Search Results (1,110)

Alopecia areata (AA) is an autoimmune-mediated nonscarring alopecia with limited therapeutic options and frequent relapses. Exosomes, nanosized extracellular vesicles secreted by various cell types, have recently emerged as potential regenerative and immunomodulatory therapies. The aim of the study is to review the clinical and preclinical evidence regarding the efficacy and safety of EV-based therapies for alopecia areata. a systematic search of PubMed, Embase, Web of Science, and Cochrane Library was performed from 2020 to 2 October 2025. Inclusion criteria were original studies (clinical, preclinical, in vivo, in vitro) investigating exosome-derived interventions for AA. Outcomes of interest were hair regrowth, immune modulation, follicular regeneration, and safety. A total of 499 records were retrieved from electronic database searches. After deduplication and application of the inclusion/exclusion criteria, 40 studies met the eligibility criteria for the review. Of these, two were clinical studies (one retrospective cohort, one case report), while the remainder comprised five animal (in vivo) studies, six in vitro studies, and sixteen mixed translational studies (in vitro/in vivo ± clinical). Experimental studies reported hair coverage improvements of 50–99% and, in one instance, 30% regrowth in totalis and 16% in partialis, with nearly complete regrowth in incipient alopecia. Clinical reports noted density increases of 9–31 hairs per cm² (e.g., from 121.7 to 146.6 hairs/cm², p < 0.001) and improvements in hair count, length, and thickness. Several studies detailed activation of the Wnt/β-catenin pathway along with enhanced dermal papilla and hair follicle stem cell function, as well as anti-inflammatory effects. Reported safety profiles were favorable; when adverse events occurred, they were limited to mild, transient local reactions with no severe systemic issues. EV-based therapy is a novel and biologically plausible approach for AA, but robust randomized controlled trials (RCTs) are lacking. Standardization of small EV sources, doses, and delivery methods is essential before clinical translation. Full article

Static voltage stability (SVS) is a critical aspect of the safe and efficient operation of electrical power systems (EPS), as it reflects the system’s ability to maintain adequate voltage levels in the face of progressive increases in demand under steady-state conditions. Traditionally, improving SVS has been addressed by compensating reactive power using FACTS devices. However, this research introduces an alternative methodology based on the hybridization of transmission technologies, integrating HVAC and HVDC links in parallel, to increase the stability margin and optimize performance in the event of contingencies. The proposed methodology is based on the resolution of the optimal AC power flow (OPF-AC) and the analysis of P-V curves to evaluate the displacement of the critical collapse point. The validity of the approach was verified through simulations in the Generation-Infinite Busbar and IEEE 9-busbar models, using the DIgSILENT PowerFactory environment. The results obtained show significant improvements in the SVS margin: an increase of 4.6% in the infinite busbar generation system, 9.5% in the critical busbar of the IEEE 9-busbar system, and 7.6% in the critical busbar of the IEEE 30-busbar system. In addition, the hybrid scheme showed a 17.1% reduction in real power losses and a more efficient redistribution of energy flows, which translates into a decrease in line load capacity. It should be noted that, under an N-1 contingency scenario, the hybrid system showed a 13.3% improvement in maximum power transfer before collapse, confirming its effectiveness under critical conditions. These findings position HVAC/HVDC hybridization as a robust and scalable alternative for strengthening voltage stability in modern electrical systems subject to operational variability. Full article

Background: Traumatic brain injury (TBI) initiates a complex sequence of inflammatory, glial, and metabolic events that evolve dynamically and contribute substantially to secondary brain injury. This study aimed to characterize the temporal serum dynamics of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), glial fibrillary acidic protein (GFAP), and insulin following severe diffuse TBI in a rat model, with the goal of delineating the coordinated progression of inflammatory, astroglial, and metabolic responses. Methods: Severe diffuse TBI was induced in adult male Sprague–Dawley rats using the Marmarou weight-drop model. Animals were randomized into five groups (sham, 1 h, 6 h, 24 h, 72 h; n = 10 per group). Serum TNF-α, IL-6, GFAP, and insulin levels were quantified using ELISA assays. Group differences were assessed using one-way ANOVA with Tukey’s post hoc test or Kruskal–Wallis analysis with Dunn’s correction where appropriate. Results were expressed as mean ± SD. Results: TNF-α demonstrated a biphasic pattern, declining at 6 h before peaking significantly at 24 h (p < 0.05) and subsequently decreasing at 72 h. IL-6 exhibited mild suppression at 6 h followed by a significant secondary elevation at 24 h (p < 0.05), with persistently elevated levels at 72 h. GFAP showed delayed kinetics, decreasing at 6 h but rising progressively to a peak at 24 h, consistent with subacute astroglial activation. Insulin levels declined at 6 h and increased significantly at 24 h and 72 h (p < 0.05), indicating evolving metabolic adaptation. Overall, cytokine activity preceded glial and endocrine changes, revealing a sequential inflammatory–glial–metabolic cascade. Conclusions: This study delineates the temporal serum profiles of TNF-α, IL-6, GFAP, and insulin after severe diffuse TBI, revealing a coordinated transition from acute inflammation to astroglial activation and metabolic adaptation. These results support the utility of multimodal biomarker panels for phase-specific characterization of secondary injury and identify GFAP and IL-6 as promising subacute markers with translational relevance. The findings should be interpreted as descriptive temporal patterns rather than mechanistic evidence, pending confirmation with complementary molecular analyses. Full article

Background: Insect metamorphosis is one of the most fascinating developmental processes in the natural world. Complete metamorphosis requires the breakdown and reorganisation of larval tissues and the coordinated construction and development of adult structures. The molecular events that achieve this transformation are, however, incompletely understood, and there is a particular shortage of data describing changes in protein abundance that occur during the process. Methods: Here, using a label-free quantitative bottom-up approach, we perform a novel whole-organism proteomic analysis of consecutive developmental stages of male Bicyclus anynana butterflies as they develop from caterpillars into adults via pupation. Results: Our analysis generated a dynamic reference dataset representing 2749 detected proteins. Statistical analysis identified 90 proteins changing significantly in abundance during metamorphosis, and functional interpretation highlights cuticle formation, apoptosis and autophagy during the pupal stages, and the up-regulation of respiration and energy metabolism upon completion of the fully formed adult. A preliminary search for potential peptide phosphorylation modifications identified 15 candidates, including three proteins with roles in muscle function. Conclusions: The study provides a basis for future protein-level analysis of butterfly metamorphosis and suggests the importance of dissecting the post-translational regulation associated with this fascinating developmental transformation. Full article

This study introduces a new ensemble framework for demographic forecasting that systematically incorporates stylised crisis scenarios into rate and population projections. While scenario reasoning is common in qualitative foresight, its quantitative application in demography remains underdeveloped. Our method combines autoregressive lags, global predictors, and robust regression with a trend-anchoring mechanism, enabling stable projections from short official time series (15–20 years in length). Scenario shocks are operationalised through binary event flags for pandemics, refugee inflows, and financial crises, which influence fertility, mortality, and migration models before translating into cohort and population trajectories. Results demonstrate that shocks with strong historical precedence, such as Germany’s migration surges, are convincingly reproduced and leave enduring effects on projected populations. Conversely, weaker or non-recurrent shocks, typical in Norway and Portugal, produce muted scenario effects, with baseline momentum dominating long-term outcomes. At the national level, total population aggregates mitigate temporary shocks, while cohort-level projections reveal more pronounced divergences. Limitations include the short length of the training series, the reduction of signals when shocks do not surpass historical peaks, and the loss of granularity due to age grouping. Nevertheless, the framework shows how robust statistical ensembles can extend demographic forecasting beyond simple trend extrapolation, providing a formal and transparent quantitative tool for stress-testing population futures under both crisis and stability. Full article

Valine-glutamine motif proteins (VQ), plant-specific transcriptional co-regulators harboring the conserved FxxhVQxhTG motif, play pivotal roles in coordinating plant stress adaptation through dynamic interactions with WRKY transcription factors (WRKY), mitogen-activated protein kinases (MAPKs) cascades, and hormone signaling pathways. Evolutionary analyses reveal the characteristics of their evolutionary protection and ancient origin, with lineage-specific expansion via genome duplication events. Structurally, compact genes lacking introns and the presence of intrinsic disordered regions (IDRs) facilitate rapid stress responses and versatile protein interactions. Functionally, VQ proteins orchestrate abiotic stress tolerance (e.g., drought, salinity, temperature extremes) by modulating reactive oxygen species (ROS) homeostasis, osmotic balance, and abscisic acid/salicylic acid (ABA/SA)-mediated signaling. Concurrently, they enhance biotic stress resistance via pathogen-responsive WRKY-VQ modules that regulate defense gene expression and hormone crosstalk. Despite advances, challenges persist in deciphering post-translational modifications, tissue-specific functions, and cross-stress integration mechanisms. Harnessing CRISPR-based editing and multi-omics approaches will accelerate the exploitation of VQ genes for developing climate-resilient crops. This review synthesizes the molecular architecture, evolutionary dynamics, and multifunctional regulatory networks of VQ proteins, providing a roadmap for their utilization in sustainable agriculture. Full article

In plants, cytidine-to-uridine (C-to-U) and uridine-to-cytidine (U-to-C) editing events are directed by pentatricopeptide repeat (PPR) proteins, modular RNA-binding factors that recognize their RNA targets through a predictable amino acid–nucleotide recognition code. Deciphering this code has enabled the rational design of synthetic PPR (synPPR) proteins with programmable RNA-binding specificity and robust stability in heterologous systems. Recent advances have extended these synthetic scaffolds to active RNA editors by fusing them to catalytically competent DYW deaminase domains, generating customizable enzymes capable of precise base conversion in bacteria, plants, and even human cells. This review summarizes current understanding of the structural and mechanistic principles underlying PPR-mediated RNA editing and highlights recent progress in the design and application of synPPR proteins. We discuss how synthetic PPR proteins have been used as programmable RNA stabilizers, translational regulators, and targeted C-to-U or U-to-C editors, as well as their emerging therapeutic potential in RNA-mediated diseases. The development of compact, cofactor-independent editors derived from early-diverging plant lineages further expands the versatility of this platform. Together, these efforts establish synthetic PPR proteins as a powerful and flexible class of RNA engineering tools with applications spanning basic research, biotechnology, and biomedicine. Continued refinement of targeting specificity, catalytic efficiency, and effector modularity will propel PPR-based editors toward broader use in synthetic biology and therapeutic RNA modulation. Full article

Cardiac implantable electronic devices (CIEDs) such as pacemakers, implantable cardioverter-defibrillators (ICDs), and cardiac resynchronisation therapy (CRT) devices are generating unprecedented volumes of data in both inpatient and remote settings. Artificial intelligence (AI) techniques are increasingly being applied to enhance the management of these devices and the patients who rely on them. Recent advances demonstrate that machine learning (ML) and deep learning (DL) can improve diagnostic capabilities (for example, by detecting arrhythmias and predicting clinical events), streamline remote monitoring workflows, and optimise device-based therapies. Key applications include AI-driven algorithms that accurately detect true arrhythmias while filtering out false alerts from pacemakers and implantable monitors, neural network models that predict ventricular arrhythmias weeks before ICD shocks, and personalised models that forecast which heart failure patients will respond to CRT. Moreover, novel approaches such as natural language processing (NLP) and reinforcement learning are being explored to integrate diverse data sources and to enable devices to self-adjust their programming. This narrative review summarises the major applications of AI in the CIED domain—diagnostics, remote monitoring, and therapy optimisation—with an emphasis on the recent literature over the past five years. The review highlights important studies and randomised trials in each area, discusses the variety of AI techniques employed, and outlines future directions and challenges (including data standardisation, validation in clinical trials, and regulatory considerations) for translating these innovations into routine clinical care. Full article

Neurological disorders and cardiovascular disease (CVD) remain leading causes of global morbidity and mortality and often coexist, in part through shared mechanisms of chronic inflammation and oxidative stress. Neuroinflammatory signaling, including microglial activation, cytokine release, and impaired autonomic regulation, contributes to endothelial dysfunction, atherosclerosis, hypertension, and stroke, while cardiac and metabolic disturbances can reciprocally exacerbate brain pathology. Increasing evidence shows that several antidiabetic agents exert pleiotropic anti-inflammatory and antioxidant effects that extend beyond glycemic control. Metformin, SGLT2 inhibitors, DPP-4 inhibitors, and GLP-1 receptor agonists modulate key pathways such as AMPK, NF-κB, Nrf2 activation, and NLRP3 inflammasome suppression, with demonstrated vascular and neuroprotective actions in preclinical models. Clinically, GLP-1 receptor agonists and SGLT2 inhibitors reduce major cardiovascular events, improve systemic inflammatory markers, and show emerging signals for cognitive benefit, while metformin and DPP-4 inhibitors exhibit supportive but less robust evidence. This review synthesizes molecular, preclinical, and clinical data across drug classes, with particular emphasis on GLP-1 receptor agonists, and highlights outstanding translational questions including blood–brain barrier penetration, biomarker development, optimal patient selection, and timing of intervention. We propose a unified framework to guide future trials aimed at leveraging antidiabetic therapies such as DDP-4 anti-inflammatory and antioxidant interventions for neurological and cardiovascular diseases. Full article

Despite the groundbreaking impact of currently approved CAR T-cell therapies, substantial unmet clinical needs remain. This highlights the need for CAR T treatments that are easier to tune, combine, and program with logic rules, in oncology and autoimmunity. Modular CAR T cells use a two-part system: the CAR on the T cell binds an adaptor molecule (AM), and that adaptor binds the tumour-associated antigen (TAA). This design separates recognition of the target antigen and activation of the T cells, resulting in a cellular therapy concept with better control, flexibility, and safety compared to established direct-targeting CAR T-cell systems. The key advantage of the system is the adaptor molecule, often an antibody-based reagent, that targets the TAA. Adaptors can be swapped or combined without re-engineering the T cells, enabling straightforward multiplexing and logic-gated control. The CAR itself is designed to recognise the AM via a unique tag on the adaptor. Only when the CAR, AM, and antigen-positive target cell assemble correctly is T-cell effector function activated, leading to cancer cell lysis. This two-component system has several features that need to be considered when designing a modular CAR: First, the architecture of the CAR, i.e., how the binding domain and the backbone are designed, can influence tonic signalling and activation/exhaustion parameters. Second, the affinity of CAR–AM and AM–TAA will mostly define the engagement kinetics of the system. Third, the valency of the AM has an impact on exhaustion and non-specific activation of CAR T cells. And lastly, the architecture of the AM, especially the size, defines the pharmacokinetics and, consequently, the dosing scheme of the AM. The research conducted on direct-targeting CAR T cells have generated in-depth knowledge of the advantages and disadvantages of the technology in its current form, with remarkable clinical success in relapsed/refractory disease and long-term survival in otherwise difficult-to-treat patient populations. On the other hand, CAR T-cell therapy poses the risk of severe adverse events and antigen loss coupled with antigen-negative relapse which remains the main reason for failed therapies. Addressing these issues in the traditional setting of one CAR targeting one antigen will always be difficult due to the heterogeneous nature of most oncologic diseases, but the flexibility to change target antigens and the modulation of CAR T response by dosing the AM in a modular CAR system might be pivotal to mitigate these hurdles of direct CAR T cells. Since the first conception of modular CARs in 2012, there have been more than 30 constructs published, and some of those have been translated into phase I/II clinical trials with early signs of success, but whether these will progress into a late-stage clinical trial and gain regulatory approval remains to be seen. Full article

In volatile economic environments, corporate resilience is a prerequisite for sustainable development. This study explores the non-linear impact of controversial events on corporate resilience using a sample of 4430 listed Chinese firms from 2018 to 2023. By applying the Double Machine Learning (DML) framework and Generalized Additive Models (GAMs), we uncover a distinct non-linear effect: mild controversies act as “stress tests” enhancing resilience, while severe events diminish it. Furthermore, we validate a novel “Controversial Events→ESG→VaR→Resilience” chain-mediating mechanism, where ESG improvements translate into reduced financial tail risk (VaR). Theoretically, this research bridges the gap between non-financial performance and financial risk management, while methodologically overcoming linear model limitations by pinpointing crisis “tipping points”. Practically, the findings imply that managers should prioritize ESG disclosure as a strategic “risk buffer” to stabilize market expectations. For policymakers and investors, the study suggests that regulatory frameworks and capital allocation strategies must account for the non-linear dynamics of controversies to foster long-term sustainability. Full article

Abusive supervision remains a pervasive and damaging phenomenon in organizations, prompting a critical need to understand preventive mechanisms. We adopt a leader-centric, actor-focused perspective to investigate how a positive organizational context can inhibit leaders’ destructive behaviors. Drawing on Affective Events Theory (AET), we propose that leaders’ awareness of their organization’s philanthropic activities serves as a positive, morally salient event that generates feelings of moral pride. This pride, in turn, is theorized to reduce the likelihood of abusive supervision. Furthermore, we posit that this process is contingent on leaders’ moral reputation maintenance concerns, such that the negative relationship between moral pride and abusive supervision is stronger for leaders who are highly concerned with being perceived as moral. We tested this model using a three-wave survey study involving 434 leaders. The results support our hypotheses, indicating that perceived philanthropy awareness is positively associated with moral pride, which, in turn, predicts lower abusive supervision. This indirect effect is significantly stronger for leaders with high moral reputation maintenance concerns. Our findings contribute to the literature by identifying a novel, positive, and self-regulatory pathway for preventing abusive supervision and showing that applying AET to understand how macro-level organizational good deeds can translate into improved micro-level leader conduct. Full article

In this study, a machine learning-based quality control algorithm for heavy rainfall was developed by integrating automatic weather station observations with remote sensing data, minute-level data, and metadata. Based on heavy rainfall samples from 1 June 2022 to 31 December 2024, the performances of four gradient boosting models—eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), and Gradient Boosted Regression Trees (GBRT)—significantly outperformed precipitation-threshold-based conventional methods, including regional extreme value checks, temporal consistency checks, and others. Specifically, the XGBoost in particular achieves an increase in precision by 0.110 and recall by 0.162. This translates to a substantial reduction in both false alarms (higher precision) and missed detections (higher recall) of anomalous heavy rainfall events, thereby significantly enhancing the reliability of the quality-controlled data. The radar composite reflectivity, satellite cloud-top temperature, and minute-level precipitation were identified as dominant contributors to model predictions. The integration of multi-sensor observations effectively addressed limitations inherent in conventional threshold-based approaches. Through SHapley Additive exPlanations (SHAP)-based interpretability analysis, the model’s decision logic was shown to align with meteorological physical principles. Characteristic patterns such as combinations of low radar reflectivity and elevated cloud-top temperatures were flagged as anomalous rainfall events, typically corresponding to manual operational errors. Moreover, the model identified anomalous minute-level precipitation extremes to be critical signals for detecting instrument malfunctions, data encoding and transmission errors. The physical consistency of the model’s reasoning enhances its trustworthiness and supports its potential for operational implementation in heavy rainfall quality control. Full article

Background/Objectives: Perinatal depression poses significant risks to maternal and fetal health, yet biomarkers for treatment response in the field remain limited. Given the overlap in symptoms with major depressive disorder (MDD) and the comparatively more vast MDD literature, identifying promising MDD biomarkers for treatment response and examining corresponding perinatal depression biomarkers can reveal translational opportunities. Methods: PUBMED searches were conducted for individual biomarkers and MDD and perinatal depression, as well as with treatment response to antidepressant pharmacological treatment and neuromodulation treatments. When available, evidence from meta-analyses and systematic reviews were preferentially summarized. Review: This narrative review presents the current evidence on MDD and perinatal depression treatment response biomarkers, including brain-derived neurotrophic factor (BDNF), S100 calcium-binding protein B (S100B), electroencephalography, event-related potentials, metabolomics, hypothalamic–pituitary–adrenal axis hormones, neuroimaging markers, inflammatory markers, and neuroactive steroids. Conclusions: Biomarker research in MDD yields insights on promising biomarkers for treatment response, including BDNF, S100B, theta band density and cordance, inflammatory markers IL-8, CRP, and TNF- α, and neuroactive steroids. Full article

Cardiovascular diseases (CVDs) remain the leading cause of death worldwide, with a substantial proportion of events occurring prematurely. Atherosclerosis (AS), the central driver of cardiovascular pathology, results from the convergence of metabolic disturbances, vascular inflammation, and organelle dysfunction. Among intracellular organelles, mitochondria have emerged as critical regulators of vascular homeostasis. Beyond their canonical role in adenosine triphosphate (ATP) production, mitochondrial dysfunction—including impaired mitochondrial oxidative phosphorylation (OXPHOS), excessive generation of reactive oxygen species (ROS), accumulation of mitochondrial DNA (mtDNA) damage, dysregulated dynamics, and defective mitophagy—contributes to endothelial dysfunction, vascular smooth muscle cell (VSMC) phenotypic switching, macrophage polarization, and ultimately plaque initiation and destabilization. These insights have established the rationale for mitochondrial “reprogramming”—that is, the restoration of mitochondrial homeostasis through interventions enhancing biogenesis, dynamics, and quality control—as a novel therapeutic paradigm. Interventions that enhance mitochondrial biogenesis, restore mitophagy, and rebalance fission–fusion dynamics are showing promise in preclinical models of vascular injury. A growing array of translational strategies—including small-molecule activators such as resveratrol and Mitoquinone (MitoQ), gene-based therapies, and nanoparticle-mediated drug delivery systems—are under active investigation. This review synthesizes current mechanistic knowledge on mitochondrial dysfunction in ASand critically appraises therapeutic approaches aimed at vascular protection through mitochondrial reprogramming. Full article