This article explores the intersection of the Eight-Circuit Model of Consciousness (ECM), initially conceptualized by Timothy Leary and expanded by Robert Anton Wilson, and its implications for mind-body health. By an...This article explores the intersection of the Eight-Circuit Model of Consciousness (ECM), initially conceptualized by Timothy Leary and expanded by Robert Anton Wilson, and its implications for mind-body health. By analyzing each circuit’s role in human consciousness, we discuss how activating and balancing these circuits can lead to enhanced psychological well-being, stress reduction, and overall physical health. Recent research is integrated to provide a contemporary understanding of how the ECM can be applied to modern mind-body therapies, with a focus on both theoretical implications and practical applications.展开更多
Background:Based on a profound awareness of crisis sense,Huai Nan-zi warns people of the importance of being prepared for danger in times of peace by using the principle that Heaven and Earth are ever-changing and nev...Background:Based on a profound awareness of crisis sense,Huai Nan-zi warns people of the importance of being prepared for danger in times of peace by using the principle that Heaven and Earth are ever-changing and never-ending.It also consciously constructs a cosmic life view of the organic,homologous,isomorphic,and harmonious unity of heaven,earth,and man based on human existence and destiny with a rational thinking attitude.The life philosophy of the Huai Nan-zi offers modern people a completely new holistic view of life and medicine.Not only in China,but also in Western countries,studying its medical philosophical ideas helps us better explore the theoretical roots of TCM in the era of globalized medicine.Methods:This paper mainly uses the analysis method of literature review and chinese philosophy intellectual concepts.It employs the I Ching’s image-number logic thinking method to compare images through analogy and the holistic thinking method of the three-talent view of heaven,earth,and human to understand life consciousness.Results:This article mainly interprets medical philosophy in Huai Nan-zi through three aspects:1)The body of Taoism:heaven,earth,and humanity constitute one unity within the body of the universe;2)The spirit of Taoism:keeping the spirit inward,preserving the essence and suppressing the superficial;and 3)The mind of Taoism:the principle of life governed by the dynamics of gain and loss,prosperity and decline.Conclusion:The philosophy of life presented in the Huai Nan-zi ultimately charts a course toward a state of profound theoretical integration.Its“conscious map”does not lead to a fixed destination,but to a continuous and dynamic mode of being–a life of flourishing known in Chinese as yang sheng,the nurturing of life.The destination,therefore,is the journey itself,undertaken with unwavering cosmic awareness and harmony.This ancient text reminds us that a truly healthy life is not a fragmented pursuit of physical fitness,mental peace,or spiritual insight in isolation.Instead,it is the symphony of all three(Taoist body,spirit,and mind),orchestrated by the fundamental principles of the cosmos.By aligning our inner nature with the outer Tao,we transform our existence from a series of reactive struggles into a graceful and spontaneous free flow.In a modern world characterized by fragmentation,overstimulation,and a relentless push against natural rhythms,the Huai Nan-zi’s life consciousness map is more relevant than ever.The philosophy of Huai Nan-zi not only plays a vital role in the construction of the theoretical system of TCM in ancient East life wisdom,but also is worthwhile for Western life sciences to conduct in-depth exploration and discovery in the age of AI.展开更多
Healthy behavior has long been linked to mental health outcomes.However,the role of artificial intelligence(AI)literacy in shaping healthy behaviors and its potential impact on mental health remains underexplored.This...Healthy behavior has long been linked to mental health outcomes.However,the role of artificial intelligence(AI)literacy in shaping healthy behaviors and its potential impact on mental health remains underexplored.This paper presents a scoping review offering a novel perspective on the intersection of healthy behaviors,mental health,and AI literacy.By examining how individuals’understanding of AI influences their choices regarding nutrition and their susceptibility to mental health issues,the current study explores emerging trends in health behavior decision-making.This emphasizes the need for integrating AI literacy into mental health and health behaviors education,as well as the development of AI-driven tools to support healthier behavior choices.It highlights that individuals with low AI literacy may misinterpret or overly depend on AI guidance,resulting in maladaptive health choices,while those with high AI literacy may be more likely to engage reflectively and sustain positive behaviors.The paper outlines the importance of inclusive education,user-centered design,and community-based support systems to enhance AI literacy for digitally marginalized groups.AI literacy may be positioned as a key determinant of health equity,better allowing for interdisciplinary strategies that empower individuals to make informed,autonomous decisions that promote both physical and mental health.展开更多
Recently,inflammatory cytokine profiles have been linked to suicide risk in adolescents with non-suicidal self-injury,highlighting a promising biological dimension of suicide risk assessment.Clinical translation of th...Recently,inflammatory cytokine profiles have been linked to suicide risk in adolescents with non-suicidal self-injury,highlighting a promising biological dimension of suicide risk assessment.Clinical translation of the cytokine profiles into practice will require frontline engagement of the workforce.Mental health nurses are frequently the most accessible professionals in schools,communities,and low-resource settings and are prime candidates to bridge this gap.By integrating psychosocial evaluation with emerging biomarker data,they can deliver systematic risk assessment,continuous monitoring,and timely intervention.This role would not replace psychiatric expertise;it would extend the reach of psychiatric services,embedding suicide prevention across the continuum of care.For health systems,nurse-led integration may enhance capacity,equity,and resilience in responding to adolescent suicide risk.This editorial demonstrates that empowering nurses to operationalize biomarker-informed strategies is needed for advancing effective and sustainable suicide prevention in this vulnerable population.展开更多
With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or p...With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.展开更多
BACKGROUND Timely and accurate evaluation of mental disorders in adolescents using appropriate mental health literacy assessment tools is essential for improving their mental health literacy levels.AIM To develop an e...BACKGROUND Timely and accurate evaluation of mental disorders in adolescents using appropriate mental health literacy assessment tools is essential for improving their mental health literacy levels.AIM To develop an evaluation index system for the mental health literacy of adolescent patients with mental disorders,providing a scientific,comprehensive,and reliable tool for the monitoring and intervention of mental health literacy of such patients.METHODS From December 2022 to June 2023,the evaluation index system for mental health literacy of adolescents with mental disorders was developed through literature reviews,semi-structured interviews,expert letter consultations,and the analytic hierarchy process.Based on this index system,a self-assessment questionnaire was compiled and administered to 305 adolescents with mental disorders to test the reliability and validity of the index system.RESULTS The final evaluation index system for mental health literacy of adolescents with mental disorders included 4 first-level indicators,10 second-level indicators,and 52 third-level indicators.The overall Cronbach’sαcoefficient of the index system was 0.957,with a partial reliability of 0.826 and a content validity index of 0.975.The cumulative variance contribution rate of 10 common factors was 66.491%.The correlation coefficients between each dimension and the total questionnaire ranged from 0.672 to 0.724,while the correlation coefficients in each dimension ranged from 0.389 to 0.705.CONCLUSION The evaluation index system for mental health literacy of adolescents with mental disorders,developed in this study,demonstrated notable reliability and validity,making it a valuable tool for evaluating mental health literacy in this population.展开更多
Excessive levels of Fluoride(F−)and Cadmium(Cd)in drinking groundwater may pose health risks.This study assessed the health risks associated with F−and Cd contamination in rural drinking groundwater sources in Wutai C...Excessive levels of Fluoride(F−)and Cadmium(Cd)in drinking groundwater may pose health risks.This study assessed the health risks associated with F−and Cd contamination in rural drinking groundwater sources in Wutai County,Shanxi Province,China,to support population health protection,water resource management,and environmental decision-making.Groundwater samples were collected and analyzed,and a Human Health Risk Model(HHRA)was applied to evaluate groundwater quality.The results showed that both contents of F−and Cd in groundwater exceeded the Class III limits of China's national groundwater quality standard(GB/T 14848—2024).Fluoride levels met the Class V threshold,with enrichment area mainly located in the east part of the study area.Cadmium levels reached Class IV,with elevated concentrations primarily observed in the western and northwestern regions.Correlation analysis revealed that F−showed weak or no correlation with other measured substances,indicating independent sources.Health risk assessment results indicated that F−poses potential health risks to rural residents,while cadmium,due to its relatively low concentrations,does not currently present a significant health risk.Among different demographic groups,the health risk levels of F−exposure followed the order:Infants>children>adult females>adult males.The findings highlight that fluoride is the primary contributor to health risks associated with groundwater consumption in the study area.Strengthened monitoring and prevention of F−contamination are urgently needed.This research provides a scientific basis for the prevention and control of fluoride pollution in groundwater and offers practical guidance for safeguarding drinking water safety in rural China.展开更多
Ambient fine particulate matter(PM_(2.5))pollution causes the largest environmental health risk globally,yet ex-posure levels and the resulting health risks vary across countries with different income levels.Global we...Ambient fine particulate matter(PM_(2.5))pollution causes the largest environmental health risk globally,yet ex-posure levels and the resulting health risks vary across countries with different income levels.Global wealth inequality has intensified in recent years,yet the relationship between wealth inequality and health risks related to PM_(2.5) pollution remains poorly understood.In this study,we evaluated the global mortality and health cost at-tributable to PM_(2.5) exposure from 2017 to 2021,and analyzed the relationship between wealth inequality,PM_(2.5) pollution,and the associated health risks across regions with varying economic levels.We found a consistent decline in mortalities and health costs attributable to PM_(2.5) exposure from 2017 to 2020,followed by a rebound after 2020,driven primarily by the resurgence of PM_(2.5) concentrations and a deceleration in the reduction of baseline mortality rates.We also found that the average PM_(2.5) concentration and associated risks decrease as domestic wealth inequality decreases and national income level increases.However,regions with extremely high levels of wealth inequality consistently show lower national average PM_(2.5) concentrations and health risks.These findings highlight the need to consider healthcare security during emergencies,as well as policy fairness across economic regions,in the formulation of global PM_(2.5) pollution control measures to promote sustainable,more equitable economic growth and coordinated air pollution management.展开更多
Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),a...Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),and autism spectrum disorder(ASD)frequently arise from the complex interplay of demographic,biological,and socioeconomic factors,resulting in aggravated symptoms.This review investigates machine intelligence approaches for the early detection and prediction of mental health conditions.Methods:The preferred reporting items for systematic reviews and meta-analyses(PRISMA)framework was employed to conduct a systematic review and analysis covering the period 2018 to 2025.The potential impact of machine intelligence methods was assessed by considering various strategies,hybridization of algorithms,tools,techniques,and datasets,and their applicability.Results:Through a systematic review of studies concentrating on the prediction and evaluation of mental disorders using machine intelligence algorithms,advancements,limitations,and gaps in current methodologies were highlighted.The datasets and tools utilized in these investigations were examined,offering a detailed overview of the status of computational models in understanding and diagnosing mental health disorders.Recent research indicated considerable improvements in diagnostic accuracy and treatment effectiveness,particularly for depression and anxiety,which have shown the greatest methodological diversity and notable advancements in machine intelligence.Conclusions:Despite these improvements,challenges persist,including the need for more diverse datasets,ethical issues surrounding data privacy and algorithmic bias,and obstacles to integrating these technologies into clinical settings.This synthesis emphasizes the transformative potential of machine intelligence in enhancing mental healthcare.展开更多
AIM:To assess risk factors for epiretinal membranes(ERM)and examine their interactions in a nationally representative U.S.dataset.METHODS:Data from the 2005–2008 National Health and Nutrition Examination Survey(NHANE...AIM:To assess risk factors for epiretinal membranes(ERM)and examine their interactions in a nationally representative U.S.dataset.METHODS:Data from the 2005–2008 National Health and Nutrition Examination Survey(NHANES)were analyzed,a nationally representative U.S.dataset.ERM was identified via retinal imaging based on the presence of cellophane changes.Key predictors included age group,eye surgery history,and refractive error,with additional demographic and health-related covariates.Weighted univariate and multiple logistic regression models were used to assess associations and interaction effects between eye surgery and refractive error.RESULTS:Totally 3925 participants were analyzed.Older age,eye surgery,and refractive errors were significantly associated with ERM.Compared to those under 65y,the odds ratio(OR)for ERM was 3.08 for ages 65–75y(P=0.0014)and 4.76 for ages 75+years(P=0.0069).Eye surgery increased ERM risk(OR=3.48,P=0.0018).Moderate to high hyperopia and myopia were also associated with ERM(OR=2.65 and 1.80,respectively).A significant interaction between refractive error and eye surgery was observed(P<0.0001).Moderate to high myopia was associated with ERM only in those without eye surgery(OR=1.92,P=0.0443).Eye surgery was most strongly associated with ERM in the emmetropic group(OR=3.60,P=0.0027),followed by the moderate to high myopia group(OR=3.01,P=0.0031).CONCLUSION:ERM is significantly associated with aging,eye surgery,and refractive errors.The interaction between eye surgery and refractive error modifies ERM risk and highlights the importance of considering combined effects in clinical risk assessments.These findings may help guide individualized ERM risk assessment that may inform personalized approaches to ERM prevention and management.展开更多
The intersection of visual impairment and mental health has profound effects on quality of life and warrants attention from healthcare providers,educators,and policymakers.With 20 million children under the age of 14 ...The intersection of visual impairment and mental health has profound effects on quality of life and warrants attention from healthcare providers,educators,and policymakers.With 20 million children under the age of 14 affected globally,older adults also experience significant psychological impact including depression,anxiety,and cognitive impairment.The implications of vision-related challenges extend far beyond mere sight.Depression and anxiety,exacerbated by social isolation and reduced physical activity,underscore the need for comprehensive interventions that address both medical and psychosocial dimensions.By recognizing the profound impact of ocular morbidities like strabismus,myopia,glaucoma,and age-related macular degeneration on mental health and investing in effective treatments and inclusive practices,society can pave the way for a healthier,more equitable future for affected individuals.There is evidence that myopic children experience a higher prevalence of depressive symptoms compared to their normal peers,and interventions like the correction of strabismus can enhance psychological outcome-demonstrating the value of an integrated management approach.展开更多
The sleep-wake cycle stands as an integrative process essential for sustaining optimal brain function and,either directly or indirectly,overall body health,encompassing metabolic and cardiovascular well-being.Given th...The sleep-wake cycle stands as an integrative process essential for sustaining optimal brain function and,either directly or indirectly,overall body health,encompassing metabolic and cardiovascular well-being.Given the heightened metabolic activity of the brain,there exists a considerable demand for nutrients in comparison to other organs.Among these,the branched-chain amino acids,comprising leucine,isoleucine,and valine,display distinctive significance,from their contribution to protein structure to their involvement in overall metabolism,especially in cerebral processes.Among the first amino acids that are released into circulation post-food intake,branched-chain amino acids assume a pivotal role in the regulation of protein synthesis,modulating insulin secretion and the amino acid sensing pathway of target of rapamycin.Branched-chain amino acids are key players in influencing the brain's uptake of monoamine precursors,competing for a shared transporter.Beyond their involvement in protein synthesis,these amino acids contribute to the metabolic cycles ofγ-aminobutyric acid and glutamate,as well as energy metabolism.Notably,they impact GABAergic neurons and the excitation/inhibition balance.The rhythmicity of branchedchain amino acids in plasma concentrations,observed over a 24-hour cycle and conserved in rodent models,is under circadian clock control.The mechanisms underlying those rhythms and the physiological consequences of their disruption are not fully understood.Disturbed sleep,obesity,diabetes,and cardiovascular diseases can elevate branched-chain amino acid concentrations or modify their oscillatory dynamics.The mechanisms driving these effects are currently the focal point of ongoing research efforts,since normalizing branched-chain amino acid levels has the ability to alleviate the severity of these pathologies.In this context,the Drosophila model,though underutilized,holds promise in shedding new light on these mechanisms.Initial findings indicate its potential to introduce novel concepts,particularly in elucidating the intricate connections between the circadian clock,sleep/wake,and metabolism.Consequently,the use and transport of branched-chain amino acids emerge as critical components and orchestrators in the web of interactions across multiple organs throughout the sleep/wake cycle.They could represent one of the so far elusive mechanisms connecting sleep patterns to metabolic and cardiovascular health,paving the way for potential therapeutic interventions.展开更多
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.Num...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.展开更多
Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,curr...Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.展开更多
Nitrate contamination of groundwater is a worldwide problem, particularly in agricultural countries. Exposure to high levels of nitrates in groundwater can have adverse effects on the health of residents who use groun...Nitrate contamination of groundwater is a worldwide problem, particularly in agricultural countries. Exposure to high levels of nitrates in groundwater can have adverse effects on the health of residents who use groundwater for drinking. This study aims to assess the health risk associated with the ingestion of nitrates in well water in the town of M’bahiakro. Health risk maps were created on the basis of hazard quotients (HQ) using the US Environmental Protection Agency (USEPA) health risk assessment model. The results indicate that residents of the Koko, Dougouba and Baoulekro neighbourhoods, whatever their age, are potentially exposed to the toxic effects of NO3−during their daily intake of nitrate-contaminated well water, with reference to hazard quotients (HQ) greater than 1. Nitrate concentrations in the groundwater should therefore be controlled in order to prevent their harmful effects on the health of the population and guarantee its use in rice-growing activities in M’Bahiakro.展开更多
To study the volatile organic compounds(VOCs)emission characteristics of industrial enterprises in China,6 typical chemical industries in Yuncheng City were selected as research objects,including the modern coal chemi...To study the volatile organic compounds(VOCs)emission characteristics of industrial enterprises in China,6 typical chemical industries in Yuncheng City were selected as research objects,including the modern coal chemical industry(MCC),pharmaceutical industry(PM),pesticide industry(PE),coking industry(CO)and organic chemical industry(OC).The chemical composition of 91 VOCs was quantitatively analyzed.The results showed that the emission concentration of VOCs in the chemical industry ranged from 1.16 to 155.59 mg/m^(3).Alkanes were the main emission components of MCC(62.0%),PE(55.1%),and OC(58.5%).Alkenes(46.5%)were important components of PM,followed by alkanes(23.8%)and oxygenated volatile organic compounds(OVOCs)(21.2%).Halocarbons(8.6%-71.1%),OVOCs(9.7%-37.6%)and alkanes(11.2%-27.0%)were characteristic components of CO.The largest contributor to OFP was alkenes(0.6%-81.7%),followed by alkanes(9.3%-45.9%),and the lowest onewas alkyne(0%-0.5%).Aromatics(66.9%-85.4%)were the largest contributing components to SOA generation,followed by alkanes(2.6%-28.5%),and the lowest one was alkenes(0%-4.1%).Ethylene and BTEX were the key active species in various chemical industries.The human health risk assessment showed workers long-term exposed to the air in the chemical industrial zone had a high cancer and non-cancer risk during work,and BTEX and dichloromethane were the largest contributors.展开更多
BACKGROUND Coronavirus disease 2019(COVID-19)disrupted healthcare and led to increased telehealth use.We explored the impact of COVID-19 on liver transplant evaluation(LTE).AIM To understand the impact of telehealth o...BACKGROUND Coronavirus disease 2019(COVID-19)disrupted healthcare and led to increased telehealth use.We explored the impact of COVID-19 on liver transplant evaluation(LTE).AIM To understand the impact of telehealth on LTE during COVID-19 and to identify disparities in outcomes disaggregated by sociodemographic factors.METHODS This was a retrospective study of patients who initiated LTE at our center from 3/16/20-3/16/21(“COVID-19 era”)and the year prior(3/16/19-3/15/20,“pre-COVID-19 era”).We compared LTE duration times between eras and explored the effects of telehealth and inpatient evaluations on LTE duration,listing,and pretransplant mortality.RESULTS One hundred and seventy-eight patients were included in the pre-COVID-19 era cohort and one hundred and ninety-nine in the COVID-19 era cohort.Twentynine percent(58/199)of COVID-19 era initial LTE were telehealth,compared to 0%(0/178)pre-COVID-19.There were more inpatient evaluations during COVID-19 era(40%vs 28%,P<0.01).Among outpatient encounters,telehealth use for initial LTE during COVID-19 era did not impact likelihood of listing,pretransplant mortality,or time to LTE and listing.Median times to LTE and listing during COVID-19 were shorter than pre-COVID-19,driven by increased inpatient evaluations.Sociodemographic factors were not predictive of telehealth.CONCLUSION COVID-19 demonstrates a shift to telehealth and inpatient LTE.Telehealth does not impact LTE or listing duration,likelihood of listing,or mortality,suggesting telehealth may facilitate LTE without negative outcomes.展开更多
This study addressed the critical need for an integrated,personalized approach to perimenopausal mental health,addressing both biological and psychosocial fac-tors.Current research highlighted the influence of hormona...This study addressed the critical need for an integrated,personalized approach to perimenopausal mental health,addressing both biological and psychosocial fac-tors.Current research highlighted the influence of hormonal fluctuations,genetic predispositions,and lifestyle factors in shaping perimenopausal mental health outcomes.This transitional period is marked by significant hormonal fluctuations contributing to heightened anxiety,depression,and sleep disturbances,affecting the women’s quality of life.Traditional pharmacological treatments,including selective serotonin reuptake inhibitors and hormone replacement therapy,have limitations due to variable efficacy and side effects,emphasizing the need for precision medicine.Advancements in pharmacogenomics and metabolomics provide new avenues for individualized treatments,with genetic markers(e.g.,Solute carrier organic anion transporter family member 1B1,estrogen receptor 1/estrogen receptor 2,and tachykinin receptor 3)guiding hormone therapy resp-onses.Emerging digital health technologies,such as artificial intelligence-driven diagnostics,wearable monitoring,and telehealth platforms,offer scalable,real-time mental health support,though regulatory and clinical validation challenges remain.Furthermore,integrative treatment models combining hormone-based therapy with non-pharmacological interventions demonstrate significant efficacy in alleviating perimenopausal symptoms.Future directions should prioritize the clinical validation and ethical implementation of digital health solutions,ensuring safety,efficacy,and user accessibility.A multidisciplinary,patient-centric model,incorporating genetics,endocrinology,digital health,and psychosocial interventions,is essential for optimizing perimenopausal mental health outcomes.展开更多
Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of data.Recently,both deep learning and ensem...Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of data.Recently,both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/decisions.With the growth in popularity of deep learning and ensemble learning algorithms,they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big data.Ensemble deep learning has exhibited significant performance in enhancing learning generalization through the use of multiple deep learning algorithms.Although ensemble deep learning has large quantities of training parameters,which results in time and space overheads,it performs much better than traditional ensemble learning.Ensemble deep learning has been successfully used in several areas,such as bioinformatics,finance,and health care.In this paper,we review and investigate recent ensemble deep learning algorithms and techniques in health care domains,medical imaging,health care data analytics,genomics,diagnosis,disease prevention,and drug discovery.We cover several widely used deep learning algorithms along with their architectures,including deep neural networks(DNNs),convolutional neural networks(CNNs),recurrent neural networks(RNNs),and generative adversarial networks(GANs).Common healthcare tasks,such as medical imaging,electronic health records,and genomics,are also demonstrated.Furthermore,in this review,the challenges inherent in reducing the burden on the healthcare system are discussed and explored.Finally,future directions and opportunities for enhancing healthcare model performance are discussed.展开更多
BACKGROUND Effective health management for high-risk stroke populations is essential.The hospital-community-home(HCH)collaborative health management(CHM)model leverages resources from hospitals,communities,and familie...BACKGROUND Effective health management for high-risk stroke populations is essential.The hospital-community-home(HCH)collaborative health management(CHM)model leverages resources from hospitals,communities,and families.By integrating patient information across these three domains,it facilitates the delivery of tailored guidance,health risk assessments,and three-in-one health education.AIM To explore the effects of the HCH-CHM model on stroke risk reduction in highrisk populations.METHODS In total,110 high-risk stroke patients screened in the community from January 2019 to January 2023 were enrolled,with 52 patients in the control group receiving routine health education and 58 in the observation group receiving HCH-CHM model interventions based on routine health education.Stroke awareness scores,health behavior levels,medication adherence,blood pressure,serum biochemical markers(systolic/diastolic blood pressure,total cholesterol,and triglyceride),and psychological measures(self-rating anxiety/depression scale)were evaluated and compared between groups.RESULTS The observation group showed statistically significant improvements in stroke awareness scores and health behavior levels compared to the control group(P<0.05),with notable enhancements in lifestyle and dietary habits(P<0.05)and reductions in postintervention systolic blood pressure,diastolic blood pressure,total cholesterol,triglyceride,self-rating anxiety scale,and self-rating depression scale scores(P<0.05).CONCLUSION The HCH-CHM model had a significant positive effect on high-risk stroke populations,effectively increasing disease awareness,improving health behavior and medication adherence,and appropriately ameliorating blood pressure,serum biochemical marker levels,and negative psychological symptoms.展开更多
文摘This article explores the intersection of the Eight-Circuit Model of Consciousness (ECM), initially conceptualized by Timothy Leary and expanded by Robert Anton Wilson, and its implications for mind-body health. By analyzing each circuit’s role in human consciousness, we discuss how activating and balancing these circuits can lead to enhanced psychological well-being, stress reduction, and overall physical health. Recent research is integrated to provide a contemporary understanding of how the ECM can be applied to modern mind-body therapies, with a focus on both theoretical implications and practical applications.
基金funded by the National Social Science Foundation project“Research on Chinese Life Wisdom from the Perspective of Creative Transformation and Innovative Development”(22ZDA082).
文摘Background:Based on a profound awareness of crisis sense,Huai Nan-zi warns people of the importance of being prepared for danger in times of peace by using the principle that Heaven and Earth are ever-changing and never-ending.It also consciously constructs a cosmic life view of the organic,homologous,isomorphic,and harmonious unity of heaven,earth,and man based on human existence and destiny with a rational thinking attitude.The life philosophy of the Huai Nan-zi offers modern people a completely new holistic view of life and medicine.Not only in China,but also in Western countries,studying its medical philosophical ideas helps us better explore the theoretical roots of TCM in the era of globalized medicine.Methods:This paper mainly uses the analysis method of literature review and chinese philosophy intellectual concepts.It employs the I Ching’s image-number logic thinking method to compare images through analogy and the holistic thinking method of the three-talent view of heaven,earth,and human to understand life consciousness.Results:This article mainly interprets medical philosophy in Huai Nan-zi through three aspects:1)The body of Taoism:heaven,earth,and humanity constitute one unity within the body of the universe;2)The spirit of Taoism:keeping the spirit inward,preserving the essence and suppressing the superficial;and 3)The mind of Taoism:the principle of life governed by the dynamics of gain and loss,prosperity and decline.Conclusion:The philosophy of life presented in the Huai Nan-zi ultimately charts a course toward a state of profound theoretical integration.Its“conscious map”does not lead to a fixed destination,but to a continuous and dynamic mode of being–a life of flourishing known in Chinese as yang sheng,the nurturing of life.The destination,therefore,is the journey itself,undertaken with unwavering cosmic awareness and harmony.This ancient text reminds us that a truly healthy life is not a fragmented pursuit of physical fitness,mental peace,or spiritual insight in isolation.Instead,it is the symphony of all three(Taoist body,spirit,and mind),orchestrated by the fundamental principles of the cosmos.By aligning our inner nature with the outer Tao,we transform our existence from a series of reactive struggles into a graceful and spontaneous free flow.In a modern world characterized by fragmentation,overstimulation,and a relentless push against natural rhythms,the Huai Nan-zi’s life consciousness map is more relevant than ever.The philosophy of Huai Nan-zi not only plays a vital role in the construction of the theoretical system of TCM in ancient East life wisdom,but also is worthwhile for Western life sciences to conduct in-depth exploration and discovery in the age of AI.
文摘Healthy behavior has long been linked to mental health outcomes.However,the role of artificial intelligence(AI)literacy in shaping healthy behaviors and its potential impact on mental health remains underexplored.This paper presents a scoping review offering a novel perspective on the intersection of healthy behaviors,mental health,and AI literacy.By examining how individuals’understanding of AI influences their choices regarding nutrition and their susceptibility to mental health issues,the current study explores emerging trends in health behavior decision-making.This emphasizes the need for integrating AI literacy into mental health and health behaviors education,as well as the development of AI-driven tools to support healthier behavior choices.It highlights that individuals with low AI literacy may misinterpret or overly depend on AI guidance,resulting in maladaptive health choices,while those with high AI literacy may be more likely to engage reflectively and sustain positive behaviors.The paper outlines the importance of inclusive education,user-centered design,and community-based support systems to enhance AI literacy for digitally marginalized groups.AI literacy may be positioned as a key determinant of health equity,better allowing for interdisciplinary strategies that empower individuals to make informed,autonomous decisions that promote both physical and mental health.
文摘Recently,inflammatory cytokine profiles have been linked to suicide risk in adolescents with non-suicidal self-injury,highlighting a promising biological dimension of suicide risk assessment.Clinical translation of the cytokine profiles into practice will require frontline engagement of the workforce.Mental health nurses are frequently the most accessible professionals in schools,communities,and low-resource settings and are prime candidates to bridge this gap.By integrating psychosocial evaluation with emerging biomarker data,they can deliver systematic risk assessment,continuous monitoring,and timely intervention.This role would not replace psychiatric expertise;it would extend the reach of psychiatric services,embedding suicide prevention across the continuum of care.For health systems,nurse-led integration may enhance capacity,equity,and resilience in responding to adolescent suicide risk.This editorial demonstrates that empowering nurses to operationalize biomarker-informed strategies is needed for advancing effective and sustainable suicide prevention in this vulnerable population.
基金funded by the Hunan Provincial Natural Science Foundation of China(Grant No.2025JJ70105)the Hunan Provincial College Students’Innovation and Entrepreneurship Training Program(Project No.S202411342056)The article processing charge(APC)was funded by the Project No.2025JJ70105.
文摘With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.
基金Supported by Inter Disciplinary Direction Cultivation Project of Hunan University of Chinese Medicine,No.2025JC01032025 Hunan Province Science and Technology Innovation Plan Project,No.2025RC9012+2 种基金2022"Unveiling and Leading"Project of Discipline Construction at Hunan University of Chinese Medicine,No.22JBZ044Changsha Municipal Natural Science Foundation,No.kq2402174Hunan Provincial Science Popularization Fund Project,No.2025ZK4223.
文摘BACKGROUND Timely and accurate evaluation of mental disorders in adolescents using appropriate mental health literacy assessment tools is essential for improving their mental health literacy levels.AIM To develop an evaluation index system for the mental health literacy of adolescent patients with mental disorders,providing a scientific,comprehensive,and reliable tool for the monitoring and intervention of mental health literacy of such patients.METHODS From December 2022 to June 2023,the evaluation index system for mental health literacy of adolescents with mental disorders was developed through literature reviews,semi-structured interviews,expert letter consultations,and the analytic hierarchy process.Based on this index system,a self-assessment questionnaire was compiled and administered to 305 adolescents with mental disorders to test the reliability and validity of the index system.RESULTS The final evaluation index system for mental health literacy of adolescents with mental disorders included 4 first-level indicators,10 second-level indicators,and 52 third-level indicators.The overall Cronbach’sαcoefficient of the index system was 0.957,with a partial reliability of 0.826 and a content validity index of 0.975.The cumulative variance contribution rate of 10 common factors was 66.491%.The correlation coefficients between each dimension and the total questionnaire ranged from 0.672 to 0.724,while the correlation coefficients in each dimension ranged from 0.389 to 0.705.CONCLUSION The evaluation index system for mental health literacy of adolescents with mental disorders,developed in this study,demonstrated notable reliability and validity,making it a valuable tool for evaluating mental health literacy in this population.
基金supported by the Northeast Geological Science and Technology Innovation Center of China Geological Survey(Grant NO.QCJJ2022-43)the Natural Resources Comprehensive Survey Project(Grant Nos.DD20230470,DD20230508)the National Groundwater Monitoring Network Operation and Maintenance Program(Grant No.DD20251300109).
文摘Excessive levels of Fluoride(F−)and Cadmium(Cd)in drinking groundwater may pose health risks.This study assessed the health risks associated with F−and Cd contamination in rural drinking groundwater sources in Wutai County,Shanxi Province,China,to support population health protection,water resource management,and environmental decision-making.Groundwater samples were collected and analyzed,and a Human Health Risk Model(HHRA)was applied to evaluate groundwater quality.The results showed that both contents of F−and Cd in groundwater exceeded the Class III limits of China's national groundwater quality standard(GB/T 14848—2024).Fluoride levels met the Class V threshold,with enrichment area mainly located in the east part of the study area.Cadmium levels reached Class IV,with elevated concentrations primarily observed in the western and northwestern regions.Correlation analysis revealed that F−showed weak or no correlation with other measured substances,indicating independent sources.Health risk assessment results indicated that F−poses potential health risks to rural residents,while cadmium,due to its relatively low concentrations,does not currently present a significant health risk.Among different demographic groups,the health risk levels of F−exposure followed the order:Infants>children>adult females>adult males.The findings highlight that fluoride is the primary contributor to health risks associated with groundwater consumption in the study area.Strengthened monitoring and prevention of F−contamination are urgently needed.This research provides a scientific basis for the prevention and control of fluoride pollution in groundwater and offers practical guidance for safeguarding drinking water safety in rural China.
基金supported by the National Natural Science Foundation of China(Nos.42305089 and 42175106)the Self-supporting Program of Guangzhou Laboratory(No.SRPG22-007)+1 种基金the Youth Science and Technology Fund Project of Gansu(No.22JR5RA512)the Fundamental Research Funds for the Central Universities(No.lzujbky-2022-pd05).
文摘Ambient fine particulate matter(PM_(2.5))pollution causes the largest environmental health risk globally,yet ex-posure levels and the resulting health risks vary across countries with different income levels.Global wealth inequality has intensified in recent years,yet the relationship between wealth inequality and health risks related to PM_(2.5) pollution remains poorly understood.In this study,we evaluated the global mortality and health cost at-tributable to PM_(2.5) exposure from 2017 to 2021,and analyzed the relationship between wealth inequality,PM_(2.5) pollution,and the associated health risks across regions with varying economic levels.We found a consistent decline in mortalities and health costs attributable to PM_(2.5) exposure from 2017 to 2020,followed by a rebound after 2020,driven primarily by the resurgence of PM_(2.5) concentrations and a deceleration in the reduction of baseline mortality rates.We also found that the average PM_(2.5) concentration and associated risks decrease as domestic wealth inequality decreases and national income level increases.However,regions with extremely high levels of wealth inequality consistently show lower national average PM_(2.5) concentrations and health risks.These findings highlight the need to consider healthcare security during emergencies,as well as policy fairness across economic regions,in the formulation of global PM_(2.5) pollution control measures to promote sustainable,more equitable economic growth and coordinated air pollution management.
文摘Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),and autism spectrum disorder(ASD)frequently arise from the complex interplay of demographic,biological,and socioeconomic factors,resulting in aggravated symptoms.This review investigates machine intelligence approaches for the early detection and prediction of mental health conditions.Methods:The preferred reporting items for systematic reviews and meta-analyses(PRISMA)framework was employed to conduct a systematic review and analysis covering the period 2018 to 2025.The potential impact of machine intelligence methods was assessed by considering various strategies,hybridization of algorithms,tools,techniques,and datasets,and their applicability.Results:Through a systematic review of studies concentrating on the prediction and evaluation of mental disorders using machine intelligence algorithms,advancements,limitations,and gaps in current methodologies were highlighted.The datasets and tools utilized in these investigations were examined,offering a detailed overview of the status of computational models in understanding and diagnosing mental health disorders.Recent research indicated considerable improvements in diagnostic accuracy and treatment effectiveness,particularly for depression and anxiety,which have shown the greatest methodological diversity and notable advancements in machine intelligence.Conclusions:Despite these improvements,challenges persist,including the need for more diverse datasets,ethical issues surrounding data privacy and algorithmic bias,and obstacles to integrating these technologies into clinical settings.This synthesis emphasizes the transformative potential of machine intelligence in enhancing mental healthcare.
基金Supported by Chengdu Municipal Science and Technology Bureau Key R&D Support Program(No.2023-YF09-00041-SN)。
文摘AIM:To assess risk factors for epiretinal membranes(ERM)and examine their interactions in a nationally representative U.S.dataset.METHODS:Data from the 2005–2008 National Health and Nutrition Examination Survey(NHANES)were analyzed,a nationally representative U.S.dataset.ERM was identified via retinal imaging based on the presence of cellophane changes.Key predictors included age group,eye surgery history,and refractive error,with additional demographic and health-related covariates.Weighted univariate and multiple logistic regression models were used to assess associations and interaction effects between eye surgery and refractive error.RESULTS:Totally 3925 participants were analyzed.Older age,eye surgery,and refractive errors were significantly associated with ERM.Compared to those under 65y,the odds ratio(OR)for ERM was 3.08 for ages 65–75y(P=0.0014)and 4.76 for ages 75+years(P=0.0069).Eye surgery increased ERM risk(OR=3.48,P=0.0018).Moderate to high hyperopia and myopia were also associated with ERM(OR=2.65 and 1.80,respectively).A significant interaction between refractive error and eye surgery was observed(P<0.0001).Moderate to high myopia was associated with ERM only in those without eye surgery(OR=1.92,P=0.0443).Eye surgery was most strongly associated with ERM in the emmetropic group(OR=3.60,P=0.0027),followed by the moderate to high myopia group(OR=3.01,P=0.0031).CONCLUSION:ERM is significantly associated with aging,eye surgery,and refractive errors.The interaction between eye surgery and refractive error modifies ERM risk and highlights the importance of considering combined effects in clinical risk assessments.These findings may help guide individualized ERM risk assessment that may inform personalized approaches to ERM prevention and management.
文摘The intersection of visual impairment and mental health has profound effects on quality of life and warrants attention from healthcare providers,educators,and policymakers.With 20 million children under the age of 14 affected globally,older adults also experience significant psychological impact including depression,anxiety,and cognitive impairment.The implications of vision-related challenges extend far beyond mere sight.Depression and anxiety,exacerbated by social isolation and reduced physical activity,underscore the need for comprehensive interventions that address both medical and psychosocial dimensions.By recognizing the profound impact of ocular morbidities like strabismus,myopia,glaucoma,and age-related macular degeneration on mental health and investing in effective treatments and inclusive practices,society can pave the way for a healthier,more equitable future for affected individuals.There is evidence that myopic children experience a higher prevalence of depressive symptoms compared to their normal peers,and interventions like the correction of strabismus can enhance psychological outcome-demonstrating the value of an integrated management approach.
基金supported by a grant from the French Society of Sleep Research and Medicine(to LS)The China Scholarship Council(to HL)The CNRS,INSERM,Claude Bernard University Lyon1(to LS)。
文摘The sleep-wake cycle stands as an integrative process essential for sustaining optimal brain function and,either directly or indirectly,overall body health,encompassing metabolic and cardiovascular well-being.Given the heightened metabolic activity of the brain,there exists a considerable demand for nutrients in comparison to other organs.Among these,the branched-chain amino acids,comprising leucine,isoleucine,and valine,display distinctive significance,from their contribution to protein structure to their involvement in overall metabolism,especially in cerebral processes.Among the first amino acids that are released into circulation post-food intake,branched-chain amino acids assume a pivotal role in the regulation of protein synthesis,modulating insulin secretion and the amino acid sensing pathway of target of rapamycin.Branched-chain amino acids are key players in influencing the brain's uptake of monoamine precursors,competing for a shared transporter.Beyond their involvement in protein synthesis,these amino acids contribute to the metabolic cycles ofγ-aminobutyric acid and glutamate,as well as energy metabolism.Notably,they impact GABAergic neurons and the excitation/inhibition balance.The rhythmicity of branchedchain amino acids in plasma concentrations,observed over a 24-hour cycle and conserved in rodent models,is under circadian clock control.The mechanisms underlying those rhythms and the physiological consequences of their disruption are not fully understood.Disturbed sleep,obesity,diabetes,and cardiovascular diseases can elevate branched-chain amino acid concentrations or modify their oscillatory dynamics.The mechanisms driving these effects are currently the focal point of ongoing research efforts,since normalizing branched-chain amino acid levels has the ability to alleviate the severity of these pathologies.In this context,the Drosophila model,though underutilized,holds promise in shedding new light on these mechanisms.Initial findings indicate its potential to introduce novel concepts,particularly in elucidating the intricate connections between the circadian clock,sleep/wake,and metabolism.Consequently,the use and transport of branched-chain amino acids emerge as critical components and orchestrators in the web of interactions across multiple organs throughout the sleep/wake cycle.They could represent one of the so far elusive mechanisms connecting sleep patterns to metabolic and cardiovascular health,paving the way for potential therapeutic interventions.
文摘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.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant(No.51677058).
文摘Precisely estimating the state of health(SOH)of lithium-ion batteries is essential for battery management systems(BMS),as it plays a key role in ensuring the safe and reliable operation of battery systems.However,current SOH estimation methods often overlook the valuable temperature information that can effectively characterize battery aging during capacity degradation.Additionally,the Elman neural network,which is commonly employed for SOH estimation,exhibits several drawbacks,including slow training speed,a tendency to become trapped in local minima,and the initialization of weights and thresholds using pseudo-random numbers,leading to unstable model performance.To address these issues,this study addresses the challenge of precise and effective SOH detection by proposing a method for estimating the SOH of lithium-ion batteries based on differential thermal voltammetry(DTV)and an SSA-Elman neural network.Firstly,two health features(HFs)considering temperature factors and battery voltage are extracted fromthe differential thermal voltammetry curves and incremental capacity curves.Next,the Sparrow Search Algorithm(SSA)is employed to optimize the initial weights and thresholds of the Elman neural network,forming the SSA-Elman neural network model.To validate the performance,various neural networks,including the proposed SSA-Elman network,are tested using the Oxford battery aging dataset.The experimental results demonstrate that the method developed in this study achieves superior accuracy and robustness,with a mean absolute error(MAE)of less than 0.9%and a rootmean square error(RMSE)below 1.4%.
文摘Nitrate contamination of groundwater is a worldwide problem, particularly in agricultural countries. Exposure to high levels of nitrates in groundwater can have adverse effects on the health of residents who use groundwater for drinking. This study aims to assess the health risk associated with the ingestion of nitrates in well water in the town of M’bahiakro. Health risk maps were created on the basis of hazard quotients (HQ) using the US Environmental Protection Agency (USEPA) health risk assessment model. The results indicate that residents of the Koko, Dougouba and Baoulekro neighbourhoods, whatever their age, are potentially exposed to the toxic effects of NO3−during their daily intake of nitrate-contaminated well water, with reference to hazard quotients (HQ) greater than 1. Nitrate concentrations in the groundwater should therefore be controlled in order to prevent their harmful effects on the health of the population and guarantee its use in rice-growing activities in M’Bahiakro.
基金supported by the National Natural Science Foundation of China(No.41905108)the National Research Program for Key Issues in Air Pollution Control(No.DQ GG0532).
文摘To study the volatile organic compounds(VOCs)emission characteristics of industrial enterprises in China,6 typical chemical industries in Yuncheng City were selected as research objects,including the modern coal chemical industry(MCC),pharmaceutical industry(PM),pesticide industry(PE),coking industry(CO)and organic chemical industry(OC).The chemical composition of 91 VOCs was quantitatively analyzed.The results showed that the emission concentration of VOCs in the chemical industry ranged from 1.16 to 155.59 mg/m^(3).Alkanes were the main emission components of MCC(62.0%),PE(55.1%),and OC(58.5%).Alkenes(46.5%)were important components of PM,followed by alkanes(23.8%)and oxygenated volatile organic compounds(OVOCs)(21.2%).Halocarbons(8.6%-71.1%),OVOCs(9.7%-37.6%)and alkanes(11.2%-27.0%)were characteristic components of CO.The largest contributor to OFP was alkenes(0.6%-81.7%),followed by alkanes(9.3%-45.9%),and the lowest onewas alkyne(0%-0.5%).Aromatics(66.9%-85.4%)were the largest contributing components to SOA generation,followed by alkanes(2.6%-28.5%),and the lowest one was alkenes(0%-4.1%).Ethylene and BTEX were the key active species in various chemical industries.The human health risk assessment showed workers long-term exposed to the air in the chemical industrial zone had a high cancer and non-cancer risk during work,and BTEX and dichloromethane were the largest contributors.
文摘BACKGROUND Coronavirus disease 2019(COVID-19)disrupted healthcare and led to increased telehealth use.We explored the impact of COVID-19 on liver transplant evaluation(LTE).AIM To understand the impact of telehealth on LTE during COVID-19 and to identify disparities in outcomes disaggregated by sociodemographic factors.METHODS This was a retrospective study of patients who initiated LTE at our center from 3/16/20-3/16/21(“COVID-19 era”)and the year prior(3/16/19-3/15/20,“pre-COVID-19 era”).We compared LTE duration times between eras and explored the effects of telehealth and inpatient evaluations on LTE duration,listing,and pretransplant mortality.RESULTS One hundred and seventy-eight patients were included in the pre-COVID-19 era cohort and one hundred and ninety-nine in the COVID-19 era cohort.Twentynine percent(58/199)of COVID-19 era initial LTE were telehealth,compared to 0%(0/178)pre-COVID-19.There were more inpatient evaluations during COVID-19 era(40%vs 28%,P<0.01).Among outpatient encounters,telehealth use for initial LTE during COVID-19 era did not impact likelihood of listing,pretransplant mortality,or time to LTE and listing.Median times to LTE and listing during COVID-19 were shorter than pre-COVID-19,driven by increased inpatient evaluations.Sociodemographic factors were not predictive of telehealth.CONCLUSION COVID-19 demonstrates a shift to telehealth and inpatient LTE.Telehealth does not impact LTE or listing duration,likelihood of listing,or mortality,suggesting telehealth may facilitate LTE without negative outcomes.
基金Supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education,No.RS-2023-00237287。
文摘This study addressed the critical need for an integrated,personalized approach to perimenopausal mental health,addressing both biological and psychosocial fac-tors.Current research highlighted the influence of hormonal fluctuations,genetic predispositions,and lifestyle factors in shaping perimenopausal mental health outcomes.This transitional period is marked by significant hormonal fluctuations contributing to heightened anxiety,depression,and sleep disturbances,affecting the women’s quality of life.Traditional pharmacological treatments,including selective serotonin reuptake inhibitors and hormone replacement therapy,have limitations due to variable efficacy and side effects,emphasizing the need for precision medicine.Advancements in pharmacogenomics and metabolomics provide new avenues for individualized treatments,with genetic markers(e.g.,Solute carrier organic anion transporter family member 1B1,estrogen receptor 1/estrogen receptor 2,and tachykinin receptor 3)guiding hormone therapy resp-onses.Emerging digital health technologies,such as artificial intelligence-driven diagnostics,wearable monitoring,and telehealth platforms,offer scalable,real-time mental health support,though regulatory and clinical validation challenges remain.Furthermore,integrative treatment models combining hormone-based therapy with non-pharmacological interventions demonstrate significant efficacy in alleviating perimenopausal symptoms.Future directions should prioritize the clinical validation and ethical implementation of digital health solutions,ensuring safety,efficacy,and user accessibility.A multidisciplinary,patient-centric model,incorporating genetics,endocrinology,digital health,and psychosocial interventions,is essential for optimizing perimenopausal mental health outcomes.
基金funded by Taif University,Saudi Arabia,project No.(TU-DSPP-2024-263).
文摘Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of data.Recently,both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/decisions.With the growth in popularity of deep learning and ensemble learning algorithms,they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big data.Ensemble deep learning has exhibited significant performance in enhancing learning generalization through the use of multiple deep learning algorithms.Although ensemble deep learning has large quantities of training parameters,which results in time and space overheads,it performs much better than traditional ensemble learning.Ensemble deep learning has been successfully used in several areas,such as bioinformatics,finance,and health care.In this paper,we review and investigate recent ensemble deep learning algorithms and techniques in health care domains,medical imaging,health care data analytics,genomics,diagnosis,disease prevention,and drug discovery.We cover several widely used deep learning algorithms along with their architectures,including deep neural networks(DNNs),convolutional neural networks(CNNs),recurrent neural networks(RNNs),and generative adversarial networks(GANs).Common healthcare tasks,such as medical imaging,electronic health records,and genomics,are also demonstrated.Furthermore,in this review,the challenges inherent in reducing the burden on the healthcare system are discussed and explored.Finally,future directions and opportunities for enhancing healthcare model performance are discussed.
基金Supported by Guiding Project of Hebei Provincial Health Commission,No.20201190 and 20180220.
文摘BACKGROUND Effective health management for high-risk stroke populations is essential.The hospital-community-home(HCH)collaborative health management(CHM)model leverages resources from hospitals,communities,and families.By integrating patient information across these three domains,it facilitates the delivery of tailored guidance,health risk assessments,and three-in-one health education.AIM To explore the effects of the HCH-CHM model on stroke risk reduction in highrisk populations.METHODS In total,110 high-risk stroke patients screened in the community from January 2019 to January 2023 were enrolled,with 52 patients in the control group receiving routine health education and 58 in the observation group receiving HCH-CHM model interventions based on routine health education.Stroke awareness scores,health behavior levels,medication adherence,blood pressure,serum biochemical markers(systolic/diastolic blood pressure,total cholesterol,and triglyceride),and psychological measures(self-rating anxiety/depression scale)were evaluated and compared between groups.RESULTS The observation group showed statistically significant improvements in stroke awareness scores and health behavior levels compared to the control group(P<0.05),with notable enhancements in lifestyle and dietary habits(P<0.05)and reductions in postintervention systolic blood pressure,diastolic blood pressure,total cholesterol,triglyceride,self-rating anxiety scale,and self-rating depression scale scores(P<0.05).CONCLUSION The HCH-CHM model had a significant positive effect on high-risk stroke populations,effectively increasing disease awareness,improving health behavior and medication adherence,and appropriately ameliorating blood pressure,serum biochemical marker levels,and negative psychological symptoms.
