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Stress granule–mediated ZBP1 activation drives necroptotic cell death in non-obstructive azoospermia and testicular aging

Male infertility remains a major unmet medical challenge, with poorly defined molecular mechanisms and no effective therapies. Here, we identify a stress granule–mediated necroptotic pathway as a key driver of non-obstructive azoospermia, a severe form of male infertility marked by the loss of spermatogenesis. Environmental or physiological stress activates eIF2α kinases, inducing stress granule formation and the recruitment of ZBP1 and RIPK3 into a cytoplasmic complex. This assembly triggers RIPK3 activation, MLKL phosphorylation, and necroptotic death of spermatogonia and Sertoli cells. Genetic ablation of Zbp1 or Ripk3 protects mice from heat-induced testicular degeneration, establishing their essential role in stress-induced testicular damage. Importantly, activation of this pathway is also observed in aged human testes, linking stress-responsive necroptosis to both pathological infertility and the broader process of reproductive aging. These findings reveal an unrecognized mechanism that couples cellular stress responses to regulated cell death in the male reproductive system.

DFT investigation of efficient hydrogen storage utilizing Li and Na decorated co-doped graphene (B/N)

Abstract This study investigates the hydrogen storage capacity of co-doped graphene with non-bonded B and N atoms (BC4N) using density functional theory (DFT). The optimized structure reveals the introduction of co-doping ripples the surface, enhancing potential hydrogen storage applications. The adsorption behavior of Li and Na atoms on the BC4N surface is examined, demonstrating a higher binding energy, surpassing their cohesive energies. Density of State (DOS), Partial Density of State (PDOS), and charge transfer analyses indicate electron donation from Li and Na to BC4N monolayer, establishing BC4N as an electron acceptor. The investigation extends to H2 adsorption on Li/BC4N and Na/BC4N systems, revealing a non-dissociative form and a cooperative effect with increasing H2 molecules. The hydrogen storage gravimetric density is calculated, and desorption temperatures are determined, highlighting the potential of Li/BC4N and Na/BC4N as promising candidates for efficient hydrogen storage.

Solution spillover: Pairing issues with ideological solutions fuels polarization

Recent research has focused on understanding ideological and affective political polarization in the United States; however, the polarization of issue salience—defined as personal beliefs about how important or concerning an issue is, and how much attention or priority it deserves—has garnered less attention, especially regarding its underlying causes. We propose that issue salience polarization, particularly that of ideologically neutral issues, is driven in part by two psychological processes: solution aversion (denying problem severity when solutions are introduced that conflict with political beliefs) and solution attraction (exaggerating problem severity when solutions are introduced that align with political beliefs). If this is so, we should be able to observe issues that are nonpolarized at baseline become suddenly polarized when paired with solutions that are consistent with a specific ideological position (liberal or conservative). Consistent with this prediction, four experimental studies demonstrate the emergence of issue salience polarization arising from solution aversion and solution attraction (Studies 1–4). Results also demonstrate support for the role of zero-sum beliefs, or the belief that one political party’s gains must come at the expense of the other party, in driving this process (Study 3).

”My AI is Lying to Me”: User-reported LLM hallucinations in AI mobile apps reviews

Abstract Large Language Models (LLMs) are increasingly integrated into AI-powered mobile applications, offering novel functionalities but also introducing the risk of “hallucinations” generating plausible yet incorrect or nonsensical information. These AI errors can significantly degrade user experience and erode trust. However, there is limited empirical understanding of how users perceive, report, and are impacted by LLM hallucinations in real-world mobile app settings. This paper presents a large-scale empirical study analyzing  3 million user reviews from 90 diverse AI-powered mobile apps to characterize these user-reported issues. Using a mixed-methods approach, a heuristic-based User-Reported LLM Hallucination Detection algorithm were applied to identify  20,000 candidate reviews, from which 1,000 are manually annotated. This analysis estimates the prevalence of user reports indicative of LLM hallucinations, which was found to be approximately 1.75% within reviews initially flagged as relevant to AI errors. A data-driven taxonomy of seven user-perceived LLM hallucination types, were developed with Factual Incorrectness (H1) emerged as the most frequently reported type, accounting for 38% of instances, followed by Nonsensical/Irrelevant Output (H3) at 25%, and Fabricated Information (H2) at 15%. Furthermore, linguistic patterns were identified using N-grams generation, Non-Negative Matrix Factorization (NMF) topics and sentiment characteristics using VADER, showing significantly lower scores for hallucination reports associated with these reviews. These findings offer critical implications for software quality assurance, highlighting the need for targeted monitoring and mitigation strategies for AI mobile apps. This research provides a foundational, user-centric understanding of LLM hallucinations, paving the way for improved AI model development and more trustworthy mobile applications.

Multiple marginalized identities and internalized HIV stigma among people living with HIV in South Florida: An intersectional approach

Internalized HIV stigma is associated with several adverse mental and physical health outcomes among people living with HIV (PLWH). PLWH and other marginalized identities may experience worse internalized HIV stigma due to minority stress and structural oppression. This study conceptualized intersectionality via a multiplicative approach and examined the associations between intersectional marginalized-group identities and internalized HIV stigma among a sample of PLWH in South Florida (N = 1343) using hierarchical linear regression models. Interaction terms within these models were used to test the effects of having more than one marginalized identity over and above the main effects of each single marginalized identity. Overall, participants reported moderate levels of internalized HIV stigma (M = 2.47, SD = 1.93, range 1−6) with main effects for those identifying with age, race, ethnicity, and/or gender marginalization experiencing higher levels. The interaction between gender and ethnic marginalization (b = 0.82) and the interaction between age and ethnic marginalization (b = 0.32), predicted significantly higher levels of internalized HIV stigma over and above the main effects of these variables on their own. Additionally, the interaction between age and racial marginalization (b = −0.54) and the interaction between age and sexual orientation marginalization (b = −0.47) both significantly predicted less internalized HIV stigma over and above the main effects of these variables on their own. Findings highlight the importance of considering how intersectional marginalized identifies influence PLWH’s internalized stigma. Data offers insight into the subgroups of PLWH who could benefit from targeted interventions to reduce internalized HIV stigma and improve HIV care outcomes.

Correction to Supporting Information for Rei et al., Basolateral amygdala bidirectionally modulates stress-induced hippocampal learning and memory deficits through a p25/Cdk5-dependent pathway

Digital occlusal force analysis in anterior crossbite

Abstract Anterior crossbite is a common malocclusion in pediatric patients that can negatively affect occlusal force distribution and periodontal health. This study aimed to evaluate changes in occlusal force distribution and periodontal health parameters before and after interceptive orthodontic treatment using a digital occlusal analysis device. Thirty-five children aged 6–11 years with anterior crossbite were treated using a Hawley appliance with a Z-spring. Occlusal forces were recorded at baseline (T0) and after treatment (T1) using the OccluSense digital analysis system. Periodontal parameters, including plaque index, gingival index, probing depth, and clinical attachment level, were also assessed. Data were analyzed using paired t-tests, Wilcoxon signed-rank tests, and Kruskal–Wallis tests, with the significance level set at p < 0.05. Following treatment, the regional force distribution decreased from 65.3% ± 8.2–52.1% ± 6.7% (p < 0.001), and the percentage force distribution decreased from 68.7% ± 9.1–54.3% ± 7.2% (p < 0.001). The plaque index declined from 1.8 ± 0.6 to 1.2 ± 0.4, gingival index from 1.5 ± 0.5 to 0.9 ± 0.3, probing depth from 3.2 ± 0.8 to 2.7 ± 0.6 mm, and clinical attachment level from 2.8 ± 0.7 to 2.4 ± 0.5 mm (p < 0.05). Interceptive treatment with a Z-spring appliance resulted in statistically significant changes in occlusal force distribution and periodontal health parameters. Digital occlusal analysis may serve as a valuable tool for objectively evaluating treatment outcomes in pediatric patients.

Advanced investing with deep learning for risk-aligned portfolio optimization

This study introduces a deep learning-based framework for portfolio optimization tailored to different investor risk preferences. We combine two prediction models, Long Short-Term Memory (LSTM) and One-Dimensional Convolutional Neural Network (1D-CNN), with three portfolio frameworks: Mean-Variance with Forecasting (MVF), Risk Parity Portfolio (RPP), and Maximum Drawdown Portfolio (MDP). Each framework represents a distinct risk profile: return-seeking, moderate-risk and conservative. The dataset is constructed from daily returns of VN-100 stocks in Vietnam, covering the period from 2017 to 2024. Forecasts from the deep learning models are integrated into each optimization approach. Results from the 2023–2024 test period showed that LSTM outperforms 1D-CNN in both accuracy and stability. Portfolios using LSTM achieved better performance. LSTM+MVF delivers the best risk-adjusted returns, while LSTM+MDP achieves the highest total return. The study highlights the value of aligning predictive models with appropriate optimization strategies for improved investment outcomes. Future work may include other asset classes, transaction cost modeling, and dynamic rebalancing. Combining deep learning with macroeconomic or alternative data could also improve forecasting and portfolio outcomes.

Correction for Ota et al., Engineering a protease-stable, oral single-domain antibody to inhibit IL-23 signaling

Effects of measurement errors on relationships between resting-state functional connectivity and psychological phenotypes

Abstract Recent neuroscientific studies have focused on interindividual relationships between resting-state functional connectivity (RSFC) and psychological phenotypes using large datasets with repeated measurements, including the Human Connectome Project (HCP). However, previous studies on RSFC-phenotype relationships have failed to differentiate trait, state, and error effects of RSFC. Latent functional connectivity, which can be estimated in structural equation model (SEM), can be useful in finding RSFC-phenotype relationships controlling state and error effects. We also accounted for measurement errors in psychological phenotypes at the test-, subscale-, or item-level. This study investigates: (i) how measurement errors, including state effects, weaken the associations between RSFC and psychological phenotypes, including cognition, mental health, and personality, and influence sample size planning and (ii) predictive accuracy on the phenotypes from RSFC, using SEM. We found that the extent of the weakening of RSFC-phenotype associations ranged from 15.3 to 33.8% across the phenotypes, and they were higher in sensorimotor networks than in higher order cognitive networks. Importantly, measurement errors can lead to requirement of about double sample size to find RSFC-phenotype associations in general. Factor scores of RSFC enhanced the coefficients of determination under some conditions. Future studies should explore more effective predictive methods by accounting for measurement errors.

Scorpion (Hottentotta tamulus) venom pre-exposure delays functional recovery in mice following peripheral nerve injury

Scorpion sting leads to profound challenges of central nervous system (CNS) impairments such as neuro-inflammation, unconsciousness, aberrant ion channels physiology, epilepsy and may become fatal due to heart failure. However persistence of Hottentotta tamulus venom in peripheral nerves and subsequent influence on regenerative process of injured peripheral nerve remains unknown. Current study reports the persistence of H. tamulus venom components 30-days following its intraperitoneal administration in sciatic nerves (SN) of mice pre-exposed through either a single-toxin exposure (STE) or multiple-toxin exposure (MTE). Of note, venom pre-exposure delays and compromises the sensori-motor functional recovery in STE and MTE mice following standard sciatic nerve crush injury. Histological investigations of regenerating SN and gastrocnemius muscles (GCM) 14-days post crush injury exhibited reduced myelination and limited numbers of motor axons in SNs and GCM of MTE mice, respectively. Consistently, a marked reduction in expression of regeneration-promoting markers including transcription factors (such as Atf-3 and c-Jun), regeneration associated genes (such as Sprr1a and Gap-43) and ion channel proteins encoding genes (such as Scn9a and Kcc2) was observed in lumber dorsal root ganglia (DRG) and regenerating SN 14-days post crush injury. Collectively, this study reports the persistence and regeneration-inhibiting effects of H. tamulus venom in peripheral nerve of pre-exposed mice leading to compromised functional recovery.

Repeated polyploidization shapes divergence in floral morphology in <i>Lithophragma bolanderi</i> (Saxifragaceae)

Polyploidization is an important driver of evolution and diversification in flowering plants. Here, we assess how repeated polyploidization may have shaped diversification of floral morphology in Lithophragma bolanderi (Saxifragaceae). This species comprises multiple cytotypes and varies geographically in its interactions with specialized pollinating moths in the genus Greya (Prodoxidae). Past studies have shown that coevolution with these moths has favored particular suites of floral characters but does not fully explain local and regional floral diversification. We combined phenotypic and genomic data from more than 1,800 individuals from 40 L. bolanderi populations spread across its entire range. Flow-cytometric analyses revealed a geographic mosaic of populations comprising one to four of three dominant (diploid, tetraploid, hexaploid) and three rare (triploid, pentaploid, octoploid) cytotypes. Whole-genome resequencing of a subset of populations suggested that polyploids arose from multiple autopolyploidization events, rather than a single event and/or through hybridization, albeit with some signals consistent with low levels of introgression from the congener Lithophragma glabrum . Quantification of flower traits from plants grown in a common garden showed that cytotype explained more than 15% of the variation in floral morphology, with polyploids showing more variability than diploids. Experimental induction of neopolyploids directly induced phenotypic changes but also indicated that local selection may have favored subsequent convergence in floral morphology among cytotypes in natural populations. Collectively, this comprehensive and integrative approach provides insights into how variability generating processes, such as polyploidization integrates with selection from species interactions to shape local floral diversification.

Enhancing confidentiality and access control in electronic health record systems using a hybrid hashing blockchain framework

Characterization of congenital hyperinsulinism in Argentina: Clinical features, genetic findings, and treatment outcomes

Introduction Congenital hyperinsulinism (CHI) is a heterogeneous disorder of insulin dysregulation, leading to hypoglycemia. This study describes the clinical characteristics, genetics, and management of CHI in Argentina. Methods We retrospectively reviewed 70 probands diagnosed with CHI (2008–2021) at multiple centres across Argentina. Clinical, biochemical, imaging, and treatment data were analyzed. Genetic testing was performed in 49 probands using Sanger and targeted next-generation sequencing of CHI-related genes. Results Transient CHI was identified in 23/70 (33%) probands, with a median duration of 2 months. Risk factors for perinatal stress-induced hyperinsulinism (PSHI) were present in 85% of transient cases. Persistent CHI was diagnosed in 44/70 (63%) individuals, of whom 31 responded to diazoxide. Late-onset CHI (diagnosed &gt;3 years) was identified in 3 children. A pathogenic variant was detected in 19/49 (39%) probands, all had persistent CHI. ABCC8 variants were most common accounting for 68% (13/19) of diagnoses. Imaging in 17 cases revealed focal disease in 8, diffuse disease in 8, and atypical disease in 1 individual. Seven individuals with focal disease underwent lesionectomy, which was curative in 5 (71%). Three children with diffuse disease required near-total pancreatectomy, with one developing postoperative diabetes. Conclusions This study provides the largest CHI cohort reported from South America and highlights the clinical and genetic heterogeneity of the condition. Transient CHI was often associated with PSHI risk factors, while persistent CHI was predominantly linked to K-ATP channel variants. The findings underscore the importance of genetics and imaging for CHI management and emphasize the need for increased access to molecular diagnostics.

Ensemble approaches for short-term dengue fever forecasts: A global evaluation study

Dengue fever, a tropical vector-borne disease, is a leading cause of hospitalization and death in many parts of the world, especially in Asia and Latin America. Where timely dengue surveillance exists, decision-makers can better implement public health measures and allocate resources. Reliable near-term forecasts may help anticipate healthcare demands and promote preparedness. We propose ensemble modeling approaches combining mechanistic, statistical, and machine learning models to forecast dengue cases 1 to 3 mo ahead at the province level across multiple countries. We assess these models’ predictive ability out-of-sample and retrospectively in over 180 locations worldwide, including provinces in Brazil, Colombia, Malaysia, Mexico, Thailand, plus Iquitos, Peru, and San Juan, Puerto Rico, during at least 2 to 3 y. We also evaluate ensemble approaches in a real-time, prospective dengue forecasting platform during 2022–2023, considering data availability limitations. Our ensemble modeling leads to an improvement to previous efforts that may help decision-making in the context of large uncertainties. This contrasts with the variable performance of individual component models across locations and time. No single model achieves optimal predictions across all scenarios, but while ensemble models may not always perform best in specific locations, they consistently rank among the top 3 performing models both retrospectively and prospectively.

OPM-MEG reveals dynamics of beta bursts underlying attentional processes in sensory cortex

Abstract Human brain electrophysiology is dominated by rhythmic activity—neural oscillations—which play an important role in coordinating brain function. In the somatosensory cortices, the dominant oscillations occur in the beta (13–30 Hz) band and are thought to mediate top-down inhibition of primary cortices. The non-invasive measurement of such oscillations has traditionally been made possible using either electroencephalography (EEG) or magnetoencephalography (MEG), yet both modalities have significant limitations. Here, we use a new MEG technology—based on optically pumped magnetometers (OPM-MEG)—to measure attentional modulation of beta oscillations in the somatosensory cortex. Using piezo-electric crystal stimulators, we present ‘braille-like’ patterns to the left and right index fingers; participants are asked to respond to pre-specified target patterns presented to an attended hand, whilst ignoring patterns presented to their non-attended hand. In agreement with previous findings, we measure significantly decreased beta amplitude during attended stimuli relative to non-attended stimuli, with the most pronounced effect immediately following an attentional cue. Moreover, we extend our analyses to demonstrate that attention has a similar effect on the occurrence of pan-spectral bursts (which underlie the beta rhythm). Specifically, we measure significant changes in the probability of burst occurrence in primary somatosensory cortices in a time window related to attentional shifts. In sum, our results provide evidence that attentional modulation of beta oscillations is driven by changes in pan-spectral burst occurrence and add weight to the argument that OPM-MEG is becoming the technique of choice for non-invasive electrophysiological measurements.

The effect of travoprost on primary human limbal epithelial cells and the siRNA-based aniridia limbal epithelial cell model, in vitro

Purpose Haploinsufficiency of the PAX6 gene is the primary pathogenic mechanism underlying classical congenital aniridia. Notably, at least 50% of patients with this condition develop glaucoma. Prostaglandin analogues, such as travoprost, are widely used to lower intraocular pressure in this patient population. At the same time, limbal epithelial cells (LECs) are believed to play a key role in the development and progression of aniridia-associated keratopathy (AAK). Therefore, the aim of this study was to investigate the effects of travoprost on cell viability, proliferation, migration, and the expression of key genes and proteins in both normal and PAX6-knockdown LECs. Materials and Methods Primary human LECs were isolated from seven corneal donors. To simulate the haploinsufficient state characteristic of congenital aniridia, a PAX6-knockdown model, using siRNA was employed. LECs were treated with 0.039–40 μg/mL travoprost concentration for 20 minutes. Cell viability was assessed using the XTT assay, while cell proliferation was evaluated by the BrdU assay. Based on XTT results, 0.156 and 0.313 μg/mL travoprost were selected for further measurements in both LECs and PAX6-knockdown LECs. A scratch assay was conducted to measure cell migration. The expression levels of PAX6, FOSL2, MAPKs, inflammatory markers, caspase-3, and MMP9 were analyzed at both the gene and protein levels using qRT-PCR, Western blot, and ELISA. Results Travoprost significantly reduced LEC viability at 0.156 μg/mL (p = 0.028), while only higher concentrations (20 and 40 μg/mL) inhibited significantly LEC proliferation (p ≤ 0.004). PAX6-knockdown LECs exhibited reduced migration compared to control cells (p ≤ 0.046); however, treatment with 0.313 μg/mL travoprost significantly enhanced their migration (p = 0.047), accompanied by upregulation of JNK1/2 protein (p = 0.039) and MMP9 mRNA and protein levels (p = 0.021, p = 0.027). PAX6 knockdown led to suppression of inflammation-related genes (p ≤ 0.031) and travoprost did not exacerbate inflammatory responses (p ≥ 0.155). Additionally, 0.313 μg/mL travoprost significantly increased caspase-3 protein levels in PAX6-knockdown LECs (p = 0.044). Conclusions Travoprost, at specific concentrations, can reduce the viability and proliferation of limbal epithelial cells. At 0.313 μg/mL, it significantly upregulates JNK1/2 and MMP9 expression, thereby enhancing the migratory capacity of PAX6-knockdown LECs. These findings may offer valuable insights for the selection of antiglaucomatous medications in patients with congenital aniridia.

A critical role for <i>Vibrio parahaemolyticus</i> LPS to mediate evasion of host immune response during infection

Vibrio parahaemolyticus ( V. para ), a Gram-negative halophilic bacterium, is the leading cause of seafood-borne gastroenteritis. While the cytotoxic and enterotoxic capabilities of this bacterium have been previously investigated, the strategies it employs to circumvent innate immune detection by the host remain poorly understood. Bacterial lipopolysaccharide (LPS) serves as the major pathogen-associated molecular pattern that triggers host inflammatory responses, and its lipid A structure contributes to the degree of innate immune system activation. We sought to define the structure of the lipid A component of LPS in V. para and probe its role in host innate immunity. Free living V. para synthesizes a predominantly hepta-acylated lipid A structure, while also producing a minor subset of hexa-acylated lipid A. This altered acylated lipid A elicits a weakened TLR4 immune response as compared to Escherichia coli , allowing V. para entry and replication inside host cells. Deletion of the V. para lipid A biosynthesis enzyme lpxM (VP0213) leads to a mutant strain with altered lipid A secondary acylation, enhanced immune response, and impaired replication within epithelial cells. This study provides evidence that the unique LPS structure synthesized by V. para plays a crucial role in evading host innate immunity, facilitating the bacterium’s survival and replication within host cells.

Structural and ion transport characteristics of transparent proton conducting biopolymer electrolytes based on chitosan and cold water fish skin gelatin

Coping with stressful life disruptions due to long COVID: A qualitative study

Background Long COVID impacts people’s physical health and cognition which immensely affects their psychosocial well-being. A larger study was conducted that explored the psychosocial impacts of Long COVID on individuals and caregivers. This paper focuses on the impact of these stressful disruptions on one’s health and psychosocial well-being, and how individuals cope with them. Methods Utilizing a qualitative descriptive approach, we conducted interviews with 67 participants (52 people with Long COVID (mean age: 50.4) and 15 caregivers (mean age: 50.0)). People with Long COVID and caregivers were recruited from healthcare institutions through care team referrals, patient partners, and health organizations and via social media platforms. A thematic codebook developed through inter-coder agreement processes was used to analyze the data. Findings Three key themes were identified: (1) Disruptions in people with Long COVID and caregivers’ lives are characterized by a deviation from their perceived ‘normalcy’, (2) Disruptions lead to substantial stress, loss and grief (independence, agency, meaning, and purpose), and (3) People with Long COVID and caregivers cope with stressful disruptions by adapting their daily activities. Conclusion Our findings make the case for supportive rehabilitation strategies that address the psychosocial repercussions of Long COVID to help mitigate feelings of loss and grief, thereby increasing individuals’ overall quality of life and well-being.