Genomics, Proteomics & Bioinformatics

Background and ObjectiveThe dysbiosis of human gut microbiome has been increasingly seen to have a relation in the development of autoimmune diseases, with specific microbial signatures having causative association with specific conditions. Inflammatory bowel disease (IBD) is one such autoimmune ailment. This paper proposes a predictive tool that can identify the IBD status of an individual based on the composition of the gut microbiome using machine learning and AI agents driven techniques. The technology can strengthen the suspicion of a potential IBD diagnosis a patient may have based on their gut microbiome profile. MethodsThe tool processes patient gut metagenome using integrated Kneaddata and MetaPhlAn to generate taxonomic profiles. These are fed into an XGBoost classifier to predict IBD or healthy status. Dysbiotic taxa are identified via Z-score and fold change. CrewAI delivers personalized probiotic recommendations based on diagnosis and dysbiosis. ResultsThe tuned XGBoost model achieved 86.6% accuracy. On validation using single ulcerative colitis sample, the tool correctly predicted IBD status but misclassified it as Crohns disease(possibly due to overlapping microbial signatures), identifying Faecalibacterium and Flavonifractor as dysbiotic taxa.The probiotic recommended was Faecalibacterium prausnitzii, backed with reasoning basedon scientific literature. ConclusionsDespite limited validation sample size, the high accuracy, correct IBD detection, dysbiosis analysis and elaborate probiotic recommendation suggest promising potential; further validation needed

This study aimed to evaluate the prognostic value of quantitative analysis of {superscript 1}F-FDG positron emission tomography (PET)/computed tomography (CT) metabolic parameters in patients with pancreatic ductal adenocarcinoma (PDAC) after neoadjuvant chemotherapy (NACT). A retrospective analysis was conducted on the clinical and imaging data of 44 patients with pathologically confirmed PDAC who received NACT. All patients completed standard chemotherapy regimens and underwent {superscript 1}F-FDG PET/CT examinations within 2 weeks before and after chemotherapy. Multiple metabolic parameters of lesions were extracted, their percentage changes were calculated, and the optimal cut-off values for each parameter were determined. Kaplan-Meier survival analysis and Cox proportional hazards regression analysis were applied to explore the prognostic value of the metabolic parameters, and the prognostic stratification performance of PET Response Criteria in Solid Tumors (PERCIST) 1.0 was compared with that of Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. PERCIST 1.0 demonstrated significantly superior prognostic stratification compared with RECIST 1.1. A peak standardized uptake value corrected for lean body mass (SULpeak2) > 3.07 and a percentage change in SULpeak between pre- and post-treatment scans ({Delta}SULpeak%) [≤] 37.66% were identified as independent risk factors for poor prognosis. Furthermore, SUL-related parameters exhibited markedly better predictive efficacy than traditional metabolic parameters such as the standardized uptake value and metabolic tumor volume. Quantitative analysis of {superscript 1}F-FDG PET/CT metabolic parameters can effectively predict prognosis in PDAC after NACT, and PERCIST 1.0 is a more optimal criterion for efficacy and prognostic assessment. A post-NACT SULpeak > 3.07 and {Delta}SULpeak% [≤] 37.66% were core independent indicators for predicting poor prognosis in these patients.

PurposeSepsis-associated immunothrombosis significantly contributes to high mortality, yet the role of N-glycosylation in this process remains poorly understood. This study aimed to comprehensively profile the plasma N-glycosylation landscape in sepsis and elucidate how its specific reprogramming in the complement and coagulation cascades influences immunothrombotic balance and patient outcomes. MethodsWe performed in-depth 4D-DIA proteomic and N-glycomic analyses on plasma from 43 sepsis patients and 9 healthy controls. Differential expression, weighted gene co-expression network analysis (WGCNA), and protein-glycosylation correlation analyses were used to characterize molecular features. Clinical relevance was assessed via correlation and survival analyses. ResultsExtensive N-glycosylation reprogramming was observed in sepsis plasma,with marked enrichment in complement and coagulation pathways(KEGG p=7.76x10- {superscript 2}{superscript 1}).Pro-coagulant proteins(eg,vWF,fibrinogen)showed increased abundance together with enhanced site-specific glycosylation,potentially amplifying their activity.In contrast,key anticoagulant proteins(eg,SERPINC1)displayed unchanged glycosylation at critical sites despite abundance changes,which may impair function.Survival analysis revealed distinct prognostic values of glycoproteins and specific glycosylation sites.For instance,high vWF protein levels predicted mortality(HR=2.83),whereas elevated glycosylation at vWF N211 was associated with improved survival(HR=0.135),suggesting a negative regulatory role.These glycosylation markers correlated closely with disease severity and prognosis,representing potential early-warning biomarkers independent of current clinical coagulation indicators. ConclusionOur study demonstrates widespread reprogramming of the plasma proteome and N-glycome in sepsis.We propose that decoupling of protein function from abundance through N-glycosylation in the complement-coagulation network contributes to immunothrombotic imbalance.Specific N-glycosylation sites may serve as novel prognostic biomarkers,offering new perspectives for early risk stratification and glycosylation-targeted therapies in sepsis. Key PointsO_LISepsis plasma exhibits specific N-glycosylation reprogramming overwhelmingly focused on the complement and coagulation cascade. C_LIO_LIA dominant "glycosylation-dominated co-upregulation" mode in procoagulant factors, coupled with a "silent" glycosylation state in key anticoagulants, drives prothrombotic imbalance. C_LIO_LISite-specific N-glycosylation levels provide prognostic information distinct from, and often superior to, their carrier protein abundance, offering novel early-risk biomarkers. C_LI

Immunotherapy with immune checkpoint inhibitors and immunotherapy combined with chemotherapy have represented promising treatments for NSCLC patients leading to prolonged survival. However, the majority of patients with advanced NSCLC have a poor prognosis. The identification and development of biomarkers for stratifying responders and non responders to immune checkpoint inhibitors contribute to unravel the mechanism of immune checkpoint pathway and the immune tumor interaction underlying the responses and are urgently needed to improve clinical outcomes of immune checkpoint inhibitor treatment. In this study, we analyzed the clinical and gene mutation data of NCSLC patients treated with nivolumab containing immunotherapy or nivolumab containing immunotherapy combined with chemotherapy (the immunotherapy treated group, n=119) and chemotherapy alone (the chemotherapy alone treated group, n=991) extracted from the MSK CHORD dataset. A DeevSurv model, a deep learning based extension of the Cox proportional hazards model was trained to generate survival risk score of each patient with binary statuses of thirty one gene mutations as input features into the model. The thirty one genes were selected based on population level mutation frequency, patient level variance in mutation status, and univariate Cox proportional hazards analyses evaluating the association between the presence or absence of each gene mutation and overall survival. The performance of the trained DeepSurv model was evaluated on the test set of the immunotherapy treated group using the concordance indexes (C index). The trained model was subsequently applied without retraining to the entire chemotherapy alone treated group as a control. The resulting C indexes for the immunotherapy treated group and chemotherapy alone treated group were 0.789 and 0.483, respectively. All patients within each group were divided into high and low risk groups according to the median predicted risk score. Kaplan Meier survival curves of high and low risk groups (n=43 vs n=70) in the immunotherapy treated group revealed a significant separation (log rank p<0.001), whereas no separation was observed in chemotherapy alone treated group (p=0.62). In the combined cohort of the immunotherapy treated group and chemotherapy alone treated group, the interaction between the DeepSurv derived risk score and treatment modality was significant (HR for interaction 1.47, 95% CI from 1.32 to 1.65, p<0.005), suggesting the DeepSurv derived risk score predictive value specific to the immunotherapy. Principal component analysis and permutation importance analysis were performed as complementary analyses to assess individual genes associated with the DeepSurv derived risk score and identified ZFHX3, SMARCA4, ALK, BTK, and NOTCH2 as major contributors to survival risk stratification. Collectively. we suggested that nonlinear coupling pattern of 31 tumor gene mutation statuses in the DeepSurv model captures the heterogeneity of survival risk among nivolumab containing immunotherapy or nivolumab containing immunotherapy combined with chemotherapy treated patients with NSCLC which was visualized as clear separation between high risk and low risk groups divided by the median value of the risk scores.

BackgroundScalable, non-invasive markers for cognitive-decline risk are limited. Olfactory dysfunction is predictive, and oral dysbiosis is mechanistically linked to neurocognitive pathways. Hence, we tested whether pairing smell and global cognition with salivary microbiome profiling yields a targeted, clinically useful signal. MethodsWe enrolled 113 Memory Center attendees and community controls. Same-day MMSE, UPSIT, and saliva were obtained for 16S rRNA gene sequencing and cytokine measurement. Unsupervised k-means clustering on standardized MMSE-UPSIT defined two groups of participants: CNN (cognitively normal, normosmia) and CIH (cognitively impaired, hyposmia). Ordination and elastic-net models adjusted for age, sex, BMI, and sequencing depth. Functions were inferred with PICRUSt2 and were integrated with taxa via DIABLO. ResultsOverall, the 16S-based microbial community structure was similar between groups, indicating minor compositional shifts. CIH showed enrichment of periodontal anaerobes (Porphyromonas, Treponema and Prevotella), whereas CNN retained nitrate-reducing commensals (e.g. Neisseria subflava, Aggregatibacter aphrophilus). Functional shifts showed mixed consistency with literature, aligning for outer membrane usher proteins and alkyldihydroxy phosphate synthase, but diverging for thiaminase, alpha-glucuronidase, and chemotaxis protein CheX. Most salivary cytokines levels did not differ between groups. ConclusionsThis integrated smell, cognition, and saliva workflow delineates an olfactory- cognitive phenotype linked to a targeted, potentially modifiable salivary dysbiosis, periodontal anaerobes vs nitrate-reducers, rather than diffuse salivary inflammatory elevation. This approach may support non-invasive triage and monitoring along the oral- brain axis, pending independent, longitudinal validation.

Background The microbial aetiology of early childhood caries (ECC) in sub-Saharan African populations remains poorly characterised, with most studies focusing on conventional cariogenic pathogens like Streptococcus mutans. This study aimed to characterise the salivary microbial profile of children with ECC in urban Kano, northern Nigeria. Methods In this cross-sectional study of 162 children aged 3-5 years in urban Kano, unstimulated saliva samples were collected and analysed using standard bacteriological culture methods. Caries status was assessed using decayed, missing, and filled teeth (dmft) index and International Caries Detection and Assessment System (ICDAS). Microbial isolates were identified through Gram staining, colony morphology, and biochemical tests (catalase, coagulase, oxidase). Results Of 32 microbial isolates obtained, Staphylococcus aureus was the most prevalent (43.8%, n=14), followed by Streptococcus species (28.1%, n=9), Klebsiella species (12.5%, n=4), non-aureus staphylococci (6.3%, n=2), yeast (6.3%, n=2), and Pseudomonas species (3.1%, n=1). Only one isolate demonstrated direct association with dmft-detectable caries. Polymicrobial colonisation occurred in four cases (12.5%), predominantly featuring S. aureus-yeast combinations (n=2). White spot lesions (ICDAS 1-2) were associated with S. aureus and Klebsiella species in two separate cases. Conclusion This study reveals an unexpected predominance of S. aureus in the salivary microbiome of children in northern Nigeria, challenging conventional paradigms of ECC microbiology. The low correlation between microbial isolates and clinical caries suggests complex, multifactorial aetiology. These findings highlight the need for molecular characterisation of oral microbiomes in African populations and reconsideration of caries pathogenesis models in this unique epidemiological context.

BackgroundPancreatic ductal adenocarcinoma is one of the most aggressive and lethal malignancies of the gastrointestinal tract. The poor prognosis is largely attributed to late-stage diagnosis, pronounced tumor heterogeneity, and limited therapeutic efficacy. These challenges underscore the urgent need for the identification of robust molecular biomarkers and novel therapeutic targets. MethodsGene expression data from a total of 146 pancreatic tissue samples, comprising 72 normal and 74 tumor specimens obtained from the Pan-Cancer Atlas(TCGA) were analyzed. Differential gene expression analysis was conducted using the DESeq2 package, followed by functional enrichment analysis based on GO and KEGG. A classification model was developed using the XGBoost algorithm and evaluated through 500 bootstrapping iterations and 5-fold cross-validation to ensure robustness and generalizability. Model interpretability was assessed using SHAP (SHapley Additive exPlanations) values to identify genes with the highest predictive contribution. ResultsA comprehensive transcriptomic analysis revealed significant dysregulation of multiple genes between normal and tumor pancreatic tissues. Genes such as GJB3, S100A2, MSLN, and SLC2A1 were notably overexpressed, whereas DEFA6, APOB, and RBP2 exhibited marked downregulation, indicative of impaired exocrine function and aberrant epithelial reprogramming. The XGBoost classification model achieved an average area under the curve (AUC) of 0.9868 and an overall accuracy of 98.6%. SHAP (SHapley Additive exPlanations) analysis identified GJB3, LINC02086, and TSPAN1 as key predictive features. Six genes were concurrently identified as differentially expressed and highly influential within the model, supporting their potential utility as robust biomarkers for pancreatic tumor characterization. ConclusionsPancreatic ductal adenocarcinoma is marked by extensive transcriptomic reprogramming. The integration of differential gene expression analysis with interpretable machine learning enabled the identification of a molecular signature with potential diagnostic and therapeutic relevance.

ObjectivePreterm birth (PTB) is a leading cause of neonatal morbidity and mortality worldwide, with India alone contributing nearly 27% of the global PTB burden. Although alterations in the vaginal microbiome have been implicated in PTB, its association in the Indian context is underexplored. This study aimed to investigate the association of vaginal microbiome and PTB in Indian women at the time of delivery. Study designThe vaginal swabs were collected at the time of delivery from 72 women (31 term, 41 preterm) admitted to a tertiary care hospital in Western India. Microbial DNA was extracted, and the V3-V4 region of the 16S rRNA gene was sequenced. Community composition, alpha and beta diversity, and differential taxonomic abundance were assessed using bioinformatics pipelines. ResultsAt the time of delivery, there were no significant differences in alpha or beta diversity between term and preterm groups. Principal coordinate and unsupervised clustering analyses showed no group-wise segregation. The relative abundance of individual Lactobacillus species, including L. iners and L. helveticus, did not differ significantly between the two groups. However, a modest difference in the relative abundance of Streptococcus was observed between the two groups after adjustment. ConclusionThis study found no major microbial shifts in the vaginal microbiome associated with preterm birth in this cross sectional cohort of Indian women, suggesting that vaginal dysbiosis at the time of delivery may not be a principal driver of PTB in this population. These findings underscore the need for larger, longitudinal, and ethnically diverse studies using standardized methodologies better to understand the microbiomes role in PTB risk.

Our previous studies identified three microRNAs (miR-92a-1-5p, miR-375 and miR-148a-3p) potentially associated with prostate cancer (PCa), particularly in advanced stages such as bone-metastatic PCa. To evaluate their clinical diagnostic utility, we isolated extracellular vesicles (EVs) from the plasma of patients with benign prostatic hyperplasia (BPH) and PCa (including localized and bone-metastatic disease). The absolute quantification of these three miRNAs within plasma EVs was performed using digital PCR. Results indicated that miR-148a-3p alone possessed a good ability to discriminate between PCa and BPH. Notably, a combined panel of all three miRNAs demonstrated improved diagnostic performance, achieving an area under the curve (AUC) of 0.736 for distinguishing PCa from BPH. These findings suggest that the plasma EV-derived miRNA panel (miR-92a-3p, miR-148a-3p, and miR-375-3p) holds promise as an auxiliary diagnostic biomarker for PCa and may aid in identifying bone metastasis.

Cancer heterogeneity is traditionally attributed to multiple parallel signaling pathways. This belief is challenged here by proposing the ER/PR axis as the dominant pathway underlying the full spectrum of breast cancer. Absolutely quantitated ER, PR, Her2 and Ki67 protein levels were accumulated over 8 years from 1652 specimens collected non-selectively and measured with Quantitative Dot Blot (QDB) method over time. Cox analysis showed ER and Ki67 as independent adverse prognostic factors while PR was an independent favorable factor statistically. Their optimized stratification framework demonstrated that prognosis across all clinical subtypes was predominantly aligned along the ER/PR axis rather than being subtype-specific, including repeated identification of a subgroup with near-perfect 10-year survival probability from three independent cohorts to be proposed as the biological basis of the ultra-safe group in MINDACT trial. A parsimonious model is proposed where the ER/PR signaling hierarchy supersedes current prevailing clinical subtyping, with its balance essential for survival until ER levels become uncontrollable. This concept of pathway hierarchy may also exist in other major cancer types, and cannot be addressed without clinical epidemiology.

Background Short-chain fatty acids (SCFAs) are widely used as functional readouts of gut microbial activity in vivo. The growing adoption of decentralised study designs and self-collection protocols has amplified the need for reliable room-temperature storage and shipment strategies. However, SCFAs volatility and the persistence of post-collection microbial metabolism raise concerns regarding pre-analytical stability and the interpretability of measured concentrations. Methods We assessed the temporal stability of fatty acids (FAs) across intestinal and systemic matrices under room-temperature storage. Untreated stool was compared with two nucleic acid stabilisation devices (eNAT and OMNIgene-GUT), while whole blood, plasma and dried blood spots (DBS) were evaluated as minimally invasive systemic sampling strategies. Profiles were quantified using complementary GC-MS and LC-MS/MS workflows. Results Untreated stool showed fermentation-driven increases in major SCFAs, whereas immediate freezing preserved baseline profiles. eNAT maintained faecal FA stability for up to 21 days, while OMNIgene-GUT exhibited baseline and time-dependent alterations. In systemic matrices, plasma and whole blood showed upward drift, whereas DBS declined initially before stabilising after approximately 14 days. Conclusions FA measurements are highly matrix- and device-dependent. Our findings provide practical guidance for the selection of sampling strategies in microbiome-associated FA studies and emphasise the need for controlled pre-analytical conditions in decentralised microbiome studies.

Aim and ObjectivesThe study aimed to evaluate the effectiveness of titanium inserts for interdental papilla reconstruction, comparing it with the Han and Takei technique using subepithelial connective tissue grafts. The objectives included assessing the black triangle height, papilla height and papilla presence index (PPI) at baseline, 1 month and 3 months postoperatively along with the evaluation of Early Wound Healing Score (EHS) during the first week of post operative healing period. Patients and MethodsThis single-blind randomized clinical trial included systemically healthy individuals aged 18-35 years with Nordland and Tarnows Class I-III papillary loss. A total of 18 participants were randomly assigned to either test group or control group. Clinical parameters were measured pre- and post-operatively at specified intervals. Both groups received standard presurgical care and postoperative follow-up. The surgical protocol for the test group involved titanium insert placement in the interdental bone, while the control group received a connective tissue graft using the Han and Takei method. ResultsBoth groups showed significant intragroup improvements in all parameters from baseline to 1 and 3 months (p<0.05). However, intergroup comparisons showed no significant differences at most time points, except at 3 months for PPI, where the control group showed significantly better results (p=0.04). EHS scores were not significant between the groups. ConclusionTitanium inserts and CTG both demonstrated clinical effectiveness in enhancing interdental papilla dimensions. These findings support the titanium insert as a viable, less invasive alternative, offering clinicians a practical option for esthetic papilla reconstruction.

BackgroundPancreatic cancer is a leading cause of cancer mortality, and early recognition is challenging. To achieve early diagnosis using symptoms alone, we examined patterns across different stages using network analysis to derive clinically useful insights. MethodsSymptom variables from a de-identified dataset of 50,000 pancreatic cancer patients were analyzed. Stratification by stage was done, followed by bootstrap resampling to address imbalances across strata. Symptom networks were then constructed with nodes representing symptoms and edges representing conditional dependencies estimated via an Ising-style neighborhood selection approach implemented through L1-regularized logistic regression. Strength, betweenness, and closeness centrality indices were then calculated, and their stability was analyzed using the case-dropping bootstrap. Network comparison tests were done, and difference networks were analyzed. Spring-layout algorithm was used for visualization, with node size being the predictability (pseudo-R{superscript 2}), and the edge weight being the mean partial correlation magnitude. ResultsOn average, symptoms were present in about one out of four patients (M = 0.26). Weight loss and abdominal discomfort were the most prevalent of the symptoms, followed by jaundice and back pain. Network structures became sparser across stages with a decreasing number of edges and centrality indices. Jaundice emerged as the dominant hub in Stage I, but shared dominance with Weight Loss in Stage II. Node predictability (pseudo-R2) was effectively zero across all disease stages. ConclusionOur network analysis of pancreatic cancer symptomatology across stages revealed distinct patterns that may improve understanding of its clinical presentation and support earlier recognition.

BackgroundAlthough DCIS has a relatively low recurrence rate, many patients still receive adjuvant radiotherapy or endocrine therapy, raising concerns about overtreatment. Reliable biomarkers are therefore needed to predict an individual patients risk and guide treatment decisions. Recent studies suggest that the composition of the tumour-associated stroma (TAS) affects progression and outcome, highlighting TAS-derived biomarkers as promising candidates for further investigation. MethodsWe trained AI models for cell and tumour segmentation using whole slide digital pathology images acquired as part of a retrospective cohort study. We investigated the effects of cell density within both the tumour and the TAS to determine how they correlated with recurrence in the ipsilateral breast. ResultsWe found that the concentration of DCIS lesions on the slide and the density of mitotic figures inside the TAS region were significantly associated with recurrence risk. Additionally, we found some predictive value in the lymphocyte and red blood cell densities in different tumour regions. Stromal composition was shown to associate with recurrence risk, and density-based biomarkers were identified and used to cluster patients into phenotypes with significantly different risk profiles. ConclusionOur findings highlight the prognostic relevance of stromal composition in DCIS, and we identify novel density-based biomarkers that can be used to identify patients who are more likely to experience a local recurrence after breast-conserving surgery alone. These results may aid in developing future risk-stratification tools for breast cancer patients, thereby reducing overtreatment and improving patient care.

ObjectiveTo explore the application value of dual-staining for specific AT sequence binding protein 2 (SATB2) immunohistochemistry and elastic lamina in detecting elastic lamina invasion (ELI) in pT3 colon cancer, and to assess its association with clinicopathological characteristics, staging, and prognosis. MethodsThis retrospective cohort study enrolled 176 pT3 colon cancer patients who underwent radical resection at Affiliated Jinhua Hospital Zhejiang University School of Medicine. The deepest tumor-infiltrated paraffin blocks were collected for SATB2 immunohistochemistry and elastin dual-staining. Correlations between ELI status and clinicopathological characteristics and prognosis were analyzed. Survival data of 74 pT4a stage patients were collected for comparative analysis. ResultsELI (+) was positively associated with high tumor budding grade, vascular invasion, lymph node metastasis, and reduced tumor infiltrating lymphocytes (TILs) (all P < 0.001). No correlations were observed with age, gender, tumor location, histological subtype, tumor grade, or perineural invasion (all P > 0.05). The ELI (+) group exhibited significantly shorter disease-free survival (DFS) and overall survival (OS) compared to ELI (-) group (P < 0.05). Additionally, the ELI (+) group demonstrated inferior OS than the pT4a group, though DFS did not differ significantly. ConclusionDual-staining of SATB2 immunohistochemistry and elastic lamina provides a reproducible and objective method for assessing ELI. ELI correlates with key clinicopathological features and functions as an independent adverse prognostic indicator in pT3 colon cancer.

BackgroundAdjuvant abemaciclib+endocrine therapy (ET) improves long-term outcomes in high-risk, hormone receptor-positive (HR+)/HER2-negative early breast cancer (eBC). However, treatment is frequently complicated by diarrhea, affecting adherence and quality of life (QoL). Increasing evidence suggests that abemaciclib-induced gastrointestinal toxicity may involve gut microbiota alterations. We conducted a prospective case-control pilot study evaluating the efficacy of MBR-01, a standardized prebiotic/probiotic formulation, in mitigating abemaciclib-induced diarrhea. MethodsWe enrolled 20 patients with high-risk HR+/HER2-negative eBC considered unfit for adjuvant chemotherapy. Patients received abemaciclib+letrozole (control, n=10) or abemaciclib+letrozole+MBR-01 (experimental, n=10). The primary endpoint was the incidence and severity of diarrhea; secondary endpoints included treatment adherence, QoL assessments and exploratory baseline/week-12 microbiota characterization according to treatment arm. Trial registration number: ISRCTN11948182. ResultsDiarrhea occurred in all patients. In the control group, diarrhea was predominantly grade 1 (50%) or grade 2 (40%), with one grade 3 event (10%). In the MBR-01 group, diarrhea frequency and severity were reduced by [~]70% at the end of week-12; 80% of patients experienced only grade 1 diarrhea or none by week-12, and no grade [&ge;]3 events. Dose modification was only required in one control. Alpha-diversity and depletion of F.prausnitzii were associated with earlier diarrhea onset and longer duration; enrichment in E.coli correlated with higher grade events. MBR-01 supplementation seemed to preserve microbial diversity and limited E.coli expansion. QoL was significantly improved with MBR-01. ConclusionMBR-01 may effectively mitigate abemaciclib-induced diarrhea, likely through the achievement of stabilization of gut microbiota composition. Larger prospective studies are warranted to validate these preliminary findings. HighlightsO_LIMBR-01, a prebiotic/probiotic, was given to reduce abemaciclib-induced diarrhea. C_LIO_LIMBR-01 reduced diarrhea by [~]70%, most patients had G0-1, one G [&ge;]3 at week 12. C_LIO_LIMBR-01 patients keep abemaciclib drug dose; 10% of controls required reduction. C_LIO_LIMBR-01 halved stool frequency and improved quality of life. C_LIO_LIMBR-01 preserved gut diversity, maintaining F. prausnitzii and limiting E. coli. C_LI

There is an unmet need to identify biomarkers of active thyroid eye disease (TED). scRNAseq revealed that orbital fibroblasts from orbital decompressions in people with TED express high levels of thyroid hormone receptors, growth factor receptors, including insulin-like growth factor 1 receptor (IGF1R), and extracellular matrix proteins including SPARC (osteonectin), whereas orbital fat endothelial cells expressed thyroid peroxidase (TPO). SPARC was significantly raised in the serum of people with thyroid disease compared to healthy controls. Furthermore, those with moderate, severe and sight threatening TED had higher SPARC levels than those with thyroid disease but free of TED or mild TED. Free-triiodothyronine (FT3) levels were positively correlated with SPARC in moderate-sight threatening TED. SPARC and IGF1 were positively correlated across people with thyroid disease alone, as well as TED. Thyroid stimulating hormone (TSH) levels were negatively correlated with SPARC in moderate-sight threatening TED. When participants were followed longitudinally, SPARC decreased after the active phase of TED. At the protein level, immunohistochemistry indicated that SPARC was heterogeneously expressed by fibroblasts in both control and TED orbital fat. SPARC is a key mediator of fibrosis and deposition of extracellular matrix and the correlation of SPARC serum levels to TED status and FT3 make it a promising biomarker of active TED.

ObjectivePost-thrombotic syndrome (PTS), a common complication of deep vein thrombosis, lacks objective diagnostic biomarkers and its molecular mechanisms remain poorly understood. This study aimed to identify plasma biomarkers and clarify pathways using integrated multi-omics and machine learning. MethodsProteomic and metabolomic profiling of 75 PTS patients and 75 controls was performed. Differential expression analysis, pathway enrichment, and protein-metabolite network analysis were conducted. A multi-algorithm machine learning with 8 feature selection methods prioritized biomarkers. Validations and 14 models were assessed. Results1,104 proteins and 1,891 metabolites were differentially expressed. Citrate cycle and unsaturated fatty acid biosynthesis were enriched. Three proteins, namely DIP2B, KNG1, and SUCLG2, were consistently selected as core biomarkers. All of these proteins were significantly downregulated in PTS and externally validated. A random forest model utilizing these proteins achieved an accuracy of 97.7% in independent testing, with SUCLG2 being the most influential predictor. ConclusionThis study identifies a novel three - protein biomarker panel for the diagnosis of PTS and reveals an immunometabolic axis in the pathogenesis of PTS, which links inflammatory regulation with mitochondrial energy metabolism. These findings provide valuable insights into the development of diagnostic tools and targeted therapeutic approaches.

The risk of esophageal adenocarcinoma (EAC) in Barretts esophagus (BE) varies substantially by segment length and dysplasia grade. This study evaluated the cost-effectiveness and health impacts of dysplasia-stratified EAC surveillance strategies for the Japanese BE population. A state-transition model was developed comparing endoscopy, sponge test, breath test, and miRNA test with no surveillance from a healthcare payer perspective over a lifetime. Non-invasive strategies were assessed as primary surveillance tools, with positive results triggering confirmatory endoscopy, and a scenario analysis evaluated AI-assisted endoscopy. Five BE populations of 50-year-old individuals were modeled: ultra-short segment BE (USSBE), short-segment BE (SSBE), long-segment nondysplastic BE (LSBE-NDBE), LSBE with low-grade dysplasia (LSBE-LGD), and LSBE with high-grade dysplasia (LSBE-HGD). Each modality was evaluated at surveillance intervals of 1, 2, 3, 4, 5, or 10 years. Primary outcomes included net monetary benefits, costs, quality-adjusted life-years, incremental cost-effectiveness ratios, and EAC deaths, with sensitivity analyses assessing parameter uncertainty. Surveillance was not cost-effective for USSBE, SSBE, or LSBE-NDBE. For LSBE-LGD, annual endoscopy was most cost-effective, averting 83 EAC deaths per 10,000 individuals, while for LSBE-HGD, annual breath testing was most cost-effective, averting 295 deaths. These findings support dysplasia-specific surveillance in LSBE with implications for global surveillance practice.

PurposeHirschsprung-associated enterocolitis remains a major postoperative complication of Hirschsprungs disease (HD), and impaired epithelial barrier integrity has been proposed as a contributing factor. In this study, we investigated whether 12-hydroxyheptadecatrienoic acid (12-HHT), an endogenous leukotriene B4 receptor 2 (BLT-2) agonist, enhances the epithelial barrier and exerts anti-inflammatory effects in patient-derived colonic organoids. MethodsNormoganglionic specimens from rectal/rectosigmoid HD at pull-through (HD-N; n = 8) and transverse colon specimens from anorectal malformation (ARM) at colostomy closure (n = 10) were used to generate colonic organoids. Epithelia were isolated using ethylenediaminetetraacetic acid and subsequently embedded in Matrigel. Baseline expression of TJP1, TJP2, F11R (encoding junctional adhesion molecule-A), JAM2, CLDN1, CLDN3, CLDN4) and LTB4R2 (encoding BLT-2) was assessed by qPCR and immunoblotting. Organoids were then treated with 12-HHT (0.4, 2, or 10 M) for 7 days, followed by qPCR. Additional experiments assessed cytokine expression (IL1B, IL6) and TJPs after 24 h with tumor necrosis factor- (TNF-, 100 ng/mL) plus phosphate buffered saline or 12-HHT. Barrier function was evaluated using FITC-dextran influx assays. ResultsHD-N and ARM organoids exhibited similar growth efficiencies. Baseline expression for F11R, JAM2, CLDN1, CLDN3, CLDN4, and LTB4R2 was significantly lower in HD-N than in ARM. TJPs were upregulated by 12-HHT at 2 and 10 M in both groups, with stronger effects in ARM. In HD-N organoids, 10 M 12-HHT suppressed TNF--induced IL1B and IL6 elevation mitigated tight junction proteins (TJPs) downregulation more effectively than 2 M. 12-HHT attenuated TNF--induced FITC-dextran influx in HD-N organoids. Conclusion12-HHT may exert anti-inflammatory effects by integrating TJPs of HD-N.