How to Effectively Detect and Manage People Living with HIV/AIDS in China:Establishment of a Community-based Model

This study explored a novel systemic community-based model for detecting and manag-ing people living with HIV/AIDS (PLWHA). Both quantitative and qualitative research methods were used in this study. A quantitative questionnaire investigation was conducted in a sample of 1192 subjects which were randomly selected from two areas with high HIV prevalence, Xiangfan City and Shiyan City of Hubei Province, China. Twenty-two medical and health service staffs were inter-viewed by semi-structured questionnaire focusing on awareness, status, problems, and suggestions about community-based Voluntary Counseling and Testing and Provider Initiated Testing and Coun-seling (VCT/PITC). And they were organized to discuss about the aforementioned issues in Xiangfan City and Shiyan City, respectively. Our results showed that the accessibility and availability of the general VCT/PITC were bad. About 28.3% had known and only 4.9% had made use of VCT/PITC. Developing community-based VCT/PITC had some special advantages that can overcome some ex-isting problems to remedy the aforementioned defects. We are led to conclude that, to maximize the availability and uptake rate of the VCT/PITC, we plan to detect PLWHA by developing the commu-nity-based VCT/PITC through 4 paths. Then we establish the community HIV health care center con-stituted of 8 sectors to provide an overall management. Thus, we can effectively detect and manage the PLWHA with a new systemic community-based model.

Ecosystem-Based and Community-Based Model Integration to Designate Coral Reef No-Take Marine Protected Areas: A Case Study from Puerto Rico

Ecosystem-based management and community-based participation in governance of Marine Protected Areas (MPAs) have been identified as key elements to improve management success, local stakeholder support, and compliance with regulations. However, both are often rarely achieved, resulting in poor MPA governance, support and success. A quantitative assessment of the spatio- temporal change (1997-2012) of coral reef fish communities within Arrecifes La Cordillera Natural Reserve in northeastern Puerto Rico was carried out. We also identified community expectations of and support for the designation of a network of small no-take MPAs within the reserve’s boundaries. A holistic approach employing biophysical and socioeconomic methods was used as part of a participatory model to identify priorities for the designation of candidate no-take MPAs. Populations of the most important fishery-targeted species showed a significant temporal decline, particularly in areas subjected to intense recreational activities and spearfishing. Most groupers (Serranidae), snappers (Lutjanidae), barracudas (Sphyraenidae), and some parrotfishes (Scaridae) were nearly absent at most sites. Most individuals belonged to smaller size categories. Herbivores represented the majority of the total fish biomass, suggesting strong fishing impacts on apex predators. Fish declines also occurred after two massive coral bleaching events in 1998 and 2005 that were followed by mass coral mortalities, suggesting combined negative impacts of fishing and climate change. A no-take MPA designation was supported by 80% of the artisanal fishermen, 73% of the concessionaires (i.e., SCUBA diving, charter boats), and 52% of registered vessel operators. Stakeholders agreed that coral reef conditions in the reserve had declined over time, as well as water quality which affected reef health and fisheries. Stakeholders did not recognize climate change and sea surface warming as threats to coral reefs and fisheries. Nonetheless, stakeholder perceptions of candidate no-take MPA sites remarkably matched those identified through fish counts. This study also highlighted the pervasive views held by many stake-holders concerning MPA management and enforcement, and recommended that any no-take MPA designation process considers improving stakeholder participation, understanding of management objectives, actions, and accomplishments, and building stakeholders trust. The integration of ecosystem-based and community-based participatory models may be critical to foster improved support of no-take MPAs and foster a long-term community-based integration to develop and implement mitigation strategies for climate change impacts in novel future scenarios.

Does community-based health insurance affect lifestyle and timing of treatment seeking behavior?Evidence from Ethiopia

Objectives This paper aims to investigate the effects of enrollment in the Ethiopian community-based health insurance(CBHI)scheme on household preventive care activities and the timing of treatment-seeking behavior for illness symptoms.There is growing concern about the financial sustainability of CBHI schemes in developing countries.However,few empirical studies have identified potential contributors,including ex-ante and ex-post moral hazards.Methods We implement a household fixed-effect panel data regression model,drawing on three rounds of household survey data collected face to face in districts where CBHI scheme is operational and in districts where it is not operational in Ethiopia.Results The findings show that enrolment in CBHI does not significantly influence household behaviour regarding preventive care activities such as water treatment before drinking and handwashing before meals.However,CBHI significantly increases delay in treatment-seeking behaviour for diseases symptoms.Particularly,on average,we estimate about 4-6 h delay for malaria symptoms,a little above 4 h for tetanus,and 10-11 h for tuberculosis among the insured households.Conclusions While there is evidence that CBHI improve the utilization of outpatient or primary care services,our study suggests that insured members may wait longer before visiting health facilities.This delay could be partly due to moral hazard problems,as insured households,particularly those from rural areas,may consider the opportunity costs associated with visiting health facilities for minor symptoms.Overall,it is essential to identify the primary causes of delays in seeking medical services and implement appropriate interventions to encourage insured individuals to seek early medical attention.

Research on Design and Management of Community-Based Ecotourism Model in Aksu-Zhabagly Nature Reserve of Kazakhstan

This study investigates the community-based ecotourism (CBE) model using a sample of the Aksu-Zhabagly nature reserve (NR). The aim is to propose a suitable CBE model for Aksu-Zhabgly nature-based tourism destinations by employing a combination of field observation, examination, evaluation, and SWOT analysis. The study determines the strategic suggestions for CBE model designing by the results of SWOT analysis. It concludes that convenient transportation and superior location, diversified wild animals and plants, rich in ethnocultural resources, traditional and tranquil life in a typical rural setting, hospitality and positive attitude of locals to tourism and great potential of the region for sustainable development of ecotourism are the strengths. At the same time, the far residential location from the provincial cities, low-quality service, outdated facilities and shortage of skilled employees in tourism management are the main weakness. Another group of constraints to tourism development is lack of tourism marketing and promotion agencies, lack of transparency, poor institution arrangement and corruption, and lack of preferential policies for CBE development. Finally, the paper recommends that economic development, environmental protection, culture and heritage, marketing and image, favorable political environment, and local residents’ empowerment are the main essential to effectively implement the sustainable development of CBE in the Aksu-Zhabagly tourist destination.

Community-Based Hepatitis B Campaign in Asian Americans

Chronic hepatitis B (CHB) disproportionately affects minority groups in the US, particularly Asian Americans, with numerous factors contributing to this disparity. Of the 2.4 million people living with chronic HBV in the US, 60% are Asian American. Many are unaware of their status and lack access to proper clinical care, with less than ten percent receiving necessary antiviral treatment. Barriers to screening and care include lack of disease awareness, language and cultural barriers, and financial constraints. Additionally, healthcare providers and systems in the US often overlook the importance of CHB, leading to inadequate care. In response, the Center for Viral Hepatitis (CVH) has implemented a community-based outreach program over the past sixteen years, employing a multifaceted approach involving all sectors of society and various organizations to combat health disparities in CHB. This grassroots campaign has proven highly effective, leveraging CVH’s leadership in spearheading numerous collaborative activities with community members, healthcare professionals, and policymakers. We have summarized the key points of CVH's efforts and their significance in combating CHB-related health disparities. The CHB Screening and Awareness Campaign, tailored to the Asian American community, serves as a successful model for increasing CHB screening, linkage-to-care, and addressing socio-cultural barriers and health literacy. Insights from these outreach programs have guided the development of culturally relevant resources and education initiatives. These findings suggest that such community-driven approaches are essential for addressing health disparities. The strategies and outcomes of CVH’s efforts can inform future health initiatives for other minority communities in the US and globally.

Establishment of Community-Based Dissemination Model and Its Effective Function for Sustainable Food Security and Poverty Reduction

Vietnam ranks as one of the largest rice exporters in the world.Two major deltas,the Mekong River Delta and the Red River Delta,contribute around 70%of the country’s rice production and 90%of its rice exports,while people in the mountainous areas of Central Vietnam still suffer from food shortage for three to four months per year.To combat this incongruous situation,the Foundation for International Development/Relief(FIDR),a Japan-based non-governmental organization(NGO),launched a project to introduce system of rice intensification(SRI)methods and developed the community-based dissemination(CBD)model.One of the difficulties that the government and aid agencies generally face is to create an effective and sustainable dissemination model,when the newly-introduced technology is totally unfamiliar to targeted region and residents.Vietnam consists of 54 ethnic groups,and 14%of the population is comprised of 53 ethnic minorities,mainly living in mountainous areas.Almost 50%of them still live below the poverty line and are threatened by food shortage.Within their cultural context,it is hard to adapt to the new concepts of farming,and they are not used to paddy rice cultivation yet.As the result of the project,the model successfully reduced the food shortage of 1.5 months less in average with more than 4,000 households involved.The model is now turned over to the local government as a case of sustainable management practices.This paper aimed to examine what made the model effectively function.

Comparative analysis of empirical and deep learning models for ionospheric sporadic E layer prediction

Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems.In this study,we present Es predictions made by an empirical model and by a deep learning model,and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations.The deep learning model exhibited significantly better performance,as indicated by its high coefficient of correlation(r=0.87)with RO observations and predictions,than did the empirical model(r=0.53).This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally,and into predicting Es layer occurrences and characteristics,in particular.

Construction of Industrialization Development Model for"1+4+X"Community-Based Elderly Care in Gannan Old Revolutionary Base Area in the Context of Population Aging

In the context of controlling population development in a planned way,China entered an aging society in the early 21 st century.In this context,how to meet the needs of the elderly care and form an effective elderly care model has become a key problem to be solved urgently by local government departments.With the continuous advancement of the elderly care policy,the community-based elderly care has gradually become the mainstream and is in the stage of vigorous promotion.Taking the Gannan old revolutionary base area in Jiangxi Province as an example,this study puts forward"1+4+X"community-based elderly care model based on the policy system of community-based elderly care,the physical and mental health of the elderly,the material space and the construction of evaluation system,and explores how to promote the application of this community model efficiently in the form of industrialization,so as to drive the economic growth of the Gannan old revolutionary base area,promote the employment development and improve the community-based elderly care service.

Community-Based Oncology Nursing:Current Status and Prospects-A Secondary Publication

Objective:Currently,it is difficult to determine whether a comprehensive and systematic community-based cancer care project is properly implemented.The purpose of this article is to present the current status and prospects of community-based oncology nursing by reviewing related literature.Methods:A new cancer care model was established through the analysis of existing models and a literature review of community-based cancer prevention,cancer screening,cancer survivor care,and cancer patient hospice care.Results:The Comprehensive Cancer Care Model(CCCM)was esrablished.The CCCM organizes the stages of cancer treatment and the cancer journey on the horizontal axis.On the vertical axis,public health centers,oncology hospitals,cancer convalescent hospitals,primary care physicians,and prayer centers were involved.The levels of participation of human resources among institutions and the degree of interconnection between organizations was observed.Conclusion:It is expected that CCCM will be used for holistic,sustainable,and cost-effective cancer management.In community-based oncology nursing,the team of health promotion nurses in public health centers is in charge of population-based cancer prevention and early detection programs,while the visiting health management team of nurses is in charge of individual-based cancer survivor and hospice programs.

Dynamics of a Stochastic Epidemic Model with Age-group

A stochastic epidemic model with two age groups is established in this study,in which the susceptible(S),the exposed(E),the infected(I),the hospitalized(H)and the recovered(R)are involved within the total population,the aging rates between two age groups are set to be constant.The existence-and-uniqueness of global positive solution is firstly showed.Then,by constructing several appropriate Lyapunov functions and using the high-dimensional Itô’s formula,the sufficient conditions for the stochastic extinction and stochastic persistence of the exposed individuals and the infected individuals are obtained.The stochastic extinction indicator and the stochastic persistence indicator are less-valued expressions compared with the basic reproduction number.Meanwhile,the main results of this study are modified into multi-age groups.Furthermore,by using the surveillance data for Fujian Provincial Center for Disease Control and Prevention,Fuzhou COVID-19 epidemic is chosen to carry out the numerical simulations,which show that the age group of the population plays the vital role when studying infectious diseases.

A Model to Estimate the Impact of Thresholds and Caps on Coverage Levels in Community-Based Health Insurance Schemes in Low-Income Countries

Background: Community-based health insurance (CBHI) schemes are increasingly implemented in low-income settings. These schemes limit the coverage they offer both by the types of care considered, and by applying thresholds and/or caps to costs reimbursed. The consequences of these thresholds and/or caps on insurance coverage have hitherto been usually ignored, for lack of data on the distributions of healthcare costs or understanding of their impact on effective coverage levels. This article describes a theoretical model to obtain the distributions even without data collection in the field, and demonstrates the quantitative impact of thresholds and/or caps on claim reimbursements. Methods: This model applies to applications on healthcare expenditures in low-income settings, following research methods examined in the Western world. We looked at hospitalizations and tests;we compared the simulated distributions to empirical data obtained through 11 household surveys conducted between 2008 and 2010 in rural locations (9 in India and 2 in Nepal). Results: We found that the shape of the distributions was very similar in all locations for both benefits, and could be represented by a model based on a lognormal distribution. The agreement between theoretical and empirical results was satisfactory (mostly within 10% difference). Conclusions: The model makes it possible to simulate the expected performance of the CBHI (represented by the percentage of costs or bills covered). The aim is to match costs with local levels of willingness-to-pay for health insurance. This model makes it possible to determine at the stage of package-design the optimal levels of thresholds and/or caps for each benefit-type included.

Aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders:progress of experimental models based on disease pathogenesis

Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism remains unknown.Therefore,experimental models of neuromyelitis optica spectrum disorders are essential for exploring its pathogenesis and in screening for therapeutic targets.Since most patients with neuromyelitis optica spectrum disorders are seropositive for IgG autoantibodies against aquaporin-4,which is highly expressed on the membrane of astrocyte endfeet,most current experimental models are based on aquaporin-4-IgG that initially targets astrocytes.These experimental models have successfully simulated many pathological features of neuromyelitis optica spectrum disorders,such as aquaporin-4 loss,astrocytopathy,granulocyte and macrophage infiltration,complement activation,demyelination,and neuronal loss;however,they do not fully capture the pathological process of human neuromyelitis optica spectrum disorders.In this review,we summarize the currently known pathogenic mechanisms and the development of associated experimental models in vitro,ex vivo,and in vivo for neuromyelitis optica spectrum disorders,suggest potential pathogenic mechanisms for further investigation,and provide guidance on experimental model choices.In addition,this review summarizes the latest information on pathologies and therapies for neuromyelitis optica spectrum disorders based on experimental models of aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders,offering further therapeutic targets and a theoretical basis for clinical trials.

Solar flare forecasting based on a Fusion Model

Solar flare prediction is an important subject in the field of space weather.Deep learning technology has greatly promoted the development of this subject.In this study,we propose a novel solar flare forecasting model integrating Deep Residual Network(ResNet)and Support Vector Machine(SVM)for both≥C-class(C,M,and X classes)and≥M-class(M and X classes)flares.We collected samples of magnetograms from May 1,2010 to September 13,2018 from Space-weather Helioseismic and Magnetic Imager(HMI)Active Region Patches and then used a cross-validation method to obtain seven independent data sets.We then utilized five metrics to evaluate our fusion model,based on intermediate-output extracted by ResNet and SVM using the Gaussian kernel function.Our results show that the primary metric true skill statistics(TSS)achieves a value of 0.708±0.027 for≥C-class prediction,and of 0.758±0.042 for≥M-class prediction;these values indicate that our approach performs significantly better than those of previous studies.The metrics of our fusion model’s performance on the seven datasets indicate that the model is quite stable and robust,suggesting that fusion models that integrate an excellent baseline network with SVM can achieve improved performance in solar flare prediction.Besides,we also discuss the performance impact of architectural innovation in our fusion model.

Engine Misfire Fault Detection Based on the Channel Attention Convolutional Model

To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precisely pinpointing misfire faults.In the experiment,we established a total of 11 distinct states,encompassing the engine’s normal state,single-cylinder misfire faults,and dual-cylinder misfire faults for different cylinders.Data collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,840Hz.The collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and prevent overlap between the two sets.The results revealed that,with a vibration acceleration sequence of 1000 time steps(approximately 50 ms)as input,the SENET model achieved a misfire fault detection accuracy of 99.8%.For comparison,we also trained and tested several commonly used models,including Long Short-Term Memory(LSTM),Transformer,and Multi-Scale Residual Networks(MSRESNET),yielding accuracy rates of 84%,79%,and 95%,respectively.This underscores the superior accuracy of the SENET model in detecting engine misfire faults compared to other models.Furthermore,the F1 scores for each type of recognition in the SENET model surpassed 0.98,outperforming the baseline models.Our analysis indicated that the misclassified samples in the LSTM and Transformer models’predictions were primarily due to intra-class misidentifications between single-cylinder and dual-cylinder misfire scenarios.To delve deeper,we conducted a visual analysis of the features extracted by the LSTM and SENET models using T-distributed Stochastic Neighbor Embedding(T-SNE)technology.The findings revealed that,in the LSTMmodel,data points of the same type tended to cluster together with significant overlap.Conversely,in the SENET model,data points of various types were more widely and evenly dispersed,demonstrating its effectiveness in distinguishing between different fault types.

Interprofessional Work Model for Dementia Care in Hospitals for Community-Based Care

In this manuscript the authors have studied interprofessional work model for dementia care in hospitals for community-based care. As present situations and problems of dementia patients in hospitals for community-based care, 8 core categories (19 categories) were extracted and as actual situations of interprofessional work for dementia care, 8 core categories (13 categories) were obtained. The authors examined a function of interprofessional work model and practice contents using these categories. The results revealed that better interprofessional work can be expected by six specialists of nurses rehabilitation specialists, MSW, pharmacists, dietitians and care workers developing dementia care based on “Family handling function” “ADL maintenance and improved function” “Staff member education and empowerment function”.

Optimizing Fine-Tuning in Quantized Language Models:An In-Depth Analysis of Key Variables

Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.

Prognostic model for esophagogastric variceal rebleeding after endoscopic treatment in liver cirrhosis: A Chinese multicenter study

BACKGROUND Rebleeding after recovery from esophagogastric variceal bleeding(EGVB)is a severe complication that is associated with high rates of both incidence and mortality.Despite its clinical importance,recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking.AIM To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding.METHODS This study included 477 EGVB patients across 2 cohorts:The derivation cohort(n=322)and the validation cohort(n=155).The primary outcome was rebleeding events within 1 year.The least absolute shrinkage and selection operator was applied for predictor selection,and multivariate Cox regression analysis was used to construct the prognostic model.Internal validation was performed with bootstrap resampling.We assessed the discrimination,calibration and accuracy of the model,and performed patient risk stratification.RESULTS Six predictors,including albumin and aspartate aminotransferase concentrations,white blood cell count,and the presence of ascites,portal vein thrombosis,and bleeding signs,were selected for the rebleeding event prediction following endoscopic treatment(REPET)model.In predicting rebleeding within 1 year,the REPET model ex-hibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort,alongside 0.862 and 0.127 in the validation cohort.Furthermore,the REPET model revealed a significant difference in rebleeding rates(P<0.01)between low-risk patients and intermediate-to high-risk patients in both cohorts.CONCLUSION We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive per-formance,which will improve the clinical management of rebleeding in EGVB patients.

Exploiting fly models to investigate rare human neurological disorders

Rare neurological diseases,while individually are rare,collectively impact millions globally,leading to diverse and often severe neurological symptoms.Often attributed to genetic mutations that disrupt protein function or structure,understanding their genetic basis is crucial for accurate diagnosis and targeted therapies.To investigate the underlying pathogenesis of these conditions,researchers often use non-mammalian model organisms,such as Drosophila(fruit flies),which is valued for their genetic manipulability,cost-efficiency,and preservation of genes and biological functions across evolutionary time.Genetic tools available in Drosophila,including CRISPR-Cas9,offer a means to manipulate gene expression,allowing for a deep exploration of the genetic underpinnings of rare neurological diseases.Drosophila boasts a versatile genetic toolkit,rapid generation turnover,and ease of large-scale experimentation,making it an invaluable resource for identifying potential drug candidates.Researchers can expose flies carrying disease-associated mutations to various compounds,rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and,ultimately,clinical trials.In this comprehensive review,we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis,pathophysiology,and potential therapeutic implications.We discuss rare diseases associated with both neuron-expressed and glial-expressed genes.Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay,mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay,and mutations in IRF2BPL causing seizures,a neurodevelopmental disorder with regression,loss of speech,and abnormal movements.And we explore mutations in EMC1 related to cerebellar atrophy,visual impairment,psychomotor retardation,and gain-of-function mutations in ACOX1 causing Mitchell syndrome.Loss-of-function mutations in ACOX1 result in ACOX1 deficiency,characterized by very-long-chain fatty acid accumulation and glial degeneration.Notably,this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology,offering a platform for the rapid identification of potential therapeutic interventions.Rare neurological diseases involve a wide range of expression systems,and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia.Furthermore,mutations within the same gene may result in varying functional outcomes,such as complete loss of function,partial loss of function,or gain-of-function mutations.The phenotypes observed in patients can differ significantly,underscoring the complexity of these conditions.In conclusion,Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases.By facilitating the modeling of these conditions,Drosophila contributes to a deeper understanding of their genetic basis,pathophysiology,and potential therapies.This approach accelerates the discovery of promising drug candidates,ultimately benefiting patients affected by these complex and understudied diseases.

A promising approach for quantifying focal stroke modeling and assessing stroke progression:optical resolution photoacoustic microscopy photothrombosis

To investigate the mechanisms underlying the onset and progression of ischemic stroke,some methods have been proposed that can simultaneously monitor and create embolisms in the animal cerebral cortex.However,these methods often require complex systems and the effect of age on cerebral embolism has not been adequately studied,although ischemic stroke is strongly age-related.In this study,we propose an optical-resolution photoacoustic microscopy-based visualized photothrombosis methodology to create and monitor ischemic stroke in mice simultaneously using a 532 nm pulsed laser.We observed the molding process in mice of different ages and presented age-dependent vascular embolism differentiation.Moreover,we integrated optical coherence tomography angiography to investigate age-associated trends in cerebrovascular variability following a stroke.Our imaging data and quantitative analyses underscore the differential cerebrovascular responses to stroke in mice of different ages,thereby highlighting the technique's potential for evaluating cerebrovascular health and unraveling age-related mechanisms involved in ischemic strokes.

Vertical gradients of neutral winds observed by ICON and estimated by the Horizontal Wind Model during the geomagnetic storm on August 26−28,2021

The Michelson Interferometer for Global High-resolution Thermospheric Imaging(MIGHTI)onboard the Ionospheric Connection Explorer(ICON)satellite offers the opportunity to investigate the altitude profile of thermospheric winds.In this study,we used the red-line measurements of MIGHTI to compare with the results estimated by Horizontal Wind Model 14(HWM14).The data selected included both the geomagnetic quiet period(December 2019 to August 2022)and the geomagnetic storm on August 26-28,2021.During the geomagnetic quiet period,the estimations of neutral winds from HWM14 showed relatively good agreement with the observations from ICON.According to the ICON observations,near the equator,zonal winds reverse from westward to eastward at around 06:00 local time(LT)at higher altitudes,and the stronger westward winds appear at later LTs at lower altitudes.At around 16:00 LT,eastward winds at 300 km reverse to westward,and vertical gradients of zonal winds similar to those at sunrise hours can be observed.In the middle latitudes,zonal winds reverse about 2-4 h earlier.Meridional winds vary more significantly than zonal winds with seasonal and latitudinal variations.According to the ICON observations,in the northern low latitudes,vertical reversals of meridional winds are found at 08:00-13:00 LT from 300 to 160 km and at around 18:00 LT from 300 to 200 km during the June solstice.Similar reversals of meridional winds are found at 04:00-07:00 LT from 300 to 160 km and at 22:00-02:00 LT from 270 to 200 km during the December solstice.In the southern low latitudes,meridional wind reversals occur at 08:00-11:00 LT from 200 to 160 km and at 21:00-02:00 LT from 300 to 200 km during the June solstice.During the December solstice,reversals of the meridional wind appear at 20:00-01:00 LT below 200 km and at 06:00-11:00 LT from 300 to 160 km.In the northern middle latitudes,the northward winds are dominant at 08:00-14:00 LT at 230 km during the June solstice.Northward winds persist until 16:00 LT at 160 and 300 km.During the December solstice,the northward winds are dominant from 06:00 to 21:00 LT.The vertical variations in neutral winds during the geomagnetic storm on August 26-28 were analyzed in detail.Both meridional and zonal winds during the active geomagnetic period observed by ICON show distinguishable vertical shear structures at different stages of the storm.On the dayside,during the main phase,the peak velocities of westward winds extend from a higher altitude to a lower altitude,whereas during the recovery phase,the peak velocities of the westward winds extend from lower altitudes to higher altitudes.The velocities of the southward winds are stronger at lower altitudes during the storm.These vertical structures of horizontal winds during the storm could not be reproduced by the HWM14 wind estimations,and the overall response to the storm of the horizontal winds in the low and middle latitudes is underestimated by HWM14.The ICON observations provide a good dataset for improving the HWM wind estimations in the middle and upper atmosphere,especially the vertical variations.