Background: The gut microbiome maintains human health through homeostasis and immune function. Disruptions may be caused by various aspects of leukemia therapy and result in long-term changes that contribute to late effects seen in survivors. We previously reported on significant differences in composition of the gut microbiome in pediatric ALL survivors compared to healthy siblings using 16S sequencing. There remains limited data on the microbial taxa and functional profiles that drive such changes. Procedure: Gut microbiome shotgun metagenomic sequencing was completed on stool DNA samples obtained from 9 survivors of childhood ALL and 10 healthy sibling controls. Stool samples were collected a minimum of 6 months after completion of chemotherapy. Results: Within survivors, beta diversity was significant when looking at time elapsed since chemotherapy. Survivors’ microbiomes were more similar to siblings further from treatment but only two families had sample pairs as similar as untreated siblings. The functional potential of gluconate-5-dehydrogenase enzyme (Ga5DH) decreased significantly with time from treatment. The species Faecalibacterium prausnitzii was identified as the major contributor in most samples to the relative abundance of Ga5DH in subjects. Conclusions: Shotgun metagenomic sequencing demonstrated time from treatment has a significant effect on microbial composition. Increased time from chemotherapy corresponds to microbiomes more similar to siblings. However, microbial dysbiosis may be long lasting given the limited number of families who demonstrated microbiome pairs that were as similar in survivors as their siblings. Additional studies are needed to investigate the role of Ga5DH in the gut microbiome in survivors of ALL.

Uncertainty related to biogeochemical model structure, i.e. the equations, parameters, and variables used to simulate nutrient cycling and lower trophic level dynamics, can contribute significantly to overall uncertainty of regional model predictions of living marine resources metrics like primary production and trophic transfer efficiency. This may be particularly true in shallow coastal regions, where there is growing interest in using these types of regional models to inform ecosystem management. Here, we use a biogeochemical model intercomparison to quantify the uncertainty of key ecosystem metrics in the eastern Bering Sea shelf region and isolate poorly constrained biogeochemical processes that may lead to this uncertainty. We run three biogeochemical models with varying complexity coupled to the same regional ocean model and run a series of 30-year hindcast simulations spanning 1990-2020. We find that the models differ widely in their spatial and temporal patterns of simulated primary production, and that these differences propagate to most of the higher trophic level metrics examined. We highlight key structural elements that lead to these differences, including a) the representation of benthic processes and their role in retaining nitrogen on the shelf, b) the role of grazing control on spring bloom timing, and c) the role of micro- and mesozooplankton groups in supporting regenerated production through the summer months. Overall, we conclude that even well-validated biogeochemical models may have high uncertainty, particularly when pushed beyond the original contexts under which they were developed.

Phytoplankton size classes (PSCs) determine many fundamental biogeochemical processes including nutrient uptake, energy transfer through marine food webs, ocean carbon export, and gas exchange with atmosphere. Discerning the causes of spatio-temporal variability of PSCs is a scientific priority for understanding the ocean’s role in and response to climate change. This study intends to decipher the relationships between the abundance of PSCs and environmental predictors using machine learning (ML) and explainable AI (XAI) techniques. The target variables were PSCs obtained using satellite products, i.e. SeaWiFS/ MODIS/ Copernicus products. The environmental predictors were nutrients, light, mixed layer depth, salinity, temperature, and upwelling. The ML algorithm used was the Random Forest Regressor (RFR) and XAI techniques were used to discern the relationship between predictors and PSCs abundance. About 85\% to 95\% of the variability of the size classes in the observational datasets was accounted for by environmental variables known to influence phytoplankton biomass. Although different size classes responded similarly to the environmental drivers, their scale of response varied. The dominant predictors were found to be shortwave radiation, dissolved iron and, temperature. Out of the twelve satellite products across PSCs, ten showed a contrast between the sub-tropical gyres and remaining parts of the World. The different satellite products show sensitivity to iron, shortwave radiation and sea surface temperature across the same range of values, but with different magnitudes. The Copernicus products show less sensitivity to iron with picoplankton being the only product positively related to sea surface temperature.

Invasive fungal disease (IFD) remains a challenging complication and a leading cause of death in the treatment of childhood acute leukemia (AL). Blinatumomab is a novel bispecific antibody targeting CD19, with excellent anti-tumor effects against B cell malignancies, including B cell acute lymphoblastic leukemia (B-ALL). Compared to standard chemotherapy, blinatumomab causes less immunosuppression. This article reports two children with B-ALL who experienced recurrent high fever during induction chemotherapy. Next-generation sequencing (NGS) detected Aspergillus in bronchoalveolar lavage fluid, skin tissue, blood, and cerebrospinal fluid. After antifungal therapy, they received 9 courses of blinatumomab combined with reduced-dose chemotherapy. Symptoms, signs, and imaging findings improved significantly, B-ALL remained in continuous remission in both patients, and no cytokine release syndrome, neurotoxicity, or fungal infection recurrence occurred. These cases suggest that alternating blinatumomab with reduced-dose chemotherapy is both effective and safe for patients with chemotherapy intolerance.

An Atypical Case of Adrenal Insufficiency Presenting with Seizures, Complicated by Developmental Venous Anomaly and Takotsubo CardiomyopathyKatrina Villegas MD1, Brittany Eason2, Karolina Janiec MD1, Samia Ammar Aldwaik3, Alaa Musallam MD1, Ahmed Hammouda MD1, Radhika Tailor MD11 \RL Internal Medicine Department, St. Joseph’s University Medical Center, 703 Main St, Paterson, New Jersey, USA2 Rowan-Virtua School of Osteopathic Medicine, 113 Laurel Rd, Stratford, New Jersey, USA3 Faculty of Medicine and Health Sciences, An-Najah National University, Nablus, Palestine

Background: Left ventricular (LV) aneurysm is a severe complication with significant clinical implications. Differences between ischemic and non-ischemic LV aneurysms remain unclear. This study aimed to analyze their distinct clinical profiles, locations, and associations with ventricular tachycardia (VT). Methods: In this retrospective cross-sectional study, we analyzed inpatients with LV aneurysm at the First Affiliated Hospital of Nanjing Medical University from January 1, 2018, to October 5, 2023. Data included demographics, medical history, laboratory results, echocardiographic findings, and electrocardiographic findings. Multivariable logistic regressions assessed the association between aneurysm etiology and pre-admission VT. Results: We included 436 patients, comprising 401 with ischemic LV aneurysms and 35 with non-ischemic LV aneurysms. Non-ischemic LV aneurysms were associated with younger age and lower prevalence of hypertension, diabetes, and smoking (all P < 0.05). Patients with non-ischemic LV aneurysms had a higher Simpson’s LV ejection fraction (46.2 ± 14.5% vs. 40.8 ± 8.9%, P = 0.037). Apex involvement was less prevalent in non-ischemic LV aneurysms (68.6% vs. 91.3%; P < 0.001), which were more commonly located in basal segments like the basal inferolateral region (22.9% vs. 1.3%; P < 0.001). Pre-admission VT was more prevalent in non-ischemic LV aneurysms (42.9% vs. 13.3%; P < 0.001). Non-ischemic LV aneurysm was independently associated with increased odds of pre-admission VT (fully-adjusted OR: 8.49, 95% CI: 2.60–27.74; P < 0.001). Conclusions: Non-ischemic LV aneurysms had distinct baseline characteristics compared to ischemic LV aneurysms. Non-ischemic LV aneurysm was also associated with a higher prevalence of pre-admission VT. Further studies should explore underlying mechanisms and targeted strategies.

Crusted scabies, a severe form of Sarcoptes scabiei infestation, is increasingly reported in immunocompromised individuals. We present a case of a 77-year-old male with bullous pemphigoid (BP) who developed crusted scabies during treatment with dupilumab, systemic corticosteroids (methylprednisolone 60 mg/day), and methotrexate (15 mg/week). Clinical improvement of crusted scabies was observed after topical 20% sulfur ointment application. However, mite clearance was confirmed via follow-up at one month. We reviewed cases of scabies associated with biologics and emphasize the compounded immunosuppressive risks of corticosteroids, methotrexate, and biologics. Clinicians should maintain high vigilance for parasitic infections in patients on multidrug immunosuppressive regimens.

Etomidate is a widely utilized anaesthetic agent for the induction of general anesthesia, recognized for its rapid onset and minimal hemodynamic suppression effects. However, its clinical application is constrained by several adverse effects, including adrenal cortex suppression, postoperative nausea and vomiting, and myoclonus. In recent years, there has been a growing global research focus on structural modifications of the etomidate molecule, aiming to simultaneously ameliorate its adverse effects and optimize its hemodynamic stability efficacy. These concerted research efforts have led to the successful synthesis and characterization of numerous etomidate analogs with improved pharmacological profiles. This comprehensive review systematically examines recent scientific advancements in this field, focusing on structural modifications, pharmacological properties, and clinical translation of these novel compounds.

This study aims to determine the prevalence of mild cognitive impairment and major neurocognitive disorder among adults aged greater than or equal to 60 in Kaniyambadi block, Vellore and the factors associated with cognitive impairment. Settings and Design: A community based cross sectional study was conducted on 360 adults greater than or equal to the age of 60 residing in Kaniyambadi block, Vellore. Methods and Material: A semi-structured interviewer-based questionnaire was administered to the participant. Their subjective and objective cognitive abilities was assessed along with their ability to perform their activities of daily living. The participants were also screened for depression. Statistical analysis used: Univariate analysis was done using measures of central tendencies and proportions. Bivariate analysis was done using Chi square test and logistic regression was also performed. Results: The prevalence of mild cognitive impairment among adults aged more than or equal to 60 residing in Kaniyambadi block was 20% (95% CI 15.9 - 24.5). The prevalence of major neurocognitive disorder in the same population was 4.4% (95% CI 2.5 - 7.1). and the prevalence of depression was 18.9% (95% CI 14.9 -23.3). Age greater than or equal to 70 (AOR 2.24 [1.38 – 3.64]), no formal education (AOR 2.62 [1.52 – 4.48]) and depression (AOR 3.64 [1.90 – 6.99]) were found to be statistically significantly associated with cognitive impairment. Conclusions: The overall prevalence of mild cognitive impairment and major neurocognitive disorder in Kaniyambadi block was found to be similar to the prevalence in other parts of the nation. Adults aged more than 70 and those with no formal education are at greater risk of developing cognitive impairment. Since depression is also associated with cognitive impairment, it is imperative to screen the elderly with depression and other psychiatric illnesses for cognitive impairment and those with cognitive impairment for depression

Background: Catheter ablation (CA) combined with left atrial appendage closure (LAAC) for non-valvular atrial fibrillation (NVAF) has become a hot topic in clinical research in recent years. Aims: This study aimed to investigated the safety and effect of CA combined with LAAC for NVAF. Methods: 150 patients diagnosed with NVAF who received CA combined with LAAC (combined group) in Shanghai Changhai Hospital from October 2020 to December 2021 were retrospectively included. Patients who underwent CA-Only (CA-only group, n=150) were selected using propensity score matching (PSM) in a 1:1 ratio. Results: At one-year follow-up, no significant differences in procedure-related complications（1.33% vs 1.33%,P=1.000）and the incidence of stroke（1.33% vs 1.33 %,P=1.000）were found. The success rate between the combined procedure and the CA-only procedure (persistent AF: 62.5% vs 68.8%, P=0.839; paroxysmal AF: 73.8% vs 76.2%, P=0.775) were similar. Furthermore, there was a significant difference in the incidence of heart failure with preserved ejection fraction (HFpEF) before and after procedure (combined group: 32% vs 18.67%,P=0.049; CA-only group: 18.7% vs 5.3%,P=0.024). Multivariate Logistic regression demonstrated longer short diameter of the LAA orifice (OR=1.196, P=0.049) might be a risk factor for residual leakage around the LAA occluder. Conclusion: The combined procedure was feasible, safe and effective in NVAF patients with a high risk for stroke. Longer short diameter of the LAA orifice might be a risk factor for leakage around the LAA occluder.

Soybean is a major plant protein source worldwide and its cultivation in central and northern Europe is still emerging. To understand the influence of the environment in the northern latitudes and its interactions with different soybean genotypes, a 3-year multi-location trial was carried out from 2019 to 2021 in Northern Germany. The objectives were to (i) quantify the grain yield and stability of six soybean genotypes across eight environments using the AMMI and BLUP models to identify superior genotypes as well as optimal environmental conditions for growing soybeans in northern latitudes, and (ii) assess the GEI influence on soybean grain yield, crude protein, and protein yield to explore the influencing factors contributing to yield variability. The mean soybean grain yield was 2060 kg ha-1 and it varied among locations and across years. A large portion of the total variance in all parameters was explained by environment (67.6% to 82.8%), followed by GEI (7.7% to 14.6%), while a small portion was attributed to genotypes (1.3% to 10.5%). The growing conditions at site Müncheberg produced a stable soybean yield but were less productive than sites Dahlem and Dedelow. Regular precipitation in July and August corresponded with increased grain yield. The BLUP and AMMI models ranked the feed-grade cultivar Merlin as superior in terms of stability and performance. In contrast, the food-grade cultivar Comandor may be risky for grain production in rainfed conditions. The study highlighted soybean’s agronomic potential in northern latitudes and the influence of the prevailing environment on yield and stability.

Postherpetic neuralgia (PHN) is a common neuropathic pain syndrome in which chronic pain is often accompanied by severe sleep disturbances that affect patients’ quality of life. At present, the evaluation of sleep disorders in patients with PHN mainly relies on patients’ self-report and monitoring of related devices, and there is still a lack of unified standards. This paper systematically evaluates the current sleep disorder assessment tools used in PHN patients from the aspects of assessment content, application advantages, limitations and characteristics of PHN patients, aiming to reveal their application characteristics and research status in PHN patients, help optimize clinical management strategies for PHN patients, and provide a basis for improving existing tools or developing new tools. This will drive technological innovation and progress in the assessment of sleep disorders.

Recent studies have highlighted a significant association between the use of proton pump inhibitors (PPIs) and an increased risk of cardiovascular diseases, with alterations in gut microbiota—induced by PPIs—potentially influencing the pathogenesis of ischemic stroke (IS). To investigate the causal relationship between PPI use and IS, we employed Mendelian Randomization (MR), utilizing single nucleotide polymorphisms (SNPs) associated with commonly used PPIs, including esomeprazole, lansoprazole, omeprazole, and rabeprazole, as instrumental variables. A two-step MR approach was further employed to examine the potential mediating effect of gut microbiota on these relationships. Our findings revealed a significant causal association between the use of esomeprazole and lansoprazole and the risk of large artery atherosclerotic stroke (LAS). Although specific shifts in gut microbiota were observed following the administration of these PPIs, these changes did not mediate the increased LAS risk, suggesting that the association between PPI use and LAS is not primarily driven by microbiota alterations. This study establishes a direct causal link between certain PPIs and the risk of specific IS subtypes, providing important insights into the cardiovascular risks associated with PPI use and underscoring the need for cautious clinical application, particularly in individuals at heightened risk of cardiovascular events.

This paper presents BiSe-AIHM, a novel method for heart rate estimation from lip microvessel dynamics in video. The approach integrates BiSeNet, a semantic segmentation algorithm, to accurately track lip regions, followed by absorption fluctuation heartbeat modulation (AIHM) for signal extraction. AIHM utilizes the absorption fluctuation modulation effect (AIFM) to derive heart rate-related signals from the green channel of the segmented lip region. These signals are then analyzed using fourier transform and the average shift histogram (ASH) to determine heart rate. Extensive experiments validate the method’s accuracy and stability, achieving 98.83% accuracy with an RMSE of 0.91 bpm. BiSe-AIHM surpasses traditional contact-based and non-contact methods, offering a low-cost, accessible solution for heart rate monitoring using standard camera equipment. This innovation holds great promise for cardiovascular disease prevention, medical diagnostics, and personal health management.

To break through the diffraction limit of light, various super-resolution techniques have been developed. Super-resolution Radial Fluctuations (SRRF) is an emerging super-resolution microscopy technique that utilizes a standard wide-field fluorescence microscope and open-source software plugins compatible with ImageJ. As a result, SRRF has relatively low hardware and software requirements, making it highly accessible to researchers. Here we first introduce the basic principles and workflow of SRRF, then describe the open-source ImageJ software tools NanoJ-SRRF and NanoJ-SQUIRREL. NanoJ-SRRF is used for reconstructing super-resolution images, while NanoJ-SQUIRREL is used for quantitatively assessing the quality of super-resolution images. Next, We summarize the advantages and disadvantages of SRRF as well as optimization methods. Finally, we present the applications of SRRF technology, along with potential avenues for future technical improvements.

Precise IMDb movie ratings predictions are vital to the stakeholders of the film industry since it will dictate investment, marketing, and content recommendation. Standard machine learning techniques are not capable of handling high-cardinality categorical columns, bad interactions between features, and non-linear relations between reviews and metadata. This paper proposes the Meta-Ensemble Predictor (MEP), a new state-of-the-art hybrid framework that integrates various gradient boosters (CatBoost, LightGBM, XGBoost) among themselves and with a deep neural network and tuned using a meta-learning algorithm with a Random Forest as the final predictor. With a database of 33,600 movies from 1960 to 2024 having metadata details like genre, director, cast, runtime, box office, and voter ratings, MEP model uses TF-IDF text feature vectorization, polynomial interaction of features, and dimensionality reduction methods for improved feature representation. The model presented here has achieved RMSE of 0.4389 and accuracy of 96.96% at a difference of 1-point from actual IMDb ratings when compared to other state-of-the-art models. The study showcases the strength of ensemble learning to learn the sophisticated patterns of the ratings of the movies in becoming a good film industry predictive analytics tool.

Photothermal therapy (PTT) employs photoabsorbing nanomaterials to convert near-infrared (NIR) light into localized hyperthermia for targeted tumor ablation. In this study, biocompatible and stable MXene photothermal fiber membranes were synthesized by integrating metal carbide MXene with the biodegradable polymer polylactic acid (PLA) through electrospinning techniques. The photothermal efficiency of these membranes was assessed via NIR laser irradiation experiments. For PTT treatment evaluation, a melanoma model was developed using C57BL/6J mice, into which the innovative MXene photothermal fiber membranes(MXene-PEG/PLA fiber membranes) was implanted. The findings indicated that the MXene-PEG/PLA fiber membranes significantly diminished both the volume and weight of tumors. Furthermore, there was a notable reduction in tumor cell apoptosis and vascular density, alongside a marked improvement in the integrity of the heart, spleen, lungs, and kidneys in the treated mice. These results suggest that the MXene-PEG/PLA fiber membranes represent a safe and effective molecular material for use in photothermal therapy.

Blood cells are vital components of the circulatory system, playing critical roles in oxygen transport, immune defense, and clot formation. The morphology of white and red blood cells can reveal diseases and disorders, making accurate segmentation and classification essential for hematological diagnostics. Beside standard histologygocal cytolpgica appaoches, biophotonics is emering as new set of tools fur such diagnostics. These bio-photonic processes enable label-free imaging of unstained blood smears, leveraging intrinsic cellular properties such as morphology, refractive index, and texture. Unlike stained blood smear analysis, which relies on color differentiation, unstained analysis depends on intrinsic cell properties, presenting challenges such as low contrast, subtle feature variations, and imaging artifacts. This review evaluates rule-based, machine learning, and deep learning techniques for segmentation and classification of unstained samples of cells, highlighting their strengths, challenges, and potential to improve diagnostic accuracy, clinical applications, and innovations in biophotonics.

High-fidelity models play an essential role in advancing the structural optimization and motion simulation of amphibious vehicles. However, the complexity of hydrodynamics poses significant challenges in dynamic modeling, parameter identification, and experimental validation. To address these challenges, this research derives a six-degree-of-freedom dynamic model for a vector propelled amphibious vehicle based on maneuvering theory, including a dedicated propulsion system dynamic model. Given the system identification challenges posed by the highly coupled multi-parameter dynamics, a systematic experimental framework is devised, featuring decoupled measurements of the propulsion and maneuvering dynamics. A staged parameter identification methodology integrating the genetic algorithm and the least squares method is proposed. The methodology initially identifies a subset of parameters through decoupled reduced-order models, and subsequently performs a systematic identification of the remaining parameters based on the complete coupled model. For model validation, a simulation platform based on numerical integration methods is developed, with real-time visualization implemented in Unreal Engine 4 (UE4). Cross-validation results demonstrate that the established model with identified parameters can accurately capture the motion characteristics of the amphibious vehicle.

Biochar/Layered double hydroxide composites are of priority when dealing with wastewater treatment since they are of low cost and have high effects. The incorporation of rare earth elements into Biochar/LDH further enhances its adsorption performance and magnetic ability toward pollutant removal. Such Lanthanide-based layered double hydroxide (LDH) biochar nanocomposites have gained considerable attention as sustainable materials for advanced wastewater treatment due to their exclusive physiochemical properties, extensive surface area, adsorption characteristics, and environmental compatibility. The review provides insight into current progressions in the formation, analysis, and application of LDH biochar nanocomposites in wastewater treatment. Key aspects such as the inclusion of lanthanide elements, optimization of synthesis methods, and enhancement of adsorption capacities are discussed. In addition, the mechanisms underlying pollutant removal, including adsorption, ion exchange, and photo-catalytic degradation, are elucidated. The review also highlights the challenges and future perspectives in the development and scale-up of LDH biochar nanocomposites for sustainable wastewater treatment solutions. Overall, this review facilitates scientific discovery for researchers and practitioners in the field of ecological engineering seeking to use the potential of lanthanide-based LDH biochar nanocomposites for efficient wastewater remediation.

The current study proposes a novel theory, the consciousness-entropy field theory (CEFT), with a conceptual framework, particularly through the lens of string theory. We hypothesize that the frequency of energy vibration is modulated by consciousness, suggesting fundamental consciousness field Ψ(𝐫, 𝑡) that dynamically interacts with the vibrational modes of strings. This interaction implies that the vibrational states of strings are emergent phenomena influenced by consciousness rather than purely fundamental entities. Moreover, we posit a profound connection between entropy and consciousness, where the latter modifies entropy in physical systems, thereby affecting their evolution. Through a series of mathematical derivations, including the mass-energy relationships, we introduce a modified wave equation that considers the conscious state as a determinant in energy dynamics and ultimately leads to a new consciousness-entropy quantum equation (CEQE) and consciousness-entropy field equation (CEFE). Furthermore, we explore the implications of consciousness within the general relativity framework, suggesting potential modifications to the stress-energy tensor that account for the consciousness field. By examining the role of consciousness in quantum interference phenomena, we propose experimental pathways to validate our framework, opening avenues for further research that connects consciousness studies with theoretical physics. The current theory opens a new branch of research area which combines quantum mechanics, general relativity, and the nature of consciousness.

AbstractBioengineered smart textiles represent a transformative advancement in fabric technology, integrating living biological materials to achieve self-healing, adaptability, and biodegradability. This study investigates whether bioengineered smart textiles offer superior performance compared to traditional smart textiles, which rely on synthetic coatings, embedded electronics, and energy-intensive components. A comparative analysis approach was employed, synthesizing data from recent experimental studies, case reports, and industry reports to evaluate self-healing efficiency, adaptability to environmental changes, and biodegradability. Findings indicate that bioengineered textiles outperform conventional smart textiles in key sustainability metrics. Bacterial and polymer-embedded self-repair systems demonstrated up to 90% restoration efficiency, significantly extending fabric lifespan. Algae-infused and fungal-based textiles exhibited dynamic responsiveness to temperature and humidity, offering passive climate control without external power sources. Biodegradable mycelium-based and bacterial-engineered fibers present a viable alternative to synthetic fabrics, reducing textile waste and microplastic pollution. Despite these advantages, challenges such as microbial stability, large-scale production feasibility, and regulatory acceptance remain barriers to widespread adoption. Further research is needed to optimize microbial encapsulation techniques, AI-driven adaptive textiles, and scalable biofabrication processes. As synthetic biology and material science advance, bioengineered textiles have the potential to revolutionize healthcare, military gear, sportswear, and sustainable fashion, aligning textile innovation with ecological responsibility.1. IntroductionSmart textiles, an interdisciplinary innovation at the intersection of material science, electronics, and biotechnology, have transformed fabrics from passive materials into interactive, responsive systems. Initially, textiles served primarily for protection and comfort, but advancements in synthetic polymers and embedded electronics have expanded their functionality. Conventional smart textiles integrate conductive fibers, nanomaterials, and electronic components to enable sensing, energy harvesting, temperature regulation, and communication functions. However, these materials often rely on non-biodegradable synthetic coatings and electronics, leading to environmental concerns. Recent developments in bioengineered smart textiles offer an alternative by incorporating living microorganisms such as bacteria, fungi, algae, and biofilms, allowing fabrics to exhibit self-healing, environmental responsiveness, and biodegradability [1-5].The textile industry is one of the largest contributors to environmental pollution, accounting for nearly 10% of global carbon emissions and generating over 92 million tons of waste annually [6-8]. Traditional synthetic fibers, such as polyester and nylon, release microplastics into the environment and require energy-intensive production processes [9]. While conventional smart textiles offer enhanced functionalities, their reliance on electronic components poses challenges for recyclability and waste management. Bioengineered smart textiles represent a promising sustainable alternative, utilizing renewable biological materials and self-regenerative mechanisms that could reduce environmental impact while maintaining or exceeding the performance of traditional smart fabrics.Despite significant advancements, several critical gaps remain in the study of bioengineered smart textiles. While individual studies highlight the potential of bioengineered fabrics, there is limited quantitative comparison between bioengineered and conventional smart textiles regarding self-healing efficiency, adaptability, and biodegradability. The long-term durability and stability of bioengineered textiles, particularly concerning microbial viability and environmental resistance, remain uncertain. Additionally, scalability and commercial viability present challenges, as most research is still confined to laboratory settings without clear pathways to large-scale production.This study aims to address these research gaps by investigating the effectiveness of bioengineered smart textiles in self-healing, adaptability, and sustainability compared to conventional smart textiles. The core hypothesis of this research is that bioengineered smart textiles demonstrate superior self-healing capabilities, adaptive responses, and biodegradability, making them a viable alternative for sustainable textile applications.To evaluate this hypothesis, the study employs a comparative analysis approach, synthesizing findings from recent experimental studies and industry reports. The research quantitatively assesses self-healing efficiency by examining bacterial-based and polymer-embedded healing mechanisms, evaluates adaptive functionality through real-time responsiveness of algae- and fungi-based textiles to environmental stimuli, and analyzes sustainability metrics, including biodegradation rates and carbon footprint reduction [10,11]. By addressing these aspects, this study provides a comprehensive evaluation of bioengineered smart textiles, offering insights for future research, industrial adoption, and sustainable textile development.2. Literature ReviewSmart textiles have emerged as an important innovation in material science, combining textiles with advanced functionalities such as sensing, energy harvesting, and environmental adaptability [12-15]. While conventional smart textiles rely on synthetic coatings, conductive materials, and embedded electronics, bioengineered smart textiles utilize living materials to achieve self-healing, adaptability, and biodegradability. The integration of biological systems into textiles presents a novel approach to sustainability and performance, yet key technological and research gaps remain that hinder their widespread adoption.Traditional smart textiles primarily rely on synthetic polymers, nanomaterials, and electronic components to provide interactive functionalities. These textiles incorporate phase-change materials for temperature regulation, conductive polymers for electrical conductivity, and microelectromechanical systems (MEMS) for sensing and actuation [16]. However, their dependence on synthetic coatings and electronic circuits presents challenges related to environmental sustainability and long-term durability. Many of these materials are non-biodegradable and contribute to microplastic pollution. Additionally, embedded electronics increase power consumption, require external energy sources, and may compromise fabric flexibility and comfort over time. While traditional smart textiles have proven effective in various applications, including healthcare, military, and sportswear, their reliance on energy-intensive and non-renewable materials limits their long-term viability.Bioengineered smart textiles represent a shift towards sustainable, self-regenerative fabric technologies by integrating living biological components into textile structures [17,18]. These materials harness bacteria, fungi, algae, and biofilms to enable functionalities that traditional textiles achieve through synthetic materials. Bacteria such as Bacillus subtilis and Escherichia coli facilitate self-healing through protein synthesis, restoring fiber integrity after damage. Fungi, particularly mycelium-based materials, serve as biodegradable leather substitutes with moisture-sensitive properties. Algae, including Chlorella vulgaris and Spirulina , are incorporated into textiles for oxygen regulation, carbon sequestration, and thermal adaptability. Biofilms, composed of microbial colonies, contribute to self-cleaning properties and structural reinforcement. Unlike conventional smart textiles, which rely on electronic sensors to achieve smart functions, bioengineered textiles leverage the intrinsic biological activity of living organisms to create dynamic, responsive fabrics.Key technological advancements in bioengineered smart textiles focus on three main areas: self-healing, adaptability, and biodegradability. Self-healing textiles incorporate microbial networks that synthesize repair proteins, mimicking biological wound-healing mechanisms [19-22]. Studies on polymer-embedded bacterial healing systems demonstrate significant improvements in fabric longevity and durability, reducing the need for frequent replacements [23]. Adaptive textiles dynamically respond to environmental stimuli such as temperature and humidity. Fungal biofilms expand or contract based on moisture levels, regulating breathability, while algae-infused textiles adjust their metabolic activity to enhance thermal regulation. Biodegradability remains a critical advantage of bioengineered textiles, as microbial-engineered fibers decompose safely after use, minimizing landfill waste. Innovations such as mycelium-based leather alternatives and bacterial dyeing techniques further support the sustainability of bioengineered fabrics by eliminating the need for harmful chemical processes.Despite these advancements, several research gaps persist in the development of bioengineered smart textiles. Quantitative comparisons between bioengineered and conventional smart textiles in terms of self-healing efficiency, adaptability, and biodegradability remain limited. Existing studies provide promising experimental results, but long-term durability, stability, and real-world performance have not been fully established. Another challenge is the integration of living materials within textiles while maintaining microbial viability over extended periods. Ensuring compatibility between biological components and fabric substrates without compromising functionality is an ongoing area of research. Additionally, scalability remains a significant hurdle, as large-scale manufacturing of bioengineered textiles requires optimized production processes and regulatory approvals. The commercial adoption of these textiles depends on addressing these limitations and improving public acceptance of fabrics embedded with living organisms.As bioengineered textiles continue to evolve, further interdisciplinary research is needed to enhance their stability, scalability, and functional longevity. Addressing these gaps will be essential for transitioning bioengineered smart textiles from laboratory research to widespread commercial use, providing sustainable alternatives to traditional smart fabrics.3. MethodologyThis study employs a comparative analysis approach to evaluate the effectiveness of bioengineered smart textiles in self-healing, adaptability, and biodegradability relative to conventional smart textiles. The methodology integrates systematic literature review and quantitative synthesis to assess the performance metrics based on experimental findings, industry reports, and case studies.3.1 Research ApproachThe study follows a structured comparative analysis of published research on bioengineered smart textiles, focusing on:Self-healing mechanisms (bacterial/polymer-embedded healing efficiency)Adaptive functionality (real-time responsiveness to environmental stimuli)Biodegradability (decomposition rate and sustainability impact)Power Consumption (energy efficiency of textile functions)Manufacturing Cost (economic feasibility of bioengineered textiles)Durability (long-term wear resistance and microbial stability)The research combines qualitative synthesis from systematic literature reviews with quantitative analysis of reported experimental data.3.2 Data Collection Methods3.2.1 Literature Search StrategyData sources include peer-reviewed journal articles, experimental studies, and reports from industry advancements. A structured search was conducted in Scopus, IEEE Xplore, PubMed, and ScienceDirect using the following keywords:”Bioengineered textiles””Smart textiles self-healing””Fungal and bacterial textile adaptability””Biodegradable textile polymers””Sustainable fabric technology”“Self-healing fabric mechanisms”“Microbial textile engineering”“Adaptive response in smart textiles”“Textile biodegradation analysis”“Energy-efficient wearable textiles”“Cost analysis of bioengineered textiles”“Durability testing in smart fabrics”“Polymer-based textile reinforcement”“Sustainable textile manufacturing”3.2.2 Inclusion and Exclusion CriteriaA total of 210 research articles were initially identified. After screening for relevance, methodological rigor, and recency (published no later than 2015), 46 studies were selected for in-depth analysis.

Background: The neutrophil-percentage-to-albumin ratio (NPAR) is a readily accessible biomarker reflecting systemic inflammation and nutritional status. This study aimed to investigate the association between NPAR and serum α-Klotho(a well-established anti‐aging biomarker) levels among the elderly population. Patients and methods: This cross-sectional investigation was carried out among participants from NHANES cycles spanning 2009 to 2016. The data of NPAR were log-transformed (ln-NPAR) to normalize the skewed distribution. The relationship between ln-NPAR and α‐Klotho was investigated using stratified multivariable linear regression. To explore possible non‐linear connection, we employed a generalized additive model, together with smoothed curve fitting. Additionally, subgroup analysis was conducted to assess the strength of the association across various populations. Results: A total of 3986 participants with an average age of 67.57 ± 0.09 years were included after applying exclusion criteria. After adjusted by all covariates, ln-NPAR was negatively associated with serum α‐Klotho concentration [β(95% CI) = -80.47(-129.20,-31.74)]. Furthermore, a significant V‐shaped association between ln-NPAR and klotho protein level was found in fully-adjusted smoothed curve fitting and two-piecewise linear regression, with its inflection point at 2.965 (corresponding to NPAR = 18.39). Subgroup analysis and interaction test indicated no detectable dependence on stratification factors (all p for interaction > 0.05). Conclusions: The logarithmically transformed NPAR exhibited a V-shaped association with serum α-klotho protein concentration in elderly adults in the U.S. Further studies are warranted to validate upon these findings.

Novel humanized mouse model for steroid-resistant asthmaShiho Yamada, MD, PhDa,b,*, Shuichiro Maruoka, MD, PhDa,b,*,**, Shota Toyoshima, PhDa,c, Yusuke Kurosawa, MD, PhDa,b, Ryosuke Ozoea,b , MD, Yutaka Kozu, MD, PhDa,b, Kenji Mizumura, MD, PhDa,b, Yoshimichi Okayama, MD, PhDa,b,d,e.f, Ryoji Ito, PhDg,**, and Yasuhiro Gon, MD, PhDa,baDivision of Respiratory Medicine, Department of Internal Medicine, Nihon University School of Medicine, Tokyo, JapanbCenter for Allergy, Nihon University Itabashi Hospital, Tokyo, JapancDepartment of Biochemistry and Molecular Biology, Nippon Medical School, Tokyo, JapandDepartment of Allergy, Internal Medicine, Misato Kenwa Hospital, Saitama, JapaneDepartment of Internal Medicine, Division of Respiratory Medicine and Allergology, Showa University School of Medicine, Tokyo, JapanfAdvanced Medical Science Research Center, Gunma Paz University, Graduate School of Health Sciences, Gunma, JapangCentral Institute for Experimental Medicine and Life Science (CIEM), Kanagawa, Japan