Search Results (13,813)

A sensitive and reliable analytical method based on ultra-high performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) was developed and validated for the simultaneous determination of bifenazate, cyflumetofen, etoxazole, and abamectin B_1a in citrus leaves, whole fruit, peel, and pulp. The method exhibited good linearity (0.0001–0.1 mg/L, R² > 0.999), a limit of quantification (LOQ) of 0.001 mg/kg, mean recoveries of 77.3–110.5%, and relative standard deviations of 3.1–19.8%. This method was applied to compare the dissipation dynamics and dietary risks of the four acaricides following knapsack spraying versus unmanned aerial vehicle (UAV) spraying. Compared to knapsack application, UAV spraying resulted in 2.2- to 4.1-fold higher initial deposits on citrus leaves and shorter dissipation half-lives. After 21 days, all terminal residues were below the maximum residue limits (MRLs) established by China, the Codex Alimentarius Commission, the United States, Australia, Korea, the European Union, and Japan. Chronic dietary risk assessment revealed risk quotients below 1% for cyflumetofen and etoxazole, and approximately 47% for bifenazate and 93% for abamectin B_1a. Although all values were below the acceptable threshold of 100%, the risk for abamectin B_1a approached this limit, indicating that its cumulative dietary risk should not be overlooked. This study provides scientific evidence for residue monitoring of acaricides in citrus and for the safety evaluation of UAV-based pesticide application. Full article

Background/Objectives: Celiac disease (CD) is common, but its cancer-risk profile remains incompletely defined. Estimates vary because of referral patterns, diagnostic era, outcome definitions, and surveillance around diagnosis. We evaluated cancer-category-specific associations in a matched cohort of clinically recognized CD. Methods: We used longitudinal electronic health record (EHR) data from Clalit Health Services for a propensity-matched cohort. Adults with EHR-coded CD were matched to controls on demographic, socioeconomic, comorbidity, and inflammatory variables. Pre-index invasive malignancies and non-invasive neoplasms were excluded. Dated EHR-coded invasive oncology outcomes were analyzed using Cox models. A restricted dated-event cohort, lag analyses, competing-risk modeling, hemoglobin adjustment, and age-at-index strata assessed robustness. Results: The primary matched cohort included 8143 individuals: 1006 with CD and 7137 controls, contributing 49,330.5 person-years. CD was associated with increased hazard of an EHR-coded invasive oncology outcome (hazard ratio [HR] 1.61, 95% confidence interval [CI] 1.47–1.77; $p<0.001$). Strongest signals were hematological malignancy codes (HR 1.99), lymphoma codes (HR 1.90), and gastrointestinal (GI) cancer codes (HR 2.71). Associations persisted after one-year and two-year lags. In the dated-event sensitivity cohort (161 CD; 1610 controls), CD remained associated with invasive cancer (HR 1.68, 95% CI 1.31–2.14), with the strongest signals for lymphoma (HR 2.81) and GI cancer (HR 2.25). The association was essentially unchanged under competing-risk modeling (Fine–Gray subdistribution HR 1.69) and after hemoglobin adjustment (HR 1.61), and was present in both age strata. Neither breast nor lung cancer was associated. Lymphoma codes included peripheral T-cell lymphomas recorded at intra-abdominal and extranodal sites, the pattern most consistent with enteropathy-associated T-cell lymphoma (EATL). Conclusions: In clinically recognized CD, cancer hazard was elevated and category-specific, concentrated in hematological, lymphoid, and GI codes with a gut-oriented T-cell lymphoma signal. The findings support targeted clinical vigilance, not expanded screening, and describe relative associations that require registry-linked confirmation. Full article

Background: Rejection remains a major barrier to long-term kidney allograft survival, and pre-transplant risk stratification remains incomplete. This study evaluated whether pre-transplant serum Fourier-transform infrared (FTIR) spectra, analyzed using machine learning methods, could identify kidney transplant recipients at increased risk of subsequent biopsy-proven rejection. Methods: In this retrospective single-center study, 80 pre-transplant serum samples collected on the day of transplantation were initially evaluated; after spectral quality control, 79 samples were retained for analysis. FTIR spectra were acquired in transmission mode and analyzed in the 600–1900 cm⁻¹ and 2800–3400 cm⁻¹ regions. Multiple preprocessing strategies were assessed, including Rubber Band baseline correction, vector normalization, and first- and second-derivative transformation, with and without normalization. Naïve Bayes classifiers with Leave-One-Out Cross-Validation and Fast Correlation-Based Filter feature selection were applied. Results: Exploratory analysis showed broad overlap between groups, indicating a subtle multivariate spectral signal. In the initial exploratory workflow, classifier performance depended strongly on preprocessing and feature selection. Because non-nested feature selection may produce optimistic estimates, the main supervised analysis was repeated using FCBF nested within each LOOCV training fold. The best-performing nested model was obtained using second derivative transformation followed by normalization in the combined 600–1900 and 2800–3400 cm⁻¹ regions, achieving an AUC of 0.837, accuracy of 0.747, sensitivity of 0.675, specificity of 0.821, balanced accuracy of 0.748, and F1-score of 0.730. Permutation testing with 1000 label-randomized repetitions supported performance above chance expectation, with no permuted model reaching the observed AUC (empirical p = 0.000999). Conclusions: Pre-transplant serum FTIR spectroscopy combined with leakage-aware nested machine learning analysis identified an internally validated spectral signal associated with subsequent biopsy-proven rejection. These findings support FTIR as a promising complementary and hypothesis-generating approach for pre-transplant biochemical risk phenotyping, requiring external multicenter validation before clinical application. Full article

Background/Objectives: Depression is a heterogeneous disorder shaped by both inherited liability and environmental exposures. Physical activity is a scalable, modifiable behavior consistently linked to lower depressive symptoms and reduced incident depression, but interpretation is complicated by measurement error, genetic confounding, and bidirectional pathways in which depression can also reduce activity. This systematic review synthesizes evidence on gene–physical activity interplay in depression across four complementary frameworks: (i) candidate–gene interaction studies, (ii) genome-wide susceptibility indexed by depression polygenic risk scores (PRS), (iii) lifestyle-context and activity-architecture analyses (e.g., timing and accumulation patterns), and (iv) Mendelian randomization (MR) studies testing bidirectional causal effects between activity-related traits and depression. Methods: A PRISMA-aligned search and narrative synthesis were conducted due to substantial heterogeneity in populations, exposure measurement (self-report vs. accelerometer), genetic approaches, and depression phenotypes. Twenty-seven studies met inclusion criteria. Results: Across designs, the most consistent pattern was that higher physical activity (or lower inactivity) tracked with lower depression risk or symptom burden even when genome-wide genetic susceptibility was modeled, supporting largely additive contributions of PRS and activity rather than strong, generalizable PRS × activity interactions. MR evidence most consistently supported a protective effect of physical activity on depression when activity was indexed by accelerometer-derived phenotypes, whereas self-reported activity instruments yielded weaker or more heterogeneous findings. Bidirectional genetic evidence also indicated that depression liability can causally suppress physical activity, consistent with a feedback loop relevant for prevention and intervention. Candidate-gene moderation effects were mixed and typically emerged only in specific contexts (e.g., stress history, developmental stage, sex, or treatment setting), underscoring limited replicability and sensitivity to how activity is operationalized. Conclusions: Overall, the literature supports physical activity as broadly protective across levels of genetic risk, while emphasizing that robust inference depends on objective exposure measurement, careful handling of confounding and reverse causation, and improved generalizability beyond predominantly European-ancestry genetic resources. Full article

Psychosocial factors such as diabetes distress, depression, anxiety, social support, stigma, coping styles, and quality of life play a critical role in shaping mental health outcomes among males living with type 2 diabetes. Despite their importance, systematic evidence on how these factors influence mental health remains limited. Objective: This protocol aims to synthesize existing evidence on psychosocial factors associated with mental health outcomes among males living with type 2 diabetes. Eligibility Criteria: Studies will be included if they focus on males aged 18 years and above diagnosed with type 2 diabetes and examine psychosocial factors in relation to mental health outcomes. Qualitative, quantitative, and mixed-methods designs will be considered. Exclusion criteria include studies focused on females, males under 18, or those with type 1 diabetes; studies exclusively evaluating interventions (e.g., CBT trials, self-management programs); non-English publications; and studies published before 2015. Confounders such as co-morbidities and lifestyle factors will be included if reported alongside psychosocial exposures, but studies focusing solely on these without mental health outcomes will be excluded. Information Sources: The search strategy will be guided by the PICO framework, and such searches will be conducted in Scopus, MEDLINE (PubMed), CINAHL, and Web of Science using Medical Subject Headings (MeSH) for articles published in English between January 2016 and December 2026. Risk of Bias: Two independent reviewers will screen studies, with disagreements resolved by a third reviewer. Risk of bias will be assessed using the JBI Critical Appraisal Checklist. Data Synthesis: Eligible studies will undergo narrative thematic synthesis. Confidence in findings will be evaluated using the JBI ConQual approach. Ethics and Dissemination: Ethical approval is not required. This protocol is registered with PROSPERO (CRD420261299482). Results will be disseminated through peer-reviewed journals and conferences. Conclusion: This protocol outlines a transparent plan to review psychosocial factors influencing mental health in men with type 2 diabetes, guiding gender-sensitive strategies that integrate mental health into diabetes care. Full article

Background/Objectives: Chronic low-grade inflammation and lipid metabolism play an important role in the development of obesity and type 2 diabetes mellitus (T2DM). The ratio of high-sensitivity C-reactive protein to high-density lipoprotein cholesterol (hs-CRP/HDL-C) integrates these pathways and it has been proposed as a novel biomarker of metabolic risk. However, evidence regarding its association with obesity and T2DM remains limited. Methods: This retrospective cross-sectional study included data from 413 adults, comprising 251 individuals with T2DM and 162 individuals without T2DM. Obesity status was assessed separately according to BMI categories. The hs-CRP/HDL-C ratio was divided into quartiles, with the lowest quartile used as the reference category. Its associations with obesity and T2DM were evaluated using multivariable logistic regression and restricted cubic spline analysis. Subgroup analyses were conducted to evaluate the consistency of these associations across different population subgroups. Results: A significantly positive association was noted between the hs-CRP/HDL-C ratio and the presence of T2DM. Compared with individuals in the lowest quartile, those in the higher quartiles of the hs-CRP/HDL-C ratio had significantly higher odds of T2DM. In the fully adjusted model, each unit increase in the hs-CRP/HDL-C ratio was associated with higher odds of T2DM (OR = 1.59, 95% CI: 1.14–2.20, p = 0.006), whereas the association with obesity was attenuated and lost statistical significance after full adjustment. RCS analysis confirmed a significant overall association between the hs-CRP/HDL-C ratio and T2DM risk (p-overall = 0.034). Although the hs-CRP/HDL-C ratio was associated with obesity in the crude model (OR = 1.40, 95% CI: 1.05–1.70, p = 0.019), the association was not significant after adjustment (OR = 1.18, 95% CI: 0.90–1.56, p = 0.229). Conclusions: In our study, an elevated hs-CRP/HDL-C ratio was significantly associated with the presence of T2DM and may represent a marker associated with metabolic inflammation, particularly in individuals with glycometabolic disorders. Full article

This study addresses maritime traffic risks in the Labuan Bajo–Komodo marine tourism corridor, a spatially constrained archipelagic environment characterized by mixed vessel traffic, intensive tourism activity, and high ecological sensitivity. An integrated decision-support framework was developed by combining the Analytic Network Process (ANP) with stakeholder-supported grid-based spatial risk analysis. Expert pairwise comparisons from eight respondents were used to evaluate eight interdependent criteria: Natural Conditions, Navigational Channel, Vessel Factors, Maritime Traffic Conditions, Port Control, Authority/Stakeholders, Tourism, and Environmental Impact. The ANP calculation was conducted using geometric mean group aggregation, consistency ratio assessment, and targeted follow-up clarification for matrices requiring refinement. The final ANP results show that Port Control received the highest priority weight (0.172), followed by Natural Conditions (0.148), Maritime Traffic Conditions (0.144), Environmental Impact (0.135), Vessel Factors (0.121), Navigational Channel (0.120), Authority/Stakeholders (0.104), and Tourism (0.0566). At the global subcriteria level, communication effectiveness, channel complexity, environmental compliance, local traffic density, and seasonal traffic variation emerged as the dominant contributors to risk. A stakeholder-supported partial spatial risk index (SRI) was then calculated for 21 grid cells using spatially mappable ANP criteria. The highest-risk cells were grids 3, 5, 6, 8, 9, 10, and 14, while sensitivity analysis confirmed that grids 3, 5, 6, 9, 10, and 14 remained high risk across all tested spatial-weight scenarios. The findings indicate that maritime traffic risk in Komodo National Park is not driven by environmental exposure alone, but by the interaction of traffic control capacity, natural hazards, traffic concentration, environmental sensitivity, and institutional coordination. The proposed framework supports spatially informed traffic management, environmental compliance, and emergency preparedness planning in marine protected tourism corridors. Full article

Background: Burkholderia pseudomallei, the causative agent of melioidosis, infects diverse animal species and reflects environmental contamination. However, the global seroprevalence of B. pseudomallei in animals remains incompletely characterized. Methods: A systematic review and meta-analysis were conducted according to PRISMA guidelines and registered in PROSPERO (CRD420261306404). PubMed, Embase, and Scopus were searched for observational studies reporting seroprevalence of B. pseudomallei in animals. Random-effects meta-analysis was performed to estimate the pooled prevalence with 95% confidence intervals (CIs). Subgroup analyses were conducted by animal group, geographic region, diagnostic method, and indirect hemagglutination assay (IHA) cut-off value. Risk of bias was assessed using the Joanna Briggs Institute checklist. Results: Twenty studies involving 78,914 animals were included. The pooled seroprevalence of B. pseudomallei was 11% (95% CI: 6–19%), with substantial heterogeneity (I² = 98.1%, p < 0.0001). Wildlife showed the highest prevalence (16%; 95% CI: 10–25%), followed by livestock (11%; 95% CI: 6–19%). Significant geographic variation was observed (p < 0.0001), with higher prevalence reported in North America (18%) and Southeast Asia (10%). Seroprevalence estimates varied according to diagnostic method and IHA cut-off values. Sensitivity analyses yielded similar pooled prevalence estimates after exclusion of small studies, supporting the stability of the overall findings despite persistent heterogeneity. Conclusions: Exposure to B. pseudomallei is widespread among animal populations and influenced by geographic and methodological factors. Standardized diagnostic approaches and expanded animal surveillance are needed to improve understanding of melioidosis epidemiology within a One Health framework. Full article

Pollinators play a critical role in agricultural productivity and the maintenance of flowering plant diversity, yet their health is increasingly threatened by multiple environmental stressors. While research has traditionally focused on pathogens, pesticides, habitat loss, and nutritional limitation, fungal secondary metabolites, mycotoxins, remain an underappreciated risk factor. This review synthesizes current knowledge on the presence, exposure pathways, and biological impacts of key mycotoxins, including aflatoxin B1, ochratoxin A, deoxynivalenol, zearalenone, and T-2 toxin, in bee-collected pollen and bee bread. We discuss how contaminated food matrices act as reservoirs of chronic exposure, linking forager activity, nurse bee physiology, brood development, and colony-level outcomes. Evidence from laboratory studies highlights sublethal effects on survival, hypopharyngeal gland development, immunity, and gut microbiota, with potential interactions with pathogens, nutritional stress, pesticides, and climate change. Furthermore, we extend these insights to wild pollinators, emphasizing differences in colony size, diet breadth, and detoxification capacity. Analytical methods for detecting mycotoxins, including HPLC, LC-MS/MS, and ELISA, are evaluated in terms of sensitivity, specificity, and relevance to field exposure. By integrating environmental concentrations with laboratory toxicity thresholds, this review identifies critical knowledge gaps and proposes a mechanistic framework linking mycotoxin exposure to colony-level risk. The findings underscore the need for targeted monitoring, improved risk assessment, and multi-stressor evaluation to safeguard both managed and wild pollinator populations. Full article

Despite the success of antiretroviral therapy, people living with HIV remain at heightened risk of multimorbidity spanning cardiovascular, renal, hepatic, oncologic and neuropsychiatric domains. We investigate whether routinely collected electronic health record data (30 laboratory variables plus seven demographic/social descriptors) can support early, multi-label classification of recorded comorbidities in a real-world cohort of 2200 HIV-positive patients receiving continuous care at a major London hospital. We benchmark classical machine and deep learning models under two settings: a demographic-aware configuration that includes sensitive attributes (age, gender, race and continent of birth) and a demographic-unaware configuration that excludes them. XGBoost yields the best macro-$F_1$ performance, and demographic-aware variants consistently outperform their unaware counterparts. Permutation feature importance revealed physiologically coherent drivers (e.g., creatinine/eGFR for renal and cardiometabolic labels, hemoglobin for hematologic labels, albumin for respiratory labels) and suggested that the relative contribution of demographic variables varied across comorbidity categories. These findings indicate that (i) routinely collected EHR data contain informative patterns associated with the multi-label comorbidity profiles of people living with HIV and (ii) carefully governed use of demographic context can improve accuracy while motivating transparent consideration of fairness and bias. Full article

Background: Healthcare-associated infections (HAIs) and antimicrobial resistance are major burdens in tertiary care hospitals. Repeated point prevalence surveys (PPSs) offer a pragmatic approach to monitor temporal changes and guide infection prevention. Objectives: Characterize healthcare-associated infections (HAI) prevalence trends, microbiological profiles, antimicrobial resistance (AMR) patterns, and risk factors to refine prevention strategies and hospital policy. Methods: Four annual cross-sectional PPSs were conducted between 2022 and 2025 using the standardized ECDC protocol. Data from all eligible inpatients present at 08:00 on survey days were collected through systematic medical record review. Multivariable logistic regression was used to identify factors independently associated with HAI, with additional sensitivity analyses evaluating invasive device burden and hospital ward type. Results: Across the surveys, 3314 patients were included. Overall HAI prevalence was 11.3%. Infections were most frequent in intensive care units (31.2%), followed by medical (14.6%) and surgical (14.2%) wards. Bloodstream infections (25.7%) and lower respiratory tract infections (19.8%) were the most common. Multivariable analysis identified invasive device exposure as the strongest predictor, with central venous and urinary catheters showing robust independent associations and a clear dose–response relationship according to the number of devices. Pathogens were predominantly Gram-positive cocci (40.5%) and Enterobacterales (30.8%), with Klebsiella pneumoniae being the most frequent isolate (13.0%). Notably, 57.6% of K. pneumoniae isolates were resistant to third-generation cephalosporins. All tested Acinetobacter baumannii isolates were resistant to carbapenems. Conclusions: This repeated PPS reveals a persistently high HAI burden, associated with invasive device exposure and resistant pathogens. Because of the repeated cross-sectional design, causal inference cannot be established. Hospital-wide device stewardship and integrated surveillance are essential for guiding targeted prevention measures, refining antimicrobial policies, and adapting local responses to evolving resistance profiles. Full article

The risk of exposure to mycotoxins in livestock farming is still poorly characterized, particularly in Italy where human biomonitoring data are scarce. Livestock farms represent a high-risk setting due to frequent handling of contaminated feed and dust-generating activities. This pilot study applied a human biomonitoring approach to assess internal exposure to multiple mycotoxins among pig farmers in Southern Italy. Urinary biomarkers of aflatoxin B1 (AFB1), aflatoxin M1 (AFM1), ochratoxin A (OTA), and fumonisin B1 (FB1), together with oxidative stress biomarkers (8-oxo-7,8-dihydroguanine (8-oxoGua), 8-oxo-7,8-dihydro-2′-deoxyguanosine (8-oxodGuo), 8-oxo-7,8-dihydroguanosine (8-oxoGuo), 3-nitrotyrosine (3-NO₂Tyr), and 5-methylcytidine (5-MeCyt)), were measured in urine samples from 35 workers and 30 non-exposed controls. A sensitive and validated HPLC–MS/MS multi-mycotoxin method was developed and applied. Biomonitoring results were also discussed in relation to previous environmental monitoring. AFM1 emerged as the most frequently detected biomarker in the exposed group, with concentrations above the limit of detection (LOD) in 22.8% of samples; 11.4% exceeded the limit of quantification (LOQ). In contrast, only 10% of the control samples had values above the LOD and none exceeded the LOQ, suggesting a possible contribution linked to occupational tasks. This study provides original biomonitoring evidence of low-dose, mixed mycotoxin exposure among Italian swine farmers and highlights the value of integrating environmental and biological monitoring to improve occupational exposure assessment in livestock production systems. Full article

Artificial intelligence is reshaping employment in smart cities, yet centralized hiring platforms can deepen exclusion for persons with disabilities through privacy risk, biased models, weak multilingual support, and limited accommodation awareness. Because disability-related records are highly sensitive, no single institution holds enough representative data to train fair models, and centralizing such data is rarely permissible across borders. We propose FedAgent-Chain, a framework that integrates federated learning, blockchain-based auditability, multilingual processing, rule-based agentic services, and human-in-the-loop governance, extended with an education-to-employment module that builds individualized, accessible job-readiness pathways. Institutions across Saudi Arabia, the United States, China, and Europe train shared models without exchanging raw data. In a prototype evaluation on synthetic records over five seeds, the framework reached a mean F1 of 0.7207 (95% CI: [0.6506, 0.7909]), comparable to a centralized logistic-regression baseline while preserving data locality, with a formal $(\epsilon =3.2,\delta ={10}^{-5})$ differential-privacy guarantee after 20 rounds. Multi-dimensional fairness regularization lowered disability-category and work-mode disparity by 32.3% and 40.3% relative to local-only training. We report the fairness behavior transparently, including a case where the penalty does not outperform standard FedAvg on disability-category disparity, and we position cross-institutional integration with accountable governance, rather than raw metric superiority, as the central contribution. Full article

The Yasuní National Park in Ecuador’s Amazon, one of Earth’s most biodiverse regions, faces unprecedented threats from oil extraction and increasing risks to Kichwa communities. This paper provides a baseline analysis of off-the-grid hydrocarbon extraction affecting ecosystems and communities living within Oil Blocks 12 and 43. Our aim is to integrate analysis of per- and polyfluoroalkyl substances (PFAS) and total petroleum hydrocarbons (TPH) to better understand the impacts of oil-extractive contamination at off-the-grid sites in sensitive Amazonian ecosystems. To achieve that, we center the Yasuní Park and Kichwa communities as a case study. Despite Kichwa environmental concerns about contamination, conventional total hydrocarbon testing has failed to detect elevated levels due to hydrocarbon degradation, necessitating testing for other contaminants associated with extractive activities, such as PFAS, a forever chemical commonly used in drilling fluids, and other contaminants from petroleum transportation via pipelines. This research was conducted at the request of and with the participation of Kichwa residents, who needed to understand the nature of contaminants in their environment. Two participatory mapping exercises were conducted in Oil Block 12 to pinpoint 16 sampling locations, given the block’s long history of contamination. In Oil Block 43, where extraction is more recent, we sampled 5 sites where community members had observed contamination in the last year. TPH and PFAS analyses were performed using EPA methods 1633 and 1664. Results revealed 7 PFAS compounds across Oil Blocks, 11 TPH compounds in Oil Block 12, and overlap between TPH and PFAS at 6 sampling locations. Contamination was detected near community housing, food gardens, and swamped forest, which is concerning because communities rely on traditional subsistence activities, including forest gathering, fishing, and gardens for survival. This is the first environmental assessment to examine the combined presence of hydrocarbons and PFAS in the Yasuní Park and the Ecuadorian Amazon, providing communities with empirical evidence of environmental contamination. Full article

Background/Objectives: Chronic lymphocytic leukemia (CLL) is characterized by a highly heterogeneous clinical course, with some patients remaining stable for years while others require early treatment. Identifying reliable and easily accessible predictors of treatment requirement at diagnosis remains an important clinical challenge. Methods: This retrospective cohort study included 226 patients diagnosed with CLL between 2015 and 2024 at a tertiary care center. Baseline demographic, clinical, and laboratory parameters were analyzed. Univariate and multivariable logistic regression analyses were performed to identify independent predictors of treatment requirement. A hierarchical mixed-effects model was constructed to account for temporal clustering across diagnostic periods. A clinical risk score was derived from independent predictors, using regression coefficient-based weighting, and its discriminative performance was evaluated using receiver operating characteristic (ROC) analysis. Results: A total of 226 patients were included (mean age: 62.4 ± 13.8 years; 56.6% male). During follow-up, 104 patients (46.0%) required treatment. Lower hemoglobin and platelet levels, higher lymphocyte counts and LDH levels, and the presence of B symptoms, splenomegaly, and advanced disease stage were independently associated with treatment requirement. These associations remained significant in hierarchical modeling. The derived risk score demonstrated acceptable discriminative ability (AUC: 0.84; 95% CI: 0.79–0.89), with a cut-off value of ≥4 yielding a sensitivity of 81.7% and specificity of 73.8%. Conclusions: Baseline clinical and laboratory parameters are associated with treatment requirement in CLL. A combination of readily available variables may support risk stratification at diagnosis. The proposed risk score may provide a practical adjunct to routine clinical assessment, particularly in settings where advanced molecular testing is not readily available; however, external validation in independent cohorts is required before clinical implementation. Full article