Progress,implications,and challenges in using humanized immune system mice in CAR-T therapy-Application evaluation and improvement

In recent years,humanized immune system(HIS)mice have been gradually used as models for preclinical research in pharmacotherapies and cell therapies with major breakthroughs in tumor and other fields,better mimicking the human immune system and the tumor immune microenvironment,compared to traditional immunodeficient mice.To better promote the application of HIS mice in preclinical research,we se-lectively summarize the current prevalent and breakthrough research and evaluation of chimeric antigen receptor(CAR)-T cells in various antiviral and antitumor treat-ments.By exploring its application in preclinical research,we find that it can better reflect the actual clinical patient condition,with the advantages of providing high-efficiency detection indicators,even for progressive research and development.We believe that it has better clinical patient simulation and promotion for the updated design of CAR-T cell therapy than directly transplanted immunodeficient mice.The characteristics of the main models are proposed to improve the use defects of the existing models by reducing the limitation of antihost reaction,combining multiple models,and unifying sources and organoid substitution.Strategy study of relapse and toxicity after CAR-T treatment also provides more possibilities for application and development.

Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners:A Recommendation System

Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not.Typically,smartphones and their associated sensing devices operate in distributed and unstable environments.Therefore,collecting their data and extracting useful information is a significant challenge.In this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical activities.Using the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities.This system is based on the calculation of calories burned by each user during physical activities.In this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts.To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional LSTM.Since human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information simultaneously.While Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data.The results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and uncertainty.To overcome this constraint and improve performance(96%),we proposed to use the fusion mechanisms.The last combines deep learning classifiers tomodel non-accurate and ambiguous data to obtain synthetic information to aid in decision-making.The Voting and Dempster-Shafer(DS)approaches are employed.The results showed that fused classifiers based on DS theory outperformed individual classifiers(96%)with the highest accuracy level of 98%.Also,the findings disclosed that participants engaging in physical activities are healthy,showcasing a disparity in the distribution of physical activities between men and women.

Design of a Lightweight Compressed Video Stream-Based Patient Activity Monitoring System

Inpatient falls from beds in hospitals are a common problem.Such falls may result in severe injuries.This problem can be addressed by continuous monitoring of patients using cameras.Recent advancements in deep learning-based video analytics have made this task of fall detection more effective and efficient.Along with fall detection,monitoring of different activities of the patients is also of significant concern to assess the improvement in their health.High computation-intensive models are required to monitor every action of the patient precisely.This requirement limits the applicability of such networks.Hence,to keep the model lightweight,the already designed fall detection networks can be extended to monitor the general activities of the patients along with the fall detection.Motivated by the same notion,we propose a novel,lightweight,and efficient patient activity monitoring system that broadly classifies the patients’activities into fall,activity,and rest classes based on their poses.The whole network comprises three sub-networks,namely a Convolutional Neural Networks(CNN)based video compression network,a Lightweight Pose Network(LPN)and a Residual Network(ResNet)Mixer block-based activity recognition network.The compression network compresses the video streams using deep learning networks for efficient storage and retrieval;after that,LPN estimates human poses.Finally,the activity recognition network classifies the patients’activities based on their poses.The proposed system shows an overall accuracy of approx.99.7% over a standard dataset with 99.63% fall detection accuracy and efficiently monitors different events,which may help monitor the falls and improve the inpatients’health.

Kinematic calibration under the expectation maximization framework for exoskeletal inertial motion capture system

This study presents a kinematic calibration method for exoskeletal inertial motion capture (EI-MoCap) system with considering the random colored noise such as gyroscopic drift.In this method, the geometric parameters are calibrated by the traditional calibration method at first. Then, in order to calibrate the parameters affected by the random colored noise, the expectation maximization (EM) algorithm is introduced. Through the use of geometric parameters calibrated by the traditional calibration method, the iterations under the EM framework are decreased and the efficiency of the proposed method on embedded system is improved. The performance of the proposed kinematic calibration method is compared to the traditional calibration method. Furthermore, the feasibility of the proposed method is verified on the EI-MoCap system. The simulation and experiment demonstrate that the motion capture precision is significantly improved by 16.79%and 7.16%respectively in comparison to the traditional calibration method.

Intelligent 3D garment system of the human body based on deep spiking neural network

Background Intelligent garments,a burgeoning class of wearable devices,have extensive applications in domains such as sports training and medical rehabilitation.Nonetheless,existing research in the smart wearables domain predominantly emphasizes sensor functionality and quantity,often skipping crucial aspects related to user experience and interaction.Methods To address this gap,this study introduces a novel real-time 3D interactive system based on intelligent garments.The system utilizes lightweight sensor modules to collect human motion data and introduces a dual-stream fusion network based on pulsed neural units to classify and recognize human movements,thereby achieving real-time interaction between users and sensors.Additionally,the system incorporates 3D human visualization functionality,which visualizes sensor data and recognizes human actions as 3D models in real time,providing accurate and comprehensive visual feedback to help users better understand and analyze the details and features of human motion.This system has significant potential for applications in motion detection,medical monitoring,virtual reality,and other fields.The accurate classification of human actions contributes to the development of personalized training plans and injury prevention strategies.Conclusions This study has substantial implications in the domains of intelligent garments,human motion monitoring,and digital twin visualization.The advancement of this system is expected to propel the progress of wearable technology and foster a deeper comprehension of human motion.

Ethics of Human Resources Management in the Cameroonian Health System, Medical Nomadism and the Ineffectiveness of the Fight against High Blood Pressure

Context/objectives: The fight against Chronic Non-Communicable Diseases (NCDs) is a long-term undertaking, which requires available, motivated and well-managed human resources (HR). The administrative management of skills on both qualitative and quantitative levels is one of the essential functions of a health system. To better implement policies of fight against High Blood Pressure (HBP) and other chronic diseases, it is important to establish strategies to retain health personnel. This loyalty requires favorable working conditions and consideration of the contribution-reward couple. Good working conditions are likely to reduce the phenomenon of medical nomadism;conversely, poor HR management can contribute to their exodus towards exotic “green pastures”, thus leading to an additional crisis in the Cameroonian health system. The fight against HBP is a complex, multifaceted and multifactorial reality that requires appropriate management model for all types of resources mainly HR. The main objective of this research is to show the impact of poor management of human resources in Cameroon health system on medical nomadism and the ineffectiveness of the fight against High Blood Pressure. Method: A cross-sectional descriptive survey among five hundred (500) health facilities in the center region of Cameroon has been conducted. A stratified probabilistic technique has been used, and the number of health facilities to be surveyed has been determined using the “sample size estimation table” of Depelteau. The physical questionnaires have been printed and then distributed to data collectors. After data collection, the latter were grouped during processing in Excel sheets. The Chi-square test was used for data with a qualitative value and that of Kolmogorov-Sminorf for data with a quantitative value to assess the normality and reliability of data. The Crochach’s Alpha reliability test allowed us to have a summary of the means and variances and then to search for intragroup correlations between variables. Descriptive analysis was possible with the XLSTAT 2016 software. Results: 43.60% of Health Facilities (HF) managers were unqualified. 82.20% of HF managers have staff in a situation of professional insecurity. They are mainly contractual (49.00), decision-making agents (24.40%), casual agents (08.80). The proportion of unstable personnel is average of 22.00% and very unstable, 12.00%.

Impact of Plastic Waste on the Human Health in Low-Income Countries: A Systematic Review

Background: Plastic pollution is the accumulation of waste composed of plastic and its derivatives all over the environment. Whether in the form of visible garbage or microparticles, as it slowly degrades, plastic pollution poses significant threats to terrestrial and aquatic habitats and the wildlife that call them home, whether through ingestion, entanglement or exposure to the chemicals contained in the material. Unfortunately, there is a lack of documentation on the impact of plastic waste on human health in low- and middle-income countries (LMICs). Methods: We searched five electronic databases (PubMed, Embase, Global Health, CINAHL and Web of Science) and gray literature, following the preferred reporting elements for systematic reviews and meta-analyses (PRISMA), for the impact of plastic waste on human health in developing countries. We included quantitative and qualitative studies written in English and French. We assessed the quality of the included articles using the Mixed Methods Appraisal tool (MMAT). Results: A total of 3779 articles were initially identified by searching electronic databases. After eliminating duplicates, 3167 articles were reviewed based on title and abstract, and 26 were selected for full-text review. Only three articles were retained. The three articles dealt with practices likely to lead to oral exposure to plastic chemicals in human health, as well as the level of awareness of participants concerning the possible impact of plastic on human health, namely, the use of plastic baby bottles, the use of microwaves to cook food and reheat precooked food, the use of plastic bottles to store water in the refrigerator, water purifier containers with plastic bodies and plastic lunch boxes, the reuse of plastic bags and the inadequacy of treatment facilities. Conclusion: Plastic waste poses different risks to human health at every stage of its life cycle. Hence, strategies must be adopted to raise public awareness of the dangers of plastic waste to their health. Trial registration: The review protocol is registered in the PROSPERO international prospective register of systematic reviews (ID = CRD42023409087).

The Dynamic System Theory of Legal Protection for Personal Information Rights Infringement

The advent of the big data era has presented unprecedented challenges to remedies for personal information infringement in areas such as damage assessment,proof of causation,determination of illegality,fault assessment,and liability.Traditional tort law is unable to provide a robust response for these challenges,which severely hinders human rights protection in the digital society.The dynamic system theory represents a third path between fixed constitutive elements and general clauses.It both overcomes the rigidity of the“allor-nothing”legal effect evaluation mechanism of the“element-effect”model and avoids the uncertainty of the general clause model.It can effectively enhance the flexibility of the legal system in responding to social changes.In light of this,it is necessary to construct a dynamic foundational evaluation framework for personal information infringement under the guidance of the dynamic system theory.By relying on the dynamic interplay effect of various foundational evaluation elements,this framework can achieve a flexible evaluation of the constitutive elements of liability and the legal effects of liability for personal information infringement.Through this approach,the crisis of personal information infringement in the era of big data can be mitigated,and the realization of personal information rights as digital human rights can be promoted.

Systematic and Theoretical Unfolding of Research on Xi Jinping’s Discourses on Respecting and Protecting Human Rights

Xi Jinping’s discourses on respecting and protect-ing human rights stand as a shining example of the sinicization and modernization of Marxist human rights theory,embodying profound theoretical,political,practical,and cultural logic.Existing research has conducted comprehensive and systematic theoretical analysis and academic extractions on the following contents:the core essence in-herent in these important discourses,including the“theory of human rights concepts,”the“theory of human rights paths,”the“theory of human rights practices,”the“theory of human rights protection,”and the“theory of human rights governance,”along with their profound theoretical significance,practical significance,and global signifi-cance.In the future,researchers should emphasize efforts on studying the original texts and understanding the original principles.While focusing on the precision of concepts,the scientific nature of the prop-ositions,the maturity of theoretical systems,and the rigor of internal logic related to Xi Jinping’s discourses on respecting and protecting human rights,researchers should also pay attention to constructing a discourse system on human rights from the dimensions of discourse power,discourse cluster,and discourse field.Researchers should be adept at drawing innovative insights into human rights theory from China’s vibrant human rights practices and the vast masses of people.This approach will facilitate the systematic unfolding,academic trans-formation,and innovative development of Xi Jinping’s discourses on respecting and protecting human rights.

Computer Vision-Based Human Body Posture Correction System

With the development of technology and the progress of life,more and more people,regardless of entertainment,learning,or work,cannot do without computer desks and cannot put down their mobile phones.Due to prolonged sitting and often neglecting the importance of posture,incorrect posture can often lead to health problems such as hunchback,lumbar muscle strain,and shoulder and neck pain over time.To address this issue,we designed a computer vision-based human body posture detection system.The system utilizes YOLOv8 technology to accurately locate key points of the human body skeleton,and then analyzes the coordinate positions and depth information of these key points to establish a criterion for distinguishing different postures.With the assistance of an SVM classifier,the system achieves an average recognition rate of 95%.Finally,we successfully deployed the posture detection system on Raspberry Pi hardware and conducted extensive testing.The test results demonstrate that the system can effectively detect various postures and provide real-time reminders to users to correct poor posture,demonstrating good practicality and stability.

Model-Based Systems Engineering Approach to Design a Human Settlement to Better Serve Displaced People

The challenge of transitioning from temporary humanitarian settlements to more sustainable human settlements is due to a significant increase in the number of forcibly displaced people over recent decades, difficulties in providing social services that meet the required standards, and the prolongation of emergencies. Despite this challenging context, short-term considerations continue to guide their planning and management rather than more integrated, longer-term perspectives, thus preventing viable, sustainable development. Over the years, the design of humanitarian settlements has not been adapted to local contexts and perspectives, nor to the dynamics of urbanization and population growth and data. In addition, the current approach to temporary settlement harms the environment and can strain limited resources. Inefficient land use and ad hoc development models have compounded difficulties and generated new challenges. As a result, living conditions in settlements have deteriorated over the last few decades and continue to pose new challenges. The stakes are such that major shortcomings have emerged along the way, leading to disruption, budget overruns in a context marked by a steady decline in funding. However, some attempts have been made to shift towards more sustainable approaches, but these have mainly focused on vague, sector-oriented themes, failing to consider systematic and integration views. This study is a contribution in addressing these shortcomings by designing a model-driving solution, emphasizing an integrated system conceptualized as a system of systems. This paper proposes a new methodology for designing an integrated and sustainable human settlement model, based on Model-Based Systems Engineering and a Systems Modeling Language to provide valuable insights toward sustainable solutions for displaced populations aligning with the United Nations 2030 agenda for sustainable development.

Integrative multi-omics and systems bioinformatics in translational neuroscience:A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information,with its application to neuroscience termed neuroinformatics.Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms,which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases.Importantly,integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile.In this review,we first summarize data mining studies utilizing datasets from the individual type of omics analysis,including epigenetics/epigenomics,transcriptomics,proteomics,metabolomics,lipidomics,and spatial omics,pertaining to Alzheimer's disease,Parkinson's disease,and multiple sclerosis.We then discuss multi-omics integration approaches,including independent biological integration and unsupervised integration methods,for more intuitive and informative interpretation of the biological data obtained across different omics layers.We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks.Finally,we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery,therapeutic development,and elucidation of disease mechanisms.We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

Humanization for neurological disease modeling:A roadmap to increase the potential of Drosophila model systems

Neuroscience and neurology research is dominated by experimentation with rodents.Around 75%of neurology disease-associated genes have orthologs in Drosophila mel-anogaster,the fruit fly amenable to complex neurological and behavioral investiga-tions.However,non-vertebrate models including Drosophila have so far been unable to significantly replace mice and rats in this field of studies.One reason for this situ-ation is the predominance of gene overexpression(and gene loss-of-function)meth-odologies used when establishing a Drosophila model of a given neurological disease,a strategy that does not recapitulate accurately enough the genetic disease condi-tions.I argue here the need for a systematic humanization approach,whereby the Drosophila orthologs of human disease genes are replaced with the human sequences.This approach will identify the list of diseases and the underlying genes that can be adequately modeled in the fruit fly.I discuss the neurological disease genes to which this systematic humanization approach should be applied and provide an example of such an application,and consider its importance for subsequent disease modeling and drug discovery in Drosophila.I argue that this paradigm will not only advance our un-derstanding of the molecular etiology of a number of neurological disorders,but will also gradually enable researchers to reduce experimentation using rodent models of multiple neurological diseases and eventually replace these models.

Biological factors controlling starch digestibility in human digestive system

Starch digestion rate and location in the gastrointestinal tract(GIT)are critical for human health.This review aims to present a comprehensive summary on our current understanding of physiological,biochemical,anatomical and geometrical factors of human digestive system that are related to in vivo starch digestibility.It is shown that all digestive compartments including mouth,stomach,small intestine,and large intestine play critical roles in regulating the overall starch digestion process.A proper investigation of starch digestion pattern should thus be based on the consideration of all these compartments.Main biological factors are summarized as oral mastication and salivation,gastric emptying and motility,small intestinal enzymes and motility,large intestinal resistant starch(RS)-microbiota interactions and gut-brain feedback control,as well as glucose adsorption and hormonal feedback control.However,connections among these biological factors in determining starch digestive behaviors remain elusive.This is due to the inherent complexity of human GIT anatomy,motility and biochemical conditions,as well as ethical,financial and technical issues in conducting clinical studies.Much technological and scientific efforts from both clinical studies and in vitro simulation models are required for a better understanding of in vivo starch digestion behaviors.

Effects of coal mining and tunnel excavation on groundwater flow system in karst areas by modeling:A case study in Zhongliang Mountain,Chongqing,Southwest China

A karst groundwater system ranks among the most sensitive and vulnerable types of groundwater systems.Coal mining and tunnel excavation can greatly change the natural hydrogeological flow system,groundwater-dependent vegetation,soil,as well as hydrology of surface water systems.Abandoned coal mine caves and proposed highway tunnels may have significant influences on groundwater systems.This study employs MODFLOW,a 3D finite-difference groundwater model software,to simulate the groundwater system's response to coal mining and tunnel excavation impact in Zhongliang Mountain,Chongqing,from 1948 to 2035.The results show a regional decline in groundwater levels within the study area following mining and tunnel construction.The groundwater flow system in the study area evolves from the Jialing River groundwater flow system to encompass the Jialing River,Moxinpo highway tunnel,Moxinpo,and the Liujiagou coal mine cave groundwater flow systems between 1948 and 2025.With the completion of tunnel construction,the groundwater level at the top of the tunnel is gradually restored to the water level in the natural state.The model also predicts groundwater level variations between 2025 and 2035.The groundwater level will rise further initially,however,it may take about 10 years for the system to stabilize and reach a new equilibrium.In light of these findings,it is advised that changes in groundwater flow systems caused by tunnel construction should be modeled prior to the practical construction.This approach is crucial for evaluating potential engineering and environmental implications.

Systematic review and meta-analysis of seroprevalence of human immunodeficiency virus serological markers among pregnant women in Africa, 1984-2020

BACKGROUND Human immunodeficiency virus(HIV)is a major public health concern,particularly in Africa where HIV rates remain substantial.Pregnant women are at an increased risk of acquiring HIV,which has a significant impact on both maternal and child health.AIM To review summarizes HIV seroprevalence among pregnant women in Africa.It also identifies regional and clinical characteristics that contribute to study-specific estimates variation.METHODS The study included pregnant women from any African country or region,irrespective of their symptoms,and any study design conducted in any setting.Using electronic literature searches,articles published until February 2023 were reviewed.The quality of the included studies was evaluated.The DerSimonian and Laird random-effects model was applied to determine HIV pooled seroprevalence among pregnant women in Africa.Subgroup and sensitivity analyses were conducted to identify potential sources of heterogeneity.Heterogeneity was assessed with Cochran's Q test and I2 statistics,and publication bias was assessed with Egger's test.RESULTS A total of 248 studies conducted between 1984 and 2020 were included in the quantitative synthesis(meta-analysis).Out of the total studies,146(58.9%)had a low risk of bias and 102(41.1%)had a moderate risk of bias.No HIV-positive pregnant women died in the included studies.The overall HIV seroprevalence in pregnant women was estimated to be 9.3%[95%confidence interval(CI):8.3-10.3].The subgroup analysis showed statistically significant heterogeneity across subgroups(P<0.001),with the highest seroprevalence observed in Southern Africa(29.4%,95%CI:26.5-32.4)and the lowest seroprevalence observed in Northern Africa(0.7%,95%CI:0.3-1.3).CONCLUSION The review found that HIV seroprevalence among pregnant women in African countries remains significant,particularly in Southern African countries.This review can inform the development of targeted public health interventions to address high HIV seroprevalence in pregnant women in African countries.

The Wandering EarthⅡ:Exploring the Macrocosm and Thinking about the Future of Humanity

With the shocking power of sci-fi scenes,the creativity of sci-fi imagination and the cognition of sci-fi humanities,The Wandering Earth II has given multiple expressions of Chinese emotions and Chinese spirit.It creates a national,scientific and popular new narrative discourse for Chinese science fiction oriented to modernization,the world and the future.

Concept, Interests and Systems: Logical Consciousness of the China Campus Football Policy Trip

Based on the “Healthy China 2030 Planning Outline”, the literature method and logical analysis method are used to review and analyze the implementation process of China’s school football policy from three dimensions: value, interest appeal and institutional background. The study believes that in order to break through the bottleneck of policy implementation and improve the effect of policy implementation, it is necessary to establish correct values and form broad recognition of policies;meet the reasonable interests of all parties and form a synergy for policy implementation;optimize the institutional environment for policy implementation and form effective incentives.

The Challenges Posed by Autonomous Weapon Systems to Human Rights and Humanitarian Concerns and Relevant Legal Responses

By 2050,autonomous weapon systems may potentially replace humans as the main force on the battlefield,as per predictions.The development of autonomous weapon systems poses risks to human rights and humanitarian concerns and raises questions about how international law should regulate new technologies.From the perspectives of international human rights law and international humanitarian law,autonomous weapon systems present serious challenges in terms of invasiveness,indiscriminate killing,cruelty,and loss of control,which impact human rights and humanitarian principles.Against the backdrop of increased attention to the protection of human rights in China,it is necessary to clarify the existing regulatory framework and fundamental stance regarding autonomous weapon systems and proactively consider and propose countermeasures to address the risks associated with such systems.This will help prevent human rights and humanitarian violations and advance the timely resolution of this issue,which affects the future and destiny of humanity,ultimately achieving the noble goal of universal enjoyment of human rights.

Glial Cell-Targeted Treatments for Bipolar Disorder: A Systematic Review of Available Data and Clinical Perspectives

This paper is a systematic review of the treatment of bipolar disorder: a systematic Google Scholar search aimed at treatment guidelines and clinical trials. The search for treatment guidelines returned 375 papers and was last performed from June 1, 2022 to August 30, 2022. The literature suggests that lithium helps control and alleviate severe mood episodes, and olanzapine is effective for acute manic or mixed episodes of bipolar I disorder. Achieving effectiveness or remission is better with Cariprazine. Lurasidone improves cognitive performance. Quetiapine improves sleep quality and co-morbid anxiety. Lamotrigine helps delay depression, mania, and mild manic episodes. Antidepressants are best used in conjunction with mood stabilizers. For co-morbid treatment, carbamazepine and lithium in combination are more effective in the treatment of psychotic mania. Co-morbid anxiety treatment considers adjunctive olanzapine or lamotrigine. Co-morbid bulimia treatment considers a mood stabilizer. Co-morbid fatigue treatment considers a dawn simulator. For diet, pay attention to a healthy diet, patients can ingest probiotics and pay attention to the balance of fatty acids.