Search Results (9,044)

Renal cell carcinoma (RCC) is a biologically heterogeneous malignancy characterized by variable clinical behavior and diverse molecular phenotypes. Although immune checkpoint inhibitors and targeted therapies have transformed the treatment landscape of advanced RCC, clinically validated biomarkers capable of improving risk stratification, therapeutic-decision making and disease monitoring remain lacking. Kidney injury molecule-1 (KIM-1), also known as hepatitis A virus cellular receptor-1 (HAVCR1) or T-cell immunoglobulin and mucin domain-containing protein-1 (TIM-1), has emerged as a biologically compelling investigational biomarker e because of its close relationship to proximal tubular epithelial injury and renal carcinogenesis. KIM-1 is a transmembrane glycoprotein minimally expressed in normal kidney tissue but markedly upregulated in dedifferentiated proximal tubular epithelial cells following injury, and in clear cell RCC, where its extracellular domain can be shed into plasma and urine. Beyond its role as a marker of tubular injury, KIM-1 participates in immune regulation, phagocytosis, inflammatory signaling and tissue remodeling, supporting its potential relevance to tumor biology. Clinical studies have demonstrated associations between elevated circulating KIM-1 levels and RCC diagnosis, recurrence risk, and survival outcomes, particularly in localized and postoperative disease settings. KIM-1 has additionally been investigated as a therapeutic target through antibody–drug conjugate approaches. Despite promising translational data, important limitations yet remain. Current evidence is predominantly prognostic rather than predictive, and substantial analytical and biological challenges continue to limit implementation. Assay standardization, clinically meaningful cutoffs, specimen selection, timing of sampling, and confounding by chronic kidney disease or nonmalignant renal injury remain incompletely resolved. Furthermore, evidence supporting incremental value beyond established clinicopathologic models remains limited. This review critically evaluates the biological rationale, analytical considerations and clinical evidence supporting KIM-1 in RCC. Particular emphasis is placed on distinguishing prognostic, predictive, pharmacodynamic, and therapeutic applications, as well as defining the evidentiary gaps that must be addressed before clinical implementation. Current evidence is derived predominantly from retrospective and exploratory analyses, and important limitations remain regarding assay standardization, biological specificity, chronic kidney disease-related confounding, and prospective validation. The review concludes with a summary of the evolving landscape of KIM-1-directed biomarker strategies in RCC, which may ultimately contribute to improved biologic risk stratification and biomarker-driven clinical investigation in RCC. Full article

The plastic pieces of synthetic polymers, which were previously regarded as primary pollutants of the environment, are increasingly being discovered as internal pollutants of the human body. This review provides a comprehensive overview of the available evidence on human exposure, tissue distribution, and associated biological effects of micro- and nanoplastics. Ingesting contaminated food and water is the major exposure pathway, with inhalation and dermal contact being secondary routes. Various organ systems have been identified as containing polymer particles through the use of advanced analytical methods, including blood, liver, lungs, placenta, breast milk, and brain tissue. Experimental animal studies suggest associations with tissue injury, metabolic illness, and neurotoxicity. Polyethylene, polypropylene, polystyrene, and polyethylene terephthalate are the most frequently found polymers in human samples. New clinical findings indicate potential health implications, though current human evidence remains largely associative rather than causal: a cardiovascular study observed more than a two-fold rise in mortality among patients with polymer-containing arterial plaques, and recent evidence demonstrates over-accumulation of polymers in brain tissue, raising questions about neuroinflammatory processes. Detection technologies have advanced substantially, with deep learning-based polymer classification achieving 95–99% accuracy and ultrasensitive electrochemical and surface plasmon resonance biosensors reaching detection limits approaching 10⁻¹¹ M. Despite these advances, critical issues remain, including lack of standardized analytical procedures, absence of chronic exposure models for humans, and insufficient longitudinal epidemiological data. To address these gaps, physiologically relevant experimental systems including organoids and organ-on-chip platforms will be required, in addition to well-designed prospective cohort studies. Full article

Background: The click-evoked Auditory Brainstem Response (ABR) is the gold standard electrophysiological tool for assessing auditory pathway integrity in infants and young children. As normative data are inherently equipment-specific, the absence of pediatric reference values for the NeuroAudio system (Neurosoft, Ivanovo, Russia) represents a significant gap in clinical practice, given that existing normative datasets for this system are restricted to adult populations. Objective: To establish normative data for click ABR recorded with the NeuroAudio system in children under three years of age, stratified by age group according to auditory maturation patterns. Methods: A prospective, cross-sectional study was conducted at the Electrophysiology Laboratory of the Department of Speech Therapy, Paulista School of Medicine, Federal University of São Paulo (UNIFESP/EPM), under the approval of the Research Ethics Committee (protocol 7.939.564). A total of 203 children (121 males, 82 females; age range: 2 weeks to 36 months) with confirmed normal peripheral auditory function were included. Click stimuli (0.1 ms, rarefaction polarity) were delivered monaurally via ER-3A insert earphones at 80 dB nHL and a repetition rate of 19.3/s. Two average runs of 2000 artifact-free sweeps were recorded per ear. Absolute latencies of waves I, III, and V, interpeak intervals I–III, III–V, and I–V, and amplitudes of waves I and V were analyzed. Results: Statistical modeling supported the consolidation of 12 initial age bins into three clinically and statistically validated categories: 0–3, 4–12, and 13–36 months. Wave I latency remained stable across age groups, whereas waves III and V and all interpeak intervals showed progressive shortening with increasing age. Wave V amplitude increased progressively with age, while wave I amplitude remained unchanged. Females presented shorter latencies than males for waves III and V and for all interpeak intervals. The right ear exhibited a shorter III–V interpeak interval than the left ear, with a significant ear × age interaction indicating that this asymmetry is modulated during early maturation. Age, sex, and ear-stratified normative values (two SD and three SD reference limits) are reported. Conclusion: This study provides the first pediatric normative dataset for click-evoked ABR acquired with the NeuroAudio system in children under three years of age. The proposed three age stratifications, together with sex- and ear-specific reference values for the III–V interpeak interval, offer a clinically actionable framework for the accurate interpretation of pediatric ABR recordings and for the early identification of auditory pathway abnormalities. Full article

Human digital twins (HDTs) are patient-specific computational models that combine medical imaging, physiological measurements and predictive algorithms. They are moving from an exciting concept to a realistic clinical opportunity. The key question is no longer whether HDTs can be built. The key question is which methods are mature enough to support clinical decisions and what is still missing for routine use. This systematic review maps the methodological landscape of HDTs and highlights practical bottlenecks that limit clinical translation. A PRISMA 2020 guided search of PubMed, Scopus, IEEE Xplore, and the Cochrane Library, covering publications from 2016 to 2026, identified 151 eligible studies. Bibliometric mapping and thematic synthesis were used to characterize research clusters, computational paradigms, and collaboration patterns. Three dominant application streams were identified: cardiovascular HDTs for hemodynamic simulation and procedural planning, musculoskeletal HDTs for biomechanics-driven orthopedic innovation, and neurological HDTs integrating neuroimaging with computational neuroscience. Across domains, the strongest technical trend is the rise in hybrid pipelines that combine physics-based simulation, including finite element and computational fluid dynamics models, with machine learning for segmentation, parameter identification, reduced-order modeling, and faster inference. However, reporting of verification, validation, uncertainty quantification, and explicit context of use remains uneven and prospective clinical evidence is still limited. Overall, the literature shows rapid progress toward clinically credible HDTs, while highlighting the need for scalable computation, standardized credibility pipelines, and workflow-integrated platforms to support safe and reproducible clinical adoption. Full article

Background/Objectives: This preliminary, single-centre study evaluated infectious diseases consultation (IDC) patterns as indicators of hospital workflow and care complexity, aiming to characterise routinely available variables that may inform future organisational research and EHR-based clinical decision support development. Methods: In this retrospective study, 39,275 IDC requests from 16,430 patients were analysed using hospital information management system records. Paediatric patients and specialised immunosuppressed patient units were excluded. Request volumes, diagnostic categories, consultation purposes, and factors associated with in-hospital mortality were evaluated. Multivariable logistic regression models were constructed separately for two hospital blocks. Results: A total of 39,275 IDC records for 16,430 unique patients were reviewed. Mean consultation access time was 82.2 ± 64.3 min. Requests originated from surgical clinics (43.8%), followed by intensive care units (37.6%) and medical/internal clinics (18.6%). Pneumonia was the most common indication (30.5%), followed by unspecified infections (25.4%) and skin/soft tissue infections (17.2%). Consultation objectives included treatment, diagnostic assessment, and clinical guidance as non-mutually exclusive components. Significant block-level differences were observed in consultation timing, ICU-related consultation, diagnostic profiles, consultation purposes, and mortality. Age and ICU-related consultation were independently associated with mortality in both blocks, whereas consultation access time and COVID-19 diagnosis showed block-specific associations. Conclusions: IDC patterns may reflect not only diagnostic demand but also case severity, ICU-related care, consultation timing, and hospital location. As a preliminary single-centre study, these hypothesis-generating findings highlight the importance of integrating clinical, organisational, and contextual variables in future prospective, multi-centre studies aimed at developing EHR-based decision-support models. External validation, incorporation of comorbidity indices and microbiological data, and assessment of explainability are required before clinical implementation. Full article

Background: The management of diabetic foot disease and knee osteoarthritis (OA) with smart orthotics holds significant importance during the early stages of these conditions, given their potential consequences, including functional impairment, chronic pain, and economic burden. Real-time monitoring of plantar foot pressure enables early detection of abnormal force distribution and gait biomechanics, allowing for the redirection of forces away from affected ulcers or arthritic joints. This is the first systematic review to synthesise clinical evidence for smart orthotics technology with real-time plantar pressure sensor biofeedback across both diabetic foot ulcer prevention and knee osteoarthritis management simultaneously. A search of the PROSPERO register confirmed no existing registration covers this specific combination. Objectives: To examine the clinical evidence for the use of standard and smart orthotics in the prevention and management of diabetic foot ulcers (DFUs) and knee OA, and to evaluate their impact on plantar pressure redistribution, ulcer recurrence, pain, biomechanics, and economic burden. Eligibility criteria: Studies published in English involving human adult participants (≥18 years) with a clinical diagnosis of diabetes mellitus (at risk of DFU or with peripheral neuropathy) or knee OA, where the intervention involved any orthotic device or smart/intelligent insole with clinical outcomes reported, were included. Studies on healthy individuals only, those not reporting participant age, and non-weight-bearing protocols not differentiated from weight-bearing were excluded. Information sources: Five databases were searched: CINAHL (EBSCO Information Services, Ipswich, MA, USA), PubMed Advanced (National Library of Medicine, Bethesda, MD, USA), Wiley Online Library (John Wiley & Sons, Hoboken, NJ, USA), Cochrane Library (Cochrane Collaboration, London, UK), and Google Scholar (Google LLC, Mountain View, CA, USA). Searches were completed in May 2026. Methods: We conducted a comprehensive literature review. This review was structured and reported with reference to the PRISMA 2020 statement (Preferred Reporting Items for Systematic Reviews and Meta-Analysis; University of Ottawa, Ottawa, ON, Canada) to guide transparency of reporting. It does not constitute a full Cochrane-style systematic review; risk of bias assessment was applied to key included studies and GRADE (Grading of Recommendations Assessment, Development and Evaluation; McMaster University, Hamilton, ON, Canada) certainty ratings were applied informally and narratively rather than as formal per-outcome evidence profiles. Five databases were searched yielding 92,637 records. After removal of 398 duplicates by Rayyan, 92,239 records remained. A subsequent automated keyword-based relevance filter applied within Rayyan (Rayyan AI, Doha, Qatar), prior to human screening, excluded 84,572 records that did not contain any terms related to orthotics, diabetic foot, or knee osteoarthritis, yielding 7667 records for human title/abstract screening. A narrative synthesis approach was adopted owing to the heterogeneity of study designs and outcome measures across included studies, which precluded meta-analysis. This review was not prospectively registered. A complete list of all 78 included studies, including those not individually discussed in the results and discussion. Results: The available clinical studies report promising findings for orthotics and smart orthotics in pain reduction, ulcer prevention, and potential reduction in economic burden, though conclusions are limited by small sample sizes, heterogeneity, and predominantly open-label designs. Recent research found that orthotics can be used to alter the gait pattern that influences knee OA by reducing excessive force on the affected joint. A randomised controlled trial demonstrated an 80% relative risk reduction in DFU recurrence (RR = 0.20; 95% CI: 0.06–0.79; p = 0.022), with absolute event rates of 6.3% in the intervention group versus 30.8% in controls (ARR = 24.5%); a second trial reported a 71% reduction in ulcer incidence over 18 months; and a third randomised controlled trial demonstrated statistically significant plantar pressure reduction (p < 0.01) in patients with diabetic neuropathy. Conclusions: The available evidence suggests that orthotics may be associated with improved pressure redistribution, reduced ulcer incidence, and benefit in the management of knee OA. Although the number of studies directly comparing smart orthotics with standard orthotics remains limited, the limited comparative studies suggested that smart orthotics showed promising results in reducing ulcer incidence, providing the patient with real-time feedback to offload via their electronic devices. These findings, while preliminary, highlight the potential of smart orthotic technology as an adjunct to standard orthotic care in reducing the overall burden of diabetic foot disease and knee osteoarthritis. Limitations: The primary methodological limitation of this review is the open-label design of all included smart orthotic trials, which precludes participant blinding and introduces performance bias. However, this limitation is structural and inherent to the wearable technology field—analogous to surgical trials—and is substantially mitigated by the use of objective primary outcome measures (plantar pressure and ulcer recurrence) across the three included RCTs, the consistency of effect direction across independent RCTs conducted in different countries, and a narrative sensitivity analysis confirming robustness of findings (Risk of Bias Across Studies Section). Formal per-outcome GRADE evidence profiles were not produced; overall certainty of evidence was assessed narratively with reference to GRADE domains and is judged to be low to moderate for smart orthotics in DFU prevention and low for knee OA management, consistent with the Level 2–3 evidence base and open-label study designs. Future adequately powered, multi-site RCTs with standardised outcome reporting, minimum 24-month follow-up, and integrated health economic modelling are the highest priority to extend these preliminary findings. Registration: This review was not prospectively registered. Full article

Background/Objectives: Early recurrence after curative-intent resection is a major determinant of poor prognosis in hepatocellular carcinoma (HCC). Artificial intelligence (AI)-driven predictive models have emerged to identify patients at high risk of recurrence but remain incompletely synthesized for early recurrence specifically. This review aimed to identify and appraise AI-driven models predicting early recurrence after surgical resection. Methods: PubMed/MEDLINE, Scopus and Web of Science were searched from inception to November 2025. Eligible studies developed and evaluated AI-driven models predicting early recurrence (≤24 months) after curative-intent hepatectomy as first-line treatment for HCC. Risk of bias and applicability were assessed using PROBAST+AI, and findings were synthesized narratively due to methodological heterogeneity. The review was registered in PROSPERO. Results: Thirty-six studies involving 14,716 patients were included. Most studies originated from China (33/36, 91.7%), were single-center (27/36, 75%), and retrospective (35/36, 97.2%). Magnetic resonance imaging (MRI) was the predominant imaging modality (15/36, 41.7%), followed by computed tomography (CT) (11/36, 30.6%) and ultrasound (US)/contrast-enhanced ultrasound (CEUS) (6/36, 16.7%). Three studies developed non-imaging models, and one combined CT and MRI. In within-study comparisons, multimodal models generally showed better discrimination than unimodal approaches. Peritumoral, habitat-based, and multiphasic strategies appeared promising. However, external validation was reported in only 6/36 studies (16.7%), calibration and decision-curve analysis were inconsistently reported, and most studies had high risk of bias. Conclusions: AI-driven models show potential to predict early recurrence of HCC after curative-intent resection. Nevertheless, evidence remains limited by methodological heterogeneity and restricted geographical diversity, while clinical utility remains inconsistently evaluated, and no model has yet been generalized in clinical practice. Prospective multicenter studies with standardized outcomes, transparent reporting, and external validation are needed for clinical implementation. Full article

Background/Objectives: Vaginal microbiota is relevant to pregnancy, but evidence on neonatal anthropometry is mainly molecular and focused on pre-term birth. This study evaluated whether microscopy-based vaginal lactobacillary dominance in late pregnancy is associated with neonatal anthropometric measures after accounting for maternal and gestational determinants. Methods: This prospective observational study included 144 mother–newborn pairs recruited at a tertiary hospital in Romania between February and August 2025. Gram-stained smears were assessed for lactobacillary dominance, leukocyte density, and candidiasis; a composite vaginal indicator was derived. Outcomes were birth weight, length, and head circumference. Associations were assessed using correlation, adjusted regression, hierarchical models, and sensitivity analyses. Results: Lactobacillary dominance was not associated with birth weight or length in bivariate analyses, but correlated weakly with head circumference (ρ = 0.186, p = 0.025). In adjusted models, it was nominally associated with higher birth weight (B = 133.5 g, p = 0.043) and larger head circumference (B = 0.47 cm, p = 0.034), but not birth length; these associations did not remain significant after multiple-testing correction. Incremental explanatory contribution was modest (ΔR² = 0.022 and 0.025), and associations attenuated after socioeconomic adjustment. Leukocyte density and candidiasis were not associated with outcomes; intermediate versus balanced vaginal status was associated with lower birth weight and head circumference in exploratory analyses. Conclusions: Routine microscopy-based lactobacillary dominance showed modest nominal associations with birth weight and head circumference, limited incremental explanatory value, and no robustness after multiplicity correction. These findings suggest a context-dependent association with limited clinical relevance. Full article

Objective: To characterize the structural organization of Enhanced Recovery After Surgery (ERAS) component implementation in gynecologic oncology and determine whether ERAS elements operate as an interconnected perioperative system during early pathway integration. Methods: This study represents a secondary analysis of the prospective multicenter Enhanced Recovery in Gynecologic Oncology (ERGO) cohort, including the first 300 consecutive patients undergoing surgery for gynecologic malignancy across five tertiary institutions. Components with prevalence between 5% and 95% were included in a regularized Ising network model to estimate conditional dependencies between pathway elements. Node-level centrality metrics and global network characteristics were calculated to identify structurally influential ERAS components and to describe the overall implementation architecture. Results: Thirteen central ERAS components met the predefined prevalence criterion (5–95%) and were included in the conditional dependency network. The estimated network demonstrated substantial inter-component connectivity, indicating that ERAS practices were frequently implemented in coordinated patterns rather than as isolated interventions. Centrality analysis identified postoperative laxatives or chewing gum, tranexamic acid administration, perioperative intravenous fluid management, and avoidance of drain placement as highly connected elements within the network. Early nutritional advancement and postoperative bowel stimulation measures also demonstrated relatively central positions within the recovery-related component cluster. Community detection analysis revealed distinct modules of co-adopted ERAS practices spanning multiple perioperative phases. Conclusions: ERAS implementation in gynecologic oncology appears to follow a structured architecture characterized by interconnected perioperative practices rather than independent protocol elements. Understanding these implementation structures may help guide targeted quality-improvement strategies aimed at optimizing ERAS integration in routine clinical practice. Full article

Background: Patients with mild traumatic brain injury (mTBI) have a small but clinically relevant risk of intracranial injury (ICI), requiring timely detection. Computed tomography (CT) remains the diagnostic gold standard but is costly and exposes patients to ionising radiation. Combining blood-based biomarkers, glial fibrillary acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), with clinical decision rules may allow safe exclusion of ICI without CT, reducing unnecessary imaging, radiation exposure, and resource use. Methods: A systematic review of clinical and economic studies in patients with mTBI was registered in PROSPERO (CRD420251051158). Searches were conducted in January 2025 and updated in May 2025 in MEDLINE, Embase, and the Cochrane Library. The aim was to assess the diagnostic accuracy and economic value of the combination of GFAP and UCH-L1 compared with CT scanning to rule out ICI in both adults and children with mTBI. Where available, studies directly comparing GFAP and UCH-L1 with S100β were also analysed descriptively. The quality of the clinical evidence was assessed with QUADAS-2 and GRADE. Meta-analyses used a bivariate random-effects model, with heterogeneity and sensitivity analyses explored. Results: Overall, 21 studies were considered in our review. Moderate- to high-quality evidence indicates that GFAP and UCH-L1, when used together with clinical assessment, have very high sensitivity and can reliably rule out ICI in adults with mTBI presenting within 12 h to the emergency department. Evidence for paediatric populations shows promise but remains very limited. Specificity is low, particularly in older adults, which limits the ability to reduce CT use in this high-risk group. Research on age-adjusted cut-offs is ongoing and may help to reduce the proportion of false positive tests without compromising sensitivity. Few studies directly compared GFAP and UCH-L1 with S100β, with slightly higher to equivalent sensitivity for GFAP and UCH-L1. Economic evaluations suggest possible cost savings and reduced CT utilisation, but these analyses rely on assumptions unsupported by robust data and are highly context-dependent. There is a lack of clarity in the included studies regarding whether existing clinical head rules were used to define the study populations (i.e., to determine which patients would be recommended for CT scanning) and, if so, which specific rules were applied. Conclusions: Evidence shows that GFAP and UCH-L1 can safely exclude ICI in adults with mTBI in whom a CT scan would otherwise be considered based on clinical assessment or decision rules. Nevertheless, real-world evidence and cost-effectiveness data are scarce. Further prospective studies, including paediatric and elderly populations, and integration with clinical decision rules will be informative to ensure optimal use in clinical practice. Full article

Background/Objectives: We investigated the association of serum ferritin and procalcitonin (PCT) with Sepsis-related Organ Failure Assessment (SOFA)-2 score-based organ dysfunction severity in intensive care patients with COVID-19-associated sepsis. Methods: Patients were stratified by day 5 ferritin (ng/mL) and PCT (μg/L) levels; associations were analysed across severity groups defined by an SOFA-2 score of <5 (mild) or ≥5 (severe). Results: Day 5 PCT did not predict the SOFA-2 score (p > 0.05). The optimal day 5 ferritin cut-off was >1191 ng/mL (35.78% sensitivity, 82.38% specificity; area under the curve (AUC) = 0.608). Day 5 ferritin was associated with SOFA-2 severity in the univariable analysis but did not remain an independent correlate after adjustment for C-reactive protein (CRP) and lactate dehydrogenase (LDH); in a mortality model, neither ferritin nor PCT independently predicted intensive care unit (ICU) death. PCT provided no predictive value beyond existing inflammatory markers, consistent with its suppression during viral infections. Conclusions: Day 5 ferritin reflects, rather than independently predicts, organ dysfunction severity and may complement, rather than replace, established multi-parameter scoring. Relative to the independent determinants of severity and mortality (PaO₂/FiO₂ ratio, LDH, CRP, and age), day 5 ferritin is a specific, rule-in adjunctive marker of concurrent organ dysfunction rather than a standalone prognostic tool. Whether these associations extend to non-COVID sepsis populations requires prospective study. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor control, balance, and gait impairments that significantly elevate fall risk. Traditional gait analysis focuses on spatiotemporal parameters, while gait variability, asymmetry, and balance measures offer more sensitive indicators of PD-related motor deficits. Machine learning studies using wearable gait data frequently report high classification accuracy but lack biomechanical interpretability and methodological rigor. Using the PhysioNet Gait in Parkinson’s Disease database, 93 individuals with PD and 72 healthy controls were analyzed during level-ground walking. Key biomechanical differences were identified: stride time coefficient of variation was significantly higher in PD bilaterally (left p = 0.001; right p = 0.003); swing-phase time was significantly reduced in both limbs (left p = 0.003; right p = 0.001); anterior–posterior center of pressure (COP) variability was significantly lower in PD for both limbs (p < 0.001); and COP path symmetry index was the most prominent asymmetry marker, significantly elevated in PD relative to controls (p = 0.003). A machine-learning analysis pipeline identified HistGradientBoosting as the best-performing classifier (AUC = 0.992; accuracy = 97.6%), but leave-one-study-out evaluation exposed substantial cross-protocol heterogeneity (AUC: 0.500–1.000), indicating that the model relied partly on dataset-specific patterns and may not generalize to independent acquisition protocols. Shapley Additive Explanations (SHAP) analysis showed classification was driven by a multimodal combination of clinical severity measures and biomechanical gait features rather than wearable metrics alone. A pre-specified gait-only sensitivity analysis that excluded clinical severity variables (UPDRS, UPDRSM, Hoehn and Yahr) confirmed that biomechanical features alone retained moderate, but substantially reduced, discriminative ability (gait-only holdout AUC = 0.844), supporting the interpretation that the headline performance reflects multimodal clinical separation rather than a stand-alone wearable-gait biomarker. These findings indicate that Parkinsonian gait impairment is characterized by timing instability and constrained forward COP progression. The combination of biomechanical analysis with interpretable predictive modeling represents a structured analysis pipeline for gait-based PD assessment; however, external validation in independent cohorts and prospective testing across acquisition protocols are required before such a pipeline can be deployed as a clinically generalizable digital biomarker. Full article

Background: Spinal cord stimulation (SCS) is an established intervention for refractory chronic neuropathic pain, but response to trial stimulation and long-term benefit remain heterogeneous. Clinicians need practical tools to document patient-selection domains discussed in the neuromodulation literature without overstating the precision of currently available evidence. Methods: We conducted a narrative synthesis of randomized trials, cohort studies, registry analyses, systematic reviews, and consensus recommendations addressing SCS outcomes and candidate selection. The objective was not to derive or validate a multivariable prediction model, but to construct a transparent, bedside-oriented framework organizing clinically accessible domains relevant to SCS trial candidacy. Results: Six domains were incorporated into the proposed SCS Trial Success Score (STSS): primary indication, psychological status, smoking status, opioid burden, body mass index, and pain duration. The resulting 0 to 12 point score is presented as an evidence-informed clinical profile rather than a validated prognostic instrument. Three descriptive categories are proposed: more favorable profile, optimization-sensitive profile, and less favorable profile. These categories are intended to guide documentation, shared decision-making, and optimization of modifiable factors, not to determine eligibility automatically. Conclusions: Pending prospective validation, checklist-mode use is the preferred interim application of the STSS. The framework may support structured pre-trial assessment, identification of modifiable factors, and shared decision-making. It should not be used as a standalone numerical decision rule, to deny access to neuromodulation, or to replace multidisciplinary judgment. Prospective derivation, calibration, external validation, and decision-curve analysis are required before the STSS can be considered a clinical prediction rule. Full article

Background and Objectives: Angiographic no-reflow (NRF) after percutaneous coronary intervention (PCI) reflects impaired microvascular reperfusion despite successful treatment of the epicardial culprit lesion. Brain-derived neurotrophic factor (BDNF) is a neurotrophin involved in endothelial signaling, platelet biology, inflammation, and angiogenesis. Its relationship with NRF in non-ST-segment elevation myocardial infarction (NSTEMI) remains insufficiently characterized. Materials and Methods: This single-center prospective observational cohort study included 700 consecutive NSTEMI patients undergoing culprit-lesion PCI. Admission serum total BDNF was measured before PCI using a standardized enzyme-linked immunosorbent assay protocol. Angiographic NRF was defined as final thrombolysis in myocardial infarction (TIMI) flow <3 and/or TIMI 3 flow with myocardial blush grade (MBG) 0–1 in the absence of residual stenosis, dissection, severe spasm, or other mechanical obstruction. Four sequential logistic regression models were used to evaluate the stability of the association between BDNF and NRF: Model 1 adjusted for clinical variables; Model 2 further adjusted for laboratory and inflammatory variables; Model 3 further adjusted for cardiac injury and functional variables; and Model 4 further adjusted for angiographic and procedural variables. Discrimination, calibration, reclassification, decision-curve analysis, and internal validation were assessed. Results: NRF occurred in 114 of 700 patients (16.3%). Serum total BDNF was higher in the NRF group than in the reflow group [555 (465–688) vs. 386 (292–496) pg/mL, p < 0.001]. BDNF remained independently associated with NRF across sequential models: Model 1 OR 1.67 per 100 pg/mL (95% CI 1.43–1.96), Model 2 OR 1.49 (95% CI 1.24–1.79), Model 3 OR 1.41 (95% CI 1.16–1.72), and Model 4 OR 1.31 (95% CI 1.07–1.60). The BDNF-only AUC was 0.787, while the final BDNF-enriched Model 4 reached an AUC of 0.866. The optimism-corrected bootstrap AUC was 0.852 and the 10-fold cross-validated AUC was 0.845. Conclusions: Higher admission serum total BDNF was independently associated with angiographic NRF in NSTEMI patients undergoing PCI and improved risk discrimination when added to clinical, biochemical, cardiac, and angiographic predictors. These findings suggest that serum total BDNF may reflect a context-dependent biomarker signal related to acute thrombo-inflammatory, platelet-associated, and microvascular injury pathways; however, the observed incremental value was modest and requires external validation. Full article

Calcrete-type uranium prospectivity prediction is challenged by the strong heterogeneity of multi-source geoscientific raster datasets, weak anomaly responses, and the lack of explicit heterogeneous information organization in conventional deep learning models. In this study, the Yilgarn Craton of Western Australia was selected as the study area, and a geology-guided fixed-group fusion ResUNet model (GGF-ResUNet) was developed based on 12-channel multi-source geoscientific raster datasets. At the input stage, the evidence layers were divided into four fixed geoscientific proxy groups according to their data modality and geological interpretation, namely gravity, aeromagnetic, radiometric, and geochemical groups, and intra-group channel weighting together with inter-group gating was introduced to enhance the hierarchical representation and adaptive fusion of heterogeneous information. Ablation results showed that GGF-ResUNet achieved better performance than the baseline ResUNet, with AUC increasing from 0.9340 to 0.9740 and F1-score improving from 0.7264 to 0.8356. Further comparative experiments with Attention U-Net, U-Net, SegNet, and FCN showed that GGF-ResUNet achieved comparatively better quantitative performance and more spatially coherent prediction results under the current experimental setting. Without substantially increasing model complexity, the proposed method improves the representation and integration of heterogeneous geoscientific information and provides a feasible technical pathway for calcrete-type uranium prospectivity prediction under weak-anomaly conditions. Full article