Search Results (41,015)

Ashwagandha, a revered herb in Ayurvedic medicine for over 3000 years, is recognized for its potential benefits in regulating sleep and supporting overall vitality. This study evaluated the comparative effects of Ashwagandha root extract (ARE) and melatonin (MLT) on sleep quality in adults. In this prospective, randomized, double-blind, placebo-controlled trial, 200 men and women aged 18–50 years were randomized to receive ARE (300 mg twice daily; n = 50), MLT (3 mg/day; n = 50), a combination of ARE (600 mg/day) and MLT (3 mg/day; n = 50), or placebo (n = 50) for eight weeks. The primary outcome was the change in sleep onset latency (SOL) from baseline to week eight, measured by actigraphy. Secondary outcomes included actigraphy-based changes in total sleep time (TST), wake after sleep onset (WASO), and sleep efficiency (SE), as well as subjective measures such as the Pittsburgh Sleep Quality Index (PSQI) and the Hamilton Anxiety Scale (HAM-A). At week eight, SOL was significantly reduced across treatment groups, with the ARE–MLT (p < 0.0001) combination showing the greatest improvement. The combination group also demonstrated significant improvements in TST (p < 0.0001), WASO (p < 0.0001), and SE (p < 0.0001), whereas ARE and MLT monotherapy produced moderate but comparable benefits. Inferential analyses confirmed statistically significant improvements in objective and subjective sleep measures (p < 0.0001). Safety analyses indicated that mild adverse events occurred across all groups, with no clinically significant between-group differences. Overall, both Ashwagandha and melatonin improved sleep disturbances in adults, with combination therapy producing the most consistent and pronounced benefits. Full article

The increasing global demand for sustainable water purification technologies has accelerated research on catalytic degradation and advanced oxidation processes for the removal of refractory pollutants. This study provides a comprehensive bibliometric analysis of global research trends in catalytic water and wastewater treatment from 2010 to 2025, combining quantitative mapping with a qualitative synthesis of emerging technological directions. Bibliographic data were retrieved from the Scopus database and screened using the PRISMA framework, followed by analysis using VOSviewer (v1.6.20) and OriginPro (version 2023, OriginLab Corporation, Northampton, MA, USA) to examine publication growth, citation patterns, international collaboration networks, and thematic evolution. A total of 1550 publications, including 1265 research articles and 285 review papers, were analyzed. The results show a significant increase in research output after 2015, reflecting growing global attention to water sustainability and environmental remediation. China, the United States, and India were identified as the leading contributors, with strong international collaboration networks. Keyword co-occurrence analysis revealed three dominant research themes: photocatalytic degradation and semiconductor engineering, Fenton and Fenton-like advanced oxidation processes, and emerging hybrid catalytic systems involving carbon-based materials and metal–organic frameworks. The analysis also indicates a recent shift toward multifunctional hybrid catalysts designed to improve efficiency, stability, and performance in complex wastewater systems. These findings highlight key scientific developments and suggest future research priorities, including green catalyst synthesis, reactor and process scale-up, AI-assisted catalyst design, and life-cycle sustainability assessment to support the transition from laboratory research to practical water treatment applications. Full article

Human papillomavirus (HPV)-driven oropharyngeal squamous cell carcinoma (OPSCC) has emerged as a biologically distinct entity, typically affecting younger, non-smoking patients and showing improved survival compared to HPV-negative tumors. Accurate HPV status determination is essential for correct staging, prognostic assessment, and treatment de-escalation. Despite advances, substantial variability persists among diagnostic methods and clinical workflows. A narrative review of PubMed, Scopus, and Web of Science databases was conducted up to July 2025. Studies addressing HPV detection techniques in OPSCC—including p16^INK4a^ immunohistochemistry (IHC), HPV DNA and RNA assays, liquid biopsy approaches, and computational surrogates—were critically analyzed regarding diagnostic accuracy, clinical applicability, and emerging innovations. Tissue-based assays remain the diagnostic reference standard. p16 IHC provides high sensitivity but limited specificity and should be confirmed with nucleic acid-based methods such as DNA PCR, in situ hybridization (ISH), or E6/E7 mRNA detection. Combined or “orthogonal” testing minimizes discordance and refines risk stratification. Liquid biopsy detection of circulating HPV DNA using droplet digital PCR or next-generation sequencing has shown high sensitivity and specificity in cohorts of patients with HPV-associated OPSCC, supporting its potential role as a complementary biomarker for treatment monitoring and surveillance. However, circulating HPV DNA alone does not unequivocally identify the anatomic source of HPV DNA and should be interpreted together with clinical, radiologic, and tissue-based findings. Oral rinse and saliva assays show moderate diagnostic performance, while artificial intelligence-based radiomic and histopathologic models are emerging as complementary tools. Reliable HPV attribution in OPSCC requires a multimodal diagnostic strategy integrating p16 IHC, molecular confirmation, and ctHPV-DNA monitoring. Methodological standardization and prospective validation are essential to implement precision-guided, cost-effective workflows in routine clinical practice. Full article

The development of stable and high-yielding fodder pearl millet genotypes with improved quality traits is crucial for enhancing livestock productivity under diverse environments. In this study, twenty-six elite genotypes, including brown midrib (bmr) lines and two check cultivars, were evaluated across four locations, which fall broadly under two agro-climatic zones of India, during the summer season of 2024 to assess their stability for yield and fodder quality traits. Significant genotypic differences and genotype × environment interactions (GEIs) were observed for all traits, indicating substantial genetic variability and environmental influence on trait expression. Additive Main Effects and Multiplicative Interaction (AMMI) and Weighted Average of Absolute Scores (WAAS) analyses identified IGPM 100 as a high-yielding and stable genotype across environments, whereas Baif Bajra 1 and IGBV 97 exhibited specific adaptation. Among quality traits, ICMbmr 2401, ICMbmr 2402, and ICMbmr 2404 recorded consistently low lignin content, confirming their potential for improving forage digestibility. Further, ICFPM 05 recorded high tillering and longer leaves, while ICMFV 2308 exhibited late flowering across locations, indicating their potential for use in developing leafy, late-flowering genotypes. The multi-trait stability index (MTSI) efficiently identified IGPM 100, ICFPM 02, ICMbmr 2404, and IGBV 9 as superior and stable genotypes across multiple traits. High selection differentials for green fodder yield and negative differentials for lignin and fibre fractions highlight the possibility of a simultaneous improvement in yield and quality traits. Overall, the integration of AMMI, WAAS, and MTSI models facilitated the identification of broadly adapted and trait-specific genotypes, which, after evaluating their combining ability, can be used for developing fodder pearl millet composites and hybrids. Full article

The selection of variables that can be used to predict another variable is usually a challenge, considering that national and international databases contain a considerable amount of information and that the literature, in some circumstances, is unclear about the most adjusted predictors and the most accurate models. Without appropriate approaches to select the variables, there are actual risks of considering irrelevant information and ignoring important data in the prediction analysis. Artificial intelligence and, in particular, machine learning methodologies provide interesting support for identifying the most important predictors and the most accurate algorithms. In this way, this research intends to identify the most important variables to predict bioenergy production and consumption and select the most accurate models. For this, statistical information from the FAOSTAT database for the year 2023 was considered. This information was analysed considering machine learning approaches following IBM SPSS Modeler (Version 18.4) procedures. The results obtained indicate that 50% of bioenergy is produced and consumed worldwide by five countries (India, China, the United States of America, Brazil and Ethiopia) and most of this energy comes from firewood (60%). Out of a total of 456 inputs (consideration of this set of FAOSTAT variables is a novelty in the literature), bioenergy production and consumption are mainly explained by fuelwood production, with elasticities of 0.75% and 0.7%, respectively. The explanatory variable “fuelwood production” was identified from the most significant variables found by the machine learning approaches and was subsequently used as an independent variable in linear regressions. XGBoost Linear, XGBoost Tree, Linear, CHAID, Tree-AS, and C&R Tree are the most accurate models (lower relative error) for predicting bioenergy production and consumption worldwide. Full article

Climate change is intensifying winters in temperate regions, posing a serious threat to Apis mellifera health. The gut microbiome, a distinct community of core bacterial species, is central to overwintering success by supporting immune function, nutrient assimilation, and pathogen resistance, but is highly sensitive to environmental stressors such as cold temperatures and dietary shifts. Stress-induced perturbations can reshape the composition and relative abundance of the gut microbiome in honey bees, leading to adverse effects on host health, physiological functions, and overwinter survival. Cold temperatures and additional stressors further destabilize the microbiome, compounding these effects. This review is the first to synthesize current knowledge on how extrinsic factors, such as diet, antibiotics, and pathogens, and intrinsic factors, including age and strain, influence the composition and function of the honey bee gut microbiota during the overwintering period. Given the increasing severity of winter conditions under climate change, a deeper understanding of microbiome–host–environment interactions is essential for improving honey bee resilience. By integrating evidence on the microbiome’s roles in nutrient utilization, immune modulation, and pathogen defense, this review outlines a framework to guide future research aimed at sustaining pollinator health and nutrition in a changing global climate. Full article

Background and Objectives: To provide a systematic narrative review of published literature on atypical hangman’s fractures focusing on pathophysiology, treatment options and clinical outcomes. Materials and Methods: A systematic review was performed according to PRISMA guidelines. MEDLINE (PubMed), EMBASE, Scopus, and Cochrane Library were searched until March 2025. Studies reporting outcomes of atypical hangman’s fractures treated conservatively or surgically were included. Data on demographics, mechanism of injury, treatment modality, outcomes, and complications were extracted and analyzed. Results: Thirteen studies with a total of 275 patients were included. The average age was 54.36 years. High-energy trauma was the predominant mechanism of injury. Conservative treatment was performed in 210 patients, with 204 (97.14%) achieving fusion and 6 (2.86%) converted to surgical treatment. Surgical fixation was performed in 71 patients, most commonly via a posterior approach. Failure of surgical treatment occurred in 5 patients, all treated with isolated anterior fusion. Neurologic injury was reported in 21 patients (7.63%), with full recovery in 14 (66%). Conclusions: Atypical hangman’s fractures represent a distinct subgroup of C2 fractures with diverse morphology and stability. Most fractures are stable and may be managed conservatively. Surgical fixation should be reserved for unstable patterns. If surgery is pursued, posterior fixation is recommended. Outcomes are generally favorable for both conservative and surgical treatment. Full article

The use of marginal sedimentary aggregates in pavement construction remains a major challenge in mountainous regions due to their high porosity, weak lamination planes, and susceptibility to moisture-induced deterioration. This study investigates the potential of low-density polyethylene (LDPE) plastic waste to enhance the engineering performance of laminated Miocene soft rock aggregates used in bituminous concrete. Aggregates sourced from the Surma Group (Bhuban Formation) in Mizoram, India, were characterized through physico-mechanical, geochemical, and mineralogical analyses to evaluate their durability and moisture sensitivity. X-ray fluorescence (XRF) analysis revealed elevated feldspar and total alkali contents (≈5.15%), indicating a mineralogical composition prone to hydrophilic behavior and stripping within bituminous mixtures. To mitigate these limitations, aggregates were coated with varying proportions of LDPE plastic using the dry process. An optimum LDPE content of 9% by weight of aggregate produced significant improvements in aggregate performance, resulting in a 70.03% reduction in Aggregate Impact Value (from 17.72% to 5.31%), a decrease in Los Angeles Abrasion Value from 42.93% to 31.45%, and an 89.82% reduction in water absorption (from 4.52% to 0.46%). The polymer coating effectively sealed lamination planes and reduced moisture ingress within the sedimentary structure. Bituminous concrete mixtures incorporating LDPE were further evaluated using Marshall stability and indirect tensile strength tests. The addition of 1.1% LDPE by weight of mix significantly enhanced moisture resistance. For mixtures with nominal maximum aggregate sizes (NMASs) of 13 mm and 19 mm, the Tensile Strength Ratio (TSR) increased from 52.59% and 58.58% in the control mixtures to 82.81% and 87.10%, respectively, thereby satisfying the minimum requirement of 80% specified by MoRTH. The results indicate that LDPE functions as a hydrophobic barrier and structural sealant that improves binder–aggregate adhesion and prevents stripping along weak lamination planes. The findings demonstrate that LDPE-modified bituminous concrete provides a sustainable and technically viable strategy for upgrading marginal sedimentary aggregates into durable pavement materials while simultaneously promoting the beneficial reuse of plastic waste. Full article

BLDC motors are utilized in electric cars, robotics, drones, home appliances and medical equipment due to their effectiveness, dependability, and accurate control. PI controllers have been put forward to enhance the dynamic performance of brushless direct current (BLDC) motors, and they have been tested in many papers with various algorithms (such as PSO, GA, GWO, ACO and ABC) and strategies (such as PI/PID control, FOC, FLC, SMC and MPC). Meanwhile, in this research, and for the first time, the PI controller was tuned by the proposed Sailfish Optimization algorithm (SFO) with a direct torque control (DTC) strategy to enhance the dynamic performance of BLDC motors. Although DTC provides a very fast torque response, it still suffers from high torque ripple and noticeable instability at low speeds. These issues persist even when using conventional PI tuning or common optimization algorithms. Hence, in this research, we proposed an improved control strategy that combines DTC with PI tuning optimized by the Sailfish Optimization algorithm (SFO), which delivers smoother torque, more stable low-speed operation, and stronger robustness during sudden changes in load. In this regard, the PI controller was tested under different levels of torque and compared with the traditional Gray Wolf Optimization (GWO-PI) algorithm controller, as well as PI and PID controllers, and the performance of each of them was evaluated for different torque levels at speeds of 600 rpm and 2000 rpm during physical experiments. The simulation results showed that the Sailfish-PI controller, compared to the others, recorded the fastest response with a rise time of 2.1 ms and settling time of 2.9 ms under 2.39 Nm nominal torque at 2000 rpm speed; in addition, it continuously showed the lowest values of overshoot and undershoot as torque increased. It also maintained the most accurate and consistent performance, keeping the peak rpm almost flat and extremely near to the target of 2001 rpm. Therefore, in systems that require variable speed and torque while operating, such as electric automobiles, the proposed method is suitable for application. Full article

Despite significant progress in general-purpose diffusion-based models capable of producing high-quality media, this approach is still too difficult to implement on consumer/gamer hardware. We present here a memory-optimized DreamBooth framework designed for consumer-grade GPUs with 16 GB of VRAM, that allows for end-to-end image personalization and addresses some of the limitations of existing solutions. Our system reduces peak GPU memory from 22 GB (baseline DreamBooth) to 14.2 GB through novel hierarchical memory management, including attention slicing, Variational Autoencoder (VAE) tiling, gradient accumulation, and gradient checkpointing integrated within the Hugging Face Accelerate ecosystem. The framework further incorporates state-of-the-art techniques for preserving facial features and a comprehensive automated quality management system. The result is a complete end-to-end pipeline achieving a peak memory of 14.2 GB, with quantitative performance (LPIPS: 0.139, SSIM: 0.879, identity: 0.852, and FID: 23.1) competitive with methods requiring significantly more hardware resources. Full article

Background: T-cell-engaging bispecific antibodies (TCEs) have transformed haematological malignancy treatment (blinatumomab > 40% complete remission), yet solid tumour efficacy remains limited (<15% response rates) due to antigen heterogeneity, immunosuppressive microenvironments, and T-cell dysfunction. Systematic molecular engineering, biomarker-driven patient selection, and rational tumour microenvironment modulation are now collectively transforming TCEs from experimental agents into an adaptable platform therapy for solid tumours. Methods: Review of 55 phase I–III trials of CD3-based TCEs in solid tumours, including tarlatamab (DLL3-targeted, small-cell lung cancer) and xaluritamig (STEAP1-targeted, prostate cancer). Analysis of next-generation engineering strategies and resistance mechanisms via genomic and immunohistochemical data. Result: Response rates now approach ~40% in selected settings, marking an inflection point. In extensive-stage small-cell lung cancer, tarlatamab achieved ~40% responses with definitive survival benefit (phase III HR 0.60, 95% CI 0.47–0.77; p < 0.001; median OS 13.6 months). In metastatic castration-resistant prostate cancer, xaluritamig produced ~41% responses in heavily pretreated patients. Step-up dosing reduced severe cytokine release syndrome to <1% (as low as 0.6% with teclistamab), enabling outpatient administration. Neurological adverse events require monitoring but are less frequent than with cellular therapies. Together these results mark a decisive transition from proof-of-concept to clinically validated platform therapy. Discussion: Three resistance mechanisms limit durability: (i) antigen heterogeneity (28–60% of progressors develop antigen-negative subclones); (ii) immunosuppressive microenvironments (stromal barriers, myeloid-derived suppressor cells, hypoxia); (iii) T-cell exhaustion (PD-1/TIM-3/LAG-3 co-expression). Conclusions: Next-generation TCE platforms integrating conditional activation, cytokine payloads, and checkpoint modulation—deployed with biomarker-guided selection and TME-modulating combinations—represent a transformative therapeutic strategy. With tarlatamab’s phase III survival benefit establishing clinical proof-of-concept, and pivotal trials underway for xaluritamig and next-generation agents, TCEs are positioned to become standard-of-care platform therapies in biomarker-defined solid tumours by 2028–2030. Full article

Salinity stress impairs maize growth by inducing osmotic stress, pigment degradation, and ionic imbalance, particularly during early seedling development. This study investigated the morpho-physiological and ionic responses of different maize genotypes exposed to increasing salinity levels (control, 3, 6, and 9 dS/m) at the seedling stage. Salinity caused a reduction in biomass accumulation (shoot fresh weight and shoot dry weight), plant height, and K⁺/Na⁺ ratio, with pronounced effects under severe stress. Significant genotypic variability was detected for photosynthetic pigments (chlorophyll a, chlorophyll b, total chlorophyll and carotenoids) growth traits, and ionic regulation, indicating diverse physiological adaptation strategies. Stress tolerance indices and multivariate analysis revealed that chlorophyll stability, carotenoid accumulation, and maintenance of ionic homeostasis (K⁺/Na⁺ ratio) were the dominant physiological determinants of salinity tolerance. Additionally, principal component analysis showed a shift from biomass-driven variation under non-stress conditions to pigment- and ion-driven variation under higher salinity. Based on the results, genotypes BML 6 and HKI 163 maintained higher pigment content and improved K⁺/Na⁺ balance, enabling better growth under saline conditions. These findings highlight key physiological traits underlying salinity tolerance and provide insight into early-stage adaptive mechanisms in maize. Full article

Artificial neural network and neuron models have made significant contributions to the area of neurodynamics. Investigating the dynamics of artificial neurons and neural networks is vital in developing brain-like systems and understanding how the brain functions. Neural network models and memristive neurons are currently demonstrating a lot of promise in the study of neurodynamics. In order to model the dynamics of biological synapses, this study explores the complex dynamical behavior of a discrete fractional Hopfield-type neural network using a flux-controlled memristive element with periodic memductance. Hyperbolic tangent and sine are the heterogeneous activation functions that are implemented in the proposed system to improve nonlinearity and replicate various forms of brain activity. Stability and bifurcation analyses are used to illustrate the nonlinear dynamical nature of the constructed network model. We examine how the fractional order $\left(\nu \right)$ and periodical memductance aspects influence the dynamics of the system to emphasize the emerging complex phenomena like multi-layered dynamics and the presence of several distinct dynamical states throughout the system variables. Randomness and complexity of the time series data for the proposed system are illustrated with the help of approximate entropy analysis. These findings could help researchers better understand brain-like memory networks, neuromorphic computers, and the theoretical study of neurological and mental abilities. The study of multi-layer attractors can be useful in advanced sensory devices, neuromorphic devices, and secure communication. Full article

Oligometastatic prostate cancer represents a distinct biological state between localized and widely metastatic disease, characterized by a limited number of lesions. Stereotactic body radiotherapy (SBRT) has emerged as a key metastasis-directed therapy (MDT), enabling precise ablation of metastatic lesions with minimal toxicity. Prospective clinical trials such as SABR-COMET, STOMP, ORIOLE, RADIOSA, and EXTEND have shown that SBRT delays disease progression, prolongs progression-free survival, and postpones the need for systemic therapy, while maintaining a favorable safety profile. Nevertheless, methodological limitations persist, including heterogeneity in defining oligometastatic disease, variability in dosing and fractionation, and the lack of predictive biomarkers. Ongoing phase III trials aim to validate the integration of SBRT with modern systemic therapies, including next-generation androgen receptor pathway inhibitors, to optimize clinical outcomes in hormone-sensitive and castration-resistant oligometastatic prostate cancer. This review summarizes current evidence, clinical applications, and future directions for SBRT in this patient population. Full article

Precise assessment of battery state of health (SoH) is vital for certifying consistent performance in order to enable maintenance of energy storage system. This work compares different deep learning methods to learn and predict the complex and nonlinear dynamics of battery. The models are developed and tested for predicting SoH using sequential degradation data from batteries. The effectiveness of these models is assessed using matrices such as RMSE, MAE and R², along with qualitative analysis. The experiment results show that the BiLSTM model performs better than the others. It has the lowest RMSE (0.90), the lowest MAE (0.72), and the highest R² (0.99), which highlights its enhanced ability to capture long-term temporal dependencies. The proposed models are validated using NASA lithium-ion battery aging dataset (B0005), which is widely used as a benchmark for battery health predictions studies. Overall, the findings indicate that bidirectional network architecture significantly improves the accuracy and consistency of SoH predictions when compared to unidirectional models. Full article