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Exploring the association between secondhand smoke exposure and hearing loss among U.S. nonsmokers

Objective Secondhand smoke (SHS) exposure has been implicated as a risk factor for hearing loss. However, there is a relative paucity of inconsistent findings with limited frequency-specific details. The goal of this study is to better characterize the relationship between SHS exposure and hearing loss among adult nonsmokers in the U.S. Study design Cross-sectional. Setting 2015-2016 NHANES cycle. Patients 1644 nonsmokers between ages 20 and 69 and without diabetes, stroke, or heart disease. Intervention Serum cotinine level (ng/ml) indicated SHS exposure. Main outcome measures Outcomes were hearing thresholds at low-frequencies and high-frequencies as well as hearing loss defined by hearing threshold 20 dB in the better ear. Linear regressions between hearing thresholds and SHS exposure stratified by Body Mass Index (BMI) category and controlled for socio-demographic variables. Logistic regression modeling hearing loss by SHS exposure controlled for the same. Results SHS exposure was associated with elevated hearing thresholds at low-frequencies (β = 0.47, p = .03) only in the obese (BMI 30) population. SHS exposure was associated with greater odds of hearing loss (Odds Ratio: 1.17, 95% Confidence Interval: 1.06–1.29, p = .005) and demonstrated a dose-response relationship. Conclusion While SHS exposure was associated with hearing loss and showed a dose-response relationship, its relationship with hearing thresholds was not demonstrated across all hearing frequencies or BMI categories. Additional research is needed to establish the clinical significance of these findings and clarify the role of obesity in this relationship.

Reconfigurable photothermal doping filament for selective spin manipulation and addressing

The room temperature manipulation of solid-state spins provides an opportunity to develop quantum applications under ambient conditions. Local electromagnetic fields, that usually produced by current in micro/nanoscale metal wires, have been employed for the coherent driving and addressing of spin qubit. However, the fixed distribution limits the spatial selectivity and efficiency of qubit manipulation, which is of central importance in a scaled-up quantum system. Here, we report a solution by demonstrating a reconfigurable current with arbitrary shape to engineer microwave and DC magnetic field at microscale. A “photothermal doping” method was proposed to optically control local insulator-to-metal transition in vanadium dioxide. It generates a conducting filament with adjustable position, direction, and width. Universal manipulation and selective addressing of spins at arbitrary sites are realized, by freely changing the filament and electromagnetic field on demand. Our work paves the way for developing quantum devices with large-scale spin qubits.

Neuronal differentiation of human dental pulp stem cells induced by co-treatment of ergothioneine

Objective Human dental pulp stem cells (hDPSCs) are promising adult stem cells that present multilineage differentiation ability. Interestingly, ergothioneine (ERGO) has the potential to uptake into the organic cation transporter N1 (OCTN1) to promote neuronal differentiation. Therefore, this study aims to demonstrate the effect of co-treatment of ergothioneine on the neuronal differentiation of hDPSCs. Methods The hDPSCs were established from the impacted third molars. Subsequently, the hDPSCs investigated the cell viability with ergothioneine at concentrations of 0–500 µM for 30 hours. The non-cytotoxic concentration of ergothioneine was synergistically induced with the neuronal induction medium. The characteristics of differentiated cells were verified as neuronal cells (d-hDPSCs) by identification of the Nissl substance. The optimal concentration of ergothioneine, which triggered the highest neuronal differentiation of hDPSCs, was further confirmed by neuronal phenotypes via immunofluorescent staining, gene expression, and the ability of neurotransmitter release by intracellular calcium oscillation. Results The isolated cells from human dental pulp tissue were characterized as mesenchymal stem cells (MSCs), verified as hDPSCs. The cellular toxicity of ergothioneine was not observed up to 500 µM for 30 hours. The d-hDPSCs presented a neuronal-like shape and positively expressed the Nissl substance. Interestingly, the highest number of neuronal-like cells was detected at 500 µM of ergothioneine. These neuronal-like cells exhibited the synaptic vesicle glycoprotein 2A (SV2A) expression and dynamic change of intracellular Ca2+, suggesting potential functional neuronal characteristics. Furthermore, co-treatment of ergothioneine at 500 µM triggered neurogenic maturation by decreasing Nestin and NES expression and increasing Beta-III tubulin, TUBB3, and microtubule-associated protein 2 (MAP2) expression, respectively. Conclusion Co-treatment of ergothioneine at 500 µM can enhance neuronal differentiation, which has the potential to promote neurogenic maturation. Therefore, these findings suggest the alternative of using hDPSCs and the potential of ergothioneine co-treatment as stem cell-based therapy for further transplantation to cure various neurological diseases.

A genome-scale drug discovery pipeline uncovers therapeutic targets and a unique p97 allosteric binding site in <i>Schistosoma mansoni</i>

Schistosomes are parasitic flatworms that infect more than 200 million people globally. However, there is a shortage of molecular tools that enable the discovery of potential drug targets within schistosomes. Thus, praziquantel has remained the frontline treatment for schistosomiasis despite known liabilities. Here, we have conducted a genome-wide study in Schistosoma mansoni using the human druggable genome as a bioinformatic template to identify essential genes within schistosomes bearing similarity to catalogued drug targets. Then, we assessed these candidate targets in silico using a set of unbiased criteria to determine which possess ideal characteristics for a ready-made drug discovery campaign. Following this prioritization, we pursued a parasite p97 ortholog as a bona-fide drug target for the development of therapeutics to treat schistosomiasis. From this effort, we identified a covalent inhibitor series that kills schistosomes through an on-target killing mechanism by disrupting the ubiquitin proteasome system. Fascinatingly, these inhibitors induce a conformational change in the conserved D2 domain P-loop of schistosome p97 upon modification of Cys519. This conformational change reveals an allosteric binding site adjacent to the D2 domain active site reminiscent of the “DFG” flip in protein kinases. This allosteric binding site can potentially be utilized to generate new classes of species-selective p97 inhibitors. Furthermore, these studies provide a resource for the development of alternative therapeutics for schistosomiasis and a workflow to identify potential drug targets in similar systems with few available molecular tools.

Stroke care in the United Kingdom before, during, and after the COVID-19 lockdowns: A retrospective nationwide cohort study

Background and aim The COVID-19 pandemic imposed significant pressures on healthcare services such as stroke care. This study aimed to assess the quality of stroke care and, outcome before, during and post lockdown. Methods Nationwide registry-based cohort study of patients with acute stroke admitted to hospitals in England, Wales, and Northern Ireland were analyzed. This included 114 hospitals for study cohorts of 261,451 in the pre-pandemic control period (01/04/2017–25/03/2020). The exposures studied include 16,843 during the first lockdown (26/03/2020–23/06/2020), 48,004 during the second lockdown (05/11/2020-17/05/2021), and 82,732 post-lockdowns (18/07/2021–30/06/2022). Logistic regression was used to compare odds of receiving aspects of acute stroke care across pandemic periods compared to the pre-pandemic period. Survival after stroke was assessed using restricted mean survival time (RMST) analysis, with models adjusted for age, sex, and stroke severity. Results Admission to a stroke unit within 4-hours increased by 8% during the first lockdown but fell by 7% in the second lockdown and remained lower post-pandemic. During the first lockdown, brain imaging within 1-hour increased by 3%, but was not maintained thereafter. Stroke multidisciplinary access increased during the first lockdown but decreased in subsequent periods. Access to thrombectomy sequentially increased across time periods; by 40% during first lockdown (adjusted Odd-Ratio: 1.4; 95% CI:1.24–1.58), second lockdown (aOR: 1.77; 1.66–1.92), and 2-folds post-lockdown (aOR: 2.03; 1.92–2.15). Thrombolysis rates fell during the pandemic and did not recover post-pandemic. Although proportion of patients discharged with good recovery did not alter, 7-day mortality increased by 10% (hazard ratio: 1.10, 1.04-1.18) in the first lockdown period but improved thereafter. Conclusion This nationwide population data showed unprecedented levels of pressure from the COVID-19 pandemic which have had an enduring effect on the quality of hospital stroke care and patient outcomes. Stroke care has not fully recovered post-pandemic period suggesting limited resilience.

A theoretical framework for scaling ecological niches from individuals to species

The niche is a key concept that unifies ecology and evolutionary biology. However, empirical and theoretical treatments of the niche are mostly performed at the species level, neglecting individuals as important units of ecological and evolutionary processes. So far, a formal mathematical link between individual-level niches and higher organismal-level niches has been lacking, hampering the unification of ecological theories and more accurate forecasts of biodiversity change. To fill in this gap, we propose a bottom–up approach to derive population and higher organismal-level niches from individual niches. We demonstrate the power of our framework by showing that 1) the statistical properties of higher organismal-level niches (e.g., niche breadth, skewness, etc.) can be partitioned into individual contributions and 2) the species-level niche shifts can be estimated by tracing the responses of individuals. By using individual-level GPS (Global Positioning System) tracking data from three different species, we show that climate change could have contrasting consequences on population-level niche shift depending on individual niche compositions. Our method paves the way for a unifying niche theory and enables mechanistic assessments of organism–environment relationships across organismal scales.

Unveiling a novel S-Box strategy: The dynamic 3D scrambling approach

A close study about the varied anatomies of the S-Box algorithms published in the literature indicates that no attempt has been made using the notion of 3D. To cement the more cryptographic security, this research article ventures to present a novel S-Box algorithm exploiting the notion of 3D. First of all, the algorithm initializes a 3D scrambled S-Box with the value of -1. After that, a 1D array is initialized with all the numbers of the potential S-Box. Now, the values placed in the 1D array are inserted randomly to the different cells of the 3D scrambled S-Box until all the numbers from the 1D array are not shifted. The way, the S-Box has been developed bears an ample promise to raise the inherent non-linearity of the required S-Box which, in turn, equips the S-Box to defy the potential cryptanalytic threats from the hackers and other adversaries. Lorenz chaotic system has been employed to produce streams of random numbers. Through rigorous analysis and experimentation, we demonstrate the efficacy of our approach in enhancing cryptographic security, offering robust protection against various cyber threats. Our research contributes to the advancement of S-Box design methodologies, providing a promising avenue for strengthening encryption algorithms in contemporary cryptographic systems. Potential real-world applications include secure communications in IoT devices, encryption in smart grid infrastructures, and data protection in medical imaging systems.

Patient stratification reveals the molecular basis of disease co-occurrences

Epidemiological evidence shows that some diseases tend to co-occur; more exactly, certain groups of patients with a given disease are at a higher risk of developing a specific secondary condition. Here, we develop an approach to generate a disease network that uses the accumulating RNA-seq data on human diseases to significantly match an unprecedented proportion of known comorbidities, providing plausible biological models for such co-occurrences and effectively mirroring the underlying structure of complex disease relationships. Furthermore, 64% of the known disease pairs can be explained by analyzing groups of patients with similar expression profiles, highlighting the importance of patient stratification in the study of comorbidities. These results solidly support the existence of molecular mechanisms behind many of the known comorbidities, with most captured co-occurrences implicating the immune system. Additionally, we identified new and potentially underdiagnosed comorbidities, providing molecular insights that could inform targeted therapeutic strategies. We provide a functional and comprehensive resource to explore diseases, disease co-occurrences, and their underlying molecular processes at different resolution levels at http://disease-perception.bsc.es/rgenexcom/ .

The influence of kinematic viscosity of oils on the energy consumption of a gear pump used for pumping oil in machines and vehicles

The general theory of oil pumping using gear pumps shows that as kinematic viscosity increases, so does the energy requirement to drive the pump shaft. However, modern oils used in machines and vehicles are characterized by a wide range of modifiers that alter their physical and chemical properties. This article presents a study on the energy demand for driving a gear pump while pumping commercial oils used in machines and vehicles (16 types), such as those for combustion engines in single-drive and hybrid vehicles, gearboxes, hydraulic systems, shock absorbers, chainsaw lubrication, and two-stroke engine fuel mixtures. For the tested oils, kinematic viscosity was determined at 25°C and 50°C, and the mechanical power required to pump them at an ambient temperature of approximately 25°C was measured. Based on the measured power and rotational speed, the energy demand for driving the gear pump was calculated. The main analysis was conducted under recommended operating conditions, where the pump operates most efficiently—namely, at a rotational speed of 2000 rpm and a pressure of 20 MPa. It was shown that within the tested group, kinematic viscosity is not the primary factor determining the energy intensity of the oil pumping process. However, this relationship becomes more evident when oils are grouped by application. The energy consumption during pumping of oils with kinematic viscosity at 25°C ranging from 21 to 784.5 mm2/s varies from 606 to 734 Wh. The difference due to the type of oil is approximately 21%. The lowest energy consumption was observed for the HL 46 hydraulic oil, which, although not having the lowest kinematic viscosity, was specifically designed by the manufacturer for use with gear pumps—indicating that pump designs are tailored to the specific type of oil being pumped.

ARRDC4-mediated glycolysis enhances innate immunity to influenza A virus through fructose-1,6-bisphosphate

Glucose metabolism impacts the innate immune response against viral infection. However, the key enzymes or the natural products and mechanisms involved are not well elucidated. Here, we found that arrestin domain containing 4 (ARRDC4), a critical regulator of glucose metabolism, senses influenza A virus (IAV) infection by interacting with viral PA protein. Upregulated ARRDC4 increases the enzymatic activity of phosphofructokinase, muscle type (PFKM) via binding its His298 site to promote the production of the metabolite fructose-1,6-bisphosphate (FBP). Consequently, FBP inhibits the K48-linked ubiquitination degradation of HSP90β, subsequently enhances its interaction with IKKβ and IKKε, and enhances NF-κB- and IRF7-mediated antiviral innate immunity, respectively. Importantly, FBP supplementation enhanced IFN-β-mediated antiviral innate immunity in vitro and in vivo. Our findings highlight a unique immunometabolic regulatory mechanism in which ARRDC4 senses IAV infection and regulates antiviral innate immunity through the PFKM-FBP metabolic axis and provide a strategy for manipulating FBP-related metabolism to treat viral infection.

Investigating factors influencing fatalities and injuries in animal-vehicle crashes using a random parameters logit model and ensemble machine learning approaches

Animal-vehicle crashes (AVC) pose risks in rural areas, often leading to casualties and injuries. Despite their infrequent occurrence, AVC can have significant consequences, especially when larger animals are involved. This study investigates factors contributing to fatalities and injuries resulting from animal-involved collisions. It examines 24 variables using 1403 animal-vehicle crash observations on intercity and major intra-city roads from 2016–2021. The study employs a random parameters logit model (RPLM) and ensemble machine learning approaches to explore the contributory factors in crashes. The RPLM accounts for unobserved heterogeneity, identifying significant variables. Meanwhile, the ensemble learner and Shapley Additive exPlanations (SHAP) provide further insights. Key findings show that expressways, roads with one or two lanes per direction, horizontal curvature, and structurally poor pavement surfaces increase the risk of severe crashes, i.e., fatalities and injuries. Side fence barriers and speed bumps also impact crash severity. The absence of side fencing and damaged fencing both positively influence severe crashes, while the presence of speed bumps is likely to increase severe crashes. Camel exposure, vacation-period crashes, and adverse weather also play positive roles. However, heavy truck involvement is negatively associated with severe crashes. Policymakers and road safety authorities can use these findings to implement effective countermeasures to prevent such collisions.

Correction for Soni et al., BIK polymorphism and proteasome regulation unveil host risk factor for severe influenza

Macrophytes and their sedimentary phosphorus niche in lowland rivers

Macrophytes in lowland rivers have traditionally been studied with a focus on surface water chemistry, particularly nutrients. However, unlike in lakes, the relationship between macrophytes and surface water nutrients in rivers is generally weaker, especially in highly alkaline lowland rivers, which are often found more downstreams. In these systems, elevated sediment nutrient levels may better explain macrophyte community compositions than surface water nutrients alone. This study investigates the associations between macrophytes and sediment pore water nutrients, particularly Total Phosphorus (TP), while also considering hydromorphological factors such as flow velocity and water depth. Sampling was conducted at 76 locations in wadable lowland rivers in North Rhine-Westphalia, Germany, where macrophyte species, surface water chemistry, and sediment pore water chemistry were recorded. Relationships were analysed using Canonical Correspondence Analysis, absolute niche quantification, and a Generalised Linear Mixed Model (GLMM). Despite the potential role of pore water chemistry in macrophyte nutrient uptake, our results indicate that species niches along pore water TP did not strongly differ. Species niches extended to at least 3,000 μg L-1, although they preferred lower concentrations. Instead, hydromorphological variables, particularly water depth and flow velocity, exerted a stronger influence on macrophyte distribution than either surface or pore water nutrients. Tolerant species such as Ceratophyllum demersum and Potamogeton crispus were more prevalent in deeper waters with higher pH levels, while more sensitive species like Glyceria fluitans were found in shallower areas with lower pH levels. The GLMM estimated that the surface water TP concentrations increase by approximately 0.37% for every 1% rise in pore water TP concentrations, suggesting a notable but complex link between sediment and surface water nutrients. These findings highlight the challenges of using macrophytes as indicators of water column and pore water nutrients levels in lowland rivers. The results suggest that either these rivers are nutrient-saturated and dominated by eutrophic species, limiting their bioindication potential, or that macrophyte communities are completely impoverished. Additionally, hydromorphological alterations, such as river straightening and embankments, constrain ecotone habitats and should also be considered in successful river management strategies.

Ingroup solidarity drives social media engagement after political crises

Social media are often said to exacerbate polarization by platforming hostility between groups. However, positive social emotions like ingroup solidarity may also drive social media engagement, particularly after major threats such as military invasions or terror attacks. In this preregistered study, we examine the socioemotional drivers of engagement following group threats in the context of the US 2024 presidential campaign trail, where both major political parties faced crises in July of 2024. We test how ingroup solidarity and outgroup hostility predicted social media users’ engagement with 62,118 posts by 484 US partisan accounts before and after the first Trump assassination attempt (July 13) and Biden’s re-election campaign suspension (July 21). We find that, while outgroup hostility is typically the dominant predictor of engagement, interactions with ingroup solidarity surged among Republicans after the Trump shooting and among Democrats after Biden’s withdrawal. We show that negativity toward other groups is not always key to going viral. Rather, positive ingroup emotions appear to play a leading role in times of crisis.

Development and validation of a deep learning–based assessment tool for teacher leadership: A case study from Xinjiang, China

Teacher leadership is widely regarded as a critical driver of school reform and educational quality improvement. Although the field has been extensively studied, empirical research remains limited in Xinjiang, China—a region characterized by its multiethnic and multilingual context. To address this gap, the present study developed and validated a culturally sensitive assessment tool based on a sample of 371 primary and secondary school teachers from Xinjiang. A structured questionnaire was designed encompassing four dimensions: professional guidance, educational collaboration, cross-cultural ICT-based teaching competence, and leadership cognition. In addition, we introduced an interpretable deep learning model—ITL-LSTM (Interpretable Teacher Leadership LSTM)—which employs a Diagonal BiLSTM structure for dynamic classification of teacher leadership profiles, achieving a prediction accuracy of 90.10%. The findings indicate that the proposed tool demonstrates strong applicability and scalability within the Xinjiang context, providing effective support for dynamic evaluation, personalized development, and evidence-based decision-making in multicultural educational settings.

Correction for An et al., Impact of women’s political empowerment through gender quotas on improved drinking water access in Africa

Journal data-sharing policies and its impact in publications: A cross-sectional study protocol

Responsible data sharing in clinical research can enhance the transparency and reproducibility of research evidence, thereby increasing the overall value of research. Since 2024, more than 5,000 journals have adhered to the International Committee of Medical Journal Editors (ICMJE) Data Sharing Statement (DSS) to promote data sharing. However, due to the significant effort required for data sharing and the scarcity of academic rewards, data availability in clinical research remains suboptimal. This study aims to explore the impact of biomedical journal policies and available supporting information on the implementation of data availability in clinical research publications This cross-sectional study will select 303 journals and their latest publications as samples from the biomedical journals listed in the Web of Science Journal Citation Reports based on stratified random sampling according to the 2023 Journal Impact Factor (JIF). Two researchers will independently extract journal data-sharing policies from the submission guidelines of eligible journals and data-sharing details from publications using a pre-designed form from Apr 2025 to Dec 2025. The data sharing levels of publications will be based on the openness of the data-sharing mechanism. Binomial logistic regression analyses will be used to identify potential journal factors that affect publication data-sharing levels. This protocol has been registered in Open Science Framework (OSF) Registries: https://doi.org/10.17605/OSF.IO/EX6DV.

Amyloid β–dependent neuronal silencing through synaptic decoupling

Amyloid β (Aβ)-dependent circuit dysfunction in Alzheimer’s disease (AD) is determined by a puzzling mix of hyperactive and inactive (“silent”) brain neurons. Recent studies identified excessive glutamate accumulation as a key Aβ-dependent determinant of hyperactivity. The cellular mechanisms underlying neuronal silence depend on both Aβ and tau protein pathologies, with an unknown role of Aβ. Here, by using single-cell-initiated rabies virus (RV) tracing in mouse models of β-amyloidosis, we demonstrate that the presynaptic connectivity of silent, but not that of hyperactive, neurons is severely disrupted. Furthermore, silent neurons display a major spine loss and strongly suppressed synaptic activity. Thus, we suggest that synaptic decoupling is an Aβ-dependent cellular mechanism underlying progressive neuronal silencing and a critical factor for the cognitive impairments encountered in AD.

An integrated genetic algorithm-machine learning approach for morphological optimization of high-rise residential districts in Yulin

The pursuit of global carbon neutrality necessitates addressing the dual challenge of enhancing solar energy utilization while improving thermal comfort in high-rise residential areas, particularly in Yulin, northern Shaanxi, China, where abundant solar resources exist but maximizing solar acquisition often compromises summer thermal environment quality. This resource-comfort contradiction highlights the need for balanced architectural strategies in regions with pronounced seasonal variations. Building morphological parameter optimization is crucial for balancing annual solar energy capture against summer overheating risks, yet research remains insufficient. This study developed parametric layout models using Rhino-Grasshopper, considering key parameters including building length, width, height, density, floor area ratio, and south-facing angle deviation. Multi-objective optimization was conducted using NSGA-II genetic algorithm under regulatory constraints, while combining traditional regression analysis with convolutional neural networks (CNN) to investigate the influence mechanisms of these morphological parameters. Results indicate that the optimized building morphology can increase annual solar radiation acquisition (SRA) by 2.57% while maintaining comfortable summer Universal Thermal Climate Index (UTCI) values, effectively balancing solar energy capture and outdoor thermal comfort. Regression analysis revealed a positive correlation between building length and summer UTCI (r = 0.73), whereas CNN identified a negative correlation (−0.45). Both methods identified similar parameter combinations affecting SRA, with CNN demonstrating superior capability in capturing complex non-linear relationships. These findings provide evidence-based design guidelines specific to Yulin while offering implications for sustainable residential development in similar climates, advancing the integration of climate-adaptive design strategies.

Organoid-based neutralization assays reveal a distinctive profile of SARS-CoV-2 antibodies and recapitulate the real-world efficacy

The efficacy of VIR-7831, a class 3 anti-SARS-CoV-2 monoclonal antibody (mAb), was demonstrated repeatedly in clinical trials; yet, reduced neutralization against Omicron variants in cell-line-based neutralization assays led to its withdrawal from clinical use. We developed organoid-based neutralization assays to measure mAb potency. We found that most class 3 mAbs, especially those not blocking receptor-binding domain-ACE2 binding, including VIR-7831, were substantially underestimated in cell-line-based assays. Nasal organoids adequately recapitulated the real-world effectiveness of VIR-7831 because of biologically relevant low ACE2 expression, and exclusively reproduced the in vivo protection of S2 mAbs due to the high TMPRSS2 expression, reminiscent of native human respiratory epithelial cells. Collectively, the robust organoid culture system and biologically relevant expression profiles of ACE2 and TMPRSS2 make nasal organoids present a correlate of in vivo protection of neutralizing mAbs exclusively. The organoid-based neutralization assays, superior to conventional cell-line-based assays, can recapitulate and predict the real-world efficacy of mAbs.