Search Results (29,581)

Cosmetic dermatology has largely focused on topical applications targeting the stratum corneum. However, emerging evidence suggests that visible aging is a systemic readout of internal “organ clocks” and molecular dysregulation across the epidermis and dermis. This review proposes an “inside–out strategy” that seeks to re-conceptualize aesthetic vitality as a measurable indicator of systemic physiological resilience. The author describes theoretically proposed organ–skin axes, including the role of molecular signaling of kidney-derived klotho (KL1 fragment) via FGFR1-α–klotho complexes and muscle-derived irisin through the AMPK/PGC-1-α pathway in modulating skin homeostasis. Drawing on recent breakthroughs in non-human primate models (2023–2025), this synthesis explores the potential of systemic interventions—including nicotinamide adenine dinucleotide (NAD+) precursors (sirtuin 1 SIRT1 activators), senolytics (targeting BCL-2/p16), and glucagon-like peptide-1 (GLP-1) receptor agonists—as candidates to potentially synchronize these internal clocks. Furthermore, the review identifies direct regenerative interventions, such as retinoids (RAR/RXR signaling), chemical peels (HIF-1-α induction), exosomes (miR-21/29 delivery), and poly-L-lactic acid PLLA (mechanotransduction via YAP/TAZ), positioning them as potential physical and chemical epigenetic modulators that may support the restoration of cellular transcriptional fidelity. This article proposes a new paradigm for regenerative aesthetics that focuses on restoring the youthful phenotype by optimizing systemic molecular crosstalk and epigenetic transcriptional fidelity. Full article

Accurate fresh weight (FW) estimation is essential for growth monitoring and yield prediction in greenhouse fruit vegetables, but remains challenging due to the dynamic allocation between vegetative and reproductive organs. This study aimed to systematically evaluate modeling strategies for FW estimation in sweet pepper and identify which approach is most suitable under conditions of dynamic biomass partitioning. Non-destructive morphological measurements were collected under greenhouse cultivation, and allometric models based on geometric equations were established as baselines. Their performance was compared with machine learning (ML) models and ensemble learning frameworks. To address limited data availability, numerical data augmentation with Gaussian noise and a variational autoencoder was applied. Among the allometric models, the stick model combined with a sigmoid function showed the highest performance, with an $R^2$ of 0.80 for shoot FW and 0.54 for fruit FW. All ML models outperformed the allometric models, and the ensemble model achieved the highest predictive accuracy, with an $R^2$ of 0.96 for shoot FW and 0.89 for fruit FW. Data augmentation further improved predictive performance across all ML models, particularly for fruit FW prediction. Feature contribution analysis revealed that temporal progression was the dominant predictor of fruit FW, while structural traits played the primary role in shoot FW estimation. Ensemble-based ML, combined with data augmentation, provides a methodological framework for non-destructive FW estimation of sweet pepper in controlled environments such as greenhouses and smart farming systems. Full article

This study addresses the challenge of the difficult identification of organ structures in the standard sections of fetal abdominal ultrasounds. A deep learning-based multi-task model named SEG-FAUSP was developed to segment the core anatomical structures of seven key fetal abdominal ultrasound sections. We collected fetal abdominal ultrasound images from pregnant women in the mid-pregnancy period (18–24 weeks) using various mainstream ultrasound devices, and professional physicians annotated key anatomical structures (e.g., umbilical veins, gastric bubbles, spine) in the images. Based on an improved deep learning framework, the model accurately segments and locates the target organ structures through a parallel dual-branch semantic segmentation network, which avoids the over-reliance on large-scale pre-trained data in traditional methods. Experimental results show that the model achieves excellent performance in anatomical structure segmentation, with the intersection over union of the bladder and gastric bubble both reaching above 0.84; its segmentation accuracy for complex structures such as the inferior vena cava is also significantly superior to the baseline model. As an end-to-end model, it simplifies the clinical interpretation process of fetal abdominal ultrasound, reduces the risk of missed diagnoses caused by unclear organ identification, provides an efficient auxiliary tool for prenatal screening in grassroots medical institutions, and is of great significance for improving the quality of newborns. Full article

Background/Objectives: Plant architecture is a critical determinant of high-density tolerance and yield potential in maize (Zea mays L.), yet the genetic networks orchestrating these complex traits require deeper elucidation. Methods: In this study, we utilized a Multi-parent Advanced Generation Inter-cross (MAGIC) population comprising 935 recombinant inbred lines (RILs) derived from 16 diverse elite founders. A comprehensive phenotypic characterization of six pivotal architectural traits—plant height (PH), ear height (EH), ear leaf length (LL), ear leaf width (LW), tassel main axis length (TL), and tassel branch number (TBN)—was conducted across three distinct agro-ecological environments. Results: Phenotypic analysis revealed substantial natural variation and high broad-sense heritability (H² ranging from 60% to 86%), with TBN exhibiting the most pronounced variability. Correlation architecture demonstrated a strong coupling between vertical growth traits (PH and EH, r = 0.73), while lateral leaf expansion (LW) and tassel complexity (TBN) showed significant genetic independence. Using a mixed linear model (MLM) for genome-wide association studies (GWAS), we identified 21 significant SNP–trait associations, including distinct chromosomal clusters on chromosome 8 for EH and chromosome 7 for TBN. By integrating genomic intervals with tissue-specific expression profiling, 23 core candidate genes were prioritized. Notably, Zm00001d042528 (FAS1), involved in chromatin assembly, was implicated in modulating meristematic cell division for plant stature. Other key regulators included Zm00001d020537 (O5) and Zm00001d025360 (F-box protein), which were associated with reproductive organ development and leaf elongation, respectively. Conclusions: These results indicate that maize plant architecture is regulated by a modular genetic framework, with specific loci independently regulating canopy structure and source–sink components. It should be noted that the findings of this study are based solely on statistical models identifying significant associations between genetic loci and phenotypes; the biological regulatory functions of the candidate genes have not yet been experimentally validated. Nevertheless, this study provides new insights into the molecular mechanisms underlying maize morphogenesis and lays a solid theoretical foundation for molecular design breeding aimed at developing high-yielding varieties tolerant of high planting densities. Full article

Background: Artificial intelligence (AI) is transforming leadership and raising critical questions about decision-making, leadership capabilities, and ethical accountability in increasingly digitalized organizations. Objective: This systematic review synthesizes peer-reviewed evidence to answer: How does AI integration transform leadership and decision-making in organizations? Methods: A PRISMA 2020-compliant systematic review was conducted using structured Boolean searches in Scopus and Web of Science Core Collection on 26 February 2026. Eligibility was restricted to English-language, peer-reviewed, open-access journal articles with an explicit AI–leadership integration signal. Records were deduplicated and screened by two reviewers, with full-text assessment conducted against predefined criteria. A qualitative, narrative (conceptual) synthesis integrated heterogeneous empirical and conceptual contributions. Results: From 452 records, 84 studies met inclusion criteria. The synthesis identified three recurring analytical dimensions: (i) AI-augmented decision-making, (ii) leadership competencies and role shifts, and (iii) ethical challenges (accountability, transparency/opacity, fairness, privacy, and human agency). Integrating these dimensions, the review conceptualizes AI-driven leadership as a hybrid decision phenomenon in which AI accelerates and expands decision cycles, leaders reconfigure roles toward decision architecture and orchestration, and ethical conditions shape legitimacy, adoption, and authority dynamics. Conclusions: The review advances theory by specifying a mechanism-oriented model of AI-driven leadership and proposing testable propositions linking AI modality, role reconfiguration, and ethically conditioned legitimacy under key boundary conditions (e.g., sectoral stakes, governance capacity, and data/infrastructure readiness). Practically, it outlines an implementation pathway emphasizing decision criticality assessment, formalized human–AI task allocation, and institutionalized oversight mechanisms. Limitations: Findings are bounded by database selection and the open-access full-text constraint, which may under-represent paywalled scholarship. Full article

Regional reassessments of agricultural carbon emission efficiency are essential for improving the sustainability of food production systems under climate constraints. This study evaluates agricultural carbon emission efficiency (ACEE) across China’s major grain-producing zone (GPZ), major grain-consuming zone (GSZ), and grain production–consumption balanced zone (GBZ) during 2003–2022, excluding Hong Kong, Macao, Taiwan, and Tibet due to data limitations. A super-efficient EBM–GML model incorporating both desirable and undesirable outputs is employed to measure ACEE at the provincial level, with comparisons conducted within each functional zone and nationally unified efficiency values used as a benchmark. Spatial dependence is examined using Moran’s I, and a spatial Durbin model is applied to identify driving factors and spatial spillover effects. The results indicate that the average efficiency levels differ systematically across functional grain zones, following the order GBZ > GPZ > GSZ, while several provinces experience notable changes in their relative rankings. Carbon emissions increase in the earlier period and decline in later years, whereas efficiency exhibits an opposite temporal pattern, reflecting a gradual transition of grain production systems from extensive input-driven growth toward more sustainability-oriented practices. Substantial regional disparities in ACEE are also observed. Rational industrial organization and efficient allocation of production resources contribute to positive spillover effects on neighboring regions, whereas natural disasters and inefficient resource distribution tend to weaken such effects. These findings suggest that functional grain zones provide an effective framework for capturing intra-regional heterogeneity and should be adopted as the basic unit for efficiency assessment and the formulation of differentiated governance strategies. Full article

Background: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection and remains a leading cause of mortality in intensive care units (ICUs). Although the Sequential Organ Failure Assessment (SOFA) score is widely used for prognostic stratification, organ dysfunction represents a downstream manifestation of sepsis, whereas immune and inflammatory dysregulation may precede overt organ failure. Monocyte distribution width (MDW) is a novel hematological parameter reflecting monocyte activation and is approved for the diagnosis of sepsis; however, its prognostic value and potential role within composite biomarker models in critically ill surgical patients with sepsis remain incompletely defined. Methods: We conducted a prospective, observational, single-center pilot study in two surgical intensive care units between November 2022 and December 2023. Adult patients with sepsis defined according to Sepsis-3 criteria were enrolled. Laboratory and clinical variables—including MDW, neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), procalcitonin (PCT), and SOFA score—were measured on admission and during the first five days of ICU stay. Patient-level median values across five days were used for analysis. The primary outcome was in-hospital mortality. Prognostic performance was assessed using receiver operating characteristic (ROC) curve analysis and logistic regression. A composite bioscore was constructed by combining dichotomized MDW, NLR, CRP, and PCT values. Results: Sixty patients were included; 24 (40%) died during hospitalization. Non-survivors were older and had significantly higher SOFA scores. MDW, NLR, CRP, and PCT were significantly higher in non-survivors. SOFA demonstrated the strongest discriminative ability for mortality prediction (AUC 0.839, 95% CI 0.730–0.948). Among biomarkers, NLR (AUC 0.741) and PCT (AUC 0.714) showed good discriminative performance, while MDW (AUC 0.690) and CRP (AUC 0.662) showed moderate discrimination; MDW exhibited the highest specificity (80.6%). In multivariable analysis with individual biomarkers, only SOFA remained an independent predictor of mortality. The composite bioscore demonstrated good discriminative ability (AUC 0.805) and, when evaluated alongside SOFA, remained independently associated with fatal outcome (OR 11.92, 95% CI 1.76–80.75); however, given the modest sample size and wide confidence intervals, this finding should be interpreted with caution. Repeated-measures correlation analysis revealed no strong collinearity among biomarkers. Conclusions: A composite bioscore incorporating MDW, NLR, CRP, and PCT provides prognostic information comparable to SOFA and remains independently associated with mortality. This approach may complement organ dysfunction-based assessment and support early risk stratification in sepsis. Full article

Sustaining employees’ work engagement remains a critical challenge for contemporary organizations, particularly in contexts marked by increasing complexity and changing job demands. Despite extensive attention to engagement, less is known about how leadership behaviors contribute to engagement through employees’ perceptions of it with their organization. Drawing on fit theory, the present study examines the relationship between leadership behaviors and work engagement, focusing on the role of person–organization fit. To this end, the current paper presents a theoretical model positing value congruence (a core element of person–organization fit) as a key factor in mediating the positive relationship between the perceptions of leadership behaviors and work engagement. To test this assertion, a two-wave study using a final sample of 143 employees from a food industry company was designed. Results from structural equation modelling (SEM) supported the hypothesized model, such that, over time, the perceptions of leadership behaviors were associated with higher value congruence, which, in turn, was associated with higher work engagement. In addition, value congruence fully mediated the relationship between the perceptions of leadership behaviors and work engagement, even after controlling for the effect of gender, age, and organizational tenure. Practical implications, limitations, and directions for future research are discussed. Full article

To address the current issues in soil organic carbon (SOC) content prediction where data preprocessing relies on expert experience to formulate fixed rules, resulting in a lack of uniform standards and insufficient consideration of regional soil heterogeneity; while hyperparameter tuning faces problems of high computational costs and excessively long runtimes, this study proposes an intelligent modeling workflow driven by Large Language Models (LLM). This workflow focuses on optimizing two key aspects of SOC Random Forest modeling: data preprocessing and hyperparameter tuning. Results: The LLM-defined rules achieved sample retention rates of 55.33% and 61.90% in the two regions, respectively, showing more significant differences compared to traditional hard-coded rules (56.2% and 59.3%), and the mean soil organic carbon content deviations (30.27% and 20.05%) were both lower than those of traditional hard-coding. At the same time, the mean soil organic carbon content values in both regions closely matched the effectiveness of other methods, indicating that the large language model has effectively captured regional soil differences. With only a single evaluation of hyperparameter optimization, the adaptive model achieved test set R² values of 0.394 and 0.694 in the black soil region and the aeolian sandy soil region, respectively, with root mean square error values of 8.76 g/kg and 6.07 g/kg—its performance is comparable to that of Grid Search and Random Search, while computational efficiency improved by over 95%. Performance comparisons with eXtreme Gradient Boosting (XGBoost) and Partial Least Squares Regression (PLSR) show that the LLM-optimized Random Forest achieved R² = 0.394 and RMSE = 8.76 g/kg in the black soil region, and R² = 0.694 and RMSE = 6.07 g/kg in the windblown sandy soil region, demonstrating practical application value. Full article

With the continuous integration of a large amount of renewable energy sources such as wind and solar power into the power system, the economic and secure scheduling of the power grid, as a crucial carrier for electricity transmission, becomes of paramount importance. However, issues such as voltage fluctuations at grid nodes, low renewable energy consumption rates, and increased active power losses, caused by the widespread integration of high proportions of renewable energy, urgently need to be addressed. To effectively solve these problems, this paper proposes a multi-objective coordinated optimization scheduling method for the economy and security of source–grid–load–storage based on an effective scenario-screening approach. Firstly, an iterative self-organizing data analysis algorithm based on density noise application spatial clustering is designed to efficiently generate typical output scenarios for renewable energy sources such as wind and solar power. Meanwhile, to achieve low-carbon scheduling objectives, green certificate and carbon trading mechanisms are introduced. A multi-objective coordinated scheduling and trading model for the economy and security of large power grids, sources, loads, and storage is constructed with the goal of enhancing renewable energy consumption, and it is solved using the weight assignment method and an improved particle swarm optimization algorithm. Finally, the effectiveness and feasibility of the proposed method are validated and illustrated based on an improved IEEE standard node test system. Full article

Background: Addictive disorders are highly heterogeneous and frequently comorbid, limiting the clinical utility of categorical diagnoses. Transdiagnostic pharmacology seeks to address these limitations by targeting symptom dimensions and shared neurobiological processes across addictions. Methods: We conducted a theory-driven narrative review of studies indexed in MEDLINE, PubMed, LILACS, and Web of Science (October–November 2025), integrating clinical, mechanistic, and dimensional evidence. Findings were organized using the Dysregulation Phenomena of the Three Main Modes of the Predostatic Mind and the Advanced Cognitive Emotional Regulation Therapy (DREXI3/ACERT) framework, which conceptualizes addiction as dysregulation across three interacting systems—Alarm, Seeking, and Balance—and six transdiagnostic symptom dimensions, with a proposed expansion into twenty clinically observable domains (TDPM-20). Results: Pharmacological interventions consistently target neurobiological systems related to stress, reward, impulsivity, and compulsivity. Across studies, the most clinically relevant outcomes remain abstinence, reduction in substance use, and treatment retention. While these outcomes are essential, expanding outcome frameworks to incorporate dimensional and mechanistically informed measures may enhance the identification of clinically meaningful subgroups. Across studies, multiple pharmacological classes show transdiagnostic potential, but their clinical application remains variably aligned with dimensional clinical profiles. Conclusions: A dimensionally oriented approach grounded in neurobiological principles may improve alignment between clinical processes and therapeutic strategies. The DREXI3/ACERT model provides a structured framework for individualized treatment planning and research integration. This approach should be understood as complementary to, rather than a replacement for, established evidence-based treatments for specific substance use disorders, particularly in contexts where therapeutic options remain limited or insufficient. Advancing transdiagnostic pharmacology will require broader dimensional stratification, expanded outcome frameworks capable of capturing patient heterogeneity, and integrative trial designs to strengthen precision psychiatry in addictive disorders. Full article

Background/Objectives: Systemic lupus erythematosus (SLE) is frequently accompanied by psychological distress. Alexithymia, an impairment in identifying and describing emotions, has been reported in SLE, but its clinical and serological correlates remain insufficiently characterized. We aimed to estimate the prevalence of clinically significant alexithymia in SLE and to explore its clinical, laboratory, and coping-related correlates. Methods: In this cross-sectional observational study, adult outpatients fulfilling the 2019 ACR/EULAR SLE classification criteria were assessed at a tertiary referral centre (2024–2025). Alexithymia was measured using the Toronto Alexithymia Scale-20 (TAS-20), and clinically significant alexithymia was defined as a total score >60. Coping strategies were assessed with the 60-item COPE inventory (Italian version). Clinical indices (SLEDAI-2K, Lupus Low Disease Activity State (LLDAS), and SLICC/ACR Damage Index (SDI)), organ involvement, antiphospholipid syndrome (APS), selected autoantibodies, complement levels, and treatments were recorded. Group comparisons and exploratory logistic regression were performed. Results: Sixty-eight patients were included (94.1% female). Clinically significant alexithymia was present in 23.5%. In univariate analysis, alexithymia was more frequent among patients with APS. Alexithymic participants reported higher use of emotional venting and lower use of positive reinterpretation. In an exploratory multivariable logistic regression model, APS (adjusted OR 35.79, 95% CI 3.74–341.7), emotional venting (adjusted OR 1.684, 95% CI 1.162–2.44), and positive reinterpretation (adjusted OR 0.514, 95% CI 0.349–0.755) remained associated with alexithymia. Conclusions: Alexithymia was frequent in this SLE cohort and, in exploratory analyses, was associated with APS and specific coping patterns. These findings suggest that assessment of emotional processing and coping may provide complementary clinical information, particularly in patients with APS, but should be interpreted as associative and hypothesis-generating. Full article

Care home staff are routinely exposed to stressful and traumatic events, increasing risks of psychological distress, burnout, and reduced workforce resilience. Psychological First Aid (PFA), recommended by the World Health Organization, provides an evidence-based framework for delivering immediate emotional and practical support; however, its adaptation for care home contexts is limited. This study aimed to co-produce and adapt an existing PFA training resource for care home staff using a person-based approach (PBA) to enhance contextual relevance, acceptability, and feasibility. A two-phase qualitative design guided by PBA principles was used. Phase 1 integrated stakeholder workshops, semi-structured interviews, and literature review to generate guiding principles, a logic model, and preliminary training content. We adapted the WHO PFA “Look–Listen–Link” framework alongside existing open-access materials. Phase 2 used think aloud interviews to optimize usability and contextual fit. Thematic and sentiment analysis identified key needs: high exposure to traumatic events, inconsistent organisational support, desire for measurable skill development, the importance of transferable competencies, and motivational factors. Participants emphasized the need for flexibility, inclusivity, and realistic care-home-specific examples. Adaptations included bite-sized interactive modules, blended delivery options, and reflective exercises. The final co-produced intervention aligns with trauma-informed principles and organisational realities. Further work is needed to access feasibility, acceptability, and fidelity in real-world settings, offering a transferable model for adapting psychological interventions in other high-stress care environments internationally. Full article

Ubiquitin-specific protease 2 (USP2) is a deubiquitinase that controls various cellular events, including cell cycle progression and tumorigenesis. Along with cell culture models, mouse models induced using chemical blockers and gene engineering have substantially contributed to our knowledge of the crucial roles of USP2 in energy metabolism and metabolic disorders. This review summarizes the evidence of the role of USP2 in regulating energy metabolism in mice and cells under physiological and pathological conditions. In hepatocytes, a short isoform of USP2, USP2b, aggravates type 2 diabetes and metabolic dysfunction-associated steatotic liver disease. Meanwhile, a long isoform of USP2 in adipose tissue macrophages, USP2a, attenuates the onset of diabetes. USP2a mitigates insulin resistance and subsequent muscle atrophy. In ventromedial hypothalamic neurons, USP2b inhibits an increase in blood glucose by repressing hepatic glycogenolysis. In addition to regulating diabetes, USP2 isoforms potentially regulate the progression of atherosclerosis by modulating macrophages and hepatocytes. In brown adipose tissue, USP2a regulates thermogenesis, thus influencing systemic energy control. Meanwhile, in testicular macrophages, USP2 protects the mitochondrial respiration of sperm and consequently contributes to maintaining the quality of frozen sperm for use in the treatment of male infertility. As USP2 is distributed to multiple cellular components, it mediates the polyubiquitination of various molecules. For instance, USP2 modulates the stability of various transcription regulators, including C/EBP-α, PPARγ, EBF2, and PGC1α. The accumulating evidence indicates that USP2 functions as a modulatory molecule for energy metabolism across organs. Full article

From falcons spotting prey to humans recognizing faces, the ability to rapidly process visual information depends on a foveated retinal organization that provides high-acuity central vision while preserving low-resolution peripheral vision. This organization is conserved along early visual pathways, yet remains under-explored in machine learning. Here, we examine the impact of embedding a foveated retinotopic transformation as a preprocessing layer on convolutional neural networks (CNNs) for image classification. By applying a log-polar mapping to off-the-shelf models and retraining them, we achieve comparable accuracy while improving robustness to scale and rotation. We demonstrate that this architecture is highly sensitive to shifts in the fixation point and that this sensitivity provides an effective proxy for defining saliency maps that facilitate object localization. Our results demonstrate that foveated retinotopy encodes prior geometric knowledge, providing a solution for visual searches and a meaningful classification robustness and localization trade-off. These findings provides a proof of concept in order to connect principles of biological vision with artificial networks, suggesting new, robust and efficient approaches for computer vision systems. Full article