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A single-nucleotide enhancer mutation overrides chromosomal sex to drive XX male development

Validation of the Arabic Levels of Emotional Awareness Scale (LEAS-Arb)

Emotional awareness is a cognitive skill that has gained recognition as being important for psychological and physical health. It is measured with the Levels of Emotional Awareness Scale (LEAS), which is a 20-item written performance measure in which an individual is asked to write about how they and another person would feel in a series of hypothetical scenarios. The LEAS has been translated and validated in 19 languages. The aim of the current paper is to describe the translation and validation of the LEAS in Arabic (LEAS-Arb). To achieve our goal, we recruited 134 Arabic-speaking adults ages18 and above to complete the LEAS in Arabic, along with additional self-report and performance measures that have been used in previous LEAS validation studies. Our findings support the reliability and validity of the Arabic translation of the LEAS originally created in English. The interrater reliability between the two scorers for the first ten study participants was very high, Cronbach’s α = 0.951 for self, 0.997 for other, and 0.962 for total LEAS scores. Internal consistency of the 20 LEAS items alpha scores for LEAS-Arb were self-Cronbach’s α = .81, other α = .96, and total α = .86. In addition, LEAS-Arb scores demonstrated convergent validity through expected associations with related constructs, including positive mood and alexithymia, consistent with prior LEAS validation studies. This study can facilitate the use of LEAS-Arb in varied settings across different Arabic speaking populations, which will allow better understanding of awareness of emotion in the Arab world and cross-culturally. This easily accessible measure can be used in clinical and research settings. Further research is needed to examine cross-cultural differences in emotional awareness as a function of culture and language.

Functional nutrition: a non-pharmacological approach to supporting cognitive Health

Serpina1e mediates the exercise-induced enhancement of hippocampal memory in male mice

Change management in hospital digital transformation: A roadmap to support planning and implementation for humanisation and efficiency through technology

Digitalisation offers promising solutions for improving efficiency and accessibility in healthcare. However, it is crucial to balance digital advancements with humanisation. The aim of this study is to support digital transformation in healthcare by striking a balance between the digital and human components, by proposing a roadmap for digital transformation, prioritizing people and organisational culture. An initial theoretical and contextual investigation guided the preliminary roadmap, addressing concerns and solutions from the literature. Then, an empirical study was conducted via interviews with Portuguese hospital managers, offering practical insights into initiatives for hospital digitalisation. These findings refined the roadmap with real-world perspectives. The roadmap is presented as a theoretically informed and empirically refined planning proposal; it has not yet been piloted in real-world settings or compared with existing implementation frameworks. Managers highlighted the human dimension as critical to successful digital transformation, reinforcing the need for such a roadmap, an integrated approach that considers technology, processes and people within organisational culture. Humanisation is treated as a measurable target, monitored through patient-experience and staff-experience indicators. Theoretically, this study advances the literature on change management and digital transformation in healthcare. Practically, it offers an exploratory roadmap to guide healthcare organisations in structuring, sequencing and monitoring digital transformation initiatives. Transferability to other contexts requires local tailoring and future piloting with broader stakeholder involvement.

AI-driven integration of Framingham Heart Study data with machine learning, deep learning, and explainable AI for enhanced pharmaceutical marketing

Abstract AI algorithms, in drug discovery, support target identification by recognizing biological patterns and molecular interactions linked to disease mechanisms. They further aid in lead compound optimization, virtual screening of large chemical libraries, and do drug design, thereby reducing time and cost constraints traditionally associated with laboratory-based approaches. This research proposes a novel framework that integrates the Framingham Heart Study (FHS)—a gold standard longitudinal dataset in cardiovascular research—with advanced machine learning (ML), deep learning (DL), and explainable artificial intelligence (XAI) techniques to predict the risk of death or survival probability based on cardiovascular risk factors and to enhance pharmaceutical marketing precision. The study leverages structured data from FHS, encompassing risk factors such as age, cholesterol levels, blood pressure, smoking status, and diabetes incidence, to model predictive relationships that inform patient-specific therapeutic interventions. Using reliability-centric ensemble ML approaches like random forest and XG Boost, alongside DL architectures including feedforward neural networks, the approach uncovers non-linear patterns and latent associations in patient behavior and treatment outcomes. To address the opacity often associated with complex models, XAI methods such as SHAP values and LIME are deployed to render outputs interpretable, thus aligning with medical ethics and regulatory standards. The results and Explainable AI methods, such as SHAP and LIME that are employed to interpret complex model predictions and ensure transparency for stakeholders, demonstrate how the insights can be translated into evidence-based pharmaceutical marketing mix modeling, supporting targeted interventions, efficient market segmentation, and personalized patient engagement strategies. The proposed approach bridges the gap between clinical research and marketing strategy, offering a data-driven pathway to enhance decision-making in the pharmaceutical sector while maintaining patient-centered practices. In this study, multiple machine learning and deep learning models, including support vector machine (SVM), random forest, XGBoost, logistic regression, feed forward neural network (FFNN), and multi-layer perceptron (MLP), were systematically evaluated for predicting cardiovascular disease outcomes using the Framingham Heart Study dataset. The SVM model demonstrated superior performance, achieving the highest test accuracy (96.65%), precision (96.55%), recall (87.50%), F1 score (91.80%), and ROC AUC (99.00%), outperforming all other baseline and deep learning models. In contrast, the FFNN and MLP models exhibited moderate performance, with final test accuracies of approximately 79.88% and 95.98%, respectively. The ensemble base learners, including XGBoost, random forest, and logistic regression, achieved lower accuracies (ranging from 71% to 79%) and reduced recall rates, indicating limitations in correctly identifying high-risk cases. Interpretability through LIME further validated the SVM’s robust decision boundaries and its alignment with clinically significant risk factors. Overall, the comparative results establish the SVM as the most reliable and generalizable predictive model for early cardiovascular risk stratification in the Framingham cohort.

Mode of action guided metagenomic natural product discovery reveals convergent evolution of a ClpP-targeting motif

Voxel-wise deep learning segmentation of hydroxyapatite and iodine in spectral photon-counting CT: A quantitative phantom study

Accurate non-invasive identification of hydroxyapatite (HA) deposits is important for diagnosing calcific musculoskeletal disease and quantifying vascular calcification, but conventional and dual-energy CT often struggle to distinguish HA from iodinated contrast because of overlapping attenuation, noise, and beam-hardening artifacts. Spectral photon-counting CT (SPCCT) offers improved energy resolution and spatial fidelity, yet most deep-learning approaches in spectral CT focus on continuous density regression or anatomical segmentation rather than direct voxel-wise material labeling. We developed SPFF–UNet, a spectral-preserving 3D segmentation model for direct classification of HA and iodine concentrations from five-bin SPCCT volumes without material-decomposition preprocessing. A cylindrical phantom containing twelve materials was scanned at 0.1 mm isotropic resolution, including five HA concentrations, three iodine concentrations, three soft-tissue equivalents, and water. SPFF–UNet integrates spectral squeeze-excitation, EnergyFiLM, and FourierGate to preserve and exploit multi-energy information throughout the network. The model was trained for thirteen-class voxel-wise segmentation and compared with five established 3D architectures under matched training conditions. SPFF–UNet achieved the best macro-averaged performance on a held-out phantom scan (Dice 0.72 ± 0.01, IoU 0.59 ± 0.01, sensitivity 0.73 ± 0.01, precision 0.71 ± 0.01), outperforming the strongest comparator, ResUNet++ (Dice 0.66 ± 0.02, IoU 0.46 ± 0.02, sensitivity 0.67 ± 0.02, precision 0.61 ± 0.03). Performance gains were concentrated in mid/low-contrast HA and low-concentration iodine, with reduced slice-wise variability and fewer HA–iodine misclassifications. These results suggest that preserving spectral information and applying targeted spectral modulation can improve concentration-aware voxel classification from SPCCT. This phantom-based proof-of-concept provides a basis for future in vivo validation.

Insulin resistance prediction from wearables and routine blood biomarkers

Correction: Predicting camouflage treatment outcomes in skeletal class III malocclusion using machine learning

Abiotic CO2 reduction promoted by carbonate and phyllosilicate minerals on the primitive seafloor

A new criterion for defining tunnel portal failure using the strength reduction method

Recently, the application of the strength reduction method (SRM) to stability analysis of tunnel portals has become a trend. The key to employing the SRM lies in selecting an appropriate failure criterion. It is analyzed that the application characteristics of traditional criteria. Additionally, it is proposed that a new failure criterion—the variational criterion. Based on the numerical models, the effectiveness of the aforementioned work is validated. The results show that the displacement mutation at characteristic points (Criterion Ⅰ) is cumbersome to apply and involves a substantial workload. The plastic zone penetration (Criterion Ⅱ) lacks quantitative and clear standards. The calculation program non-convergence (Criterion Ⅲ) lacks a mechanical explanation. The energy mutation (Criterion Ⅳ) can effectively reflect the failure mechanism of the model. But it requires considerable computational effort. The variational criterion addresses these shortcomings while providing results with a relative error of no more than 1.6% compared to other criteria. Moreover, this applicability and accuracy are largely unaffected by mesh densities, geometric dimensions, strength reduction factor intervals, mechanical parameters, and convergence criteria. The variational criterion offers a comprehensive indicator—the variational value, and employs a clear discrimination method—judging the sign of the variational value. This criterion can provide a new reference for failure discrimination in tunnel portals.

The potential directing role of chemokines for specific metastatic sites in breast cancer

Abstract Metastasis is the most life-threatening sequel in breast cancer (BC). The aim of the study is to assess the association of certain cytokines including IL4, IL-11, CCL-2, CCL-4, and CXCL12 with the site of metastases (lung, bone, brain, ovaries, and liver) in BC. The serum levels of IL4, IL-11, CCL-2, CCL4 and CXCL-12 were assessed in 175 BC patients compared to 50 control subjects using ELISA. The data were correlated to the sites of metastases, patients’ clinicopathological features and response to treatment. The mean age of the BC patients was 49.3 ± 9.5 years old. Distant metastasis was found in 69.1% (121/175) of the patients. There was a significant increase in IL4 ( p = 0.012 ), CXCL12 ( p < 0.001 ), and CCL4 ( p < 0.001 ) in BC patients compared to controls. Lung metastasis associated significantly with increased IL11 concentration (OR = 1.008, p  = 0.023). Brain metastasis associated with increased IL4 (OR = 1.009, p  = 0.025), and CXCL12 concentrations (OR = 1.004, p  = 0.031). Bone metastasis linked with CCL4 (OR = 0.991, p  = 0.017) and CCL2 (OR = 0.996, p  = 0.001). Low ER expression, brain, and liver metastasis were considered as potential independent risk factors for shorter disease-free survival (DFS) of BC patients ( p = 0.040 , 0.001 , and 0.003 ; respectively). IL4 (AUC = 0.699, p = 0.011) and CXCL12 (AUC = 0.700, p = 0.010) showed a diagnostic potential for BC brain metastasis. Combined expression of both IL4 and CXCL12 exhibited a 75% sensitivity and a 71.4% specificity (AUC = 0.768, p  = 0.001). IL11 associated with the diagnosis of BC lung metastasis (AUC = 0.629, p  = 0.024). While CCL2 showed a significant potential for the diagnosis of BC liver metastasis (AUC = 0.717, p  = 0.006). Chemokines have an important role in directing the tumor cells and colonization in a specific metastatic site.

Steric hindrance-mediated extracellular vesicle size fractionation for rapid prehospital diagnosis of intracerebral hemorrhage

Identification of neuronatin as a SERCA2b regulin-like protein and assessment of its aggregation propensity via coarse grained simulations

Neuronatin (NNAT) is small transmembrane protein involved in a wide range of physiological processes, such as white adipose tissue browning and neuronal plasticity, as well as pathological ones, such as Lafora disease caused by the formation of NNAT aggregates. However, its 3D structure is unknown, and its mechanism of action is still unclear. In this study the two most well-known NNAT isoforms (α and β) were modelled and the interaction with the SERCA2b calcium pump was assessed using computational methods. First, molecular docking identified the same binding region as the one described for phospholamban, a thoroughly described SERCA inhibitor. Then, analyses of the flux of water molecules during molecular dynamics simulations highlighted significant similarities between the behavior of SERCA2b when in complex with phospholamban, and when in complex with either NNAT isoform. These results suggest that NNAT could be considered a “regulin-like” protein. Additional all-atom and coarse-grained simulations of multiple copies of NNAT highlighted a significant aggregation potential of both NNAT isoforms, supporting experimental data.

Regular physical activity in midlife cuts risk of early death

Psychometric evaluation of the patient health questionnaire-9 in helping professionals: factor structure, gender invariance, and construct validity

Genetic genealogy of the Piast dynasty and related European royal families

Grassland restoration in typical wind-eroded regions effectively increase soil organic carbon

Soil organic and inorganic carbon (SOC and SIC, respectively) are the two most important carbon pools in the terrestrial carbon cycle, yet their responses to land use change in typical wind-eroded regions remain poorly understood. This study analyzed the carbon change patterns of four land use types Yanchi County, including the seasonal dynamics and driving factors of SOC, SIC, and total carbon storage under wind erosion background. According to filed measurement, the SOC and SIC contents in cropland were 3.0 g kg -1 and 12%, respectively. Compared with cropland, grassland restoration markedly increased SOC to 4.4 g kg -1 but reduced SIC to 2.7%, primarily due to enhanced organic matter inputs and the suppression of wind erosion. In contrast, shrubland restoration resulted in lower SOC (~2.4 g kg -1 ) and SIC (~2.5%) contents, likely because the slow decomposition of recalcitrant litter and coarse root biomass limited carbon turnover. Both SOC and SIC exhibited distinct vertical distribution patterns with depth, with SOC mainly concentrated in the 0–1 cm layer and SIC in the 1–5 cm layer. These contrasting profiles can largely be attributed to their dominant controlling factors: SOC was primarily regulated by vegetation cover, whereas SIC was strongly influenced by soil pH. Nevertheless, both carbon pools were sensitive to variations in wind erosion intensity and soil texture. These findings highlight distinct control processes over SOC and SIC, as well as underscore the surface soil (0–5 cm) as a critical interface mediating vegetation, erosion, and soil properties.

Data from smart watches reveal early signs of insulin resistance