Browse Articles

Genetic predisposition to elevated BMI and adult asthma phenotypes in a Japanese population

Obesity is a well-established risk factor for asthma, with genetic factors influencing both conditions. This study investigates the impact of genetic predisposition to increased body mass index (BMI) on adult asthma phenotypes. We recruited 1532 non-asthmatic healthy individuals and 779 adult asthma patients to assess the relationship between BMI-related genetic risk scores (BMI-GRS) and asthma. Among the 85 single nucleotide polymorphisms (SNPs) previously associated with BMI in Japanese populations, significant associations with BMI were confirmed for 6 SNPs in the healthy individuals. Using these, BMI-GRS was calculated for both groups. While asthma patients had higher BMI than healthy individuals (p = 0.004), no significant difference in BMI-GRS was observed between the groups (p = 0.56). A cluster analysis identified six distinct phenotypes of adult asthma patients: two overweight/obese clusters (one with elevated BMI-GRS, one without) and four non-obese clusters (with one showing significantly elevated BMI-GRS). This study demonstrates a genetic heterogeneity in the phenotype of adult asthma among a Japanese population, showing that genetic variants associated with BMI contribute to specific subtypes of asthma. Prospective longitudinal studies are essential to delineate the interactions between genetic predisposition, elevated BMI, subsequent changes in adiposity, and the evolution of asthma phenotypes, which would facilitate the development of mechanism-based therapeutic strategies tailored to genetically-defined patient subgroups.

Wikipedia is needed now more than ever, 25 years on

Protective effect of EVA layers under quasi-static loading for veneer-like ceramic discs

Correction: Design of a multi-epitope recombinant BCG vaccine targeting Brucella OMP31, LptE and VirB2 in immunoinformatics approaches

A bendable AI chip for wearable technology

The metabolic dysfunction-associated steatohepatitis (MASH) drug resmetirom exhibits broad nuclear receptor activity with minimal functional impact

Abstract In 2024, the FDA granted approval for resmetirom, a selective but moderately potent agonist of the thyroid hormone receptor (THR) β, marking the first drug for the treatment of metabolic dysfunction-associated steatohepatitis (MASH) in the US. Given the absence of selectivity data, we conducted a comprehensive screening against other nuclear receptors implicated in metabolic regulation, in addition to THRβ. Reporter gene assays revealed that resmetirom also binds several off-target nuclear receptors, including constitutive androstane receptor (CAR), retinoic acid receptor-related orphan receptors (RORs), and hepatocyte nuclear factor 4α (HNF4α). However, subsequent in vitro experiments, designed to better recapitulate physiological conditions, showed that THRβ modulation is functionally dominant. These preliminary findings suggest that pharmacotherapeutic efficacy of resmetirom remains intact despite the identified off-target effects, supporting its clinical application in the treatment of MASH.

Correction: Renal cancer survival in clear cell renal cancer compared to other types of tumor histology: A population-based cohort study

Metadata driven malicious URL detection using RoBERTa large and multi source network threat intelligence

Retraction: Enhancing pipa tuning stability with piezoelectric materials: An adaptive system for real-time performance adjustment

Medical knowledge representation enhancement in large language models through clinical tokens optimization

Abstract During the training of medical large language models (LLMs), conventional tokenizers frequently segment domain-specific medical terms into multiple subword tokens, resulting in suboptimal recognition and representation of specialized vocabulary. As a consequence, the model encounters difficulties in effectively acquiring medical domain knowledge during the fine-tuning process. To address this limitation, the present study introduces “clinical tokens”—medical subword units—by augmenting the vocabulary of the original LLaMA2 tokenizer. This adapted tokenizer retains medical terms as whole tokens wherever feasible, thereby enhancing tokenization accuracy and enabling the model to learn and interpret medical knowledge more effectively. For downstream task adaptation, this study employs the Byte Pair Encoding (BPE) algorithm to construct a domain-specific vocabulary and tokenization model, ensuring the inclusion of medical subword units (clinical tokens). We compare the tokenization performance of three variants: the original LLaMA2 tokenizer, the Chinese-LLaMA2 tokenizer (expanded with an extended Chinese vocabulary), and the clinical token-augmented tokenizer. This was followed by fine-tuning the large language models on curated medical datasets. The experimental results indicate that the enhanced tokenizer improves encoding and decoding efficiency, extends the model’s effective context window, and yields superior performance on downstream medical tasks.

Symmetry, presumptions, and the judges design

An instrumental variables approach called ‘the judges design’ used frequently in social sciences relies on an assumption called ‘average monotonicity’. This assumption pertains to how different judges’ (or other classifiers’) decision making processes relate to each other. Violations of it are hard to detect, which raises the importance of it being supported by a plausible theory. Decisions of judges who solve Bayesian decision problems violate average monotonicity as long as the signals they process are symmetric and they do not possess strong presumptions. This result is extended to cases where judge presumptions are symmetrically distributed and may include strong presumptions. The analysis reveals factors that can be considered while discussing the plausibility of an assumption made to identify causal effects whose violations are difficult to detect and has important policy implications. “In other cases, however where instrumental variables are used [monotonicity is] not so plausible. Specifically in what are now called judge lenience designs” Guido Imbens, 2021 Nobel Prize Lecture

A synergistic strategy of crosslinking and filler toughening enabling stretchable organic photovoltaics for wearable applications

Optimizing the operational conditions for microalgae biomass drying using tray dryers

Abstract Drying is a critical yet energy-intensive step in the valorization of microalgae biomass, essential for ensuring long-term stability and enabling downstream processing. This study investigates the technical feasibility and performance of tray drying as an alternative to conventional methods, using Chlorella sp. biomass. Experiments were conducted at drying temperatures ranging from 60 to 80 °C and biomass layer thicknesses between 0.3 and 1.0 cm, simulating industrial tray dryer conditions. Drying kinetics were assessed through moisture ratio and water content conditions, with a power-law model applied to describe the drying rate as a function of moisture content. The results demonstrated that thinner layers and higher temperatures significantly reduced drying time, with full dehydration within 5 h at 80 °C and 0.3 cm thickness. However, spectrophotometric analysis revealed a trade-off between drying efficiency and biomass quality, with pigment degradation increasing with temperature and time. A polynomial model was developed to predict pigment deterioration based on operational parameters. These findings provide a robust foundation for the design and scale-up of tray drying systems and offer a practical framework for optimizing the balance between process efficiency and product quality in microalgae biorefineries.

Health information anxiety in social media users during public health emergencies: A qualitative comparative analysis using attribution theory

This article investigates the mechanisms influencing health information anxiety among social media users during sudden public health emergencies, aiming to provide insights for managing social media users’ negative emotions in such contexts. By employing literature analysis and case studies and integrating Three-Dimensional Attribution Theory, the factors contributing to health information anxiety are classified into individual, informational, and situational dimensions. Questionnaire data were gathered via scenario simulation, and a Qualitative Comparative Analysis (QCA) method was used to validate causal configurations leading to health information anxiety among social media users. The findings indicate that, within the context of sudden public health emergencies, the emergence of health information anxiety is the result of the interplay among individual, situational, and informational dimensions. Specifically, six key factors, including event severity, involvement, textual sentiment, collective emotions, information overload, and information asymmetry, are identified as playing a critical role in the development of severe health information anxiety. Notably, the situational dimension is found to exert a crucial and decisive influence on the generation of health information anxiety among social media users.

The mosaic memory of large language models

Abstract As Large Language Models (LLMs) become widely adopted, understanding how they learn from, and memorize, training data becomes crucial. Memorization in LLMs is widely assumed to only occur as a result of sequences being repeated in the training data. Instead, we show that LLMs memorize by assembling information from similar sequences, a phenomenon we call mosaic memory. We show major LLMs to exhibit mosaic memory, with fuzzy duplicates contributing to memorization as much as 0.8 of an exact duplicate and even heavily modified sequences contributing substantially to memorization. Despite models displaying significant reasoning capabilities, we somewhat surprisingly show memorization to be predominantly syntactic rather than semantic. We finally show fuzzy duplicates to be ubiquitous in real-world data, untouched by deduplication techniques. In this work, we show memorization to be a complex, mosaic process, with real-world implications for privacy, confidentiality, model utility and evaluation.

Resilience-oriented optimization of hospital microgrids with critical load support using ESS and PV under grid outage conditions

Abstract This study develops a resilience-oriented optimization framework for hospital microgrids that integrates photovoltaic (PV) generation, multi-node battery energy storage systems (BESS), and medical load prioritization under grid outage conditions. A mixed-integer linear programming (MILP) model is formulated to jointly optimize ESS scheduling, critical-load support, and renewable utilization across a set of Monte Carlo outage scenarios. The framework introduces a multi-tier hospital load hierarchy (ICU, OR, imaging, pharmacy) based on Value of Lost Load (VOLL), and employs a composite resilience index combining ENS, LOLP, and critical-load survivability. The model is evaluated on modified IEEE 13-, 33-, and 69-bus systems. Results show that coordinated multi-node ESS placement improves resilience significantly, reducing Energy Not Supplied (ENS) by 55–63% compared with baseline configurations, while maintaining ≥ 95% supply to life-critical loads across most stochastic outage realizations. The proposed strategy also ensures stable Resilience Index (RI) values with a variance below 10%, highlighting robustness against PV variability and outage timing uncertainty. Sensitivity analysis demonstrates that ESS capacity, PV penetration, and outage duration are the dominant factors influencing resilience. Overall, the framework provides a practical and quantitatively validated tool for hospital energy planners seeking enhanced survivability and operational security during grid disruptions.

Surveillance and molecular characterization of banana viruses associated with Musa germplasm in Malawi

Malawi has diverse local banana germplasms that are preferred by its population. However, the epidemics of banana bunchy top disease (BBTD), caused by the banana bunchy top virus (BBTV) is wiping out the preferred germplasms and limiting their cultivation. A survey was conducted to characterize banana germplasm and evaluate the presence, incidence and prevalence of banana viruses. PCR products from infected germplasm were sequenced and aligned for each detected virus to construct a phylogenetic tree. BBTV, banana mild mosaic virus (BanMMV) and six banana streak virus (BSV) species were detected in Malawi. Malawi’s BBTV isolates belonged to the Pacific Indian Ocean group, and BanMMV isolates clustered to three sub-branches. The six BSV species detected in Malawi belonged to clade 1. Among the genetic groups of Musa , the characterized banana germplasms belonged to AA, AAA, AAB, and ABB groups with some germplasms being unique compared to those already genotyped. The ABB group was dominant in Malawi and was significantly more often infected by BSV species (possibly originating from endogenous viral sequences), while BBTV and BanMMV infected the AAA and AAB group more frequently, respectively. The primary source of banana planting materials was banana propagule exchange among relatives which posed a higher risk of spreading virus diseases. The survey underlined the importance of establishing a banana seed industry and implementing policies that promote farmers’ access to virus-tested planting materials, ultimately helping to prevent future virus epidemics.

Olutasidenib in recurrent/relapsed locally advanced or metastatic IDH1-mutated chondrosarcoma: phase 1b/2 trial

Design and performance analysis of a vertically stacked gate-all-around nanosheet FET with embedded nanocavity for biosensing applications

Abstract In this article, we designed and analyzed a Vertically Stacked Gate All Around Dielectric Modulated Nano Sheet Field Effect Transistor (DM-NSFET) based biosensor through TCAD simulations. The DM-NSFET is designed for detection of Cancer biomolecules like SW 620, HEK293, and other biomolecules like DNA, gelatin. This functionality comes out through the modulation of its electrical properties by incorporating cavity all around at two sides of dielectric material (HfO 2 ) under the gate electrode to allow biomolecules. The proposed device contains gate all around to increase the sensitivity of device. The sensitivity variation of biosensors is analyzed in terms of subthreshold swing (SS), Selectivity and response time (τ). Further, the effect of filling positions on sensitivity is examined under different cases, this biosensor sensitivity mainly depends on number of biomolecules filling rather than the specific filling position. The obtained results indicates that the proposed device is reaches its current sensitivity of 3.1 × 10 3 , subthreshold swing (SS) of 27.72 mV/dec. The proposed device exhibits significantly enhanced sensitivity compared to existing biosensors and therefore, this biosensor is highly suitable for diagnosis of Cancer Biomolecule.

PFKFB3 exacerbates myocardial injury by accelerating CXCR4hi neutrophil mobilization after acute myocardial infarction

Background CXCR4 hi neutrophil mobilization is a key cause of myocardial damage after acute myocardial infarction (AMI). 6-Phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3), a key glycolytic enzyme, plays a crucial role in regulating neutrophil function. However, researchers have not clearly determined whether PFKFB3 is involved in AMI-induced CXCR4 hi neutrophil mobilization. Methods First, the circulating CXCR4 hi neutrophil percentage and neutrophil Pfkfb3 mRNA expression were measured in AMI patients and left anterior descending coronary artery (LADCA)-ligated mice. Next, we explored the relationship between PFKFB3 and CXCR4 expression in lipopolysaccharide (LPS)-stimulated cell models. Neu-PFKFB3 –/– mice were used to investigate the effect of conditional knockout of the Pfkfb3 gene in neutrophils on AMI-induced myocardial inflammatory injury. Results In AMI patients, the expression level of Pfkfb 3 gene was markedly regulated in AMI-induced neutrophils and was positively related to the content of plasma inflammatory factors in AMI patients. Further study revealed that PFKFB3 promotes CXCR4 hi neutrophil mobilization by reprogramming glycolytic metabolism and subsequently exacerbates inflammatory injury in the myocardial tissues of AMI model mice. However, specific knockout of Pfkfb3 gene in neutrophils protects mice from AMI-induced myocardial inflammatory injury by inhibiting the mobilization of CXCR4 hi neutrophils. Conclusions PFKFB3 exacerbates AMI-induced myocardial inflammatory injury by accelerating CXCR4 hi neutrophil mobilization. The mechanism involves PFKFB3-mediated reprogramming of glycolytic metabolism.