Browse Articles

Multimorbidity patterns and their associations with demographic characteristics, behavioral factors, and urban–rural residence: A latent class analysis in the Thai population

Multimorbidity, defined as the presence of two or more chronic conditions, is a growing public health concern. Understanding disease clustering and risk profiles is crucial for prevention and planning purposes. This study analyzed data from the National Health Survey in Thailand, which included 142,753 participants. Latent class analysis investigated nine non-communicable diseases, while multinomial logistic regression explored the characteristics associated with class membership. Four distinct classes were identified: low burden, hypertension–metabolic, predominant hypertension, and high multimorbidity. Advancing age revealed an increased probability of membership in all multimorbidity classes. Females exhibited a higher propensity to belong to the hypertension–metabolic and predominant hypertension classes. Urban residency was associated with an elevated probability of membership in the hypertension–metabolic and high-multimorbidity classes. The body mass index displayed significant variations across classes. Overweight and obesity were linked to the hypertension–metabolic and predominant hypertension classes, with obesity also associated with the high multimorbidity class. Underweight individuals demonstrated a reduced likelihood of membership in the hypertension–metabolic class but an increased likelihood of membership in the predominant hypertension class. Smoking was correlated with a higher probability of membership across classes, particularly among current smokers. Dietary behaviors were distinctly associated across classes. Frequent consumption of high-fat and instant foods was linked to the hypertension–metabolic class, whereas instant food consumption was also associated with the predominant hypertension class. Fruit consumption was correlated with a higher probability of membership in the hypertension–metabolic class, whereas vegetable consumption was associated with a reduced likelihood of membership in the predominant hypertension class. Moderate consumption of non-alcoholic sugary drinks was associated with a lower probability of hypertension–metabolic class membership. These findings underscore the heterogeneity of multimorbidity patterns in Thailand and delineate the demographic, behavioral, and residential profiles associated with non-communicable disease clustering.

Burden of heart failure in Asian Countries from 1990 to 2021: Update from the Global Burden of Disease Study 2021

Heart failure (HF) remains a major public health challenge in Asia, with rising prevalence and disability burden. This study analyzed data from the 2021 Global Burden of Disease study (GBD 2021) to assess HF trends across 48 Asian countries from 1990 to 2021. In 2021, there were 29.5 million HF cases in Asia, a 155% increase since 1990, with an age-standardized prevalence rate rising from 583.62 to 633.76 per 100,000. Years lived with disability (YLDs) also surged by 155%, reaching 2.86 million in 2021. China accounted for 44.34% of Asia’s HF cases, with the highest YLDs. Treated HF cases were most common, but severe HF contributed most to YLDs. The middle-high socio-demographic index (SDI) region had the highest age-standardized prevalence rate, while most SDI regions saw increasing trends, except Japan and Cyprus. The findings highlight the growing HF burden in Asia, urging targeted interventions to address this escalating health crisis.

Quantitative logics for directed evolution of an asexual population

Directed evolution of asexual populations is expected to offer a wide range of benefits to humanity. Achieving efficient directed evolution (DE) requires a quantitative formulation of experimental methodologies—or logics—that can address potential challenges throughout the evolutionary process. In this article, ten logics designed for resolving such difficulties when performing DE are introduced. To illustrate their application, a hypothetical scenario is considered in which an imaginary asexual population limb evolves into a wing, using a matrix-based discretization method to represent the trait of interest. Specifically, based on the operator model, fifty simulation iterations with logics applied and thirty iterations without logics were done. The results indicate that the introduced logics can accelerate the evolutionary process by roughly 2.6 times, while achieving an average accuracy for reaching the objective trait of approximately 82%. Based on these findings, key considerations for implementing quantitative logic-based DE are discussed, along with approaches for improving alignment between the final outcome and the target reference trait. Moreover, factors that could contribute to making the quantitative logic-based DE process more practical and rigorous are also examined.

A flashing success: A community-engaged approach to finding western U.S. fireflies

Fireflies are well-documented in many regions of the USA. However, the presence of flashing fireflies in much of the western United States remains largely unknown. Leveraging citizen science, this study aimed to locate populations of bioluminescent fireflies across Utah. Through a multifaceted public outreach campaign and an interactive website for data collection, citizen scientists were invited to report firefly sightings. Over nearly a decade, the project successfully amassed reports from diverse locations, significantly expanding the known distribution of bioluminescent fireflies in Utah and beyond. Validation of reports was conducted through on-site observations and specimen collection. Despite challenges in data reports (e.g., reports from outside desired area) and public interest management (e.g., protecting private property sites), the project achieved remarkable success, with over 135 unique localities documented. Media coverage, social media engagement, and educational outreach further amplified the impact of the project. Limitations in data quality and public engagement were addressed through iterative improvements in reporting protocols and outreach strategies. Future directions include expanding the project to encompass the western United States and exploring innovative communication strategies. By partnering with organizations in neighboring states, the project aims to create a robust dataset and foster public awareness of these charismatic invertebrates. This study highlights the effectiveness of community-engaged approaches in biodiversity research and underscores the importance of public involvement in scientific endeavors.

Ferroelectric Switchable Altermagnetic-Like Compensated Ferrimagnets with Charge Ordering

Abstract Unconventional collinear magnets with almost zero magnetization but prominent nonrelativistic spin splitting, such as altermagnets, can inherit the advantages of both ferromagnets and antiferromagnets. By incorporating more degrees of freedom such as ferroelectricity and charge ordering, these unconventional magnets can be even more interesting and functionalized. With this design principle, the Fe3O5 monolayer is predicted to exhibit a hybrid spin-splitting mechanism, with the superposition of the altermagnetic-like k-path alternating splitting and ferrimagnet-like Zeeman splitting. Benefiting from the hidden magnetoelectricity based on spin-charge coupling, such spin splitting can be fully switched by an electric field. Its conductivity is highly spin-polarized, with a polarization ratio above 99%, comparable to half-metals but with zero magnetization.

A lightweight hybrid framework integrating convolutional neural networks and fast Fourier transform for reliable and calibrated ECG-based cardiac abnormality detection

The electrocardiogram (ECG) is an essential non-invasive tool for detecting cardiac abnormalities; however, accurate interpretation often requires specialized expertise that may be unavailable in resource-limited clinical settings. While deep learning models have demonstrated high classification performance, many existing architectures remain computationally intensive and lack assessments of predictive reliability, hindering their deployment in clinical decision support systems. In this study, we propose a lightweight hybrid framework integrating Convolutional Neural Networks (CNN) and Fast Fourier Transform (FFT) components. This architecture combines time-domain morphological representations learned from raw ECG signals with physiologically relevant spectral features to enable accurate and efficient classification. Unlike previous approaches, this work emphasizes reliable model evaluation by incorporating probability calibration and a rigorous patient-wise validation protocol. The proposed method was evaluated on the publicly available PTB-XL dataset using the official 10-fold cross-validation protocol for both binary and five-class multi-label classification. In addition to conventional discrimination metrics, model reliability was assessed using Expected Calibration Error (ECE). The model achieved an accuracy of 92.42% and an AUC of 97.8% for binary classification, alongside a macro-AUC of 92.46% for five- class multi-label classification. Calibration analysis demonstrated well-calibrated probability estimates with low ECE values. Despite its competitive performance, the architecture is highly efficient, containing only 87K parameters and 0.26 GFLOPs. These findings highlight the potential of lightweight hybrid architectures combined with calibration-aware evaluation to support reliable AI-assisted ECG diagnostics in resource-constrained healthcare settings.

Beyond Geometric Effects: Particle Size-Dependent Electronic Promotion in Ru Catalysts for Ammonia Synthesis

Abstract Metal particle-size effects in heterogeneous catalysis are commonly interpreted in geometric terms, where catalytic trends arise from variations in the density of active surface ensembles while the intrinsic properties of the sites are generally assumed to remain unchanged. Here we demonstrate that metal particle size also governs the intrinsic properties of active sites via size-dependent electronic promotion, beyond conventional geometric effects. Using well-defined Ru catalysts supported on multiwalled carbon nanotubes for ammonia synthesis, we separate the geometric contribution of B5-like site density from changes in the intrinsic properties of the sites induced by electronic promotion. Without promoters, the adsorption and catalytic properties of these sites remain essentially invariant with particle size, consistent with classical geometric interpretations. In contrast, with electronic promotion using BaO, interfacial charge storage and capacitive effects enable smaller Ru particles, with higher surface-to-volume ratios, to accumulate greater electron densities. This size-dependent electronic enrichment directly tunes the intrinsic reactivity of individual B5-like sites, strengthening N2 activation through enhanced π-backdonation and alleviating hydrogen poisoning, leading to higher site-specific activity. These findings establish particle size as a dual control parameter that modulates both site density and intrinsic site properties via electronic effects, providing new insight into the complex interplay between catalyst structure, charge distribution, and intrinsic catalytic activity.

Body fat, skin tone, and the accuracy of smartwatch caloric expenditure estimates

Smart watches are commonly used to provide continuous feedback on activity and caloric expenditure and are leveraged for weight management, clinical decisions, and public health strategies. Most wrist-worn wearables combine photoplethysmography, accelerometry, and proprietary algorithms to estimate caloric expenditure. Prior research indicates significant errors, yet the roles of potential moderators, specifically skin tone and body fat percentage (BF%), remain insufficiently examined. Therefore, the primary objective of this study was to quantify the accuracy of smartwatch-derived physical activity energy expenditure (PAEE) estimates relative to indirect calorimetry and to examine whether error varies by device brand, body fat percentage, and skin tone. We tested whether brand, BF%, and Fitzpatrick skin type (III–V) predict caloric expenditure error versus indirect calorimetry. Hispanic adults ( n  = 58) completed a single 10-minute recumbent-cycle protocol with alternating 2-minute intervals at ~64–76% and ~77–95% HRmax (Tanaka formula), bracketed by 5-minute rest/recovery. Participants wore Apple Watch Series 8, Fitbit Sense 2, Samsung Galaxy Watch 5, and Garmin Forerunner 955; COSMED K5 metabolic system provided the criterion. After device-specific data quality filters, analyzable participant–device pairings were Apple = 52, Garmin = 51, Samsung = 50, Fitbit = 44. One-sample tests indicated significant mean bias for three of four devices, p  < .05. Importantly, the non-significant Fitbit bias depended on device-specific outlier removal. Bias ( M , SD ) and 95% CI (kcal): Apple 21.60 (36.63), 11.59–31.60; Garmin 68.61 (55.86), 53.28–83.94; Samsung 56.76 (42.03), 45.11–68.41; Fitbit 3.14 (40.95), −8.96 to 15.24. Mixed-effects models showed a device main effect ( p  < .001), a BF% main effect ( p  < .01), and a device by BF% interaction ( p  = .02): Physical activity energy expenditure (PAEE) error increased with adiposity across all brands ( p  < .01). Common smart watches substantially misestimate PAEE relative to indirect calorimetry, with error magnitude increasing as BF% rises and varying by brand. Current consumer devices do not yet provide reliable caloric monitoring for individuals or for research; improving accuracy across body types is essential for clinical and public health applications.

Synergistic induction of apoptosis and metabolic reprogramming by ursolic acid and acetyl-11-keto-β-boswellic acid in chemoresistant ovarian cancer cell lines

Mapping the chaperonin TRiC/CCT interactome in mouse photoreceptors reveals functional significance for energy metabolism

The eukaryotic chaperonin TRiC/CCT is essential for folding a diverse set of proteins, yet its interactome and functional roles in specialized neurons remain incompletely understood. To investigate TRiC-mediated folding in rod photoreceptors, we generated a transgenic mouse line expressing an epitope-tagged Tcp-1α subunit, enabling purification of intact TRiC complexes from retinal tissue. Mass spectrometry identified 226 TRiC-interacting proteins, including known TRiC substrates and co-chaperones as well as numerous novel candidates enriched in RNA processing, cytoskeletal organization, and cell-cycle regulation. Using a TRiC loss-of-function model in which expression of a short splice isoform of phosducin-like protein (PhLPs) competitively inhibits TRiC activity, we observed marked reductions in canonical TRiC substrates, including tubulins, transducin β subunits, and triosephosphate isomerase, as well as secondary alterations in proteins involved in cytoskeletal stability, membrane trafficking, energy metabolism, and phototransduction. Quantitative metabolomic profiling revealed that TRiC deficiency induces a metabolic “energy crisis” characterized by reduced glycolytic- and tricarboxylic acid cycle intermediates, acylcarnitines, ATP, NAD, and NADH, implicating widespread impairment of glucose utilization, mitochondrial bioenergetics, and fatty acid oxidation. Integrative proteomic–metabolomic analysis identified a small subset of proteins, including Rab10 and Anxa1, as potential drivers of these metabolic disruptions, with defective Rab10-dependent GLUT4 trafficking emerging as a plausible mechanism underlying impaired glucose uptake in TRiC-deficient rods. Finally, experiments using a perpetually unfolded Gβ 1 mutant and Gγ 1 -knockout mice demonstrated that substrate overload sequesters TRiC and competitively displaces other clients, exacerbating proteostasis imbalance. Together, our study provides a comprehensive in vivo mapping of the TRiC interactome in mammalian rods, reveals a connection between TRiC-dependent proteostasis and energy metabolism in rods, and indicates a mechanism by which misfolded TRiC substrates exacerbate a proteostasis imbalance that ultimately results in neurodegeneration.

Current practices in revision/conversion surgery after Roux-en-Y gastric bypass: an international binary expert survey

Supramolecular Seeding-Induced Fibrillar Refinement for High Efficiency and Stable Organic Solar Cells

Abstract Achieving optimal morphology and long-term stability in organic solar cells (OSCs) remains challenging because the donor–acceptor crystallization sequence is often poorly coordinated during solution processing. Here, we develop a supramolecular crystallization seeding strategy by introducing a hydroxyl-terminated, highly crystalline acceptor (Y–OH) into the D18/L8-BO system. By engineering a hierarchical thermodynamic compatibility gradient (χD18/Y–OH > χL8-BO/Y–OH > χD18/L8-BO), Y–OH becomes interface-active and exhibits an interfacial distribution tendency in the multicomponent film. In situ UV–vis measurements reveal that Y–OH undergoes early stage ordering on a time scale closer to the donor, thereby participating in the initial morphology evolution and steering the subsequent organization of L8-BO into a refined interpenetrating fibrillar network with reduced fibril diameters. This morphology simultaneously promotes efficient exciton harvesting/charge generation and enables balanced charge transport, leading to concurrent enhancements in short-circuit current density (JSC) and fill factor (FF). As a result, the optimized ternary devices deliver a PCE of 20.90% (vs 20.05% for the binary control). Moreover, hydroxyl-enabled supramolecular interactions provide noncovalent anchoring that retards thermally driven morphology relaxation, allowing the devices to retain ≈83% of their initial efficiency after 900 h at 65 °C. This work highlights supramolecular crystallization seeding as an effective design principle for simultaneously improving efficiency and thermal stability in OSCs.

Quantitative comparative study on the verb-direction constructions “V+ Xia (下)”, “V+ Xialai (下来)” and “V+ Xiaqu (下去)” in mandarin Chinese

“V+ Xia (下)”, “V+ Xialai (下来)” and “V+ Xiaqu (下去)” are common verb-direction constructions in Mandarin Chinese, exhibiting certain similarities in syntactic distribution and semantic content. Employing collostructional analysis, this study quantitatively and visually calculates the collocational strength and attraction between different types of verbs and these verb-direction constructions. It analyzes the polysemy of the three constructions, summarizes the differences in their prototype meaning, and examines the distribution of collocational strength and relative semantic distance between prototypical and non-prototype meanings. Furthermore, it explores the characteristics of the semantic network structures presented by the three constructions. The findings indicate that “V+ Xialai ” possesses the largest number of attracted verbs, the broadest verb selection range, and the highest productivity among the three constructions. “V+ Xia ”, constrained by prosody, exhibits the lowest productivity but the highest average collocational strength. “V+ Xia ” has the highest number of semantic types with a relatively uniform distribution of collocational strength, whereas the distributions for “V+ Xialai ” and “V+ Xiaqu ” are more concentrated around their prototype meanings. The semantic networks of “V+ Xia ” and “V+ Xiaqu ” overall display a gradient continuum, while that of “V+ Xialai ” is relatively discrete, with certain semantic types showing a tendency to form independent clusters. The prototype meanings of all three constructions demonstrate strong semantic coherence. Non-prototype meanings with high collocational strength also exhibit relatively strong internal coherence, while those with low collocational strength are more discrete, displaying characteristics of diversity.

Associating raindrop size distribution types with microphysical structure of precipitation systems in the Indian core monsoon zone

Optimization of CNC milling parameters for YXR-7 tool steel using fuzzy MARCOS: A multi-response approach to improve machining productivity

This study presents an integrated optimisation method for CNC milling of heat-treated YXR7 tool steel using carbide cutting inserts under varying lubrication and process parameters. A full factorial experimental design comprising 27 runs was employed to assess the influence of depth of cut ( d c ), feed per tooth ( f t ), cutting speed ( C s ), and nano-cutting fluid ( C f ) on critical performance responses such as surface roughness ( Ra ), material removal rate ( MRR ), and tool wear rate ( TWR ). An advanced modelling through regression and ANOVA showed complex interactive and non-linear effects among process parameters. To effectively navigate these interdependencies, a novel hybrid decision-making model combining the Full Consistency Method (FUCOM) and fuzzy-MARCOS was employed. This multi-criteria decision-making (MCDM) method was described for uncertainties in machining performance and successfully ranked experimental alternatives based on their proximity to ideal performance. The optimal configuration (Experiment 21) accomplished a superior balance across all criteria, notably achieving a low surface roughness (Ra ≈ 0.42 µm) and TWR (~0.148 mm³/min) while maintaining a high MRR (~109.4 mm³/min). The proposed fuzzy-FUCOM-MARCOS method reveals high robustness, adaptability, and decision reliability, contributing a valuable strategy for precision machining of hard-to-cut steels. This work bridges experimental understandings with intelligent optimisation, fostering sustainable and high-performance manufacturing practices in the tooling industry.

Development and pilot evaluation of an electronic trigger-based surveillance model for sedative-hypnotic drug misuse in a tertiary hospital

Programmable Helicity and Spin Polarization in Pt6L4 Coordination Capsules through Guest-Mediated Chiral Control

Abstract Programmable regulation of spin polarization enables molecular-scale spin filtering, yet reversible control of its sign in supramolecular systems remains challenging. Herein, a chiral-directing strategy modulates multiple helical states of octahedral capsules, enabling programmable and reversible control of chirality-induced spin selectivity (CISS). The strategy transfers (R)-1,1′-bi-2-naphthol chirality to induce capsule helicity, followed by controlled helicity inversion upon introduction of (R)-1,1′-binaphthyl-2,2′-diylhydrogen phosphate via host–guest interactions. Magnetic circular dichroism (MCD) reveals chirality-dependent changes in electronic states, while magnetic conductive atomic force microscopy (mc-AFM) independently demonstrates spin-selective charge transport. Systematic modulation of host–guest electronic coupling enables reversible switching of spin polarization. These results demonstrate that dynamic, chirality-matched host–guest interactions emulate enzymatic stereochemical control, offering molecular-level insight and a general design principle for chiral coordination architectures with programmable spin functions.

Investigating the adaptive coping mechanisms of rewilded elephants: A comparison of behavioural and physiological variables with wild elephants

Literature on the adaptability and environmental processing abilities of ex-captive (rewilded) African elephants ( Loxodonta africana ) is sparse, emphasising the importance of this research area. By broadening our knowledge on the topic of rewilded elephant behaviour, we can generate a realistic expectation for stakeholders wanting to secure locations for these captive elephants with the aim of rewilding them. We studied 11 African elephants who have been rewilded onto various fenced reserves in South Africa. We measured their adaptability and response to their environment by comparing their behavioural frequencies within four categories and their physiological responses (faecal glucocorticoid metabolite (fGCM) concentrations), to those observed for free-roaming elephants in South Africa. All study animals were grouped into three age/sex categories (Adult females (AF); Adult males (AM); Sub-adult females (SAF)). The results demonstrated that rewilded elephants expressed similar behavioural frequencies to their wild counterparts, except within the “Attentive” category, where both rewilded AMs and SAFs showed significantly lower frequencies than wild elephants. On the contrary, rewilded AFs and SAFs had comparatively higher fGCM concentrations than wild elephants. Through individual comparisons, it was evident that rewilded elephants have adopted unique mechanisms to process environmental stimuli. When the relationship between behavioural and physiological responses was investigated, certain rewilded elephants showed a strong relationship between the two parameters. However, for other elephants, there was no clear correlation between behavioural frequencies and fGCM concentrations. These distinctive differences might largely be influenced by personality traits that will determine whether the elephants respond actively or passively to their environment. This study has shown that rewilded elephants can adapt to wild environments after long-term captivity and that each of them implemented unique ways of coping with both natural and management-related pressures in the different reserves.

Development of hydrophobic deep eutectic solvent-ferrofluid based magnetic-assisted liquid–liquid microextraction for the extraction of PAHs from water and food samples

Hexagonal Close-Packed 2H-Cu Nanocrystals

Abstract Tuning the morphology and structure of Cu nanomaterials could effectively regulate their property, functions, and applications. However, it still remains challenging to directly synthesize Cu nanomaterials with an unconventional phase. Here, we report a one-pot wet-chemical synthesis of Cu nanocrystals (NCs) with a hexagonal close-packed (hcp, 2H type) phase, which is different from their thermodynamically stable face-centered cubic (fcc) phase. Compared to the conventional fcc-Cu NCs, the obtained 2H-Cu NCs exhibit enhanced catalytic activity and selectivity in the electrochemical carbon dioxide reduction reaction (CO2RR), achieving a high Faradaic efficiency (FE) of 73.1% toward multicarbon (C2+) products at 600 mA cm–2 under alkaline conditions in a flow cell. Moreover, in situ characterizations and density functional theory (DFT) calculations reveal that the 2H-Cu NCs can optimize the adsorption of the *CO intermediate, leading to a low energy barrier for the formation of C2+ products. This work not only demonstrates an improvement in CO2RR performance of Cu NCs by using the strategy of phase engineering of nanomaterials (PEN) but also opens up an avenue to explore the intrinsic properties and applications of unconventional-phase nanomaterials.