Search Results (1,946)

Background: Individuals with type 2 diabetes mellitus (T2DM) are at markedly increased risk of developing coronary heart disease (CHD); however, the generalizability and transportability of existing prediction models remain uncertain. Objective: To identify and evaluate multivariable prognostic models developed to predict CHD in adults with T2DM. Methods: We conducted a PRISMA-guided systematic review and meta-analysis of multivariable prognostic models predicting CHD in T2DM populations. Model characteristics and performance metrics were extracted following the CHARMS and TRIPOD-SRMA frameworks, and pooled discrimination was estimated on the logit-transformed AUC scale using a random-effects model (REML, Hartung–Knapp adjustment). Between-study heterogeneity and 95% prediction intervals were quantified, while risk of bias and applicability were assessed using the PROBAST tool. Results: Thirteen studies encompassing clinical, imaging-based, and omics-augmented models met the inclusion criteria. The pooled AUC was 0.69 (95% CI: 0.66–0.71), with high heterogeneity (I² = 97.4%; τ² = 0.0979) and a wide 95% prediction interval (0.54–0.81). Classical regression-based models demonstrated modest discrimination, whereas machine learning, imaging, and proteomic approaches achieved higher AUC estimates but were frequently constrained by small sample sizes, internal-only validation, and poor calibration reporting. The analysis domain emerged as the principal source of bias in PROBAST evaluations, and applicability issues were most frequent in models requiring advanced imaging or molecular platforms. Conclusions: Prognostic models for CHD in T2DM demonstrate moderate-to-good discrimination but substantial heterogeneity and frequent miscalibration across studies. Their clinical utility depends on rigorous external validation and local recalibration, particularly when incorporating imaging or molecular predictors. Future research should prioritize standardized CHD outcomes, consistent calibration reporting, decision-analytic assessments, and the development of transportable multimodal prediction models across diverse populations. Full article

Casing collar locator (CCL) measurements are widely used as reliable depth markers for positioning downhole instruments in cased-hole operations, enabling accurate depth control for operations such as perforation. However, autonomous collar recognition in downhole environments remains challenging because CCL signals are often corrupted by toolstring- or casing-induced magnetic interference, while stringent size and power budgets limit the use of computationally intensive algorithms and specific operations require real-time, in situ processing. To address these constraints, we propose Collar Recognition Nets (CRNs), a family of domain-specific lightweight 1-D convolutional neural networks for collar signature recognition from streaming CCL waveforms. With depthwise separable convolutions and input pooling, CRNs optimize efficiency without sacrificing accuracy. Our most compact model achieves an F1-score of 0.972 on field data with only 1985 parameters and 8208 MACs, and deployed on an ARM Cortex-M7-based embedded system using the TensorFlow Lite for Microcontrollers (TFLM) library, the model demonstrates a throughput of 1000 inferences per second and 343.2 μs latency, confirming the feasibility of robust, autonomous, and real-time collar recognition under stringent downhole constraints. Full article

Culturally adapted cognitive behavioral therapy (CaCBT) is increasingly used to reduce disparities in mental health outcomes among ethnoculturally diverse populations. Although CBT is a well-established evidence-based intervention, little is known about CaCBT’s effectiveness across diagnostic groups and global contexts. This meta-analysis synthesizes CaCBT efficacy for common mental health conditions. Using PRISMA guidelines, five electronic databases were used to search for RCTs reporting mental health variables for CaCBT. Funnel plots, Egger’s test, and the trim-and-fill method were used to evaluate publication bias. Hedges’ g was used to compute effect sizes, and heterogeneity was assessed through DerSimonian and Laird I² statistics. Variations in populations, settings, and adaptation strategies were accounted for through random-effects models. Sixteen articles (n = 4787) met the inclusion criteria. CaCBT was associated with significant reductions in anxiety (g = −0.86, 95% CI [−1.66, −0.07], p = 0.032), somatic symptoms (g = −0.89, 95% CI [−1.61, −0.16], p = 0.016), and improved emotion regulation (g = 1.50, 95% CI [0.72, 2.28], p = 0.0002), though adjusted models reduced effects. For depression, PTSD, stress, and quality of life, pooled estimates favored CaCBT but did not reach statistical significance and were characterized by substantial heterogeneity. Significant heterogeneity was noted across studies, demonstrating diverse cultural contexts and intervention methods. CaCBT demonstrated significant benefits for anxiety, somatic symptoms, and emotional regulation across diverse groups. While depression and PTSD had varying outcomes, overall trends support this culturally responsive intervention’s efficacy. Further research on CaCBT, including understudied populations and standardized adaptation methods, could improve global mental health equity. Full article

Background/Objectives: The rates of adverse maternal and neonatal outcomes—including preterm birth < 37 weeks’ gestation (PTB), hypertensive disorders of pregnancy (HDP), gestational diabetes mellitus (GDM), small for gestational age (SGA), and large for gestational age (LGA)—remain elevated in the United States. Preventive strategies beyond the current standard of care (SOC) may be needed, particularly in diverse and socioeconomically vulnerable populations. The study evaluated a targeted diet and lifestyle intervention incorporating selected nutrient and gene variant analysis with personalized trimester-based counseling and supplementation (Standard of Care Plus, PLUS). Methods: The prospective observational study compared outcomes among participants receiving PLUS in addition to SOC with regional SOC data. A Nevada PLUS cohort (n = 15), consisting of high-risk participants with 100% Medicaid coverage, received the intervention virtually. An Oregon PLUS cohort (n = 387), consisting of moderate-risk participants with approximately 50% Medicaid coverage, received PLUS through in-person group sessions. Outcomes were compared with regional SOC rates and between PLUS cohorts. Cochran–Mantel–Haenszel (CMH) analyses were performed to account for site-level differences in pooled analyses. Primary outcome was PTB < 37 weeks’ gestation; secondary outcomes included HDP, GDM, SGA, and LGA. Results: The Nevada PLUS application was associated with lower adverse outcome rates compared with regional SOC; however, statistical significance was not observed, likely reflecting limited sample size. The Oregon PLUS cohort experienced statistically significant association with reductions across all five outcomes (all p < 0.001) compared to regional SOC. No statistically significant differences were observed between the Nevada (virtual) and Oregon (in-person) PLUS cohorts. In pooled analyses (n = 402), significant reductions compared with SOC were observed for PTB (RR = 0.23), HDP (RR = 0.11), GDM (RR = 0.06), SGA (RR = 0.25), and LGA (RR = 0.35) (all p < 0.001). Conclusions: The implementation of selected nutrient and gene variant analysis combined with targeted nutritional and lifestyle interventions, delivered in collaboration with standard obstetric care, was associated with reduced adverse maternal and neonatal outcomes. Interpretation of virtual delivery remains limited by small sample size. Full article

While extensive research on laser powder bed fusion has focused on optimizing process parameters to improve the performance of manufactured parts, the critical role of effective spatter particle removal in mitigating defects during manufacturing has not received commensurate attention. To address these issues, this study investigates the influence of a key parameter in the atmosphere protection system, namely, airflow velocity, on part performance. Methodologically, a combined approach of numerical simulation and experimental methods was employed to examine in detail the effect of airflow velocity on spatter removal efficiency and its corresponding contribution to the enhancement of formed Inconel 718 part quality. First, Computational Fluid Dynamics–Discrete Phase Model simulations identified an optimal airflow velocity of 0.57 m/s. Subsequently, experimental observations using a high-speed camera system revealed that velocities below 0.6 m/s led to spatter redeposition, resulting in pore and defect formation, whereas velocities exceeding 0.6 m/s increased spatter size and reduced molten-pool stability. The simulation and experimental results are consistent, demonstrating that an appropriate airflow velocity can effectively suppress defects and thereby improve the quality of the fabricated components. This research provides a viable pathway for significantly enhancing the mechanical properties of laser powder bed-fused Inconel 718. Full article

In metal additive manufacturing, the molten pool directly influences the performance of the fabricated components. Therefore, a comprehensive understanding of the molten pool behavior is essential for improving the quality of the parts and mitigating the formation of defects. Selective arc melting (SAM) is a promising additive manufacturing method for fabricating metal matrix composites. However, the melting and solidification process of the powder layer under the arc heat source remains unrevealed. This study aims to elucidate the formation mechanisms of surface morphology during SAM processing and the influence of carbide addition on the melting and solidification behavior of Inconel 718 powder. In this study, thin-walled parts of Inconel 718 and TiC/Inconel 718 composite were fabricated and their microstructures were studied. The melting and solidification behavior of Inconel 718 and TiC/Inconel 718 composite during single-track single-layer deposition was investigated using high-speed photography. Focusing on the differences in the sidewall surface morphology of the Inconel 718 and TiC/Inconel 718 composite parts, the edge feature formation of the deposition track of both materials was studied. Furthermore, the formation mechanism of the differences in forming height at different positions of the deposition track was explored. The results indicate that the melted material in the molten pool of Inconel 718 mainly comes from the mass transport of the beads generated around the molten pool, while the liquid material in the molten pool of TiC/Inconel 718 composite mainly comes from the in situ powder melted under the arc center. During the melting process of Inconel 718 powder, beads at the edge of the heating area come into contact with the boundary of the molten pool and solidify in situ, forming protrusion features. The randomness in the bead size leads to different volumes of molten material at different positions within the same time, thereby causing variations in building height. Full article

Background/Objectives: Mental health conditions such as anxiety, depression, and stress remain among the leading global causes of disability. Mindfulness-Based Interventions (MBIs) have gained increasing attention as effective non-pharmacological strategies for reducing psychological distress. Methods: This systematic review examined 30 randomized controlled trials and quasi-experimental studies involving over 24,000 participants to evaluate the impact of MBIs on mental health outcomes, with a specific focus on the contribution of gratitude-based components. Results: Studies varied in terms of population, duration, and format, with most demonstrating moderate to strong effects on symptom reduction, particularly in programs lasting 8 to 12 weeks. A random-effects meta-analysis was conducted, yielding a pooled effect size of Hedges’ g = −0.45, indicating a moderate improvement in psychological outcomes. Subgroup analyses revealed slightly stronger effects for anxiety (g = −0.56) than depression (g = −0.45). Gratitude-integrated MBIs demonstrated modestly enhanced emotional benefits, suggesting a synergistic role in improving well-being. Conclusions: The review found low evidence of publication bias and acceptable risk of bias, supporting the moderate results. The findings underscore the value of MBIs, particularly those integrating gratitude, as scalable, cost-effective interventions in clinical and educational settings. Full article

Plant domestication studies have traditionally focused on morphological factors that are under direct selection, e.g., fruit size, overlooking the consequences of domestication on ecosystem services. We addressed this knowledge gap by documenting for first time the soil carbon (C) sequestration potential in wild relatives and domesticated cultivars of breadfruit (Artocarpus), a long-lived tree crop. We evaluated aggregate-bound and bulk organic C pools in breadfruit wild relatives and domesticates in soils that varied in nitrogen (N) and phosphorus (P) fertility with management practices (fertilizer and mulch). We determined whether C levels were linked to plant domestication, abiotic factors (N, P, pH, and texture), or biotic factors with known links to C accrual (arbuscular mycorrhizal fungi (AMF), and microbial biomass). In low N or N: P soils, increasing breadfruit domestication was associated with reductions in macroaggregate C (by 50%) and bulk C (host determinism); these shifts were associated with AMF hyphal productivity (50% lower than in wild relatives), soil N and P, and microbial biomass. With a high soil N fertility, the levels of aggregate and bulk soil C were similar between wild relatives and domesticates (plasticity). Despite the limited number of cultivars sampled (n = 10) and the different management practices among sites, our findings suggest domestication effects on ecosystem services, especially those modulated by AMF and soil N fertility. The calculated soil C stocks averaged 99.5 Mg C/ha (range 70–122 Mg C/ha), supporting the possibility of C accrual in breadfruit agroforestry. Full article

This research examined the long-run effect of climate change on food security in Malawi, Madagascar, Mozambique and Zimbabwe using the Autoregressive Distributed Lag (ARDL) model. The study used nine variables for quantitative analysis using data for the four countries from the World Bank spanning from 2000 to 2023, using two models. The results were validated using the pooled mean group (PMG) estimator. The results from model 1 show that environmental temperature, fertiliser consumption, credit access, age dependency ratio, urbanisation and land size significantly affect the percentage of crop yields. The model 2 results show that all the aforementioned factors, including cereal temperature and yields, have an effect on the prevalence of malnutrition, which was a proxy for food security in this study. Furthermore, the study used the Granger causality test to indicate a unidirectional causality direction from both models’ independent variables to dependent variables. From the econometric analysis conducted, the findings highlight the urgent need for targeted interventions, such as promoting climate-resilient agriculture, expanding access to credit and social protection policies, to enhance nutritional well-being and improve resilience to climate shocks. Full article

Background/Objectives: Musculoskeletal disorders and their surgical treatment significantly affect global disability, healthcare utilization, and costs. Artificial intelligence (AI) is a key enabler of data-driven musculoskeletal care. Their applications include diagnostic imaging, surgical planning, risk prediction, rehabilitation, and digital health ecosystems. This narrative review synthesizes current evidence on the use of AI in orthopaedics and musculoskeletal care across five areas: diagnostic imaging, surgical planning and intraoperative augmentation, predictive analytics and patient-reported outcomes, rehabilitation intelligence and teleorthopaedics, and system-level management. An additional task is to identify translational gaps and priorities for safe, ethical, and equitable implementation of AI. Methods: A structured narrative review was conducted using targeted searches in PubMed, Scopus, and Web of Science supplemented by semantic and citation-based explorations in Semantic Scholar, OpenAlex, and Google Scholar. The main search period was January 2019 to December 2025. The retrieved peer-reviewed articles were analyzed for clinical relevance to human musculoskeletal care, quantitative outcomes, and the translational implications of the results. From the broader pool of eligible publications, 40 clinically relevant studies were selected for detailed synthesis covering imaging, surgical planning, predictive modeling, rehabilitation, and system-level applications. Owing to the significant heterogeneity in the model architectures, datasets, and endpoints, the results were organized into five predefined thematic areas. Results: The most mature evidence is for AI-assisted detection of bone fractures on radiographs, identification of implants, and use of sizing templates in preoperative planning for arthroplasty, where deep learning systems have achieved expert-level diagnostic performance (e.g., fracture detection sensitivity of approximately 90% and specificity of approximately 92% and implant identification accuracy of 97–99%) and improved the accuracy of preoperative planning compared to conventional templating. AI-based planning increases the likelihood of reducing intraoperative corrections, shortening surgery time, reducing blood loss, and improving the final functional outcomes. Predictive models can support the stratification of risk for complications, rehospitalizations, and patient-reported outcomes, although external validation remains limited and is often single-center at this stage of research. Emerging applications in rehabilitation and teleorthopaedics, including sensor-based monitoring and learning systems integrated with Patient-Reported Outcome Measures (PROMs), are conceptually promising, but are mainly limited to feasibility or pilot studies. Conclusions: AI is beginning to influence musculoskeletal care, moving beyond pattern recognition toward integrated, patient-centered decision support throughout the perioperative and rehabilitation periods. Its widespread use remains constrained by limited multicenter validation, dataset bias, algorithmic opacity, and immature regulatory and governance frameworks. Future work should prioritize prospective multicenter impact studies, repeatable revalidation of local models, integration of PROM and teleorthopedic data with health learning systems, and adaptation to changing regulatory requirements to enable safe, ethical, effective, and equitable implementation in routine orthopedic practice. Full article

Diagnosing cervical spine instability with flexion-extension radiographs is challenging, as current guidelines are based on limited cadaver studies and do not adequately account for level, vertebral size, or patient effort. There is a need for automated cervical instability metrics anchored to normative reference data, accompanied by evidence on how often abnormal findings occur in real clinical populations and which soft-tissue injury patterns they can detect. We developed and evaluated fully automated, radiographic-based cervical intervertebral motion (IVM) metrics—adapted from prior lumbar methods—using an FDA-cleared analysis pipeline that segments C2–C7 and derives rotation, translation, disc heights, and regression-based instability indices. Normative reference data were first established from flexion-extension radiographs of 341 asymptomatic volunteers after excluding radiographically degenerated levels. Abnormality prevalence was then estimated in two symptomatic cohorts: pooled preoperative clinical-trial radiographs and 881 patients with symptoms attributed to motor-vehicle accidents, excluding levels with <5° rotation to reduce unreliable data due to insufficiently stressed spines. Finally, potential diagnostic performance was assessed in a controlled cadaveric ligament-sectioning model (12 cadavers) using ROC analysis and Youden’s J thresholds. Across clinical cohorts, objective IVM abnormalities were uncommon. Prevalence increased when studies demonstrated adequate total C2–C7 motion, emphasizing the importance of patient effort. In cadavers, vertical instability metrics were most discriminative (AUC 0.96–0.97) with high sensitivity (0.89) and perfect specificity at optimal thresholds, whereas translation changed minimally with sectioning. These results support regression-based instability indices as promising candidates for standardized, physiology-guided cervical instability assessment. Full article

Oil spills represent a severe threat to aquatic ecosystems, requiring rapid and reliable detection methods to support environmental response. Hyperspectral imaging (HSI) offers high spectral resolution for distinguishing hydrocarbon types, but its effective use depends on the performance and robustness of deep learning (DL) models, especially under data-limited conditions. This study presents a systematic evaluation of convolutional neural network (CNN) configurations for oil spill classification in visible-near-infrared (VNIR) hyperspectral data, examining the influence of architectural depth and hyperparameters such as the number of convolutional kernels, neuron density, and dropout rate. Two architectures were tested across 54 configurations and two training set sizes (259 and 518 samples). Results show that a compact architecture with an additional max pooling layer achieved near-perfect accuracy (>0.99) with reduced complexity and greater robustness, outperforming its deeper counterpart. Importantly, this study reveals that under small-sample scenarios, optimal performance can still be achieved by carefully balancing model capacity, favouring moderate convolutional depth and high neuron density, while avoiding over-regularisation. These findings provide practical guidance for designing efficient CNNs for UAV-based oil spill monitoring and lay the groundwork for future integration into local real-time processing pipelines and transfer learning applications. Full article

Background/Objectives: In the field of computer vision, prominent solutions often rely on transformers and convolutional neural networks (CNNs). Researchers frequently incorporate CNNs and transformers in developing image classification models. This study aims to introduce an innovative CNN model inspired by the Generative Pretrained Transformer (GPT) architecture and assess its image classification capabilities. Methods: This study utilized three distinct biomedical image datasets to evaluate the efficacy of the proposed GPTNeXt model. The datasets encompassed (i) Alzheimer’s disease (AD) magnetic resonance (MR) images, (ii) blood images, and (iii) lung cancer images. The choice of these datasets aimed to showcase the GPTNeXt model’s versatile classification performance. The GPTNeXt model and a deep feature engineering approach based on it were developed. In this deep feature engineering model, features were extracted from the global average pooling layer of GPTNeXt, and a novel deep feature extraction method was employed. This method extracted features from the entire image and generated nine fixed-size patches. To identify the most informative features, iterative neighborhood component analysis (INCA) was applied. The classification phase involved three shallow classifiers to produce classification results. Results: The GPTNeXt-based feature engineering model was applied to the three aforementioned biomedical image datasets, achieving classification accuracies exceeding 98% for all of them. Conclusions: This study demonstrates the high effectiveness of the proposed approach, as evidenced by the exceptional classification performance on the selected biomedical image datasets. Additionally, a lightweight CNN was introduced, showcasing outstanding classification performance. Full article

As renewable energy sources (RESs) become increasingly prevalent, limitations on connecting new sources arise due to insufficient suitable locations and grid constraints. Existing RES installations introduce challenges such as generation variability, the necessity for costly reserves, and overproduction, which can lead to forced outages. In response, grid operators have adopted more flexible connection policies, notably “cable pooling”, which only restricts the power injected at a given node rather than the total capacity of the connected sources. This article proposes a method for optimal sizing of diverse RES combinations connected to high-voltage networks under cable pooling conditions from an investor’s perspective. The most prominent findings show the existence of a strong relationship between optimal RES sizing and composition on financial objectives, revenue sources, and market prices. Subsequent achievements involve demonstrating that the profitability of energy storage without subsidies is essentially limited to participation in the capacity market and that the reduction of RES generation depends on the investor’s financial objective, not on the market type. Full article

Background and Objectives: Gingival recession (GR) is a recognized periodontal condition that can expose the tooth root, imposing aesthetic, functional, and hypersensitivity concerns. We conducted this study to investigate xenogenic soft tissue substitutes as potential alternatives to the gold standard connective tissue graft (CTG) for the treatment of multiple GR. Materials and Methods: This systematic review and meta-analysis adhered to PRISMA guidelines and was registered in PROSPERO. A comprehensive search of PubMed, Web of Science, Scopus, and the Cochrane Library was conducted until October 2025 for randomized controlled trials (RCTs) comparing connective tissue graft (CTG) to xenogeneic substitutes (XCM or P-XADM) for treating multiple gingival recessions. Two reviewers independently performed study selection, data extraction, and risk of bias assessment using the RoB 2 tool, 2019 version. Data were pooled using a random-effects model to calculate mean differences (MD) and risk ratios (RR) with 95% confidence intervals (CI) for primary (mean root coverage, MRC; complete root coverage, CRC) and secondary outcomes (clinical attachment level, CAL; keratinized tissue width, KTW; gingival thickness, GT; probing depth, PD). Results: Sixteen RCTs (632 patients, 1878 recessions) were included. At 6 and 12 months, CTG demonstrated a significantly greater MRC than both XCM (MD −13.4% and −11.05%) and P-XADM (MD −11.63% at 12 months). CTG was also superior to XCM in achieving CRC at 6 months (RR = 0.71, 95% CI [0.62 to 0.82]). For secondary outcomes, CTG showed superior gains in CAL and KTW at 12 months compared with both xenogeneic materials. GT was significantly greater in the CTG than in the XCM group in 12 months. No significant differences were found in PD at all time points. Conclusions: CTG continues to have superior clinical outcomes in the treatment of multiple GR. However, xenogenic materials are a promising alternative, particularly when patient comfort and satisfaction are prioritized. Future well-designed trials with larger sample sizes and standardized outcomes are needed to validate their clinical benefits and long-term stability. Full article