Browse Articles

Risk factors for perioperative nerve injury associated with total knee arthroplasty: Analysis of a national administrative database

Introduction Nerve injury related to total knee arthroplasty (TKA) is a rare but serious complication. Previous studies identifying risk factors for nerve injury related to TKA have been constrained by institutional data or small cohorts. The current study utilized a comprehensive, national, administrative database to investigate independent risk factors for nerve injury associated with TKA. Materials and Methods The PearlDiver M161 database was queried for adult TKA procedures performed between 2010 and 2022. Cases with postoperative nerve injury within 90 days of surgery were identified. Factors such as patient age, sex, body mass index (BMI), Elixhauser Comorbidity Index (ECI), fracture indication, and type of surgery (primary vs. revision) were evaluated for their correlation with nerve injury using multivariate analyses. Results Out of 1,517,637 TKA procedures, nerve injury was identified for 4,480 (0.3%). Multivariate analysis identified the following independent risk factors for nerve injury, listed in decreasing order of odds ratio (OR): revision surgery (OR: 1.68), female sex (OR: 1.31), ECI ≥ 5 (OR: 1.27), and younger age (OR: 1.02 per decreasing decade) (P < 0.05 for each). Factors not significantly associated with nerve injury included underweight BMI (<20 kg/m2) and fracture indication. A decreased risk of nerve injury was observed in individuals with a BMI ≥ 35 kg/m2 (OR: 0.80, P = 0.002). Discussion As expected, the incidence of nerve injury following TKA was low at 0.3%. Independent risk factors were identified for this adverse outcome, with the highest risk associated with revision surgeries. These findings, drawn from the largest cohort studied to date, offer valuable insights for risk stratification, and should inform patient discussions.

Radiation enhancement in shear horizontal surface acoustic wave driven magnetoelectric antenna

Magnetoelectric (ME) antennas driven by shear horizontal surface acoustic waves (SH-SAWs) exhibit significant potential for miniaturized high-frequency systems owing to their structural simplicity and acoustic–magnetic coupling characteristics. In this work, we demonstrate an SH-SAW driven ME antenna based on a 42° YX-LiTaO3/FeGa heterostructure, achieving a reference gain of −18.4 dBi and radiation efficiency of 0.52%. Through bias magnetic field modulation, we experimentally observe enhanced coupling between shear horizontal mode and magnetization of FeGa film. These findings provide a method for optimizing radiation efficiency in acoustically driven ME antennas, for the design and performance improvement of ME antennas.

Inflammatory diseases and risk of lung cancer among individuals who have never smoked

Abstract Lung cancer in never-smokers (LCINS) is a leading cause of cancer death globally, but no screening programs for LCINS exist. To identify medical conditions that could serve as markers of LCINS risk, we conducted a nested case-control study within the United Kingdom’s Clinical Practice Research Datalink (CPRD-GOLD), consisting of 1581 LCINS cases and 14,318 never-smoking controls. Conditions significantly associated with LCINS 1-10 years before the index date were validated in an independent dataset, CPRD-Aurum (2188 LCINS cases, 19,597 never-smoking controls). These conditions include Chronic Obstructive Pulmonary Disease/Emphysema (COPD); gastroesophageal reflux disease (GERD); bronchitis and tracheitis; diabetes mellitus type 1; and gastritis and non-infective gastroenteritis and colitis. Adjusting for medication use only slightly attenuated these associations. Overall, inflammatory diseases appear to be important in LCINS pathogenesis although further studies need to confirm these associations. Conditions such as GERD or COPD could be considered as part of eligibility criteria for future LCINS screening programs.

Intelligent and precise auxiliary diagnosis of breast tumors using deep learning and radiomics

Background Breast cancer is the most common malignant tumor among women worldwide, and early diagnosis is crucial for reducing mortality rates. Traditional diagnostic methods have significant limitations in terms of accuracy and consistency. Imaging is a common technique for diagnosing and predicting breast cancer, but human error remains a concern. Increasingly, artificial intelligence (AI) is being employed to assist physicians in reducing diagnostic errors. Methods We developed an intelligent diagnostic model combining deep learning and radiomics to enhance breast tumor diagnosis. The model integrates MobileNet with ResNeXt-inspired depthwise separable and grouped convolutions, improving feature processing and efficiency while reducing parameters. Using AI-Dhabyani and TCIA breast ultrasound datasets, we validated the model internally and externally, comparing it to VGG16, ResNet, AlexNet, and MobileNet. Results: The internal validation set achieved an accuracy of 83.84% with an AUC of 0.92, outperforming other models. The external validation set showed an accuracy of 69.44% with an AUC of 0.75, demonstrating high robustness and generalizability. Conclusions: We developed an intelligent diagnostic model using deep learning and radiomics to improve breast tumor diagnosis. The model combines MobileNet with ResNeXt-inspired depthwise separable and grouped convolutions, enhancing feature processing and efficiency while reducing parameters. It was validated internally and externally using the AI-Dhabyani and TCIA breast ultrasound datasets and compared with VGG16, ResNet, AlexNet, and MobileNet.

Brookite-phase vanadium (IV) oxide formation for semiconductor-to-metal transition free bolometric membrane via atomic layer deposition technique

In this study, we investigated the formation of brookite-phase vanadium (IV) oxide (VO2) thin films, focusing on their potential application in bolometric membranes without semiconductor-to-metal transition (SMT) behavior. Using atomic layer deposition (ALD), we controlled the crystallization of VO2 thin films by varying the deposition temperature from 140 to 250 °C. X-ray diffraction (XRD) and x-ray photoelectron spectroscopy (XPS) analyses revealed that films deposited at 250 °C predominantly crystallized into the brookite phase, while lower temperatures led to monoclinic-phase VO2, which undergoes SMT behavior. Electrical measurements showed that films deposited at 250 °C demonstrated superior bolometric properties with a stable temperature coefficient of resistance (TCR) and minimal 1/f noise, even after thermal stress testing. In contrast, films with higher monoclinic-phase content exhibited significant SMT behavior and degraded performance. These results suggest that brookite-phase VO2 films, free from SMT behavior, are promising candidates for high-performance microbolometer applications.

Spectral physical unclonable functions: downscaling randomness with multi-resonant hybrid particles

Mathematical modelling of inflammatory process and obesity in osteoarthritis

Osteoarthritis (OA) is prevalent in obese people due to the inflamed adipose tissue surrounding the joints. The increase in obesity level upregulates adipokines enhancing inflammation. Whilst a few main inflammatory mediators including cytokines and adipokines have been identified, the multi-effects of obesity and exercise on OA inflammation are elusive. This study aimed to develop a five-variable mathematical model elucidating the dynamics of OA inflammation associated with obesity and physical activity. Within this model, pro- and anti-inflammatory cytokines, adipokines, matrix metalloproteinases and fibronectin fragments interact to regulate the inflammatory process. The damage of cartilage is considered crucial to stimulate the production of fibronectin fragments, subsequently leading to chronic inflammation. The adipokine production is dependent on the obesity level measured by body mass index (BMI). Hill functions are used to describe the interactions (stimulation and inhibition) between mediators and the nonlinear impacts of physical activity level on adiposity. The dynamics of this inflammation system was verified and analysed through bifurcation diagrams. Results indicate that a high BMI reduces the bistability of the system up to a BMI value of 33 for which inflammation is persistent in the non-dimensionalised model. In codimension-2 bifurcations, parameters of adipokine production can govern the transition of system behaviours. This shows the variability of individuals susceptible to OA inflammation related to obesity. The minimum damage leading to persistent inflammation is decreased as BMI increases and the correlation is nonlinear, which suggests a significant rise in OA risk with a high level of obesity. Additionally, the simulations of multiple physical activity intervention strategies suggest that physical activity can minimise and postpone inflammation by downregulating adipokines within a window period after injury. This novel computational model describes the roles of obesity and physical activity in OA inflammation, providing a mathematical framework to evaluate the risk of OA inflammation from the perspective of obesity.

Acoustically coupled reflectors for multiplexed wavefront engineering

Acoustic standing wave fields, with stable pressure nodes and antinodes, are valuable in biology, medicine, chemistry, and engineering due to their noninvasive nature. While standing waves from high-impedance boundaries have been used, these boundaries typically exceed transducer size. The coupling effect between acoustic waves and objects with sizes above the wavelength of the acoustic field on particle/cell manipulation remains unexplored. We combined theoretical analysis with experimental investigation to study reflector effects on acoustic fields for particle manipulation. Our research shows that in the aqueous phase, objects whose dimensions exceed the wavelength scale (∼1λ), with impedances greater than 6 MRayl, can modulate the angular threshold of the acoustic standing wave pattern formation maximum to 55°. This method enables controlled particle and cell patterning with spacing variation conforming to a secant relationship d=λ sec θ. Object size determines the angle range of standing wave control and can locally modify both angle and wavelength. Multiple boundary coupling creates various biocompatible acoustic field modes. This method offers a reliable approach for local acoustic field control with implications for lab-on-a-chip technologies.

Author Correction: Dietary fiber content in clinical ketogenic diets modifies the gut microbiome and seizure resistance in mice

Plastics of the Future? An Interdisciplinary Review on Biobased and Biodegradable Polymers: Progress in Chemistry, Societal Views, and Environmental Implications

AbstractGlobal demand to reduce polymer waste and microplastics pollution has increased in recent years, prompting further research, development, and wider use of biodegradable and biobased polymers (BBPs). BBPs have emerged as promising alternatives to conventional plastics, with the potential to mitigate the environmental burdens of persistent plastic waste. We provide an updated perspective on their impact, five years after our last article, featuring several recent advances, particularly in exploring broader variety of feedstock, applying novel chemical modifications, and developing new functionalities. Life‐cycle assessments reveal that environmental performance of BBPs depends on several factors including feedstock selection, production efficiency, and end‐of‐life management. Furthermore, the introduction of BBPs in several everyday life products has also influenced consumer perception, market dynamics, and regulatory frameworks. Although offering environmental advantages in specific applications, BBPs also raise concerns regarding their biodegradability under varying environmental conditions, potential microplastic generation, and soil health impacts. We highlight the need for a circular approach considering the entire polymer life cycle, from feedstock sourcing, modification and use, to end‐of‐life options. Interdisciplinary research, collaborative initiatives, and informed policymaking are crucial to unlocking the full potential of BBPs and exploiting their contribution to create a circular economy and more sustainable future.

PCB-YOLO: Enhancing PCB surface defect detection with coordinate attention and multi-scale feature fusion

Nowadays, industrial electronic products are integrated into all aspects of life, with PCB quality playing a decisive role in their performance. Ensuring PCB factory quality is thus crucial. Common PCB defects serve as key references for evaluating quality. To address low detection accuracy and the bulky size of existing models, we propose an improved PCB-YOLO model based on YOLOv8n.To reduce model size, we introduce a novel CRSCC module combining SCConv convolution and C2f, enhancing PCB defect detection accuracy and significantly reducing model parameters. For feature fusion, we propose the FFCA attention module, designed to handle PCB surface defect characteristics by fusing multi-scale local features. This improves spatial dependency capture, detail attention, feature resolution, and detection accuracy. Additionally, the WIPIoU loss function is developed to calculate IoU using auxiliary boundaries and address low-quality data, improving small-target recognition and accelerating convergence. Experimental results demonstrate significant improvements in PCB defect detection, with mAP50 increasing by 5.7%, and reductions of 13.3% and 14.8% in model parameters and computational complexity, respectively. Compared to mainstream models, PCB-YOLO achieves the best overall performance. The model’s effectiveness and generalization are further validated on the NEU-DET steel surface defect dataset, achieving excellent results. The PCB-YOLO model offers a practical, efficient solution for PCB and steel defect detection, with broad application prospects.

Generating large complete momentum gaps in temporally modulated dispersive media

This study investigates the generation of a large complete momentum gap (k-gap) in the Lorentzian media with a plasma frequency varying periodically in time. By using the energy expression for the Lorentz model, Maxwell's equations are reformulated as a time-dependent Hamiltonian, allowing an analytical solution of the Floquet band structure. Our simulations reveal a large complete k-gap whose width remains nearly unchanged as the resonance frequency ω0 increases. Numerical analyses further indicate that the k-gap emerges when the modulation frequency Ω approaches the magnitude of unmodulated plasma frequency ωp0. This large k-gap, in contrast to those in previous studies on Floquet systems, suggests potential applications for the developments in non-Hermitian physics or gain mechanisms.

Constraints on sea-level rise during meltwater pulse 1B from the Great Barrier Reef

The effects of low-load resistance training combined with blood flow restriction on knee rehabilitation in middle-aged and elderly patients: A systematic review and meta-analysis

This meta-analysis evaluates the effectiveness of low-load resistance training combined with blood flow restriction in knee rehabilitation. Methods: Randomized controlled trials investigating the effects of blood flow restriction training on knee injury rehabilitation were systematically searched in the PubMed, EBSCO, and Web of Science databases for studies published between January 2000 and May 2024. The Cochrane Risk of Bias Tool was used to assess study quality, and statistical analyses were performed using Review Manager 5.3 software. Results: (1) Compared to low-load control training, blood flow restriction training showed no significant difference in pain scores (standardized mean difference = -0.10, P = 0.46) but significantly improved muscle strength (standardized mean difference = 1.11, P < 0.00001). (2) When compared to high-intensity resistance training, blood flow restriction training demonstrated no significant differences in muscle strength (standardized mean difference = -0.11, P = 0.74) or pain scores (standardized mean difference = -0.84, P = 0.17). (3) Preoperative blood flow restriction training did not significantly improve postoperative pain scores (standardized mean difference = 0.77, P = 0.37); however, among 241 patients undergoing preoperative training, blood flow restriction training significantly enhanced postoperative muscle strength (standardized mean difference = 0.97, P = 0.03). Conclusions: Although blood flow restriction training has limited effects on reducing pain, it significantly improves muscle strength, particularly in preoperative rehabilitation and low-load training settings, making it a valuable alternative in clinical knee rehabilitation strategies.

N-polar gallium nitride doping by atomic layer deposition for improved electrical performance

Atomic layer deposition (ALD) is an excellent growth technique to achieve high-quality, high-uniformity, and highly conformal films with precise growth control at low (<400 °C) substrate temperatures. In this work, ALD was used to deposit low-resistance GaN layers on nitrogen-polar (N-polar) semi-insulating (S.I.) GaN substrates at 300 °C; film conductivity was significantly increased by adding a Si-precursor dose step immediately after the Ga-precursor step in the group III half-cycle and before the nitrogen-based plasma step in the group V half-cycle. Hall measurements revealed remarkably lower resistivities and five orders of magnitude increases in charge density in the Si-doped GaN films (∼3.5 × 1019 cm−3) relative to unintentionally doped films (∼2 × 1014 cm−3), with a Hall mobility of ∼30 cm2/V-s. Moreover, the charge density was further increased to 6.0 × 1019 cm−3 by utilizing a dual plasma process and a sub-saturation dosing of the Ga-precursor prior to the Si dose in the group III half-cycle. The sample surface remained smooth in most experiments.

Mitigating alcohol inhibition of oxide chemiresistors: bilayer sensors with HZSM-5 zeolite overlayers

Multiuser wireless network enhancement via an innovative rime optimization search strategy

This paper introduces an Improved Rime Optimization Algorithm (IROA) designed to maximize achievable rates in multiuser wireless communication networks equipped with Reconfigurable intelligent surfaces (RISs). The proposed technique incorporates the Quadratic Interpolation Method (QIM) into the classic Rime Optimization Algorithm (ROA), which improves solution diversity, facilitates broader exploration of the search space, and enhances robustness against local optima. Finding the ideal quantity and positioning of RIS components to optimize system performance is the main goal of the optimization framework. Two objective models are taken into consideration: one that maximizes the lowest achievable rate in order to prioritize fairness, and another that maximizes the average achievable rate for all users. The performance of IROA is evaluated on systems with 20 and 50 users and compared against established algorithms such as Differential Evolution (DE), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Augmented Jellyfish Search Optimization Algorithm (AJFSOA), and Jellyfish Search Optimization Algorithm (JFSOA). Results demonstrate that the proposed IROA achieves relative performance improvements ranging from 5% to 46% across different scenarios and objective models. In the 20-user case with the first objective model, IROA achieves improvements of 28.02%, 42.07%, 46.54%, 1.74%, 35.46%, and 25.95% compared to AJFSOA, JFSOA, PSO, ROA, GWO, and DE, respectively, in terms of average achievable rate. Similarly, for the second objective model, IROA achieves relative improvements of 5.94%, 13.29%, 14.55%, 7.1%, 15.97%, and 46.26% over ROA, DE, PSO, AJFSOA, JFSOA, and GWO, respectively, in terms of minimum achievable rate. On contrary, the IROA shows lower standard deviation compared to the current ROA. However, the proposed IROA achieves superior performance over ROA in terms of the best, mean and worst objective outcomes. These findings demonstrate that in RIS-assisted wireless communication networks, the suggested IROA achieves strong flexibility and reliable performance benefits across a range of multiuser optimization tasks.

Giant tunneling magnetoresistance effect of van der Waals magnetic tunnel junction Fe3GaTe2/InSe/Fe3GaTe2

Van der Waals (vdW) magnetic tunnel junctions (MTJs), with a two-dimensional (2D) material barrier between two vdW ferromagnetic electrodes, present unprecedented opportunities to design innovative spintronic devices. In this study, we employ density functional theory and non-equilibrium Green's function methods to investigate the spin-dependent electronic transport properties of a vdW MTJ, Fe3GaTe2/InSe/Fe3GaTe2. The MTJ with a monolayer InSe barrier demonstrates nearly 100% spin filtering and a large tunneling magnetoresistance (TMR) of 7.48 × 105%, where the resistance changes nearly 10 000% as the magnetization alignment of the electrodes transitions from parallel (P) to antiparallel. When the barrier layer increases from monolayer InSe to bilayer InSe, the TMR ratio (3.64 × 107%) is significantly enhanced. The large TMR originates from the high spin polarization of the magnetic electrodes, Fe3GaTe2. Our results highlight that room-temperature vdW MTJs pave the way for potential applications of nonvolatile spintronic devices.

Machine learning-powered activatable NIR-II fluorescent nanosensor for in vivo monitoring of plant stress responses

Low‐Entropy and Fast‐Li⁺‐Conducting Electrolyte With Cascade Reaction‐Induced Robust Interphase for Fast‐Charging Lithium Metal Batteries

AbstractLithium metal is considered the most promising next‐generation anode, while its fast‐charging application is hindered by the problems of dendrite growth and side reactions. Herein, a low‐entropy electrolyte with cascade reaction‐induced stable interphase and fast Li+ transport kinetics is proposed to provide excellent fast‐charging performance of lithium metal batteries. In this electrolyte with an extremely simple formula, only two co‐solvents with a lithium salt are used without any other additives. Fast lithium transport and desolvation kinetics are maintained, owing to the solvents’ low viscosity and weak interaction with lithium. Particularly, the intermediate products of lithium salt can further promote rapid defluorination of the F‐containing solvent to form LiF, constructing a LiF‐rich interphase. Fast Li+ transport kinetics and robust interphase enable the fast‐cycling performance of lithium metal anodes. The Li||Li symmetric cell can even withstand a high current density of 10 mA cm−2. Good cycling stability under fast charging is also achieved with a capacity of 123 mAh g−1 maintained with a capacity retention rate reaching 90% after 200 cycles at a charging rate of 6C. Our results demonstrate a simple yet effective electrolyte design strategy facilitating the fast cycling of lithium metal batteries.