Search Results (25,749)

Chronic limb-threatening ischemia (CLTI) in patients with no conventional targets for revascularization presents a formidable challenge in limb salvage. Deep venous arterialization (DVA) is an emerging endovascular approach that redirects arterial blood flow into the venous system to perfuse the ischemic foot. Despite early promising results, appropriate wound management of the ischemic foot following a DVA procedure has been described in the literature, albeit infrequently and with limited standardization. Here, we present a case of an 85-year-old male with multiple comorbidities, including peripheral artery disease and a prior right above-knee amputation (AKA), who underwent a successful left-sided DVA following an open transmetatarsal amputation (TMA) for infection. A staged wound care approach with guillotine amputation, delayed revision and skin grafting ultimately preserved his only remaining limb and allowed for ambulation. This case underscores the potential of DVA as a limb-saving option in complex “no-option” patients when paired with multidisciplinary care and tailored wound management. Full article

The development of biocompatible materials has gained traction due to the increasing clinical demands for customizable and functional medical devices. Chitosan, a deacetylated derivative of chitin, is a naturally occurring biopolymer with strong antimicrobial properties, immunocompatibility, and structural adaptability, making it a promising candidate for biomedical applications. Through mechanisms such as crosslinking, ionic bonding, gas formation, and UV radiation, the mechanical properties and stimulus responses of chitosan-based hydrogels can be tailored for drug delivery at specific sites or under specific pH, light, or electrical conditions. Beyond drug delivery, chitosan hydrogels have shown considerable potential for vascular tissue repair. The porous structure of chitosan allows patient specific vascular scaffolding to be created that promotes the recovery rate veins and stenting procedures. Thermally sensitive hydrogels can deliver drugs to target regions to further assist in vascular healing. Furthermore, recent developments with composite polymers and coatings engineered to self-assemble within veins provide scaffolds for vascular tissue growth. This manuscript reviews chitosan hydrogel fabrication methods and their corresponding materials properties, with particular emphasis on drug delivery to vascular tissues. Furthermore, relevant findings from clinical trials are summarized to support the potential of chitosan hydrogels for future clinical use. Challenges of chitosan hydrogels, such as insufficient mechanical strength, high degradation rates, and complex manufacturing, remain as areas for research break-through. Full article

The transient heat-transfer behavior of hybrid natural-fiber-reinforced epoxy composites containing 0–5 wt% conch shell filler and 20–35 wt% combined banana–bagasse fiber reinforcement was evaluated using active infrared thermography. A standardized protocol comprising 30 s of convective heating with 100 °C hot air followed by 60 s of natural cooling was applied to seven composite configurations tested in triplicate. The transient response was analyzed in three phases: active heating (0–30 s), thermal lag (30–57 s), and natural cooling (57–90 s). Maximum temperature ($T_{max}$), heating rate ($R_h$), cooling rate ($R_c$), and a thermal retention ratio ($TR$) were extracted and statistically validated by one-way ANOVA with Bonferroni correction. For specimens exhibiting zero within-group variance at the camera display resolution, significance was confirmed using exact permutation tests. Filler incorporation (3–5 wt%) was the dominant factor governing peak-temperature reduction; F5B15S10 (5 wt% filler, 25 wt% total fiber) achieved the lowest $T_{max}$ (33.80 °C, 4.57 °C below neat epoxy). Cooling efficiency was primarily governed by fiber content; F3B15S20 (3 wt% filler, 35 wt% total fiber) demonstrated the most efficient heat dissipation ($TR=0.721$). These findings demonstrate that heating resistance and cooling efficiency are governed by partially independent mechanisms, enabling tailored material design. This study indicates that the proposed transient thermographic protocol provides a valuable reference to thermal management design of hybrid biocomposites in automotive interior and building envelope applications. Full article

Maceration techniques like cold maceration before fermentation (CM) and skin-contact fermentation (SF) are widely used in winemaking. However, their application remains limited in white winemaking, representing an important objective for the production of diverse white wines. This study systematically investigated the impacts of CM and SF on phenolics, volatile aromas and sensory properties of Chardonnay and Italian Riesling wines in Yantai wine region. Both CM and SF significantly increased the total phenolic content, especially with gallic acid and quercitrin contents rising 11.65- and 10.02-fold in Chardonnay, as well as catechin and quercitrin increasing 9.05- and 10.82-fold in Italian Riesling under 100% SF. Moreover, 72 h CM and 100% SF showed higher volatile aroma contents in both wines compared to other CM and SF treatments. Esters, including ethyl octanoate, ethyl hexanoate and isoamyl acetate, contributed to the improved floral and fruity aromas in maceration-treated Chardonnay wines, whereas esters and terpenes drove the aromatic profile of Italian Riesling wines, with terpenes rising 1.67- and 3.27-fold after CM and SF. The two varieties differed in wine color, with Italian Riesling wines displaying a stronger yellow hue and reduced lightness under 72 h CM, 50% SF and 100% SF. Sensory evaluation by a panel containing seven trained assessors found that SF-treated wines exhibited more intense flavor and balanced taste and CM-treated wines showed more freshness. These findings provide theoretical support for tailored maceration to enhance varietal expression in the specific region and diversify white wine production. Full article

Plunger lift systems are vital for optimizing production in gas wells, but their performance can be compromised by various operational anomalies. Traditional diagnostic methods and conventional convolutional neural network (CNN) approaches often struggle with the complex, transient data from these systems, particularly in capturing long-range temporal dependencies and generalizing from limited, imbalanced datasets. This study presents an enhanced diagnostic framework for plunger lift anomaly detection by leveraging the strengths of a pre-trained Vision Transformer (ViT). The methodology transforms one-dimensional time-series pressure data into two-dimensional image representations using the element-wise summation of Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF), which simultaneously preserves global operational trends and local transient dynamics for vision model analysis. The ViT model, initialized with pre-trained weights, is further optimized using Bayesian optimization (BO) for hyperparameter tuning, and a tailored data augmentation pipeline is employed to improve robustness. Comparative evaluations demonstrate that the proposed ViT-based approach, particularly the ViT + GAF + BO model, significantly outperforms baseline CNN models and their optimized variants, achieving the highest Precision, Recall, and F1-score, with an F1-score of 0.93. Visualizations using t-SNE confirm the ViT’s superior capability in learning discriminative features, showcasing well-separated clusters for different operational conditions compared to CNNs. This research underscores the potential of pre-trained ViTs combined with appropriate data representation and optimization techniques for achieving accurate and reliable anomaly detection in plunger lift systems. Full article

Background: Adjuvant therapy decisions for T3N0 stage II colon cancer remain controversial. This study evaluates long-term outcomes, recurrence patterns, and conditional relapse-free survival (RFS) in pathologic T3N0 colon cancer. Methods: This retrospective study included 306 patients undergoing curative resection for T3N0 colonic adenocarcinoma (1995–2020). Early recurrence was defined as recurrence or death within 3 years after surgery. Survival was estimated via Kaplan–Meier. Cox regression, adjusted for treatment eras, evaluated survival factors. Inverse Probability of Treatment Weighting (IPTW) minimized selection bias. Conditional RFS utilized a 5-year landmark analysis. Results: Over a 133-month median follow-up, 72 patients (23.5%) recurred. Most recurrences (81.9%) occurred within 3 years; only 9.7% after 5 years. Five- and 10-year OS rates were 80.9% and 70.4%. Inadequate lymph node dissection (<12 nodes) was performed in 29.7% of the entire cohort and was found to be an independent adverse prognostic factor for OS. Adjuvant chemotherapy lacked overall OS benefit, though IPTW analysis suggested potential benefit in patients with inadequate dissection. Conditional RFS (5–10 years) for patients recurrence-free at 60 months was 95.0%. Exploratory analyses showed descriptive differences in post-relapse survival based on the clinical triggers prompting radiological evaluation (marker-triggered versus symptom-triggered presentations). Conclusions: T3N0 colon cancer recurrences occur predominantly within the first 3–5 years after surgery. Inadequate lymph node dissection is the primary adverse prognostic factor. Although a 5-year follow-up period appears adequate for most patients, individualized extended surveillance may be considered for selected high-risk patients. Adjuvant treatment and follow-up strategies should be tailored according to surgical quality and risk factors. Full article

Time-Sensitive Networking (TSN) has emerged as a critical communication protocol for automotive Ethernet to support the high-bandwidth, real-time, and deterministic transmission requirements of next-generation in-vehicle networks. However, a clear and effective TSN mechanism combination tailored to the mixed and bursty traffic characteristics of automotive scenarios remains lacking. To address this issue, this paper proposes a combined TSN scheduling mechanism for automotive scenarios. The highest-priority traffic is scheduled by class-based Time-Aware Shaper (TAS), periodic bursty sensor traffic is shaped by Credit-Based Shaper (CBS), and medium-priority traffic adopts Multi-Cyclic Queueing and Forwarding (MCQF). Based on Compositional Performance Analysis (CPA), this paper derives the worst-case latency upper bound expressions for CQF streams and optimizes the schedulability analysis to reduce conservative errors. Simulation verifies that the theoretically calculated bounds cover the maximum simulation latency, and the optimized analysis reduces conservatism, with peak conservative error of 3.07% in the ring scenario and 10.59% in the automotive scenario. Compared with the strict priority and TAMCQF (a combination of TAS and Multi-CQF), the proposed mechanism combination suppresses the latency jitter of mixed traffic, mitigates long-duration blocking of medium-priority traffic caused by high-priority burst data, and provides reliable deterministic transmission guarantees for automotive in-vehicle networks. Full article

Background/Objectives: Prehospital advanced airway management significantly affects patient outcomes. The King Laryngeal Tube (King LT) has been a standard method for managing compromised airways in various emergency medical services (EMSs). However, in-depth analyses of first-attempt success and influencing factors are limited. This study explores the use of the King LT in Saudi Arabia to assess the first-attempt success rate and predictors of successful management. Methods: This retrospective cross-sectional study was conducted to analyze cases requiring the King LT in the main EMS provider in Saudi Arabia between October 2021 and September 2022. A descriptive analysis was employed for categorical data, and Chi-square test, Fisher’s exact test, and a regression analysis were applied to assess the significance of the association. Results: Of the 239 analyzed cases, adults (58.6%) and males (70.7%) were predominant. The highest proportions of cases were medical cases (36.8%) and indoor incidents (69.9%), with a significant association of indoor incidents with female and elderly patients (p = 0.001). The first-attempt success rate reached 82.4%, with significant success likelihood in afternoon incidents (adjusted odds ratio [OR] = 2.92, 95% confidence interval [CI] [0.53–3.57]; p = 0.03). Conclusions: This first nationwide study of King LT outlines advanced airway management characteristics in Saudi Arabia. The high use rates in adults, males, medical cases, and indoor incidents could suggest tailored training strategies. Noted temporal variations may provide insights for policy improvements. While first-attempt success rates are high, reflecting literature findings; performance could improve with further training. Full article

Background/Objectives: Prediabetes significantly increases the risk of type 2 diabetes and related complications. Limited data exist for prediabetes among minority groups in Israel, particularly Bedouins. In the Negev region, Jewish and Bedouin populations differ markedly in culture and socioeconomic status. This study aimed to identify gender- and ethnicity-specific predictors of prediabetes. Methods: This retrospective, population-based observational exploratory study used data from 28,754 adults aged 20–65 years insured by Clalit Health Services in Southern Israel (2010–2020). Individuals with prediabetes were matched 1:1 with controls by age, gender, ethnicity, and year of diagnosis. Multivariate logistic regression models stratified by gender and ethnicity identified independent predictors. Results: Prediabetes was identified at significantly younger ages among Bedouins than Jews (6.8 years in men, 11.3 in women). The strongest predictor across all subgroups was metabolic syndrome (OR 2.0–4.0). Gestational diabetes was a major risk factor in women, particularly Jewish (OR 3.6). Cardiovascular disease and the use of statins or thiazide diuretics were independently associated with increased odds of prediabetes. Triglyceride-to-HDL cholesterol ratio was consistently elevated among prediabetes patients. Conclusions: Metabolic and medication-related factors contribute significantly to prediabetes-associated risk, with distinct gender and ethnic patterns. Culturally tailored early interventions and individualized risk profiling may enhance diabetes prevention in Southern Israel. Full article

Background: Polyendocrine metabolic ovarian syndrome (PMOS/PCOS) is the most common hormonal disorder in women of reproductive age and is linked to infertility as well as long-term metabolic and psychological problems. In the Gulf Cooperation Council (GCC) region, rising obesity, dietary changes, and sedentary lifestyles may be increasing its burden. However, prevalence estimates remain highly inconsistent due to differences in diagnostic criteria and measurement methods rather than true variation in disease rates. Objective: This study aimed to describe the situation by systematically pooling available evidence on the prevalence of PMOS among women in GCC countries and by summarizing the range of clinical features reported across included studies. Methods: We conducted a systematic review and meta-analysis following PRISMA 2020 guidelines. We searched five major bibliographic databases (PubMed, Scopus, Web of Science, Cochrane Library, and Embase) and the Google Scholar search engine for observational studies published up to 1 June 2026. Studies were eligible if they reported PMOS prevalence and related clinical features among women of reproductive age residing in GCC countries. After removing duplicates and screening 570 initially identified records, 25 studies met our inclusion criteria; 24 were included in the quantitative meta-analysis after excluding one high-risk study. Risk of bias was appraised using the Joanna Briggs Institute Checklist for Prevalence Studies. A random-effects meta-analysis using the DerSimonian-Laird method, combined with the Freeman-Tukey double arcsine transformation, was used to estimate the pooled prevalence. Heterogeneity was quantified using the I² statistic and Cochran’s Q test. Subgroup analyses explored differences by country, diagnostic method, study setting, and publication period. Meta-regression was used to identify study-level factors that explained between-study variability. Results: Across 24 studies involving 77,890 women, the pooled prevalence of PMOS was 17.59% (95% CI: 12.98–23.40%). Country-level estimates ranged from 6.56% in Oman to 23.0% in Saudi Arabia. Heterogeneity across all analyses was extremely high (I² = 99.6%), and meta-regression identified the diagnostic tool as the single most important source of variation, explaining 42.7% of between-study variance. Studies using structured clinical criteria (Rotterdam or NIH) yielded prevalence estimates around 13–14%, while those relying on self-report or physician diagnosis without standardized criteria reported considerably higher figures (20–37%). Common clinical features included menstrual irregularity (up to 100% of PMOS cases in clinical cohorts), hirsutism (5–100%), acne and oily skin (17–74%), and obesity (17–73%). Awareness of PMOS among women in the region was highly variable, ranging from under 3% to nearly 100%. Conclusions: PMOS is a significant public health concern across the GCC region. The markedly higher pooled prevalence combined with high rates of obesity and metabolic risk in this population calls for urgent, coordinated action. Standardizing diagnostic practices, investing in population-level screening, and developing culturally tailored awareness programs are essential steps toward reducing the clinical and social burden of PMOS. Full article

Incremental automatic modulation recognition is essential for the awareness of complex electromagnetic environments but is prone to catastrophic forgetting. This is fundamentally precipitated by shortcut learning, a phenomenon where deep models prioritize stable but non-essential channel artifacts (e.g., noise, fading) over intrinsic modulation characteristics. Consequently, models rely on spurious correlations that collapse during incremental task updates or environmental shifts, leading to representation drift. To bridge this gap, we propose the ParalIMR framework, which integrates a parallel reconstruction architecture with the segment substitution (SS) strategy to decouple modulation signatures from environmental fingerprints. Specifically, the parallel branch utilizes a Denoising AutoEncoder (DAE) as a task-agnostic structural anchor, purifying feature representations and maintaining geometric consistency across varying signal-to-noise ratios without propagating noise-overfitting to the classifier. In the meantime, the SS strategy actively disrupts the temporal coupling between class labels and hardware fingerprints through random reorganization, forcing the model to extract modulation-invariant structural cues. Experimental results on the RML2016a datasets demonstrate that in a three-stage incremental setup, our method achieves an overall accuracy of 84.32% at 0 dB SNR, representing a 2.69% improvement over the iCaRL baseline. Notably, this advantage expanded to 4.46% on RML2018, demonstrating that ParalIMR effectively arrests catastrophic forgetting. Ultimately, this research provides a robust learning paradigm tailored for cognitive radio and electronic warfare in dynamic electromagnetic landscapes. Full article

Hyperspectral imaging (HSI) provides rich spatial and spectral information for remote sensing, mineral exploration, and biomedical analysis, but its limited spatial resolution and sensor imperfections lead to blurred, noisy, and mixed-pixel observations. Addressing these degradations jointly—rather than sequentially—has been shown to improve physical interpretability, yet existing joint deblurring–unmixing methods rely primarily on hand-crafted regularizers that do not fully exploit spatial–spectral structure. Meanwhile, recent plug-and-play (PnP) approaches applied to HSI leverage deep priors but focus solely on either deconvolution or unmixing in isolation. To bridge this gap, we formulate the joint inverse problem of hyperspectral deblurring and spectral unmixing and propose, to our knowledge, the first plug-and-play framework tailored for this coupled task using the Alternating Direction Method of Multipliers (ADMM) and a pretrained deep denoiser (DnCNN) as an implicit PnP prior. Our method uses the natural splitting properties of ADMM to separate a physics-driven subproblem that enforces fidelity to the hyperspectral forward model, which includes linear mixing and blur under a linear, space-invariant convolution approximation, from the data-driven prior step. This synergy of model-based fidelity and learned spatial prior enables more accurate abundance estimates than those obtained with approaches relying solely on analytical regularizers. Experimental results on real hyperspectral datasets demonstrate that the proposed Plug-and-Play Joint Deconvolution and Unmixing (PnP-JDU) method outperforms conventional unmixing baselines, stand-alone PnP unmixing methods, and the Deblurring and Sparse Unmixing via the Alternating Direction Method with Total Variation (DSUnADM-TV) baseline in reconstruction and abundance accuracy metrics. Across the tested datasets and imaging conditions, PnP-JDU achieves lower RMSE, higher PSNR, lower reconstruction and abundance errors, and lower SAD values, while preserving fine spatial details and producing physically meaningful abundance maps. Full article

Trajectory optimization during the terminal area energy management (TAEM) phase is pivotal for achieving accurate runway alignment and enhancing landing safety in autonomous aircraft operations. In the presence of initial state uncertainties in TAEM phase, conventional pseudo-spectral methods still suffer from robustness limitations and exhibit a strong dependence on the quality of the initial guess. Therefore, this paper proposes the composite African vulture optimization algorithm (CAVOA), a meta-heuristic framework designed to automate trajectory optimization. An in-depth examination of the heading alignment cone (HAC) trajectory model enables effective heading adjustments prior to landing, augmented by a tailored dynamic pressure profile to ensure safe touchdown velocities. By incorporating dynamic opposition learning, intelligent boundary processing, and composite exploration, CAVOA enhances global search efficiency. These enhancements are substantiated through comparisons with benchmark function optimization, Wilcoxon rank sum tests, and convergence analysis. Numerical simulations validate that CAVOA reliably directs autonomous aircraft to predefined touchdown states, demonstrating superior performance in complex aerial environments. Full article

We develop a physics-facing version of the persistence/transition-variety framework for scalar-field cosmology, which is tailored to inflationary dynamics. The guiding idea is that observationally viable inflationary models are often best understood not as single asymptotic phases but rather as concatenations of persistent regimes separated by universal transition episodes. In this picture, slow roll appears as a robust persistent balance, ultra-slow roll as a bottleneck passage near a nonhyperbolic organising set, and oscillatory post-inflationary behaviour as a recurrent exit sector. Using the exponential model as a reference regime atlas and the massive case as a dynamical realisation of slope drift, we show how such histories may be organised and read geometrically. The resulting framework makes explicit that the relevant regime transitions are organised precisely where hyperbolicity is lost or the spectrum crosses the imaginary axis, and they are therefore invisible to a purely hyperbolic or asymptotic treatment. Full article

Across all kingdoms of life, ribosomes are indispensable molecular machines that translate genetic information into the proteome of living cells. The fundamental catalytic centers of the ribosome, constructed primarily from ribosomal RNA (rRNA), exhibit remarkable conservation between the major domains of life. The ribosome’s A-site deciphers the mRNA’s triplet code, while the P-site synthesizes the growing protein chain and the E-site provides exit for deacylated tRNA; a distinct tunnel facilitates nascent polypeptide export. While the conservation of ribosomal proteins is less pronounced between bacteria and eukaryotes, striking homology exists from simple eukaryotes to humans. Ribosomal proteins were traditionally viewed mainly as scaffolding agents, steering rRNA folding during ribosome biogenesis and maintaining structural stability during translation. However, since the early 2000s, advances in structural and functional ribosome analysis have ushered in a more nuanced paradigm: ribosomes are no longer considered uniform machines. Instead, an array of rRNA and ribosomal protein modifications generates a spectrum of ribosome populations capable of specialized translation. RiboScreen^TM technology leverages this regulatory potential of individual ribosomal proteins, enabling deliberate modulation of target protein output and representing a promising tool for correcting dysregulated protein expression involved in rare and common diseases. This review will first introduce relevant aspects of ribosome biology and then showcase the tools of this new technology. Finally, we report examples for the delivery of small molecules to target ribosomal proteins for tailored restoration of protein production levels in rare and prevalent diseases. Full article