Browse Articles

Impact of the amount of parathyroid autotransplantation in total parathyroidectomy for secondary hyperparathyroidism: a prospective randomized trial

MIDAS: rapid, multiplexed molecular profiling for integrated host–pathogen analysis

Electronic conduction in water-adsorbed kenaf cellulose

Synchronous mid-Holocene marine and terrestrial deglaciation in the Ross Sea, Antarctica

Abstract The Ross Ice Shelf buttresses ice draining from both East and West Antarctica and its collapse could accelerate the loss of inland ice sheets, rapidly raising sea level. Documenting the location, timing and rate of past glacial retreat can help reveal processes driving rapid mass loss, informing projections of ice sheet responses to a warming climate. Here, we present a record of mid-Holocene ice retreat from the southwestern Ross Sea using facies succession and paired ramped pyrolysis oxidation 14 C/ 210 Pb chronology. This record shows rapid ice shelf retreat from 6.9-5.4 cal kyr BP, coeval with thinning of adjacent outlet glaciers. Our findings reconcile earlier discrepancies in terrestrial and marine reconstructions, and indicate that synchronous grounding line retreat from west of Ross Island to the Siple Coast at ~7-6.2 cal kyr BP was likely driven by warm-water incursions, a process active in parts of Antarctica today.

RETRACTED ARTICLE: Structural configuration of sustainable sports industry based on deep learning and genetic algorithm

Highly coherent organic lasing based on etch-free metasurface

Habitat-elevation and plant organ-type affect the phytochemical contents and the medicinal properties of Senna italica in the arid climate of Fujairah

Structural determinants of non-covalent PPARγ inverse agonism and their therapeutic implications

Network security analysis based on feature selection and optimized fireworks algorithm

Fullerene-decorated PdCo nano-resistor network hydrogen sensors with sub-second response and parts-per-billion detection at room temperature

Abstract Hydrogen detection with rapid response and ultra-low detection limits remains a critical challenge for safety and energy applications. Here, we report a fullerene-decorated PdCo nano-resistor network sensor that integrates nanostructuring, alloying, and surface-engineering approaches. The C 60 layer enhances sensor performance by increasing the surface-to-volume ratio, enabling fast hydrogen diffusion, relieving mechanical stress during cycling, and guiding nanostructure morphology. Our composite device (20 nm C 60 /3 nm Teflon AF/5 nm Pd 63 Co 37 /30 nm Teflon AF) achieves a response time of 0.40 ± 0.06 s across 1–100 mbar H 2 and detects 40 ppb H 2 with a signal-to-noise ratio of 10 at room temperature. A poly(methyl methacrylate) (PMMA) topcoat further improves cycling stability and selectivity under 90% relative humidity and interfering gases. This design provides a scalable approach and opens the door to future adaptation of porous carbon-based frameworks and polymeric interlayers to realize robust, high-performance hydrogen sensors for real-world applications.

Role of innate immune and inflammatory signaling in West Nile virus tropism and neuronal and glial cell death

Abstract West Nile virus (WNV) is a mosquito-borne virus that causes severe neurological disease in humans. Despite substantial advances, our knowledge of the mechanisms involved in damaging the human brain is still limited. To address this gap, we developed a physiologically relevant in vitro model using human neuronal/glial cells and aimed to determine WNV tropism, assess whether the virus induces innate immune and inflammatory responses, and elucidate the resulting pathophysiological consequences. We found that WNV productively infected glial cells, whereas neurons exhibited a remarkable and unexpected resistance to infection. Despite the induction of a robust innate immune response mediated by IFN signalling and a rapid control of WNV replication in glial cells, we observed substantial death of astrocytes, oligodendrocytes, and neurons. Analysis of cytokine and chemokine expression further revealed that infection triggered an inflammatory response, potentially contributing to bystander cell death. We also showed that IFN signaling did not contribute to the resistance of neurons and identified IFI6 as an effector of the antiviral response in human glial cells. Together, our results underscore the importance of human neural models for confirming previous findings obtained in less physiologically relevant models and for unravelling novel cellular and molecular mechanisms.

Manganese-engineered Lactobacillus Reuteri with enhanced antitumor and immunomodulatory activities for colorectal cancer prevention and treatment

YOLO-TME: A UAV landing detection algorithm that is suitable for Polar ice Floe base stations

Reaching elimination of onchocerciasis transmission with long-term vector control and ivermectin treatment in Togo

Abstract The Onchocerciasis Control Programme in West Africa implemented vector control (VC) and ivermectin mass drug administration (MDA) to eliminate blindness, intensifying efforts in Special Intervention Zones (SIZ). Togo aims to eliminate onchocerciasis transmission (EOT) by 2030. We use the EPIONCHO-IBM model to project microfilarial prevalence trends across Togo’s five regions by SIZ status, MDA coverage (65%-80%) and VC efficacy (60%-100%). We compare projections with prevalence surveys (400 villages, 1970–2017) stratified by hypoendemic, mesoendemic, hyperendemic, and holoendemic baseline endemicity, and calculate EOT probabilities for 2024, 2027, and 2030. Combined VC and MDA reduced prevalence nationwide. After cessation of VC, prevalence continued to decline in hypo-to-mesoendemic areas under annual MDA, while hyperendemic areas required biannual MDA. In holoendemic areas, prevalence rebounded even with biannual MDA, indicating that alternative strategies are needed. EPIONCHO-IBM reproduces Togo’s onchocerciasis trends throughout five decades of intervention and provides a transferable framework to guide policy towards 2030 goals.

DFT investigation of boron- and zinc-doped C24 fullerenes as efficient nanosensors for molly detection

Abstract Reliable detection of 3,4-methylenedioxymethamphetamine (Molly) remains a major analytical challenge due to its widespread recreational use and high risk of combination with toxic substances, as well as the limitations of conventional laboratory methods such as GC-MS and Raman spectroscopy. This study employs Density Functional Theory (DFT), Time-Dependent DFT (TD-DFT), Quantum Theory of Atoms in Molecules (QTAIM), Natural Bond Orbital (NBO), and Non-Covalent Interaction (NCI) analyses to design and evaluate pristine and doped C24 fullerenes (BC23 and ZnC23) as potential colorimetric and electrochemical nanosensors for Molly detection. Computational findings reveal that boron and zinc doping enhance structural stability, with cohesive energies increasing from 149 kcal mol − 1 (C 24 ) to 194 kcal mol − 1 (BC 23 ) and 188 kcal mol − 1 (ZnC 23 ). Electronic analysis shows that doping reduces the HOMO-LUMO gap from 6.12 eV (C 24 ) to 5.68 eV (BC 23 ) and 5.24 eV (ZnC 23 ), improving reactivity and charge transfer. The BC 23 @Molly complex (Conformer 4) exhibited the highest adsorption energy (− 18.19 kcal mol − 1 ) and a remarkable redshift in λmax from 444 to 660 nm, confirming its superior colorimetric sensitivity. Conversely, the ZnC 23 @Molly complex (Conformer 6) demonstrated the fastest recovery time (3.8 × 10 − 4 s) and highest electrical conductivity (2.78 × 10 9 A m − 2 ), identifying it as the most efficient electrochemical sensor. QTAIM and NCI analyses confirmed the presence of medium-strength hydrogen bonding and dispersive interactions, while NBO data revealed strong π→π (21.13 kcal mol − 1 )* and LP→π (55.97 kcal mol − 1 )* transitions in BC 23 @Molly. Collectively, these results establish BC 23 as the most effective colorimetric sensor and ZnC 23 as the optimal electrochemical sensor for rapid, sensitive, and field-deployable Molly detection.

Nanoscale multi-gradient ordered architectures driven exceptional strength and ductility in low thermal expansion magnesium alloy

Propylene glycol and vegetable glycerin e-cigarette aerosols impact mucociliary function and cause cytotoxicity in human airway epithelium

Quantum sensing with spin defects in boron nitride nanotubes

Abstract Spin defects in semiconductors are widely investigated for various applications in quantum sensing. Conventional host materials such as diamond and hexagonal boron nitride (hBN) provide bulk or low-dimensional platforms for optically addressable spin systems, but often lack the structural properties needed for chemical sensing. Here, we introduce a new class of quantum sensors based on naturally occurring spin defects in boron nitride nanotubes (BNNTs), which combine high surface area with omnidirectional spin control, key features for enhanced sensing performance. First, we present strong evidence that these defects consist of weakly-coupled spin pairs, akin to recently identified centers in hBN, and demonstrate coherent spin control over ensembles embedded within randomly oriented, dense, BNNTs networks. Using dynamical decoupling, we enhance spin coherence times by a factor exceeding 300 times and implement high-resolution detection of radiofrequency signals. By integrating the BNNT mesh sensor into a microfluidic platform we demonstrate chemical sensing of paramagnetic ions in solution, with detectable concentrations reaching levels nearly 1000 times lower than previously demonstrated using comparable hBN-based systems. This highly porous and flexible architecture positions BNNTs as a powerful new host material for quantum sensing.

Simultaneous determination of vitamin B12 and cobalt (II) in infant milk power using liquid chromatography-inductively coupled plasma mass spectrometry

Leading predictors and their associations with combination opioid pain therapy in older adults with cancer: Application of machine learning approaches

Combined use of opioids and other pharmacological therapies used for pain management, such as non-steroidal anti-inflammatory drugs (NSAIDs), benzodiazepines, gabapentinoids, and/or skeletal muscle relaxants (SMRs), in older adult cancer survivors can increase the risk of mortality. The objective of this study was to identify the leading predictors of opioid combination therapy with other therapies that may be inappropriate in older adults with cancer using interpretable machine learning approaches. A retrospective cohort design of older (> 66 years at diagnosis) cancer survivors (N = 2,682) diagnosed with primary and incident cancer in 2014. The Surveillance, Epidemiology, and End Results (SEER) cancer registry linked with Medicare claims database was used. Recursive feature elimination with random forest was used to extract the optimal number of features out of 119 for predictive modeling. The eXtreme Gradient Boosting (XGBoost), SHapley Additive exPlanations (SHAP), and global feature importance were used to identify the leading predictors and their associations with opioid combination therapy. Overall, 37.4% of older adults with cancer used opioid combination therapy. We included 34 features in the final predictive model.The predictive model had a good high area under the curve (AUC, 0. 758) and high recall (0.821) with the test dataset. We included 34 features in the final predictive model. Baseline opioids, NSAIDs, benzodiazepines, gabapentinoids, chemotherapy, surgery, and female sex generally positively predicted opioid combination therapy. We observed relationships of zip code percentage residents and Native American residents living below poverty with opioid combination therapy. Patient-level baseline medication use, biological factors, cancer treatment, and zip code-level poverty were leading predictors of opioid combination therapy. Our study findings contribute to the knowledge for targeted interventions to reduce the risk of opioid combination therapy.