Browse Articles

Retraction: Induction of heat shock protein expression in SP2/0 transgenic cells and its effect on the production of monoclonal antibodies

Mitophagy-driven prognosis in pediatric acute myeloid leukemia: a new frontier

Non-chelation control in allylations of α-oxy ketones using group-14 allylatranes

Abstract Stereoselective nucleophilic additions to α -substituted carbonyl compounds are a crucial area of contemporary research in organic chemistry. Of the various advancements in π-facial selectivity in addition reactions of carbonyl compounds, the (polar) Felkin-Anh model and the chelation model are well recognized for accurately explaining the selectivity of the allylic products. For reactions that involve α -oxy carbonyl groups - known for their broad applications in natural-product synthesis and as effective building blocks in organic synthesis - the stereoselective reaction typically follows the chelation model, favoring syn -selective addition. In contrast to the well-established syn -selective additions of α -oxy carbonyls, anti -selective additions through a non-chelation pathway remain largely unexplored. In this study, we present the anti -selective allylation of α -oxy ketones using allylatranes that feature a highly coordinated group-14-element center. These atranes demonstrate high nucleophilicity and low chelating ability due to their transannular interactions and rigid framework, facilitating anti -selective allylations. A combined experimental and theoretical approach has been used to highlight the unique electronic properties of these atranes. This method is applicable to a wide variety of substrates, producing anti -1,2-diols with a homoallylic moiety in high yield and excellent diastereoselectivity compared to traditional methods.

Class switching toward IgG4 six months after primary mRNA-based COVID-19 vaccination in kidney patients

Background Class switching toward spike (S)-binding IgG4 antibodies after mRNA-based COVID-19 vaccination has been observed, an antibody subclass with strong neutralizing but limited effector activity. While this has been reported in healthy individuals, subclass dynamics in immunocompromised kidney patients are unclear. We assessed IgG subclass patterns and S-specific B-cell phenotypes up to 6 months after a two-dose mRNA-1273 vaccination schedule in kidney transplant recipients (KTRs), dialysis patients, and patients with chronic kidney disease (CKD). Methods In this exploratory study, KTRs (n = 11), dialysis patients (n = 5), CKD stage G4–5 patients (eGFR < 30 ml/min/1.73m2, n = 5), and controls without known kidney disease (eGFR > 45 ml/min/1.73m2, n = 8) received two mRNA-1273 doses 28 days apart. Blood was collected pre-vaccination (V1), and at 28 days (V3) and 6 months (V4) after the second dose. S1-specific IgG antibodies were measured by a validated fluorescent bead-based multiplex-immunoassay, and participants seronegative at V1 and seropositive at V3 were included. B cells were phenotyped by flow cytometry. Results Five of 11 KTRs had no detectable S-binding B cells, whereas all other groups mounted responses. Across responders, the frequency of S-binding B cells increased from V1 (median 0.08%) to 0.49% at V3 and to 0.84% at V4 (both p < 0.0001). S-binding B cells mainly comprised IgG⁺ plasmablasts. The IgG4:IgG1 log-ratio increased significantly from V3 to V4 (p < 0.001), indicating a relative shift toward IgG4; absolute frequencies were comparable across the groups. Conclusions Approximately half of KTRs lacked detectable S-binding B cells after two mRNA-1273 doses, despite antibody formation. Among responders, S-binding B cells persisted up to 6 months after vaccination with a relative shift toward IgG4, a pattern also observed in dialysis patients, CKD patients and controls. The clinical significance of this subclass skewing requires confirmation in larger cohorts with functional antibody readouts.

Unveiling the poroelastic evolution of agar hydrogels through the drying process

Femtosecond laser synthesis of multiscale high-entropy alloys/graphene composites for high-performance Joule heating

Abstract High-entropy alloy nanoparticles (HEA-NPs) have garnered significant interest across diverse fields. However, thus far, research on their applications has predominantly focused on electrocatalysis. Expanding the applications of HEA-NPs beyond current fields is timely and desirable but remains a challenge. Here, we demonstrate the successful femtosecond laser synthesis of HEA-NPs on the laser-induced graphene (LIG) for realizing high-performance Joule heating applications. This prepared composites (HEAs/LIG) exhibits exceptional electrothermal conversion ability with efficiency up to ~285.4 °C cm 2 W −1 . Furthermore, the HEAs/LIG also shows high broadband infrared emissivity of ~0.98 across the wavelength range from 2.5 to 20 μm. Finally, we present the applications of HEAs/LIG as an efficient Joule heater, which consumes ~49.1% less energy compared to conventional electrical heaters in winter. This work expands the application of HEA-NPs into the Joule heating field, and underlining the importance of further development in efficient energy utilization technology.

Mental health help-seeking intentions among health workers in the east coast of peninsular Malaysia: Perceived barriers and predictive factors

Introduction Mental health problems among health workers are a growing concern globally, including in Malaysia. Despite the availability of mental health services, some health workers do not seek professional help. This study aims to determine the level of health workers’ intention to seek professional help, examine the barriers they perceive, and identify predictors of mental health help-seeking intention. Methods This cross-sectional study involved 470 health workers in the East Coast region of Peninsular Malaysia. Data was collected using a self-administered questionnaire. Linear regression analysis was employed to identify the predictors of professional help-seeking intention. Results The mean score for mental health help-seeking intention was 4.90 (SD = 1.03). Perceived need for help positively predicted help-seeking intention (B = 0.532, p < 0.001), while perceived stigma barriers negatively predicted help-seeking intention (B = −0.588, p < 0.001). Notable barriers perceived included concerns about perceptions of weakness, feelings of embarrassment, a preference for handling problems independently, and challenges in taking time off work. Conclusions This study highlights the roles of the perceived need for help and perceived stigma barriers in predicting health workers’ help-seeking intentions, offering a basis for targeted interventions and policies to enhance mental health support within Malaysian healthcare settings.

Interpretable hybrid ensemble with attention-based fusion and EAOO-GA optimization for lung cancer detection

Abstract Lung cancer’s high mortality rate underscores the critical need for early and accurate diagnosis, as late-stage diagnoses often lead to 5-year survival rates as low as 5% compared to 56% for early detection, imposing significant economic burdens on healthcare systems and diminishing patient quality of life. While deep learning models offer promising tools for analyzing Computed Tomography (CT) scans, they often suffer from limitations in generalizability, interpretability, and sensitivity to imbalanced data. This paper introduces SE-FusionEAOO Ensemble, a new robust framework for lung cancer classification. Our approach leverages the strengths of multiple deep learning architectures through a sophisticated two-stage process. First, we construct three powerful feature fusion models by strategically pairing diverse pre-trained networks (DenseNet201/EfficientNetB6, Inception v3/MobileNetV2, DenseNet121/ResNet50), each integrated with Squeeze-and-Excitation (SE) blocks for adaptive feature recalibration. Second, we amalgamate the predictions of these expert models using an intelligently weighted aggregation scheme. The key innovation of our framework is the deployment of a new metaheuristic, the Enhanced Animated Oat Optimization algorithm with Genetic Operators (EAOO-GA), to precisely optimize these ensemble weights, ensuring optimal contribution from each model. To address class imbalance in the IQ-OTH/NCCD lung cancer dataset, we employ the Synthetic Minority Over-sampling Technique (SMOTE), significantly improving the model’s sensitivity to minority classes. Extensive experimental results demonstrate that our framework achieves a state-of-the-art accuracy of 99.40%, with 99.2% precision, 99.5% recall, and 99.3% F1-score, outperforming individual models, conventional ensemble methods, and other metaheuristic optimizers. Additionally, the model was externally validated on the LIDC-IDRI dataset, achieving 97.9% accuracy and 97.8% F1-score, confirming its strong generalization capability across independent clinical domains. The proposed framework provides a highly accurate, reliable, and interpretable tool for automated lung cancer detection.

Generative modeling enables molecular structure retrieval from Coulomb explosion imaging

The 30-year evolution of motor vehicle road injuries: Can the future come from the shadows?

Background This study examines temporal changes from 1990 to 2021 in the burden of vertebral fractures (VFs) attributable to motor vehicle road injuries (MVRIs), with a particular focus on age- and sex-specific patterns in China and India. These national trends are compared with global patterns to better understand population distribution characteristics and injury mechanisms underlying this public health challenge. Methods Data were obtained from the 2021 Global Burden of Disease (GBD) study. Crude rates and age-standardized rates (ASRs) of incidence, prevalence, and years lived with disability (YLDs) for MVRI-related VFs were estimated. Joinpoint regression was applied to assess temporal trends, while age–period–cohort (APC) modeling was used to disentangle the independent effects of age, calendar period, and birth cohort. Results From 1990 to 2021, the global age-standardized incidence rate (ASIR) of MVRI-related VFs declined by 48.8%, with an average annual percentage change (AAPC) of −1.839% (95% confidence interval [CI], −1.869 to −1.808). In contrast, China showed no significant reduction in ASIR (AAPC = −0.478%, 95% CI, −0.531 to −0.426), whereas India demonstrated minimal variation over the study period (AAPC = −0.013%, 95% CI, −0.054 to 0.028). Regional analyses revealed heterogeneous drivers of disease burden. In China, period effects during 2000–2021 were strongly associated with elevated risk among males aged 20–40 years, likely reflecting hazardous driving behaviors, while cohort effects were most prominent among individuals born between 1980 and 1990. Conversely, individuals older than 60 years experienced an increasing burden, potentially related to osteoporosis and rapid motorization. Across all regions, males consistently exhibited higher ASIRs, age-standardized prevalence rates (ASPRs), and YLD rates than females, with the greatest sex disparities observed among younger males in China. Conclusion The persistently high burden of MVRI-related VFs in China, which diverges from declining trends observed in countries with a high sociodemographic index (SDI), highlights the need for targeted prevention strategies. Interventions should prioritize behavioral risk reduction in younger male populations and address age-related biomechanical vulnerability in older adults. In India, strengthening road safety enforcement and trauma care infrastructure remains essential. These findings underscore the heterogeneous demands for road injury prevention in China and India and provide evidence to support more effective allocation of public health resources.

Study on the high-temperature static and dynamic constitutive model of silicon carbide-modified concrete

Machine learning discovers numerous new computational principles supporting elementary motion detection

Abstract Motion direction detection is a fundamental visual computation that transforms spatial luminance patterns into directionally tuned outputs. Classical models of direction selectivity rely on temporal asymmetry, where motion detection arises through either delayed excitation or inhibition. Here, I used biologically inspired machine learning applied to retinal and cortical circuits to uncover receptive field architectures capable of direction selectivity. These include mechanisms based on asymmetric synaptic properties, spatial receptive field variations, new roles for pre- and postsynaptic inhibition, and previously unrecognized kinetic implementations. Conceptually, these circuit architectures cluster into eight computational primitives underlying motion detection, four of which are previously undescribed. Many of the solutions rival or outperform classical models in both robustness and precision, and several exhibit enhanced noise tolerance. All mechanisms are biologically plausible and correspond to known physiological and anatomical motifs, offering fresh insights into motion processing and illustrating how machine learning can uncover general principles of neural computation.

A biologically plausible decision-making model based on interacting neural populations

We present a novel decision-making model with two populations. Each population is composed of Regularly Spiking (excitatory) and Fast Spiking (inhibitory) cells in cortical layer 2/3. Each population votes for one of the two visual alternatives shown on a monitor in human and macaque experiments. The model is biophysically plausible since it is based on long-range cortico-cortical connections between the layer 2/3 populations. These connections are excitatory. They contact both Regularly Spiking and Fast Spiking cells. This long-range excitation is conflicted by an inhibition based on local connections within the populations. This configuration introduces a competition between the layer 2/3 populations, sufficient for making a decision to choose between two alternatives shown on the monitor. We integrate the model with a reward-driven learning mechanism. This allows the model to learn the optimal strategy maximizing the cumulative reward in the long term. We test the model on two decision-making tasks applied on human and macaque. This model elaborates certain biophysical details which were not considered by simpler phenomenological models proposed for similar decision-making tasks. Finally, it can be embedded in a brain simulator such as The Virtual Brain to study decision-making in terms of large-scale brain dynamics.

Association between the blood urea nitrogen to albumin ratio and 30-day mortality in critically ill children with acute kidney injury

sRNA centered signaling activates nitrate respiration and enhances Cronobacter sakazakii virulence in host environments

UHPLC-QTOF-MS/MS-based metabolomic discovery of anticancer compounds in ethanolic extracts of Ficus hispida L. f.

Ficus hispida L. f. ( F. hispida ) is commonly used in traditional medicine for various health problems. No comprehensive analysis of all its components has yet been undertaken, despite researchers having explored its chemical constituents and biological activities. Using untargeted metabolomics, we aimed to assess the chemical compositions and metabolic differences among the five parts of F. hispida : bark, fruits, leaves, twigs, and stalks. To discover the compounds that might be accountable for the noted efficacy, our study assessed the correlation between the identified metabolites and anticancer activities. We applied untargeted metabolomics using UHPLC-QTOF-MS/MS to confirm a total of 82 metabolites. These compounds were classified into six phytochemical groups with predominant accumulation in each part: fatty acids and their conjugates (twigs), terpenoids (stalks), phenylpropanoids (bark and twigs), alkaloids (bark and leaves), saccharides and their conjugates (bark), and amino acids and peptides (twigs and stalks). Multivariate analysis showed markedly distinct metabolic patterns across the five tested parts, especially leaf and bark extracts. The sulforhodamine assay (SRB) for the cytotoxicity test revealed that the bark and leaf extracts had the highest potential to inhibit the viability of cholangiocarcinoma (CCA) cells, with IC 50 values of 0.71 ± 0.17 µg/mL and 0.82 ± 0.22 µg/mL for KKU-213A, and 0.78 ± 0.13 µg/mL and 1.03 ± 0.21 µg/mL for KKU-055. Univariate analysis revealed that four metabolites, including catharanthine, cianidanol, procyanidin B2, and quinic acid, had significant correlation with these anticancer abilities. Subsequent cytotoxicity studies on these candidate metabolites revealed that catharanthine suppressed CCA cell viability at the IC 50 of 41.0 ± 0.99 µM (KKU-213A) and 47.4 ± 4.34 µM (KKU-055) over other candidates. Our research suggests that catharanthine, which is a terpene indole alkaloid found in the bark and leaves of F. hispida, is responsible for the anticancer efficacy against CCA cells.

A compliant, morphing gripper for handling diverse leafy vegetables in vertical farming systems

Optimizing cross-domain transfer for universal machine learning interatomic potentials

Abstract Accurate yet transferable machine-learning interatomic potentials are essential for accelerating materials and chemical discovery. However, many existing universal models are overfitted to narrow chemical spaces or computational protocols, limiting their reliability across diverse chemical and functional domains. Here, we introduce a transferable multi-domain training strategy that jointly optimizes parameters through selective regularization, coupled with a domain-bridging set that aligns potential-energy surfaces across datasets. Systematic ablation experiments show that suggested strategies synergistically enhance out-of-distribution generalization while preserving in-domain fidelity. Based on our observation, we train SevenNet-Omni on 15 open datasets spanning molecules, crystals, and surfaces. Our model achieves state-of-the-art accuracy in cross-domain benchmarks, reaching chemical accuracy in various scenarios including adsorption-energy in catalytic surfaces and metal–organic frameworks. SevenNet-Omni also accurately reproduces high-fidelity properties by effectively transferring knowledge learned from larger, lower-accuracy databases. This framework offers a scalable route toward universal, transferable models that bridge quantum-mechanical fidelities and chemical domains.

Reply to Fernández-Quevedo García et al.: Surface tension in phase-separated active Brownian particles

Computational frameworks for automated detection and quantification of paroxysmal sympathetic hyperactivity among traumatic brain injury patients

Paroxysmal sympathetic hyperactivity (PSH) is a syndrome that occurs in a large subset of critically ill traumatic brain injury (TBI) patients and is associated with complications and poor recovery. PSH is defined by recurrent episodic vital sign elevations in the appropriate clinical context. However, standard diagnostic criteria rely heavily on subjective judgment, leading to challenges and delays in recognition, monitoring, and management. The objective of this study was to develop automated PSH detection and quantification tools that exclusively utilize objective bedside continuous vital sign data. Using a cohort of 221 critically ill acute TBI patients with at least 14 days of continuous physiologic data (of which 107 were clinically diagnosed with PSH) we developed a high-resolution clinical feature scale based on established PSH-Assessment Measure criteria and two artificial intelligence-based episode detection models including an expert system approach and a machine learning model approach, using a clinician-annotated case example as ground truth. For the episode detection methods, PSH was quantified as the number, duration, and overall temporal burden of detected episodes. To evaluate performance, we compared quantifications across PSH cases and controls and explored precision and recall. All three methods demonstrated initial face validity to delineate PSH cases from non-PSH TBI controls. Future optimization and implementation of the described computational frameworks with real-time patient data could improve the standard monitoring and management of this challenging clinical syndrome.