Search Results (2,112)

Background: Thymic epithelial tumors (TETs) are the most common primary neoplasms of the anterior mediastinum and present a dual classification challenge, namely anatomical staging according to the Masaoka–Koga system and histological risk stratification according to the World Health Organization (WHO) classification. Both tasks rely on expert pathological assessment and may be affected by interobserver variability. This study applied supervised machine learning (ML) to quantitative immunohistochemical (IHC) H-score profiles to predict Masaoka–Koga stage and WHO risk group in TETs. Methods: Logistic regression (LR) and XGBoost were applied to 19 biomarkers, including cellular localization, across two parallel analyses. Masaoka–Koga stage prediction was performed in 81 patients, including 59 early-stage and 22 advanced-stage cases, using the Synthetic Minority Oversampling Technique (SMOTE) across 100 train/test splits. WHO risk group prediction was performed in 89 patients, including 45 low-risk and 44 high-risk tumors, without oversampling. A cross-endpoint analysis applied the optimal Masaoka–Koga model to the WHO endpoint. Results: LR consistently outperformed XGBoost. The optimal Masaoka–Koga model combined Eph receptor A6 (EphA6) membranous, Yes-associated protein (YAP) nuclear, and histone deacetylase 4 (HDAC4) cytoplasmic H-scores, achieving an area under the curve (AUC) of 0.756. The optimal WHO model combined transcriptional coactivator with PDZ-binding motif (TAZ) cytoplasmic, EphA6 membranous, and YAP nuclear H-scores, achieving an AUC of 0.936. The Masaoka–Koga triad predicted WHO risk group with an AUC of 0.901. No tetrad improved trivariate performance. Conclusions: IHC H-score profiling combined with supervised ML identifies biologically interpretable candidate signatures for TET classification, although prospective external validation is required before clinical application. Full article

Background/Objectives: Dolphins inhabiting coastlines can be influenced by anthropogenic factors. As biochemical changes accumulate in blubber over weeks to months, blubber metabolites may be informative biomarkers of molecular adaptations to environmental changes. Methods: We investigated the blubber metabolomic signatures of live free-ranging bottlenose dolphins for the first time. This exploratory study analyzed blubber samples from 35 common bottlenose dolphins (Tursiops truncatus) in South Texas waters using untargeted ultra-high-performance liquid chromatography-Orbitrap metabolomics. Results: Blubber samples exhibited distinct temporal and spatial metabolic patterns. Pathway enrichment analyses comparing detected metabolites (n = 2777) revealed that dolphins sampled in the spring had enhanced lipid quality and immune regulation, while dolphins sampled in the summer showed stress-associated metabolic responses. Dolphins inhabiting areas previously reported to experience heavy vessel traffic and contaminant burdens exhibited enriched immune- and inflammation-associated pathways. Dolphins that visually appeared to have poorer body condition exhibited metabolite profiles suggestive of increased protein catabolism. Dolphins in extreme salinity conditions had more abundant membrane maintenance and endocrine pathways. Conclusions: Dolphins from each system exhibited distinct metabolic signatures that may be associated with differing physiological responses, highlighting the potential utility of blubber biomarkers for assessing physiological adaptations in free-ranging marine mammals. Improved understanding of habitat-specific physiological responses offers critical insights into how cumulative impacts may affect the health and adaptive capacity of vulnerable species in dynamic coastal ecosystems. Full article

Background/Objectives: Oral squamous cell carcinoma (OSCC) remains clinically heterogeneous, and prognosis is not always fully explained by conventional clinicopathological parameters. Systemic inflammation and tumor metabolic alterations may provide complementary information on tumor biology. This study aimed to assess associations between preoperative inflammatory markers, isotope ratio mass spectrometry (IRMS)-derived tumor signatures, clinicopathological features, and survival outcomes in OSCC. Methods: This exploratory retrospective secondary analysis included 50 consecutive patients with surgically treated, histologically confirmed OSCC. Preoperative blood-based markers, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), white blood cell count, lymphocyte count, and C-reactive protein, were retrieved from routine laboratory tests. Matched tumor, surgical margin, and healthy oral mucosa samples were analyzed by IRMS for δ¹³C, δ¹⁵N, carbon and nitrogen content, and [N]/[C] ratio. Associations with clinicopathological variables, nodal status, overall survival (OS), and disease-free survival (DFS) were evaluated using non-parametric tests, Spearman correlations, and Cox regression models. Results: Tumor tissue showed a consistent isotope and elemental phenotype compared with healthy mucosa, including higher nitrogen content, lower carbon content, increased [N]/[C] ratio, lower δ¹⁵N, and less negative δ¹³C values. NLR, PLR, SII, and CRP were not robustly associated with standard clinicopathological features after correction for multiple testing. Correlations between inflammatory and isotope-derived parameters were modest. Higher NLR was associated with worse OS and DFS and remained significant after adjustment for pathologic nodal status. Less negative tumor δ¹³C showed a potential adverse prognostic signal. Conclusions: Systemic inflammatory markers and IRMS-derived tumor signatures appear to reflect partly distinct biological domains in OSCC. NLR may provide accessible prognostic information, while tumor δ¹³C warrants further validation as a metabolic biomarker. Full article

Metabolic dysfunction-associated fatty liver disease (MAFLD) is now the leading cause of hepatocellular carcinoma (HCC) globally. HCC surveillance is currently restricted to patients with cirrhosis, leaving those without cirrhosis, who present with more advanced disease and poorer outcomes without adequate risk stratification tools. This study compared lipid profiles across MAFLD and MAFLD-related HCC (MAFLD-HCC) patients, with and without cirrhosis, to characterise metabolic dysregulation underlying non-cirrhotic MAFLD-HCC (ncMAFLD-HCC). Plasma and liver lipidomic profiles were obtained from 221 patients (140 MAFLD, 66 cirrhotic MAFLD-HCC (cMAFLD-HCC), and 15 ncMAFLD-HCC) using untargeted liquid chromatography mass spectrometry. Univariate, multivariable and enrichment analyses were performed for statistically determining the lipid profile difference between the groups. Seventy percent of lipid classes were more abundant in MAFLD than in ncMAFLD-HCC and cMAFLD-HCC. Multivariate analysis revealed distinct lipid profiles across the three groups in both plasma and liver. Over 100 lipid species including diglyceride (DAG), sphingomyelin (SM), triglyceride (TG), dihydroceramide (DHCer), and linoleic acid derivatives were differentially expressed in ncMAFLD-HCC versus MAFLD, with enrichment in pathways such as glycerolipid metabolism, G-protein signalling, MAPK signalling, EGFR-TKI resistance pathway, implicated in HCC development. ncMAFLD-HCC exhibits a distinct lipid signature, offering preliminary mechanistic insight and a foundation for non-invasive biomarker development. Full article

Metabolic dysfunction-associated steatohepatitis (MASH; formerly non-alcoholic steatohepatitis, NASH) is characterized by oxidative stress, inflammatory activation, hepatocellular injury, and progressive liver dysfunction. However, the global transcriptomic landscape underlying stress-induced hepatic injury remains incompletely understood. In this study, we employed a methionine–choline-deficient (MCD) diet-induced murine model to characterize the phenotypic and transcriptomic alterations associated with liver injury. Male C57BL/6J mice were fed either a control or MCD diet, and hepatotoxicity was assessed by survival analysis, body and liver weight measurements, serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels, histopathological examination, RNA sequencing, quantitative real-time PCR (qRT-PCR), and tumor necrosis factor-alpha (TNF-α) enzyme-linked immunosorbent assay (ELISA). MCD feeding markedly reduced survival and body weight while inducing hepatomegaly and significant elevations in serum ALT and AST, indicating severe hepatocellular injury. Histopathological analysis demonstrated hepatic steatosis, hepatocellular ballooning, and lobular inflammation without histological evidence of fibrosis. Transcriptomic profiling revealed extensive gene expression remodeling, characterized by activation of inflammatory pathways, enrichment of MAPK-related signaling, dysregulation of lipid metabolism, suppression of antioxidant defense systems, impairment of cytochrome P450-mediated detoxification, and upregulation of apoptosis-associated genes. qRT-PCR further validated the differential expression of representative genes involved in inflammatory signaling (Tlr4, Nfkb1, Nlrp3, and Casp1), MAPK signaling (Fos), xenobiotic metabolism (Cyp4f18), lipid metabolism (Apoa4 and Lpl), extracellular matrix remodeling (Mmp12), and oxidative stress responses (Sod1 and Gstp1). In addition, elevated serum TNF-α levels provided protein-level evidence supporting activation of the TLR4/NF-κB/TNF-α/NLRP3 inflammatory axis. Although fibrosis-associated transcriptional responses were detected, the absence of histological fibrosis suggests transcriptional priming of fibrogenic pathways rather than established fibrogenesis. Collectively, these findings provide a transcriptomic framework linking oxidative stress, impaired detoxification, inflammatory activation, and stress-responsive signaling to MCD-induced hepatic injury. The MCD model provides a valuable experimental platform for characterizing hepatic stress-response transcriptomes and for generating hypotheses that can subsequently be evaluated in environmentally relevant toxicological models. Nevertheless, caution should be exercised when extrapolating these findings to obesity-associated human MASLD, as the MCD model lacks key metabolic features of the human disease, including obesity and insulin resistance. Therefore, the present findings should be interpreted primarily as transcriptomic signatures of stress-induced hepatic injury rather than as a direct representation of the pathophysiological processes underlying human obesity-associated MASLD. Full article

Objectives: To assess the feasibility and reproducibility of predicting antimicrobial resistance (AMR) in Escherichia coli from MALDI-TOF mass spectrometry data using a standardized, open-source machine learning (ML) workflow, we systematically compared four ML algorithms, evaluated the impact of culture conditions, extract storage, and spectral preprocessing on model performance, and validated results through nested cross-validation with statistical significance testing. Methods: A total of 282 clinical E. coli isolates were analyzed. Two MALDI-TOF MS datasets were generated from freshly cultured extracts (T₁) and recultured isolates one year later (T₃), yielding 4468 spectra. A third dataset from the T₁ extracts stored at −20 °C for one year (T₂) was evaluated for spectral stability but excluded from primary modeling likely due to storage-induced degradation. Protein spectra (m/z 2000–15,000) were preprocessed using an in-house developed MALDI-TOF preprocessing pipeline (MTPP) comprising variance stabilization, Savitzky–Golay smoothing, SNIP baseline correction, TIC normalization, LOWESS alignment, and MAD-based peak detection (SNR ≥ 3), yielding 121 m/z features. Four classifiers—Random Forest (RF), Logistic Regression, Support Vector Machine, and Gradient Boosting—were trained to predict resistance to 11 antibiotics using nested cross-validation: outer GroupShuffleSplit (5-fold, isolate-level) for evaluation and inner GroupKFold for recursive feature elimination (RFECV) and hyperparameter tuning (RandomizedSearchCV). Classification thresholds were optimized via the precision–recall curve. Model performance was assessed using AUROC, AUPRC, F1-score, Matthews Correlation Coefficient (MCC), and bootstrap 95% confidence intervals (1000 replicates). Pairwise model comparisons were tested with McNemar’s chi-squared test. Results: Among the 12 antibiotics included in the analysis (meropenem excluded for absence of resistance), resistance prevalence ranged from 1.1% (colistin) to 59.9% (amoxicillin). Colistin was subsequently also excluded from ML modeling due to insufficient resistant isolates (n = 3), leaving 11 antibiotics for prediction. The best predictive performance was observed for ciprofloxacin (AUROC 0.76 [95% CI 0.74–0.77]; F1 0.54; MCC 0.38) and ceftazidime (AUROC 0.68 [0.65–0.71]; F1 0.36; MCC 0.29), using 13 and 37 RFECV-selected features, respectively. Amoxicillin achieved the highest F1-score (0.76), driven by high recall (0.98) but modest AUROC (0.58). No meaningful predictive signal was detected for amikacin, cefepime, or tigecycline (AUROC ≤ 0.57, F1 ≤ 0.17), attributable to extreme class imbalance, and no robust multi-peak resistance signature was detected in this dataset. McNemar’s test confirmed that RF significantly outperformed Logistic Regression for all antibiotics (p < 0.01), while Gradient Boosting performed comparably to RF for ciprofloxacin (p = 0.17) and ceftazidime (p = 0.28). Frozen extracts (T₂) produced lower spectral similarity and were excluded from model training; the aligned T₁₊₃ dataset yielded the most stable performance across metrics. Conclusions: Machine learning analysis of MALDI-TOF spectra enables reproducible AMR prediction for selected antibiotics in E. coli, with ciprofloxacin and ceftazidime showing the strongest signal. Nested isolate-level cross-validation, multi-model comparison with statistical testing, and open-source code provide a transparent, reproducible foundation for integrating ML-assisted MALDI-TOF analysis into diagnostic AMR surveillance. Extract storage at −20 °C degrades spectral quality and should be avoided in ML training workflows. Full article

Background/Objectives: Parkinson’s disease (PD) exhibits marked sexual dimorphism, with a higher incidence and earlier onset in men than in women. However, the impact of biological sex on systemic molecular alterations in PD remains poorly understood. This pilot study aimed to identify sex-specific circulating signatures associated with PD. Methods: Serum samples from a selected cohort of PD patients and healthy controls (HC) of both sexes were analyzed using an integrated biochemical and ¹H NMR-based metabolomic approach. Oxidative stress markers, antioxidant proteins, inflammatory mediators, matrix metalloproteinases, α-synuclein species, and circulating antibodies were evaluated. Results: This analysis indicated that, while global oxidative stress markers were unchanged, sex-related differences in antioxidant pathways were observed as suggested by the reduced Nrf2 expression observed in PD females and increased IL-6 levels, above all in male PD patients. MMP3 levels were significantly higher in female PD patients compared with males. Male patients showed higher levels of 52 kDa protease-resistant α-synuclein species, while females exhibited increased antibody titers against both monomeric and aggregated forms. Metabolomic profiling suggested a disease-associated metabolic remodeling in PD, with distinct sex-related metabolic signatures and a more pronounced and widespread metabolic dysregulation in males. Conclusions: These findings suggest that biological sex may contribute to systemic molecular heterogeneity in PD, with trends indicating more pronounced inflammatory and metabolic alterations in males and distinct immune-related responses in females. Given the exploratory nature of the study and the limited sample size, these observations should be interpreted cautiously and require validation in larger, independent cohorts. Nevertheless, the results support the importance of considering sex-related molecular differences in future biomarker studies and precision medicine approaches for PD. Full article

Triple negative breast cancer (TNBC) is an aggressive and heterogeneous subtype of breast cancer characterized by the absence of the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), resulting in limited options for targeted therapy and high rates of metastasis, recurrence and death. Extracellular vesicles (EVs) have emerged as central mediators of TNBC pathophysiology, functioning as key intercellular communication vehicles transporting oncogenic proteins, nucleic acids; lipids, and metabolites. These EV-mediated interactions promote tumor microenvironment (TME) remodeling, immune evasion, metastatic niche formation, and therapeutic resistance. Given their stability, accessibility, and molecular complexity, EVs also represent promising diagnostic and prognostic biomarkers for TNBC. Advances in isolation and molecular profiling technologies have enabled the identification of EV-associated signatures that predict therapeutic response and stratify patient risk. Beyond their utility as biomarkers, EVs are rapidly emerging as therapeutic targets and delivery platforms, demonstrating efficacy in transporting chemotherapeutics, RNA-based therapeutics, immune modulators, and photosensitizers with enhanced targeting specificity and therapeutic efficiency. Collectively, EVs play a multifaceted role in TNBC biology, serving simultaneously as drivers of disease progression, minimally invasive biomarkers, and versatile therapeutic vehicles. The integration of EV-centered diagnostics, multi-omic profiling, and engineered therapeutics holds significant potential to transform TNBC management and advance precision oncology for this challenging breast cancer subtype. Full article

Non-obstructive azoospermia (NOA) is characterized by focal and quantitatively limited spermatogenesis, making preoperative prediction of sperm retrieval difficult. Seminal plasma is a biologically plausible liquid-biopsy compartment because it contains testicular, epididymal and accessory-gland secretions enriched with extracellular vesicles, cell-free nucleic acids, proteins and metabolites. This narrative molecular review examines the mechanisms by which germ-cell-derived molecular cargo reaches the ejaculate and organizes seminal-plasma biomarkers by cargo class and spermatogenic stage. Particular attention is given to extracellular-vesicle non-coding RNAs, cell-free seminal mRNAs, germ-cell-enriched proteins including TEX101 and ECM1, and metabolomic and lipidomic signatures. Although several markers show promising discrimination, most remain discovery-stage, single-center and insufficiently validated. The central argument is that the field should move from isolated biomarker nomination toward locked, stage-mapped multi-analyte panels integrated with clinical and genetic predictors under modern prediction-model standards. Seminal plasma is best viewed not as a ready clinical test, but as a biologically coherent platform for future calibrated, externally validated and artificial-intelligence (AI)-ready sperm-retrieval decision support. Full article

Background: Diabetic cardiomyopathy (DCM) is characterized by metabolic dysfunction, inflammation, extracellular matrix (ECM) remodeling, and myocardial fibrosis. Increasing evidence suggests that ferroptosis-associated oxidative injury may contribute to cardiac remodeling; however, the interaction between ferroptosis-related pathways and fibrosis-associated molecular networks remains incompletely understood. This study explored the ferroptosis–fibrosis axis using an integrative transcriptomic and systems pharmacology framework. Methods: Differentially expressed genes were identified from the GSE5406 myocardial transcriptomic dataset comparing nonfailing donor hearts with ischemic and idiopathic cardiomyopathy samples and analyzed using functional enrichment, protein–protein interaction, and disease-association approaches. Cross-dataset comparison and exploratory sample-level external evaluation were performed using the independent GSE263297 DCM-related dataset. Candidate genes were further evaluated by receiver operating characteristic (ROC) analysis and machine learning-based feature selection using least absolute shrinkage and selection operator (LASSO), random forest, and support vector machine-recursive feature elimination (SVM-RFE). Representative compounds associated with fibrosis-, oxidative stress-, inflammation-, and ferroptosis-related pathways were subsequently assessed by molecular docking against TGFBR1, STAT3, GPX4, AKT1, SMAD3, and ACSL4. Results: Transcriptomic analyses highlighted ECM organization, collagen-containing ECM, and fibrosis-related pathways as dominant biological themes. Cross-dataset comparison showed partial preservation of transcriptional patterns between independent myocardial cohorts, with 20 of 51 evaluated genes demonstrating concordant expression direction across datasets. ROC analysis identified LUM and ASPN as having the highest area under the curve (AUC) values among candidate genes, whereas COL1A1, COL1A2, and COL3A1 also showed elevated AUC values. Machine learning analyses identified FCN3, HOPX, CNN1, and GLUL as the core signature consistently prioritized across all three algorithms, whereas LUM was additionally identified by two of three algorithms. Internal validation yielded a cross-validated AUC of 0.934 (95% CI: 0.820–1.000), and exploratory sample-level external evaluation of the four-gene signature in GSE263297 yielded an AUC of 0.673 (95% CI: 0.380–0.967). Exploratory docking analyses suggested potential structural compatibility between several candidate compounds and fibrosis-, inflammation-, and ferroptosis-associated targets, with comparatively lower predicted binding-energy values observed for selected ligand–target combinations. Conclusions: The findings are consistent with a fibrosis-dominant remodeling signature and suggest potential network-level links between ferroptosis-associated processes and cardiac fibrosis. These observations should be regarded as exploratory and hypothesis-generating and require validation in independent cohorts and experimental studies. Full article

Background/Objectives: Systemic sclerosis (SSc) is a heterogeneous systemic autoimmune rheumatic disease characterized by immune dysregulation, vasculopathy, and fibrosis involving the skin and internal organs. Interstitial lung disease (ILD), pulmonary arterial hypertension (PAH), and cardiac involvement remain major causes of morbidity and mortality, yet prediction of disease progression and therapeutic responsiveness remains difficult. Methods: This narrative review summarizes studies of circulating blood biomarkers in SSc, with emphasis on literature published since 2020 and on Japanese multicenter longitudinal cohort studies. Disease-specific autoantibodies were intentionally excluded from the main scope, and the review focuses on soluble biomarkers measurable in peripheral blood that reflect inflammation, endothelial injury, and fibrotic remodeling. Results: Multiple cytokines, chemokines, adhesion molecules, endothelial markers, extracellular vesicle-associated molecules, and extracellular matrix (ECM)-related molecules have been associated with disease activity, organ involvement, prognosis, and therapeutic response in SSc. Clinically established biomarkers such as KL-6 and surfactant protein-D (SP-D) for SSc-associated interstitial lung disease (ILD), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) for pulmonary arterial hypertension (PAH), are already used as adjunctive tools in routine clinical assessment, whereas many other candidate biomarkers, including interleukin (IL)-6, CCL2, CXCL8, CXCL4, intercellular adhesion molecule-1 (ICAM-1), CCL18, periostin, endostatin, endothelin-1, extracellular vesicle signatures, and ECM turnover markers remain at varying stages of clinical validation. In particular, Japanese multicenter longitudinal studies have demonstrated the prognostic significance of circulating chemokines and adhesion molecules in early SSc and, more recently, identified biomarker-based clusters associated with distinct pulmonary trajectories. Recent multidimensional proteomic and transcriptomic approaches further support biologically based patient stratification in SSc. Conclusions: Blood biomarkers may contribute to risk stratification, prediction of organ progression, and future precision medicine in SSc. Integrated biomarker signatures may better capture the biological heterogeneity of SSc than single biomarkers alone. However, most candidate biomarkers still require external validation, assay standardization, and demonstration of incremental value over conventional clinical variables before routine clinical implementation. Full article

(TB) induces a pronounced thromboinflammatory state during active disease, characterized by elevated fibrinogen, D-dimer, and thrombin-related activity, reduced levels of endogenous anticoagulants, impaired fibrinolysis, platelet activation, and endothelial dysfunction. Although many of these abnormalities improve after treatment initiation, accumulating evidence suggests that microbiological cure may not fully restore vascular, immune, and hemostatic homeostasis. This raises the possibility that TB leaves a persistent thromboinflammatory imprint after cure. This narrative synthesizes current evidence on tuberculosis-associated hypercoagulability during active disease and after treatment, and proposes a conceptual framework for post-tuberculosis thromboinflammatory memory grounded in cellular persistence, tissue remodeling, and extracellular vesicle-mediated signaling. Candidate storage compartments include hematopoietic stem and progenitor cells, monocyte/macrophage lineages, alveolar macrophages, remodeled pulmonary endothelium, and fibrotic post-TB lung tissue. EVs may function as mobile vectors that transfer procoagulant phospholipids, tissue factor, inflammatory proteins, and regulatory microRNAs between these compartments, thereby linking local post-TB remodeling to systemic vascular and coagulation pathways. A mechanistic evidence ladder is proposed, encompassing phenotypic persistence, EV cell-of-origin attribution, molecular persistence, paired longitudinal validation, functional transfer, and clinical outcome linkage. Current data support the biological plausibility of this framework but remain insufficient to establish post-TB thromboinflammatory memory as a defined clinical entity. Direct evidence in long-term TB survivors is still lacking, particularly with respect to persistent EV signatures, cell-specific reservoirs, and the functional transfer of procoagulant phenotypes. Longitudinal, cell-resolved, multi-omic, and functionally validated studies are required to determine whether TB leaves a durable thromboinflammatory memory, where it is stored, and whether it contributes to long-term thrombotic and cardiovascular risk. This article should be interpreted as a narrative review with a conceptual framework rather than as evidence that post-tuberculosis thromboinflammatory memory is already a formally established clinical entity. Full article

Strictosidine synthases (STRs) are catalytic enzymes involved in terpenoid indole alkaloid biosynthesis, whereas STR-like (STRL) genes in cereal crops remain poorly understood. Previous studies of the maize STR-like (STRL) gene family have mainly provided genome-wide identification, phylogenetic classification, structural annotation and expression profiling, but the evolutionary constraints and molecular mechanisms underlying STRL diversification remain insufficiently resolved. In this study, we investigated the maize STRL gene family from an evolutionary and structural perspective by integrating sequence divergence, codon usage bias, selection pressure, protein structural modelling, Gene Ontology (GO) enrichment and tissue-specific expression analysis. A total of 21 ZmSTRL genes were analyzed and their comparative and phylogenetic analyses revealed conserved lineages together with maize-associated expansion patterns. Codon usage and neutrality analyses indicated heterogeneous evolutionary constraints among ZmSTRL genes, suggesting that mutational pressure alone does not explain their sequence divergence. Protein conservation and three-dimensional structural modelling showed a generally conserved STR-related catalytic framework, while member-specific variation in terminal and loop regions suggested localized structural divergence. GO enrichment supported conserved catalytic and metabolic signatures, but these associations were interpreted as putative functional evidence rather than direct functional confirmation. Tissue-specific qRT-PCR analysis revealed divergent expression patterns among selected ZmSTRL genes in root, stem, leaf, and anther tissues, indicating possible regulatory specialization. Overall, this study provides an evolutionary-constraint-based framework for understanding STRL diversification in maize and identifies candidate genes and structural features for future functional validation. Full article

Background: Phosphatidylcholine (PC) is a major phospholipid that contributes to intestinal barrier protection and is essential for hepatic secretion of lipids and bile acids. Because inflammatory bowel disease (IBD) and primary sclerosing cholangitis (PSC) are closely linked, we hypothesized that individual serum PC species would reflect disease activity. We therefore investigated whether serum PC profiling could identify clinically useful biomarkers across the gut–liver axis. Methods: Serum concentrations of 21 PC species were quantified by direct flow injection high-resolution mass spectrometry in 16 healthy controls, 57 patients with IBD, and 20 patients with PSC. Results: In IBD, multiple serum PC species were inversely associated with inflammatory activity, showing negative correlations with serum C-reactive protein and fecal calprotectin. Patients with fecal calprotectin concentrations above the diagnostic cut-off of 120 µg/g had lower levels of PC 34:3, 36:1, 36:2, 36:3, 36:4, 36:5, 38:3, 38:4, 38:5, 38:7, 40:5, and 40:6, as well as lower total PC. In contrast, in PSC, PC 30:0, 32:0, 32:1, and 34:1 were increased compared with IBD and correlated positively with gamma-glutamyltransferase and alkaline phosphatase. Furthermore, these shorter-chain PC species as well as PC 36:1 were markedly elevated in PSC with advanced liver fibrosis compared with PSC without fibrosis. Conclusions: Serum PC species show a reciprocal disease-associated pattern in IBD and PSC. In IBD, lower concentrations of predominantly unsaturated PC species are associated with active intestinal inflammation, whereas in PSC, higher concentrations of shorter-chain PC species are associated with cholestatic injury and advanced liver fibrosis. IBD and PSC exhibit opposing serum PC signatures, suggesting that dysregulated PC metabolism is a pathophysiological feature of intestinal inflammation and PSC-associated liver fibrosis. Full article

Background: Minimal change disease (MCD) is a leading cause of childhood nephrotic syndrome. Endoplasmic reticulum stress (ERS) and autophagy are implicated in its pathogenesis, but the precise mechanisms remain unclear. This study aimed to identify ERS and autophagy-related key genes (ERS-RGs and ARGs) in MCD using bioinformatic and experimental approaches. Methods: Transcriptomic data from GSE216841 and GSE246206 were analyzed. ERS-RGs and ARGs were obtained from prior literature. Candidate genes were selected by integrating weighted gene coexpression network analysis and differential expression analysis. Feature genes were identified via protein–protein interaction network analysis and machine learning (Least Absolute Shrinkage and Selection Operator and Boruta). Key genes were validated by expression analysis and receiver operating characteristic evaluation. A multilayer perceptron (MLP) model was constructed, and regulatory networks, immune infiltration, and chemical compound prediction were analyzed. The expression levels of the identified key genes were preliminarily assessed in peripheral blood samples using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Results: LIG4 and ZRANB3 were identified as key genes, both significantly downregulated in the MCD group, and the gene-based MLP model effectively predicted MCD probability. Overall, 13 significantly different immune cell types (e.g., CD56⁺ natural killer and activated dendritic cells) were detected. Regulatory networks (transcription factor-messenger RNA (mRNA) and long non-coding RNA-microRNA-mRNA) and 8 common chemical compounds (e.g., bisphenol A, acetaminophen) targeting these genes were predicted. Notably, peripheral blood RT-qPCR analysis revealed significant LIG4 and ZRANB3 downregulation, suggesting a systemic expression signature. Conclusion: LIG4 and ZRANB3 are key genes associated with ERS and autophagy in MCD, providing insights for diagnosis and targeted therapy. Full article