Risk Stratification Research Articles

Serum biomarkers as early indicators of outcomes in spontaneous subarachnoid hemorrhage

Abstract Objectives Spontaneous subarachnoid hemorrhage (sSAH) is a life-threatening neurological event with high morbidity and mortality. Predicting patient outcomes remains challenging, necessitating novel prognostic tools. This study evaluates the prognostic value of central and systemic serum biomarkers, including S100, neuron-specific enolase (NSE), glial fibrillary acidic protein (GFAP), ubiquitin carboxy-terminal hydrolase L1 (UCHL-1), soluble suppression of tumorigenicity 2 (sST2), and soluble urokinase plasminogen activator receptor (suPAR) in acute sSAH. Methods A prospective observational study was conducted on 91 sSAH patients admitted to the Emergency Department. Biomarkers were measured 24 h post-admission and correlated with clinical severity using the modified Rankin Scale (mRS) at 24 h and 3 months. Statistical analyses included correlation tests, receiver operating characteristic (ROC) curves, and partial least squares discriminant analysis with 10-fold cross-validation (PLS-DA) to assess predictive accuracy. Results Patients with unfavorable outcomes (mRS 3–6) exhibited significantly higher median levels of all biomarkers. GFAP (ρ=0.74, p<0.0001) and S100 (ρ=0.65, p<0.0001) strongly correlated with hemorrhage volume. ROC analysis confirmed GFAP and S100 as the most effective central biomarkers (AUC=0.951), while sST2 demonstrated the highest prognostic sensitivity (97.1 %). PLS-DA further validated the prognostic relevance of sST2, GFAP, and S100. Conclusions Early biomarker assessment enhances sSAH prognosis, complementing neuroimaging. GFAP and S100 strongly correlate with brain injury severity, while sST2 predicts 3-months outcomes. Integrating these biomarkers into routine practice may improve early risk stratification and patient management.

Dexrazoxane protects against doxorubicin-induced cardiotoxicity in susceptible human living myocardial slices: A proof-of-concept study.

The increasing number of cancer survivors has caused growing concern over chemotherapy-induced cardiotoxicity. This study aimed to investigate a novel human model of cardiotoxicity and explore cardioprotection. Living myocardial slices (LMS) were obtained from explanted end-stage heart failure hearts, then exposed to doxorubicin (Dox) to investigate cardiotoxic effects and to dexrazoxane (Dex) to explore cardioprotection. We assessed contractile function and glucose consumption, followed by evaluation of calcium transients, structural integrity and transcriptomic changes. Additionally, electrocardiogram (ECG) alterations were analysed in patients treated with anthracyclines to corroborate the cardiotoxicity findings from LMS. We observed distinct functional responses to Dox, with LMS derived from some patients exhibiting high susceptibility to Dox-induced cardiotoxicity. LMS from susceptible patients displayed reduced contractile function and excitability, myofibre dyssynchrony, structural damage and decreased metabolic activity. Dex pretreatment partially mitigated these effects, preserving contractile function and preventing structural damage. Consistent with ex vivo findings, patients treated with anthracyclines exhibited acute and chronic alterations in T-, P- and R-wave morphology of the ECG, confirming variable susceptibility at the clinical level. We highlight the value of human LMS in studying Dox-induced cardiotoxicity and the cardioprotective potential of Dex, even when sourced from end-stage heart failure patients. Susceptible patients harboured cardiomyopathy-associated genetic mutations, suggesting that genetic screening including cardiomyopathy-associated genes, prior to anthracycline treatment, could enable improved patient risk stratification. We demonstrate the potential utility of ECG changes for early detection of subclinical cardiotoxicity.

Endoscopic management of infected necrotizing pancreatitis: Advancing through standardization

Infected necrotizing pancreatitis (INP) remains a life-threatening complication of acute pancreatitis. Despite advancements such as endoscopic ultrasound (EUS)-guided drainage, lumen-apposing metal stents, and protocolized step-up strategies, the clinical practice remains heterogeneous, with variability in endoscopic strategies, procedural timing, device selection, and adjunctive techniques contributing to inconsistent outcomes. This review synthesizes current evidence to contribute to a structured framework integrating multidisciplinary team decision-making, advanced imaging (three-dimensional reconstruction, contrast-enhanced computed tomography/magnetic resonance imaging), EUS assessment, and biomarker-driven risk stratification (C-reactive protein, procalcitonin) to optimize patient selection, intervention timing, and complication management. Key standardization components include endoscopic assessment and procedural strategies, optimal timing of intervention, personalized approaches for complex pancreatic collections, and techniques to reduce the number of endoscopic debridements and mitigate complications. This work aims to enhance clinical outcomes, minimize practice heterogeneity, and establish a foundation for future research and guideline development in endoscopic management of INP.

Integrative analysis of epigenetic subtypes in acute myeloid Leukemia: A multi-center study combining machine learning for prognostic and therapeutic insights.

Acute Myeloid Leukemia (AML) exhibits significant heterogeneity in clinical outcomes, yet current prognostic stratification systems based on genetic alterations alone cannot fully capture this complexity. This study aimed to develop an integrated epigenetic-based classification system and evaluate its prognostic value. We performed multi-omics analysis on five independent cohorts totaling 1,103 AML patients. The Cancer Genome Atlas-Acute Myeloid Leukemia (TCGA-LAML) cohort (n = 83) provided comprehensive multi-omics data including DNA methylation profiles (Illumina 450K platform), RNA sequencing (mRNA, lncRNA, and miRNA), and somatic mutation profiles. The BEAT (n = 649), TARGET (n = 156), GSE12417 (n = 79), and GSE37642 (n = 136) cohorts contributed transcriptome data. Molecular subtypes were identified using empirical Bayes-based clustering on the TCGA cohort. LSC17 scores were calculated using a validated 17-gene expression signature. A random survival forest model was developed integrating molecular features with LSC17 scores, validated across all cohorts. Immune microenvironment analysis employed multiple deconvolution methods (ESTIMATE, CIBERSORT, xCell) and pathway analysis (GSVA, GSEA). Drug sensitivity was predicted using the pRRophetic algorithm with GDSC database reference. Multi-omics integration revealed two molecularly distinct AML subtypes with significant survival differences (CS2 vs CS1, P < 0.001). The random survival forest model, incorporating 20 key epigenetic features (including CPNE8, CD109, and CHRDL1) and LSC17 scores, achieved superior prognostic accuracy (C-index: 0.72-0.78) across validation cohorts. Both epigenetic risk score (HR = 2.45, 95%CI: 1.86-3.24) and LSC17 score (HR = 1.89, 95%CI: 1.42-2.51) maintained independent prognostic value in multivariate analysis. Integration of both scores in a nomogram improved 1-, 3-, and 5-year survival predictions (C-index: 0.81). High-risk patients exhibited distinct immune profiles with elevated M2 macrophages (1.8-fold) and Tregs (2.3-fold), while low-risk patients showed enhanced NK cell activity (2.1-fold). Drug sensitivity analysis identified differential responses to epigenetic regulators (LAQ824, P = 0.000139; MS-275, P = 0.00104) and proteasome inhibitors (Bortezomib, P = 0.00747; MG-132, P = 0.0106) between risk groups. This integrated classification system combining epigenetic features and stem cell signatures provides new insights into AML heterogeneity and therapeutic targeting. The complementary nature of epigenetic and stem cell-related prognostic factors suggests potential for improved risk stratification in clinical practice. Future prospective validation studies are warranted to confirm these findings.

Prognostic Value of Blood-Based Protein Biomarkers in Traumatic Brain Injury: A Living Systematic Review and Meta-Analysis.

Circulating biomarkers might improve the prediction of outcomes in patients with traumatic brain injury (TBI) beyond current approaches. Robust and up-to-date evidence is required to support their clinical utility and integration into medical practice to guide decision-making. Our objective was to critically appraise the existing evidence for six core blood-based TBI biomarkers (S100 calcium-binding protein B, glial fibrillary acidic protein [GFAP], neuron-specific enolase, ubiquitin C-terminal hydrolase-L1 [UCH-L1], tau and neurofilament proteins), in predicting outcome after TBI. Electronic databases, including Medline and Embase, were searched for articles published from their inception to October 2023. Studies were included if they evaluated the accuracy of blood biomarker concentrations at hospital presentation for outcome prediction in adult patients with TBI. Outcomes assessed were mortality, Glasgow Outcome Scale (GOS)/GOS extended (GOS-E), or the Rivermead Post-Concussion Symptoms Questionnaire (RPQ). Study selection, data extraction, and quality assessment using the modified Quality Assessment of Prognostic Accuracy Studies tool were performed by two authors independently, with disagreements being resolved through discussion or arbitration. If appropriate, a meta-analysis was conducted by calculating the weighted summary area under the curve (AUC) and using a bivariate regression model. Of 12,792 retrieved records, 32 articles, including 7481 patients with TBI, were selected as relevant. Two biomarkers showed strong associations with in-hospital and 6-month mortality: GFAP (unadjusted pooled AUC 0.81 [95% confidence interval [CI] 0.75-0.87] and 0.82 [0.80-0.85], respectively) and UCH-L1 (0.80 [0.74-0.85] and 0.83 [0.77-0.88]). Their addition to models that included established risk factors consistently improved the predictive value, though models and performance varied substantially across studies. In four studies measuring both markers, UCH-L1 outperformed GFAP in improving risk stratification when added to established prediction models. At ∼1.5 ng/mL (five studies), the summary sensitivity of GFAP for predicting mortality was 78% (95% CI 67-85%), and the summary specificity was 79% (95% CI 64-89%). The other assessed biomarkers had fair to good performance in mortality prediction with unclear added benefits. Neurofilament light (NfL) (three studies) demonstrated the strongest association in predicting a 6-month poor outcome (GOS-E ≤4; GOS ≤3) (unadjusted pooled AUC 0.81 [95% CI 0.75-0.87]), whereas the other assessed biomarkers had a fair performance with unclear or irrelevant added value. All core biomarkers had only marginal or no association with incomplete recovery and post-concussion symptoms/syndrome, as assessed by RPQ. Serious problems were found in the design and analysis of many of the studies. We conclude that admission measurements of core blood TBI biomarkers, in particular GFAP and UCH-L1, are strongly associated with mortality. There remains little evidence that any of these markers are ready for clinical implementation for prognostic purposes. Future work focused on the intended use and applying unbiased rigorous analysis methods is necessary to demonstrate that the biomarker test results are "prognostically actionable."

Patient-Reported Swallowing Outcomes in HPV+ Oropharyngeal Cancer by Postoperative Chemoradiation Dose in MINT and E3311.

Evaluate the effect of deintensified postoperative adjuvant (chemo)radiation therapy (POA(C)RT) on patient-reported dysphagia outcomes in patients with human papillomavirus (HPV+) oropharyngeal squamous cell carcinoma (OPSCC). Retrospective. Multiple institutions, 2014 to 2021. Patients with HPV+ OPSCC underwent transoral robotic surgery and reduced-dose POA(C)RT by pathologic risk stratification. The Minimalist Trial (MINT) participants received 42 Gy radiation therapy (RT) with one dose of cisplatin 100 mg/m2 (intermediate-risk arm) or no cisplatin (low-risk arm). The intermediate-risk E3311 participants were randomized to 50 or 60 Gy RT. Analysis was per-protocol by RT dose group. The primary outcome was change in MD Anderson Dysphagia Inventory (MDADI) composite score from baseline to 1-year posttreatment, with a clinically meaningful decline (CMD) of ≥10 points. In total, 156 included patients received POA(C)RT: n = 28 at 42 to 49 Gy (n = 19 no cisplatin, n = 9 cisplatin), n = 82 at 50 to 59, and n = 46 at 60 Gy. Mean (SD) change in MDADI was -7.2 (10.6) in the 42 to 49 Gy group, -11.3 (17.2) in the 50 to 59 group, and -9.1 (15.1) in the 60 Gy group (analysis of variance [ANOVA] P = .46). The rate of CMD was 11/28 (39%) in the 42 to 49 Gy group, 43/82 (52%) in the 50 to 59 group, and 20/46 (44%) in the 60 Gy group (chi-square P = .42). The rate of CMD was similar in those receiving 42 to 49 Gy with (3/9, 33%) and without cisplatin (8/19, 42%) (diff. 1%, 95% CI -29% to 47%). Gastrostomy tube rates were similar across dose groups. Changes in dysphagia-related quality-of-life (MDADI) from baseline to 1 year after POA(C)RT did not differ by radiation dose in the range of 42 to 60 Gy.

A two-phase approach to identifying HFpEF in heart failure patients: Risk score evaluation and decision tree development.

Heart failure (HF) with preserved ejection fraction (HFpEF) poses significant diagnostic challenges due to its complex aetiology and overlapping symptoms with other HF types. The heterogeneity of HFpEF, compounded by frequent comorbidities, complicates diagnosis. This study aimed to enhance HFpEF prediction through a two-phase approach: a simplified risk score and a decision tree model. In Phase 1, an 8-point risk score based on accessible clinical parameters was developed. In Phase 2, we conducted comprehensive predictive modelling using decision tree analysis. Data from 560 HF patients were analysed. It achieved an accuracy of 63.13% (sensitivity: 62.87%, specificity: 54.24%). In Phase 2, a decision tree model using broader clinical variables improved accuracy to 73.04% (sensitivity: 53.89%, specificity: 81.17%). This dual framework provides tools for both quick screening and detailed risk stratification in various clinical settings.

PROS1-MERTK Axis Drives Tumor Microenvironment Crosstalk and Progression in Papillary Thyroid Microcarcinoma.

The incidence of papillary thyroid carcinoma (PTC) has been rising annually, with papillary thyroid microcarcinoma (PTMC) accounting for more than half of the cases. While most PTMCs exhibit indolent growth and a favorable prognosis, some undergo clinical progression with poor outcomes. Thus, identifying biomarkers associated with PTC, particularly those related to PTMC progression, is crucial for precise risk stratification and treatment planning. This study utilized single-cell RNA sequencing on 19 surgical tissue specimens from 15 patients, including four para-tumor tissues, four non-progressive PTMCs, five progressive PTMCs, and six progressive PTCs. Key findings are corroborated through in vivo and in vitro experiments. Single-cell RNA sequencing and spatial transcriptomics characterized the cellular ecosystem within PTC, revealing multi-directional evolutionary patterns as PTMC progresses. Analysis of progression-specific alterations in intercellular communication networks highlighted the PROS1-MERTK signaling interaction as pivotal in PTMC progression. In vitro and in vivo models confirm that the PROS1-MERTK axis accelerates PTMC progression via paracrine and autocrine signaling. Furthermore, NFYB and FOXP2 are identified as activators of PROS1 transcription in fibroblasts, promoting PTMC progression through the MERTK/WNT/TGF-β signaling. These findings underscore the PROS1/MERTK axis as a critical component of the cellular microenvironment and a key regulatory mechanism in PTMC progression.

Reward magnitude-specific delay discounting differentiates mania versus depression risk.

Mania/hypomania, the pathognomonic feature of bipolar disorder (BD), is characterized by elevated impulsivity, often assessed via delay discounting-the preference for smaller, immediate versus larger, delayed rewards. It remains unclear whether delay discounting differentiates BD from non-BD individuals or serves as an objective behavioral marker of mania/hypomania versus depression risk. Bipolar disorder (n = 40) and non-BD (n = 187) individuals were recruited, with the latter encompassing a range of mania/hypomania and depression risk and stratified into mania/hypomania and depression risk tertiles. Kruskal-Wallis and Dunn's tests evaluated delay discounting rates (k values), assessed via the 27-Item Monetary Choice Questionnaire, across both risk groups compared to the BD group. Significant group effects were found for overall and geomean k values in both mania/hypomania (overall k: χ2(3) = 8.15, p = 0.043; geomean k: χ2(3) = 8.40, p = 0.038) and depression risk groups (overall k: χ2(3) = 8.30, p = 0.04; geomean k: χ2(3) = 8.75, p = 0.033). Only k values for medium reward magnitudes were significant for both mood risk stratifications (corrected α = 0.05/3 = 0.0167). Bipolar disorder had significantly higher k versus low-risk mania/hypomania individuals (adjusted p = 0.012), as did high-risk versus low-risk mania/hypomania individuals (adjusted p = 0.039). Bipolar disorder had higher k versus high-risk depression individuals (adjusted p = 0.005), as did low-risk versus high-risk depression individuals (adjusted p = 0.029). Bipolar disorder had significantly higher k for medium reward magnitudes versus high-risk depression-only (W = 398, p < 0.001), but not versus high-risk mania/hypomania-only (W = 587.5, p = 0.368) individuals. Delay discounting for medium reward magnitudes differentiates BD from non-BD individuals and distinguishes heightened mania/hypomania risk from depression risk, supporting its potential as an objective behavioral marker for mania/hypomania risk detection.

Gender Disparities in Breast Cancer Survival According to Clinical Treatment Score Post-5 Years (CTS5) Risk Stratification

Gender Disparities in Breast Cancer Survival According to Clinical Treatment Score Post-5 Years (CTS5) Risk Stratification

Biomarker Profiling for Discrimination of High-Risk Asymptomatic Carotid Artery Stenosis Patients with Ulcerated Plaques: A Pilot Study

Although various methods are used to treat patients with asymptomatic carotid artery stenosis (ACAS), approaches are controversial, and combining imaging of carotid plaque features with biomarkers to identify plaques prone to rupture may be crucial in identifying high-risk ACAS patients. This study aimed to investigate a blood-based biomarker for discriminating ulceration in ACAS patients by analyzing plaque surface morphology through RNA sequencing of blood samples. Peripheral blood samples were collected from ACAS patients with plaque morphology determined by Doppler ultrasonography. Then, total RNA was isolated, and RNA-Seq was performed to analyze differentially expressed genes (DEGs). The KEGG, Reactome, and Gene Ontology (GO) terms pathway enrichment analyses were performed to investigate the molecular functions and biological processes involved in plaque formation. The pilot study included 7 ACAS patients, 57.1 % exhibiting ulcerated plaques. RNA-Seq results revealed significant upregulation of genes related to immune response, cell cycle regulation, and oxidative stress in ulcerated plaques. Especially, TP73, CCL3L3, and PXDNL genes showed the highest fold changes, indicating their role in endothelial damage, immune activation, and oxidative stress. KEGG and Reactome analyses identified TNF and chemokine signaling pathways as key regulators in ulcerated plaque formation. Our findings indicate that TP73, CCL3L3, and PXDNL may be potential biomarkers for identifying high-risk ACAS patients with ulcerated plaques due to their involvement in immune system regulation and oxidative stress-related processes. Thus, these genes and the pathways may be candidate biomarkers for early diagnosis and risk stratification, improving treatment approaches for ACAS.

Albuminuria as a diagnostic criterion and a therapeutic target in heart failure and other cardiovascular disease.

The high disease burden and bidirectional relationship of chronic kidney disease (CKD), heart failure (HF) and other cardiovascular disease (CVD) necessitate the need for early diagnosis of these diseases. While current screening and detection methods are recommended by CKD and CVD guidelines, their adoption in practice is low. Urine albumin-to-creatinine ratio (uACR) is recognized as a diagnostic marker for CKD and a prognostic marker for CKD progression, HF and CVD outcomes, therefore albuminuria changes have been accepted as a surrogate outcome for kidney and cardiovascular endpoints. Furthermore, clinical trials investigating guideline-directed medical therapies have shown that uACR reductions are accompanied by risk reductions for cardiovascular, HF and other CKD outcomes. However, uACR is not routinely measured in patients at risk of kidney and heart disease, and its utility for detection, risk stratification and prediction models may not be fully appreciated in routine clinical practice. This review will discuss the effectiveness and implications of uACR screening as a method for heart and kidney disease diagnosis and risk assessment.

Malignancy Risk Stratification of Suspicious Breast Microcalcifications Detected on Mammograms Using Morphological and Distribution Characteristics Based on the Fifth Edition of BI-RADS

AbstractBreast cancer is a major cause of mortality and morbidity in women. Hence, detecting suspicious microcalcifications on mammograms can be crucial for early diagnosis.To determine the malignancy risk of suspicious microcalcifications detected on mammograms in terms of positive predictive value (PPV) based on morphology and distribution characteristics and correlate results with BI-RADS Atlas, fifth edition and world literature.This is a hospital-based observational study conducted at our institute over 15-month duration and included all symptomatic and asymptomatic females who underwent mammogram, detected with suspicious breast microcalcifications, followed by stereotactic or ultrasound-guided breast biopsy and histopathology.The chi-square test was used to test the association of the outcome. A p &lt; 0.05 was considered to be statistically significant.Among 77 lesions, 56 were not associated with mass and 21 were associated with mass. Overall PPV for malignancy of suspicious microcalcifications not associated with mass was 37.5%, and PPV of these according to morphology descriptors was: amorphous 9.5%; coarse heterogeneous 45.4%; fine pleomorphic 50%, and fine linear/fine linear branching 100% (p &lt; 0.001). Overall PPV when associated with mass was 71.4%. PPV of suspicious microcalcifications for distribution descriptors was: regional 0%, grouped 38.9%, linear 66.7%, and segmental 63.2%.Results of our study correlated well with BI-RADS, fifth edition. Subcategorizing morphology and distribution descriptors provides accurate risk stratification, determines the need for image-guided biopsy, and guides further management strategies.

Dynamic cytokine monitoring enhances CAP severity scores in elderly patients: a prospective pilot study.

Current severity scoring systems (PSI and CURB-65) have limitations in risk stratification for elderly patients with community-acquired pneumonia (CAP). Given the complex immune responses in elderly populations, dynamic biomarker monitoring may provide additional prognostic value. This study evaluates whether integrating early cytokine dynamics with traditional severity scores improves mortality prediction in elderly CAP patients. This prospective observational study included 81 CAP patients aged ≥ 65years. Multiple cytokines were measured at admission and within 48h. Traditional severity scores (PSI and CURB-65) were calculated at baseline. Patients were categorized into survival (n = 67) and mortality (n = 14) groups. The predictive value of cytokine dynamics alone and in combination with severity scores was assessed using ROC curve analysis. Among measured cytokines, IL-6 demonstrated significant prognostic value. The mortality group showed an 88% increase in IL-6 levels within 48h, contrasting with a 49% decrease in survivors (p = 0.040). While individual PSI (AUC = 0.6631) and CURB-65 (AUC = 0.6231) showed modest discrimination, integration with IL-6 dynamics significantly improved predictive accuracy (PSI + IL-6: AUC = 0.7676, p = 0.0017; CURB-65 + IL-6: AUC = 0.7564, p = 0.0027). Early dynamic monitoring of cytokines, particularly IL-6, significantly enhances the prognostic accuracy of traditional severity scores in elderly CAP patients. This pilot study suggests that this integrated approach provides a more precise risk stratification tool, potentially enabling more personalized clinical decision-making. Larger multicenter studies are warranted to validate these findings and establish standardized cutoff values for clinical implementation.

Neutrophil Myo5c gene downregulation is associated with postoperative organ dysfunction following pediatric cardiac surgery with cardiopulmonary bypass

IntroductionPediatric cardiac surgery with cardiopulmonary bypass (CPB) carries substantial risks of postoperative organ dysfunction and mortality, making the identification of biomarkers for postoperative organ dysfunction crucial for enhancing patient outcomes. As neutrophils play a major role in the perioperative setting and act as double-edge swords to the host, we examined neutrophil transcriptomic profiles in pediatric patients undergoing cardiac surgery with CPB.MethodsWe enrolled into this study from May 31, 2022, to February 22, 2023.Results32% developed postoperative complications, mainly in the respiratory and cardiovascular systems. Patients in the complication group showed higher PELOD-2 scores on postoperative day 2. These patients experienced longer duration of mechanical ventilation and extended ICU and hospital stays. RNA sequencing of neutrophils revealed significant changes in gene expression after CPB, with the myo5c gene emerging as a key downregulated transcript. Its expression was inversely correlated with PELOD-2 score, IL-6 levels, and markers of neutrophil and platelet activation. Furthermore, myo5c-knockout HL60 cells exhibited enhanced neutrophil extracellular traps (NETs) formation upon stimulation, supporting a potential regulatory role for myo5c in neutrophil activation and systemic inflammation.DiscussionWhile myo5c was not an independent predictor of complications, its expression was consistently associated with clinical severity, suggesting it may serve as a useful biomarker for early risk stratification of postoperative complications in this vulnerable pediatric population.

Comprehensive analysis of risk factors and nomogram development for predicting hepatic metastasis following radical resection of adenocarcinoma of the esophagogastric junction

BackgroundAdenocarcinoma of the esophagogastric junction (AEG) often presents with subtle early symptoms and delayed diagnosis, frequently resulting in liver metastasis and a poor prognosis. This study aimed to investigate the primary risk factors influencing postoperative liver metastasis in AEG and to develop a simple predictive model to facilitate clinical risk stratification and individualized follow-up strategies.MethodsThis retrospective study analyzed data from 524 patients with AEG who underwent radical resection, with patients randomly divided into a training group (368 cases) and a validation group (156 cases). Clinical and pathological information was collected, and independent factors significantly associated with postoperative liver metastasis were identified using univariate and multivariate Cox regression analyses. Based on these findings, a nomogram model was constructed to predict the 1-year and 3-year liver metastasis-free survival rates, and the model’s predictive performance and clinical utility were evaluated using the C-index, ROC curves, and calibration curves.ResultsMultivariate analysis revealed that thoracoabdominal surgery, higher N stage (N1 and N2/N3), moderate-to-poor differentiation, the presence of vascular tumor thrombus, intestinal type according to Lauren classification, and P53 status were independent risk factors for postoperative liver metastasis. The nomogram model based on these six indicators demonstrated high predictive accuracy in both the training group (C-index = 0.966) and the validation group (C-index = 0.976), with ROC AUCs for both the 1-year and 3-year predictions exceeding 0.96 and favorable calibration curves, confirming the model’s strong predictive efficacy.ConclusionsThe predictive model developed in this study can effectively assess the risk of postoperative liver metastasis in patients with AEG, thereby providing a scientific basis for postoperative monitoring and individualized treatment, with the potential to improve patient outcomes in clinical practice.

Impact of Raised CRP in Mortality and Morbidity among Neonatal Intestinal Obstruction Cases

Background: Neonatal intestinal obstruction is a grave reason for morbidity and mortality. C-reactive protein (CRP), an acute-phase reactant, may have prognostic implications in predicting the outcome in such patients. This research aimed to identify whether there was any relationship between admission CRP and clinical outcome in neonates presenting with intestinal obstruction. Materials and Methods: The retrospective observational study examined 80 neonates (0-28 days) with intestinal obstruction over a 12-month period. Admission CRP values were categorized as normal (&lt;6 mg/L), mildly elevated (6-15 mg/L), moderately elevated (16-30 mg/L), and highly elevated (&gt;30 mg/L). The primary outcomes were mortality and morbidity. Statistical analysis included frequency distributions and Chi-square tests with p&lt;0.05 as the significance level. Results: Out of the study population (60% male), 31.25% were preterm neonates and 27.5% were of low birth weight. The intestinal obstruction was congenital in 56.25% and acquired in 43.75%. CRP values were normal in 31.25%, slightly raised in 18.75%, moderately raised in 20%, and extremely raised in 30% of the neonates. Increasing CRP values were associated with mortality (20% in normal versus 35% in highly elevated groups, p=0.02). Moreover, morbidity also rose with increased CRP (32% normals vs. 50% high raised groups, p=0.01). Combined mortality or morbidity was observed in 85% neonates with high raised CRP compared to 52% normal CRP. Conclusion: Elevated CRP, particularly when moderately or significantly elevated, is highly associated with increased mortality and morbidity in neonates with intestinal obstruction. Measurement of CRP upon admission can be a valuable predictive marker for risk stratification and clinical management in this high-risk population.

Temporal muscle thickness as a preoperative predictor of motor aphasia in Moyamoya disease

ObjectivePostoperative motor aphasia is a common complication following left-sided combined revascularization surgery for Moyamoya disease (MMD), yet reliable preoperative predictors remain unavailable. This study evaluates preoperative temporal muscle thickness (TMT), a novel MRI-based parameter, as a predictive biomarker for this complication.MethodsWe retrospectively analyzed 34 adult MMD patients who underwent left-sided combined revascularization between April 2021 and June 2023. Preoperative TMT was measured on axial MRI using multi-planar reformation. Statistical analyses (e.g., t-tests) were used to assess the association between preoperative TMT and the incidence, timing, and duration of postoperative motor aphasia.ResultsExcluding complications such as infarction, postoperative aphasia occurred in 28 of 34 patients (82.35%), predominantly pure motor aphasia (25/34, 73.53%), typically emerging on the third postoperative day with a median duration of 4 days. Patients who developed aphasia had significantly greater mean preoperative TMT than those who did not (7.08 ± 1.00 mm vs. 5.70 ± 0.68 mm, respectively; p = 0.003). Furthermore, greater preoperative TMT showed a positive correlation with a longer duration of postoperative aphasia (r = 0.4907, p = 0.0032).ConclusionOur findings confirm that TMT independently predicts the occurrence and severity of postoperative motor aphasia in MMD patients after left-sided revascularization. This MRI metric enhances risk stratification, guiding surgical planning and patient counseling. Further studies are needed to validate its utility and explore preventive measures.

AI-based multimodal prediction of lymph node metastasis and capsular invasion in cT1N0M0 papillary thyroid carcinoma

BackgroundAccurate preoperative evaluation of cT1N0M0 papillary thyroid carcinoma (PTC) is essential for guiding appropriate treatment strategies. Although ultrasound is widely used for clinical staging, it has limitations in detecting lymph node metastasis (LNM) and capsular invasion (CI), which may lead to misclassification of high-risk patients. Such undetected risks pose safety concerns for those undergoing radiofrequency ablation. This study aimed to develop an artificial intelligence (AI)-assisted predictive model that integrates ultrasound radiomics and deep learning features to improve the identification of LNM and CI, thereby enhancing risk stratification and optimizing treatment strategies for cT1N0M0 PTC patients.MethodsA total of 203 PTC patients were divided into high-risk (CI or LNM) and low-risk groups, with 142 assigned to the training set and 61 to the internal test set. Regions of interest delineation was performed using ITK-Snap. Radiomic features were extracted with PyRadiomics, and embedding features were obtained through the Vision Transformer (ViT) model. Risk-related features were selected using least absolute shrinkage and selection operator (LASSO), variance thresholding, and recursive feature elimination (RFE). Single-modal and multimodal models were developed using feature-level and decision-level fusion. Feature importance was assessed using Shapley Additive exPlanations (SHAP). Model performance was evaluated using recall, accuracy, and area under curve (AUC).ResultsAmong 1,001 radiomics features, 47 were selected via LASSO and RFE, and 15 relevant features from 768 ViT features. In the internal test set, NeuralNet models based on radiomics and 2D deep learning achieved AUCs of 0.756 and 0.708, respectively, and 0.829 and 0.840 in the training set. The multimodal RandomForest model outperformed single-modality models, with an AUC of 0.763 in the test set and 0.992 in the training set. Decision-level fusion models, such as DLRad_LF_Avg and DLRad_LF_Max, improved the external test set AUC to 0.843. SHAP analysis identified key features linked to tumor heterogeneity.ConclusionThe multimodal AI model effectively predicts high-risk cT1N0M0 PTC, outperforming single-modality models and aiding clinical decision-making.

An integrative and comprehensive analysis of blood transcriptomes combined with machine learning models reveals key signatures for tuberculosis diagnosis and risk stratification

Tuberculosis (TB) remains a major global health challenge, contributing substantially to morbidity and mortality worldwide. The progression from Mycobacterium tuberculosis (Mtb) infection to active disease involves a complex interplay between host immune responses and Mtb’s ability to evade them. However, current diagnostic tools, such as interferon-gamma release assays (IGRAs) and tuberculin skin tests (TSTs), have limited ability to distinguish between different stages of TB or to predict the progression from infection to active disease. In this study, we performed an integrative analysis of 324 previously acquired blood transcriptome samples from TB patients, TB contacts, and controls across diverse geographical regions. Differential gene expression analysis revealed distinct transcriptomic signatures in TB patients, highlighting dysregulated pathways related to immune responses, antimicrobial peptides, and extracellular matrix organization. Using machine learning, we identified a 99-transcript signature that accurately distinguished TB patients from controls, demonstrated strong predictive performance across different cohorts, and identified potential progressors or subclinical cases. Validation in an independent dataset comprising 90 TB patients and 20 healthy controls confirmed the robustness of the 10-gene signature (BATF2, FAM20A, FBLN2, AK5, VAMP5, MMP8, KLHDC8B, LINC00402, DEFA3, and GBP6), achieving high area under the curve (AUC) values in both receiver operating characteristic (ROC) and precision–recall analyses. This 10-gene signature offers promising candidates for further validation and the development of diagnostic and prognostic tools, supporting global efforts to improve TB detection and risk stratification.