Search Results (532)

Adverse pregnancy outcomes (APOs) such as prematurity, low birth weight, stillbirth, and birth defects remain significant global health challenges. While many risk factors are known, APOs encompass a wide range of outcomes with diverse, sometimes poorly understood etiologies. Pregnancy-related acute kidney injury (PR-AKI) and liver injury are particularly associated with increased maternal and fetal mortality. This study investigated the association between hematological parameters, kidney and liver injury markers and adverse pregnancy outcomes. This cross-sectional study involved 714 pregnant women aged 18–40 years, conducted between August 2021 and August 2022. Maternal blood samples were collected before and after delivery to compare hematological parameters. Kidney and liver injury markers were measured using standard methods. The study analysed the association of these parameters with adverse pregnancy outcomes. The median age of participants was 24 years (Q1, Q3: 21, 26). Women with adverse pregnancy outcomes had statistically significant serum creatinine levels [0.52 mg/dL (0.45, 0.58)] compared to those without [0.50 mg/dL (0.44, 0.56)], although the difference was not clinically significant. Elevated Aspartate Transaminase (AST) levels (>90th percentile) were statistically associated with adverse pregnancy outcomes. Pairwise comparisons with Bonferroni corrections revealed significant differences in Hemoglobin (Hb), White Blood Cell (WBC), Red Blood Cell (RBC), platelet, and Packed Cell Volume (PCV) levels before and after delivery (p < 0.05) in both groups. Elevated AST levels, but not other hematological or biochemical parameters, were independently associated with adverse pregnancy outcomes, whereas creatinine differences lacked clinical impact. Full article

Change in direction ability (COD) is a fitness component that may be related to safe and effective participation in pickleball. The general aim of the research was to examine a COD test that may be specific to the movement demands of the sport. Therefore, we tested the inter-trial reliability of the modified spider test for pickleball, compared learning effects between younger and older adults, and examined the reliability and validity of hand timing compared to timing gates. In this cross-sectional study, 36 participants (ages 19–78) were grouped as adults (ages 18–49) or seniors (ages 50+) according to the USA Pickleball age groupings. Participants completed a standard warm-up, one practice trial, and five full-effort trials with 4–6 min of rest between trials. Intraclass correlation coefficient (ICC) was used to determine reliability across five trials. Inter-rater reliability and validity of hand timing were also examined with ICCs. Pairwise comparison t-tests of individual trials were performed using the Hochberg method to determine learning effect. Linear regression analyses were used to determine if any segment could predict total trial time. During participation, older players provided unsolicited feedback that they were concerned about the safety of the backpedaling in the spider test. We observed that one person fell while backpedaling, though suffered no injury. Results indicate that the spider test was reliable across all five trials (ICC = 0.977). A learning effect was detected between the first and second trial (p = 0.001), and the magnitude of the effect was significantly different between age groups (p = 0.009). Hand timing demonstrated excellent inter-rater reliability (ICC = 0.993) and validity (ICC = 0.990). Splits 2, 3, and 4 significantly predicted total test time (R² = 0.973, 0.973, and 0.986, respectively). The test demonstrated reliability, but older players expressed concern about backpedaling. This raises questions about backpedaling safety in pickleball. Therefore, we do not recommend this test. Future research needs to determine appropriate tests to screen for fall risk in the dynamic movements relevant to pickleball. Full article

Global air transport has become the dominant mode of long-distance travel, carrying more than four billion passengers in 2019 and projected to exceed 8 billion by 2040. Nevertheless, limited demand and economic inefficiencies often make direct connections unfeasible, forcing many passengers to rely on transfers. In such cases, synchronizing arrivals and departures at hub airports is crucial to minimizing transfer times and maximizing passenger retention. This study investigates the synchronization problem at Istanbul Airport, one of the world’s largest hubs, using metaheuristic optimization. Three algorithms—Genetic Algorithms (GA), Modified Discrete Particle Swarm Optimization (MDPSO), and Evolutionary Strategies (ES)—were applied in parallel to optimize arrival and departure schedules for a major airline. The proposed chromosome-based framework was tested through parameter tuning and validated with statistical analyses, including ANOVA and Games–Howell pairwise comparisons. The results show that MDPSO achieved strong improvements, while ES consistently outperformed both GA and MDPSO, increasing successful passenger transfers by more than 200% compared to the original schedule. These findings demonstrate the effectiveness of evolutionary metaheuristics for large-scale airline scheduling and highlight their potential for improving hub connectivity. This framework is generalizable to other hub airports and airlines, and future research could extend it by integrating hybrid metaheuristics or applying enhanced forecasting methods and more dynamic scheduling approaches. Full article

Background and Objectives: To evaluate the association of surgeon experience, simulation-based training, and hand dominance on drilling accuracy using a synthetic bone model, with the hypothesis that training improves resident performance and left-handed individuals show superior bilateral accuracy. Materials and Methods: A prospective observational study was conducted in the Orthopedic Surgery Division of a tertiary academic center. Drilling accuracy was assessed before and after a standardized simulation-based training program. Twenty-five orthopedic surgeons participated: 9 junior residents (≤3 years of training), 8 senior residents (>3 years), and 8 board-certified experts. All participants completed baseline assessments; only residents were evaluated immediately after training and at a 2-week follow-up. Results: Experts showed superior baseline accuracy, particularly with the non-dominant hand. Senior residents showed a significant overall effect of time on right-hand accuracy (F(2,14) = 5.85, p = 0.014); post hoc pairwise comparisons showed trends toward improvement from baseline to post-training (p = 0.06) and from post-training to 2-week follow-up (p = 0.105); Junior residents showed no significant changes. Left-handed participants consistently outperformed right-handed peers with their non-dominant hands (p = 0.034). Among residents, this pattern persisted across all sessions. At baseline, senior residents and experts had similar right-hand accuracy (p = 0.59), but senior residents performed worse with the left hand (p = 0.038). No significant differences were found between junior and senior residents in either hand across all time points, indicating that residency duration alone does not improve performance without targeted training. Conclusions: Drilling accuracy in orthopedic surgery is influenced by experience level, targeted training, and hand dominance. Experts show greater precision, and senior residents showed a significant overall effect of time on right-hand accuracy, with trends toward improvement following training, while junior residents may need different training strategies. Tailored educational interventions are needed to improve accuracy and ambidexterity across all training stages. Level of evidence: II. Full article

Background: Traditional dietary assessment methods face limitations including reporting bias and scalability challenges. Large language models (LLMs) offer potential for automated food classification, yet their validation in morphologically complex, non-English languages like Polish remains limited. Methods: We analyzed 1992 food items from a Polish long-term care facility (LTCF) cohort using three advanced LLMs (Claude Opus 4.5, Gemini 3 pro, and GPT-5.1-chat-latest) with two prompting strategies: a structured double-step prompt integrating NOVA and World Health Organization (WHO) criteria, and a simplified single-step prompt. Classifications were compared against consensus judgments from two human experts. Results: All LLMs showed high agreement with human experts (90.3–94.2%), but there were statistically significant differences in all pairwise comparisons (χ² = 1174.5–1897.1; p < 0.001). The structured prompt produced very high Recall for UNHEALTHY items at the cost of lower Specificity, whereas the simplified prompt achieved higher overall Accuracy and a more balanced Recall–Specificity profile, indicating a trade-off between strict guideline adherence and alignment with general human judgment. Conclusions: Advanced LLMs demonstrate near-expert accuracy in Polish-language dietary classification, enhancing workflow efficiency by shifting effort toward validation. Expert oversight remains essential, and multi-model consensus alongside language-specific validation can improve AI reliability in nutrition assessment. Full article

This paper examines decision-making challenges that arise when information is incomplete, specifically when judgments are missing or unavailable in the context of individual and group applications of the Analytic Hierarchy Process (AHP). Two illustrative examples are provided. The first, adapted from a recently published study in the field of artificial intelligence, demonstrates how different methods for generating missing judgments can affect the outcomes of an individual decision-maker. The second example addresses a real-world problem of allocating farmland among three crops, wheat, corn, and soybeans, using four evaluation criteria: expenses, labor, reliability, and market considerations. In this example, two decision-makers form a group, and their incomplete judgments leave gaps in pairwise comparison matrices at different levels of the hierarchy. The solution incorporates both transition-based approaches (general transition rule and First-Level Transition Rule) and established methods such as Harker’s and van Uden’s. In addition, aggregation of individual judgments (AIJ) is applied where at least one judgment exists, while geometric aggregation is used when multiple judgments are available. This enables prioritization of decision elements in both examples, with particular attention to cases requiring a priori and a posteriori aggregation of individual judgments across hierarchical levels. A critical analysis of the results highlights key differences between methods, revealing ongoing controversies regarding their reliability in practice. Although it is shown that the First-Level Transition Rule method in the presented examples and other authors’ tests outperforms other methods used, the findings suggest that further research is needed to refine and establish more trustworthy procedures for handling incomplete information in AHP applications. Full article

Renewable energy communities are emerging as key players in the sustainable energy transition, yet there is a lack of systematic approaches for evaluating the digital tools that support their development and operation. This study proposes a comprehensive methodology for assessing tools for supporting renewable energy communities, based on a system of key performance indicators and the multi-criteria decision analysis framework method. Twenty-three specific sub-criteria were defined and scored for each tool, and a weighted sum model was applied to aggregate performance. To ensure robust comparison, criteria weights were derived using both expert judgement (pairwise comparisons of ranking and analytical hierarchy process) and objective data-driven methods (the entropy-based method and the criteria importance through intercriteria correlation weighting method). The framework was applied to a diverse sample of contemporary renewable energy community’s tools, including open-source, commercial, and European Union project tools. Key findings indicate that some of the tools have shown noticeable rank shifts between expert-weighted and data-weighted evaluations, reflecting that expert opinions emphasize technical and operational features while objective variability elevates environmental and economic criteria. This assessment enables stakeholders to compare energy community tools based on structured criteria, offering practical guidance for tool selection and highlighting areas for future improvement. Full article

Background/Objectives: Artificial intelligence (AI)-based tools are increasingly being used in tailored nutrition management, and evaluating their compliance with guidelines is significant in clinically sensitive areas, including bariatric surgery. This study aimed to investigate the extent to which diet plans recommended by AI models in the early period following sleeve gastrectomy (SG) align with current clinical nutrition guidelines (ASMBS, AACE/TOS). Methods: A total of 360 menu plans were generated using three AI platforms—ChatGPT-4o, DeepSeek V3, and Grok-3—for 40 simulated patients (20 females, 20 males; BMI 32–45 kg/m²) across three postoperative stages: liquid (day 5), puree (day 16), and solid (day 35). The energy and nutrient contents of the menus were analyzed using BeBiS 8.1; an experienced dietitian assessed compliance with the guidelines using a structured checklist. Nutrient intakes and guideline compliance scores were examined using within-patient Friedman tests followed by Bonferroni-adjusted pairwise comparisons. Results: ChatGPT-4o demonstrated the highest overall compliance scores, particularly in the liquid and puréed phases, while DeepSeek produced higher values for several micronutrients. All models showed substantial gaps in essential postoperative recommendations, most notably thiamine and multivitamin supplementation. Conclusions: Although LLMs can generate partially guideline-concordant postoperative diet plans, they consistently omit several critical elements of bariatric nutrition care. These findings indicate that LLM-generated menus may serve as supportive educational tools, and diet planning must be performed under the guidance of a specialist dietitian. This simulation does not assess clinical safety, efficacy, or patient outcomes and should not be used as a substitute for dietitian-led postoperative nutrition care. Full article

Background/Objectives: The safety of intraperitoneal mesh placement in contaminated fields remains controversial because of the increased risk of inflammation and adhesion formation. N-acetyl-L-cysteine (NAC) has antioxidant, pro-fibrinolytic and antibiofilm actions that could attenuate this response. The aim of this study is to determine whether NAC reduces mesh-related inflammation in a septic model created by intraperitoneal Escherichia coli (E.coli) inoculation. The primary comparison was prospectively defined between E. coli–inoculated animals treated with NAC (D) and those without NAC (B). Groups without E. coli (A,C,E) are presented for context and were compared previously. Methods: In this randomized, double-blind experimental model (five groups, n = 20 per group), all rats underwent midline laparotomy with intraperitoneal placement of a composite mesh, followed by standardized ciprofloxacin administration. The septic groups received intraperitoneal E. coli, while the NAC-treated groups additionally received intraperitoneal NAC (150 mg/kg). Serum levels of IL-1α, IL-6, and TNF-α were measured on postoperative days 7, 14, and 21. On day 21, adhesions were graded using the Modified Diamond system, histology (inflammatory infiltration, fibrosis, neovascularization) was scored, and mesh cultures were obtained. Cytokine data were analyzed with repeated-measures ANOVA, while categorical or ordinal outcomes were assessed using χ² or Fisher’s exact tests with Bonferroni-adjusted pairwise comparisons. Results: E. coli inoculation significantly increased adhesion burden and worsened histologic scores compared with controls (both p < 0.001). NAC administration in the septic model significantly reduced adhesions and improved all histologic domains relative to E. coli alone (all p ≤ 0.003), with values comparable to controls (non-significant across domains). For cytokines, there was a significant overall group effect for IL-1α, IL-6, and TNF-α (all p < 0.001), without a main effect of time or time × group interaction. Pairwise contrasts showed lower IL-1α (p = 0.024), IL-6 (p < 0.001), and TNF-α (p < 0.001) levels in group D versus B, and lower IL-6 and TNF-α in group D versus A (both p < 0.001). Mesh culture positivity rate was higher in group B than A (p < 0.001) and showed a non-significant reduction in group D versus B (p = 0.10). No perioperative deaths occurred. Conclusions: NAC attenuated septic, mesh-associated inflammation—normalizing adhesions and histology and reducing IL-6 and TNF-α— supporting its role as a host-directed adjunct alongside antibiotics. Further translational studies are warranted to define the optimal dose, timing, and clinical indications. Full article

Nanosatellites in Low Earth Orbit (LEO) are an attractive platform for commercial and scientific missions, but their downlink capacity is constrained by bandwidth and by low ground station duty cycles (often under 5%). These limitations are particularly acute in heterogeneous cooperative networks, where operators seek to maximize “good-put”: the number of unique messages successfully delivered to the ground. In this paper, we present and evaluate three complementary algorithms for scheduling nanosatellite passes to maximize good-put under realistic traffic and link variability. First, a Cooperative Reception Algorithm uses Shapley value analysis from cooperative game theory to estimate each station’s marginal contribution (considering signal quality, geography, and historical transmission patterns) and prioritize the most valuable upcoming satellite passes. Second, a pair-utility optimization algorithm refines these assignments through local, pairwise comparisons of reception probabilities between neighboring stations, correcting selection biases and adapting to changing link conditions. Third, a weighted bidding algorithm, inspired by the Helium reward model, assigns a price per message and allocates passes to maximize expected rewards in non-commercial networks such as SatNOGS and TinyGS. Simulation results show that all three approaches significantly outperform conventional scheduling strategies, with the Shapley-based method providing the largest gains in good-put. Collectively, these algorithms offer a practical toolkit to improve throughput, fairness, and resilience in next-generation nanosatellite communication systems. Full article

Reverse transcription quantitative PCR (RT-qPCR) is extensively used to quantify gene expression under drought conditions; however, its reliability depends on the validation of the reference genes under specific conditions. In cucumber, reference genes have rarely been validated under drought conditions. This study identified stable housekeeping genes for RT-qPCR normalization in the leaves of two inbred lines with contrasting drought responses. Plants underwent a 7-day drought period, with leaf samples collected at multiple points along with watered controls. The expression stability of 13 candidate genes was evaluated using four algorithms: geNorm, NormFinder, BestKeeper, and the comparative ΔCt method, with the results integrated using RefFinder. Ten genes producing specific and efficient amplicons were analyzed for stability. CACS and UBI-1 consistently ranked among the most stable genes, with TIP41-like as an additional reliable option, whereas GAPDH and HEL were unstable. GeNorm pairwise variation analysis showed that the two reference genes were sufficient for accurate normalization. Functional validation with three drought-responsive targets (LOX, HsfC1, and CYP72A219) and comparison with RNA sequencing (RNA-seq) fold changes confirmed that normalization using CACS and UBI-1 yielded the most biologically credible expression profiles. These reference genes will facilitate robust RT-qPCR analyses of drought response in cucumber leaves and provide a starting point for validating suitable normalizers in other cucumber organs and related cucurbits. Full article

Repetitive non-concussive head impacts (NCHIs) may contribute to long-term neurodegenerative conditions. However, objective, multimodal methods for monitoring acute changes in brain health biomarkers following NCHIs remain underdeveloped. In this exploratory study, we examined the effects of ten kicking and ten heading trials related to association football on linear and nonlinear measures of postural control and corticospinal inhibition. Postural control was assessed via force platform analysis in dual-stance and single-leg protocols, and corticospinal inhibition was measured using transcranial magnetic stimulation with electromyography. Large effects of condition were found for anteroposterior postural complexity (CI-AP), anteroposterior sway amplitude, mediolateral centre of pressure shift and cortical silent period (η² > 0.14). Pairwise comparisons revealed large post-heading effects, particularly in CI-AP, which decreased significantly relative to baseline (d_z = 0.71, p = 0.018) and showed a moderate negative effect relative to post-kicking testing (d_z = 0.53, p = 0.069). These findings suggest a possible reduction in postural control adaptability following exposure to ten NCHIs, consistent with patterns observed in mild traumatic brain injury. Whilst confirmatory research with larger samples is warranted, nonlinear measures of postural control complexity demonstrate promise as a sensitive biomarker for detecting acute NCHI-related changes. Full article

Background: The clinical utility of risk scores predicting shunt dependency after aneurysmal subarachnoid hemorrhage (aSAH) remains limited due to scarce validation data. This multicenter pooled analysis aimed to assess the predictive accuracy of existing post-aSAH shunt risk scores. Methods: Consecutive aSAH cases treated at two German university hospitals from January 2010 to July 2023 were pooled into a validation cohort. Total scores for the CHESS, CHESS-Huckman, and SDASH risk models were calculated, and their diagnostic performance was compared using receiver operating characteristic (ROC) curve analysis. Results: A total of 813 patients were included, of whom 215 (26.4%) required ventriculoperitoneal shunt placement within a median time of 29 days post-aSAH. All three risk scores were significantly associated with shunt dependency. ROC analysis showed that the CHESS-Huckman score had the highest predictive accuracy (AUC: 0.792, 95% CI: 0.761–0.824), followed by the SDASH (AUC: 0.782, 95% CI: 0.750–0.814) and CHESS (AUC: 0.780, 95% CI: 0.748–0.812) scores. Pairwise comparisons of AUCs were not statistically significant. All three scores showed good overall calibration, with CHESS–Huckman performing best, as confirmed by calibration intercepts and slopes, Brier scores, and decile-based analysis. Higher CHESS–Huckman scores correlated with earlier shunt placement, whereas delayed shunting (>30 days after aSAH) was most common in patients with moderate CHESS–Huckman scores (7–8 points), occurring in 47.4% of cases compared to 41.4% and 33.3% in patients scoring 0–6 and 9–10 points, respectively. Conclusions: This multicenter analysis validated existing risk scores for predicting shunt dependency after aSAH, with the CHESS–Huckman score demonstrating the nominally highest diagnostic accuracy. Integrating these risk scores into clinical practice could enhance early identification of patients requiring shunting, potentially reducing external ventricular drain weaning time, shortening hospital stays, and lowering the risk of cerebrospinal fluid infections. Full article

Background/Objectives: Chronic insomnia is a prevalent condition with important health implications. However, its recognition in clinical practice is often limited. This study evaluated the alignment between population-based estimates of chronic insomnia and the prevalence recorded in primary care, assessing the diagnostic reliability of healthcare services. Design: We conducted a comparative cross-sectional analysis using two independent data sources: (1) the Epidemiology Study of Insomnia in Spain (EPINSOM) survey, which applied the International Classification of Sleep Disorders, Third Edition (ICSD-3), criteria to a representative sample of the Spanish adult population (n = 2243; Catalonia subsample n = 363) and (2) the SIDIAP Electronic Health Record (EHR) database, comprising anonymized primary care data from adults in Catalonia (N = 4,131,754). Methods: Prevalence estimates of insomnia symptoms, chronic insomnia syndrome, and chronic insomnia disorder were extracted from the EPINSOM survey and compared with ICD-10-coded insomnia diagnoses in the SIDIAP. Pairwise comparisons of proportions were conducted using z-tests for independent samples. For transparency, 95% confidence intervals (CI) were calculated using the Wilson method. Results: In Catalonia, the population-based prevalence of chronic insomnia disorder was 13.6% (95% CI: 10.1–17.1), whereas the prevalence of coded insomnia diagnoses in primary care was significantly lower at 5.1% (95% CI: 5.08–5.12). Among adults aged ≥55 years, the prevalence estimates were more closely aligned between the survey (18.2%) and SIDIAP records (18.5%). The survey-derived prevalence of insomnia symptoms reached 41.39% (95% CI: 39.2–43.6), highlighting substantial underrecognition in clinical practice. Conclusions: Our findings indicate that primary care insomnia diagnoses substantially underestimate insomnia prevalence compared with ICSD-3-based population estimates, particularly in younger adults. While diagnoses appear to be more like to prevalence in older adults (>55 years), this discrepancy highlights the need for improved identification and management of insomnia in primary care settings, especially among younger populations who may be underdiagnosed. Full article

The growing trend of using artificial intelligence models in many areas increases the need for a proper understanding of their functioning and decision-making. Although these models achieve high predictive accuracy, their lack of transparency poses major obstacles to trust. Explainable artificial intelligence (XAI) has emerged as a key discipline that offers a wide range of methods to explain the decisions of models. Selecting the most appropriate XAI method for a given application is a non-trivial problem that requires careful consideration of the nature of the method and other aspects. This paper proposes a systematic approach to solving this problem using multi-criteria decision-making (MCDM) techniques: ARAS, CODAS, EDAS, MABAC, MARCOS, PROMETHEE II, TOPSIS, VIKOR, WASPAS, and WSM. The resulting score is an aggregation of the results of these methods using Borda Count. We present a framework that integrates objective and subjective criteria for selecting XAI methods. The proposed methodology includes two main phases. In the first phase, methods that meet the specified parameters are filtered, and in the second phase, the most suitable alternative is selected based on the weights using multi-criteria decision-making and sensitivity analysis. Metric weights can be entered directly, using pairwise comparisons, or calculated objectively using the CRITIC method. The framework is demonstrated on concrete use cases where we compare several popular XAI methods on tasks in different domains. The results show that the proposed approach provides a transparent and robust mechanism for objectively selecting the most appropriate XAI method, thereby helping researchers and practitioners make more informed decisions when deploying explainable AI systems. Sensitivity analysis confirmed the robustness of our XAI method selection: LIME dominated 98.5% of tests in the first use case, and Tree SHAP dominated 94.3% in the second. Full article