Meta-Analysis of Invasive versus Non-Invasive Techniques to Predict Fluid Responsiveness by Passive Leg Raising in the Critically Ill

Objective: To analyze the accuracy and specificity of recent studies to compare the ability of predicting fluid responsiveness with Passive Leg Raising (PLR) by using invasive or non-invasive techniques during passive leg raising. Data Sources: MEDLINE, EMBASE and the Cochrane Database of Systematic Reviews were systematically searched. Study Selection: Clinical trials that reported the sensitivity, specificity and area under the receiver operating characteristic curve (AUC) between the responder and non-responder induced by passive leg raising and Volume Expansion (VE) in critical ill patients were selected. 246 studies were screened, 14 studies were included for data extraction, which met our inclusion criteria. Data Extraction: Data were abstracted on study characteristics, patient population, type and amount of VE, time of VE, definition of responders, position, techniques used for measuring hemodynamic change, number and percentage of responders, the correlation coefficient, sensitivity, specificity, best threshold and area under the ROC curve (AUC). Meta-analytic techniques were used to summarize the data. Data Synthesis: A total of 524 critical ill patients from 14 studies were analyzed. Data are reported as point estimate (95% confidence intervals). The pooled sensitivity and specificity of invasive techniques were 80% (73% - 85%) and 89% (84% - 93%) respectively with the area under the sROC of 0.94. While, the pooled sensitivity and specificity of non-invasive techniques were 88% (84% - 92%) and 91% (86% - 94%) respectively with the area under the sROC of 0.95. The pooled DOR of invasive techniques was 32.2 (13.6 - 76.8), which was much lower than that of non-invasive techniques with the value of 64.3 (33.9 - 121.7). Conclusions: The hemodynamic indexes changes induced by PLR could reliably predict fluid responsiveness. Non-invasive hemodynamic techniques with their accuracy and safety can benefit the daily work in ICUs. Because the number of patients included in the present trials was small, further studies should be undertaken to confirm these findings.

Non-invasive evaluation of liver steatosis with imaging modalities:New techniques and applications

In the world,nonalcoholic fatty liver disease(NAFLD)accounts for majority of diffuse hepatic diseases.Notably,substantial liver fat accumulation can trigger and accelerate hepatic fibrosis,thus contributing to disease progression.Moreover,the presence of NAFLD not only puts adverse influences for liver but is also associated with an increased risk of type 2 diabetes and cardiovascular diseases.Therefore,early detection and quantified measurement of hepatic fat content are of great importance.Liver biopsy is currently the most accurate method for the evaluation of hepatic steatosis.However,liver biopsy has several limitations,namely,its invasiveness,sampling error,high cost and moderate intraobserver and interobserver reproducibility.Recently,various quantitative imaging techniques have been developed for the diagnosis and quantified measurement of hepatic fat content,including ultrasound-or magnetic resonancebased methods.These quantitative imaging techniques can provide objective continuous metrics associated with liver fat content and be recorded for comparison when patients receive check-ups to evaluate changes in liver fat content,which is useful for longitudinal follow-up.In this review,we introduce several imaging techniques and describe their diagnostic performance for the diagnosis and quantified measurement of hepatic fat content.

Functional near-infrared spectroscopy in non-invasive neuromodulation

Non-invasive cerebral neuromodulation technologies are essential for the reorganization of cerebral neural networks,which have been widely applied in the field of central neurological diseases,such as stroke,Parkinson’s disease,and mental disorders.Although significant advances have been made in neuromodulation technologies,the identification of optimal neurostimulation paramete rs including the co rtical target,duration,and inhibition or excitation pattern is still limited due to the lack of guidance for neural circuits.Moreove r,the neural mechanism unde rlying neuromodulation for improved behavioral performance remains poorly understood.Recently,advancements in neuroimaging have provided insight into neuromodulation techniques.Functional near-infrared spectroscopy,as a novel non-invasive optical brain imaging method,can detect brain activity by measuring cerebral hemodynamics with the advantages of portability,high motion tole rance,and anti-electromagnetic interference.Coupling functional near-infra red spectroscopy with neuromodulation technologies offe rs an opportunity to monitor the cortical response,provide realtime feedbac k,and establish a closed-loop strategy integrating evaluation,feedbac k,and intervention for neurostimulation,which provides a theoretical basis for development of individualized precise neuro rehabilitation.We aimed to summarize the advantages of functional near-infra red spectroscopy and provide an ove rview of the current research on functional near-infrared spectroscopy in transcranial magnetic stimulation,transcranial electrical stimulation,neurofeedback,and braincomputer interfaces.Furthermore,the future perspectives and directions for the application of functional near-infrared spectroscopy in neuromodulation are summarized.In conclusion,functional near-infrared spectroscopy combined with neuromodulation may promote the optimization of central pellral reorganization to achieve better functional recovery form central nervous system diseases.

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.

FibroScan-aspartate transaminase:A superior non-invasive model for diagnosing high-risk metabolic dysfunction-associated steatohepatitis

BACKGROUND Non-alcoholic fatty liver disease(NAFLD)with hepatic histological NAFLD activity score≥4 and fibrosis stage F≥2 is regarded as“at risk”non-alcoholic steatohepatitis(NASH).Based on an international consensus,NAFLD and NASH were renamed as metabolic dysfunction-associated steatotic liver disease(MASLD)and metabolic dysfunction-associated steatohepatitis(MASH),respectively;hence,we introduced the term“high-risk MASH”.Diagnostic values of seven non-invasive models,including FibroScan-aspartate transaminase(FAST),fibrosis-4(FIB-4),aspartate transaminase to platelet ratio index(APRI),etc.for high-risk MASH have rarely been studied and compared in MASLD.AIM To assess the clinical value of seven non-invasive models as alternatives to liver biopsy for diagnosing high-risk MASH.METHODS A retrospective analysis was conducted on 309 patients diagnosed with NAFLD via liver biopsy at Beijing Ditan Hospital,between January 2012 and December 2020.After screening for MASLD and the exclusion criteria,279 patients wereincluded and categorized into high-risk and non-high-risk MASH groups.Utilizing threshold values of each model,sensitivity,specificity,positive predictive value(PPV),and negative predictive values(NPV),were calculated.Receiver operating characteristic curves were constructed to evaluate their diagnostic efficacy based on the area under the curve(AUROC).RESULTS MASLD diagnostic criteria were met by 99.4%patients with NAFLD.The MASLD population was analyzed in two cohorts:Overall population(279 patients)and the subgroup(117 patients)who underwent liver transient elastography(FibroScan).In the overall population,FIB-4 showed better diagnostic efficacy and higher PPV,with sensitivity,specificity,PPV,NPV,and AUROC of 26.9%,95.2%,73.5%,72.2%,and 0.75.APRI,Forns index,and aspartate transaminase to alanine transaminase ratio(ARR)showed moderate diagnostic efficacy,whereas S index and gamma-glutamyl transpeptidase to platelet ratio(GPR)were relatively weaker.In the subgroup,FAST had the highest diagnostic efficacy,its sensitivity,specificity,PPV,NPV,and AUROC were 44.2%,92.3%,82.1%,67.4%,and 0.82.The FIB-4 AUROC was 0.76.S index and GPR exhibited almost no diagnostic value for high-risk MASH.CONCLUSION FAST and FIB-4 could replace liver biopsy as more effectively diagnostic methods for high-risk MASH compared to APRI,Forns index,ARR,S index,and GPR;FAST is superior to FIB-4.

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.

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.

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.

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. .

Research Progress on Detection Techniques of Fungicide Residues in Chinese Chives

Chinese chive is a kind of medicinal and edible plant,with many diseases,and chemical fungicides are usually used for control.In order to find out the risk of pesticide residues in Chinese chives,this paper summarized relevant literatures published in recent years,and sorted out and analyzed the types of pesticides used and detection techniques of common diseases in Chinese chives.

Economic Power Dispatching from Distributed Generations: Review of Optimization Techniques

In the increasingly decentralized energy environment,economical power dispatching from distributed generations(DGs)is crucial to minimizing operating costs,optimizing resource utilization,and guaranteeing a consistent and sustainable supply of electricity.A comprehensive review of optimization techniques for economic power dispatching from distributed generations is imperative to identify the most effective strategies for minimizing operational costs while maintaining grid stability and sustainability.The choice of optimization technique for economic power dispatching from DGs depends on a number of factors,such as the size and complexity of the power system,the availability of computational resources,and the specific requirements of the application.Optimization techniques for economic power dispatching from distributed generations(DGs)can be classified into two main categories:(i)Classical optimization techniques,(ii)Heuristic optimization techniques.In classical optimization techniques,the linear programming(LP)model is one of the most popular optimization methods.Utilizing the LP model,power demand and network constraints are met while minimizing the overall cost of generating electricity from DGs.This approach is efficient in determining the best DGs dispatch and is capable of handling challenging optimization issues in the large-scale system including renewables.The quadratic programming(QP)model,a classical optimization technique,is a further popular optimization method,to consider non-linearity.The QP model can take into account the quadratic cost of energy production,with consideration constraints like network capacity,voltage,and frequency.The metaheuristic optimization techniques are also used for economic power dispatching from DGs,which include genetic algorithms(GA),particle swarm optimization(PSO),and ant colony optimization(ACO).Also,Some researchers are developing hybrid optimization techniques that combine elements of classical and heuristic optimization techniques with the incorporation of droop control,predictive control,and fuzzy-based methods.These methods can deal with large-scale systems with many objectives and non-linear,non-convex optimization issues.The most popular approaches are the LP and QP models,while more difficult problems are handled using metaheuristic optimization techniques.In summary,in order to increase efficiency,reduce costs,and ensure a consistent supply of electricity,optimization techniques are essential tools used in economic power dispatching from DGs.

Urethroplasty among Elderly Men, Surgical Techniques and Outcomes

Introduction: Urethroplasty remains the gold standard for the management of urethral stricture. However, the treatment of stricture disease in the elderly tends to be less invasive due to the presumption that they might not be able to stand long hours of surgery and might have higher rates of recurrence due to poor wound healing from microangiopathy. We present our experience with the outcomes of urethroplasty among elderly men seen at the Komfo Anokye Teaching Hospital from January 2012 to December 2021. Methods: This was a retrospective review of data captured in the urology database on all patients 65 years and above who underwent urethroplasty at the hospital over the study period. Data was obtained on patients’ demographics, stricture characteristics, urethroplasty technique, and outcome. A successful outcome was defined as peak flow rate > 15 mls/s, a patent urethra on retrograde urethrogram, patient satisfaction with urine stream, or restoration of the normal stream of urine with only one attempt at urethral calibration or internal urethrotomy postoperatively. Data was analyzed using PASW Statistics for Windows, Version 18.0. Results: Overall, 43 urethroplasties were done over the study period in elderly men. The age range was 65 to 87 years. The commonest aetiology was catheterization (62.79%) followed by urethritis (32.56%). Stricture length ranged from 0.5 cm to 16 cm with a mean of 3.93 cm. Most patients (60.46%) had bulbar urethral strictures. The repair methods employed were anastomotic urethroplasty (62.80%), fasciocutaneous flap (FCF) ventral onlay (13.95%), buccal mucosa graft (BMG) ventral onlay urethroplasty (4.65%), and staged urethroplasty (4.65%). Three of the patients (6.98%) had a combination of anastomotic and tissue transfer urethroplasty. The overall success rate was 88.37%. Complications included three surgical site infections, two urethral diverticula and one glans dehiscence. Conclusion: Elderly men tolerate urethroplasty well and the procedure should not be denied solely based on age.

Detection of Knowledge on Social Media Using Data Mining Techniques

In light of the rapid growth and development of social media, it has become the focus of interest in many different scientific fields. They seek to extract useful information from it, and this is called (knowledge), such as extracting information related to people’s behaviors and interactions to analyze feelings or understand the behavior of users or groups, and many others. This extracted knowledge has a very important role in decision-making, creating and improving marketing objectives and competitive advantage, monitoring events, whether political or economic, and development in all fields. Therefore, to extract this knowledge, we need to analyze the vast amount of data found within social media using the most popular data mining techniques and applications related to social media sites.

A Study on Near-Infrared Non-Invasive Blood Glucose Concentration Regression Prediction Based on PSO-MKL-SVR

To improve the accuracy of predicting non-invasive blood glucose concentration in the near-infrared spectrum, we utilized the Particle Swarm Optimization (PSO) algorithm to optimize hyperparameters for the Multi-Kernel Learning Support Vector Machine (MKL-SVR). With these optimized hyperparameters, we established a non-invasive blood glucose regression model, referred to as the PSO-MKL-SVR model. Subsequently, we conducted a comparative analysis between the PSO-MKL-SVR model and the PSO-SVR model. In a dataset comprising ten volunteers, the PSO-MKL-SVR model exhibited significant precision improvements, including a 16.03% reduction in Mean Square Error and a 0.29% increase in the Squared Correlation Coefficient. Moreover, there was a 0.14% higher probability of the Clark’s Error Grid Analysis falling within Zone A. Additionally, the PSO-MKL-SVR model demonstrated a faster operational speed compared to the PSO-SVR model.

Innovative Techniques Unveiled in Advanced Sheet Pile Curtain Design

This thorough review explores the complexities of geotechnical engineering, emphasizing soil-structure interaction (SSI). The investigation centers on sheet pile design, examining two primary methodologies: Limit Equilibrium Methods (LEM) and Soil-Structure Interaction Methods (SSIM). While LEM methods, grounded in classical principles, provide valuable insights for preliminary design considerations, they may encounter limitations in addressing real-world complexities. In contrast, SSIM methods, including the SSI-SR approach, introduce precision and depth to the field. By employing numerical techniques such as Finite Element (FE) and Finite Difference (FD) analyses, these methods enable engineers to navigate the dynamics of soil-structure interaction. The exploration extends to SSI-FE, highlighting its essential role in civil engineering. By integrating Finite Element analysis with considerations for soil-structure interaction, the SSI-FE method offers a holistic understanding of how structures dynamically interact with their geotechnical environment. Throughout this exploration, the study dissects critical components governing SSIM methods, providing engineers with tools to navigate the intricate landscape of geotechnical design. The study acknowledges the significance of the Mohr-Coulomb constitutive model while recognizing its limitations, and guiding practitioners toward informed decision-making in geotechnical analyses. As the article concludes, it underscores the importance of continuous learning and innovation for the future of geotechnical engineering. With advancing technology and an evolving understanding of soil-structure interaction, the study remains committed to ensuring the safety, stability, and efficiency of geotechnical structures through cutting-edge design and analysis techniques.

Observation on the Effect of Non-Invasive Ventilator Combined with Conventional Therapy in the Treatment of Chronic Obstructive Pulmonary Disease Complicated with Respiratory Failure

Objective:To explore the clinical effect of a non-invasive ventilator combined with conventional therapy in the treatment of patients with chronic obstructive pulmonary disease(COPD)combined with respiratory failure.Methods:68 patients with COPD combined with respiratory failure treated in our hospital from September 2021 to October 2023 were selected as the research subjects.Using the random number table method,they were divided into a control group and an experimental group of 34 cases each.The control group received conventional symptomatic treatment,and the experimental group received non-invasive ventilator treatment based on the control group.The clinical effects,blood gas indicators(partial pressure of carbon dioxide(PaCO_(2)),partial pressure of oxygen(PaO_(2)),arterial oxygen saturation(SaO_(2))),lung function(forced expiratory volume in 1 second(FEV1),forced vital capacity(FVC),6 min walking distance),complications,and inflammatory factor levels(c-reactive protein(CRP),interleukin-6(IL-6),neutrophil-to-lymphocyte ratio(NLR))of the two groups of patients were observed.Results:(1)The clinical efficacy of the patients in the experimental group(33/97.06%)was more significant as compared with the control group(25/73.53%)(P<0.05);(2)After treatment,the clinical efficacy of the two groups of patients in terms of FEV1,FEV1/FVC,6-minute walking distance,PaO_(2)and SaO_(2)all increased in the experimental group as compared to that of the control group(P<0.05);(3)After treatment,the PaCO_(2),CRP,IL-6,and NLR of the two groups of patients decreased,and the decrease in the experimental group was higher than that of the control group(P<0.05);(4)The patients’complication rate in the experimental group(2/5.88%)was lower as compared to that of the control group(9/26.46%)(P<0.05).Conclusion:Non-invasive ventilators combined with conventional therapy achieved good clinical results in treating patients with COPD and respiratory failure.

Relationship Between Gene-Phenotype and Clinical Manifestations of Chromosomal Copy Number Variations Indicated by Non-Invasive Prenatal Testing

Objective:To analyze the clinical value of non-invasive prenatal testing(NIPT)in detecting chromosomal copy number variations(CNVs)and to explore the relationship between gene expression and clinical manifestations of chromosomal copy number variations.Methods:3551 naturally conceived singleton pregnant women who underwent NIPT were included in this study.The NIPT revealed abnormalities other than sex chromosome abnormalities and trisomy 13,18,and 21.Pregnant women with chromosome copy number variations underwent genetic counseling and prenatal ultrasound examination.Interventional prenatal diagnosis and chromosome microarray analysis(CMA)were performed.The clinical phenotypes and pregnancy outcomes of different prenatal diagnoses were analyzed.Additionally,a follow-up was conducted by telephone to track fetal development after birth,at six months,and one year post-birth.Results:A total of 53 cases among 3551 cases showed chromosomal copy number variation.Interventional prenatal diagnosis was performed in 36 cases:27 cases were negative and 8 were consistent with the NIPT test results.This indicates that NIPT’s positive predictive value(PPV)in CNVs is 22.22%.Conclusion:NIPT has certain clinical significance in screening chromosome copy number variations and is expected to become a routine screening for chromosomal microdeletions and microduplications.However,further interventional prenatal diagnosis is still needed to identify fetal CNVs.

Emerging role of non-invasive and liquid biopsy biomarkers in pancreatic cancer

A global increase in the incidence of pancreatic cancer(PanCa)presents a major concern and health burden.The traditional tissue-based diagnostic techniques provided a major way forward for molecular diagnostics;however,they face limitations based on diagnosis-associated difficulties and concerns surrounding tissue availability in the clinical setting.Late disease development with asymptomatic behavior is a drawback in the case of existing diagnostic procedures.The capability of cell free markers in discriminating PanCa from autoimmune pancreatitis and chronic pancreatitis along with other precancerous lesions can be a boon to clinicians.Early-stage diagnosis of PanCa can be achieved only if these biomarkers specifically discriminate the non-carcinogenic disease stage from malignancy with respect to tumor stages.In this review,we comprehensively described the non-invasive disease detection approaches and why these approaches are gaining popularity for their early-stage diagnostic capability and associated clinical feasibility.

A Review of In‑Situ Techniques for Probing Active Sites and Mechanisms of Electrocatalytic Oxygen Reduction Reactions

Electrocatalytic oxygen reduction reaction(ORR)is one of the most important reactions in electrochemical energy technologies such as fuel cells and metal–O2/air batteries,etc.However,the essential catalysts to overcome its slow reaction kinetic always undergo a complex dynamic evolution in the actual catalytic process,and the concomitant intermediates and catalytic products also occur continuous conversion and reconstruction.This makes them difficult to be accurately captured,making the identification of ORR active sites and the elucidation of ORR mechanisms difficult.Thus,it is necessary to use extensive in-situ characterization techniques to proceed the real-time monitoring of the catalyst structure and the evolution state of intermediates and products during ORR.This work reviews the major advances in the use of various in-situ techniques to characterize the catalytic processes of various catalysts.Specifically,the catalyst structure evolutions revealed directly by in-situ techniques are systematically summarized,such as phase,valence,electronic transfer,coordination,and spin states varies.In-situ revelation of intermediate adsorption/desorption behavior,and the real-time monitoring of the product nucleation,growth,and reconstruction evolution are equally emphasized in the discussion.Other interference factors,as well as in-situ signal assignment with the aid of theoretical calculations,are also covered.Finally,some major challenges and prospects of in-situ techniques for future catalysts research in the ORR process are proposed.

Non-invasive model for predicting high-risk esophageal varices based on liver and spleen stiffness

BACKGROUND Acute bleeding due to esophageal varices(EVs)is a life-threatening complication in patients with cirrhosis.The diagnosis of EVs is mainly through upper gastrointestinal endoscopy,but the discomfort,contraindications and complications of gastrointestinal endoscopic screening reduce patient compliance.According to the bleeding risk of EVs,the Baveno VI consensus divides varices into high bleeding risk EVs(HEVs)and low bleeding risk EVs(LEVs).We sought to identify a non-invasive prediction model based on spleen stiffness measurement(SSM)and liver stiffness measurement(LSM)as an alternative to EVs screening.AIM To develop a safe,simple and non-invasive model to predict HEVs in patients with viral cirrhosis and identify patients who can be exempted from upper gastrointestinal endoscopy.METHODS Data from 200 patients with viral cirrhosis were included in this study,with 140 patients as the modelling group and 60 patients as the external validation group,and the EVs types of patients were determined by upper gastrointestinal endoscopy and the Baveno Ⅵ consensus.Those patients were divided into the HEVs group(66 patients)and the LEVs group(74 patients).The effect of each parameter on HEVs was analyzed by univariate and multivariate analyses,and a noninvasive prediction model was established.Finally,the discrimination ability,calibration ability and clinical efficacy of the new model were verified in the modelling group and the external validation group.RESULTS Univariate and multivariate analyses showed that SSM and LSM were associated with the occurrence of HEVs in patients with viral cirrhosis.On this basis,logistic regression analysis was used to construct a prediction model:Ln[P/(1-P)]=-8.184-0.228×SSM+0.642×LSM.The area under the curve of the new model was 0.965.When the cut-off value was 0.27,the sensitivity,specificity,positive predictive value and negative predictive value of the model for predicting HEVs were 100.00%,82.43%,83.52%,and 100%,respectively.Compared with the four prediction models of liver stiffness-spleen diameter to platelet ratio score,variceal risk index,aspartate aminotransferase to alanine aminotransferase ratio,and Baveno VI,the established model can better predict HEVs in patients with viral cirrhosis.CONCLUSION Based on the SSM and LSM measured by transient elastography,we established a non-invasive prediction model for HEVs.The new model is reliable in predicting HEVs and can be used as an alternative to routine upper gastrointestinal endoscopy screening,which is helpful for clinical decision making.