Personal Health Care Research Articles

Revolutionizing Healthcare with Intelligent Remote Health Monitoring

Revolutionizing healthcare through intelligent remote health monitoring involves leveraging advanced technologies such as artificial intelligence, wearable devices, and IoT to provide continuous and real-time monitoring of patient's health conditions. This approach enables proactive healthcare management by collecting and analyzing vital health data remotely, facilitating Prompt identification of possible wellness issues, and reducing the need for frequent hospital visits. It promotes personalized healthcare, improves patient outcomes, and enhances the efficiency of healthcare systems by allowing healthcare providers to make data-driven decisions. This transformation in healthcare delivery ensures better access to care, particularly for individuals in remote or underserved areas, while also addressing the growing demand for healthcare services globally. Revolutionizing healthcare through intelligent remote health monitoring involves leveraging Cutting-edge innovations like synthetic intelligence, wearable devices, and the Internet of Things (IoT) to provide continuous and real-time monitoring of patient's health conditions. This approach enables proactive healthcare management by collecting and analyzing vital health data remotely, facilitating Prompt identification of emerging health issues, and reducing the need for frequent hospital visits. The integrationof machine learning algorithms allows for predictive analytics, which can anticipate health deteriorations and recommend timely interventions. It promotes personalized healthcare by tailoring treatments to individual needs, improves patient outcomes through constant monitoring and timely responses, and enhances healthcare systems by allowing healthcare providers to make data-driven decision.

Deep Learning for Personalized Healthcare Recommendations

Personalized healthcare refers to an evolving paradigm of providing appropriate medical treatments based on the particularities of the individual patient, where evidence-based management is enhanced with the use of technologies. Deep learning (DL) is placed within the umbrella of efficient systems known as artificial intelligence (AI), it assists in performing data processing with more accuracy, and making suggestions based on the unique health information of the health record e.g. EHRs, images and genetic data among others. This article gives an overview of deep learning techniques in developing personalized healthcare recommendations, including the major achieved algorithms, data sources and existing issues. and future work in exploring the ways deep learning can be harnessed for the development of personalized healthcare systems are presented.

Portable paper-based microfluidic devices with Cu1-xAgxS NPs modification for multiplex intelligent visualized detection of adrenaline and glucose simultaneously

BackgroundAdrenaline and glucose are essential biomarkers in human body for maintaining metabolic balance. Abnormal levels of adrenaline and glucose are associated with various diseases. Therefore, it is important to design portable, on-site devices for rapid adrenaline and glucose analysis to safeguard health. Traditional paper-based analytical devices (μPADs) for multiplexed detection typically required multiple recognized probes, increasing the cost and complexity. Moreover, non-uniform color distribution, caused by lateral chromatographic elution effect, reduced results accuracy. Hence, it is necessary to construct visualized μPADs with controlled flow capability using single sensing probe for simultaneous adrenaline and glucose analysis in point-of-care testing (POCT). ResultsThe portable multiplexed 3D-folded μPADs have been developed for the visualized, accurate and simultaneous detection of adrenaline and glucose, employing Ag-doped copper sulfide nanoparticles (Cu1-xAgxS NPs) as multifunctional sensing probe. The μPADs integrated the sampling entrance layer, buffer layer and test layer into a single device through wax printing, allowing the analyte solution to flow vertically from buffer zones to detection zones, thereby minimizing lateral chromatographic elution effect. Cu1-xAgxS NPs, exhibiting laccase-like and peroxidase-like multi-enzyme activities, were modified on various detection zones of μPADs for multiplex colorimetric analysis of adrenaline and glucose. Based on Cu1-xAgxS-induced colorimetric reactions, the orange-red and blue colors were generated in adrenaline and glucose detection zones, respectively. Subsequently, color signals were converted into RGB values via smartphone application (APP) for rapid and intelligent results analysis. The Cu1-xAgxS-incorporated μPADs exhibited colorimetric uniformity and accuracy with limit of detection (LOD) of 10.2 nM and 11.5 μM, respectively. SignificanceThe adjustable laccase-like and peroxidase-like activities of Cu1-xAgxS NPs induced by Ag doping provide a new perspective for multiple biomarkers analysis. Benefiting from the excellent multienzyme activity of Cu1-xAgxS NPs, μPADs can perform on-site, visual and multiplex analysis using a single probe, demonstrating their potential for POCT in personal healthcare. The 3D folded Cu1-xAgxS-incorporated μPADs with integrated horizontal and vertical flows exhibit uniform color signals for obtaining accurate detection results.

Long-Term Efficacy of an AI-Based Health Coaching Mobile App in Slowing the Progression of Nondialysis-Dependent Chronic Kidney Disease: Retrospective Cohort Study.

Chronic kidney disease (CKD) is a significant public health concern. Therefore, practical strategies for slowing CKD progression and improving patient outcomes are imperative. There is limited evidence to substantiate the efficacy of mobile app-based nursing systems for decelerating CKD progression. This study aimed to evaluate the long-term efficacy of the KidneyOnline intelligent care system in slowing the progression of nondialysis-dependent CKD. In this retrospective study, the KidneyOnline app was used for patients with CKD in China who were registered between January 2017 and April 2023. Patients were divided into 2 groups: an intervention group using the app's nurse-led, patient-oriented management system and a conventional care group that did not use the app. Patients' uploaded health data were processed via deep learning optical character recognition, and the artificial intelligence (AI) system provided personalized health care plans and interventions. Conversely, the conventional care group received suggestions from nephrologists during regular visits without AI. Monitoring extended for an average duration of 2.1 (SD 1.4) years. The study's objective is to assess the app's effectiveness in preserving kidney function. The primary outcome was the estimated glomerular filtration rate slope over the follow-up period, and secondary outcomes included changes in albumin-to-creatinine ratio (ACR) and mean arterial pressure. A total of 12,297 eligible patients were enrolled for the analysis. Among them, 808 patients were successfully matched using 1:1 propensity score matching, resulting in 404 (50%) patients in the KidneyOnline care system group and another 404 (50%) patients in the conventional care group. The estimated glomerular filtration rate slope in the KidneyOnline care group was significantly lower than that in the conventional care group (odds ratio -1.3, 95% CI -2.4 to -0.1 mL/min/1.73 m2 per year vs odds ratio -2.8, 95% CI -3.8 to -1.9 mL/min/1.73 m2 per year; P=.009). Subgroup analysis revealed that the effect of the KidneyOnline care group was more significant in male patients, patients older than 45 years, and patients with worse baseline kidney function, higher blood pressure, and heavier proteinuria. After 3 and 6 months, the mean arterial pressure in the KidneyOnline care group decreased to 85.6 (SD 9.2) and 83.6 (SD 10.5) mm Hg, respectively, compared to 94.9 (SD 10.6) and 95.2 (SD 11.6) mm Hg in the conventional care group (P<.001). The ACR in the KidneyOnline care group showed a more significant reduction after 3 and 6 months (736 vs 980 mg/g and 572 vs 840 mg/g; P=.07 and P=.03); however, there was no significant difference in ACR between the two groups at the end of the follow-up period (618 vs 639 mg/g; P=.90). The utilization of KidneyOnline, an AI-based, nurse-led, patient-centered care system, may be beneficial in slowing the progression of nondialysis-dependent CKD.

Self-healing and transparent ionic conductive PVA/pullulan/borax hydrogels with multi-sensing capabilities for wearable sensors

Conductive hydrogels as wearable sensors have been used for numerous applications in human motion detection, personal healthcare monitoring and other diverse scenarios. However, it remains a challenge to integrate self-healing ability, multiple sensing capabilities, and transparency in one single unit. In this work, multifunctional polyvinyl alcohol (PVA)/Pullulan/Borax conductive hydrogels were fabricated by introducing borate ester bonds and hydrogen bonds. The described hydrogels showed fast self-healing properties, which could autonomously completely recover within 15 s. The hydrogels possessed high optical transparency (92.9%) in the visible light range and had multi-sensing capabilities, such as strain, temperature and humidity sensing. The assembled hydrogel sensor displayed a high strain sensitivity of 2.74 within the strain range of 300%, and it could be used to monitor human motions such as finger and wrist bending. In addition, the hydrogel sensor could sense temperature variations with a temperature coefficient of resistance of −0.914 °C−1 over 28–46 °C. Besides, the hydrogel sensor demonstrated the humidity sensing ability and can recognize human inhale and exhale. The overall sensing performance of the PVA/Pullulan/Borax hydrogel was satisfactory and repeatable. This conductive hydrogel shows great potential in wearable electronics and personal healthcare and inspires a new generation of multifunctional hydrogel sensors.

Completion Rate and Satisfaction With Online Computer-Assisted History Taking Questionnaires in Orthopedics: Multicenter Implementation Report.

Collecting the medical history during a first outpatient consultation plays an important role in making a diagnosis. However, it is a time-consuming process, and time is scarce in today's health care environment. The computer-assisted history taking (CAHT) systems allow patients to share their medical history electronically before their visit. Although multiple advantages of CAHT have been demonstrated, adoption in everyday medical practice remains low, which has been attributed to various barriers. This study aimed to implement a CAHT questionnaire for orthopedic patients in preparation for their first outpatient consultation and analyze its completion rate and added value. A multicenter implementation study was conducted in which all patients who were referred to the orthopedic department were invited to self-complete the CAHT questionnaire. The primary outcome of the study is the completion rate of the questionnaire. Secondary outcomes included patient and physician satisfaction. These were assessed via surveys and semistructured interviews. In total, 5321 patients were invited, and 4932 (92.7%) fully completed the CAHT questionnaire between April 2022 and July 2022. On average, participants (n=224) rated the easiness of completing the questionnaire at 8.0 (SD 1.9; 0-10 scale) and the satisfaction of the consult at 8.0 (SD 1.7; 0-10 scale). Satisfaction with the outpatient consultation was higher in cases where the given answers were used by the orthopedic surgeon during this consultation (median 8.3, IQR 8.0-9.1 vs median 8.0, IQR 7.0-8.5; P<.001). Physicians (n=15) scored the average added value as 7.8 (SD 1.7; 0-10 scale) and unanimously recognized increased efficiency, better patient engagement, and better medical record completeness. Implementing the patient's answers into the electronic health record was deemed necessary. In this study, we have shown that previously recognized barriers to implementing and adapting CAHT can now be effectively overcome. We demonstrated that almost all patients completed the CAHT questionnaire. This results in reported improvements in both the efficiency and personalization of outpatient consultations. Given the pressing need for personalized health care delivery in today's time-constrained medical environment, we recommend implementing CAHT systems in routine medical practice.

Level 4 seizure monitoring unit admissions are associated with reduced long-term health care costs.

This study was undertaken to determine whether admission to dedicated seizure monitoring units (SMUs) result in reduced health care use (HCU). This was a retrospective open cohort study covering the years 2010-2018 of patients residing in Alberta, Canada, who were referred to the Calgary Comprehensive Epilepsy Program and admitted to a level 4 SMU. Patients were required to have ≥3 years pre- and postadmission follow-up. The outcome was the change in trajectory of composite HCU (primary care, specialist outpatient visits, emergency department visits, and hospitalizations) for the 3 years prior to and 3 years following SMU admission using the point of admission as the "index date." Secondary outcomes were HCU limited to specific settings. We excluded the first 30 days following the point of admission to mitigate the confounding admission would have on the postadmission HCU trajectory. We used adjusted restricted maximum likelihood linear and nonlinear effects models to determine trajectories expressed as Canadian dollars. A total of 315 of 600 (53%) patients met eligibility criteria. Mean age was 40 years (SD = 17.4), 176 (56%) were female, 220 (70%) had focal epilepsy, and 60 (19%) had functional seizures or physiologic seizure mimics without epilepsy as adjudicated by the attending physician at the point of discharge. Mean per person health care costs increased by CAD$341.28 (95% confidence interval [CI] = -25.17 to 707.74) for each successive 6-month interval prior to SMU admission (p = .07). Following admission, mean per person costs decreased by CAD$802.34 (95% CI = 699.62-905.06, p < .001) for each successive 6-month interval up to 3 years postdischarge. Similar trends were noted for primary and specialist care, emergency department, admitted care, and when nonlinear models were applied. Admission to an SMU is associated with significant and enduring declines in HCU. Each 6-months following discharge overall HCU declined by a mean of CAD$802.34 and acute inpatient, emergency department, and outpatient physician interactions declined by 25%, 26%, and 18% respectively. Comprehensive epilepsy care not only reduces morbidity and mortality but also reduces cost.

Abstract 4128962: Medicaid Work Requirements and Changes in Insurance Coverage, Employment, Access to Care, and Health Status Overall and Among People with Cardiovascular Disease

Introduction: Medicaid work requirements have been at the forefront of health policy debate. Proponents argue that these policies increase employment, reduce poverty, and improve health. Critics have raised concerns that work requirements lead to loss of coverage and impede access to care. Hypothesis: We hypothesized that Medicaid work requirement implementation would be associated with decreased insurance coverage and healthcare access without impacting employment. Methods: We used the Behavioral Risk Factor Surveillance System (a nationally representative survey administered by the CDC) to estimate the impact of Medicaid work requirements on insurance coverage, employment, access to care, and self-reported health. We used a difference-in-differences (DID) approach to compare changes in outcomes in Arkansas (where work requirements were implemented) versus control states. Outcomes were self-reported and included: 1) insurance coverage, 2) employment, 3) presence of a personal healthcare provider, 4) inability to see a doctor due to cost, and 5) self-reported health. The analysis was conducted among all respondents and among respondents with cardiovascular disease, who were hypothesized to be particularly vulnerable to negative sequelae of insurance loss. Results: The implementation of Medicaid work requirements in Arkansas was associated with decreased insurance coverage compared with control states (adjusted DID estimate -4.3%, CI -8.4%, -0.1%), but no increase in employment rates (-2.2% CI -7.0%, 2.6%). There was a trend toward decreased likelihood of having a personal healthcare provider (-4.7% CI -9.9%, 0.5%). There was no change in inability to see a doctor due to cost or self-reported good health. When this analysis was repeated among participants with cardiovascular disease, work requirement implementation was not associated with changes in any of the outcome variables. Conclusions: The implementation of Medicaid work requirements in Arkansas was associated with decreased insurance coverage and no change in employment. Our findings are particularly relevant given national policy debate about work requirement expansion and the recent implementation of Medicaid work requirements in Georgia. Despite the null findings among respondents with cardiovascular disease (likely the result of a small sample size), this study has important implications for cardiovascular health given the close association between insurance coverage and cardiovascular outcomes.

Current Models of Innovation and Market Strategies in Pharmaceutical Development: A Comprehensive Review

This paper examines innovative models and market strategies in the pharmaceutical industry, highlighting how these innovations shape drug development, market dynamics, and public health outcomes. Pharmaceutical innovation spans product enhancements, process efficiencies, and organizational and marketing transformations, all aimed at improving patient outcomes and healthcare access. The methodology explored comprehensive literature review, focusing on recent developments in pharmaceutical innovation, drug marketing practices, and regulatory frameworks. Primary sources include scholarly articles from Google Scholar, ResearchGate, Scopus, and Web of Science, as well as industry reports and case studies from leading pharmaceutical firms. Key strategies explored include product and process innovations that make medications more effective and accessible, as well as open innovation models, which promote collaboration and cost-sharing in research. The review underscores the ethical and regulatory complexities of the industry, from patient-centered marketing to the rise of biosimilars. Future directions in pharmaceutical innovation, such as artificial intelligence and precision medicine, are presented as transformative tools for advancing personalized healthcare. These advancements reflect the sector's ongoing response to regulatory demands, economic pressures, and global health challenges, positioning the pharmaceutical industry as a crucial driver of public health and economic sustainability.

HARNESSING PRIVACY-PRESERVING FEDERATED LEARNING WITH BLOCKCHAIN FOR SECURE IoMT APPLICATIONS IN SMART HEALTHCARE SYSTEMS

The Internet of Medical Things (IoMT) refers to interconnected medical systems and devices that gather and transfer healthcare information for several medical applications. Smart healthcare leverages IoMT technology to improve patient diagnosis, monitoring, and treatment, providing efficient and personalized healthcare services. Privacy-preserving Federated Learning (PPFL) is a privacy-enhancing method that allows collaborative method training through distributed data sources while ensuring privacy protection and keeping the data decentralized. In the field of smart healthcare, PPFL enables healthcare professionals to train machine learning algorithms jointly on their corresponding datasets without sharing sensitive data, thereby maintaining confidentiality. Within this framework, anomaly detection includes detecting unusual events or patterns in healthcare data like unexpected changes or irregular vital signs in patient behaviors that can represent security breaches or potential health issues in the IoMT system. Smart healthcare systems could enhance patient care while protecting data confidentiality and individual privacy by incorporating PPFL with anomaly detection techniques. Therefore, this study develops a Privacy-preserving Federated Learning with Blockchain-based Smart Healthcare System (PPFL-BCSHS) technique in the IoMT environment. The purpose of the PPFL-BCSHS technique is to secure the IoMT devices via the detection of abnormal activities and FL concepts. Besides, BC technology can be applied for the secure transmission of medical data among the IoMT devices. The PPFL-BCSHS technique employs the FL for training the model for the identification of abnormal patterns. For anomaly detection, the PPFL-BCSHS technique follows three major processes, namely Mountain Gazelle Optimization (MGO)-based feature selection, Bidirectional Gated Recurrent Unit (BiGRU), and Sandcat Swarm Optimization (SCSO)-based hyperparameter tuning. A series of simulations were implemented to examine the performance of the PPFL-BCSHS method. The empirical analysis highlighted that the PPFL-BCSHS method obtains improved security over other approaches under various measures.

Editorial: Precision Medicine Meets AI

There are rapid advancements in AI-driven diagnostics and wearable technologies, which are enabling early disease prediction, especially in conditions like Parkinson's disease. Precision medicine, underpinned by molecular biology and genomic data, allows clinicians to tailor treatment based on patient phenotypes, significantly enhancing personalized healthcare. AI complements this by processing vast datasets, from health records to biomarker evaluations, supporting more precise diagnostics and treatment recommendations.

Health services consumption and barriers in the health care of persons of Roma origin in Bulgaria

Abstract Introduction Persons of Roma origin often face serious inequities concerning their state of health and their access to good quality health services. The aim of the study is to analyze the health services consumption and barriers in the health care of persons of Roma origin in Bulgaria. Methods The cross-sectional study was conducted and a semi-structured face to face interview were applied in 2022. Adult citizens of Roma origin were covered as follows: residents of Knezha - a total of 59 people and households from the towns of Knezha and Kotel - 18 families (50 persons). The cities are representative of settlements of medium size with separate neighborhoods of Roma origin. Data processing was performed by SPSS v.24. Results Over two thirds of the covered persons of Roma origin do not have health insurance. In each household an average of 1.8 uninsured people was found, but in some of the families almost all of the elderly members are not insured. Due to lack of health insurance in 14% of the households were reported a refusal to provide health care by general practitioners (GPs) and specialists in out-hospital care. 82.1% of Roma women have never had a mammography and 71.4% a smear test. In 83.3% of the households required direct payment for the provision of health services, which corresponds to the high share of informal payments in health care in Bulgaria (34 % in 2023), but among the Roma population this share is significantly higher. The households have to pay for specialized out-hospital care (66.6%), GPs (22.2%), and for emergency assistance (5,6%). 44.4% of the families have used the services of emergency care in the last one year. While in one third of the families an adult member was hospitalized in 27.8% of them there is a need for hospital treatment, which is unrealized. Conclusions The Roma population in Bulgaria is characterized by an unfavorable health profile and the health services provided to the persons of Roma origin are inadequate to their needs. Key messages • Persons of Roma origin in Bulgaria often face serious inequities concerning their state of health and their access to good quality health services. • Improving the socioeconomic status of people of Roma origin will increase their opportunities for health insurance and going to improve their access to health services.

Economic evaluation of Polygenic Risk Scores in healthcare interventions: a systematic review

Abstract Background Polygenic Risk Scores (PRS) quantify genetic susceptibility to diseases, promising personalized healthcare. This review explores current evidence regarding the economic evaluation of strategies based on PRS or other polygenic risk stratification approaches, scrutinizing their methodologies. Methods The study protocol was registered in PROSPERO (CRD42023442780). A systematic search in PubMed, Scopus, and Web of Science identified full economic evaluations of intervention based on polygenic risk stratification strategies. The quality of the included articles was assessed using the Drummond checklist. This study was supported by the EC and MUR under PNRR - M4C2-I1.3 Project PE_00000019 ‘HEAL ITALIA”. Results Nineteen articles were included in the analysis, with oncological conditions being the most frequently investigated (13), followed by cardiovascular conditions (3). In nearly 80% of the studies, PRS was employed for screening interventions, with the general population being the primary target in 14 out of the 19 studies. All the economic analysis models investigated cost-utility in terms of QALYs, with Markov models and microsimulations being the most common structures. The majority of these models were based on simulated cohorts derived mostly from North American or European data, with 9 adopting a healthcare system perspective and 6 a societal perspective. Although delivery strategies for PRS testing were rarely addressed, PRS costs were included in nearly all studies. Indirect costs were examined in fewer than half of the studies. In 12 out of 19 studies, the conclusions claimed the cost utility of PRS involving strategies. Conclusions Despite the technique’s potential, evaluations of various strategies based on PRS approaches yield heterogeneous conclusions regarding cost-utility. Our study highlights the factors contributing to this heterogeneity and underscores the need for further exploration, ideally prioritizing real-world data. Key messages • PRS strategies evaluations vary in cost-utility conclusions, highlighting the need for further investigation. • Real-world data and practical implications should be prioritised in PRS research.

Barriers and facilitators to healthcare services of people with mental ill-health in Europe

Abstract Background People with mental ill-health (PMIH) have higher cancer mortality rates than the general population. Barriers to healthcare service access may be a contributing factor. The EU-funded CO-CAPTAIN project aims to reduce health inequalities by improving overall health and reducing cancer burden by adapting and implementing the Patient Navigation (PN) Model targeted at PMIH. This qualitative study explored healthcare system experiences of PMIH in Austria, Greece, Poland, and Spain. Methods Eighty-one participants (22-76 years old), including 34 PMIH, 10 caregivers, 14 care team members, 12 mental health organization representatives, and 11 service manager representatives, were recruited. Semi-structured interviews focused on awareness of cancer risk factors, healthcare system experiences, and perspectives on PN as an intervention. Interviews were transcribed verbatim and analyzed using thematic analysis. Results Three major themes emerged: (1) somatic health relevance and preventive services utilization and (2) barriers / (3) facilitators to preventive services access. Knowledge of cancer prevention services varied greatly. Participants were aware of associations between risky health behaviors and health. Burden of mental health problems and discrimination, insufficient access to information, and lack of service flexibility were noted as barriers. Considerations of individual circumstances, social support, and empowerment were named as facilitators. Conclusions Tailored interventions involving healthcare professionals and PMIH in development of supportive environments and health literacy have the potential of overcoming identified barriers to the use of healthcare services. Key messages • People experiencing mental ill-health are faced with significant structural barriers when accessing somatic health services. • Understanding people’s needs in terms of access and utilization of health care allows us to define guidelines for future personalized healthcare solutions.

Systematic Review of Social Robots for Health and Wellbeing: A Personal Healthcare Journey Lens

Systematic Review of Social Robots for Health and Wellbeing: A Personal Healthcare Journey Lens

Barriers to oral health care for persons with disabilities: An overview of systematic reviews.

Nearly one billion individuals globally live with disabilities, facing greater risk of dental issues. Systematic reviews have identified barriers to oral health care for persons with disabilities (PWDs), but a comprehensive synthesis to inform health-policy guidelines is lacking. This overview addresses this gap by summarizing the key barriers to oral health care access for PWDs. (1) summarize key findings on the barriers PWDs encounter in seeking dental and oral healthcare, (2) evaluate the influence of disabilities on accessibility to dental and oral health services, and (3) identify facilitators to improve access and inform future health policy. Systematic review of systematic reviews of both cross-sectional and evaluative studies that identified barriers to oral health care access for PWDs. A comprehensive search of databases was conducted from inception to 24 February 2024, using specific keywords and Boolean operators. Data extraction and quality assessment were performed using AMSTAR 2 to ensure transparency and reliability. Five systematic reviews were included. These reviews highlighted financial constraints, provider reluctance, access difficulties, systemic barriers, and patient-related factors as major obstacles. Methodological variations across reviews were noted, affecting the transparency and reliability of findings. Barriers to oral health care access for PWDs include financial, provider-related, access-related, systemic, and patient-related factors. Addressing these barriers, particularly in developing countries, should be a priority for future research and interventions.

Provision of primary health care for people with disabilities: requirements for accessible environment organization

Provision of health care for persons with disabilities (PDs) requires additional conditions — creation of an accessible environment in medical facilities and surroundings.Aim. To study principles of providing inclusive and safe environment for PDs in healthcare facilities providing primary health care.Material and methods. A search and analysis of regulatory legal documents and statistical data on providing an accessible environment for PDs was conducted. Data from the official websites of the Federal State Statistics Service and the Russian Public Opinion Research Center were used. The search for regulatory legal acts was carried out using the reference legal system Consultant Plus. The following research methods were used: descriptive statistics, content analysis.Results. The tendency towards an increase in the number of people with repeated disability and the number of disabled children allows to predict a further increase in the number of disabled persons in adulthood. Accessibility and inclusive conditions are a prerequisite for such patients visiting primary health care facilities. The requirements for them are regulated by a large number of guidelines, which were systematized within the study.Conclusion. The increase in the number of PDs determines the relevance of creating an inclusive environment taking into account the needs of people with various health and mobility impairments. In addition to creating technical conditions for a inclusive and safe environment, a set of organizational solutions is needed, including regulations for employees responsible for distributing patient flows in the healthcare facilities and their interaction. Federal state educational standards and typical job functions of employees of healthcare facilities (nurse, medical registrar, medical administrator) need to be revised in terms of specifying their participation in providing assistance to PDs.

Smart Green Cotton Textiles with Hierarchically Responsive Conductive Network for Personal Healthcare and Thermal Management.

Whereas cotton as an abundant natural cellulose has been widely used for sustainable and skin-friendly textiles and clothes, developing cotton fabrics with smart functions that could respond to various stimuli is still eagerly desired while remaining a great challenge. Herein, smart multiresponsive cotton fabric with hierarchically copper nanowire interwoven MXene conductive networks that are seamlessly assembled along a 3D woven fabric template for efficient personal healthcare and thermal comfort regulation is successfully developed. The robust hierarchically interwoven conductive network was "glued" and protected by organic conductive polymer poly(3,4-ethylenedioxythiophene) along a 3D interconnected fabric template to enhance interfacial adherent and environmental stability. Benefiting from the robust multiresponsive hierarchically interwoven conductive network, smart cotton fabric exhibits real-time response to various external stimuli (light/electrical/heat/temperature/stress), and the details of human activities can be accurately recognized and monitored. Furthermore, the porous structure of 3D smart fabric induced strong capillary force and confinement to phase change materials PEG, which exhibits a wide range of phase transition temperatures for efficient thermal comfort regulation. After further encapsulation with transparent fluorosilicone resin, the smart cotton fabric exhibits excellent self-cleaning performance with water/oil repellent. The smart multiresponsive cotton fabrics hold great promise in next-generation wearable systems for efficient personal healthcare and thermal management.

Multi-omics analysis revealed significant metabolic changes in brain cancer cells treated with paclitaxel and/or topotecan

Glioblastoma (GBM) stands as the most common primary malignant brain tumor. Despite the best standard therapies, GBM survivors have a brief survival time, about 24 months on average. The treatment is troublesome because the cancer cells may not respond well to specific therapies as they grow within an extensive network of blood vessels. Our study aims to evaluate the impact of paclitaxel 5.3 μg/mL and topotecan 0.26 μM solely and in pairwise combination on the resultant metabolic and proteomic signatures of the U87 cell line while using the precise ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF) analytical technology. The U87 cells wear treated with DMSO, paclitaxel 5.3 μM, topotecan 0.26 μM, and their combinations. Using One-way ANOVA, we observed 14 significantly altered metabolites compared to those cells treated with DMSO. For combination treatment (paclitaxel and topotecan), 11 metabolites were significantly dysregulated. Sparse partial least squares-discriminant analysis (sPLS-DA) revealed minimal overlap, highlighting distinctions among the four groups. While for proteomics, a total of 79 proteins were significantly dysregulated among the groups. These findings can aid in identifying new biomarkers associated with the utilized drugs and creating a map for targeted therapy. EIF3F, GNB2L1, HINT2, and RPA3 were shown to be significantly upregulated in the combination group relative to the control. Moreover, ribosome, apoptosis, HIF-1 signaling, arginine and proline, glutathione, purine metabolism, apelin signaling pathway, and glycolysis were significantly altered in the combination group. Overall, this study underscores the effectiveness of multi-omics approaches in revealing the molecular mechanisms driving chemotherapy responses in cancer cells. Additionally, this work generates a comprehensive list of molecular alterations that can serve as a foundation for further investigations and inform personalized healthcare strategies to enhance patient outcomes.

Breathable and Stretchable Epidermal Electronics for Health Management: Recent Advances and Challenges.

Advanced epidermal electronic devices, capable of real-time monitoring of physical, physiological, and biochemical signals and administering appropriate therapeutics, are revolutionizing personalized healthcare technology. However, conventional portable electronic devices are predominantly constructed from impermeable and rigid materials, which thus leads to the mechanical and biochemical disparities between the devices and human tissues, resulting in skin irritation, tissue damage, compromised signal-to-noise ratio (SNR), and limited operational lifespans. To address these limitations, a new generation of wearable on-skin electronics built on stretchable and porous substrates has emerged. These substrates offer significant advantages including breathability, conformability, biocompatibility, and mechanical robustness, thus providing solutions for the aforementioned challenges. However, given their diverse nature and varying application scenarios, the careful selection and engineering of suitable substrates is paramount when developing high-performance on-skin electronics tailored to specific applications. This comprehensive review begins with an overview of various stretchable porous substrates, specifically focusing on their fundamental design principles, fabrication processes, and practical applications. Subsequently, a concise comparison of various methods is offered to fabricate epidermal electronics by applying these porous substrates. Following these, the latest advancements and applications of these electronics are highlighted. Finally, the current challenges are summarized and potential future directions in this dynamic field are explored.