Health Management Research Articles

Association of the CUN-BAE body adiposity estimator and other obesity indicators with cardiometabolic multimorbidity: a cross-sectional study

Cardiometabolic multimorbidity (CM), defined as the coexistence of two or three cardiometabolic disorders, is one of the most common and deleterious multimorbidities. This study aimed to investigate the association of Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE), body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR) with the prevalence of CM. The data were obtained from the 2021 health checkup database for residents of the Electronic Health Management Center in Xinzheng, Henan Province, China. 81,532 participants aged ≥ 60 years were included in this study. Logistic regression models were used to estimate the odd ratios (ORs) and 95% confidence intervals (CIs) for CUN-BAE, BMI, WC, and WHtR in CM. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory ability of different anthropometric indicators for CM. The multivariable-adjusted ORs (95% CIs) (per 1 SD increase) of CM were 1.799 (1.710–1.893) for CUN-BAE, 1.329 (1.295–1.364) for BMI, 1.343 (1.308–1.378) for WC, and 1.314 (1.280–1.349) for WHtR, respectively. Compared with BMI, WC and WHtR, CUN-BAE had the highest AUC in both males and females (AUC: 0.642; 95% CI 0.630–0.653 for males, AUC: 0.614; 95% CI 0.630–0.653 for females). CUN-BAE may be a better measure of the adverse effect of adiposity on the prevalence of CM than BMI, WC, and WHtR.

Attention-Oriented CNN Method for Type 2 Diabetes Prediction

Diabetes is caused by insulin deficiency or impaired biological action, and long-term hyperglycemia leads to a variety of tissue damage and dysfunction. Therefore, the early prediction of diabetes and timely intervention and treatment are crucial. This paper proposes a robust framework for the prediction and diagnosis of type 2 diabetes (T2DM) to aid in diabetes applications in clinical diagnosis. The data-preprocessing stage includes steps such as outlier removal, missing value filling, data standardization, and assigning class weights to ensure the quality and consistency of the data, thereby improving the performance and stability of the model. This experiment used the National Health and Nutrition Examination Survey (NHANES) dataset and the publicly available PIMA Indian dataset (PID). For T2DM classification, we designed a convolutional neural network (CNN) and proposed a novel attention-oriented convolutional neural network (SECNN) through the channel attention mechanism. To optimize the hyperparameters of the model, we used grid search and K-fold cross-validation methods. In addition, we also comparatively analyzed various machine learning (ML) models such as support vector machine (SVM), logistic regression (LR), decision tree (DT), random forest (RF), and artificial neural network (ANN). Finally, we evaluated the performance of the model using performance evaluation metrics such as precision, recall, F1-Score, accuracy, and AUC. Experimental results show that the SECNN model has an accuracy of 94.12% on the NHANES dataset and an accuracy of 89.47% on the PIMA Indian dataset. SECNN models and CNN models show significant improvements in diabetes prediction performance compared to traditional ML models. The comparative analysis of the SECNN model and the CNN model has significantly improved performance, further verifying the advantages of introducing the channel attention mechanism. The robust diabetes prediction framework proposed in this article establishes an effective foundation for diabetes diagnosis and prediction, and has a positive impact on the development of health management and medical industries.

Demographics, culture and participatory nature of multi-marathoning-An observational study highlighting issues with recommendations.

The defining achievement of a multi-marathoner is completing 100 marathons. This study aimed to comprehensively document the phenomenon of multi-marathoning, addressing its demographics, culture and participatory nature, filling a gap in peer-reviewed research on the topic. Additionally, it aimed to provide recommendations for multi-marathon governing bodies, event organisers, health professionals and participants to address identified issues. A global survey was distributed to participants and individuals interested in multi-marathoning. It was distributed with support from major national and international multi-marathon clubs through their social media channels, email groups and newsletters. The survey was conducted anonymously and online. The survey garnered responses from 830 participants across 40 countries, with an average marathon completion count of 146.54 (SD 201.83) per respondent. Gender distribution showed 60.69% men, 39.3% women and 0.1% gender variant/non-conforming. Respondents' average ages were 51.6 (SD 9.96) years for men, 48.83 (SD 9.15) years for women and 35.00 (SD 8.76) years for gender variant/non-conforming. As participants age, social and travel motivations surpass competitiveness. A majority (57%) of respondents had at least one contravention to the pre-participation screening questionnaire PARQ-+ and 67% reported taking pain relief medication around events. Notably, 93% of respondents reported multi-marathoning as beneficial for their mental health. Multi-marathoning accommodates older athletes, but a significant gender imbalance exists in participation levels. Long-term health implications warrant attention from governing bodies, event organisers, health professionals and participants alike. Multi-marathoners should seek medical advice before participation, utilise modern equipment for health monitoring and optimise training accordingly. Recommendations include encouraging diversity at events, ensuring event directors have well-resourced health plans and promoting participants' proactive health management before and during their involvement in the sport. This study not only advances our understanding of multi-marathoning as a sport but also contributes to theoretical frameworks such as SDT and HBM.

The development and future of sports medicine in China

This article mainly introduces the development history and current situation of sports medicine in China, and deeply analyzes the problems that have emerged during the current development process, and proposes corresponding solutions. Based on this, the article proposes five key directions for the development of sports medicine, which are: developing arthroscopic minimally invasive techniques, ensuring the guarantee work for competitive sports, attaching great importance to the development of sports rehabilitation, vigorously carrying out exercise prescription and exercise assessment work, and emphasizing the training and education of sports medicine professionals. Finally, the article looks forward to the future development of sports medicine from three aspects: popularizing and expanding the influence of sports medicine through popular science promotion, improving the quality and safety of sports through health management and disease prevention, and achieving precise diagnosis and treatment through scientific research and innovation. It is hoped that this article will provide reference for the development of sports medicine in China.

Machine learning prediction of health risk and spatial dependence of geogenic contaminated groundwater from the Hetao Basin, China

Geogenic contaminated groundwater (GCG), characterized by elevated arsenic, fluoride, and iodine levels, present a significant challenge to public health and government management. Conventional survey-based approaches of collecting groundwater samples, conducting physicochemical tests, and performing spatial interpolation to obtain regional groundwater chemical component maps are inefficient and costly. More importantly, it does not take into account the actual hydrogeological conditions or the characteristics of pollutant transport and enrichment. To address this issue, we utilized Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost) to analyze the likelihood of occurrence of arsenic, fluoride, and iodine as well as their spatial distribution in shallow groundwater from the Hetao Basin. Our study incorporated 20 indicators related to meteorology, soil physicochemical properties, and groundwater conditions, along with 1505 labeled samples consisting of groundwater arsenic, fluoride, and iodine concentrations and their corresponding coordinates. Subsequently, the study automatically analyzed the meteorological, soil physicochemical properties and groundwater conditions by constructing a machine learning model using the available data. In order to optimise and select the best prediction model, this paper presents a quantitative evaluation of the prediction performance of various machine learning models. The accuracy (AC), area under curve (AUC) and mean squared error (MSE) were calculated to predict the spatial distribution of CGC. Subsequently, the optimized model for predicting the spatial distribution of GCG was selected. The results showed that the XGBoost algorithm provided optimal predictions for groundwater with arsenic concentrations above 10 μg/L and fluoride concentrations exceeding 1.5 mg/L, whereas the RF model provided the best predictions for groundwater with arsenic concentrations surpassing 50 μg/L and iodine concentrations exceeding 100 μg/L. Subsequently, groundwater health risk zones were delineated based on an optimal prediction model, and demographic analysis was conducted in both the direct and potential groundwater risk zones. Model predictions indicated that hundreds of thousands of people in the Hetao Basin were facing a public health crisis caused by high concentrations of arsenic, fluoride and iodine in groundwater. These findings underscore the significant health challenge in the study area. Considering the agricultural development and increasing groundwater use in the area, our findings can guide local governments in managing the extent of groundwater development, establishing control zones, and enhancing protection measures for populations at risk from groundwater contamination.

ENVIRONMENTAL IMPACTS OF COVID-19 HOSPITALS AND ARCHITECTURAL REMEDIES

Pandemics, with their potential for widespread devastation and global impact, have historically been a critical concern for public health and society. the historical impact of pandemics on the environment underscores the interconnectedness of human lives and health, societal systems, and the environment, highlighting the need for holistic approaches to public health and environmental management. The COVID-19 pandemic has brought about significant changes in healthcare facilities worldwide, particularly in hospitals designated for COVID-19 patients to counter and serve the impact on humans which universally led to the rapid establishment of specialized hospitals to manage the surge in patients. However, such hospitals not only posed significant challenges to healthcare systems worldwide but also highlighted the need for sustainable practices in designing and operating healthcare facilities along with their significant environmental impacts. In response to these challenges, architects, designers, and healthcare professionals have explored innovative architectural and other remedies to mitigate the environmental impacts of COVID-19 hospitals. This paper explores these potential environmental impacts and proposes architectural planning strategies that include sustainable practices and green design principles through which, by implementing, COVID-19 hospitals or similar hospitals can minimize their environmental footprint and contribute not only to a more sustainable healthcare system but also create healthier and more sustainable environments for patients, healthcare workers, and the community at large. KEYWORDS: Hospital Environmental Impact, Hospital Environment Relation, COVID-19 Hospital Impact on Environment, COVID-19 Hospital Impact, Environmental Impact of Pandemics, Pandemic Hospital Impact on Environment

Corneal Stroma Analysis and Related Ocular Manifestations in Recovered COVID-19 Patients.

The purpose of this study was to assess the impact of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) on corneal stroma characteristics, ocular manifestations, and post-recovery refractive surgery outcomes after varying recovery durations. Fresh corneal lenticules from patients with post-coronavirus disease 2019 (COVID-19; recovered within 135days) and healthy controls (HCs) after small incision lenticule extraction (SMILE) surgery were obtained for experimental validation of SARS-CoV-2 susceptibility, morphological changes, and immune response of the corneal stroma. Corneal optical density (CD) was measured using the Pentacam HR. Corneal epithelium thickness (ET) and endothelium parameters were evaluated by wide-field optical coherence tomography (OCT) and non-contact specular microscopy (SP-1P), respectively. All the patients were assessed after SMILE surgery until 3 month of follow-up. The cornea was susceptible to SARS-CoV-2 with the presence of SARS-CoV-2 receptors (CD147 and ACE2) and spike protein remnants (4 out of 58) in post-recovery corneal lenticules. Moreover, SARS-CoV-2 infection triggered immune responses in the corneal stroma, with elevated IL-6 levels observed between 45 and 75days post-recovery, which were then lower at around day 105. Concurrently, corneal mid-stromal nerve length and branching were initially higher in the 60D to 75D group and returned to control levels by day 135. A similar trend was observed in CD within zones 0 to 2 and 2 to 6 and in the hexagonal cells (HEX) ratio in endothelial cells, whereas ET remained consistent. Notably, these changes did not affect the efficacy, safety, or predictability of post-recovery SMILE surgery. SARS-CoV-2 induces temporal alterations in corneal stromal morphology and function post-recovery. These findings provided a theoretical basis for corneal health and refractive surgery management in the post-COVID-19 milieu.

Seasonal antigenic prediction of influenza A H3N2 using machine learning

Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays for surveillance purposes. It is also used to determine the need for annual influenza vaccine updates as well as for pandemic preparedness. Performing antigenic characterization of IAV on a global scale is confronted with high costs, animal availability, and other practical challenges. Here we present a machine learning model that accurately predicts (normalized) outputs of HI assays involving circulating human IAV H3N2 viruses, using their hemagglutinin subunit 1 (HA1) sequences and associated metadata. Each season, the model learns an updated nonlinear mapping of genetic to antigenic changes using data from past seasons only. The model accurately distinguishes antigenic variants from non-variants and adaptively characterizes seasonal dynamics of HA1 sites having the strongest influence on antigenic change. Antigenic predictions produced by the model can aid influenza surveillance, public health management, and vaccine strain selection activities.

PET COMPANION HUB

Pet Companion Hub is a multifaceted platform designed to enhance pet care and animal welfare through its three interconnected modules: Pet Adoption, Pet Veterinary, and Pet Shelter. The Pet Adoption module provides a user-friendly interface for individuals to browse and adopt animals in need of homes, promoting responsible pet ownership and reducing shelter overpopulation. Users can access detailed profiles of animals, facilitating informed decision-making and personalized matches. The Pet Veterinary module offers comprehensive veterinary services, including appointment scheduling, medical record management, and expert advice, ensuring pets receive the care they need to thrive. This module empowers pet owners to make informed healthcare decisions and promotes proactive health management. The Pet Shelter module serves as a valuable resource for shelters and rescue organizations, streamlining operations, facilitating communication, and maximizing impact within the community. By providing support to shelters and promoting collaboration, Pet Companion Hub strengthens the infrastructure for caring for and rehoming animals in need. Together, these modules create a holistic approach to pet care and advocacy, fostering a culture of compassion, responsibility, and community engagement in the realm of animal welfare

Genome editing: An insight into disease resistance, production efficiency, and biomedical applications in livestock.

One of the primary concerns for the survival of the human species is the growing demand for food brought on by an increasing global population. New developments in genome-editing technology present promising opportunities for the growth of wholesome and prolific farm animals. Genome editing in large animals is used for a variety of purposes, including biotechnology to improve food production, animal health, and pest management, as well as the development of animal models for fundamental research and biomedicine. Genome editing entails modifying genetic material by removing, adding, or manipulating particular DNA sequences from a particular locus in a way that does not happen naturally. The three primary genome editors are CRISPR/Cas 9, TALENs, and ZFNs. Each of these enzymes is capable of precisely severing nuclear DNA at a predetermined location. One of the most effective inventions is base editing, which enables single base conversions without the requirement for a DNA double-strand break (DSB). As reliable methods for precise genome editing in studies involving animals, cytosine and adenine base editing are now well-established. Effective zygote editing with both cytosine and adenine base editors (ABE) has resulted in the production of animal models. Both base editors produced comparable outcomes for the precise editing of point mutations in somatic cells, advancing the field of gene therapy. This review focused on the principles, methods, recent developments, outstanding applications, the advantages and disadvantages of ZFNs, TALENs, and CRISPR/Cas9 base editors, and prime editing in diverse lab and farm animals. Additionally, we address the methodologies that can be used for gene regulation, base editing, and epigenetic alterations, as well as the significance of genome editing in animal models to better reflect real disease. We also look at methods designed to increase the effectiveness and precision of gene editing tools. Genome editing in large animals is used for a variety of purposes, including biotechnology to improve food production, animal health, and pest management, as well as the development of animal models for fundamental research and biomedicine. This review is an overview of the existing knowledge of the principles, methods, recent developments, outstanding applications, the advantages and disadvantages of zinc finger nucleases (ZFNs), transcription-activator-like endonucleases (TALENs), and clustered regularly interspaced short palindromic repeats associated protein 9 (CRISPR/Cas 9), base editors and prime editing in diverse lab and farm animals, which will offer better and healthier products for the entire human race.

Metal Hydrogel‐Based Integrated Wearable Biofuel Cell for Self‐Powered Epidermal Sweat Biomarker Monitoring

AbstractWearable sensors for continuous monitoring of biomarkers in body fluids have gained significant attention for their potential in disease diagnostics and health management, but lack sustainable power supply and advanced sensing strategies. Herein, sweat wearable biofuel cells (w‐BFCs) based on metal hydrogels are demonstrated with high output and outstanding stability, which harvest energy directly from human sweat and simultaneously enable self‐powered sensing of epidermal biomarkers. Experimental and computational results elucidate that the highly porous and flexible metal hydrogels exhibit superior electrocatalytic capabilities for oxidizing ascorbic acid (AA), a sweat metabolite at the anode, and reducing O2 at the cathode. Consequently, the assembled AA/O2 BFC delivers a high and stable power output, with a maximum output power density of 35 µW cm−2 at an ultralow AA concentration and long‐term stability over 30 days, and a self‐powered, sensitive sensing for AA detection. When applied to the skin of the volunteers, this integrated w‐BFC powers the biosensor using human sweat AA as fuel and allowing real‐time monitoring of AA sensing signal via smartphone. This work not only advances energy harvesting for wearable sensors but also paves new avenues for real‐time, online monitoring of epidermal sweat biomarkers.

PReventing and Approaching Crises for frail community-dwelling patients Through Innovative Care (PRACTIC): protocol for an effectiveness cluster randomised controlled trial

BackgroundDemographic changes, with an increasing number and proportion of older people with multimorbidity and frailty, will put more pressure on home care services in municipalities. Frail multimorbid people receiving home care services are at high risk of developing crises, defined as critical challenges and symptoms, which demand immediate and new actions. The crises often result in adverse events, coercive measures, and acute institutionalisation. There is a lack of evidence-based interventions to prevent and resolve crises in community settings.MethodsThis is a participatory action research design (PAR) in a 6-month cluster randomised controlled trial (RCT). The trial will be conducted in 30 municipalities, including 150 frail community-dwelling participants receiving home care services judged by the services to be at risk of developing crisis. Each municipality (cluster) will be randomised to receive either the locally adapted TIME intervention (the intervention group) or care as usual (the control group). The Targeted Interdisciplinary Model for Evaluation and Treatment of Neuropsychiatric Symptoms (TIME) is a manual-based, multicomponent programme that includes a rigorous assessment of the crisis, one or more interdisciplinary case conferences, and the testing and evaluation of customised treatment measures. PAR in combination with an RCT will enhance adaptations of the intervention to the local context and needs. The primary outcome is as follows: difference in change between the intervention and control groups in individual goal achievement to resolve or reduce the challenges of the crises between baseline and 3 months using the PRACTIC Goal Setting Interview (PGSI). Among the secondary outcomes are the difference in change in the PGSI scale at 6 months and in neuropsychiatric symptoms (NPSs), quality of life, distress perceived by professional carers and next of kin, and institutionalisation at 3 and 6 months.DiscussionThrough customised interventions that involve patients, the next of kin, the social context, and health care services, crises may be prevented and resolved. The PReventing and Approaching Crises for frail community-dwelling patients Through Innovative Care (PRACTIC) study will enhance innovation for health professionals, management, and users in the development of new knowledge and a new adapted approach towards crises.Trial registrationClinicalTrials.gov identifier: NCT05651659. Registered 15.12.22.

Some theoretical and practical problems of legal protection of occupational safety: national and international aspects

The academic article deals with certain issues of the theory and practice of regulatory regulation of labour protection in Ukraine. Main organisational and legal forms of occupational safety and health, as well as main tasks of labour protection management in the country, are defined. Philosophical, general scientific and special legal methods of cognition, including but not limited to formal-legal and comparative legal methods, were used for the research. It is noted that Ukraine’s health and safety situation is one of the most challenging in Europe. The authors also state that importance of the psychological climate in the workplace and its impact on the production process cannot be underestimated, so relevant measures in the context of its improvement are proposed. The article highlights certain aspects of labour protection regulation at the international legal level in general (ensured by the International Labour Organization) and at the level of the European Union in particular, as well as the impact of relevant standards on national legislation. The authors formulate proposals for improving the labour protection institute in modern economic conditions in Ukraine, including but not limited to: implementing innovative legal education and training programs focused on occupational safety and health;, creating/improving safe working conditions under martial law, including proper equipment of shelters for use during air raids; ensuring submission of a draft law on improving penalties for violations of labour protection legislation, which will increase employers’ responsibility for compliance with labour protection legislation. It is concluded that the occupational health and safety management system shall be based not only on measures taken by the state, but also on the interest of the parties to labour relations in maintaining the proper physical condition of the employee, which is inextricably linked to the economic and social well-being of the employer.

Perceptions of Chinese women with a history of gestational diabetes regarding health behaviors and related factors: a directed qualitative content analysis

BackgroundGestational diabetes mellitus (GDM) is one of the most common metabolic disorders during pregnancy and is associated with adverse outcomes in both mothers and their children. After delivery, women who experience GDM are also at higher risk of both subsequent GDM and type 2 diabetes mellitus (T2DM) than those who do not. Therefore, healthcare providers and public health practitioners need to develop targeted and effective interventions for GDM. In this study, we aimed to explore the perceptions regarding health behaviors and related factors during the inter-pregnancy period among Chinese women with a history of GDM through the lens of the theory of planned behavior (TPB).MethodsBetween December 2021 and September 2022, 16 pregnant Chinese women with a history of GDM were purposively recruited from a tertiary maternity hospital in Shanghai for face-to-face semi-structured interviews. They were asked questions regarding their health behaviors and related factors. The transcribed data were analyzed using a directed qualitative content analysis method based on the theory of TPB.ResultsThe health-related behaviors of the women varied substantially. We identified five domains that influenced women’s behaviors according to TPB constructs and based on the data collected: behavioral attitude (perceived benefits of healthy behaviors and the relationship between experience and attitude towards the oral glucose tolerance testing); subjective norms (influences of significant others and traditional cultural beliefs); perceived behavior control (knowledge of the disease, multiple-role conflict, the impact of COVID-19, an unfriendly external environment and difficulty adhering to healthy diets), incentive mechanisms (self-reward and external incentives); preferences of professional and institutional support (making full use of social media platform and providing continuous health management).ConclusionsThe health-related behaviors of women with a history of GDM were found to be affected by multiple factors. Healthcare professionals are recommended to provide women with sufficient information regarding the disease and to take advantage of the power of the family and other social support networks to improve women’s subjective norms and to promote the adoption of a healthy lifestyle.

THE SPARŚA PROJECT: BUILDING A COMPREHENSIVE MENSTRUAL HEALTH MANAGEMENT INTERVENTION IN NEPAL

This article addresses the pervasive silence around menstrual health management (MHM) in Nepal, emphasizing its impact on women's rights. Despite being a natural process, menstruation remains taboo, perpetuating gender discrimination and restricting women's empowerment. This study examines the lack of sufficient menstruation-related targets in international development agendas and focuses on Nepal's cultural norms concerning this issue that affect women's behaviour, health, and social participation. The paper advocates for a comprehensive understanding of women's experiences and preferences to design effective MHM interventions. It explores various strategies, including campaigns against practices like Chhaupadi, presents findings from a field survey and introduces the Sparśa project - a women-led social enterprise offering biodegradable pads to counteract menstrual discrimination across Nepal. The article emphasises the historical neglect of menstrual-related needs, resulting in adverse effects like school absenteeism and social marginalization. It highlights the crucial role of coordinated efforts and fostering knowledge-sharing to implement more impactful and comprehensive interventions in the field of MHM. The research highlights the Sparśa project's potential to transform societal perceptions of menstruation, break the silence, and contribute to women's rights. It offers insights into the challenges faced by Nepali women and provides valuable considerations for global menstrual health initiatives.

Integrating Indigenous and Western Health Practices as Synergy for African Development in the Globalization Era: The Nigerian Example

The health policy of colonial rule precipitated structural and institutional changes in the healthcare system of the colonized Africans, as Western medicine was imposed across Africa as a better system of health management. This development fundamentally altered the professional and practical acceptability of indigenous medicine even beyond the colonial era. The much-acclaimed theoretical incompatibility of western and indigenous medical systems is one of the major causes of the strain in the levels of acceptance between indigenous medicine and western medicine in post-colonial Africa. In the case of Nigeria, a majority of the populace, especially in the rural areas, still consult the indigenous medical care providers. This consultation is arguably as a result of some factors, including the people’s thin financial resources,the mythical belief in the therapeutic efficacy of indigenous medicine, and closeness of the indigenous healthcare centres to the patients, among others. Given this, it becomes imperative to explore the historical development and value of Adepoju Adewale Toyin and Onagun Rasheed: Integrating Indigenous and… 415 indigenous healthcare practices in Nigeria and determine the possibility of its integration with western medicine. Towards accomplishing this, we explore historical-analytic method, using primary and secondary sources to examine the current state of indigenous traditional health practice in Nigeria, identify the factors that drive (mis)communication between patient and practitioner, and suggest solutions that can be implemented to address existing challenges. In addition, with illustrations drawn from successful examples of integration of indigenous and modern health practices in developing countries, the paper argues in the context of globalization that an advantageous synergy between indigenous and western health practices is feasible as a development strategy for Africa.

Advancement in AI-Based Devices for Monitoring Heart Health in Patients with Cardiac Conditions

In the contemporary landscape of cardiac healthcare, the integration of Artificial Intelligence (AI) into monitoring devices represents a groundbreaking shift towards enhancing patient outcomes and streamlining the management of heart health. This review paper delves into the burgeoning domain of AI-based devices designed for the meticulous monitoring of heart patients, elucidating the methodologies behind their operation, the breadth of their applications, and the palpable impact they have had on both clinical practices and patient experiences. Through a meticulous selection and analysis of current studies, we uncover the transformative capabilities of AI technologies in detecting and predicting heart-related anomalies, offering a personalized approach to cardiac care. The findings underscore the paramount importance of AI in facilitating early intervention, reducing the incidence of heart-related morbacies, and ultimately, charting a course towards a more responsive and adaptive healthcare ecosystem. The significance of this review lies not only in its comprehensive synthesis of existing research but also in its exploration of future potentials, highlighting the ongoing evolution of AI as a pivotal ally in the quest for improved heart health monitoring. This paper aims to contribute to the academic and medical discourse surrounding AI applications in healthcare, offering insights that may guide future innovations and research directions. Keywords--- AI-driven Cardiac Monitoring, Wearable Health Technology, Remote Heart Health Monitoring, Smart Health Devices, Cardiovascular Disease Management, Machine Learning in, Cardiology, Digital Health Solutions, Real-time Heart Health Analytics, Sensor Technology in Cardiac Care, Patient-Centric Heart Monitoring, AI-based Cardiac Diagnostics, Continuous Heart Rate Monitoring, Mobile Health Applications, Personalized Cardiac Care, Telemedicine in Cardiology.

Artificial Intelligence in Enhancing the Kosovo Health Information System

The contemporary healthcare landscape faces unprecedented challenges, ranging from data fragmentation within health information systems to the need for timely and accurate diagnostics. This research explores the transformative potential of Artificial Intelligence (AI) in enhancing the Kosovo Health Information System (HIS). By leveraging the capabilities of AI, we aim to address existing limitations in data interoperability, predictive analytics, and personalized healthcare. The study incorporates a comprehensive literature review, methodological data collection, and analysis of the current state of the Kosovo HIS. Drawing inspiration from successful global implementations, we delve into the possibilities of AI applications in diagnosis, treatment personalization, and population health management. The research also examines ongoing initiatives and collaborations aimed at integrating AI into the Kosovo HIS. Through a critical assessment of technical challenges and ethical considerations, the paper provides insights into the opportunities and hurdles associated with the implementation of AI in healthcare. Ultimately, this research contributes to the discourse on the future prospects of healthcare in Kosovo, highlighting the potential long-term impacts of AI integration and offering recommendations for advancing the country's health information infrastructure. Received: 5 February 2024 / Accepted: 23 April 2024 / Published: 5 May 2024

283 Identification of new or existing risk factors associated with the development of sow pelvic organ prolapse

Abstract A survey was conducted between March and December of 2022 to examine the variation between farms that had high and low pelvic organ prolapse (POP) rate. The objective was to identify new or existing risk factors associated with the development of POP, which could open new avenues for further investigation. Sow farms (n = 53) across 26 production systems located in the US, Canada, and Mexico were used. Sampling of farms was not random. High and low POP systems were selected within each region as well as high and low POP farms within each system. Over 800 variables related to general farm information and management, performance traits, reproductive management, replacement gilt management, gestation and farrowing practices, labor and health management, and nutrition were collected during farm visits. To be included in the analysis, at least 70% of observations were needed for continuous variables with at least 10% of observation for each category for categorical variables. Univariable models were fitted to identify variables associated with annualized POP rate for 2021. Variables with P ≤ 0.10 were selected for further analysis, with 94 predictor variables selected for principal components analysis (PCA). A factor analysis of mixed data was conducted, and results were then fitted into a hierarchical clustering on PCA, which segregated the data into three clusters: 2 with low POP (2.5%) and 1 with high POP (6.1%) prevalence (low vs high P < 0.05). The resulting clusters explained 33.7% of the variability in annualized 2021 POP rate. Differences between clusters were evaluated by Kruskall-Wallis test for numerical variables and chi-square test for categorical variables. High POP cluster farms had greater total piglets born over the years, greater number of sows per worker, greater annual gestation feed usage, and a lower mean body condition score at 16 wk of gestation compared with low POP cluster farms. High POP cluster farms had a decreased proportion of farms using conventional artificial insemination, a greater proportion of farms utilizing cup waterers in gestation for sows in group housing and in farrowing, and greater proportion of farms with a routine sleeving practice. High POP cluster farms compared with low POP cluster farms had greater gestation distillers dried grains with solubles (DDGS) inclusion level (14.6% vs 4.0%, SE 3.02), greater levels of standardized total tract digestibility (STTD) P% (0.49% vs 0.39%, SE 0.014), narrower analyzed Ca to STTD P ratio (1.7 vs 2.2, SE 0.06), and lower phytase inclusion and release (0.16% vs 0.09%, SE 0.014). These variables were also the predictors with the greatest R2 values in the univariable models. These observations suggest further research is needed to evaluate the connection between dietary Ca and P and pelvic organ prolapse.

Teaching Quality Analysis of Health Management Professionals in Colleges and Universities Based on Big Data Decision Tree Algorithm

In this study, we investigate whether the big data decision tree approach can be utilised to evaluate the effectiveness of teaching in higher education by health management professors. In light of the expanding need for high-quality healthcare education by the professionals, it is absolutely necessary to evaluate and enhance the teaching efficacy of healthcare management specialists. The application of big data analytics towards the creation of a method for the evaluation of instructors that is both objective and precise is the focus of this study. Every educational system may be broken down into its two primary components: teachers and pupils. The students are not only passive recipients of the instruction that their teachers provide. All of the desired instructional achievements must be driven by the effort of the students in order to fall within the category of "subjective initiative." Therefore, the first step in any educational activity should be encouraging pupils to build their own sense of initiative and intrinsic motivation to learn. This should be the case in any educational endeavour. If teachers have access to and make use of reliable employment data for statistical analysis, they will be able to have a more significant impact on the kids they teach. Based on a preliminary set of actual university employment data from 2012, this study compares the results of employing the C4.5 approach with a decision tree generating technique for the assessment of the teaching quality by the professionals. Both approaches were used to analyse the data. The findings demonstrate that a straightforward decision tree structure may be obtained using the decision tree approach that makes use of the multiscale rough set model. In addition, our approaches don't need to use overlapping data sets, and they're quite efficient from a computing standpoint. The relevance of this study lies in the fact that it sheds light on how the expertise and motives of teachers who are healthcare professionals to be precise, reflect on the learning of their students and their relevant field skills.