing their hands frequently, wearing masks and not facing others while sneezing, etc. Autumn and winter are high seasons for the spread of epidemics and, as no vaccination for COVID-19 has yet been developed, apart from personal hygiene, the only way to control the spread is for every citizen to strictly abide by lockdown to prevent the virus from rebounding again in the USA before reaching the turning point.This study predicts the epidemic trends of COVID-19 in the USA by constructing a SIRD model and considering “social factors”. The aim here is to provide a reference for clarifying the epidemic spread rule, scientifically formulating an epidemic prevention and control plan, and for promoting the resumption of work and production in an orderly manner. However, this study has the following shortcomings. First of all, due to the large fluctuations or absence in the recovered case data of residents in some states, there will inevitably be a big deviation between the predicted results and the actual data. Second, the spread in the early outbreak stage of the epidemic was more of a state of free transmission. With the increasing attention of the government, society and residents paid to the epidemic, many temporary control measures have been introduced; in this scenario, the free spread of the virus is difficult to maintain. Although the study divides the spread process of the epidemic in the USA into stages according to government intervention measures and social emergencies, it is necessary to acknowledge the complexity of real social activities, which complicates the positing of accurate mathematical expressions and predictions. Therefore, there is uncertainty in using this model to predict the epidemic trend in the future under different intervention intensities and emergencies. How to extend the prediction period while ensuring the accuracy of prediction is a key direction for future research. Third, due to the limitations of objective factors such as virus incubation period, detection capability, and medical and health facilities, the current official epidemic data may be lower than the actual infection data, which will also lead to uncertainty in the final evaluation results. Finally, this study only predicts the future epidemic trend at the national level and in some state administrative units in the USA, and does not put forward effective prevention and control measures, thus restricting the research depth and application value of this paper.