Artificial intelligence (AI) techniques are increasingly applied to magnetic resonance imaging (MRI) for detecting temporomandibular joint (TMJ) anomalies; however, their overall diagnostic accuracy and generalizability remain uncertain. To systematically review and meta-analyse the diagnostic performance of AI models for TMJ anomaly detection on MRI and to identify factors influencing model performance. A comprehensive search of PubMed, Scopus, Embase, and Web of Science was conducted for studies published between January 2015 and September 2025. Two reviewers independently screened and extracted data. Eligible studies developed and tested AI, machine learning, or deep learning models on human TMJ MRI and reported quantitative performance metrics. Risk of bias was assessed using the QUADAS-2 tool. Pooled sensitivity and specificity were estimated using a bivariate random-effects model, while pooled accuracy was derived using logit transformation. Heterogeneity (I2) was explored through subgroup analyses by model architecture and validation strategy. Fourteen studies were included in the systematic review, of which six met the criteria for meta-analysis. Across these six studies, 18 models were analyzed for accuracy, 29 for sensitivity, and 24 for specificity. The pooled diagnostic accuracy was 0.487 (95% CI 0.403-0.571), with pooled sensitivity and specificity of 0.399 (95% CI 0.348-0.450) and 0.399 (95% CI 0.343-0.456), respectively, all showing substantial heterogeneity (I2 > 90%). Subgroup analyses indicated that advanced architectures such as ResNet-18, Inception v3, and EfficientNet-b4 achieved higher and more consistent diagnostic performance. Advanced deep learning architectures such as ResNet-18, Inception v3, and EfficientNet-b4 demonstrated superior diagnostic performance for detecting temporomandibular joint anomalies on MRI. These findings highlight the potential of AI-assisted MRI interpretation to improve diagnostic consistency, efficiency, and early detection of TMJ pathology. However, substantial heterogeneity and limited external validation currently limit clinical translation. Standardized multicenter studies and transparent model validation are essential to ensure reliable integration of AI tools into clinical TMJ imaging workflows.

The empirically supported property of absolute variations dominating quadratic variations are employed to motivate the construction of models with percentage returns bounded by unity in absolute value. Characteristic functions are developed for the log price relative for the new return models. The models are estimated on time series and option data and demonstrate improvements delivered by the inverse logistic transformation. Applications to pricing return variations and options on them are developed. Hedging strategies use Machine Learning methods on simulated sample spaces. It is observed that the log contract hedge introduces a high volatility dynamic hedge that theoretically may be compensated by the log contract leaving the variance contract as a residual. The Machine Learned hedge works with other functions estimated here by a Gaussian Process Regression that substantially reduces the volatility of the dynamic hedge and yet leaves, approximately, the variance payout as the residual.

Flowering time in maize is a complex quantitative trait regulated by multiple genes, and its genetic variation mechanisms remain to be fully elucidated. In this study, we phenotypically evaluated flowering-related traits (days to tasseling, days to pollen shedding, days to silking) across six different environments using an association population comprising 368 maize varieties. Genome-wide association studies were employed to dissect the genetic basis of these traits. A total of 261 significant association single nucleotide polymorphisms (SNPs; logarithm of odds>5.5) were detected, among which 23 SNPs were consistently identified across multiple environments or traits. By integrating population fixation index, we further screened 41 potential candidate genes, including Zm00001d006213, Zm00001d027394, and Zm00001d027395, which are likely key regulators of flowering time. These findings provide both theoretical insights into the genetic regulatory network of maize flowering time and valuable gene resources for molecular breeding.

This research discusses the strategy of strengthening defense logistics through the use of digital technology in supporting humanitarian operations by the Indonesian Military. Background departs from the fact that defense logistics so far is often understood to be limited to technical functions, even though it has a strategic role in maintaining national resilience. The focus of research is directed at multidimensional barriers, which include overlapping regulations, separatist threats, limited resources, low public participation, weak information literacy, and infrastructure constraints and geographical conditions of the island. The research method uses a qualitative approach with PMESII frame combined with data analysis through NVIVO 12. The results show the need for defense logistics transformation into a strategic system based on digital technology with a risk management approach that emphasizes aspects of planning, distribution, transportation, efficiency, and effectiveness through the support of information systems, satellite technology and UAVs, and geographical mapping. This finding confirms that the transformation of digital technology can improve efficiency, transparency, and responsiveness while strengthening legitimacy and public trust. Further research is advised to test the effectiveness of the Digital Technology Based Defense Logistics Model through empirical case studies and disaster scenario simulations and conflicts in Indonesia.

Although many studies have investigated the safety of hands-free and handheld phone calls, the levels under different cognitive demands—particularly their interaction with call modes—remain unclear. Using a driving simulator, this study tested 45 drivers (37 valid) in rear-end scenarios under various call modes and cognitive demands (baseline, hands-free/handheld with low/high cognitive load). Behavioral variables were analyzed across conditions, and K-means clustering classified risk levels. A binary Logit model identified factors affecting rear-end risk, and three car-following models (FVD, Gipps, IDM) were calibrated. Results show that drivers compensate for handheld with high cognitive load by increasing forward spacing (FBD increased 34% versus baseline). Handheld with low cognitive load raised rear-end risk by 5.4%, whereas hands-free and handheld with high cognitive load showed no significant effect, highlighting the overlooked risk of low-load handheld use. FVD outperformed IDM/Gipps in simulating phone-distracted driving. Furthermore, the calibrated parameters exhibit trend variations that correlate with changes in the behavioral variable FBD. These findings enhance our understanding of the combined impact of phone use mode and cognitive load on driving safety, thereby providing practical implications for the formulation of relevant safety policies.

Excessive use of short-form video platforms such as TikTok has raised growing concerns about digital addiction and its impact on young users’ emotional well-being. This study examines the relationship between continuous TikTok exposure and emotional engagement in young adults aged 20–23 through a multimodal experimental design. The purpose of this research is to determine whether emotional engagement increases, remains stable, or declines during prolonged exposure and to assess the degree of correspondence between facially inferred engagement and physiological arousal. To achieve this, multimodal biometric data were collected using the iMotions platform, integrating galvanic skin response (GSR) sensors and facial expression analysis via Affectiva’s AFFDEX SDK 5.1. Engagement levels were binarized using a logistic transformation, and a binomial test was conducted. GSR analysis, merged with a 50 ms tolerance, revealed no significant differences in skin conductance between engaged and non-engaged states. Findings indicate that although TikTok elicits strong initial emotional engagement, engagement levels significantly decline over time, suggesting habituation and emotional fatigue. The results refine our understanding of how algorithm-driven, short-form content affects users’ affective responses and highlight the limitations of facial metrics as sole indicators of physiological arousal. Implications for theory include advancing multimodal models of emotional engagement that account for divergences between expressivity and autonomic activation. Implications for practice emphasize the need for ethical platform design and improved digital well-being interventions. The originality and value of this study lie in its controlled experimental approach that synchronizes facial and physiological signals, offering objective evidence of the temporal decay of emotional engagement during continuous TikTok use and underscoring the complexity of measuring affect in highly stimulating digital environments.

Digital transformation in the logistics sector is crucial for improving operational performance and financial outcomes, especially amid the rapidly growing demands of Indonesia’s economy and the global market. This study examines the impact of digital logistics transformation and related factors, including top management commitment, IT capability, employees’ digital mindset and skills, work culture change, human capital capacity, as well as the operational and financial performance of PT Pos Indonesia (Persero). Using a quantitative approach with data collected from 91 senior leaders at PT Pos Indonesia and analyzed through SmartPLS, the study proves that digital transformation significantly enhances both operational and financial performance. The factors that contribute most positively to the success of digital transformation are IT capability, employees’ digital mindset, and human capital capacity. In contrast, top management commitment, individual digital skills, and work culture change did not show a direct significant impact at the early stage. Nevertheless, digital transformation was found to be a strong mediating variable in linking internal company factors with performance improvement. This confirms that digital strategies are not merely technological innovations but also concrete mechanisms for creating business value and strengthening the company’s competitiveness in the logistics industry.

Oropharyngeal leak pressure (OPLP) is an objective measure of the airway seal provided by a supraglottic airway (SGA) around the glottis. While under-vision insertion techniques improve the anatomic placement of SGAs vs. blind insertion techniques, evidence regarding their impact on OPLP remains inconclusive. Electronic databases were searched until 31st March 2025. Randomized controlled trials (RCTs) comparing blind insertion techniques vs. under-vision placement of SGAs in anesthetized adults reporting OPLP were included. The primary objective was to compare the OPLP between under-vision and blind placement of SGAs. Secondary objectives included comparison of SGA positioning, insertion time, first-attempt success rates, peak inspiratory pressures (PIP), ease of SGA and gastric tube insertion, and postoperative complications. Ten RCTs involving 1,089 patients were included. Under-vision placement of SGAs was associated with significantly higher OPLP (27.88 vs. 24.7 cmH<inf>2</inf>O; mean difference: 3.18 cmH<inf>2</inf>O; 95% confidence interval (CI): 2.42 to 3.94; P<0.00001; I2=44.33%). Under-vision techniques also demonstrated better anatomical positioning with fiberoptic bronchoscopy (log odds ratio: 1.90; 95% CI: 0.76 to 3.03; P<0.00001). Meta-regression to quantify heterogeneity of OPLP was not significant for either the type of SGAs (P=0.205) or the type of laryngoscope used (P=0.404). Lower PIP and improved ease of gastric tube insertion were noted in the under-vision groups, though a longer time was required to place the SGAs. No differences were noted in other outcomes. Under-vision placement of SGAs results in higher OPLP vs. blind insertion techniques, but the clinical significance remains uncertain.

The adoption of Good Beekeeping Practices is widely recognized as essential for enhancing productivity, sustainability, and commercial success in the beekeeping industry. In Nepal, despite the sector’s strong potential to support rural livelihoods, its growth remains constrained by reliance on traditional methods. This study investigates the extent of Good Beekeeping Practices adoption and explores the socioeconomic factors influencing their use among beekeepers in Chitwan, one of the country’s leading honey producing districts. A mixed-methods approach was employed, combining a survey of 57 randomly selected beekeepers with qualitative insights from focus group discussions and key informant interviews conducted within the Prime Minister Agriculture Modernization Project (PMAMP) area. Data were analyzed using descriptive statistics and a binary logit regression model. Findings reveal a 59.6% adoption rate of Good Beekeeping Practices. Core practices such as the use of protective gear, appropriate apiary site selection, and maintenance of colony handling (64.9%) and feeding honey-pollen mixture (52.6%) were less common. Regression results indicate that age and the number of beehives negatively influenced adoption, whereas annual income from beekeeping, off-farm income, and honey production were positive and significant. The study concludes that financial capacity and economic resilience are key to encouraging the adoption of better practices. It suggests that efforts to increase profitability and diversify income are essential for modernizing the sector.

Background: Atherosclerosis and its associated chronic inflammation of the arterial wall disrupt fatty acid metabolism, leading to changes in plasma fatty acid composition. These alterations can be used to improve disease diagnosis and risk stratification by the development and application of specific lipidomic indices. Objectives: The objectives of this study are to evaluate the performance of conventional fatty acid indices and enhance diagnostic efficiency in atherosclerosis by introducing novel index based on plasma PUFA n-6 and n-3 content (Omega-6/3 Balance Index, O6/3-BI), as well as the perspective SFA/MUFA ratio (stearic/oleic acid ratio, C18:0/C18:1n-9) and a logit function combining PUFA and SFA/MUFA biomarkers. Methods: Plasma fatty acids were quantified by LC-MS/MS in healthy controls (n = 50) and patients with carotid atherosclerosis (n = 52), stratified by atorvastatin, rosuvastatin, or no statin therapy. The conventional indices (the Omega-3 Status (EPA + DHA), AA/EPA, and the omega-6/omega-3 ratio), and pathway ratios (C18:0/C18:1n-9; and C20:4n-6/C22:4n-6), as well as the newly introduced PUFA index and combined PUFA-SFA/MUFA logit function, were calculated. Their diagnostic performance for distinguishing atherosclerosis was assessed by a receiver operating characteristic (ROC) analysis with the cross-validation and calculation of Cliff’s Δ effect size. Results: The conventional parameters demonstrated a poor to low discrimination ability of the atherosclerosis patients’ groups from healthy controls (area under the ROC curve, AUC 0.548–0.711). In statin-treated patients, these conventional markers lost significance. The newly introduced PUFA index and SFA/MUFA ratio demonstrated improved patients’ discrimination with AUC 0.734–0.780 for the former and strong predictive power with AUC 0.831–0.858 for the latter marker and maintained their diagnostic value under statin therapy. The most significant positive effect size was observed for the SFA/MUFA ratio with Cliff’s Δ = 0.67–0.71. The combined PUFA-SFA/MUFA logit function also demonstrated a strong predictive power with AUC = 0.880 (Cliff’s Δ = −0.76), outperforming any single index. Conclusions: The newly introduced lipidomic index based on the PUFA content, SFA/MUFA ratio, and a logit function combining PUFA-SFA/MUFA biomarkers demonstrated a substantially better discrimination of atherosclerosis-related fatty acid metabolic disturbances than conventional fatty acid biomarkers.

Injury severity analysis of e-bike crashes: An age-stratified study of riders aged 40 and above.

ABSTRACT This study explores the poverty driven factors in the case of women-headed households at regional level in Pakistan using a binary logit model. The data for the study were collected from the district Bhakkar (Pakistan) and grounded on a self-structured and self-administrative questionnaire. The results of the study explain that education, job experience, family size, and health expenditures have negative and significant impacts on the likelihood of poverty among women-headed households. The age of the respondent is also positively related to poverty in this case. Based upon the results of the study, policy makers may better manage women’s employment and education, especially in the remote areas of Pakistan, to reduce women’s poverty. Further, it is recommended that the government provide funds to families to educate their children.

Although neuromuscular junction disorders (NMDs) and inflammatory polyneuropathies (IPNs) are biologically distinct, direct genetic comparisons between them remain limited, suggesting that additional underlying biological differences may yet be uncovered. Few studies have explored whether differences in variant patterns within shared biological pathways can be leveraged to distinguish NMDs and IPNs using machine learning (ML). We propose an interpretable ML framework based on Pathway-based Genetic Variant Dosage Average (PGVDA) to classify NMDs and IPNs and to identify key genes and pathways differentiating diseases. Using nonsynonymous variants from 667 UK Biobank participants, logistic regression identified disease-associated variants. Significant pathways were identified via pathway enrichment analysis with adjusted P-value < 0.05. PGVDA was calculated by assigning the log odds ratio to each variant dosage and then computing a weighted average at the pathway level. Dimensionality reduction was performed via hierarchical clustering based on gene-set overlaps and then PGVDAs with a variance inflation factor (VIF) > 10 were excluded. ML models were evaluated using leave-one-out cross validation. Utilizing the best-performing model, SHAP-based interpretation was applied using two distinct input configurations. Pathway-level interpretation using PGVDA input included stages of PGVDA scaling and ML-based classification, while variant-level interpretation using variant dosage input encompassed stages from odds ratio-based weight assignment to ML-based classification. Using logistic regression model with best performance, key differentiating five PGVDAs and 10 genes within each pathway were identified, suggesting that pathway-level variant aggregation enables accurate and interpretable classification of these two neuromuscular diseases. External validation is needed to ensure generalizability across populations.

Energy poverty is a critical global challenge, with profound implications for health, climate, and sustainable development. Energy poverty is generally examined within the framework of indoor air pollution, considering acute respiratory infection (ARI) among young children in low- and lower-middle-income countries (LLMICs) acknowledged as one of its most significant health consequences. The present study employs a multidimensional perspective to comprehensively measure the association between ARI and energy poverty. A sample of 344,160 children in the under-five age group (mean age 2.05 years; 50.3% female) across 26 LLMICs, obtained from the Multiple Indicator Cluster Surveys, was analyzed. The Multidimensional Energy Poverty Index, composed of five fundamental dimensions of energy essential, was used to compute energy poverty. The results from binary logit models showed that the odds of ARI increased by 53% (aOR 1.53; 95% CI 1.46–1.60) with every unit increase in MEPI. Electricity deprivation, lack or absence of entertainment/education and household appliances, and reliance on biomass fuels for cooking were also independently associated with a greater ARI risk. These findings demonstrate that multidimensional energy poverty is a major contributor to child respiratory health. Therefore, to design result-oriented policies and interventions to minimize ARI among children in LLMICs, it is essential to address energy accessibility and affordability.

P0768 Efficacy and Safety of Advanced Therapies in pre-liver transplant PSC-IBD patients – A systematic review and meta analysis of IBD outcomes

Efficacy and safety of cenobamate in developmental and epileptic encephalopathies: A systematic review and meta-analysis.

Digital transformation of Chinese logistics enterprises: Impact on corporate innovation from the perspective of Fintech

Suicidal behavior comorbidities in old adults: A systematic review and meta-analysis.

FIBTEM, a component of rotational thromboelastometry, assesses the functional contribution of fibrin to clot formation. Notably, impaired fibrin function using FIBTEM may be observed despite normal plasma fibrinogen levels measured by the Clauss assay. The clinical implications of such isolated functional deficits are poorly understood in traumatic intracranial hemorrhage (TICH) and are the focus of this brief report. From February 2022 to July 2023, we conducted a single-center retrospective study of patients with TICH and Glasgow Coma Scale score of ≤14. All participants had paired FIBTEM and Clauss fibrinogen levels from simultaneously drawn samples. Patients were grouped as follows: (1) FIBTEM-A5 >10 mm, (2) FIBTEM-A5 ≤10 mm with Clauss fibrinogen >2 g/L (isolated fibrin dysfunction), and (3) FIBTEM-A5 ≤10 mm with fibrinogen ≤2 g/L (true fibrinogen deficiency [TD]). Rates of progressive hemorrhagic injury and mortality were compared using univariate and multivariable analyses. Of 73 included patients, 37 (51%) had reduced FIBTEM-A5; among these, 12 (32%) experienced TD. Mortality occurred in 7 of 12 TD patients (58%), 11 of 36 (31%) with FIBTEM-A5 >10 mm, and 6 of 25 (24%) with isolated fibrin dysfunction. Progressive hemorrhagic injury occurred in 6 of 9 TD patients (66%), 2 of 26 (8%) with FIBTEM-A5 >10 mm, and 9 of 23 (39%) with isolated fibrin dysfunction. In multivariate analysis, TD was associated with a 4.5-fold increase in the log odds of progressive hemorrhagic injury (95% confidence interval, 1.5-7.4), while isolated fibrin dysfunction was associated with a 3.2-fold increase (95% confidence interval, 1.0-5.4). No significant associations with mortality were observed. In TICH, low FIBTEM-A5 may reflect either fibrinogen deficiency or impaired fibrin function. Progressive hemorrhagic injury is increased in both of these phenotypes. Further research is needed to validate these findings and to guide the optimal clinical response to low FIBTEM-A5 in neurotrauma. Prognostic and Epidemiologic; Level IV.

In several clinical areas, traditional clinical trials often use a responder outcome, a composite endpoint that involves dichotomising a continuous measure. An augmented binary method that improves power while retaining the original responder endpoint has previously been proposed. The method leverages information from the undichotomised component to improve power. We extend this method for basket trials, which are gaining popularity in many clinical areas. For clinical areas where response outcomes are used, we propose the new augmented binary method for basket trials thatenhances efficiency by borrowing information on the treatment effect between subtrials. The method is developed within a latent variable framework using a Bayesian hierarchical modelling approach. We investigate the properties of the proposed methodology by analysing point estimates and high-density intervals in various simulation scenarios, comparing them to the standard analysis for basket trials that assumes binary outcomes. Our method results in a reduction of 95% high-density interval of the posterior distribution of the log odds ratio and an increase in power when the treatment effect is consistent across subtrials. We illustrate our approach using real data from two clinical trials in rheumatology.