Kidney health for all: bridging the gap in kidney health education and literacy

The high burden of kidney disease,global disparities in kidney care,and poor outcomes of kidney failure bring a concomitant growing burden to persons affected,their families,and carers,and the community at large.Health literacy is the degree to which persons and organizations have or equitably enable individuals to have the ability to find,understand,and use information and services to make informed health⁃related decisions and actions for themselves and others.Rather than viewing health literacy as a patient deficit,improving health literacy largely rests with health care providers communicating and educating effectively in codesigned partnership with those with kidney disease.For kidney policy makers,health literacy provides the imperative to shift organizations to a culture that places the person at the center of health care.The growing capability of and access to technology provides new opportunities to enhance education and awareness of kidney disease for all stakeholders.Advances in telecommunication,including social media platforms,can be leveraged to enhance persons’and providers’education;The World Kidney Day declares 2022 as the year of“Kidney Health for All”to promote global teamwork in advancing strategies in bridging the gap in kidney health education and literacy.Kidney organizations should work toward shifting the patient⁃deficit health literacy narrative to that of being the responsibility of health care providers and health policy makers.By engaging in and supporting kidney health-centered policy making,community health planning,and health literacy approaches for all,the kidney communities strive to prevent kidney diseases and enable living well with kidney disease.

迎向新世纪Health for All——如何扮演一位称职的家庭医师

一位医精术明的医生是善于诊断、精于治疗的,而要在新世纪中扮演一位称职的第一线家庭医师应该懂得善于利用社区资源,推动社区卫生保健活动及提供社区优质的医疗保健服务,充分发挥家庭医师五大特色、八大功能;他不仅是一位独善其身的名医,同时亦是兼善天下的良医,尤其更能发挥高品质的转诊后续照顾,协助国家建立完整健全的医疗网,落实分级医疗,迎合21世纪家庭医学的需求,使人人享有“均健”(Health forA ll)的健康社会。在新世纪新挑战的自我期许中除了做一位健康的现代人之外,更要负起“称职的家庭医师角色”。从个人职业服务上出发,结合社会资源及国家医疗卫生政策,提供社会超我服务精神,协助加速推行家庭医师制度,并建议政府全力支持健保政策,展现政府贤能德政及造福于全民健康的施政决心,真正营造三赢(人民、医界、政府)局面。

Challenges and opportunities for battery health estimation:Bridging laboratory research and real-world applications

Addressing climate change demands a significant shift away from fossil fuels,with sectors like electricity and transportation relying heavily on renewable energy.Integral to this transition are energy storage systems,notably lithium-ion batteries.Over time,these batteries degrade,affecting their efficiency and posing safety risks.Monitoring and predicting battery aging is essential,especially estimating its state of health(SOH).Various SOH estimation methods exist,from traditional model-based approaches to machine learning approaches.

Social Media-Based Surveillance Systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

Thermally Conductive and UV-EMI Shielding Electronic Textiles for Unrestricted and Multifaceted Health Monitoring

Skin-attachable electronics have garnered considerable research attention in health monitoring and artificial intelligence domains,whereas susceptibility to elec-tromagnetic interference(EMI),heat accumulation issues,and ultraviolet(UV)-induced aging problems pose significant constraints on their potential applications.Here,an ultra-elas-tic,highly breathable,and thermal-comfortable epidermal sensor with exceptional UV-EMI shielding performance and remarkable thermal conductivity is developed for high-fidelity monitoring of multiple human electrophysiological signals.Via filling the elastomeric microfibers with thermally conductive boron nitride nanoparticles and bridging the insulating fiber interfaces by plating Ag nanoparticles(NPs),an interwoven thermal con-ducting fiber network(0.72 W m^(-1) K^(-1))is constructed benefiting from the seamless thermal interfaces,facilitating unimpeded heat dissipation for comfort skin wearing.More excitingly,the elastomeric fiber substrates simultaneously achieve outstanding UV protection(UPF=143.1)and EMI shielding(SET>65,X-band)capabilities owing to the high electrical conductivity and surface plasmon resonance of Ag NPs.Furthermore,an electronic textile prepared by printing liquid metal on the UV-EMI shielding and thermally conductive nonwoven textile is finally utilized as an advanced epidermal sensor,which succeeds in monitoring different electrophysiological signals under vigorous electromagnetic interference.This research paves the way for developing protective and environmentally adaptive epidermal electronics for next-generation health regulation.

Principle for the Installation of Accommodation for Sick guards in Public Health Establishments in Brazzaville in the Republic of Congo

The results of scientific studies of human social facts in the field of health show that the management of a patient should involve the patient’s entourage,whatever the status or size of the health establishment.In healthcare establishments in the Congo,the following are recognised as being responsible for medical care:specialist doctors,doctors,midwives,nurses and care assistants.The patient’s family and close friends are responsible for looking after the patient and financing care.The hospital infrastructure does not provide any space for the patient warden who accompany the patient during reception and hospitalisation.This makes Congolese hospitals inefficient for patient care.How can we integrate the function of the Sick guard and the assistance of the family,in order to reduce the mortality rate and repair the harm caused to patients requiring the presence of relatives during their stay in hospital,which is considered to be a dangerous place?This article examines the functional principles for configuring the space that patient warden would occupy in the patient care system.On the basis of a documentary analysis of sociological and architectural studies of existing facilities,this article proposes a typical accommodation model with the spaces needed to ensure the well-being and effectiveness of the patient warden with the patient.These are rooms with minimum space for 2 to 4 individual beds,equipped with toilets and showers.The accommodation has a dining area,kitchen and laundry facilities.In the future,this accommodation will become part of the hospital estate and may be occupied by orderlies and patient warden recruited by the hospital administration.

Artificial Intelligence for Global Health: Difficulties and Challenges: A Narrative Review

Global health (GH) aims to improve healthcare for all people on the planet and eradicate all avoidable diseases and deaths. The inception of Artificial Intelligence (AI) is innovating healthcare practices and improving patient outcomes by shuffling enormous volumes of health data—from health records and clinical studies to genetic information analyzing it much faster than humans. AI also helps in the improvement of medical imaging and medical diagnosis. There is an increased optimism regarding the use of applications of AI locally but can these facets be translated globally in the advancement and delivery of healthcare with the help of AI. At present majority of AI developments and applications in health care provide to the needs of developed countries and there is little effort to develop programs which could help to improve healthcare delivery globally. We performed this narrative review to assess the difficulties and discrepancies in implementing AI in global health delivery and find ways to improve.

Effect of health education based on information-motivationbehavioral skills model on patients with unilateral vestibular dysfunction

BACKGROUND Vestibular dysfunction(VH)is a common concomitant symptom of late peri-pheral vestibular lesions,which can be trauma,poisoning,infection,heredity,and neurodegeneration,but about 50%of the causes are unknown.The study uses the information-motivation-behavioral skills(IMB)model for health education,effectively improve the quality of life,increase their self-confidence,reduce anxiety and depression,and effectively improve the psychological state of patients.AIM To explore the effect of health education based on the IMB model on the degree of vertigo,disability,anxiety and depression in patients with unilateral vestibular hypofunction.METHODS The clinical data of 80 patients with unilateral vestibular hypofunction from January 2019 to December 2021 were selected as the retrospective research objects,and they were divided into the control group and the observation group with 40 cases in each group according to different nursing methods.Among them,the control group was given routine nursing health education and guidance,and the observation group was given health education and guidance based on the IMB model.The changes in self-efficacy,anxiety and depression,and quality of life of patients with unilateral VH were compared between the two groups.RESULTS There was no significant difference in General Self-Efficacy Scale(GSES)scale scores between the two groups of patients before nursing(P>0.05),which was comparable;after nursing,the GSES scale scores of the two groups were higher than those before nursing.The nursing group was higher than the control group,and the difference was statistically significant(P<0.05).There was no significant difference in the scores of Hospital Anxiety and Depression Scale(HADS)and anxiety and depression subscales between the two groups before nursing(P>0.05).After nursing,the HADS score,anxiety,and depression subscale scores of the two groups of patients were lower than those before nursing,and the nursing group was lower than the control group,and the difference was statistically significant(P<0.05).After nursing,the Dizziness Handicap Inventory(DHI)scale and DHI-P,DHI-E and DHI-F scores in the two groups were decreased,and the scores in the nursing group were lower than those in the control group,and the difference was statistically significant(P<0.05).CONCLUSION Health education based on the IMB model can effectively improve patients'quality of life,increase self-efficacy of patients with unilateral vestibular hypofunction,enhance patients'confidence,enable patients to resume normal work and life as soon as possible,reduce patients'anxiety and depression,and effectively improve patients'psychological status.

Mental health in the virtual world:Challenges and opportunities in the metaverse era

Current rates of mental illness are worrisome.Mental illness mainly affects females and younger age groups.The use of the internet to deliver mental health care has been growing since 2020 and includes the implementation of novel mental health treatments using virtual reality,augmented reality,and artificial intelligence.A new three dimensional digital environment,known as the metaverse,has emerged as the next version of the Internet.Artificial intelligence,augmented reality,and virtual reality will create fully immersive,experiential,and interactive online environments in the metaverse.People will use a unique avatar to do anything they do in their“real”lives,including seeking and receiving mental health care.In this opinion review,we reflect on how the metaverse could reshape how we deliver mental health treatment,its opportunities,and its challenges.

Charting the course:India's health expenditure projections for 2035

Objectives Understanding past trends and forecasting future changes in health spending is vital for planning and reducing reliance on out-of-pocket(OOP)expenses.The current study analyzed health expenditure patterns in India and forecasted future trends and patterns until 2035.Methods Data on health expenditure in India from 2000 to 2019 was collected from the Organisation for Economic Co-operation and Development(OECD)iLibrary and National Health Accounts 2019 databases.Gross domestic product(GDP)data from the World Bank was also utilized.Descriptive statistics analyzed the composition and pattern,while the exponential smoothing model forecasted future health expenditures.Results The findings revealed that expenditure made by OOP is the primary health financing source,followed by government and pre-paid private spending.The percentage of GDP allocated to total health expenditure remains stable,while the per capita health expenditure fluctuates.Variations in expenditure among states are observed,with Karnataka relying heavily on pre-paid private coverage.Future projections suggest a decline in per capita and total health expenditure as a share of GDP,with a slight increase in the government’s share.Pre-paid private expenditure per capita and OOP health expenditure as a share of the total is projected to remain relatively constant but still high in absolute terms.Conclusion The study highlights variations in health spending in India,characterized by high OOP spending,limited public coverage,and a need for investments,and reforms to improve healthcare access and equity.

Blade-Coated Porous 3D Carbon Composite Electrodes Coupled with Multiscale Interfaces for Highly Sensitive All-Paper Pressure Sensors

Flexible and wearable pressure sensors hold immense promise for health monitoring,covering disease detection and postoperative rehabilitation.Developing pressure sensors with high sensitivity,wide detection range,and cost-effectiveness is paramount.By leveraging paper for its sustainability,biocompatibility,and inherent porous structure,herein,a solution-processed all-paper resistive pressure sensor is designed with outstanding performance.A ternary composite paste,comprising a compressible 3D carbon skeleton,conductive polymer poly(3,4-ethylene dioxythiophene):poly(styrenesulfonate),and cohesive carbon nanotubes,is blade-coated on paper and naturally dried to form the porous composite electrode with hierachical micro-and nano-structured surface.Combined with screen-printed Cu electrodes in submillimeter finger widths on rough paper,this creates a multiscale hierarchical contact interface between electrodes,significantly enhancing sensitivity(1014 kPa-1)and expanding the detection range(up to 300 kPa)of as-resulted all-paper pressure sensor with low detection limit and power consumption.Its versatility ranges from subtle wrist pulses,robust finger taps,to large-area spatial force detection,highlighting its intricate submillimetermicrometer-nanometer hierarchical interface and nanometer porosity in the composite electrode.Ultimately,this all-paper resistive pressure sensor,with its superior sensing capabilities,large-scale fabrication potential,and cost-effectiveness,paves the way for next-generation wearable electronics,ushering in an era of advanced,sustainable technological solutions.

Comparative Analysis of ARIMA and LSTM Model-Based Anomaly Detection for Unannotated Structural Health Monitoring Data in an Immersed Tunnel

Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.

Low crude protein formulation with supplemental amino acids for its impacts on intestinal health and growth performance of growing-finishing pigs

Background Low crude protein(CP)formulations with supplemental amino acids(AA)are used to enhance intestinal health,reduce costs,minimize environmental impact,and maintain growth performance of pigs.However,extensive reduction of dietary CP can compromise growth performance due to limited synthesis of non-essential AA and limited availability of bioactive compounds from protein supplements even when AA requirements are met.Moreover,implementing a low CP formulation can increase the net energy(NE)content in feeds causing excessive fat deposition.Additional supplementation of functional AA,coupled with low CP formulation could further enhance intestinal health and glucose metabolism,improving nitrogen utilization,and growth performance.Three experiments were conducted to evaluate the effects of low CP formulations with supplemental AA on the intestinal health and growth performance of growing-finishing pigs.Methods In Exp.1,90 pigs(19.7±1.1 kg,45 barrows and 45 gilts)were assigned to 3 treatments:CON(18.0%CP,supplementing Lys,Met,and Thr),LCP(16.0%CP,supplementing Lys,Met,Thr,Trp,and Val),and LCPT(16.1%CP,LCP+0.05%SID Trp).In Exp.2,72 pigs(34.2±4.2 kg BW)were assigned to 3 treatments:CON(17.7%CP,meeting the requirements of Lys,Met,Thr,and Trp);LCP(15.0%CP,meeting Lys,Thr,Trp,Met,Val,Ile,and Phe);and VLCP(12.8%CP,meeting Lys,Thr,Trp,Met,Val,Ile,Phe,His,and Leu).In Exp.3,72 pigs(54.1±5.9 kg BW)were assigned to 3 treatments and fed experimental diets for 3 phases(grower 2,finishing 1,and finishing 2).Treatments were CON(18.0%,13.8%,12.7%CP for 3 phases;meeting Lys,Met,Thr,and Trp);LCP(13.5%,11.4%,10.4%CP for 3 phases;meeting Lys,Thr,Trp,Met,Val,Ile,and Phe);and LCPG(14.1%,12.8%,11.1%CP for 3 phases;LCP+Glu to match SID Glu with CON).All diets had 2.6 Mcal/kg NE.Results In Exp.1,overall,the growth performance did not differ among treatments.The LCPT increased(P<0.05)Claudin-1 expression in the duodenum and jejunum.The LCP and LCPT increased(P<0.05)CAT-1,4F2hc,and B0AT expressions in the jejunum.In Exp.2,overall,the VLCP reduced(P<0.05)G:F and BUN.The LCP and VLCP increased(P<0.05)the backfat thickness(BFT).In Exp.3,overall,growth performance and BFT did not differ among treatments.The LCPG reduced(P<0.05)BUN,whereas increased the insulin in plasma.The LCP and LCPG reduced(P<0.05)the abundance of Streptococcaceae,whereas the LCP reduced(P<0.05)Erysipelotrichaceae,and the alpha diversity.Conclusions When implementing low CP formulation,CP can be reduced by supplementation of Lys,Thr,Met,Trp,Val,and Ile without affecting the growth performance of growing-finishing pigs when NE is adjusted to avoid increased fat deposition.Supplementation of Trp above the requirement or supplementation of Glu in low CP formulation seems to benefit intestinal health as well as improved nitrogen utilization and glucose metabolism.

Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,etc.Still,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during pregnancy.Several researchers have proposed various methods for classifying the status of fetus growth.Manual processing of CTG data is time-consuming and unreliable.So,automated tools should be used to classify fetal health.This study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal health.Various strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also illustrated.This study easily provides valuable interpretations for healthcare professionals in the decision-making process.

Evaluation of the effects of health education interventions for hypertensive patients based on the health belief model

BACKGROUND Hypertension is a major risk factor for cardiovascular disease and stroke,and its prevalence is increasing worldwide.Health education interventions based on the health belief model(HBM)can improve the knowledge,attitudes,and behaviors of patients with hypertension and help them control their blood pressure.AIM To evaluate the effects of health education interventions based on the HBM in patients with hypertension in China.METHODS Between 2021 and 2023,140 patients with hypertension were randomly assigned to either the intervention or control group.The intervention group received health education based on the HBM,including lectures,brochures,videos,and counseling sessions,whereas the control group received routine care.Outcomes were measured at baseline,three months,and six months after the intervention and included blood pressure,medication adherence,self-efficacy,and perceived benefits,barriers,susceptibility,and severity.RESULTS The intervention group had significantly lower systolic blood pressure[mean difference(MD):-8.2 mmHg,P<0.001]and diastolic blood pressure(MD:-5.1 mmHg,P=0.002)compared to the control group at six months.The intervention group also had higher medication adherence(MD:1.8,P<0.001),self-efficacy(MD:12.4,P<0.001),perceived benefits(MD:3.2,P<0.001),lower perceived barriers(MD:-2.6,P=0.001),higher perceived susceptibility(MD:2.8,P=0.002),and higher perceived severity(MD:3.1,P<0.001)than the control group at six months.CONCLUSION Health education interventions based on the HBM effectively improve blood pressure control and health beliefs in patients with hypertension and should be implemented in clinical practice and community settings.

Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering

State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and challenging.Most of existing estimations are based on a fixed constant current charging and discharging aging profiles,which overlooked the fact that the charging and discharging profiles are random and not complete in real application.This work investigates the influence of feature engineering on the accuracy of different machine learning(ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is considered.Fifteen features were extracted from the battery partial recharging profiles,considering different factors such as starting voltage values,charge amount,and charging sliding windows.Then,features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset selection.Multiple linear regression(MLR),Gaussian process regression(GPR),and support vector regression(SVR)were applied to estimate SOH,and root mean square error(RMSE)was used to evaluate and compare the estimation performance.The results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05%,2.57%,and 2.82%for MLR,GPR and SVR respectively.It was demonstrated that the estimation based on partial charging profiles with lower starting voltage,large charge,and large sliding window size is more likely to achieve higher accuracy.This work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.

Validity and Reliability of a Mental Health Recovery Checklist for Young Adults

The purpose of the present study is to evaluate the internal consistency and construct validity of a self-report checklist measuring clinical mental health recovery across six key domains:family and relationships,occupation and school,physical health,mental health,spirituality,and social support systems.The Mental Health Recovery Checklist(MHRC)was developed at The Dorm,an intensive outpatient mental health treatment program for young adults in New York,NY,and Washington DC that services individuals between the ages of 18 and 35 years old.The present study is cross-sectional,as data were pulled from clients’records who were discharged between January 2018 and May 2023.Pearson Correlations and Cronbach’s alpha were used to determine scale reliability.To establish validity,an Exploratory Factor Analysis(EFA)was conducted to assess a single-factor model using Principal Axis Factoring.Cronbach’s alpha was high(α=0.88),indicating good reliability.In the EFA,all items loaded strongly on a single factor.The unidimensional structure revealed in the EFA highlights the interconnected nature of various life domains as they pertain to mental health recovery in young adults.Future research is warranted to explore whether there are additional dimensions of mental health recovery that have not been assessed in the present scale.

Effect of care intervention with a health education form for breastfeeding on breast distension,pain,and lactation in postpartum mothers

BACKGROUND Breastfeeding not only meets the nutritional needs of newborn growth and development but also promotes uterine contraction and discharge of lochia,which helps in maternal recovery.However,some mothers experience abnormal lactation and breast swelling due to a lack of breastfeeding knowledge,painful cesarean incisions,anesthesia,negative emotions,and other factors,resulting in a reduced breastfeeding rate,which adversely affects neonatal and maternal health.AIM To explore the effects of care intervention with a health education form for breastfeeding on breastfeeding-related conditions.METHODS In this study,207 mothers with postpartum breast pain and difficulty lactating were selected and divided into intervention and control groups using a random number table.Both groups of mothers were provided with basic nursing and related treatment measures after delivery.The intervention group additionally received care intervention with a health education form for breastfeeding.The scores of lactation volume,breast distension and pain,breastfeeding rate,breastfeeding self-efficacy,treatment effect,and complication rate of the two groups were compared.RESULTS After treatment,the breast pain score of the intervention group was significantly lower than that of the control group,while the lactation score,score of Breastfeeding Self-Efficacy Scale Short Form scale,parent-child communication score,maternal-infant interaction score,total score of maternal-infant communication,and breastfeeding rate of the intervention group were significantly higher than those of the control group.After intervention,the overall therapeutic effect of the intervention group was better than that of the control group,and the complication rate of the intervention group was lower than that of the control group.CONCLUSION Breastfeeding health education and nursing intervention combined with basic clinical treatment have good clinical effects in managing postpartum breast distension and pain and increasing lactation yield.

Health education intervention and its impact on patients with unilateral vestibular dysfunction:An information-motivation-behavioral model approach

Unilateral vestibular dysfunction is a one-sided impairment of vestibular function in one ear.Incorporating health education in treatment and rehabilitation plans can improve vestibular function,keep negative emotions at bay,and reduce the extent of the condition.This letter investigates the impact of the informationmotivation-behavioral skills model as a medium for health education on patient outcomes.While offering encouraging observations,there are certain limitations,such as the study’s retrospective design,small sample size,use of subjective measures,and lack of longer follow-ups that challenge the cogency of the study.The study is a step toward transforming vestibular dysfunction treatment through health education.

Understanding the etiology of mental health problems in postrehabilitation COVID-19 patients:Insights and strategies for effective intervention

In this editorial,we comment on an article by Alhammad et al that was published in a recent issue of the World Journal of Clinical Cases(Manuscript No.:91134).We specifically focus on the mental health problems caused by coronavirus disease 2019(COVID-19),their mechanisms,and targeted rehabilitation strategies.Severe acute respiratory syndrome coronavirus 2,via its spike protein,binds to angiotensin-converting enzyme 2 and other receptors prior to infiltrating diverse cells within the central nervous system,including endothelial cells,neurons,astrocytes,and oligodendrocytes,thereby contributing to the development of mental illnesses.Epidemiological data from 2020 underscored the global upsurge in major depressive and anxiety disorders by 27.6%and 25.6%,respectively,during the pandemic.The commented research show that 30%of post-intensive care unit discharge patients with COVID-19 in the Arabic region exhibited Hospital Anxiety and Depression Scale scores that were indicative of anxiety and depression.While acknowledging psychosocial factors,such as grief and loss,it is crucial to recognize the potential neurological impact of the virus through various mechanisms.Accordingly,interventions that encompass dietary measures,health supplements,and traditional Chinese medicine with neuroprotective properties are necessary.This editorial underscores the urgency to implement comprehensive rehabilitation approaches to address the intricate interplay between COVID-19 and mental well-being.