Direct seawater splitting for hydrogen production:Recent advances in materials synthesis and technological innovation

Direct seawater splitting has emerged as a popular and promising research direction for synthesising clean,green,non-polluting,and sustainable hydrogen energy without depending on high-purity water in the face of the world’s shortage of fossil energy.However,efficient seawater splitting is hindered by slow kinetics caused by the ultra-low conductivity and the presence of bacteria,microorganisms,and stray ions in seawater.Additionally,producing hydrogen on an industrial scale is challenging due to the high production cost.The present review addresses these challenges from the catalyst point of view,namely,that designing catalysts with high catalytic activity and stability can directly affect the rate and effect of seawater splitting.From the ion transfer perspective,designing membranes can block harmful ions,improving the stability of seawater splitting.From the energy point of view,mixed seawater systems and self-powered systems also provide new and low-energy research systems for seawater splitting.Finally,ideas and directions for further research on direct seawater splitting in the future are pointed out,with the aim of achieving low-cost and high-efficiency hydrogen production.

A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

Dynamic Interaction Analysis of Coupled Axial-Torsional-Lateral Mechanical Vibrations in Rotary Drilling Systems

Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.

Optimizing care for gastric cancer with overt bleeding:Is systemic therapy a valid option?

Gastric cancer(GC)and gastroesophageal junction cancer(GEJC)represent a significant burden globally,with complications such as overt bleeding(OB)further exacerbating patient outcomes.A recent study by Yao et al evaluated the effectiveness and safety of systematic treatment in GC/GEJC patients presenting with OB.Using propensity score matching,the study balanced the comparison groups to investigate overall survival and treatment-related adverse events.The study's findings emphasize that systematic therapy can be safe and effective and contribute to the ongoing debate about the management of advanced GC/GEJC with OB,highlighting the complexities of treatment decisions in these high-risk patients.

Improving postoperative outcomes in patients with pancreatic cancer:Inflammatory and nutritional biomarkers

This editorial assesses the prognostic value of preoperative inflammatory and nutritional biomarkers in patients undergoing surgical resection for pancreatic cancer.Lu et al evaluated the ability of seven biomarkers to predict postoperative recovery and long-term outcomes.These biomarkers were albumin-to-globulin ratio,prognostic nutritional index(PNI),systemic immune-inflammation index,neutrophil-to-lymphocyte ratio,platelet-to-lymphocyte ratio,nutritional risk index,and geriatric nutritional risk index.The PNI was found to be a strong predictor of both overall and recurrence-free survival,underscoring its clinical relevance in managing patients with pancreatic cancer.

Learning the parameters of a class of stochastic Lotka-Volterra systems with neural networks

In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.

Attribution of psychiatric manifestations to systemic lupus erythematosus in Chinese patients:A retrospective study

BACKGROUND Not all neuropsychiatric(NP)manifestations in patients with systemic lupus erythematosus(SLE)are secondary to lupus.The clarification of the cause of NP symptoms influences therapeutic strategies for SLE.AIM To understand the attribution of psychiatric manifestations in a cohort of Chinese patients with SLE.METHODS This retrospective single-center study analyzed 160 inpatient medical records.Clinical diagnosis,which is considered the gold standard,was used to divide the subjects into a psychiatric SLE(PSLE)group(G1)and a secondary psychiatric symptoms group(G2).Clinical features were compared between these two groups.The sensitivity and specificity of the Italian attribution model were explored.RESULTS A total of 171 psychiatric syndromes were recorded in 138 patients,including 87 cases of acute confusional state,40 cases of cognitive dysfunction,18 cases of psychosis,and 13 cases each of depressive disorder and mania or hypomania.A total of 141(82.5%)syndromes were attributed to SLE.In contrast to G2 patients,G1 patients had higher SLE Disease Activity Index-2000 scores(21 vs 12,P=0.001),a lower prevalence of anti-beta-2-glycoprotein 1 antibodies(8.6%vs 25.9%,P=0.036),and a higher prevalence of anti-ribosomal ribonucleoprotein particle(rRNP)antibodies(39.0%vs 22.2%,P=0.045).The Italian attribution model exhibited a sensitivity of 95.0%and a specificity of 70.0%when the threshold value was set at 7.CONCLUSION Patients with PSLE exhibited increased disease activity.There is a correlation between PSLE and anti-rRNP antibodies.The Italian model effectively assesses multiple psychiatric manifestations in Chinese SLE patients who present with NP symptoms.

Catatonia induced by antipsychotics in an adolescent male patient with systemic lupus erythematosus:A case report

BACKGROUND Systemic lupus erythematosus(SLE)can affect multiple organs or systems.The involvement of the central nervous system can result in the manifestation of epilepsy,an acute confusional state,and other rare neuropsychiatric presentations,such as catatonia.CASE SUMMARY We present a case of an adolescent male patient with first-onset SLE who presented with neuropsychiatric symptoms including epilepsy and delirium.The initial utilization of olanzapine to alleviate symptoms of agitation precipitated the emergence of catatonia,which was mitigated by discontinuing olanzapine and supplementing with lorazepam.In this case,whether the catatonia was secondary to the utilization of antipsychotics or to an organic disease is a question that warrants differential diagnosis.CONCLUSION Multidisciplinary collaborative management is the cornerstone for the successful management of severe cases of SLE.

Mechanistic insights into the amelioration effects of diabetic cardiomyopathy by berberine:an integrated systems pharmacology study and experimental validation

Background:Diabetic cardiomyopathy(DCM)is a type of cardiomyopathy caused by long-term diabetes,characterized by abnormal myocardial structure and function,which can lead to heart failure.Berberine(BBR),a quaternary ammonium alkaloid isolated from Coptidis Rhizoma,a traditional Chinese medicine,has superior anti-diabetic and heart-protective properties.The purpose of this study is to assess the impact of BBR on DCM.Methods:This study used a systems pharmacology approach to evaluate the related proteins and signalling pathways between BBR and DCM targets,combined with experimental validation using diabetic mouse heart sections.Microstructural and pathological changes were observed using Hematoxylin-eosin,Masson’s trichrome stain and wheat germ agglutinin staining.Immunofluorescence and western blot were used to determine protein expression.Results:The results indicate that BBR and DCM share 21 core relevant targets,with cross-targets predominantly located in mitochondrial,endoplasmic reticulum,and plasma membrane components.BBR exerts its main effects in improving DCM by maintaining mitochondrial integrity,particularly involving the PI3K-AKT-GSK3βand apoptosis signalling pathways.In addition,post-treatment changes in the key targets of BBR,including cysteine aspartate specific protease(Caspase)-3,phosphoinositide 3-kinase(PI3K)and mitochondria-related proteins,are suggestive of its efficacy.Conclusion:BBR crucially improves DCM by maintaining mitochondrial integrity,inhibiting apoptosis,and modulating PI3K-AKT-GSK3βsignaling.Further studies must address animal model limitations and validate clinical efficacy to understand BBR’s mechanisms fully and its potential clinical use.

Neurotransmitter-mediated artificial synapses based on organic electrochemical transistors for future biomimic and bioinspired neuromorphic systems

Organic electrochemical transistors have emerged as a solution for artificial synapses that mimic the neural functions of the brain structure,holding great potentials to break the bottleneck of von Neumann architectures.However,current artificial synapses rely primarily on electrical signals,and little attention has been paid to the vital role of neurotransmitter-mediated artificial synapses.Dopamine is a key neurotransmitter associated with emotion regulation and cognitive processes that needs to be monitored in real time to advance the development of disease diagnostics and neuroscience.To provide insights into the development of artificial synapses with neurotransmitter involvement,this review proposes three steps towards future biomimic and bioinspired neuromorphic systems.We first summarize OECT-based dopamine detection devices,and then review advances in neurotransmitter-mediated artificial synapses and resultant advanced neuromorphic systems.Finally,by exploring the challenges and opportunities related to such neuromorphic systems,we provide a perspective on the future development of biomimetic and bioinspired neuromorphic systems.

Prognostic value of inflammatory markers in predicting recurrencefree survival in gastrointestinal stromal tumor patients:A nomogram-based approach

BACKGROUND There are currently no relevant studies at home or abroad that combine inflammatory indicators and nomograms to predict the prognosis of gastrointestinal stromal tumor(GIST)patients after surgery.The purpose of this study was to investigate the predictive value of related inflammatory indicators[systemic immune-inflammation index(SII),neutrophil/lymphocyte ratio(NLR),platelet/lymphocyte ratio(PLR)and monocyte/Lymphocyte ratio(MLR)]in patients undergoing GIST surgery,incorporating relevant risk factors to establish a nomogram prediction model,with the aim of better predicting the prognosis of GIST patients.AIM To explore the relationships between the SII,NLR,PLR,and MLR and postoperative recurrence in patients with GIST.METHODS This study retrospectively included patients who underwent GIST surgery from January 2014 to January 2017 and analyzed the potential relationships between the preoperative SII,NLR,PLR,and MLR and clinicopathological features.The independent risk factors influencing the prognosis of GIST patients were obtained via multivariate regression analysis,and a nomogram model based on the independent risk factors was established.RESULTS Among the 124 GIST patients included in the present study,31(25%)experienced recurrence within 5 years.Kaplan-Meier survival analysis revealed a correlation between the MLR and PLR and tumor size(P=0.016 and P=0.002,respectively).The preoperative SII,MLR,NLR,and PLR were significantly associated with recurrence-free survival(RFS)(P<0.05).The multivariate analysis results identified the PLR,MLR,and targeted therapy as independent prognostic factors for patient outcomes.CONCLUSION Preoperative MLR and PLR,which are independent risk factors for GIST recurrence,were correlated with RFS.Nomograms based on the PLR,MLR and targeted therapy can be used for clinical treatment.

Unmasking gastric carcinoma: Unveiling diagnostic biomarkers and the role of critical care

We discuss the findings of Wu et al on the utility of neutrophil-to-lymphocyte ratio,platelet-to-lymphocyte ratio,and systemic immune-inflammatory index as diagnostic markers for gastric carcinoma(GC).We commend the study's contributions to the field and suggest a prospective study to validate these markers'sensitivity and specificity for early GC detection.We also propose developing surveillance protocols that incorporate these markers with other diagnostic methods to enhance clinical decision-making.Furthermore,we highlight the need for a more diverse patient cohort to assess the generalizability of these markers across different ethnic groups and demographic factors.Our suggestions aim to refine the application of these markers in clinical practice and to understand their potential in diverse clinical scenarios.

Diagnostic implications of neutrophil-to-lymphocyte ratio,platelet-tolymphocyte ratio,and systemic immune-inflammatory index for gastric carcinoma

BACKGROUND The diagnosis of gastric carcinoma(GC)is essential for improving clinical outcomes.However,the biomarkers currently used for GC screening are not ideal.AIM To explore the diagnostic implications of the neutrophil-to-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio(PLR),and systemic immune-inflammatory index(SII)for GC.METHODS The baseline data of 133 patients with GC and 134 patients with precancerous gastric conditions admitted between January 2022 and December 2023 were retrospectively analyzed.The information on peripheral blood platelet,neutrophil,and lymphocyte counts in each patient was collected,and the NLR,PLR,and SII levels of both groups were calculated.Additionally,multivariate logistic regression analysis was conducted,and the diagnostic implications of NLR,PLR,and SII in differentiating patients with precancerous gastric conditions,compared with those with GC,were analyzed through receiver operating characteristic(ROC)curves.RESULTS The data indicated that NLR,PLR,and SII had abnormally increased levels in the patients with GC.Gender and body mass index were risk factors for the occurrence of GC.ROC data revealed that the areas under the curve of three patients with precancerous gastric conditions,who were differentiated from those with GC,were 0.824,0.787,and 0.842,respectively.CONCLUSION NLR,PLR,and SII are all abnormally expressed in GC and have diagnostic implications,especially when used as joint indicators,in distinguishing patients with precancerous gastric conditions from those with GC.

Artificial sensory neurons and their applications

With the rapid development of artificial intelligence(AI)technology,the demand for high-performance and energyefficient computing is increasingly growing.The limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a solution.Neuromorphic computing mimics the working principles of the human brain,characterized by high efficiency,low energy consumption,and strong fault tolerance,providing a hardware foundation for the development of new generation AI technology.Artificial neurons and synapses are the two core components of neuromorphic computing systems.Artificial perception is a crucial aspect of neuromorphic computing,where artificial sensory neurons play an irreplaceable role thus becoming a frontier and hot topic of research.This work reviews recent advances in artificial sensory neurons and their applications.First,biological sensory neurons are briefly described.Then,different types of artificial neurons,such as transistor neurons and memristive neurons,are discussed in detail,focusing on their device structures and working mechanisms.Next,the research progress of artificial sensory neurons and their applications in artificial perception systems is systematically elaborated,covering various sensory types,including vision,touch,hearing,taste,and smell.Finally,challenges faced by artificial sensory neurons at both device and system levels are summarized.

Improving gastrointestinal scoring systems for predicting short-term mortality in critically ill patients

Shen et al’s retrospective study aims to compare the utility of two separate scoring systems for predicting mortality attributable to gastrointestinal(GI)injury in critically ill patients[the GI Dysfunction Score(GIDS)and the Acute Gastroin-testinal Injury(AGI)grade].The authors note that this study is the first proposal that suggests an equivalence between the ability of both scores to predict mor-tality at 28 days from intensive care unit(ICU)admission.Shen et al retrospec-tively analysed an ICU cohort of patients utilising two physicians administering both the AGI grade and GIDS score,using electronic healthcare records and ICU flowsheets.Where these physicians disagreed about the scores,the final decision as to the scores was made by an associate chief physician,or chief physician.We note that the primary reason for the development of GIDS was to create a clear score for GI dysfunction,with minimal subjectivity or inter-operator variability.The subjectivity inherent to the older AGI grading system is what ultimately led to the development of GIDS in 2021.By ensuring consensus between physicians administering the AGI,Shen et al have controlled for one of this grading systems biggest issues.We have concerns,however,that this does not represent the real-world challenges associated with applying the AGI compared to the newer GIDS,and wonder if this arbitration process had not been instituted,would the two scoring systems remain equivalent in terms of predicted mortality?

A novel detection method for warhead fragment targets in optical images under dynamic strong interference environments

A measurement system for the scattering characteristics of warhead fragments based on high-speed imaging systems offers advantages such as simple deployment,flexible maneuverability,and high spatiotemporal resolution,enabling the acquisition of full-process data of the fragment scattering process.However,mismatches between camera frame rates and target velocities can lead to long motion blur tails of high-speed fragment targets,resulting in low signal-to-noise ratios and rendering conventional detection algorithms ineffective in dynamic strong interference testing environments.In this study,we propose a detection framework centered on dynamic strong interference disturbance signal separation and suppression.We introduce a mixture Gaussian model constrained under a joint spatialtemporal-transform domain Dirichlet process,combined with total variation regularization to achieve disturbance signal suppression.Experimental results demonstrate that the proposed disturbance suppression method can be integrated with certain conventional motion target detection tasks,enabling adaptation to real-world data to a certain extent.Moreover,we provide a specific implementation of this process,which achieves a detection rate close to 100%with an approximate 0%false alarm rate in multiple sets of real target field test data.This research effectively advances the development of the field of damage parameter testing.

基于WordCloud技术的MBSE发展态势研究

基于IEEE及国际系统工程协会(International Council on Systems Engineering,INCOSE)社区会刊,提取与基于模型的系统工程(model based systems engineering,MBSE)领域相关的167篇顶刊的关键词和摘要。采用Python及其第三方库WordCloud技术,以可视化形式展示MBSE领域研究内容并对MBSE发展态势进行研究。研究结果表明,MBSE在产品研发全生命周期,应用建模技术来支持系统需求、设计、分析、验证与确认等活动,在系统架构设计方面具有重要作用,将MBSE与安全性分析、可靠性分析方法结合也是MBSE的重要研究内容;系统建模语言(system modeling language,SysML)和对象过程方法(object process method,OPM)分别是目前MBSE研究领域中最受欢迎的建模语言和建模方法;将MBSE方法与本体进行结合是规范MBSE模型表达的重要手段,将MBSE与信息物理系统、数字孪生、并行工程领域进行融合研究是MBSE的重要发展方向。所提研究为使用WordCloud文本分析技术来探索当前的MBSE研究提供了技术路线参考,有助于对MBSE的未来发展态势进行预测。

Present and future of new systemic therapies for early and intermediate stages of hepatocellular carcinoma

Hepatocellular carcinoma(HCC)is a high mortality neoplasm which usually appears on a cirrhotic liver.The therapeutic arsenal and subsequent prognostic outlook are intrinsically linked to the HCC stage at diagnosis.Notwithstanding the current deployment of treatments with curative intent(liver resection/local ablation and liver transplantation)in early and intermediate stages,a high rate of HCC recurrence persists,underscoring a pivotal clinical challenge.Emergent systemic therapies(ST),particularly immunotherapy,have demonstrate promising outcomes in terms of increase overall survival,but they are currently bound to the advanced stage of HCC.This review provides a comprehensive analysis of the literature,encompassing studies up to March 10,2024,evaluating the impact of novel ST in the early and intermediate HCC stages,specially focusing on the findings of neoadjuvant and adjuvant regimens,aimed at increasing significantly overall survival and recurrence-free survival after a treatment with curative intent.We also investigate the potential role of ST in enhancing the downstaging rate for the intermediate-stage HCC initially deemed ineligible for treatment with curative intent.Finally,we critically discuss about the current relevance of the results of these studies and the encouraging future implications of ST in the treatment schedules of early and intermediate HCC stages.

Efficacy of hepatic arterial infusion chemotherapy and its combination strategies for advanced hepatocellular carcinoma:A network meta-analysis

BACKGROUND With the rapid progress of systematic therapy for hepatocellular carcinoma(HCC),therapeutic strategies combining hepatic arterial infusion chemotherapy(HAIC)with systematic therapy arised increasing concentrations.However,there have been no systematic review comparing HAIC and its combination strategies in the first-line treatment for advanced HCC.AIM To investigate the efficacy and safety of HAIC and its combination therapies for advanced HCC.METHODS A network meta-analysis was performed by including 9 randomized controlled trails and 35 cohort studies to carry out our study.The outcomes of interest comprised overall survival(OS),progression-free survival(PFS),tumor response and adverse events.Hazard ratios(HR)and odds ratios(OR)with a 95% confidence interval(CI)were calculated and agents were ranked based on their ranking probability.RESULTS HAIC outperformed Sorafenib(HR=0.55,95%CI:0.42-0.72;HR=0.51,95%CI:0.33-0.78;OR=2.86,95%CI:1.37-5.98;OR=5.45,95%CI:3.57-8.30;OR=7.15,95%CI:4.06-12.58;OR=2.89,95%CI:1.99-4.19;OR=0.48,95%CI:0.25-0.92,respectively)and transarterial chemoembolization(TACE)(HR=0.50,95%CI:0.33-0.75;HR=0.62,95%CI:0.39-0.98;OR=3.08,95%CI:1.36-6.98;OR=2.07,95%CI:1.54-2.80;OR=3.16,95%CI:1.71-5.85;OR=2.67,95%CI:1.59-4.50;OR=0.16,95%CI:0.05-0.54,respectively)in terms of efficacy and safety.HAIC+lenvatinib+ablation,HAIC+ablation,HAIC+anti-programmed cell death 1(PD-1),and HAIC+radiotherapy had the higher likelihood of providing better OS and PFS outcomes compared to HAIC alone.HAIC+TACE+S-1,HAIC+lenvatinib,HAIC+PD-1,HAIC+TACE,and HAIC+sorafenib had the higher likelihood of providing better partial response and objective response rate outcomes compared to HAIC.HAIC+PD-1,HAIC+TACE+S-1 and HAIC+TACE had the higher likelihood of providing better complete response and disease control rate outcomes compared to HAIC alone.CONCLUSION HAIC proved more effective and safer than sorafenib and TACE.Furthermore,combined with other interventions,HAIC showed improved efficacy over HAIC monotherapy according to the treatment ranking analysis.

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.