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.

Antimicrobial peptides (AMPs) are essential components of the innate immune system, exhibiting diverse mechanisms of action. This study investigates the roles of cathelicidin (LL-37), alpha-defensins, and the S100 proteins S100A8 and S100A9 in systemic inflammation associated with sepsis, severe COVID-19, and acute pancreatitis using whole-blood bulk RNA-sequencing data. Gene co-expression network analysis revealed that during septic shock and severe COVID-19, cathelicidin and alpha-defensins act synergistically in innate immune responses, while S100A8 and S100A9 function through distinct pathways related to mitochondrial metabolism and ubiquitin ligase binding. In contrast, the acute pancreatitis network displayed a different pattern, with CAMP co-expressed alongside S100A8 and S100A9, whereas alpha-defensins were downregulated and associated with inhibited mucosal immune responses. These findings suggest that antimicrobial peptides contribute variably to systemic inflammation depending on the underlying insult, underscoring their complex, context-dependent roles in critical illness.

ObjectiveProstate cancer (PCa) constitutes a considerable public health concern worldwide, primarily attributable to its elevated mortality rates. Changes in exosome are shown to significantly influence tumor development. This study aimed to investigate the prognostic value of exosome-related genes (ERGs) in PCa. MethodsPCa single-cell RNA sequencing (scRNA-seq) and transcriptome datasets were obtained from public databases, with ERGs extracted from existing literature. Candidate genes were identified by overlapping 6,004 PCa-related differentially expressed genes (DEGs) and 121 ERGs. Multiple algorithms screened prognostic genes to construct and validate a risk model. Function enrichment, immune infiltration, and drug sensitivity analyses were performed for high/low-risk groups, while scRNA-seq determined cell types via prognostic genes. ResultsA sum of 36 candidate genes was discovered at the intersection of 6,004 DEGs and 121 ERGs. NOC2L, RPS10, POSTN, and BIRC5 were selected as the prognostic genes. The survival status of PCa patients was effectively predicted by a risk model. The majority of pathways identified as significantly enriched between the 2 groups were related to cellular functions. Additionally, 7 differential immune cell types were identified between the 2 groups. RPS10 demonstrated the most significant negative correlation with immature dendritic cells. Chemotherapy drugs were more effective for PCa patients classified as low-risk group. Finally, epithelial cells, endothelial cells, and T cells were considered as key cells and played a critical role in PCa. ConclusionNOC2L, RPS10, POSTN, and BIRC5 were identified associated with exosome in PCa, providing a strong reference for exosome mechanisms in PCa.

ObjectiveThis study utilizes small-sample periodontitis data to exploratively investigate causal relationships between the oral microbiome and periodontitis in East Asian populations. We aimed to identify specific oral microbial taxa that may drive disease pathogenesis. Given the exploratory nature of the dataset, findings should be interpreted as hypothesis-generating. MethodsWe performed a two-sample Mendelian randomization (MR) analysis using genome-wide association study (GWAS) summary statistics for tongue dorsum and salivary microbiomes alongside periodontitis data in East Asian populations. Primary causal estimates were derived using the inverse-variance weighted (IVW) method, supplemented by MR-Egger, weighted median, weighted mode, and simple mode methods. To ensure robustness, we assessed heterogeneity using Cochrans Q test, evaluated horizontal pleiotropy via the MR-Egger intercept and MR-PRESSO tests, and applied Steiger filtering to rule out reverse causality. ResultsWe identified 60 species-level microbial taxa causally associated with periodontitis, comprising 29 negative and 31 positive associations. These taxa were predominantly enriched within the genera Campylobacter, Pauljensenia, Solobacterium, and Streptococcus. ConclusionThis study provides tentative evidence for causal links between specific species-level oral microbial taxa and periodontitis, highlighting potential targets for prevention and therapeutic intervention.

BackgroundColorectal cancer (CRC) is a leading cause of cancer mortality. While early detection improves outcomes, current non-invasive tests often lack sensitivity for early-stage CRC and advanced precancerous lesions (APL). Stool-based host messenger RNA (mRNA) biomarkers offer a promising approach, though the most clinically useful candidates remain undefined. MethodsWe screened for mRNA biomarkers by first using bioinformatic analysis of tissue RNA-seq datasets to identify candidate genes with strong and ubiquitous differential expression in CRC versus normal tissues. The top 135 computationally predicted biomarkers were evaluated using "gold standard" RT-PCR on clinical stool samples across two independent cohorts. ResultsSeveral biomarkers, including PPBP, MYC, MMP7, and TGFBI, exhibited strong predictive power. Integrating top-performing markers through machine learning yielded an AUC of 0.98 for CRC and 0.76 for APL detection. The optimized panel demonstrated 98% sensitivity for CRC and 50% for APL, with a specificity of 90%. ConclusionsThis study derives a high-performance mRNA-based stool test for non-invasive CRC screening. Our findings demonstrate that a multi-marker panel achieves exceptional sensitivity and good specificity, providing a viable tool for clinical diagnostics.

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.

Stoma formation diverts the flow of faeces to an opening on the skin, away from a section of bowel. Stomas are used to reduce the risks of bowel surgery and their effects are thought to be temporary and benign. However, negative effects [e.g. diversion colitis (inflammation in the excluded segment), poor quality of life, and an unclear effect on homeostasis] occur. We investigated these changes and whether distal feeding (DF) - the introduction of nutrition directly into the defunctioned bowel prior to stoma reversal - could mitigate these effects. A series of clinical, qualitative, immunological, metabolomic and microbiomic experiments conducted across 133 patients identified consistent changes in immune and microbiome response which differed according to stoma formation and underlying disease process [colorectal cancer (CRC) with or without chemotherapy, intestinal failure (IF)], including a unique immune signature in circulating memory T-cell homing in stoma patients. DF induced the proliferation and altered homing of memory T cells, particularly in patients who had received chemotherapy. DF led to changes in serum metabolites, increased circulating markers of gut health (citrulline, serotonin), and modified the faecal microbiota. DF also promoted a faster return of bowel function and earlier hospital discharge in patients with CRC, while in IF it accelerated intestinal autonomy from parenteral nutrition. This in-depth analysis of sequential experiments characterises gut homeostasis and post-operative systemic immunity after stoma formation in CRC and IF patients, and quantifies and explains the benefit of DF in these groups. This research program has wide-ranging implications for patients worldwide with stomas. Given its simplicity, affordability and wide applicability, DF has the potential to become a transformative adjunct in surgical recovery and improved long-term gut health in patients with stomas.

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.

Ovarian cancer is the deadliest of gynecological cancers and surgery is often necessary for a final diagnosis. Benign cases could be managed more conservatively, avoiding the risks and complications associated with surgery, if accurate diagnostic biomarkers existed. Underlying differences between circulating protein biomarkers and tumor gene expression also restricts interpretation and prioritization of potential biomarkers for diagnosis and potential drug targets. Here, high-throughput affinity plasma proteomics data encompassing over 5400 proteins in plasma from 404 women from two independent Swedish cohorts were analyzed alone and combined with total RNA sequencing in corresponding benign and malignant tumor tissue. A subset of 191 proteins previously identified as differentially expressed between benign and malignant conditions were used to perform correlation analyses, revealing similar patterns between groups but much stronger signals in malignant cases. Comparison with known protein interactions from the STRING database revealed a highly interconnected network consisting of 154 proteins in plasma. Differential correlation analysis (DCA) was performed on the full set of 5414 proteins and for their corresponding tumor RNA expression. DCA identified 31 plasma proteins with significant differential correlations (adjusted p < 0.05, {Delta}R > 0.5) and 759 tumor transcript pairs with significantly differentially correlating RNA expression. Distinct protein-protein correlation patterns in plasma were discovered and validated with notable differences between benign and malignant tumors. In general, these patterns were distinct from those detected on gene expression level in tumor tissue. In conclusion, our findings reveal clear differences in plasma protein co-regulation, with distinct correlation patterns between malignant and benign cases. The differences between results obtained in tumor transcriptomics and plasma proteomics results from the same patients warrants further studies into the tumor microenvironment to understand the function of promising protein biomarker candidates and the potential of these as future drug targets.

BackgroundCrohns disease (CD) is a chronic inflammatory disorder of the gastrointestinal tract characterized by high post-operative recurrence (POR) rates, reaching up to 90% within one year. Current clinical and endoscopic predictors show limited accuracy. ObjectiveThis study aimed to identify molecular mechanisms associated with POR at the time of surgery through integrated transcriptomic and bacteriomic analyses of ileal tissue. DesignIleal samples were obtained during surgery from 20 patients with CD and 10 inflammatory bowel disease-free controls, with an independent validation cohort of 49 patients with CD. POR was evaluated every six months using ileocolonoscopy and defined by Rutgeerts score. Host gene expression and tissue-associated microbiome profiles were integrated using correlation and pathway enrichment analyses to uncover host-microbe interactions linked to POR. ResultsIn the inflamed mucosa of patients who developed endoscopic POR, we identified a novel immune interaction involving the Porphyromonadaceae family, mainly Parabacteroides gordonii, which was slightly depleted. This depletion was associated with downregulation of epithelial barrier and tissue repair genes, including TFF1 and LSR, findings confirmed in the validation cohort. Porphyromonadaceae abundance positively correlated with short-chain fatty acid levels, particularly propionate. Additionally, omics integration revealed an association between Xanthomonadaceae and increased expression of ITGAM, a gene involved in neutrophil activation. ConclusionThese results highlight microbial-host gene interactions associated with POR. The pathogenic ITGAM-driven immune signature and the protective Porphyromonadaceae-TFF1-propionate axis supporting epithelial integrity may enable microbiome-informed prognostic tools and therapeutic strategies for CD POR. O_LIWhat is already known on this topic: Post-operative recurrence in Crohns disease is linked to microbial dysbiosis, particularly reduced diversity and expansion of Enterobacteriaceae. However, how microbial changes translate into host molecular mechanisms driving POR remains unclear. C_LIO_LIWhat this study adds: This prospective multi-omic study identifies a disrupted Porphyromonadaceae-SCFA-epithelial barrier axis and the participation of neutrophil responses in patients who develop POR at surgery time. C_LIO_LIHow this study might affect research, practice or policy: The findings provide mechanistic targets for microbiome-informed risk stratification and prevention of POR. They support development of microbial or metabolite-based interventions aimed at restoring epithelial barrier function after surgery. C_LI

BackgroundInflammatory breast cancer (IBC) is a rare and highly aggressive form of breast cancer with an increased propensity to metastasize to distant organs including the brain. Higher serum levels of the calcium-binding proteins S100A8/A9, particularly of S100A9, have emerged as a clinically and biologically significant factor in aggressive breast cancers that are associated with poorer prognosis, tumor progression, and resistance to therapy. However, its contribution in IBC specifically remains undefined. Here, we investigated whether serum levels of S100A8/A9 predict outcomes in patients with IBC. MethodsSerum S100A8/A9 levels were measured in a cohort of 304 IBC patients using ELISA assay. S100A8/A9 levels were categorized by their third quartile value (S100A8/A9-low [&le;] 3rd quartile; S100A8/A9-high > 3rd quartile). Overall survival (OS) and breast cancer-specific survival (BCSS) were analyzed with Kaplan-Meier curves, log-rank tests, and Cox proportional hazard regression models. The cumulative incidence of any metastases and the cumulative incidence of brain metastases were analyzed using Aalen-Johansen method, Gray test, and Fine-Gray models. ResultsThe median follow-up time was 64 months. Forty-six percent of patients had estrogen receptor (ER)-negative tumors, 61.3% were stage III-IV, 77% high grade, 16.8% received adjuvant chemotherapy and 53.6% received adjuvant radiation. On univariate analysis, S100A8/A9 levels, disease stage, ER status, PR status, HER2 status, adjuvant chemotherapy, and adjuvant radiation therapy were significantly associated with OS and BCSS. Patients with high S100A8/A9 serum levels had poor OS (P=0.01) and BCSS (P=0.007) and had a higher risk of developing brain metastasis (P=0.01) but not other metastasis. On multivariate analysis, high S100A8/A9 serum levels were independently associated with reduced OS (hazard ratio [HR]=1.7, 95% CI 1.1 to 2.6, P=0.01), reduced BCSS (HR=1.8, 95% CI 1.2 to 2.8, P=0.006), and increased cumulative incidence of developing brain metastasis (subdistribution hazard ratio (sHR)=1.8, 95% CI 1.1 to 3.0, P=0.03). ConclusionsIn patients with IBC, high serum levels of S100A8/A9 are an independent prognostic factor for brain metastasis and poor clinical outcomes. These findings support the potential of S100A8/A9 as predictive biomarker for identifying increased risk of brain metastasis and unfavorable prognosis in patients with IBC.

This study investigates the efficacy of transformer-based deep learning architectures--specifically, Vision Transformer (ViT), Class Attention in Image Transformers (CaiT), and Data-Efficient Image Transformers (DeiT)--for the binary classification of colorectal polyps using the Minimalist Histopathology Image Analysis Dataset (MHIST). The dataset comprises 3,152 hematoxylin and eosin (H&E)-stained Formalin Fixed Paraffin-Embedded (FFPE) images annotated as either Hyperplastic Polyps (HP) or Sessile Serrated Adenomas (SSA). A rigorous evaluation was conducted using a 5-fold stratified cross-validation methodology, and performance was quantified using metrics including accuracy, precision, recall, F1-score, and AUC-ROC. Experimental results revealed that transformer architectures, particularly CaiT (accuracy of 90.18%, AUC-ROC of 95.52%), outperformed traditional convolutional neural networks (CNNs). The superior performance of CaiT is attributed to its specialized class-attention mechanisms, effectively capturing nuanced morphological differences essential for accurate histopathological classification. These findings underscore the potential of transformer-based models to enhance diagnostic precision, reduce variability in pathological assessment, and facilitate earlier and more reliable colorectal cancer screening.

BackgroundHER2-positive (HER2+) cancers are associated with aggressive tumor development but also high response rates to targeted blockade treatments of the HER-2/neu signaling pathway leading to improved clinical outcome for the patient. Current clinical analysis of the HER2 status primarily relies on solid tumor biopsies low-suitable for continuous real-time monitoring needed for possible adjustment of the treatment, while serum tests targeting blood-circulating HER-2/neu fragments often show conflicting tumor-serum relations. MethodsA cellulase-linked aptamer sandwich assay was used for detection of total urokinase plasminogen activator (uPA) and its different forms in serum of cancer patients and healthy individuals. Serum uPA levels were correlated with solid biopsy results and relevant clinical data extracted from electronic patient records, and FDG-PET/CT scanning. ResultsWe show that serum uPA allows precise stratification of patients with HER2+ cancers and cancers with HER2 borderline expression. Serum levels of total uPA 96.6% accurately informed about HER2+ tumor status in a cohort of 85 patients, with a HER2+ cut-off value of 0.976 ng mL-1. ConclusionsThe established liquid biopsy test for serum uPA has potential for accurate diagnosis and staging of patients with HER2+ cancers and "borderline" cancers requiring further confirmatory (or rejection) testing.

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.

The embodied brain is highly dynamic, changing with every thought, sensory input and motor activity. It keeps us coherent and healthy via its connections to every organ within the body, particularly the heart, which in turn supplies nutrients and oxygen to all bodily organs and the brain. Listening to music can instantly alter brain-body dynamics. Yet, the acoustic, neural, and physiologic parameters and processes that facilitate these effects are not well understood. Here, we tested the hypothesis that a custom music composition can promote healing in patients recovering from liver transplant surgery within an intensive care unit (ICU). The music presented consisted of custom,15-minute music sets curated and recorded by an experienced medical musician. We obtained cortisol samples from saliva samples [~]15 minutes before and after music presentation and captured autonomic activity by recording electrocardiography for 5 minutes before, during, and 5 minutes after music presentation in normal subjects and patients. Discriminant analysis showed a significant decrease in cortisol production (n = 17) after music presentation. Detailed analysis in a single patient showed significant changes in multiple cardiac parameters, including heart-rate variability (HRV). Multidimensional scaling of twenty-five parameters related to HRV in a patient mapped all five instances of the music presentation condition outside of the mixed cluster of baseline conditions before and after music presentation. Our results show that listening to music promotes homeostasis in ICU patients by transiently shifting physiological parameters towards a state of recovery that may stabilize over time.

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.

BackgroundInflammatory responses and nutritional status are critical determinants of colorectal cancer (CRC) progression and clinical outcomes. This study aimed to evaluate the prognostic value of a novel albumin-neutrophil composite grading (ANPG) system and to develop a nomogram for predicting overall survival (OS) in CRC patients following curative resection. MethodsA retrospective analysis was conducted on 660 consecutive patients with primary CRC who underwent R0 resection between December 2017 and December 2018. The ANPG was constructed based on preoperative serum albumin levels and neutrophil counts, with optimal cutoff values determined by receiver operating characteristic (ROC) curve analysis. Prognostic factors were identified using univariate and multivariate Cox proportional hazards regression models. A predictive nomogram was developed and internally validated via bootstrap resampling (800 iterations) and time-dependent ROC analysis. Decision curve analysis (DCA) was performed to assess clinical utility. ResultsThe median follow-up duration was 2442 days (interquartile range: 2117-2537 days), during which 108 cancer-specific deaths occurred. The ANPG demonstrated superior discriminative ability (area under the curve [AUC] = 0.637, 95% confidence interval [CI]: 0.588-0.687, P < 0.001) compared to established inflammatory and nutritional markers, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), fibrinogen-to-albumin ratio (FAR), and fibrinogen-neutrophil-lymphocyte ratio (F-NLR). Kaplan-Meier survival analysis revealed significant differences in OS across ANPG grades (Log-rank {chi}{superscript 2} = 24.423, P < 0.001), with 5-year OS rates of 93.7%, 83.2%, and 74.4% for grades 0, 1, and 2, respectively. Multivariate Cox regression analysis identified ANPG (grade 1 vs. 0: hazard ratio [HR] = 2.190, P = 0.020; grade 2 vs. 0: HR = 3.256, P < 0.001), age (HR = 1.032, P < 0.001), carbohydrate antigen 19-9 (CA19-9) (HR = 1.002, P = 0.003), histological type (HR = 1.954, P = 0.005), and TNM stage as independent prognostic factors. The nomogram incorporating these variables--retaining carcinoembryonic antigen due to its clinical relevance and contribution to model performance--achieved a concordance index (C-index) of 0.806 (95% CI: 0.788-0.824) with excellent calibration, significantly outperforming TNM staging alone in predictive accuracy. Decision curve analysis (DCA) showed greater net benefit across a wide range of threshold probabilities for 5-year OS compared to TNM staging, confirming its enhanced clinical applicability. ConclusionsThe ANPG is a robust and independent prognostic indicator in CRC patients undergoing curative resection. The developed nomogram provides a clinically valuable tool for individualized survival prediction and risk stratification.