Arterial pulse harmonic alterations: a novel biomarker linking vascular dysfunction to prefrailty.

This study examined the psychometric properties of the Turkish versions of the Maladaptive Daydreaming Scale (MDS-16) and its short form (MDS-5), with a particular focus on how the scales function in adults with attention-deficit/hyperactivity disorder (ADHD). Cross-sectional psychometric validation study comparing adults with ADHD to typical controls. A total of 357 participants (251 with ADHD, 106 controls) completed the MDS-16, MDS-5, and additional measures of ADHD symptoms, excessive mind wandering, dissociation, and anxiety/depression. Confirmatory factor analysis supported the four-factor structure of the MDS-16 and indicated good model fit for the MDS-5. Internal consistency was excellent for the MDS-16 total scale (α = .93) and good for the MDS-5 (α = .87). Multivariate analyses, controlling for sociodemographic variables and comorbid symptom severity, showed significantly higher MD scores in the ADHD group across total and subscale scores (η2 = .17 for Impairment, .14 for Kinesthesia, .08 for Yearning, .04 for Music). Item-level analyses showed that Yearning and Music/Kinesthesia items clustered in the moderate-to-lower AUC range, whereas Impairment items clustered at the highest AUC levels. Convergent validity was supported by moderate correlations between MD scales and ADHD symptoms, excessive mind wandering, and dissociation (r = .51-.63), while ADHD symptoms and excessive mind wandering showed a markedly stronger association (r = .82). The Turkish MDS-16 and MDS-5 demonstrated good validity and reliability, with Yearning and Music/Kinesthesia subscales capturing more MD-specific features, whereas Impairment showed greater overlap with ADHD. Further research is needed to clarify MD-ADHD overlap and improve diagnostic specificity.

A benchmark dataset of Primitive Indian Paddy Panicle Images and identification via deep residual transfer learning.

Multicentre MRI-based machine learning model for noninvasive prediction of pulmonary metastasis in osteosarcoma integrating intra-tumoral heterogeneity features.

Early identification of gram-negative bacteremia in intensive care units (ICUs) remains challenging at the time of blood culture sampling, when clinical signs are often nonspecific and existing diagnostic approaches typically rely on single-timepoint measurements. We conducted a retrospective cohort study of adult ICU patients admitted between July 2022 and January 2024 to investigate whether short-term longitudinal patterns in routinely collected clinical and laboratory data contain diagnostically relevant information for gram-negative bacteremia. Clinical and laboratory variables were extracted at three consecutive timepoints (Day -2, -1, and 0 relative to blood culture collection), and diagnostic models incorporating this temporal information were developed using complementary statistical and machine-learning approaches. Model performance was evaluated on a held-out test set using discrimination, calibration, and decision curve analysis. Among 568 patients, models incorporating short-term longitudinal data demonstrated good and consistent discrimination for gram-negative bacteremia (AUC range 0.81-0.83) with good calibration after recalibration. Diagnostic performance was stable across modeling approaches, indicating robustness of the underlying signal rather than dependence on a specific algorithm. Decision curve analysis suggested higher net benefit for model-based risk stratification compared with treat-all or treat-none strategies across clinically relevant threshold probabilities. Hemoglobin, creatinine, and albumin consistently emerged as influential contributors. These findings indicate that short-term longitudinal clinical trajectories contain diagnostically meaningful information for gram-negative bacteremia at the time of blood culture sampling and support further external validation and prospective evaluation prior to clinical implementation.

This study aimed to establish cut-points for cardiorespiratory fitness, muscular strength, and speed-agility and to evaluate their ability to detect general and central obesity risk in preschool children aged 3-5 years. Briefly, 3179 Spanish preschoolers (52.8% boys) were evaluated. Physical fitness was assessed with the PREFIT battery. Anthropometry included BMI and waist circumference. Obesity risk was defined using age- and sex-specific percentiles according to criteria established by the World Health Organization and the International Diabetes Foundation. Receiver operating characteristic curve analyses were used to identify fitness cut-points. Age- and sex-specific cut-points were established. For cardiorespiratory fitness, cut-points ranged from 9.5 to 23.5 laps in boys and 6.5 to 21.5 in girls across the 3- to 5-year age range. Muscular strength cut-points ranged from z-scores of -1.5 to 2.2 in boys and -1.6 to 1.9 in girls. Speed-agility cut-points ranged from 18.8 to 14.9 s in boys and 19.9 to 15.3 s in girls. Predictive accuracy was moderate-to-high (AUC range: 0.61-0.74 for cardiorespiratory fitness, 0.59-0.80 for strength, 0.49-0.71 for speed-agility). This study provides fitness cut-points for detecting general and central obesity risk in preschoolers. Early integration of physical fitness assessments into health monitoring may facilitate early identification of obesity risk.

Background Consensus molecular subtypes (CMSs) are associated with prognostic and clinical outcomes in colorectal cancer (CRC); however, effective noninvasive methods for CMS4 identification are limited. Purpose To evaluate whether a radiomics-based machine learning approach can predict CMS4 status in CRC and to explore its biologic relevance and interpretability of radiomics features. Materials and Methods This multicenter, retrospective study included patients with CRC who underwent abdominal, pelvic, or rectal MRI (January 2015 to April 2020). Patients were followed until recurrence or metastasis or up to 60 months, whichever occurred first. A subgroup of patients was randomly divided into a training and an internal test set, whereas another subgroup constituted the external test set. Pathologic tissue and MRI data were collected, including T2-weighted imaging (T2WI) and contrast-enhanced (CE) T1-weighted imaging (T1WI). CMS classification was determined using immunohistochemistry. A machine learning model was developed to generate an MRI radiomics CMS4 score (MRC4s) for predicting CMS4 status. Deep learning models (ResNet50, VGG16, and DenseNet201) were also implemented as comparators. Performance was evaluated using receiver operating characteristic curve analysis. Bulk and single-cell RNA sequencing data from patients with CRC was used to investigate the association between MRC4s and biologic pathways. Results This study included 253 patients (median age, 63 years; IQR, 55-69 years; 163 men). The merged MRC4s, combining CE T1WI and T2WI features, achieved areas under the receiver operating characteristic curve (AUCs) of 0.85 (95% CI: 0.63, 1.00) in the internal and 0.84 (95% CI: 0.73, 0.95) in the external test sets, outperforming state-of-the-art deep learning models (AUC range, 0.70-0.75; all P < .01). Merged MRC4s stratified the risk of recurrent metastasis (hazard ratio, 5.96; P < .001). Transcriptomic analyses revealed the merged MRC4s were associated with transforming growth factor-β and epithelial-mesenchymal transition pathways. Conclusion A machine learning radiomics model based on preoperative multiparametric MRI predicted CMS4 of CRC from other subtypes with strong performance and biologic interpretability supported by transcriptomic analyses. © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Arita and Kim in this issue.

BackgroundResting quadriceps muscle thickness (QMT) is known to be associated with muscle strength; however, the clinical relevance of contracted QMT in identifying muscle weakness has not been fully clarified.ObjectiveThis study aimed to investigate the relationship between resting and contracted QMT and isometric peak knee-extension torque (PT) and to evaluate the diagnostic value of contracted QMT in predicting probable sarcopenia (PS).MethodsForty-two individuals with PS and sixty-two healthy controls were evaluated. QMT and PT were compared between groups. Linear regression analyses examined the associations between QMT, handgrip strength (HGS), and appendicular skeletal muscle mass. Logistic regression and receiver operating characteristic (ROC) analyses assessed the diagnostic performance of contracted QMT.ResultsIndividuals with PS had lower contracted QMT and knee-extension strength than controls (all p < 0.01). Contracted QMT, rather than relaxed measurements, showed positive associations with HGS and appendicular skeletal muscle mass (ASMM) (all p < 0.05). Contracted QMT independently predicted PS (OR = 0.90, 95% CI: 0.82-0.99), and ROC analysis indicated moderate discriminatory performance (AUC range: 0.62-0.77).ConclusionsWhile both measurements were reduced in PS, contracted QMT showed a stronger association with muscle mass and strength. These findings suggest that contraction-based ultrasound parameters may serve as a preliminary and exploratory adjunct for identifying early functional decline. However, given the moderate discriminatory power, further standardization and external validation are required before routine clinical adoption.

Ankylosing spondylitis (AS) is a chronic immune-mediated inflammatory disease affecting the axial skeleton, characterized by progressive structural damage and functional impairment. Although biologic therapies targeting tumor necrosis factor and interleukin-17 have improved clinical outcomes, a substantial proportion of patients fail to achieve sustained disease control. Emerging evidence suggests that metabolic alterations may contribute to AS pathogenesis; however, systematic characterization of metabolism-related biomarkers and their regulatory networks remains limited, and the interplay between metabolic dysfunction and immune dysregulation in AS is poorly understood. Two whole-blood GEO datasets (GSE25101, GSE73754; n = 104) were integrated as the primary analytical cohort. A third dataset (GSE11886, n = 18; monocyte-derived macrophages) was included for exploratory cross-tissue analysis. Differential expression analysis identified 847 DEGs, which were refined to 16 metabolism-related genes through weighted gene co-expression network analysis (WGCNA) and GeneCards database filtering. Eleven machine learning algorithms with 5-fold cross-validation were applied to construct diagnostic models and identify hub genes. Validation analyses included immune cell infiltration estimation using CIBERSORT, metabolic pathway activity assessment via ssGSEA, single-cell transcriptomics from GSE268839, functional enrichment through GSEA/GSVA, and chromosomal localization analysis. A competing endogenous RNA (ceRNA) regulatory network was constructed to map post-transcriptional regulation. Natural compounds from 66 AS-treating traditional Chinese medicines were screened against hub genes using deep learning-based binding prediction. Multiple machine learning algorithms achieved comparable cross-validated performance (CV AUC range 0.741–0.836; top five models: 0.805–0.836) using the six hub genes (MFN2, SLC27A3, RHOB, SMG7, AKR1B1, LCOR) identified through SHAP-based feature importance analysis of the PLS model. Leave-one-dataset-out validation between the two whole-blood cohorts showed that all algorithms exceeded an AUC of 0.77 in Round 1 (validate: GSE73754, n = 72; best AUC 0.861), while Round 2 (validate: GSE25101, n = 32) yielded more modest performance (best AUC, 0.715) reflecting the smaller validation sample. Exploratory application to GSE11886 (macrophage-derived samples) showed near-chance performance, consistent with the tissue-source discrepancy. AS patients exhibited significant downregulation of oxidative phosphorylation, TCA cycle, and glycolysis pathways (p < 0.01), accompanied by elevated glutathione metabolism (p < 0.001). Immune cell deconvolution revealed reduced CD8+ T cell proportions correlating with MFN2 downregulation, and increased neutrophil frequencies correlating with SLC27A3 upregulation. Exploratory single-cell analysis indicated that RHOB expression was relatively enriched in border-associated macrophages and fibroblasts, while AKR1B1 was more prominently expressed in vascular endothelial cells and plasmacytoid dendritic cells. The ceRNA network identified 21 miRNAs and 65 lncRNAs forming 86 regulatory interactions, with four key regulatory axes (SATB1-AS1/miR-539-5p/LCOR, FAM95B1/miR-223-3p/RHOB, LINC01106/miR-106a-5p/MFN2, AATBC/miR-185-5p/SMG7) predicted to regulate hub gene expression. Compound screening identified betaine, pyruvic acid, citric acid, etc., as top-ranking candidates, with MFN2 showing the highest binding capacity among hub genes. This study provides an integrative framework linking metabolic reprogramming with immune dysfunction in AS. The six-gene diagnostic signature showed preliminary discriminatory ability in the available datasets, while the ceRNA regulatory network and natural compound screening results prioritize candidate regulatory pathways and compounds for future validation. These findings advance our understanding of AS pathogenesis and may guide future biomarker development and targeted intervention strategies.

Obstructive sleep apnea (OSA) is highly prevalent among patients with cardiovascular (CV) risk factors, yet early detection in primary care remains difficult, particularly in individuals with subtle or absent symptoms, in whom screening questionnaires may have limited accuracy. This study aimed to evaluate the diagnostic performance of five validated questionnaires and the triglyceride-glucose (TyG) index, alone and in combination, for detecting moderate-to-severe OSA in primary care patients stratified by CV risk. In this prospective study, 189 adults aged 18-75 years with hypertension, type 2 diabetes, or dyslipidemia were consecutively recruited from a primary care center in Spain. Participants completed the Berlin, STOP, STOP-BANG, NoSAS, and BASH-GN questionnaires, and the TyG index was calculated from fasting glucose and triglyceride levels. OSA was assessed using home sleep apnea testing, with moderate-to-severe OSA defined as an apnea-hypopnea index (AHI) ≥ 15 events/h. CV risk was categorized using SCORE charts. Overall OSA prevalence was 57.7%, and 23.8% of participants had moderate-to-severe disease. Individually, questionnaires and TyG showed modest discrimination (AUC range 0.575-0.675). Diagnostic accuracy improved when strategies were tailored to CV risk: in low-to-moderate CV risk patients, TyG combined with the Berlin questionnaire achieved the best performance (AUC 0.740), whereas in high-to-very-high CV risk patients, the TyG plus STOP-BANG combination performed best (AUC 0.732). Notably, high-risk patients had more severe OSA but fewer typical symptoms, suggesting a "silent" phenotype. Integrating TyG with selected questionnaires may modestly enhance detection of clinically significant OSA in primary care, particularly when adapted to CV risk.

Tertiary lymphoid structures (TLS) are associated with improved survival and enhanced response to anticancer immunotherapy. This study aimed to develop a CT imaging-based approach for noninvasive assessment of TLSs and immunotherapy response. This study involved 3,155 patients with gastric cancer. TLSs were classified into four stages (absence, Agg, FL-I, and FL-II) according to their maturation based on IHC staining. A CT imaging-based TLS scoring model (ctTLS) was developed to assess TLS status, subsequently classified into four classes (ctTLS-0/1/2/3). We next evaluated the model's associations with prognosis and immunotherapy response. To enhance the model's interpretability, we analyzed multiomics data and employed the Shapley value strategy. The ctTLS model achieved high accuracies in predicting TLS status in the internal validation (AUC range, 0.727-0.809) and external validation (0.704-0.807) cohorts. In retrospective and prospective validation cohorts, ctTLSs exhibited significant associations with both disease-free and overall survival (HR range, 0.206-0.634; all P < 0.01). Shapley value analysis highlighted ctTLSs as the strongest predictor of TLS status. Upon analyzing multiomics data, we found that higher ctTLS levels positively correlated with tumor immune activation and apoptosis signaling, while displaying a negative correlation with tumor proliferation and metabolism signaling. Intriguingly, patients with high ctTLSs (but not low ctTLSs) exhibited substantial benefits from immunotherapy (P < 0.0001). The objective response rate of four ctTLS classes was 16.7% in ctTLS-0, 35.5% in ctTLS-1, 45.8% in ctTLS-2, and 53.8% in ctTLS-3. The ctTLS model could noninvasively assess TLS status, enabling improved prognosis evaluation and informed decisions about immunotherapy.

In recent years, there has been increasing interest in personalizing cognitive training to enhance the likelihood of positive training effects at the individual level. Machine learning methods have proven suitable for this purpose due to their ability to generate predictions at the individual level. The aim of the study was to develop supervised machine learning models to predict near and far transfer of three cognitive training interventions (memory training, reasoning training and speed-of-processing training) based on baseline characteristics of elderly individuals including sociodemographic data, measures of cognitive and everyday functioning and depressive symptoms. In addition, near-transfer models were further utilized to predict individual responsiveness to all three types of cognitive training. Publicly available data from the ACTIVE study were used, which examined the effects of memory training, reasoning training and speed-of-processing training in healthy adults. Multiple supervised machine learning classification algorithms were applied to establish optimal predictive models for each type of cognitive training and transfer measure. Selected models for predicting near transfer were then used to estimate individual responsiveness to all three interventions. The results show selected models for all three types of cognitive training and both near- and far-transfer outcomes demonstrated better discriminative ability than chance based on all included features (AUC range 0.56-0.74), although models predicting far transfer demonstrated limited performance. Predicted responsiveness to cognitive training varied according to participant characteristics. Differences between model-predicted responders indicate that initially advantaged participants would have greater likelihood of benefiting from a broader range of interventions compared to initially disadvantaged ones, which would support magnification effects. The developed models need external validation, but have practical potential for selecting effective interventions tailored to individual characteristics, which could improve the future implementation of cognitive training programs.

The Value of Time-dependent Diffusion MRI and Macromolecular Proton Fraction Imaging in Assessing Pathological Grade in Cervical Cancer.

To explore the clinical biomarkers that predict therapeutic response to bronchial thermoplasty (BT) in severe asthma. We prospectively recruited patients with severe asthma who completed three sessions of bronchial thermoplasty. Baseline demographics, Asthma Control Questionnaire-5 (ACQ5) scores, eosinophil counts, fractional exhaled nitric oxide (FeNO), spirometry, impulse oscillometry (IOS), endobronchial optical coherence tomography (EB-OCT), and BT activation counts were recorded. All subjects were followed for two years and classified as responders or non-responders according to ACQ5 improvement ≥0.5 points. Thirty patients were included (22 responders, 8 non-responders). Compared with non-responders, responders had lower body weight, BMI, and triglycerides, along with more negative X5 values, higher RV/TLC ratios, greater Collagen Type III (COL3) expression, and larger airway luminal areas with thinner airway walls on EB-OCT. Receiver operating characteristic (ROC) analysis demonstrated that body weight (AUC = 0.759), BMI (AUC = 0.733), triglycerides (AUC = 0.694), X5 (AUC = 0.938), RV/TLC (AUC = 0.756), COL3 (AUC = 0.846), and EB-OCT indices including airway luminal area from the 3rd to 6th generation (Ai3-6), 7th to 9th generation (Ai7-9), and airway wall area percentage from the 3rd to 6th generation (Aw%3-6) showed moderate-to-good discriminatory power (AUC range: 0.761-0.830). Multivariable logistic model integrating BMI, X5, and Ai3-6 achieved better discrimination (AUC = 0.988) in predicting response to BT. More negative baseline X5, lower triglyceride levels, EB-OCT-derived thinner airway walls and larger luminal areas, and higher COL3 expression, but not BT activation number, may help identify asthma patients most likely to benefit from BT and serve as potential predictors of its long-term efficacy.

Purpose To histologically validate T1 mapping for quantitative assessment of mild-to-severe myocardial fibrosis in a swine model of chronic myocardial infarction (MI). Materials and Methods In this animal study conducted from June 2021 to July 2022, 18 male miniature swine (16 MI animals; two healthy control animals) underwent cardiac MRI, including cine imaging, late gadolinium enhancement (LGE) imaging, T1 mapping, and extracellular volume fraction (ECV) mapping. Two T1 mapping techniques, modified Look-Locker inversion recovery (MOLLI) and shortened MOLLI (ShMOLLI), were evaluated. Pathologic myocardial slices were categorized as infarcted, peri-infarct, remote, and healthy based on triphenyl tetrazolium chloride staining. Fibrosis was quantified using collagen volume fraction (CVF) and classified as severe (CVF, ≥30%), moderate (CVF, >15%-30%), or mild (CVF, 3%-15%). Associations between cardiac MRI parameters and CVF were assessed, and diagnostic performance in detecting myocardial fibrosis was evaluated using the area under the receiver operating characteristic curve (AUC). Results For detection of severe fibrosis, LGE, T1 mapping, and ECV all demonstrated excellent diagnostic performance (AUC range, 0.88-0.96). ECV using ShMOLLI showed significantly higher performance than ECV using MOLLI for detecting severe fibrosis (AUC, 0.96 vs 0.88; P = .03) and MI (AUC, 0.93 vs 0.87; P = .045), as well as the strongest correlation with histologic CVF (r = 0.90 for ECV with ShMOLLI, 0.84 for ECV with MOLLI, 0.74 for T1 with ShMOLLI, 0.77 for T1 with MOLLI, and 0.74 for LGE extent). In remote myocardium with mild fibrosis (CVF, 8.81%) compared with healthy myocardium (CVF, 2.21%), only T1 with ShMOLLI and ECV with ShMOLLI demonstrated significant differences (P < .05), whereas LGE and MOLLI-based parameters did not. Conclusion Cardiac MRI helped detect mild-to-severe myocardial fibrosis in close agreement with histologic findings. While all techniques helped accurately identify severe fibrosis, T1 mapping-particularly ECV using the ShMOLLI sequence-provided unique sensitivity for detecting low-grade fibrosis, underscoring the importance of sequence selection for precise myocardial tissue characterization and clinical trial design. Keywords: Myocardial Fibrosis, Heart, Histological Techniques, Magnetic Resonance Imaging, Swine Supplemental material is available for this article. © RSNA, 2026.

Non-sputum biomarkers to monitor tuberculosis treatment and predict poor outcomes are lacking. We evaluated host-blood transcriptomic signatures for treatment monitoring and prognosis (death, treatment failure, recurrence) in adults with pulmonary tuberculosis. Adults with culture-confirmed, drug-susceptible pulmonary tuberculosis were enrolled at five Brazilian sites. Whole-blood PAXgene samples were collected at baseline, month 2 (M2), and end of treatment (EoT). Treatment failure was defined as sputum culture positivity at month 5 or later. Participants were followed for 24 months from treatment initiation for clinical or microbiological tuberculosis recurrence. Unfavourable outcomes were matched ∼1:3 to recurrence-free cure. Twenty-two published blood transcriptomic signatures were measured by microfluidic RT-qPCR and benchmarked against the WHO Target Product Profile (TPP) criteria. We matched 263 participants with recurrence-free cure to 33 with treatment failure, 24 who died (tuberculosis/unknown cause), and 9 with recurrence. Signature scores generally declined from baseline to EoT. Multiple signatures measured at baseline and M2 predicted recurrence (AUC range 0.71-0.91), with waning performance when measured at EoT (AUC range 0.42-0.89). Against the WHO TPP, 2/22 signatures met minimum criteria at baseline, 13/22 at M2, and none at EoT. Prediction of treatment failure was poor across timepoints (AUC < 0.70). In contrast, several signatures measured at baseline predicted death during treatment or follow-up (AUC ≥ 0.80). Blood transcriptomic signatures tracked treatment response and predicted recurrence and death, meeting WHO TPP benchmarks at baseline and M2. These findings support prospective, biomarker-guided trials to individualise tuberculosis therapy-shortening regimens for early responders and intensifying care for high-risk patients.

AimAcute kidney injury (AKI) is a major complication of coronary artery bypass grafting (CABG). Existing predictive models show limited accuracy and rarely include modifiable intraoperative risk factors. Therefore, a prediction model was developed to identify modifiable variables to guide targeted prevention strategies.MethodsWe collected data from 1174 patients who underwent CABG at our hospital between January 2020 and May 2025. AKI was defined according to the KDIGO 2012 criteria. Missing data (<2%) were processed using multiple imputation (MICE algorithm). We constructed a preoperative model (baseline variables only) and a complete model (baseline plus intraoperative variables). Model discrimination was performed using the area under the receiver operating characteristic curve (AUC-ROC), and internal validation was conducted using bootstrap resampling (500 iterations). The clinical utility was assessed using decision curve analysis.ResultsAKI occurred in 297 patients (25.3%). The full model had six predictors: age, body mass index, diabetes, estimated glomerular filtration rate, hemoglobin nadir during cardiopulmonary bypass, and maximum lactate. Hemoglobin nadir was the most robust modifiable predictor (odds ratio 0.73/SD, 95% CI 0.61-0.86, p < 0.001). The bootstrap-corrected AUC of 0.661 with minimal optimism (0.008) suggested solid internal validity. Model discrimination was consistent across subgroups (AUC range 0.614-0.672). Patients with chronic kidney disease demonstrated the highest AKI incidence (39.1%). AKI also increased the hospital length of stay by 26.6% (95% CI 19.6%-34.1%, p < 0.001) after adjusting for confounders. Decision curve analysis yielded a positive net benefit between clinically relevant risk thresholds (15-35%).ConclusionWe developed and internally validated a predictive model with acceptable discrimination and reasonable optimism in a contemporary CABG population. The hemoglobin nadir during cardiopulmonary bypass emerged as the most potent modifiable risk factor, with dose-response analysis suggesting a threshold of 8-9g/dL as an actionable target for AKI prevention. High-risk populations showed markedly elevated AKI incidence, and the model quantified considerable clinical and economic impacts. External validation is required.

Diabetic kidney disease (DKD) constitutes a chronic renal condition arising from type 2 diabetes mellitus after excluding other causes. Immune-inflammatory responses are pivotal in the pathogenesis of DKD, and related biomarkers may be diagnostic targets. The current meta-analysis appraises the diagnostic value of common immune-inflammatory indicators-red blood cell distribution width (RDW), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR), mean platelet volume (MPV), and systemic inflammation response index (SIRI)-for early and established DKD. We systematically retrieved the Cochrane Library, Embase, PubMed, Web of Science, CNKI, CBM, VIP, and Wanfang databases up to October 11, 2025. The QUADAS-2 tool was applied to evaluate study quality. Meta-analyses were implemented employing Stata 16.0, RevMan 5.3, and MetaDisc 1.4. Thirty-two eligible investigations were incorporated: 11 on early DKD(649 subjects) and 21 on DKD(9,120 subjects). The meta-analysis yielded pooled diagnostic performance metrics. For early DKD, PLR showed a sensitivity of 0.74 (95% CI: 0.65-0.81), specificity of 0.69(0.54-0.81), and AUC of 0.78(0.74-0.81). For established DKD, SII demonstrated a sensitivity of 0.68(0.59-0.75), specificity of 0.64 (0.54-0.72), and an AUC of 0.71(0.67-0.74). PLR, MLR, MPV, and RDW exhibited low to moderate diagnostic accuracy for both stages (AUC range: 0.68-0.74). Common immune-inflammatory markers have diagnostic value for early and established DKD. Among them, PLR offers moderate diagnostic accuracy for early DKD, while SII performs relatively better for diagnosing DKD. These findings should be verified through future high-quality studies due to limitations of eligible research. https://www.crd.york.ac.uk/prospero/, Identifier CRD420251174942.

Background: In this study, we aimed to evaluate and compare the diagnostic performance of peripapillary retinal nerve fiber layer (RNFL) thickness, macular ganglion cell-inner plexiform layer (GCIPL) thickness, and GCIPL asymmetry parameters in differentiating healthy eyes from primary angle-closure glaucoma (PACG), primary open-angle glaucoma (POAG), and secondary open-angle glaucoma (SOAG). Methods: This retrospective study included 204 eyes of 204 patients categorized into four groups: healthy controls (n = 46), PACG (n = 53), POAG (n = 58), and SOAG (n = 47). All participants underwent spectral-domain optical coherence tomography (OCT). Peripapillary RNFL thickness, sectoral and average GCIPL thickness, and GCIPL-derived asymmetry ratios were analyzed. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis. Results: Diagnostic accuracy varied according to glaucoma subtype. In distinguishing POAG from healthy controls, the average RNFL thickness (area under the ROC curve [AUC] = 0.82) demonstrated the highest diagnostic performance, followed by the superotemporal, inferotemporal, and average GCIPL thickness parameters. In contrast, no parameter reached an AUC of ≥0.80 in the PACG or SOAG comparisons. GCIPL asymmetry ratios exhibited limited discriminative ability across most analyses. Subtype differentiation was modest; POAG versus SOAG comparisons yielded AUC values up to 0.66, whereas PACG versus SOAG comparisons demonstrated minimal discrimination (AUC range: 0.47-0.63). Conclusions: Peripapillary RNFL and localized temporal GCIPL thickness measurements provide the highest diagnostic accuracy for identifying POAG. Diagnostic performance is reduced in PACG and SOAG, and the OCT parameters show limited ability to differentiate between glaucoma subtypes. GCIPL asymmetry indices do not enhance diagnostic discrimination beyond direct thickness measurements.

Efficacy of EGFR inhibitor-anti-angiogenic combination therapy varies substantially in RAS wild-type advanced colorectal cancer (CRC), and current clinical guidelines lack individualized predictive tools to identify optimal candidates and tailor regimens. This study aimed to develop and externally validate a multi-dimensional efficacy prediction model to address this unmet clinical need. This retrospective multi-center study included 600 RAS wild-type advanced CRC patients (development cohort: 420 patients from two centers; external validation cohort: 180 patients from an independent center) treated with EGFR inhibitors (cetuximab/panitumumab) plus anti-angiogenic agents (bevacizumab/fruquintinib/regorafenib) between 2018 and 2021. Candidate variables encompassed clinical, laboratory, radiomic, and biological indices. Radiomic features were screened via ANOVA-dimensionality reduction, and LASSO-Cox regression was used for variable selection and nomogram construction (with shrinkage calibration to reduce overfitting). Model performance was evaluated by discrimination (AUC), calibration (Hosmer–Lemeshow test), and clinical utility (decision curve analysis [DCA]); the overfitting risk was assessed by calculating events per variable (EPV), and model stability was verified by multi-step internal validation (tenfold cross-validation, bootstrap resampling) and subgroup/risk stratification analyses. The final nomogram integrated five core predictors: vascular density, neutrophil-to-lymphocyte ratio (NLR), carcinoembryonic antigen (CEA), metastatic sites, and ECOG score. The model exhibited moderate discrimination with clinical practical value (development cohort AUC = 0.641, 95%CI 0.588–0.691; validation cohort AUC = 0.532, 95%CI 0.445–0.617), which is consistent with the performance of most multi-dimensional clinical prediction models for advanced colorectal cancer reported in similar studies (AUC range 0.58–0.68). Meanwhile, the model showed excellent calibration in the external validation set (H–L χ2 = 1.12, p = 0.572), indicating a high consistency between the predicted PFS probability and the actual clinical outcome. The limited discrimination of the model is mainly due to the inherent biological heterogeneity of advanced colorectal cancer and the lack of dynamic monitoring indicators (e.g., circulating tumor DNA) in the study variables. Risk stratification identified low (0.7%), intermediate (57.3%), and high-risk (42.0%) groups with significantly distinct progression-free survival (PFS) and overall survival (OS) (all log-rank p < 0.001). High-risk patients who switched regimens achieved longer median PFS (11.1 vs. 5.9 months, p < 0.001). DCA confirmed superior net benefit over “treat all/none” strategies, and the model outperformed guidelines (median PFS: 9.3 months [both recommended] vs. 6.7 months [guideline-only], p < 0.05). Key biomarkers (vascular density, tumor mutational burden) correlated with treatment response and risk stratification, providing biological rationale. This externally validated nomogram integrating five readily available clinical and laboratory indicators can realize individualized PFS prediction and risk stratification for RAS wild-type advanced CRC patients receiving EGFR inhibitor-anti-angiogenic combination therapy, and provide preliminary reference for clinical regimen adjustment of high-risk patients. As a supplementary tool to current clinical guidelines, the model can partially address the problem of clinical response heterogeneity in combination therapy and provide simple decision support for clinicians in primary and secondary hospitals with limited detection conditions. However, the model has certain limitations in long-term prognostic prediction and needs to be further optimized and validated in larger, multi-center prospective cohorts before it can be translated into clinical practice of precision oncology.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-026-44562-w.