Frontiers in Molecular Biosciences

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.

Background and AimsCholedocholithiasis (CDL) is a common condition that can lead to serious complications, requiring effective risk stratification for timely intervention. While current guidelines use clinical predictors, imaging, and laboratory markers for risk assessment, the role of glutamate dehydrogenase (GLDH) in CDL remains poorly understood. This study aims to evaluate its potential as a clinical biomarker for identifying patients with CDL. MethodsThis single-center cohort study identified 23,103 patients who presented to the emergency department of the University Medical Center Hamburg-Eppendorf and underwent routine abdominal laboratory testing between May 2021 and December 2023. Patients were classified into CDL and other diagnoses. To assess the predictive value of age, sex and laboratory markers for CDL, we developed a random forest machine learning model, conducted a backward stepwise logistic regression and performed receiver operating characteristic (ROC) analysis. Results152 patients were diagnosed with CDL and 22,951 with other diagnoses. In the random forest machine learning model, GLDH emerged as the most significant feature for predicting CDL. ROC analysis revealed that GLDH had the highest area under the curve of 0.93 among laboratory markers. At the upper limit of normal, GLDH demonstrated the best sensitivity (92%) compared to aspartate aminotransferase (AST), alanine aminotransferase (ALT) and bilirubin. High GLDH levels exceeding 150 U/L demonstrate the highest specificity (99%) for CDL, outperforming AST, ALT and bilirubin. ConclusionGLDH outperforms AST, ALT and bilirubin as a screening and predictive marker for CDL, supporting its inclusion in clinical guidelines for risk stratification.

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.

Protocadherin-12 (PCDH12), a cell-adhesion protein belonging to the non-clustered protocadherin family, plays a crucial role in the establishment and regulation of neuronal connections and communication. Bi-allelic loss-of-function (LoF) variants in the PCDH12 gene have been associated with several neurodevelopmental disorders (NDDs) such as diencephalic-mesencephalic junction dysplasia (DMJD) syndrome, cerebral palsy, and cerebellar ataxia, often accompanied by ocular abnormalities. However, genotypes exhibit variable expressivity. Affected individuals sharing the same PCDH12 variant presenting differing phenotypic severities have posed major challenges towards identification of the underlying pathogenic mechanisms. Here, we report three affected individuals from two families, each harbouring non-truncating pathogenic missense variants in PCDH12. The patients are compound heterozygous, with each individual carrying one extracellular [c.1742T>G (p.Val581Gly) and c.1861_2del/insCA (p.Ile621His)] and one intracellular variant [c.3370C>T (p.Arg1124Cys) and c.3445G>A (p.Asp1149Asn] on each allele. The children present with a range of phenotypes similar to those associated with LoF variants. One child exhibited microcephaly and seizures, while the two siblings displayed developmental delays and severe behavioral disorders. All three children experienced some degree of visual impairment. The missense variants provided new insights into the neurodevelopmental consequences of compromised PCDH12 function by distinguishing the specific consequences associated with dysfunction in the extracellular versus intracellular domains of PCDH12. All identified missense variants are predicted to be deleterious and destabilizing. The expression of PCDH12 in HEK293T and HeLa cells demonstrated that PCDH12 is expressed effectively, regardless of the presence of missense variants. However, the extracellular variants p.Val581Gly and p.Ile621His compromised the stability of PCDH12's homophilic adhesion. Additionally, we found evidence of an interaction between PCDH12 and the extracellular domain of the epilepsy-associated PCDH19 protein. PCDH12 extracellular missense variants also negatively impact this interaction. Our study provides evidence that PCDH12 mediates both homophilic and heterophilic interactions. Our findings also highlight the importance of stable PCDH12-mediated adhesion, emphasizing the need to further study the functional consequences of PCDH12 missense variants on brain and visual system development.

ObjectivesTo develop and validate pediatric adaptations of the Venous Excess Ultrasound Score (P-VExUS) for noninvasive estimation of central venous pressure (CVP) in critically ill children. DesignProspective observational study. SettingPICU of a tertiary-care teaching hospital. PatientsFifty-six mechanically ventilated children (median age 7.4 months, median weight 6.0 kg) with central venous catheters. InterventionsNone. Measurements and Main ResultsVenous Doppler ultrasonography of the inferior vena cava, hepatic, portal, and intrarenal veins was performed at the bedside. Two P-VExUS models were tested: (1) a categorical grading system (0-III) and (2) a semiquantitative point-based score (0-7). Both models showed significant associations with CVP. For predicting elevated CVP (>12 mmHg), model 1 achieved an AUROC of 0.74 (95% CI 0.61-0.85) with 45% sensitivity and 98% specificity, while model 2 demonstrated superior accuracy with an AUROC of 0.94 (95% CI 0.84-0.98), sensitivity 82%, and specificity 91% (p < 0.001). For detecting low CVP (<7 mmHg), model 2 also outperformed model 1 (AUROC 0.80 vs. 0.69, p = 0.02). Among individual venous Doppler components, intrarenal veins had the highest discriminative ability (AUROC 0.92), followed by hepatic (0.89) and portal (0.80) veins. ConclusionsTwo pediatric-specific P-VExUS models were feasible and accurate for estimating CVP in critically ill children. The point-based model (model 2) demonstrated superior diagnostic performance, supporting its potential as a noninvasive tool to assess venous congestion at the bedside. Research in ContextO_LIVenous congestion, reflected by elevated central venous pressure (CVP), is associated with adverse outcomes in critically ill children, including mortality and renal dysfunction. C_LIO_LIThe Venous Excess Ultrasound Score (VExUS) is validated in adults, but pediatric-specific adaptations and cutoff values remain poorly defined. C_LIO_LIThere is a need for noninvasive, bedside tools to estimate CVP in children and guide fluid management in the PICU. C_LI What This Study MeansO_LIThis study validates pediatric-specific adaptations of the Venous Excess Ultrasound Score (P-VExUS) for estimating CVP in critically ill children. C_LIO_LIThe semiquantitative point-based model provided more consistent and accurate discrimination of venous congestion compared with categorical grading. C_LIO_LIThese findings highlight the feasibility and potential clinical utility of venous Doppler ultrasonography as a noninvasive bedside tool in the PICU. C_LI

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

BackgroundEndoscopic resection is the standard treatment for rectal neuroendocrine tumors (r-NETs) [&le;]10 mm, yet the optimal technique remains controversial. Modified endoscopic mucosal resection (m-EMR) has emerged as a potential alternative compared to endoscopic submucosal dissection (ESD), but existing evidence is largely retrospective and the results of recent randomized controlled trials (RCTs) are inconclusive. AimsTo compare the efficacy and safety of m-EMR versus ESD for r-NETs [&le;]10 mm. MethodsWe systematically searched CENTRAL, PubMed, Embase, and WanFang from January 1st, 1970 to December 23, 2025 for RCTs comparing m-EMR with ESD in r-NETs [&le;]10 mm. The GRADE framework assessed evidence certainty, while trial sequential analysis (TSA) controlled random errors and evaluated conclusion validity. ResultsSix RCTs involving 440 patients were analyzed. No significant difference between m-EMR and ESD was found in histologic complete resection (RR = 1.00, 95% CI 0.97-1.03; I2 = 0%), en bloc resection rates (P = 0.75) and procedure-related complications (P = 0.94). And m-EMR was associated with a significantly shorter procedure time (P<0.00001) and lower hospitalization cost (P<0.00001). The evidence was of moderate certainty; TSA confirmed its reliability, and both cumulative and sensitivity analyses supported the robustness. ConclusionsModerate-certainty evidence indicates m-EMR achieves oncologic outcomes comparable to ESD while offering clear advantages in procedural efficiency and cost for r-NETs [&le;]10 mm, supporting m-EMR possibly as a preferred endoscopic strategy in clinical practice.

IntroductionSequence-based typing (SBT) has been the standard molecular typing method for understanding Legionella pneumophila genetic relationships. However, genome-scale typing approaches, namely core-genome (cg) or whole-genome (wg) multilocus sequence typing (MLST), provide higher discriminatory power. To advance these capabilities, the Legionella International Typing (LIT) workgroup was established to develop, evaluate, and disseminate a novel cgMLST schema with enhanced wgMLST resolution for L. pneumophila investigations. MethodsWe created and populated the LIT cg/wgMLST schema with chewBBACA software using more than 9000 genome assemblies representative of the species diversity. We applied a multi-step refinement workflow, considering loci prevalence, diversity and presence/absence profile across the species tree, to select the final cg/wgMLST loci, and compared the performance of the LIT cgMLST schema with the previously used 1521-loci schema and assessed its congruence with SBT. ResultsThe LIT schema includes 2009 loci present in 98% of the dataset, forming the static cgMLST schema for routine genomic surveillance, plus 2698 accessory loci for an in-depth wgMLST analysis of clusters of interest. The LIT cgMLST schema maintains moderate agreement with SBT and presents high clustering congruence with the 1521-loci schema, while providing increased resolution. Analysis of epidemiologically related isolates using the LIT cgMLST schema for initial cluster delineation, followed by cluster-specific dynamic wgMLST analysis extending the cgMLST with accessory loci shared among isolates within each cluster, demonstrated increased confidence for outbreak investigation and source identification. ConclusionsThe LIT schema is expected to contribute to harmonizing genomic surveillance of Legionnaires disease at both local and global levels. The schema and associated resources for local implementation are available on Zenodo (https://doi.org/10.5281/zenodo.17871973).

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.

Background Whole-genome sequencing (WGS) has improved the diagnosis of rare genetic disorders, yet interpretation of non-coding variants that affect splicing remains challenging. In silico predictions alone are insufficient, and short-read RNA sequencing may fail to capture complex or low-abundance splicing events. Targeted amplicon-based long-read RNA sequencing (Amp-LRS) offers a cost-effective approach for functional validation of candidate splice-altering variants. Methods We applied Amp-LRS to five patients with neurological disorders (central nervous system, peripheral nervous system, or muscle) harbouring candidate non-coding variants predicted to alter splicing. RNA was extracted from fibroblasts or peripheral blood, and full-length transcript amplicons were sequenced using Oxford Nanopore Technologies. Nonsense-mediated decay (NMD) inhibition was performed on fibroblast cultures using cycloheximide. Results Amp-LRS validated all five candidate variants, including intronic and UTR variants in POLR3A, OPA1, PYROXD1, GDAP1, and SPG11. Aberrant splicing events included exon skipping, intron retention, cryptic splice site activation, and pseudoexon inclusion, often resulting in frameshifts and premature termination codons. For POLR3A and OPA1, multiple abnormal isoforms arose from single variants, highlighting the complexity of splicing disruption. Some pathogenic effects were detectable only in a minority of reads and variably enriched by NMD inhibition, consistent with being hypomorphic. The approach was successfully applied using accessible tissues and enabled multiplexed sequencing at low per-sample cost. Conclusions Amp-LRS is a sensitive, versatile, and cost-effective method for functional assessment of non-coding splice-altering variants identified by WGS. By enabling full-length transcript analysis from accessible tissues, this approach improves interpretation of variants of uncertain significance and could enhance molecular diagnosis in rare neurological diseases.

Amyotrophic Lateral Sclerosis (Lou Gehrigs disease) is a progressive neurodegenerative disease affecting hundreds of thousands of people worldwide. It is characterized by the degeneration of the neurons in the brain and spinal cord of the patients, leading to a loss of control of muscles. Over time, without nerves to stimulate them muscles tend to atrophy. ALS may occur sporadically or run in families; many mutations have been identified for the latter. Treatment of ALS is mostly limited to three approved therapeutic agents: riluzole, edaravone, and tauroursidiol/ sodium phenylbutyrate. Among these, riluzole remains the most effective despite its early discovery. There are no conclusive meta-analysis comparing riluzole monotherapy to all possible co-therapies present. In this work we have attempted to address such a concern and observed that no adjunct therapy significantly improved the performance of riluzole. However, mitochondrial/ oxidative stress modulator and neuroimmune/ neuroexcitability modulator co-therapy exhibited positive trends. Surprisingly, trials were mainly confined to the USA and European countries, indicating unequal demographic representation in ASL research. We have concluded that large double blinded inter-continental RCTs to be carried out for better understanding of the scenario.

BackgroundCurrent British Society of Gastroenterology (BSG) guidelines misclassify metachronous lesion risk after polypectomy in approximately 40% of patients. Building on evidence that immune exclusion drives progression of adenomas to colorectal cancer, this study examined immune profiles in screen-detected adenomas as a predictive biomarker for metachronous lesion risk. MethodsPatients undergoing polypectomy within the Scottish Bowel Screening Programme, with surveillance colonoscopy between 6 months and 6 years were included. Chromogenic immunohistochemistry (IHC; n=2642), 6-plex multiplex immunofluorescence (mIF; n=334), and spatially resolved 6000-plex single cell transcriptomics (n=7) were applied to adenoma microarrays. Cell density and location were measured using QuPath. Hierarchical then K-means clustering was used to define immune cell density-based clusters, which were compared to future lesion events using Kaplan-Meier curves and the log rank test. ResultsAfter adjustment for age, sex, site, size and dysplasia, adenoma CD3+ T cell density was significantly associated with future colorectal neoplasia (HR 1.43, 95% CI 1.19-1.71, p<0.001). Using mIF three immune cell density clusters were identified; 1) high T cell density, low macrophage density, 2) low T cell density, low macrophage density, and 3) high T cell, macrophage and SMA density, with significant differences in future lesion risk (Cluster 1: 22%, Cluster 2: 41%, Cluster 3: 36%, p=0.032). Bulk RNAseq and spatial transcriptomic analysis revealed significant variation in T cell and macrophage co-location and gene expression profiles between clusters. ConclusionAdenoma immune contexture emerges as a determinant of future metachronous lesion risk, offering a novel biomarker to refine surveillance and reduce disease burden. SummaryWhat is already known on this topic: O_LIPost-polypectomy surveillance is currently recommended to patients with high-risk pathological features to detect metachronous lesions and cancer. However current guidelines misclassify risk in a proportion of patients, leading to unnecessary surveillance for some, whilst falsely reassuring others. C_LI What this study adds: O_LIAnalysis of this large post-polypectomy surveillance cohort reveals that adaptive immune responses within removed index adenomas predicts low risk of metachronous lesions, while an immune excluded phenotype signals higher risk, independent of pathological characteristics, and patient risk factors. C_LI How this study might affect research, practice or policy: O_LIDefining immune cell spatial distributions and interactions that drive future adenoma and cancer risk will enable more precise risk stratification for surveillance, informing surveillance guidelines and shaping targeted colorectal cancer prevention strategies. C_LI

BackgroundSepsis is a major public health challenge, and reliable biomarkers are essential for distinguishing sepsis from other conditions. Neutrophil Gelatinase-Associated Lipocalin (Neutrophil gelatinase-associated lipocalin (NGAL)) has shown promise as a diagnostic marker due to its role in the immune response. This study evaluates plasma NGAL as a diagnostic tool at the time of ICU admission. MethodsWe analysed plasma NGAL and C-reactive protein (CRP) levels in 4732 adult patients admitted to four ICUs between 2015 and 2018. All patients were retrospectively screened for Sepsis-3 criteria at ICU admission. The discriminative performance of NGAL and CRP for sepsis was assessed using receiver operating characteristic (ROC) analysis, with NGAL levels adjusted for Chronic kidney disease (CKD) and age. Patients were stratified by renal function. ResultsPlasma NGAL levels were significantly higher in septic patients (p<0.001). For the whole cohort, NGAL alone yielded an Area under the curve (AUC) of 0.67 (Confidence interval (CI) 0.66-0.69), CRP yielded an AUC of 0.72 (CI 0.71-0.73, p<0.001), and combining NGAL with CRP nominally improved discriminative performance (AUC 0.74 vs 0.72, p<0.001). Stratified analyses indicated that NGAL, together with CRP, significantly outperformed CRP alone in patients with no kidney injury and those with Acute Kidney Injury (AKI) only. In contrast, differences were not significant in patients with CKD only or CKD and AKI. ConclusionIn this large cohort, NGAL showed modest discrimination for sepsis, with a nominal improvement when combined with CRP. These findings do not indicate that NGAL meaningfully improves sepsis diagnosis in the ICU.

Applying deep learning models to RNA-Seq data poses substantial challenges, primarily due to the high dimensionality of the data and the limited sample sizes. To address these issues, this study introduces an advanced deep learning pipeline that integrates feature engineering with data augmentation. The engineering application focuses on biomedical engineering, specifically the classification of RNA-Seq datasets for disease diagnosis. The proposed framework was initially validated on synthetic datasets generated from Naive Bayes, where MLP-based augmentation yielded a notable improvement in predictive performance. Building on this foundation, we applied the approach to chromophobe renal cell carcinoma (KICH) RNA-Seq data from The Cancer Genome Atlas (TCGA). Following standard preprocessing steps normalization, transformation, and dimensionality reduction, the analysis concentrated on three main aspects: augmentation strategies, preprocessing methods, and explainable AI (XAI) techniques in relation to classification outcomes. Feature selection was performed through PCA, Boruta, and RF-based methods. Three augmentation strategies linear interpolation, SMOTE, and MixUp were evaluated. To maintain methodological rigor, augmentation was applied exclusively to the training set, while the test set was held out for unbiased evaluation. Within this framework, we conducted a comparative assessment of multiple deep learning architectures, including MLP, GNN, and the recently proposed Kolmogorov-Arnold networks (KAN). The GNN achieved the highest classification accuracy (99.47%) when trained with MixUp augmentation combined with RF feature selection, and achieved the best F1 score (0.9948). Consequently, the GNN-based XAI framework was applied to the RF dataset enriched with MixUp. XAI analyses identified the top 20 most influential genes, such as HNF4A, DACH2, MAPK15, and NAT2, which played the greatest role in classification, thereby confirming the biological plausibility of the model outputs. To further validate model robustness, cervical cancer and Alzheimers RNA-Seq datasets were also tested, yielding consistent and reliable results. Overall, the findings highlight the value of incorporating data augmentation into deep learning models for RNA-Seq analysis, not only to improve predictive performance but also to enhance biological interpretability through explainable AI approaches.

Respiratory monitoring in daily-life settings is important for health assessment, yet extracting physiologically interpretable information from breathing signals under natural conditions remains challenging, as breathing is inherently dynamic and strongly modulated by behavior. Here, a portable breathing monitoring device based on a flexible lead zirconate titanate sensor is developed to address this challenge. By exploiting polarity-opposed piezoelectric and pyroelectric responses through sensor orientation, the recorded breathing waveform exhibits a characteristic dual-component structure, consisting of a narrow transient spike followed by a broad quasi-steady peak within each breathing phase. This intrinsic waveform structure enables phase-resolved quantification of how breathing effort is distributed between transient and quasi-steady components during inhalation and exhalation. Pilot measurements in healthy subjects and patients with chronic obstructive pulmonary disease or asthma reveal systematic shifts toward transient-enhanced breathing in patients, providing clearer differentiation than conventional descriptors based on breathing duration or amplitude. By transforming complex breathing dynamics into stable and physiologically meaningful signal components under daily-life conditions, this dual-response sensing approach enables more robust access to function-related changes in natural breathing.

Spinal muscular atrophy (SMA) is a severe neuromuscular disorder caused by reduced expression of the survival motor neuron (SMN) protein. In addition to affecting motor neuron survival, SMN deficiency impacts multisystem physiology and neurotransmission. Dopaminergic dysfunction has been reported in mouse models of SMA, leading to postural and locomotor impairments that improve upon treatment with L-DOPA and benserazide. However, whether altered dopamine metabolism contributes to clinical symptoms in SMA patients remains unclear. To investigate this issue, we conducted a real-world observational study involving pediatric patients with SMA1, SMA2, and SMA3. We performed a longitudinal measurement of the main dopamine-related catabolites - 3,4-dihydroxyphenylacetic acid (DOPAC) and homovanillic acid (HVA) - in cerebrospinal fluid (CSF) samples collected at baseline and after five intrathecal doses of Nusinersen, an SMN-enhancing therapy. No significant differences were observed in CSF DOPAC and HVA levels across SMA types or following treatment, and no association emerged with SMN2 copy number. In contrast, lower baseline DOPAC levels were detected in SMA1 patients requiring gastrostomy and tracheostomy, and were associated with reduced improvement on the CHOP-INTEND scale. These findings suggest that reduced central dopaminergic turnover reflects disease progression in SMA1 and is associated with more severe clinical impairment and limited functional recovery.

IntroductionAdverse drug reactions (ADRs) remain a major public health issue, and genetic factors contribute importantly to interindividual variability in drug response. Pharmacogenetic testing helps reduce ADR risk by optimizing drug selection and dosage, particularly in monogenic disorders. Material and MethodsWhole-exome sequencing of 6,739 samples from the Russian population was performed using the MGIEasy Universal DNA Library Prep Set on the DNBSEQ-G400 platform (MGI). Variants in 48 genes were examined, focusing on inherited arrhythmias (Long QT syndrome, Short QT syndrome, Timothy syndrome, Andersen-Tawil syndrome, Brugada syndrome, Atrial fibrillation, Catecholaminergic polymorphic ventricular tachycardia), enzyme deficiencies (Glucose-6-Phosphate Dehydrogenase Deficiency [G6PDD], Porphyrias), Dravet Syndrome (DS) and Malignant Hyperthermia (MH). All identified variants had been reported at least once as pathogenic (P) or likely pathogenic (LP) in ClinVar, along with those occasionally classified as variants of uncertain significance (VUS). Each variant was manually re-evaluated according to ACMG criteria. ResultsA total of 75 unique variants in 18 genes were observed in 119 individuals (1.77%), including 21 carriers and 13 women with a G6PD mutation. Of these, 46 variants were classified as P, 21 as LP, and 8 as VUS. Missense variants accounted for the largest proportion (73.33%). The most affected genes were KCNQ1 (24/119), which exhibited the highest number of unique variants (18), G6PD (20/119), SCN1A (15/119), and RYR1 (14/119). Regarding associated conditions, mutations linked to arrhythmias were found in 51 individuals, MH in 27, G6PDD in 20, DS in 15, and Porphyrias in 6. ConclusionsIncorporating genetic information on both common and rare clinically actionable variants into therapeutic decision-making has the potential to improve medication safety, reduce preventable ADRs, and enhance the effectiveness of personalized pharmacotherapy.

Background and AimsThe prognosis of interstitial lung diseases (ILDs) other than idiopathic pulmonary fibrosis (IPF) has not been studied as extensively as IPF. This study aimed to evaluate baseline factors associated with mortality in non-IPF ILD, including demographic characteristics, respiratory function test (RFT), comorbidities, and ILD subtypes. MethodsThis retrospective cohort study analysed prospectively collected data of patients with non-IPF ILD at a single tertiary centre in Malaysia (2010-2023). Patients without baseline RFT or HRCT were excluded. Survival was assessed using Kaplan-Meier analysis, and mortality predictors were identified using Cox regression. ResultsThe mean age was 60 {+/-} 15 years, with a male-to-female ratio of 1:3. Indian ethnicity constituted the largest group (n = 109, 47.6%). The mean baseline forced vital capacity (FVC) was 53.3 {+/-} 21% predicted. An FVC <50% predicted, age [&ge;]50 years at diagnosis, specific ILD subtypes, and ethnicity were independently associated with mortality. Compared with Malays, both Chinese (hazard ratio [HR] 9.86, 95% confidence interval [CI] 1.27-76.89, p = 0.037) and Indians (HR 8.59, 95% CI 1.14-64.69, p = 0.001) were associated with a higher risk of death. Kaplan-Meier analysis demonstrated significant differences in survival across non-IPF ILD subtypes (log-rank p = 0.048), with hypersensitivity pneumonitis showing the poorest prognosis (mean survival 6.1 years). ConclusionEthnicity emerged as an independent prognostic factor for mortality in non-IPF ILD. The underlying mechanisms remain unclear and may reflect differences in genetic variation, cultural factors, or environmental exposures. Larger prospective studies are required to validate these findings.

BackgroundComprehensive data on the prevalence of Helicobacter pylori infection and its associated risk factors among patients with gastrointestinal symptoms remain limited. Generating this evidence would help inform clinical management and improve antibiotic stewardship. H. pylori infection affects a substantial proportion of the global population, with prevalence varying widely across regions. In Uganda, previous studies have documented the presence of H. pylori infection. However, data specific to symptomatic patients are scarce. This study therefore aimed to determine the prevalence of H. pylori infection and associated factors among patients with gastrointestinal symptoms attending Mulago National Referral Hospital in Kampala, Uganda. MethodsA cross-sectional study was conducted among 353 patients with gastrointestinal symptoms attending Mulago Hospital. Data on socio-demographic characteristics, lifestyle and dietary habits, and medical history were collected using a semi-structured questionnaire. H. pylori infection status was determined using stool antigen tests. Proportions were used to determine the prevalence of H. pylori, and associated factors analyzed using STATA version 14 software by performing bivariate and multivariate analyses. ResultsAmong the 353 participants, majority were between 16 and 25 years old (69%), female (58%), and residing in peri-urban areas (74%). The prevalence of H. pylori infection in this population was 308 (87.3%). Multivariate analysis showed that H. pylori infection was significantly associated with having more than five income dependents (aPRR = 1.104, 95% CI: 1.025-1.189, p = 0.008), a history of previous H. pylori treatment (aPRR = 3.459, 95% CI: 2.138-5.595, p < 0.001), and a family history of H. pylori infection or gastrointestinal ulcers (aPRR = 1.135, 95% CI: 1.055-1.221, p = 0.001). ConclusionThis study demonstrated a high prevalence of Helicobacter pylori infection among patients presenting with gastrointestinal symptoms, with nearly nine out of ten individuals testing positive. The high burden observed suggests that routine screening for H. pylori, or carefully guided empirical treatment, may be clinically justified in symptomatic patients. These findings underscore the need for integrated clinical and public health strategies to improve diagnosis, treatment, and prevention of H. pylori infection in this setting.

Background and aimsPopulation screening for liver disease in high-risk groups is recommended. Community diagnosis of liver disease is a challenge due to the asymptomatic nature of disease until very advanced stages. Moreover, regional variation in testing availability can result in people with clinically significant liver disease being missed. Machine learning (ML) has been proposed as a method to reduce diagnostic error and automate screening. We present a novel machine learning derived algorithm (ID LIVER-ML) designed to predict the risk of clinically significant liver disease in a high-risk community population to identify those needing further investigations or specialist referral. MethodsUsing data from 2039 patients recruited to two UK cohorts, we created a parsimonious model using investigations that would be available in primary care using liver stiffness measurement as reference standard. The performance of ID LIVER-ML was compared against FIB-4 score in a second unseen hold out cohort (n=327). ResultsID LIVER-ML performed well at identifying patients at risk of clinically significant liver fibrosis (sensitivity 0.90, Specificity 0.43, PPV 0.54, NPV 0.86, AUC 0.83) and outperformed conventional risk scoring systems (FIB-4: AUC 0.65; NAFLD Fibrosis Score: AUC 0.66; APRI: AUC 0.53; BARD: AUC 0.58). ConclusionMachine learning derived algorithms can help screen high risk populations in a community setting for liver fibrosis. ClinicalTrials.gov ID: NCT04666402 Impact and ImplicationsThe prevalence of steatotic liver disease is rising globally and is an increasingly significant challenge for healthcare systems. Existing risk stratification scores are not validated in a real-world cohort where patients have risk factors for multiple aetiologies of liver disease. Our work shows that a machine learning model can predict the risk of clinically significant liver disease using routine primary care data, better than existing non-invasive risk stratification tools in a real-world cohort. This highlights a potential role for machine learning in the automation of fibrosis risk assessment in primary care. Highlights- Machine learning derived algorithms can predict the risk of clinically significant liver disease in an at risk community population with a mixed aetiology of liver diseases. - The performance of the ML algorithm (ID LIVER-ML) is not affected by metabolic, alcohol, or mixed aetiologies. - ID LIVER-ML outperforms traditional risk stratification scoring systems such as FIB-4 and NAFLD fibrosis scores. - Compared to the FIB-4 score, the use of Machine Learning can reduce the need for secondary care investigations by 59%.