Search Results (4,107)

Background: The early detection of insulin resistance (IR) is critical for the prevention of type 2 diabetes and cardiometabolic diseases. The ECORE-BF index is a simple anthropometric tool for estimating body fat percentage and overweight. However, its potential utility as a predictor of IR risk has not been previously evaluated in large populations using validated IR indices. Methods: This cross-sectional study included 418,343 Spanish workers (172,282 women and 246,061 men) who underwent occupational health evaluations. The ECORE-BF index was calculated for all participants, and its association with four validated surrogate markers of IR was analyzed: the triglyceride–glucose index (TyG), TyG-BMI, METS-IR, and SPISE. Subjects were classified into normal or high-risk IR groups based on established cut-off values. We evaluated the mean ECORE-BF values across groups, the prevalence of ECORE-BF-defined obesity, and the diagnostic performance of ECORE-BF using receiver operating characteristic (ROC) curve analysis. Results: Participants with elevated IR index values had significantly higher mean ECORE-BF scores than those with normal values (p < 0.001). The prevalence of ECORE-BF-defined obesity was substantially higher in all high-risk IR groups, exceeding 99% for METS-IR and SPISE in both sexes. ROC analysis demonstrated the high diagnostic accuracy of ECORE-BF in predicting elevated IR risk, with area under the curve (AUC) values ranging from 0.698 (TyG in men) to 0.992 (METS-IR in women). Sensitivity and specificity were also high, particularly for TyG-BMI, SPISE, and METS-IR, with optimal Youden indices above 0.75. Conclusions: ECORE-BF demonstrated high accuracy as a non-invasive tool for identifying individuals at increased insulin resistance risk; however, due to the cross-sectional design, predictive value for incident disease cannot be inferred. Its simplicity, cost-effectiveness, and high diagnostic accuracy support its potential utility in large-scale screening programs for early detection of metabolic risk. Full article

Protein–DNA and protein–RNA interactions are central to gene regulation and genetic disease, yet experimental identification remains costly and complex. Machine learning (ML) offers an efficient alternative, though challenges persist in representing protein sequences due to residue variability, dimensionality issues, and the risk of losing biological context. Traditional approaches such as k-mer counting or neural network encodings provide standardized sequence representations but often demand high computational resources and may obscure functional information. To address these limitations, a novel encoding method based on interpolation of physicochemical properties (PCPs) is introduced. Discrete PCPs values are transformed into continuous functions using logarithmic enhancement, highlighting residues that contribute most to nucleic acid interactions while preserving biological relevance across variable sequence lengths. Statistical features extracted from the resulting spectra via Tsfresh are then used for binary classification of DNA- and RNA-binding proteins. Six classifiers were evaluated, and the proposed method achieved up to 99% accuracy, precision, recall, and F1 score when amino acid highlighting was applied, compared with 66% without highlighting. Benchmarking against k-mer and neural network approaches confirmed superior efficiency and reliability, underscoring the potential of this method for protein interaction prediction. Our framework may be extended to multiclass problems and applied to the study of protein variants, offering a scalable tool for broader protein interaction prediction. Full article

Aim: Nephrotic syndrome (NS) represents a complex glomerular disorder with significant clinical heterogeneity across pediatric and adult populations. Although glucocorticosteroids have constituted the mainstay of therapeutic intervention for more than six decades, primary treatment resistance manifests in approximately 20% of pediatric patients and 50% of adult cohorts. Steroid-resistant nephrotic syndrome (SRNS) is associated with substantially greater morbidity compared to steroid-sensitive nephrotic syndrome (SSNS), characterized by both iatrogenic glucocorticoid toxicity and progressive nephron loss with attendant decline in renal function. Based on this, the current study aims to develop a robust machine learning (ML) model integrated with explainable artificial intelligence (XAI) to distinguish SRNS and identify important biomarker candidate metabolites. Methods: In the study, biomarker candidate compounds obtained from proton nuclear magnetic resonance (1 H NMR) metabolomics analyses on plasma samples taken from 41 patients with NS (27 SSNS and 14 SRNS) were used. We developed ML models to predict steroid resistance in pediatric NS using metabolomic data. After preprocessing with MICE-LightGBM imputation for missing values (<30%) and standardization, the dataset was randomly split into training (80%) and testing (20%) sets, repeated 100 times for robust evaluation. Four supervised algorithms (XGBoost, LightGBM, AdaBoost, and Random Forest) were trained and evaluated using AUC, sensitivity, specificity, F1-score, accuracy, and Brier score. XAI methods including SHAP (for global feature importance and model interpretability) and LIME (for individual patient-level explanations) were applied to identify key metabolomic biomarkers and ensure clinical transparency of predictions. Results: Among four ML algorithms evaluated, Random Forest demonstrated superior performance with the highest accuracy (0.87 ± 0.12), sensitivity (0.90 ± 0.18), AUC (0.92 ± 0.09), and lowest Brier score (0.20 ± 0.03), followed by LightGBM, AdaBoost, and XGBoost. The superiority of the Random Forest model was confirmed by paired t-tests, which revealed significantly higher AUC and lower Brier scores compared to all other algorithms (p < 0.05). SHAP analysis identified key metabolomic biomarkers consistently across all models, including glucose, creatine, 1-methylhistidine, homocysteine, and acetone. Low glucose and creatine levels were positively associated with steroid resistance risk, while higher propylene glycol and carnitine concentrations increased SRNS probability. LIME analysis provided patient-specific interpretability, confirming these metabolomic patterns at individual level. The XAI approach successfully identified clinically relevant metabolomic signatures for predicting steroid resistance with high accuracy and interpretability. Conclusions: The present study successfully identified candidate metabolomic biomarkers capable of predicting SRNS prior to treatment initiation and elucidating critical molecular mechanisms underlying steroid resistance regulation. Full article

Background: Periodontitis is a multifactorial inflammatory disease with a complex interplay between microbial, environmental, and host-related factors. Among host factors, genetic susceptibility plays a significant role in influencing both disease onset and progression. Over the past two decades, a wide range of genetic tests, ranging from single-nucleotide polymorphism (SNP) analysis to genome-wide association studies (GWAS), have been explored to assess individual risk profiles and potential treatment responses. However, despite initial enthusiasm, the clinical integration of genetic testing in periodontics remains limited. This narrative review aims to critically examine the current landscape of genetic testing in periodontitis, including commercially available tests, their scientific validity, and their clinical utility. Methods: Most relevant studies which were published in recent years were identified by using the major scientific search engines, including PubMed, Scopus, and Web of Science. Articles discussing genetic susceptibility, key gene polymorphisms, and emerging technologies were included in this narrative review. Results: Polymorphisms in genes coding for IL-1, IL-6, TNF-α, and in others involved in immune modulation and bone metabolism, are associated with periodontitis. Nevertheless, there are limitations related to heterogeneity in study design, population stratification, and gene–environment interactions. Moreover, emerging technologies, including polygenic risk scoring and machine learning approaches, may enhance the predictive value of genetic tools in periodontology. Conclusions: A deeper understanding of genetic susceptibility could pave the way for precision dentistry and personalized periodontal care, but significant hurdles remain before genetic testing can become a routine component of periodontal diagnostics. Full article

Background: Surgical aortic valve replacement (SAVR) is the main treatment for severe aortic valve disease, the most common valvular heart disease worldwide. Methods: We evaluated the in-hospital mortality risk factors and predictors following on-pump SAVR. We retrospectively reviewed data from consecutive patients treated at a tertiary center from 2022 to 2024, focusing on routine hematological data and inflammatory indexes, alongside established factors. Results: Postoperative vasoactive-inotropic score (VIS) (OR1.058, CI95%: 1.007–1.112), platelet count (OR1.033, CI95%: 1.002–1.064), lymphocyte counts (OR3.532, CI95%: 1.507–8.278), and perioperative fresh frozen plasma transfusion (OR1.335, CI95%: 1.068–1.669) were independent risk factors for SAVR in-hospital mortality. VIS best predicted the endpoint (AUC 0.929, p = 0.001). Postoperative platelet count and platelet-to-lymphocytes ratio (PLR) outperformed the additive EuroSCORE in predicting the outcome, but not EuroSCORE II. Conclusions: Although EuroSCORE II remained superior to inflammatory indexes in predicting in-hospital death, the dynamic postoperative monitoring provided added value beyond static preoperative risk scores. This dynamic approach supported personalized monitoring and targeted therapeutic interventions. Postoperative VIS, platelet, lymphocyte counts, and PLR represent dynamic, low-cost predictors of in-hospital mortality after on-pump SAVR, offering a complementary value to EuroSCORE II–based models. Full article

Background/Objectives: Demand for dermatologic services exceeds specialist capacity, with average wait times of 26–50 days in the United States. Current triage methods rely on subjective judgment or disease-specific indices that do not generalize across diagnoses or translate to operational decisions. We developed and validated the Dermatologic Complexity Score (DCS), a standardized instrument to guide case prioritization across dermatology care settings and evaluate DCS as a workload-reduction filter, enabling safe delegation of approximately half of routine teledermatology cases (DCS ≤ 40) away from specialist review. Methods: We conducted a prospective validation study of the DCS using 100 consecutive teledermatology cases spanning 30 common conditions. The DCS decomposes complexity into five domains (Diagnostic, Treatment, Risk, Patient Complexity, Monitoring) summed to a 0–100 total with prespecified bands: ≤40 (low) (41–70), (moderate) (71–89), (high), ≥90 (extreme). Five board-certified dermatologists and an automated module independently scored all cases. Two primary care physicians completed all ≤40 cases to assess feasibility. Primary outcomes were interrater reliability using ICC (2,1) and agreement with automation. Secondary outcomes included time-to-decision, referral rates, and primary care feasibility. Results: Mean patient age was 46.2 years; 47% of cases scored ≤40, 33% scored 41–70, 18% scored 71–89, and 2% scored ≥90. Interrater reliability was excellent (ICC (1,2)) = 0.979; 95% CI 0.974–0.983), with near-perfect agreement between automated and mean dermatologist scores (r = 0.998). Time-to-decision increased monotonically across DCS bands from 2.11 min (≤40) to 5 (90) min (≥90) (p = 1.36 × 10⁻¹⁴). Referral rates were 0% for ≤40, 3% for 41–70, 27.8% for 71–89, and 100% for ≥90 cases. DCS strongly predicted referral decisions (AUC = 0.919). Primary care physicians successfully managed all ≤40 cases but required 6–8 additional minutes per case compared to dermatologists. Conclusions: The DCS demonstrates excellent reliability and strong construct validity, mapping systematically to clinically relevant outcomes, including decision time and referral patterns. The instrument enables standardized, reproducible triage decisions that can optimize resource allocation across teledermatology, clinic, procedural, and inpatient settings. Implementation could improve access to dermatologic care by supporting appropriate delegation of low-complexity cases to primary care while ensuring timely specialist evaluation for high-complexity conditions. Full article

Background/Objective: Biochemical recurrence (BCR) after radical prostatectomy (RP) for prostate cancer indicates disease progression. Although type 2 diabetes mellitus (T2D) shows a paradoxical association with prostate cancer risk, the prognostic role of T2D-related genetic variants remains unclear. Methods: We analyzed 113 common T2D susceptibility-related single-nucleotide polymorphisms (SNPs) in 644 Taiwanese men with localized prostate cancer (D’Amico risk classification: 12% low, 34% intermediate, and 54% high) treated with RP. Associations between SNPs and BCR were assessed using Cox regression, adjusting for key clinicopathological factors. Functional annotation was performed using HaploReg and FIVEx, while The Cancer Genome Atlas transcriptomic data were analyzed for C2 calcium-dependent domain-containing 4A (C2CD4A) expression. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were applied to explore related biological pathways. Results: C2CD4A SNP rs4502156 was independently associated with a reduced risk of BCR (hazard ratio = 0.80, p = 0.035). The protective C allele correlated with higher C2CD4A expression. Low C2CD4A expression is associated with advanced pathological stages, higher Gleason scores, and disease progression. GSEA revealed negative enrichment of mitotic and chromatid segregation pathways in high-C2CD4A-expressing tumors, with E2F targets being the most suppressed. GSVA confirmed an inverse correlation between C2CD4A expression and E2F pathway activity, with CDKN2C as a co-expressed functional gene. Conclusions: The T2D-related variant rs4502156 in C2CD4A independently predicts a lower risk of BCR, potentially via suppression of the E2F pathway, and may serve as a germline biomarker for postoperative risk stratification. Full article

Background: Cerebral vasospasm is a frequent and severe complication after aneurysmal subarachnoid hemorrhage (aSAH), often causing delayed cerebral ischemia (DCI) and poor outcomes. Despite progress in neurocritical care, early vasospasm prediction after aSAH remains challenging due to its multifactorial nature but is essential for timely intervention. Methods: We retrospectively analyzed 503 consecutive patients with spontaneous subarachnoid hemorrhage (SAH) treated between 2013 and 2018. Of these, 345 with angiographically confirmed aSAH were included in the primary analysis, and 158 SAH cases in a sensitivity analysis. We extracted demographic, clinical, and imaging parameters including age, sex, Hunt and Hess grade, Fisher scale, aneurysm and treatment features, external ventricular drainage (EVD), and central nervous system (CNS) infection. Seven supervised machine learning (ML) models, including logistic regression and gradient-boosted trees, were trained using nested cross-validation and evaluated by AUC-ROC, AUC-PR, accuracy, precision, sensitivity, specificity, and F1 score. Results: Over half of aSAH patients developed moderate to severe vasospasm. Independent predictors included younger age, higher Hunt and Hess and Fisher grades, and EVD placement (all p < 0.001). Logistic regression achieved the best discrimination (AUC-ROC 0.723), while tree-based models reached higher sensitivity (0.867) at the expense of specificity. Aneurysmal etiology further increased vasospasm risk (OR 4.72). Conclusions: Routinely available clinical and imaging parameters enable reliable ML-based vasospasm prediction after aSAH. Logistic regression provided the best balance between accuracy and interpretability, while tree-based models optimized sensitivity. This web-based, interpretable ML tool—one of the first using routine clinical data—may support the bedside prediction of vasospasm and requires prospective validation. Full article

Background: The pathological response to neoadjuvant chemotherapy (NAC) is an established predictor of long-term outcomes in breast cancer. However, conventional binary assessment based solely on pathological complete response (pCR) fails to capture prognostic heterogeneity across molecular subtypes. This study aimed to develop an interpretable deep learning model that integrates multiple clinical and pathological variables to predict both recurrence and metastasis development following NAC treatment. Methods: We conducted a retrospective analysis of 832 breast cancer patients who received NAC between 2013 and 2022. The analysis incorporated five key variables: tumor size changes, nodal status, Ki-67 index, Miller–Payne grade, and molecular subtype. A Multi-Layer Perceptron (MLP) model was implemented on the PyTorch platform and systematically benchmarked against SVM, Random Forest, and XGBoost models using five-fold cross-validation. Model performance was assessed by calculating the area under the curve (AUC), accuracy, precision, recall, and F1-score, and by analyzing confusion matrices. Results: The MLP model achieved AUC values of 0.86 (95% CI: 0.82–0.93) for HER2-positive cases, 0.82 (95% CI: 0.70–0.92) for triple-negative cases, and 0.76 (95% CI: 0.66–0.82) for HR+/HER2-negative cases. SHAP analysis identified post-NAC tumor size, Ki-67 index, and Miller–Payne grade as the most influential predictors. Notably, patients who achieved pCR still had a 12% risk of developing recurrence, highlighting the necessity for ongoing risk assessment beyond binary response evaluation. Conclusions: The proposed deep learning system provides precise and interpretable risk assessment for NAC patients, facilitating individualized treatment approaches and post-treatment monitoring plans. Full article

Invasive candidiasis (IC) is a life-threatening fungal infection predominantly affecting critically ill patients in intensive care units (ICUs). Despite advances in antifungal therapies, IC remains a diagnostic and therapeutic challenge, with a mortality rate exceeding 40%. The current reliance on blood cultures as the diagnostic gold standard is limited by low sensitivity and prolonged turnaround times, often delaying effective treatment. This often leads to the overuse of empirical antifungal therapies, increasing resistance, healthcare costs, and inconsistent outcomes. To address these issues, this paper introduces a five-step diagnostic strategy developed by an expert panel to optimise IC diagnosis and management. The strategy integrates predictive risk scores, biomarkers, and antifungal susceptibility testing to streamline diagnosis, identify high-risk patients, and promote antifungal stewardship. It also addresses barriers such as resource disparities and variability in clinical practices, offering a practical, standardised strategy for ICUs in the UK and Ireland. The clinical utility of this approach is highlighted through two patient cases. One describes the safe discontinuation of antifungal therapy after a negative (1,3)-β-D-glucan (BDG) assay ruled out IC, reducing unnecessary treatment and adverse effects. The other showcases the use of rapid in-house antifungal susceptibility testing to precisely tailor therapy for a patient with Nakaseomyces glabratus, ensuring effective treatment and preventing resistance. This pragmatic five-step guide simplifies and standardises IC diagnosis, aiming to lower mortality, optimise therapies, and promote judicious antifungal use. Full article

Background: The aim of this study is to identify people with cystic fibrosis (pwCF) at risk for future pulmonary exacerbations (PEx) based on established and unestablished markers of chronic inflammation. There is currently no universal definition of PEx in cystic fibrosis (CF), but it is commonly characterized by clinical deterioration and a drop in FEV1 ≥10% with or without elevations in systemic inflammatory markers. PEx negatively affect clinical outcomes in pwCF; therefore, predicting and preventing PEx is a crucial goal in the treatment of pwCF. Methods: We retrospectively examined pwCF ≥18 years who had ≥2 pulmonary function tests per year for a 3-year period. The first year was marked as the baseline. The follow-up period (FU) was defined as the following two-year period after baseline. PEx were defined as a need for intravenous antibiotic treatment due to clinical deterioration. Various scoring systems and ratios (neutrophil/lymphocyte (NLR), lymphocyte/monocyte (LMR), CRP, CRP/albumin, Glasgow Prognostic Score (GPS), high-sensitivity modified Glasgow Prognostic Score (hs-GPS)) were compared in pwCF with and without PEx during the FU. Logistic regression models were used to determine the best marker for predicting PEx, considering factors such as age, sex, PEx at baseline, BMI, homozygote F508del mutation, diabetes mellitus, chronic bacterial infection, and CFTR (cystic fibrosis transmembrane conductance regulator)-modulator therapy. The results are reported as odds ratios (ORs) with p-values. Results: Out of 283 pwCF, 131 were included in the study. In total, 43.5% were female, and the mean age was 34.0 years. A total of 75 pwCF (57.3%) had PEx during FU. In the multivariate analysis, the following markers at baseline were significantly associated with having a PEx during FU: CRP(log) (OR = 7.29, p = 0.01), CRP/albumin (OR = 1.08, p = 0.006), decreased LMR (OR = 0.51, p = 0.02), increased NLR (OR = 1.52, p = 0.02), and GPS of 1 vs. 0 (OR = 2.75, p = 0.04). The results indicate that the CRP/albumin ratio was the best model for predicting PEx in pwCF during the FU, outperforming other models. Conclusions: While several inflammation-based scoring systems can predict PEx in pwCF, the easily calculated CRP/albumin proved to reliably identify pwCF with an increased risk for PEx, making it a promising tool in clinical practice. Full article

Avalanche susceptibility mapping is vital for disaster prevention and infrastructure safety in cold mountain regions under climate change. Traditional machine learning (ML) approaches have demonstrated strong predictive capacity, yet their limited interpretability and difficulty in identifying threshold effects hinder their broader application in geohazard risk management. To overcome these limitations, this study develops an explainable ML framework that integrates remote sensing data, topographic and climatic variables, and SHapley Additive exPlanations for the Kanas Scenic Area transportation corridor in the Chinese Altay Mountains. The framework evaluates five classifiers: Random Forest, XGBoost, LightGBM, Soft Voting, and Stacking, and using sixteen conditioning factors that capture topography, climate, vegetation, and anthropogenic influences. Results show that LightGBM achieved the best performance, with an AUC of 0.9428, accuracy of 0.8681, F1-score of 0.8750, and Cohen’s kappa of 0.7366. To ensure transparency for risk decisions, SHAP analyses identify Terrain Ruggedness Index, wind speed, slope, aspect and NDVI as dominant drivers. The dependence plots reveal actionable thresholds and interactions, including a TRI plateau near 5–7, a slope peak between 30° and 40°, a wind effect that saturates above about 2.5 m s⁻¹, and a near-river high-risk belt within 0–2 km. The five-class map aligns with independent field observations, with more than three quarters of events falling in moderate to very high zones. By integrating explainable ML with remote sensing, this study advances avalanche risk assessment in cold region transportation corridors and strengthens the robustness of regional susceptibility mapping. Full article

Background: Myocardial scar and fibrosis predict adverse cardiac outcomes. Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is the reference standard for detection. However, it requires gadolinium-based contrast agents (GBCAs), which may be unsuitable for some patients. Cine balanced steady-state free precession (bSSFP) sequences are universally acquired in routine CMR. They may enable contrast-free scar detection via radiomics analysis. Aim: To systematically review the diagnostic accuracy of cine CMR radiomics for myocardial scar or fibrosis detection. The reference standard is visual or threshold-based LGE. Methods: This review followed PRISMA guidelines and was registered in PROSPERO (CRD420251121699). We searched MEDLINE, Embase, and Cochrane Library up to 8 August 2025. Eligible studies compared cine CMR radiomics with LGE-based assessment in patients with suspected or known scar/fibrosis. Quality was assessed using QUADAS-2 and Radiomics Quality Score (RQS). Results: Five retrospective studies (n = 1484) were included. Two focused on myocardial infarction, two on hypertrophic cardiomyopathy, and one on ischaemic versus dilated cardiomyopathy. Diagnostic performance was good to excellent (AUC 0.74–0.96). Methodological heterogeneity was substantial in reference standards, segmentation, preprocessing, feature selection, and modelling. Only one study used external validation. QUADAS-2 showed high bias risk in patient selection and index test domains. RQS scores were low (30–42%), indicating limited reproducibility and validation. Conclusions: Cine CMR radiomics shows promise as a non-contrast alternative for detecting myocardial scar and fibrosis. However, methodological standardisation, multicentre validation, and prospective studies are needed before clinical adoption. Full article

Background/Objectives: Numerous older patients undergo transcatheter valve implantation (TAVI) and frequently experience preoperative malnutrition and anemia, which markedly influence postoperative outcomes. This study investigated whether the Hemoglobin-Geriatric Nutritional Risk Index (H-GNRI) could predict major adverse cardiovascular events (MACEs) after TAVI. Methods: Patients who underwent TAVI at a single institution were classified into three groups according to their H-GNRI scores: low-risk (H-GNRI score = two), intermediate-risk (H-GNRI score = one), and high-risk (H-GNRI score = zero). The primary outcome was the occurrence of MACEs post-TAVI, and Kaplan–Meier survival and Cox proportional-hazard analyses were performed. Results: Of the 205 patients analyzed, 123, 67, and 15 were assigned H-GNRI scores of two, one, and zero. Kaplan–Meier survival analysis revealed that patients with H-GNRI scores of one and zero developed significantly more MACEs than those with a score of two (log-rank p = 0.0030; 1 vs. 2, p = 0.0032; 0 vs. 2, p = 0.0077). In the Cox proportional-hazard analysis, factors associated with MACEs included H-GNRI score (using score two as reference; score one: hazard ratio [HR] = 2.02, 95% confidence interval [CI] = 1.10–3.60, p = 0.021; score 0: HR = 2.67, 95% CI = 1.10–6.44, p = 0.028), procedure time (HR = 1.00; 95% CI = 1.00–1.01; p = 0.0093), and length of hospital stay after TAVI (HR = 1.02; 95% CI = 1.01–1.04, p = 0.0003). Conclusions: Preoperative H-GNRI scores were markedly associated with the incidence of postoperative MACEs in patients undergoing TAVI. Full article

This paper presents a mathematics-focused AI iterative prediction framework that combines Extreme Gradient Boosting (XGBoost) for nonlinear function approximation with nonlinear autoregressive model with exogenous inputs (NARXs) for time-series modeling, applied to analyzing the Remaining Useful Life (RUL) and availability of Unmanned Aerial Vehicle (UAV) Brushless DC (BLDC) motors. The framework integrates nonlinear regression, temporal recursion, and survival analysis into a unified system. The dataset includes five UAV motor types, each recorded for 10 min at 20 Hz, totaling approximately 12,000 records per motor for validation across these five motor types. Using grouped K-fold cross-validation by motor ID, the framework achieved mean absolute error (MAE) of 4.01 h and root mean square error (RMSE) of 4.51 h in RUL prediction. Feature importance and SHapley Additive exPlanation (SHAP) analysis identified temperature, vibration, and HI as key predictors, aligning with degradation mechanisms. For availability assessment, survival metrics showed strong performance, with a C-index of 1.00 indicating perfect risk ranking and a Brier score at 300 s of 0.159 reflecting good calibration. Additionally, Conformalized Quantile Regression (CQR) enhanced interval coverage under diverse operating conditions, providing mathematically guaranteed uncertainty bounds. The results demonstrate that this framework improves both accuracy and interpretability, offering a reliable and adaptable solution for UAV motor prognostics and maintenance planning. Full article