Clinical diagnostic advances in intestinal anastomotic techniques:Hand suturing,stapling,and compression devices

The development of intestinal anastomosis techniques,including hand suturing,stapling,and compression anastomoses,has been a significant advancement in surgical practice.These methods aim to prevent leakage and minimize tissue fibrosis,which can lead to stricture formation.The healing process involves various phases:hemostasis and inflammation,proliferation,and remodeling.Mechanical staplers and sutures can cause inflammation and fibrosis due to the release of profibrotic chemokines.Compression anastomosis devices,including those made of nickel-titanium alloy,offer a minimally invasive option for various surgical challenges and have shown safety and efficacy.However,despite advancements,anastomotic techniques are evaluated based on leakage risk,with complications being a primary concern.Newer devices like Magnamosis use magnetic rings for compression anastomosis,demonstrating greater strength and patency compared to stapling.Magnetic technology is also being explored for other medical treatments.While there are promising results,particularly in animal models,the realworld application in humans is limited,and further research is needed to assess their safety and practicality.

Exploring battery material failure mechanisms through synchrotron X-ray characterization techniques

Rechargeable battery cycling performance and related safety have been persistent concerns.It is crucial to decipher the capacity fading induced by electrode material failure via a range of techniques.Among these,synchrotron-based X-ray techniques with high flux and brightness play a key role in understanding degradation mechanisms.In this comprehensive review,we summarize recent advancements in degra-dation modes and mechanisms that were revealed by synchrotron X-ray methodologies.Subsequently,an overview of X-ray absorption spectroscopy and X-ray scattering techniques is introduced for charac-terizing failure phenomena at local coordination atomic environment and long-range order crystal struc-ture scale,respectively.At last,we envision the future of exploring material failure mechanism.

Orchard Sports Injury and Illness Classification System (OSIICS) Version 15

Background:Sports medicine(injury and illnesses)requires distinct coding systems because the International Classification of Diseases is insuf-ficient for sports medicine coding.The Orchard Sports Injury and Illness Classification System(OSIICS)is one of two sports medicine coding systems recommended by the International Olympic Committee.Regular updates of coding systems are required.Methods:For Version 15,updates for mental health conditions in athletes,sports cardiology,concussion sub-types,infectious diseases,and skin and eye conditions were considered particularly important.Results:Recommended codes were added from a recent International Olympic Committee consensus statement on mental health conditions in athletes.Two landmark sports cardiology papers were used to update a more comprehensive list of sports cardiology codes.Rugby union protocols on head injury assessment were used to create additional concussion codes.Conclusion:It is planned that OSIICS Version 15 will be translated into multiple new languages in a timely fashion to facilitate international accessibility.The large number of recently published sport-specific and discipline-specific consensus statements on athlete surveillance warrant regular updating of OSIICS.

A marathon,not a sprint:Increasing population physical activity as a legacy of sports mega-events

1.Legacy matters The withdrawal of Australia as hosts for the Commonwealth Games in 2026 demonstrates that large international sporting events are more than just sporting mastery,medals,and memories.Money also matters.1 The cost of hosting a major international tournament is eye-wateringly high.Host governments need to show that the billions of dollars of investment deliver a return beyond sporting glory to justify the resources that are spent.

The Application of Analytical Techniques to Alpha-Synuclein in Parkinson’s Disease

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms such as tremors, rigidity, and bradykinesia, as well as non-motor symptoms including cognitive impairment and mood disorders. A hallmark of PD is the accumulation of alpha-synuclein, a presynaptic neuronal protein that aggregates to form Lewy bodies, leading to neuronal dysfunction and cell death. The study of alpha-synuclein and its pathological forms is crucial for understanding the etiology of PD and developing effective diagnostic and therapeutic strategies. Analytical techniques play a pivotal role in elucidating the structure, function, and aggregation mechanisms of alpha-synuclein. Biochemical methods such as Western blotting and enzyme-linked immunosorbent assay (ELISA) are employed to detect and quantify alpha-synuclein in biological samples, offering insights into its expression levels and post-translational modifications. Imaging techniques like immunohistochemistry and positron emission tomography (PET) allow for the visualization of alpha-synuclein aggregates in tissue samples and in vivo, respectively, facilitating the study of its spatial distribution and progression in PD Spectroscopic methods, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry, provide detailed structural information on alpha-synuclein and its isoforms, aiding in the identification of conformational changes associated with aggregation. Emerging techniques such as cryo-electron microscopy (Cryo-EM) and single-molecule fluorescence enable high-resolution structural analysis and real-time monitoring of alpha-synuclein aggregation dynamics, respectively. The application of these analytical techniques has significantly advanced our understanding of the pathophysiological role of alpha-synuclein in PD. They have contributed to the identification of potential biomarkers for early diagnosis and the evaluation of therapeutic interventions targeting alpha-synuclein aggregation. Despite technical limitations and challenges in clinical translation, ongoing advancements in analytical methodologies hold promise for improving the diagnosis, monitoring, and treatment of Parkinson’s disease through a deeper understanding of alpha-synuclein pathology.

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

Epileptic brain network mechanisms and neuroimaging techniques for the brain network

Epilepsy can be defined as a dysfunction of the brain network,and each type of epilepsy involves different brain-network changes that are implicated diffe rently in the control and propagation of interictal or ictal discharges.Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice.An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tra ctography,diffusion kurtosis imaging-based fiber tractography,fiber ball imagingbased tra ctography,electroencephalography,functional magnetic resonance imaging,magnetoencephalography,positron emission tomography,molecular imaging,and functional ultrasound imaging have been extensively used to delineate epileptic networks.In this review,we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy,and extensively analyze the imaging mechanisms,advantages,limitations,and clinical application ranges of each technique.A greater focus on emerging advanced technologies,new data analysis software,a combination of multiple techniques,and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.

Surgical techniques to prevent delayed gastric emptying after pancreaticoduodenectomy

Background: Delayed gastric emptying(DGE) is one of the most common complications after pancreaticoduodenectomy(PD). DGE represents impaired gastric motility without significant mechanical obstruction and is associated with an increased length of hospital stay, increased healthcare costs, and a high readmission rate. We reviewed published studies on various technical modifications to reduce the incidence of DGE. Data sources: Studies were identified by searching Pub Med for relevant articles published up to December 2022. The following search terms were used: “pancreaticoduodenectomy”, “pancreaticojejunostomy”, “pancreaticogastrostomy”, “gastric emptying”, “gastroparesis” and “postoperative complications”. The search was limited to English publications. Additional articles were identified by a manual search of references from key articles. Results: In recent years, various surgical procedures and techniques have been explored to reduce the incidence of DGE. Pyloric resection, Billroth II reconstruction, Braun's enteroenterostomy, and antecolic reconstruction may be associated with a decreased incidence of DGE, but more high-powered studies are needed in the future. Neither laparoscopic nor robotic surgery has demonstrated superiority in preventing DGE, and the use of staplers is controversial regarding whether they can reduce the incidence of DGE. Conclusions: Despite many innovations in surgical techniques, there is no surgical procedure that is superior to others to reduce DGE. Further larger prospective randomized studies are needed.

Novel fabrication techniques for ultra-thin silicon based flexible electronics

Flexible electronics offer a multitude of advantages,such as flexibility,lightweight property,portability,and high durability.These unique properties allow for seamless applications to curved and soft surfaces,leading to extensive utilization across a wide range of fields in consumer electronics.These applications,for example,span integrated circuits,solar cells,batteries,wearable devices,bio-implants,soft robotics,and biomimetic applications.Recently,flexible electronic devices have been developed using a variety of materials such as organic,carbon-based,and inorganic semiconducting materials.Silicon(Si)owing to its mature fabrication process,excellent electrical,optical,thermal properties,and cost efficiency,remains a compelling material choice for flexible electronics.Consequently,the research on ultra-thin Si in the context of flexible electronics is studied rigorously nowadays.The thinning of Si is crucially important for flexible electronics as it reduces its bending stiffness and the resultant bending strain,thereby enhancing flexibility while preserving its exceptional properties.This review provides a comprehensive overview of the recent efforts in the fabrication techniques for forming ultra-thin Si using top-down and bottom-up approaches and explores their utilization in flexible electronics and their applications.

Recent Advance in Synaptic Plasticity Modulation Techniques for Neuromorphic Applications

Manipulating the expression of synaptic plasticity of neuromorphic devices provides fascinating opportunities to develop hardware platforms for artifi-cial intelligence.However,great efforts have been devoted to exploring biomimetic mechanisms of plasticity simulation in the last few years.Recent progress in various plasticity modulation techniques has pushed the research of synaptic electronics from static plasticity simulation to dynamic plasticity modulation,improving the accuracy of neuromorphic computing and providing strategies for implementing neuromorphic sensing functions.Herein,several fascinating strategies for synap-tic plasticity modulation through chemical techniques,device structure design,and physical signal sensing are reviewed.For chemical techniques,the underly-ing mechanisms for the modification of functional materials were clarified and its effect on the expression of synaptic plasticity was also highlighted.Based on device structure design,the reconfigurable operation of neuromorphic devices was well demonstrated to achieve programmable neuromorphic functions.Besides,integrating the sensory units with neuromorphic processing circuits paved a new way to achieve human-like intelligent perception under the modulation of physical signals such as light,strain,and temperature.Finally,considering that the relevant technology is still in the basic exploration stage,some prospects or development suggestions are put forward to promote the development of neuromorphic devices.

Pretreatment and analysis techniques development of TKIs in biological samples for pharmacokinetic studies and therapeutic drug monitoring

Tyrosine kinase inhibitors(TKIs)have emerged as the first-line small molecule drugs in many cancer therapies,exerting their effects by impeding aberrant cell growth and proliferation through the modulation of tyrosine kinase-mediated signaling pathways.However,there exists a substantial inter-individual variability in the concentrations of certain TKIs and their metabolites,which may render patients with compromised immune function susceptible to diverse infections despite receiving theoretically efficacious anticancer treatments,alongside other potential side effects or adverse reactions.Therefore,an urgent need exists for an up-to-date review concerning the biological matrices relevant to bioanalysis and the sampling methods,clinical pharmacokinetics,and therapeutic drug monitoring of different TKIs.This paper provides a comprehensive overview of the advancements in pretreatment methods,such as protein precipitation(PPT),liquid-liquid extraction(LLE),solid-phase extraction(SPE),micro-SPE(μ-SPE),magnetic SPE(MSPE),and vortex-assisted dispersive SPE(VA-DSPE)achieved since 2017.It also highlights the latest analysis techniques such as newly developed high performance liquid chromatography(HPLC)and high-resolution mass spectrometry(HRMS)methods,capillary electrophoresis(CE),gas chromatography(GC),supercritical fluid chromatography(SFC)procedures,surface plasmon resonance(SPR)assays as well as novel nanoprobes-based biosensing techniques.In addition,a comparison is made between the advantages and disadvantages of different approaches while presenting critical challenges and prospects in pharmacokinetic studies and therapeutic drug monitoring.

An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart environments.To address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed analysis.This analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

Isothermal hydrogen absorption process of Pd-capped Mg films traced by ion beam techniques and optical methods

Pd-capped nanocrystalline Mg films were prepared by electron beam evaporation and hydrogenated under isothermal conditions to inves-tigate the hydrogen absorption process via ion beam techniques and in situ optical methods.Films were characterized by different techniques such as X-ray diffraction(XRD)and scanning electron microscopy(SEM).Rutherford backscattering spectrometry(RBS)and elastic recoil detection analysis(ERDA)provided a detailed compositional depth profile of the films during hydrogenation.Gas-solid reaction kinetics theory applied to ERDA data revealed a H absorption mechanism controlled by H diffusion.This rate-limiting step was also confirmed by XRD measurements.The diffusion coefficient(D)was also determined via RBS and ERDA,with a value of(1.1±0.1)·10^(−13)cm^(2)/s at 140℃.Results confirm the validity of IBA to monitor the hydrogenation process and to extract the control mechanism of the process.The H kinetic information given by optical methods is strongly influenced by the optical absorption of the magnesium layer,revealing that thinner films are needed to extract further and reliable information from that technique.

Randomization Strategies in Image Steganography Techniques:A Review

Image steganography is one of the prominent technologies in data hiding standards.Steganographic system performance mostly depends on the embedding strategy.Its goal is to embed strictly confidential information into images without causing perceptible changes in the original image.The randomization strategies in data embedding techniques may utilize random domains,pixels,or region-of-interest for concealing secrets into a cover image,preventing information from being discovered by an attacker.The implementation of an appropriate embedding technique can achieve a fair balance between embedding capability and stego image imperceptibility,but it is challenging.A systematic approach is used with a standard methodology to carry out this study.This review concentrates on the critical examination of several embedding strategies,incorporating experimental results with state-of-the-art methods emphasizing the robustness,security,payload capacity,and visual quality metrics of the stego images.The fundamental ideas of steganography are presented in this work,along with a unique viewpoint that sets it apart from previous works by highlighting research gaps,important problems,and difficulties.Additionally,it offers a discussion of suggested directions for future study to advance and investigate uncharted territory in image steganography.

An integrated spatial planning of the mountainous landscapes for ski sports in a case area at the eastern Türkiye

Mountainous regions have disadvantages in economic development because of harsh physical and climatic conditions.However,winter tourism activities are one of the key components for supporting economic development in the highlands.Establishing a ski resort area supports direct and indirect employment in a region,and it stops immigration from mountainous regions to other places.This research aimed to assess the potential ski areas using a multi criteria evaluation technique in the Van region which is located in the eastern part of Türkiye.In this context,snow cover duration,sun effect,slope,slope length,elevation,population density,distance from main roads and lake visibility were used as input factors in the decision making process.Each factor was standardized using a fuzzy technique based on existing well-known ski centers in Türkiye.The weight of inputs was defined by applying a survey to the professional skiers.The most important factors were detected as transportation opportunities and snow covers whereas,the least important factors were elevation and population density.Additionally,lake visibility was very important to make a difference from other existing facilities in the region.Therefore,it was included as constraints and lake visible areas were extracted at the final stage of the research.Potential ski areas were mapped in three levels as professional,intermediate and beginner skiers.One of the suitable areas was selected as a sample projection and for the 3D simulation of the ski investment area.Potential costs and benefits were discussed.It was found that a ski tourism area investment can be amortized in 3 years in the region.

Software Defect Prediction Using Hybrid Machine Learning Techniques: A Comparative Study

When a customer uses the software, then it is possible to occur defects that can be removed in the updated versions of the software. Hence, in the present work, a robust examination of cross-project software defect prediction is elaborated through an innovative hybrid machine learning framework. The proposed technique combines an advanced deep neural network architecture with ensemble models such as Support Vector Machine (SVM), Random Forest (RF), and XGBoost. The study evaluates the performance by considering multiple software projects like CM1, JM1, KC1, and PC1 using datasets from the PROMISE Software Engineering Repository. The three hybrid models that are compared are Hybrid Model-1 (SVM, RandomForest, XGBoost, Neural Network), Hybrid Model-2 (GradientBoosting, DecisionTree, LogisticRegression, Neural Network), and Hybrid Model-3 (KNeighbors, GaussianNB, Support Vector Classification (SVC), Neural Network), and the Hybrid Model 3 surpasses the others in terms of recall, F1-score, accuracy, ROC AUC, and precision. The presented work offers valuable insights into the effectiveness of hybrid techniques for cross-project defect prediction, providing a comparative perspective on early defect identification and mitigation strategies. .

Advances in the application of auxiliary imaging techniques in parathyroid diseases

Hypoparathyroidism is one of the main complications after total thyroidectomy,severely affecting patients’quality of life.How to effectively protect parathyroid function after surgery and reduce the incidence of hypoparathyroidism has always been a key research area in thyroid surgery.Therefore,precise localization of parathyroid glands during surgery,effective imaging,and accurate surgical resection have become hot topics of concern for thyroid surgeons.In response to this clinical phenomenon,this study compared several different imaging methods for parathyroid surgery,including nanocarbon,indocyanine green,near-infrared imaging techniques,and technetium-99m methoxyisobutylisonitrile combined with gamma probe imaging technology.The advantages and disadvantages of each method were analyzed,providing scientific recommendations for future parathyroid imaging.In recent years,some related basic and clinical research has also been conducted in thyroid surgery.This article reviewed relevant literature and provided an overview of the practical application progress of various imaging techniques in parathyroid surgery.

Discrete Choice Models and Artificial Intelligence Techniques for Predicting the Determinants of Transport Mode Choice--A Systematic Review

Forecasting travel demand requires a grasp of individual decision-making behavior.However,transport mode choice(TMC)is determined by personal and contextual factors that vary from person to person.Numerous characteristics have a substantial impact on travel behavior(TB),which makes it important to take into account while studying transport options.Traditional statistical techniques frequently presume linear correlations,but real-world data rarely follows these presumptions,which may make it harder to grasp the complex interactions.Thorough systematic review was conducted to examine how machine learning(ML)approaches might successfully capture nonlinear correlations that conventional methods may ignore to overcome such challenges.An in-depth analysis of discrete choice models(DCM)and several ML algorithms,datasets,model validation strategies,and tuning techniques employed in previous research is carried out in the present study.Besides,the current review also summarizes DCM and ML models to predict TMC and recognize the determinants of TB in an urban area for different transport modes.The two primary goals of our study are to establish the present conceptual frameworks for the factors influencing the TMC for daily activities and to pinpoint methodological issues and limitations in previous research.With a total of 39 studies,our findings shed important light on the significance of considering factors that influence the TMC.The adjusted kernel algorithms and hyperparameter-optimized ML algorithms outperform the typical ML algorithms.RF(random forest),SVM(support vector machine),ANN(artificial neural network),and interpretable ML algorithms are the most widely used ML algorithms for the prediction of TMC where RF achieved an R2 of 0.95 and SVM achieved an accuracy of 93.18%;however,the adjusted kernel enhanced the accuracy of SVM 99.81%which shows that the interpretable algorithms outperformed the typical algorithms.The sensitivity analysis indicates that the most significant parameters influencing TMC are the age,total trip time,and the number of drivers.

Breeding and Seed Reproduction Techniques of a New Tetraploid Common Buckwheat Variety‘Pintianqiao 3’

[Objectives]This study was conducted to actively carry out the breeding of new tetraploid common buckwheat varieties and its supporting breeding techniques.[Methods]Pintianqiao 3 is a new tetraploid common buckwheat variety developed by College of Agriculture of Shanxi Agricultural University and Agricultural Genetic Resources Center of Shanxi Agricultural University,using‘Pintianqiao 1’as the parent,through mutation treatment with 0.2%colchicine aqueous solution,grain selection,plant selection,isolation and identification,variety comparison,regional test and field investigation.The variety has chromosomes 2n=4X=32,and shows a spring sowing period of 101 d and a summer sowing period of 80 d,large flowers and seeds(with a 1000-grain weight of 41.4 g),and good resistance to lodging.[Results]From 2021 to 2022,Pintianqiao 3 participated in the independent joint regional test of common buckwheat varieties in Shanxi Province,and the average yield in 10 test positions was 1.8 kg,equivalent to 1800 kg/hm^(2),which was 8.4%higher than the control.It passed the field investigation conducted by Shanxi provincial expert group for identification of non-major crop varieties in Dongyang and Kelan experimental sites on September 2-3,2022.On January 4,2024,it passed the preliminary examination of Shanxi Provincial Crop Variety Approval Committee.The seed reproduction technique of Pintianqiao 3 including land selection,preparation before sowing,sowing,field management and timely harvesting has been developed.[Conclusions]This study provides technical support for the demonstration and popularization of this new variety.

Eight of ten patients return to daily activities, work, and sports after total knee arthroplasty

BACKGROUND Besides return to work(RTW)and return to sports(RTS),patients also prefer to return to daily activities(RTA)such as walking,sleeping,grocery shopping,and domestic work following total knee arthroplasty(TKA).However,evidence on the timelines and probability of patients’RTA is sparse.AIM To assess the percentage of patients able to RTA,RTW,and RTS after TKA,as well as the timeframe and influencing factors of this return.METHODS A retrospective cohort study with prospectively collected data was conducted at a medium-sized Dutch orthopedic hospital.Assessments of RTA,RTW,and RTS were performed at 3 mo and/or 6 mo following TKA.Investigated factors en-compassed patient characteristics,surgical characteristics,and preoperative patient-reported outcomes.RESULTS TKA patients[n=2063;66 years old(interquartile range[IQR]:7 years);47%male;28 kg/m2(IQR:4 kg/m2)]showed RTA ranging from 28%for kneeling to 94%for grocery shopping,with 20 d(IQR:27 d)spent for putting on shoes to 74 d(IQR:57 d)for kneeling.RTW rates varied from 62%for medium-impact work to 87%for low-impact work,taking 33 d(IQR:29 d)to 78 d(IQR:55 d).RTS ranged from 48%for medium-impact sports to 90%for low-impact sports,occurring within 43 d(IQR:24 d)to 90 d(IQR:60 d).One or more of the investigated factors influenced the return to each of the 14 activities examined,with R²values ranging from 0.013 to 0.127.CONCLUSION Approximately 80%of patients can RTA,RTW,and RTS within 6 mo after TKA.Return is not consistently in-fluenced by predictive factors.Results help set realistic pre-and postoperative expectations.