Personal Health Care Research Articles

Diverse functions of cytochrome c in cell death and disease.

The redox-active protein cytochrome c is a highly positively charged hemoglobin that regulates cell fate decisions of life and death. Under normal physiological conditions, cytochrome c is localized in the mitochondrial intermembrane space, and its distribution can extend to the cytosol, nucleus, and extracellular space under specific pathological or stress-induced conditions. In the mitochondria, cytochrome c acts as an electron carrier in the electron transport chain, facilitating adenosine triphosphate synthesis, regulating cardiolipin peroxidation, and influencing reactive oxygen species dynamics. Upon cellular stress, it can be released into the cytosol, where it interacts with apoptotic peptidase activator 1 (APAF1) to form the apoptosome, initiating caspase-dependent apoptotic cell death. Additionally, following exposure to pro-apoptotic compounds, cytochrome c contributes to the survival of drug-tolerant persister cells. When translocated to the nucleus, it can induce chromatin condensation and disrupt nucleosome assembly. Upon its release into the extracellular space, cytochrome c may act as an immune mediator during cell death processes, highlighting its multifaceted role in cellular biology. In this review, we explore the diverse structural and functional aspects of cytochrome c in physiological and pathological responses. We summarize how posttranslational modifications of cytochrome c (e.g., phosphorylation, acetylation, tyrosine nitration, and oxidation), binding proteins (e.g., HIGD1A, CHCHD2, ITPR1, and nucleophosmin), and mutations (e.g., G41S, Y48H, and A51V) affect its function. Furthermore, we provide an overview of the latest advanced technologies utilized for detecting cytochrome c, along with potential therapeutic approaches related to this protein. These strategies hold tremendous promise in personalized health care, presenting opportunities for targeted interventions in a wide range of conditions, including neurodegenerative disorders, cardiovascular diseases, and cancer.

Enhanced Interstitial Fluid Extraction and Rapid Analysis via Vacuum Tube-Integrated Microneedle Array Device.

Advancing the development of point-of-care testing (POCT) sensors that utilize interstitial fluid (ISF) presents considerable obstacles in terms of rapid sampling and analysis. Herein, an innovative strategy is introduced that involves the use of a 3D-printed, hollow microneedle array patch (MAP), in tandem with a vacuum tube (VT) connected through a hose, to improve ISF extraction efficiency and facilitate expedited analysis. The employment of negative pressure by the VT allows the MAP device to effectively gather ≈18µL of ISF from the dermis of a live rabbit ear within a concise period of 5min. This methodology enables the immediate and minimally invasive measurement of glucose levels within the body, employing personal healthcare meters for quantification. The fusion of the VT and MAP technologies provides for their effortless integration into a comprehensive and mobile system for ISF analysis, accomplished by preloading the hose with custom sensing papers designed to detect specific analytes. Moreover, the design and functionality of this integrated VT-MAP system are intuitively user-friendly, eliminating the requirement for specialized medical expertise. This feature enhances its potential to make a significant impact on the field of decentralized personal healthcare.

Improving Healthcare Quality and Clinical Outcomes for Persons with Dementia in the Sub-Acute Hospital Through Person-Centered Care Practice.

Person-centered care is considered beneficial for persons with dementia. To evaluate the impact of a person-centered knowledge translation intervention on the quality of healthcare and outcomes for persons with dementia. Over nine months, sub-acute hospital nursing, allied health, and medical staff (n = 90) participated in online and/or face-to-face person-centered education and were supported by senior nursing, allied health, and medical staff champions (n = 8) to implement person-centered healthcare. The quality of healthcare service, ward climate and care delivery were evaluated pre/post study intervention. In the week following hospital admission (Time 1) and week of discharge (Time 3), agitation incidence (co-primary outcome) was assessed in participants with dementia (n = 80). Participant delirium (co-primary outcome), accidents/injuries, psychotropic medicines, length of stay, readmission and discharge destination (secondary outcomes) were compared with a retrospective group (n = 77) matched on demographics, cognition and function in activities of daily living. Improvements occurred post-intervention in service quality by 17.5% (p = 0.369, phi = 0.08), ward climate by 18.1% (p = 0.291, phi = 0.08), and care quality by 50% (p = 0.000, phi = 0.37). Participant agitation did not change from Time 1 to Time 3 (p = 0.223). Relative to the retrospective group, significant reductions occurred in participant delirium (p = 0.000, phi = 0.73), incidents/injuries (p = 0.000, phi = 0.99), psychotropic medicine use (p = 0.030, phi = 0.09), and hospital readmissions within 30 days (p = 0.002, phi = 0.25), but not in discharge to home (p = 0.171). When person-centered healthcare knowledge is translated through staff education and practice support, persons with dementia can experience improved healthcare services and clinical outcomes, while healthcare services can benefit through reductions in unplanned service use.

Sleep duration and quality are associated with health-related quality of life in adults aged 60years and older.

The current study aimed to determine the association between sleep duration and quality with health-related quality of life (HRQoL) among the Iranian older persons. This study was conducted on a representative sample of 3452 Iranian older people (≥ 60years) who participated in the Iranian Longitudinal Study on Ageing (IRLSA) launched in Neyshabur (Razavi Khorasan province, northeastern Iran). The Persian versions of 12-Item Short Form Survey (SF-12) and Pittsburgh Sleep Quality Index (PSQI) were utilized to measure the level of HRQoL and the level of sleep duration and quality, respectively. Multiple linear regression model was performed to analyze the data and clarify the independent association of sleep duration and quality with HRQoL. More than half of the study population (52.4%) suffered from poor sleep quality. 57.3% and 3.9% of individuals were short sleepers (< 7h) and long sleepers (equal or more than 9h), respectively. The total score of HRQoL was achieved 65.87 ± 20.55. After adjusting the covariates, the results indicated that short sleep duration was negatively associated with HRQoL (P < 0.05), whereas no significant associations were found between long sleep duration and HRQoL (P > 0.05). Interestingly, the results of multiple linear regression model revealed a significant negative association between poor sleep quality and HRQoL (P < 0.001). The current study confirmed that short sleep duration and poor sleep quality were independently associated with lower HRQoL in the older persons. Therefore, interventional courses should be planned to enrich sleep-related healthcare and general HRQoL in the older persons.

Optimizing Preparation of Fish Scale Collagen Peptide/Sacha Inchi (Plukenetia volubilis L.) Seed Oil Nanoemulsion

AbstractThis study presents results of extracting hydrolyzed collagen peptide from fresh‐water fish scale and using it to prepare a nanoemulsion based on Sacha inchi (Plukenetia volubilis L.) seed oil and collagen peptide. The sacha inchi seed oil is extracted from the seeds of Sacha Inchi (abbreviated as sachi) plant and contains approximate 45 % triple omega 3–6‐9 (ω‐3, ω‐6, ω‐9) content. Pseudo‐ternary phase diagrams of three components: aqueous collagen solution, sachi seed oil, and surfactant/co‐surfactant (Smix) have been constructed. The parameters including the composition of Smix, sachi seed oil, and aqueous collagen solution have been selected to optimize the technology factors for the emulsion process. A quadratic regression equation was chosen, and experimental planning following the Box‐Behnken model using response surface methodology was used to optimize the preparation conditions for the nanoemulsion. The average droplet size of the emulsion systems is the object function for optimization. The Smix is a mixture of surfactant (Tween80) and propylene glycol (PG) with different ratios of Tween80/PG of 1/1, 2/1, 3/1, and 4/1, corresponding to the formation of four emulsion systems, code 1, 2, 3, 4, respectively. The optimal parameters for each emulsion system are different, however, the aqueous collagen solution contains a higher proportion in emulsions. Under the optimal conditions, the predicted values of the objective function of emulsions code 1–code 4 ranged from 95.62 to 185.14 nm, increasing together with the Tween80 ratio in Smix. The experiment values of the objective function of emulsions code 1–code 4 prepared under the optimal conditions are 104.22, 133.70, 159.27, and 207.87 nm, respectively. These values are close to the predicted values, suggesting that the suitability of the experiment and theory. Additionally, the stability of the nanoemulsion prepared under the optimal conditions is also evaluated. A stable nanoemulsion is potentially for loading nutraceuticals and vitamins for applications in personal health care and medicinal fields.

A Kingdom of Possibility

A Kingdom of Possibility

Impact Of Down Syndrome Among Families Who Have Children with Down Syndrome and Have Follow-Up for Their Children at Black Lion Hospital In 2019.

Background: - Worldwide Down syndrome is a common chromosomal abnormal condition. Itis also known as trisomy 21. Children with down syndrome have delay in growth and hypotonic this interferes with feeding, constipation led to malnutrition, poor motor development, delay cognitive development and mental retardation, congenital heart disease and risk of ear infection. The effect of Down syndrome differs among individual depending on the extent of abnormality and the health care service available even in developed countries despite the extensive knowledge of health conditions in children with Down syndrome, the impact of Down syndrome on families and access to health care for persons with Down syndrome remains understudied. All these will affect family life differently. Therefore, exploring the impact of Down syndrome among families who have children with Down syndrome will provide a paramount input for care providers, researcher and policy maker. Objectives: - To explore the impact of Down syndrome on family among families who have children with Down syndrome and have follow up for their children at black lion hospital 20 in 19. Methods: -The researcher use phenomenological research design. Data was collected through in-depth interview, Focus group discussion and key informant interview. These major categories were aggregate from the raw data collection and then framework analysis and identify themes across all sources of data. Result: - From the Analysis of the data six major themes were categorized. Non acceptance –Birth of an abnormal child, Lifestyle changes, family relationship, unpredictable future of the child, basic service and Societal and Community acceptance. Conclusion: -The findings show that birth of a child with Down syndrome produces a lot physical and psychological effect in parents. So, parents need adequate support from Government and concerned body in general and also from nurses in particular right from the very birth of a disabled child. Therefore, this study is important for nurses working with families having children with Down syndrome. As nurses are the eye to blind, leg to amputate based on this, it is their role to realize and aware of the problems faced by the families and should advocate necessary support.

Impact Of Down Syndrome Among Families Who Have Children with Down Syndrome and Have Follow-Up for Their Children at Black Lion Hospital In 2019.

Background: - Worldwide Down syndrome is a common chromosomal abnormal condition. Itis also known as trisomy 21. Children with down syndrome have delay in growth and hypotonic this interferes with feeding, constipation led to malnutrition, poor motor development, delay cognitive development and mental retardation, congenital heart disease and risk of ear infection. The effect of Down syndrome differs among individual depending on the extent of abnormality and the health care service available even in developed countries despite the extensive knowledge of health conditions in children with Down syndrome, the impact of Down syndrome on families and access to health care for persons with Down syndrome remains understudied. All these will affect family life differently. Therefore, exploring the impact of Down syndrome among families who have children with Down syndrome will provide a paramount input for care providers, researcher and policy maker. Objectives: - To explore the impact of Down syndrome on family among families who have children with Down syndrome and have follow up for their children at black lion hospital 20 in 19. Methods: -The researcher use phenomenological research design. Data was collected through in-depth interview, Focus group discussion and key informant interview. These major categories were aggregate from the raw data collection and then framework analysis and identify themes across all sources of data. Result: - From the Analysis of the data six major themes were categorized. Non acceptance –Birth of an abnormal child, Lifestyle changes, family relationship, unpredictable future of the child, basic service and Societal and Community acceptance. Conclusion: -The findings show that birth of a child with Down syndrome produces a lot physical and psychological effect in parents. So, parents need adequate support from Government and concerned body in general and also from nurses in particular right from the very birth of a disabled child. Therefore, this study is important for nurses working with families having children with Down syndrome. As nurses are the eye to blind, leg to amputate based on this, it is their role to realize and aware of the problems faced by the families and should advocate necessary support.

Artificial Intelligence and Machine Learning Algorithms in Modern Cardiology

BACKGROUND: Recent years have witnessed the widespread adoption of machine learning (ML) and deep learning techniques in various health-care applications. Artificial intelligence and ML algorithms using big medical data make it possible to predict diseases and enable the development of personalized treatments for patients. Heart diseases are one of the most common chronic diseases affecting human health, and early detection can reduce the mortality rate. AIM: We aimed to review different types of ML techniques and their applications in heart disease risk detection. METHODS: For different cardiovascular diseases, the choice of algorithms should be tailored based on their accuracy and efficiency RESULTS: The research presented highlights the critical global issue of heart disease and its impact on public health. The urgency to address this global problem is emphasized, as heart disease has become a significant factor in the increasing mortality rate worldwide. The introduction of ML in the prognosis of heart disease is a significant step toward realizing predictive, preventive, and personalized health care and reducing health-care costs. In this study, a comparative evaluation of ML models was made: Logistic regression, decision tree, random forest, and support vector machine. The quality of the data, as well as the choice of an appropriate algorithm, is key factors in the assessment of heart diseases. CONCLUSION: Despite the impressive performance of ML, there are doubts about its robustness in traditional health-care systems due to many security and privacy issues.

Dual One and Two‐Photon Solvent‐Free Hybrid Photoresist for High‐Resolution and Grayscale 3D Microprinting

Additive manufacturing holds a strong promise to revolutionize industry. Fabricating high‐resolution and large objects with specific physical properties remains a critical challenge. In this direction, innovation in both chemical processing and 3D‐printing techniques are needed. Here, a solvent‐free hybrid organic‐inorganic photoresist combining a radical photoinitiator and a photobase generator is photostructured by grayscale lithography and direct laser writing using both one‐ and two‐photon absorption. Infrared spectroscopy suggests that one‐photon absorption activates the photoinitiator and leads to radical polymerization of organic moieties, triggering silanol condensation in a synergetic way. In contrast, upon two‐photon absorption, the driving force of polymer formation is the photobase‐induced silanol condensation. The proposed approach produces both large area and high‐resolution 3D structures upon one‐ and two‐photon absorption induced polymerization, respectively. This new paradigm paves the way toward the efficient fabrication of on‐demand devices for personalized health care, microfluidics, or microoptics.

Experiences of volt hockey from family members’ perspectives: an interview study

Volt hockey is a team sport developed for persons with physical disabilities. Because family members are important resources to enable sports participation, the purpose of this study was to illuminate experiences of volt hockey from family members’ perspectives. Focus group interviews were conducted, including 20 family members, and the verbatim transcripts were inductively analysed using reflexive thematic analysis. Family members saw the value of enabling participation in volt hockey, being an inclusive team sport that brings enjoyment and strengthens social relationships within, and outside of, the family. Trying to facilitate sport participation, the need for material resources and other actors was emphasised, such as personal assistance, funders, and healthcare professionals informing about the sport and the engagement of the volt hockey clubs. In conclusion, involvement in volt hockey is emotionally and socially rewarding for family members, but there is a need for additional enablers to allow the sport to grow.

Revolutionizing Wellness: The IoT Smart Bottle for Health Monitoring

Intelligent and health-focusedIoT project designed to monitor and provide information about the contents of a bottle, such as temperature and water level. This system utilizes a DHT11 sensor for temperature monitoring, an ultrasonic sensor for water level detection, and an LCD to display real-time information. The project aims to promote health awareness by allowing users to monitor the temperature of liquids and ensure anadequate water supply in their bottles. The smart bottle's sensors detect when the medication is dispensed, and thedata is transmitted to a centralized platform via a secure IoT network. This not only assists healthcare professionals in tracking patient compliance but also facilitates personalized healthcare by enabling timely adjustments to treatment plans. With the ability to send alerts and notifications to both patients and healthcare providers, the Smart Bottle Health Care using IoT enhances patient engagement, fosters medication adherence, and ultimately contributes to better health outcomes. This innovative solution holds great potential in transforming medication management, particularly for individuals with chronic conditions who require consistent and precise dosage adherence for effective treatment. Through continuous monitoring and data analysis, the IoT-enabled smart bottle not only ensures timely medication reminders but also provides a holistic view of patients' medication adherence trends. This data-driven approach empowers healthcare providers to make informed decisions, intervene proactively, and tailor treatment plans for improved patient outcomes. The system's ability to send notifications and alerts fosters a collaborative healthcare ecosystem, engaging both patients and caregivers inthe medication

Circadian assessment of heart failure using explainable deep learning and novel multi-parameter polar images

Background and objectiveHeart failure (HF) is a multi-faceted and life-threatening syndrome that affects more than 64.3 million people worldwide. Current gold-standard screening technique, echocardiography, neglects cardiovascular information regulated by the circadian rhythm and does not incorporate knowledge from patient profiles. In this study, we propose a novel multi-parameter approach to assess heart failure using heart rate variability (HRV) and patient clinical information. MethodsIn this approach, features from 24-hour HRV and clinical information were combined as a single polar image and fed to a 2D deep learning model to infer the HF condition. The edges of the polar image correspond to the timely variation of different features, each of which carries information on the function of the heart, and internal illustrates color-coded patient clinical information. ResultsUnder a leave-one-subject-out cross-validation scheme and using 7,575 polar images from a multi-center cohort (American and Greek) of 303 coronary artery disease patients (median age: 58 years [50–65], median body mass index (BMI): 27.28 kg/m2 [24.91–29.41]), the model yielded mean values for the area under the receiver operating characteristics curve (AUC), sensitivity, specificity, normalized Matthews correlation coefficient (NMCC), and accuracy of 0.883, 90.68%, 95.19%, 0.93, and 92.62%, respectively. Moreover, interpretation of the model showed proper attention to key hourly intervals and clinical information for each HF stage. ConclusionsThe proposed approach could be a powerful early HF screening tool and a supplemental circadian enhancement to echocardiography which sets the basis for next-generation personalized healthcare.

Connecting cohorts of Finnish biobanks creates a research resource for the study of healthy ageing.

Connecting cohorts with biobanks is a Finnish biobank collaboration, creating an infrastructure for the study of healthy ageing. We aimed to develop a model for data integration and harmonisation between different biobanks with procedures for joint access. The heart of the collaboration is the integrated datasets formed by using data from three biobanks: (a) Arctic Biobank, hosting regional birth cohorts and cohorts of elderly; (b) hospital-affiliated Borealis Biobank of Northern Finland; and (c) THL Biobank, hosting population-based cohorts. The datasets were created by developing a data dictionary, harmonising cohort data and with a joint pseudonymisation process. The connecting cohorts with biobanks resource at its widest consists altogether of almost 1.4 million individuals from collaborating biobanks. Utilising data from 107,000 cohort participants, we created harmonised datasets that contain attributes describing metabolic risk and frailty for studies of healthy ageing. These data can be complemented with medical data available from Biobank Borealis and with samples taken at hospital settings for approximately 38,000 cohort participants. In addition, the harmonised connecting cohorts with biobanks datasets can be expanded with supplementary data and samples from the collaborating biobanks. The connecting cohorts with biobanks datasets provide a unique resource for research on ageing-related personalised healthcare and for real-world evidence studies. Following the FAIR principles on findability, accessibility, interoperability, and reusability, the reused and harmonised datasets are findable and made accessible for researchers. The same approach can be further utilised to develop additional datasets for other research topics.

Multi-Functional and Flexible Nano-Silver@MXene Heterostructure-Decorated Graphite Felt for Wearable Thermal Therapy.

Wearable heaters with multifunctional performances are urgently required for the future personal health management. However, it is still challengeable to fabricate multifunctional wearable heaters simultaneously with flexibility, air-permeability, Joule heating performance, electromagnetic shielding property, and anti-bacterial ability. Herein, silver nanoparticles (AgNPs)@MXene heterostructure-decorated graphite felts are fabricated by introducing MXene nanosheets onto the graphite felts via a simple dip-coating method and followed by a facile in situ growth approach to grow AgNPs on MXene layers. The obtained AgNPs@MXene heterostructure decorated graphite felts not only maintain the intrinsic flexibility, air-permeability and comfort characteristics of the matrixes, but also present excellent Joule heating performance including wide temperature range (30-128 °C), safe operating conditions (0.9-2.7V), and rapid thermal response (reaching 128 °C within 100 s at 2.7V). Besides, the multifunctional graphite felts exhibit excellent electromagnetic shielding effectiveness (53dB) and outstanding anti-bacterial performances (>95% anti-bacterial rate toward Bacillus subtilis, Escherichia coli and Staphy-lococcus aureus). This work sheds light on a novel avenue to fabricate multifunctional wearable heaters for personal healthcare and personal thermal management.

An Intelligent Health Surveillance System: Predictive Modeling of Cardiovascular Parameters through Machine Learning Algorithms Using LoRa Communication and Internet of Medical Things (IoMT)

In several nations, the majority of heart attacks lead to fatality prior to patients receiving any kind of medical intervention. The traditional healthcare system is mostly passive, requiring patients to initiate contact with healthcare services independently. People often do not request the treatment if they are unconscious during a heart disease episode. The use of Internet of Medical Things (IoMT) methods offers significant advantages in addressing the issue of caring for patients with cardiac problems. These techniques may transform service delivery into ubiquitous and activate healthcare services. Low-cost remote monitoring systems are essential to implementing a widespread healthcare service. In this article, we proposed a cost-effective Personal Health Care Device(PHCD) based on the Internet of Things (IoT). The PHCD transmits user somatic signals to data acquisition devices using a LoRa (Long-range and low-power) wireless communication network. The received data is uploaded to the cloud using IoT platforms like Adafruit IO. Further, various Machine learning (ML) algorithms, Naïve Bayes, ANN, CNN, and LSTM, were applied to collected data to predict heart rate and SpO2 behavior. The performance results of different forecast models were compared to identify precise modeling and reliable forecasts to prevent emergency cardiovascular conditions.

Environment‐Tolerant Conductive Eutectogels for Multifunctional Sensing

AbstractConductive hydrogels have shown significant potential in the realm of flexible electronics; however, using water as a solvent introduces problems such as low‐temperature freezing and water loss. One possible solution to address these limitations is replacing water with deep eutectic solvents (DES). In this study, the PHEAA–gelatin–MXene (PGM) eutectogels are constructed with poly(N‐hydroxyethyl acrylamide) (PHEAA) and gelatin as the main components, and MXene nanosheets are added as nanofillers. The PGM eutectogels exhibit exceptional tensile and compressive mechanical properties, including remarkable stretchability (940%), high strength (0.5 MPa), high toughness (1.39 MJ m−3), and impressive compressive strength (0.3 MPa at 80% strain). Additionally, the PGM eutectogels demonstrate excellent adhesion, anti‐freezing, and long‐term anti‐drying abilities. Moreover, the PGM eutectogels multifunctional sensors allow high sensitivity, which enables accurate real‐time and stable monitoring of human activities over a wide temperature range. Consequently, the PGM eutectogels hold great potential as candidates in fields such as flexible wearable devices, personal healthcare, and human–machine interfaces.

Ultrasensitive Flexible Temperature Sensors Based on Thermal-Mediated Ions Migration Dynamics in Asymmetrical Polymer Bilayers.

Accurately acquiring crucial data on the ambient surroundings and physiological processes delivered via subtle temperature fluctuation is vital for advancing artificial intelligence and personal healthcare techniques but is still challenging. Here, we introduce an electrically induced cation injection mechanism based on thermal-mediated ion migration dynamics in an asymmetrical polymer bilayer (APB) composed of nonionic polymer and polyelectrolyte layers, enabling the development of ultrasensitive flexible temperature sensors. The resulting optimized sensor achieves ultrahigh sensitivity, with a thermal index surpassing 10,000 K-1, which allows identifying temperature differences as small as 10 mK with a sensitivity that exceeds 1.5 mK. The mechanism also enables APB sensors to possess good insensitivity to various mechanical deformations─features essential for practical applications. As a proof of concept, we demonstrate the potential impact of APB sensors in various conceptual applications, such as mental tension evaluation, biomimetic thermal tactile, and thermal radiation detection.

Data analytics in healthcare: A review of patient-centric approaches and healthcare delivery

The integration of data analytics in healthcare has revolutionized the industry, ushering in a new era of personalized and patient-centric approaches to healthcare delivery. This review explores the multifaceted role of data analytics in reshaping the landscape of healthcare, with a specific focus on patient-centric methodologies and their impact on healthcare delivery. The advent of electronic health records (EHRs) and wearable devices has generated an unprecedented volume of healthcare data. Leveraging advanced analytics, healthcare providers can extract valuable insights from this data to enhance patient care. Patient-centric approaches involve the utilization of individualized health data to tailor treatment plans, predict disease outcomes, and optimize preventive measures. This review delves into the methodologies employed in patient-centric data analytics, examining the utilization of machine learning algorithms, predictive modeling, and artificial intelligence to develop personalized healthcare interventions. Furthermore, the review explores the transformative impact of data analytics on healthcare delivery. The optimization of operational processes, resource allocation, and the identification of cost-effective interventions are vital components of healthcare management. By harnessing the power of data analytics, healthcare systems can streamline their operations, reduce inefficiencies, and allocate resources more effectively. Additionally, predictive analytics aids in forecasting disease outbreaks, enabling proactive measures for containment and resource allocation. The review also highlights the ethical considerations and challenges associated with the implementation of data analytics in healthcare. Patient privacy, data security, and the responsible use of sensitive health information are critical aspects that demand careful attention in the era of digital healthcare. This review underscores the pivotal role of data analytics in fostering patient-centric healthcare approaches and optimizing healthcare delivery. As the healthcare industry continues to evolve, the integration of advanced analytics promises to revolutionize the way healthcare is administered, ensuring a more personalized, efficient, and effective approach to patient well-being.

The Women4Health cohort: a unique cohort to study women-specific mechanisms of cardio-metabolic regulation.

Epidemiological research has shown relevant differences between sexes in clinical manifestations, severity, and progression of cardiovascular and metabolic disorders. To date, the mechanisms underlying these differences remain unknown. Given the rising incidence of such diseases, gender-specific research on established and emerging risk factors, such as dysfunction of glycaemic and/or lipid metabolism, of sex hormones and of gut microbiome, is of paramount importance. The relationships between sex hormones, gut microbiome, and host glycaemic and/or lipid metabolism are largely unknown even in the homoeostasis status. Yet this knowledge gap would be pivotal to pinpoint to key mechanisms that are likely to be disrupted in disease context. Here we present the Women4Health (W4H) cohort, a unique cohort comprising up to 300 healthy women followed up during a natural menstrual cycle, set up with the primary goal to investigate the combined role of sex hormones and gut microbiota variations in regulating host lipid and glucose metabolism during homoeostasis, using a multi-omics strategy. Additionally, the W4H cohort will take into consideration another ecosystem that is unique to women, the vaginal microbiome, investigating its interaction with gut microbiome and exploring-for the first time-its role in cardiometabolic disorders. The W4H cohort study lays a foundation for improving current knowledge of women-specific mechanisms in cardiometabolic regulation. It aspires to transform insights on host-microbiota interactions into prevention and therapeutic approaches for personalized health care.