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Prevalence and burden of anhedonia among patients with major depressive disorder in South Korea: A cross-sectional, observational study

Background Anhedonia (ANH), a key symptom of major depressive disorder (MDD), has a substantial societal and economic burden. In South Korea, while MDD is prevalent, the evidence regarding anhedonia remains scarce. This study investigated the prevalence and impact of anhedonia in patients with MDD in South Korea, including patient and physician perceived goals and satisfaction with MDD treatment. Methods This cross-sectional study (April-May 2023) included two surveys, one specific to patients (aged ≥18 years) with self-reported physician diagnosis of MDD and 9-item Patient Health Questionnaire (PHQ-9) score≥10, and another to physicians treating MDD. The MDD group was classified as MDD-ANH (Snaith-Hamilton Pleasure Scale [SHAPS] score≥3) and MDD non-ANH (SHAPS score≤2). Multiple regression models were employed to evaluate the effect of anhedonia on health-related quality of life (HRQoL), work productivity and activity impairment, and healthcare resource utilization (HCRU). Results Of 4496 participants, the age- and gender-weighted prevalence of MDD was 9.9%, of which 61.5% self-reported anhedonia. Patients with MDD-ANH (vs. MDD non-ANH) had longer duration of depression since diagnosis and lower prior and current antidepressants use (all, p < 0.05). Similarly, patients with MDD-ANH (vs. MDD non-ANH) demonstrated lower HRQoL, and increased HCRU (all, p < 0.05). Patients with MDD-ANH prioritized treatment goal of improved sleep quality, while physicians prioritized avoiding suicidal thoughts. Similarly, patients with MDD-ANH had highest level of satisfaction with controlled depressed mood and physicians with improved sleep quality. Both groups reported lowest level of satisfaction with regaining interest in hobbies, regaining self-esteem, and improving sexual satisfaction. Conclusions This is the first study in South Korea to estimate the prevalence of anhedonia in patients with MDD, highlighting the disease burden and unmet medical needs. Furthermore, disparities observed between patients and physicians in the goals and expectations of MDD treatment underscore the need to monitor anhedonia for treatment optimization.

Resilience and flexibility optimization in solar integrated power systems via deep Q network under extreme weather

The German translation of the Oxford utilitarianism scale: Validation and the impact of the Covid-19 pandemic on the observations

The study of utilitarian inclinations is probably the most experimentally investigated aspect of morality. The Oxford Utilitarianism Scale has been developed to provide a self-report tool for reliable measurement of utilitarian views while addressing serious methodological issues with previous measures. In this study, we have translated and validated a German version of the Oxford Utilitarianism Scale (OUS-DE). The scale consists of two subscales: Impartial Beneficence (IB-DE) and Instrumental Harm (IH-DE). We conducted a procedure in a general German sample (N S1  = 378, 243 women, M age  = 25.37) before the Covid-19 pandemic. A confirmatory factor analysis demonstrated a good fit of a two-factor model for OUS-DE, while internal consistency and construct reliability were acceptable. Both in the pre-pandemic and the post-pandemic sample (N S2  = 348, 206 women, M age  = 24.61) we found a sex/gender difference, with women scoring significantly higher in the IB-DE subscale than men. We also found that the mean agreement with the IB-DE subscale decreased after the pandemic. In a separate third sample (N S3  = 39, 19 women, M age  = 23.72), we observed an inverse U-shape relationship between moral behavior related to quarantine requirements and the IH-DE subscale, as measured during the peak pandemic restrictions in late 2020. Repeated OUS-DE measurement in this sample showed stability in responders’ utilitarian beliefs post-pandemic. In sum, OUS-DE is the first available measurement of utilitarian inclinations in German. The scale will enable further research on how utilitarian preconceptions affect behavior in German-speaking populations.

Outbreak of caterpillars (Lepidoptera) in Valle de Caracas, Venezuela

Abstract Lepidopterism, a condition resulting from accidents by caterpillars or the adult forms of moths and butterflies, typically manifests as mild and self-limited hypersensitivity reactions. In August 2023, an unexpected number of accidents by caterpillars were noted in Valle de Caracas, Venezuela. This prospective descriptive study was conducted from August to September 2023. The sample includes both sightings and accidents reported in three regions of Valle de Caracas. A total of 32 sighting reports were recorded, including 13 accidents and 117 caterpillars. The caterpillars primarily belonged to the family Saturniidae, including genus Dirphia (86%) (Hübner, 1819) and Automeris (9%) (Hübner, 1819). Two caterpillars (2%) were identified as Megalopyge opercularis (Smith, 1797). Over half (54%) of the accidents involved children under nine years. Accidents were most common in residential gardens and parks (54%) and public parks and footpaths (31%). Skin lesions were present in all patients, and six patients exhibited systemic symptoms, primarily fever and palpitations. The study highlights a period of increasing accidents by caterpillars in Valle de Caracas, coinciding with multiple sightings of several species of caterpillars, mainly of the genus Dirphia . Not only were cutaneous manifestations reported, but cases of lepidopterism were previously unreported for this species in the country.

Comprehensive analysis of proline metabolizing genes reveals their functional diversification and abiotic stress response in Solanum lycopersicum

Proline plays a crucial role in plant stress responses. Proline metabolizing genes (PMGs) are a group of enzymes involved in its catabolism in mitochondria and the biosynthesis in the chloroplast and cytoplasm. A total of 21 PMGs were identified in Solanum lycopersicum . Among them, 2 gene pairs were identified as tandemly duplicated, and 6 gene pairs were segmentally duplicated. Phylogenetic analysis revealed distinct gene clusters, suggesting functional diversification. Gene structure analysis provided insights into the arrangement of coding and non-coding regions, while domain analysis highlighted conserved sequences for functional predictions and evolutionary conservation. Microarray expression data of the identified genes revealed that SlOAT8 exhibited maximum expression in different anatomical tissues, particularly in ovules, and SlOAT9 showed maximum response at developmental stages associated with shoot growth. Under stress conditions, SlOAT8 and SlP5CS1 were upregulated in exposure to drought stress but downregulated in response to heat and salt stress. Meanwhile, SlOAT4 was strongly expressed only in roots during salt stress. The qRT-PCR analysis demonstrated significant upregulation of SlOAT8 , SlP5CDH2 , and SlP5CR alongside a significant downregulation of SlP5CS1 under abiotic stress conditions. Furthermore, biochemical assay indicates the accumulation of proline and H 2 O 2 under stressed conditions. These findings provide an extensive study on the PMGs, which will help in the development of a stress-resilient tomato plant in further.

Prevalence of radix entomolaris and distolingual canals and their association with the incidence of middle mesial canals in mandibular first molars of a Saudi subpopulation

Adaptive output steps: FlexiSteps network for dynamic trajectory prediction

Accurate trajectory prediction is vital for autonomous driving, robotics, and intelligent decision-making systems, yet traditional models typically rely on fixed-length output predictions, limiting their adaptability to dynamic real-world scenarios. In this paper, we introduce the FlexiSteps Network (FSN), a novel framework that dynamically adjusts prediction output time steps based on varying contextual conditions. Inspired by recent advancements addressing observation length discrepancies and dynamic feature extraction, FSN incorporates a pre-trained Adaptive Prediction Module (APM) to intelligently determine optimal prediction horizons and a Dynamic Decoder (DD) module that enables flexible output generation across different time steps. Additionally, to balance prediction horizon and accuracy, we design a scoring mechanism that leverages Fréchet distance to evaluate geometric similarity between predicted and ground truth trajectories while considering prediction length, enabling principled trade-offs between prediction horizon and accuracy. Our plug-and-play design allows seamless integration with existing trajectory prediction models. Extensive experiments on benchmark datasets including Argoverse and INTERACTION demonstrate that FSN achieves superior prediction accuracy and contextual adaptability compared to traditional fixed-step approaches.

Environmental gradients shape microbial community structure and ecosystem processes in Antarctic lakes on King George Island

Abstract Antarctic lakes are extreme, oligotrophic habitats that contain microbial communities distinct from those of temperate freshwater systems. Our central question was whether these lakes host microbial communities distinct from those of non-Antarctic freshwater systems, and how environmental variability drives community differences among Antarctic lakes themselves. We analyzed the microbial community across five lakes on King George Island via high-throughput sequencing of amplicon sequence variants (ASVs) and biogeochemical profiling. The microbial communities were dominated by Bacteroidota, Actinomycetota, and Pseudomonadota, but varied strongly with environmental gradients such as salinity, sulfate, methane, and organic carbon. Hybrid ASVs, which were ubiquitous in both water and sediment, comprised the majority of sequences and indicate that dispersal processes, alongside environmental filtering, jointly structure lake microbial communities. Functional predictions further revealed lake- and habitat-specific pathways for carbon, nitrogen, and sulfur cycling, linking microbial diversity to ecosystem processes. These findings highlight how Antarctic lake microbes are shaped by both local selective pressures and cross-habitat exchange, providing critical insights into the resilience and vulnerability of polar freshwater ecosystems under climate change.

Investigating the causes of casing deformation induced by faults and natural fractures in shale gas platform wells

During the development of deep shale gas in Luzhou, southern Sichuan, faults and natural fractures caused extensive casing shear deformation, including those that happen during hydraulic fracturing and those that occur prior to fracturing. To investigate the mechanism of complex deformation of platform well casings caused by faults and natural fractures, this paper analyzed the characteristics of casing deformation and identified the primary types of deformation. By integrating microseismic signal data, it was determined that fault slip is the direct cause of casing deformation. Based on these findings, the Mohr–Coulomb criterion was used to evaluate fault slip conditions, using critical pore pressure as a threshold. A finite element model of platform well fracturing was built with actual engineering parameters. Simulating the fracturing process showed how pore pressure changed under different fracture conditions. Comparing these results with the critical pore pressure clarified how fractures at different scales impact casing deformation in platform wells. The findings suggest that: (1) Casing deformation in the Luzhou Block mainly involves shear deformation, with fault or large-scale fracture slip being the direct cause of these shear deformations; (2) Fault slip at the well location caused casing deformation during fracturing, while fluid migration along faults caused fault instability and slip near non-fractured wells, leading to casing deformation before fracturing; (3) If fractures of a scale similar to the well spacing are present within the platform area, nearby wells may also deform before fracturing. These results provide a scientific basis for understanding casing deformation mechanisms in shale gas platform wells and for developing effective prevention and control measures in the Luzhou Block.

Early-life endurance sports lowers frailty and falls in former male athletes

Retraction: Molecular structure and dimeric organization of the Notch extracellular domain as revealed by electron microscopy

Numerical simulation of red blood cells migration and platelets margination for blood flow in micro-vessels with fusiform aneurysms

Abstract Understanding several micro-vascular diseases depends mainly on examining the dynamic behavior of blood cells, especially the red blood cells (RBCs) and platelets. For instance, the dynamics of RBCs and platelets are significantly impacted by micro-vascular diseases such as aneurysms, which may lead to many disorders. The oxygenation process, for example, depends on the motion and velocity of the RBCs. Important hemodynamic parameters such as the wall shear stress (WSS) and the cell-free layer (CFL) thickness are affected by the motion of the RBCs and platelets. Thus, the main objective of the current study is to introduce more insights into cellular blood dynamics in micro-vessels with fusiform aneurysms, which have important clinical implications, by examining some important hemodynamic parameters. This study examines the migration of RBCs and their velocities under different hematocrit levels. Furthermore, the effect of hematocrit variation on platelets’ margination, the CFL, and the WSS is investigated. The simulations are performed using a validated code developed cellular flow simulations. The obtained results show that decreasing the hematocrit value increases the proportion of migrated RBCs, and hence the CFL thickness increases, which significantly affects blood apparent viscosity, especially at the aneurysm zone, and hence affects the local WSS and the endothelial cell in the vessel wall tissue. In addition, it is found that the fusiform aneurysm reduces the velocity of the RBCs by more than 83%, which can affect the oxygenation process. The obtained results exhibit an asymmetrical trend up and downstream of the aneurysm zone, with a thinner CFL at the divergent part of the aneurysm. These results are beneficial for medical microfluidic devices as well as for understanding many microvascular diseases.

A lightweight zero-trust authentication architecture for IoT via unified enhanced FAST-SM9 and dynamic re-authentication

Authentication is a crucial challenge for Internet of Things (IoT) security, especially in open, distributed and resource-constrained environments. Current methods have significant shortcomings in terms of efficiency, adaptability, and ability to cope with complicated security threats. Therefore, this paper proposes a lightweight authentication framework for Cloud-Edge-End, which integrates the enhanced Fast Authentication and Signature Trust for SM9 (FAST-SM9) algorithm and zero-trust Dynamic Re-authentication (zero-trust-DRA) mechanism. First, FAST-SM9 effectively reduces protocol overhead, and meanwhile ensuring security by organically integrating authentication and signature processes. Its architectural optimization reduces the number of communication rounds by 40% and simplifies trust negotiation between heterogeneous layers without affecting the integrity of encryption mechanisms. To enhance runtime protection, the designed zero-trust-DRA mechanism also introduces context-aware, time-windowed based re-authentication techniques so as to efficiently defend against risks such as session hijacking and credential leakage. In addition, the Dynamic Identity Token Generation Mechanism (DITGM) enhances the security and flexibility of the system by incorporating multi-factor attributes such as fingerprints and OTP seeds into time-sensitive tokens. Experimental results show that this scheme reduces latency by 56.6% and energy consumption by 63% compared to traditional PKI edge authentication methods, and effectively resists related attacks. The formal tool AVISPA verification further confirms its security. The scalability testing also proves its applicability in IoT. A feasible path is provided for efficient and secure identity authentication in distributed systems, which helps to promote the development of zero-trust security systems.

Using multiple machine learning algorithms to predict spinal cord injury in patients with cervical spondylosis: a multicenter study

Establishing an in vivo large animal model of one-lung ventilation and operative lung trauma

Background Respiratory complications, including acute lung injury (ALI) and acute respiratory distress syndrome (ARDS), are important causes of morbidity and mortality among lung surgery patients. Lung surgery introduces surgical and atelectatic trauma to the operated lung, while one-lung ventilation (OLV) applied to the contralateral lung is also a suspected mechanism of ventilator-induced lung injury (VILI). Our goal was to develop a large animal model to assess the relative lung injury induced by surgical and ventilator trauma during left upper lobectomy in juvenile pigs. Methods Sixteen pigs (24–32 kg) were randomly assigned to one of three OLV exposure groups. The control group (n = 5) was exposed to lung-protective ventilation (LPV) during OLV, the second group (n = 5) was exposed to potentially injurious ventilation (IMV) during OLV using higher tidal volume and peak airway pressure and the third group (n = 6) was exposed to hyperoxia with protective ventilation (LPV-HO) for the duration of OLV and surgery. Findings We describe the surgical and ventilation methods for a successful lung surgery pilot for a porcine OLV model. Initial surgeries show that our protocol is effective in reproducibly maintaining peak airway pressures, tidal volumes and oxygen delivery according to the parameters of LPV, IMV and hyperoxia during OLV. Bronchoalveolar lavage fluid IL-6 was elevated in response to IMV during OLV, hyperoxia and surgical exposure. Conclusions We describe a reproducible protocol for an in vivo large animal model of OLV lung surgery with a protective and two injurious mechanical ventilation arms with collection of physiologic data and biospecimens.

Hyperspectral reflectance spectroscopy for rapid nondestructive microstructural evaluation of high strength internally cured concrete

Use of machine learning for early prediction of short-term mortality in veterans with metabolic dysfunction-associated steatotic liver disease

Background Metabolic dysfunction associated steatotic liver disease (MASLD) is a leading cause of chronic liver disease worldwide and affects >25% in the United States population. We hypothesized that clinical features present in electronic health records (EHR) could be extracted early to characterize patients with MASLD who are at high risk of early mortality and that machine learning models would predict mortality better than noninvasive assessments of liver disease/fibrosis. Methods Using previously published criteria for MASLD, applied to data from the US Veterans Affairs EHR, we identified a cohort of 13,071 patients between 2000 and 2018 who had an initial diagnosis of MASLD without clinical evidence of cirrhosis. We subsequently used machine-learning and conducted analysis of variance and logistic regression to identify clinical variables to characterize cirrhosis risk and predict mortality within the ensuing 5-years. Results The average age of the cohort was 60 years, had a BMI of 31, and 34% diabetes prevalence. Patients who progressed to cirrhosis were younger when first diagnosed with MASLD (56), had a higher BMI (33), and had significantly higher noninvasive fibrosis scores. Having diabetes at index MASLD diagnosis significantly increased the risk of developing cirrhosis and doubled the risk cirrhosis plus HCC (2.09 CI:1.217–3.63). Our machine-learning model performed significantly better than FIB-4 at predicting mortality within 5-years of being diagnosed with MASLD (AUC 83% vs 68%). Conclusion Our data suggest that machine learning models based on data extracted from the EHR early during MASLD can identify patients likely to develop cirrhosis and predict short term mortality.

Study on urban passenger transport carbon reduction pathways based on system dynamics

Hematological toxicity of anti-tumor antibody-drug conjugates: A retrospective pharmacovigilance study using the FDA adverse event reporting system

Background Although antibody-drug conjugates (ADCs) have shown significant efficacy in cancer treatment, hematotoxicity remains a serious issue. This study aims to investigate the relationship between ADCs and hematological toxicity. Methods Our study was conducted using data extracted from the U.S. Food and Drug Administration Adverse Events Reporting System (FAERS) from the third quarter of 2011 to the second quarter of 2024. We used four disproportionality analysis methods to measure risk signals. In addition, we analyzed the time-to-onset of hematotoxicity adverse events (AEs). Results A total of 4,803 cases of hematotoxicity AEs associated with ADCs were identified, the median age of patients was 60 years (IQR: 47–72). Different ADCs have different hematotoxicity profiles, among which brentuximab vedotin (BV) and sacituzumab govitecan (SG) were more likely to lead to serious outcomes. The median time-to-onset of hematotoxicity AEs was the shortest for SG at 12 days and the longest for trastuzumab deruxtecan (TG) at 22 days. The hospitalization and mortality rates with hematotoxicity AEs were 30.38% and 18.30%, respectively. Conclusions ADCs are significantly associated with increased reporting of hematotoxicity. A novel hematotoxicity signal that was not disclosed in the drug specifications was observed. The most prominent hematotoxicity AE signals were cytopenia related to inotuzumab ozogamicin (IO), polatuzumab vedotin (PV), loncastuximab tesirine (LT), and tisotumab vedotin (TV); febrile bone marrow aplasia related togemtuzumab ozogamicin (GO), BV, and SG; and myelosuppression related to BV, trastuzumab emtansine (TE), andenfortumab vedotin (EV). Our findings need to be validated by large-scale prospective studies.

Peripheral blood cellular dynamics during the progression of human tuberculosis