Early Preventive Care Research Articles

The need for a mortality database in Pakistan: now or never.

Respected editor, With a staggering figure of 241.5 million inhabitants, Pakistan is considered the fifth most populous country in the world, with a growth rate of 2.55% per annum. (1) The crude mortality rate as per Pakistan Demographic Survey 2020, was 6.7 per 1000 persons (2), however, data regarding causes of death is doubtful, since a good number of diseases are never truly diagnosed, especially those occurring at homes or in rural areas, while many remain unregistered. Worldwide, mortality statistics serve as an essential indicator of the health and well-being of a population. Maintenance of up-to-date records is considered an important component of health-care policy making, public health research, and future resource allocation, as they help in assessing community health status, and subsequently aid in modifying factors that can ensure better lives. The CDC WONDER database, for example, contains mortality statistics of the U.S. population, collected from the millions of death certificates, from over the past two decades, and is available online. (3) However, in Pakistan, no such reliable mortality database exists at the national level, which could serve a similar purpose. Although the Civil Registration and Vital Statistics (CRVS) system for Pakistan has been in existence for long, its online record lacks statistics regarding causes of death. (4) It is a dilemma, that a significant number of deaths, 40% according to WHO (5), remain unregistered, while those that are registered do not contain an accurate diagnosis of diseases that ultimately lead to loss of lives. It is only reasonable that an accurate and timely updated mortality database be maintained on a nationwide level, based off of death certificates issued by healthcare facilities. A standardized death certificate introduced throughout the country, can not only help recognize the leading causes of death, but could also aid in overcoming inadequacies when it comes to maintaining accurate health records. Such measures can prospectively allow researchers to document long-term survival statistics for clinical research, to identify potential risk factors for certain conditions and recognize high-risk populations so that early preventative care and interventions can be undertaken.

Quantitative biomechanical analysis in validating a video-based model to remotely assess physical frailty: a potential solution to telehealth and globalized remote-patient monitoring.

Assessing physical frailty (PF) is vital for early risk detection, tailored interventions, preventive care, and efficient healthcare planning. However, traditional PF assessments are often impractical, requiring clinic visits and significant resources. We introduce a video-based frailty meter (vFM) that utilizes machine learning (ML) to assess PF indicators from a 20s exercise, facilitating remote and efficient healthcare planning. This study validates the vFM against a sensor-based frailty meter (sFM) through elbow flexion and extension exercises recorded via webcam and video conferencing app. We developed the vFM using Google's MediaPipe ML model to track elbow motion during a 20s elbow flexion and extension exercise, recorded via a standard webcam. To validate vFM, 65 participants aged 20-85 performed the exercise under single-task and dual-task conditions, the latter including counting backward from a random two-digit number. We analyzed elbow angular velocity to extract frailty indicators-slowness, weakness, rigidity, exhaustion, and unsteadiness-and compared these with sFM results using intraclass correlation coefficient analysis and Bland-Altman plots. The vFM results demonstrated high precision (0.00-7.14%) and low bias (0.00-0.09%), showing excellent agreement with sFM outcomes (ICC(2,1): 0.973-0.999), unaffected by clothing color or environmental factors. The vFM offers a quick, accurate method for remote PF assessment, surpassing previous video-based frailty assessments in accuracy and environmental robustness, particularly in estimating elbow motion as a surrogate for the 'rigidity' phenotype. This innovation simplifies PF assessments for telehealth applications, promising advancements in preventive care and healthcare planning without the need for sensors or specialized infrastructure.

Cost minimization analysis of chronic kidney disease management: Evaluating economic strategies for early intervention and treatment optimization

Chronic kidney disease is a common global health condition characterized by progressive loss of kidney function and significant cost burden on the healthcare system worldwide. The disease often coexists with conditions such as diabetes and hypertension, further complicates its management and increasing associated costs. Effective economic management, particularly in the early stages of CKD, is crucial for reducing long-term healthcare expenses and improving patient outcomes. This review examines the utility of cost minimization analysis (CMA) as a strategic tool for evaluating economic options in CKD management. CMA is an economic evaluation method that compares the costs of different therapeutic interventions that have demonstrated equivalent clinical effectiveness, thereby aiding healthcare providers and policymakers in identifying the most cost-efficient options to optimize resource utilization. This review highlights the economic impact of CKD, focusing on the increasing costs linked to advanced disease management and the essential role of early diagnosis and intervention in reducing these costs and explores various cost-effective strategies for CKD management, including pharmacological treatments, lifestyle modifications, and patient education, to identify approaches that are both clinically and economically advantageous. Additionally, the broader implications of cost minimization for healthcare policy, clinical decision-making, and resource allocation are discussed for their significance in a resource-limited healthcare setting. Evidence indicates that prioritizing cost-effective CKD management through early intervention and preventive care can significantly lower healthcare expenditures, slow the disease progression, and enhance patient’s quality of life. By adopting such strategies, healthcare systems can achieve better clinical outcomes and ensure financial sustainability.

Sex and age-related patterns in pediatric primary headaches: observations from an outpatient headache clinic.

Age reportedly affects headache prevalence differently in boys and girls. However, little empirical data exists regarding pediatric headache prevalence and headache-related burden in children and adolescents according to age and sex. In the present study, we considered age and sex while evaluating the distribution, characteristics, and impairment of primary headache disorders at a pediatric headache center in Germany. Medical records of children and adolescents attending the headache clinic of the Interdisciplinary Pain Center of the Carl Gustav Carus University Hospital in Dresden during the period 2015-2022 were retrospectively grouped and analyzed depending on age (< or ≥14 years) and sex. The study population consisted of 652 children and adolescents, aged between 3 and 18 years. Almost two-thirds of the patients (≈60%) were females, and almost two-thirds of these females (58%) were ≥14 years of age. Generally, the most prevalent headache diagnoses as defined by the International Classification of Headache Disorders 3rd edition were episodic migraine without aura and the combination of tension-type headache and episodic migraine with or without aura i.e., mixed-type headache (each ≈27%). In the younger group (<14 years), the mixed-type headache was the most prevalent in girls (28.6%), whereas, for boys, episodic migraine without aura was the most prevalent headache diagnosis (47.4%). In the older group (≥14 years), the mixed-type headache continued to be the most prevalent for girls (30%), and it became the most prevalent for boys (26.3%). Before the age of 14, about 16% of children were severely affected by their headaches. After the age of 14, this proportion increased to roughly one-third (33%) of adolescents, driven mainly by teenage girls (26%) who were severely affected by their headaches. Furthermore, the prevalence of comorbidities was significantly higher among girls (67%), particularly in the adolescent group (74%). Our data shows that headache disorders in a specialized pediatric clinic impose a significant burden, especially among teenage girls indicating high therapy needs. Enhancing awareness of early diagnosis and preventive care is crucial to mitigate the development of chronic headaches, and mitigate their adverse effects on life quality and educational capability.

Predicting Long COVID in the National COVID Cohort Collaborative Using Super Learner: Cohort Study.

Postacute sequelae of COVID-19 (PASC), also known as long COVID, is a broad grouping of a range of long-term symptoms following acute COVID-19. These symptoms can occur across a range of biological systems, leading to challenges in determining risk factors for PASC and the causal etiology of this disorder. An understanding of characteristics that are predictive of future PASC is valuable, as this can inform the identification of high-risk individuals and future preventative efforts. However, current knowledge regarding PASC risk factors is limited. Using a sample of 55,257 patients (at a ratio of 1 patient with PASC to 4 matched controls) from the National COVID Cohort Collaborative, as part of the National Institutes of Health Long COVID Computational Challenge, we sought to predict individual risk of PASC diagnosis from a curated set of clinically informed covariates. The National COVID Cohort Collaborative includes electronic health records for more than 22 million patients from 84 sites across the United States. We predicted individual PASC status, given covariate information, using Super Learner (an ensemble machine learning algorithm also known as stacking) to learn the optimal combination of gradient boosting and random forest algorithms to maximize the area under the receiver operator curve. We evaluated variable importance (Shapley values) based on 3 levels: individual features, temporal windows, and clinical domains. We externally validated these findings using a holdout set of randomly selected study sites. We were able to predict individual PASC diagnoses accurately (area under the curve 0.874). The individual features of the length of observation period, number of health care interactions during acute COVID-19, and viral lower respiratory infection were the most predictive of subsequent PASC diagnosis. Temporally, we found that baseline characteristics were the most predictive of future PASC diagnosis, compared with characteristics immediately before, during, or after acute COVID-19. We found that the clinical domains of health care use, demographics or anthropometry, and respiratory factors were the most predictive of PASC diagnosis. The methods outlined here provide an open-source, applied example of using Super Learner to predict PASC status using electronic health record data, which can be replicated across a variety of settings. Across individual predictors and clinical domains, we consistently found that factors related to health care use were the strongest predictors of PASC diagnosis. This indicates that any observational studies using PASC diagnosis as a primary outcome must rigorously account for heterogeneous health care use. Our temporal findings support the hypothesis that clinicians may be able to accurately assess the risk of PASC in patients before acute COVID-19 diagnosis, which could improve early interventions and preventive care. Our findings also highlight the importance of respiratory characteristics in PASC risk assessment. RR2-10.1101/2023.07.27.23293272.

Early Childhood Screening Practices and Barriers: A National Survey of Primary Care Pediatricians.

The American Academy of Pediatrics recommends screening during the first 3 years of life for developmental risk/delay, maternal depression, and social determinants of health (SDOH) using standardized tools. Adoption of these guidelines has been gradual, and barriers to screening are as varied as pediatric practices are themselves. We analyzed 2019 American Academy of Pediatrics Periodic Survey data. The survey included pediatricians' screening practices for developmental delay, maternal depression, and SDOH, and barriers to screening. We used Cochran's Q and McNemar's tests to compare barriers across topics, and χ2 tests and multivariable logistic regression to examine the relationship between barriers and screening practices. The survey response rate was 46.9% (n = 688). Most pediatricians reported screening/surveilling for developmental delay (98.1%), maternal depression (83.2%), and SDOH (76.7%), but fewer used standardized instruments to do so (59.0%, 44.9%, 12.6%, respectively). Those not screening/only surveilling for maternal depression were more likely to report that screening is not an appropriate role for the pediatrician and difficulty prioritizing time. For SDOH, those not screening/only surveilling were more likely to report instruments not available in the electronic health record, lack of knowledge regarding referral options, and lack of treatment options for positive screens. Rates of standardized screening, and barriers pediatricians encounter, have important implications for improving quality of early childhood preventive care. Opportunities include expanding the primary care team and integrating screening tools into the electronic health record. Programs to support social needs and maternal mental health could be strengthened to ensure robust referral options are available.

Blindness Detection – A Systematic Research

The proposed framework merges Generative Adversarial Networks (GANs) with Reinforcement Learning (RL) techniques to enhance blindness detection. GANs generate synthetic retinal images covering various eye diseases, enriching training data and improving generalization. RL optimizes screening strategies dynamically, adjusting decisions based on evolving patient profiles and environmental cues. Empirical evaluations on real-world datasets demonstrate superior performance over conventional methods, addressing data imbalance and fostering adaptable screening policies. This synergistic fusion offers a comprehensive, adaptable, and interpretable approach to early diagnosis and preventive care, highlighting the potential of advanced AI techniques in healthcare.

Early Intervention in pediatric Dentistry: Nurturing lifelong Oral health Habits

Early intervention in pediatric dentistry is crucial in influencing children’s oral health trajectory and establishing habits that may last a lifetime. Pediatric dentists strive to develop good dental hygiene habits in young children via early detection, treatment, education, and prevention (Happykidsdental, 2024). To foster good dental hygiene habits for life, this study explores the many aspects of early intervention in pediatric dentistry. Pediatric dentistry is essential for youngsters to have healthy teeth and gums. It considers the specific requirements and difficulties during infancy and goes beyond regular dental checkups (Wang, 2023). Pediatric dentists provide the groundwork for optimal dental health throughout a child’s life by concen trating on early intervention, individualized treatments, and preventative care (Krol and Whelan, 2022). They make the dentist’s office a fun place for kids, encouraging them to have good experiences there and teaching their parents how to care for their teeth better (Happykidsdental, 2024). Issues such as thumb-sucking, teething, and assisting youngsters with orthodontic troubles are also part of a pediatric dentist’s expertise (Lin, 2022). Pediatric dentists are medical professionals who focus on children’s oral and dental health (dentalcare.com, 2023). They help children maintain good teeth and jaw development, which benefits their general health. Pediatric dentistry is a subspecialty of general dentistry that focuses on the specific problems and requirements of dental health in children. It covers a lot of ground, from general dental checkups and education to more precise treatments for common problems in children and teenagers (Clinical Affairs Committee, 2012). Dental treatment for children goes beyond just fixing cavities; pediatric dentists also work to alleviate anxiety and promote a good attitude toward the dentist from a young age (Happykidsdental, 2024). Building a solid foundation for excellent dental health throughout a child’s life begins in the early years (Baker, Lee and Wright, 2019). The importance of early interventions in establishing excellent oral hygiene habits and avoiding dental disorders before they happen is acknowledged in this particular area (Krol and Whelan, 2022). Pediatric dentists play a dual role as healthcare practitioners and educators, helping children and their families maintain good oral health and wellness. The field of pediatric dentistry focuses on the dental health and welfare of children from the time they are infants until they reach puberty (Wang, 2023). Encouraging youngsters to form good habits with their teeth and gums is integral to this niche field’s mission to promote dental health (Lin, 2022). The formative years are essential to provide the groundwork for excellent dental health throughout a child’s life.

Beyond the Chair: Exploring the Boundaries of Teledentistry.

Teledentistry, a blend of dentistry and telecommunications used to provide dental care from a distance, holds great promise in enhancing public health, especially in reaching communities with limited access. However, putting it into practice has its own set of obstacles and background variables. It is frequently difficult to obtain dental treatment, especially in rural or economically underdeveloped areas. By removing geographical constraints and offering virtual consultations and diagnostics, teledentistry provides a solution. The quick development of technology, such as portable electronics and high-speed internet, has increased accessibility and efficiency in communication, which has aided in the acceptance of teledentistry. Getting traditional dental care can be costly, particularly for people without insurance. As an affordable substitute for traditional dental care, teledentistry may lower overhead expenses related to physical dental offices. Early interventions and preventive care can greatly enhance oral health results and lessen the need for later, more invasive procedures. There are disparities in the availability of digital gadgets and internet connectivity. The laws that control telehealth services might differ greatly between nations and areas. While teledentistry can help with remote consultations and triage, it might not be able to do thorough examinations or other diagnostic treatments that need in-person attendance. Delivering dependable, top-notch telemedicine services in developing areas can pose challenges. Certain dentists might hesitate to utilize teledentistry due to concerns about the quality of care, liability issues, or potential impacts on their traditional practice models. It could be difficult to get patients to accept and use teledentistry services. This review is undertaken to assess the effectiveness of teledentistry in public health as well as legal and regulatory considerations for practicing teledentistry. Teledentistry might be the future of the oral health sector, thus fully capitalizing on this enormous opportunity to change how oral therapies are administered. Patients, dentists, and the dental community at large will need to have highly open minds. For the community's well-being and to gain its trust, ethical considerations are crucial. The outcomes of teledentistry can be attained by overcoming the obstacles and using comprehensive methods and approaches. It was noted that teledentistry is a potential strategy that combines dental care with telecommunication technology to enhance patient outcomes, reduce healthcare inequalities, and expand access to oral health services. The delivery of oral healthcare is being revolutionized by teledentistry, especially in light of contemporary issues including geographic restrictions, lack of access to dental treatment, and the ongoing global health crisis.

Challenges and Opportunities in Breast Cancer Care in Low-Resourced Countries, Jordan as An Example.

Jordan is a relatively small country with a rapidly growing population and a challenged economy. Breast cancer is the most diagnosed cancer among women worldwide and also in Jordan. Though the age-standardized rate (ASR) of breast cancer incidence is still lower than that in Western societies, the number of newly diagnosed cases continues to increase, involving younger women, and new cases are usually detected at more advanced stages. Improvements in breast cancer care across the health care continuum, including early detection, prevention, treatment, and survivorship and palliative care, have become very visible, but may not match the magnitude of the problem. More organized, goal-oriented work is urgently needed to downstage the disease and improve awareness of, access to, and participation in early detection programs. The cost of recently introduced anti-cancer therapies poses a great challenge, but the impact of these therapies on treatment outcomes, including overall survival, is becoming very noticeable. Though the concept of a multidisciplinary approach to breast cancer treatment is often used at most health care facilities, its implementation in real practice varies significantly. The availability of breast reconstruction procedures, survivorship programs, germline genetic testing, counselling, and palliative care is improving, but these are not widely practiced. In this manuscript, we review the status of breast cancer in Jordan and highlight some of the existing challenges and opportunities.

Genome-wide association study of osteoporosis identifies genetic risk and interactions with Dietary Approaches to Stop Hypertension diet and sugar-sweetened beverages in a Hispanic cohort of older adults.

Osteoporosis (OP) and low bone mass can be debilitating and costly conditions if not acted on quickly. This disease is also difficult to diagnose as the symptoms develop unnoticed until fracture occurs. Therefore, gaining understanding of the genetic risk associated with these conditions could be beneficial for health-care professionals in early detection and prevention. The Boston Puerto Rican Osteoporosis (BPROS) study, an ancillary study to the Boston Puerto Rican Health Study (BPRHS), collected information regarding bone and bone health. All bone measurements were taken during regular BPROS visits using dual-energy X-ray absorptiometry. The OP was defined as T-score ≤ -2.5 (≥2.5 SDs below peak bone mass). Dietary variables were collected at the second wave of the BPRHS via a food frequency questionnaire. We conducted genome-wide associations with bone outcomes, including BMD and OP for 978 participants. We also examined the interactions with dietary quality on the relationships between genotype and bone outcomes. We further tested if candidate genetic variants described in previous GWAS on OP and BMD contribute to OP risk in this population. Four variants were associated with OP: rs114829316 (IQ motif containing J gene), rs76603051, rs12214684 (melanin-concentrating hormone receptor 2 gene), and rs77303493 (Ras and Rab interactor 2 gene), and 2 variants were associated with BMD of lumbar spine (rs11855618, cingulin-like 1 gene) and hip (rs73480593, NTRK2), reaching the genome-wide significance threshold of P≤ 5E-08. In a gene-diet interaction analysis, we found that 1 SNP showed a significant interaction with the overall Dietary Approaches to Stop Hypertension (DASH) score, and 7 SNPs with sugar-sweetened beverages (SSBs), a major contributor to the DASH score. This study identifies new genetic markers related to OP and BMD in older Hispanic adults. Additionally, we uncovered unique genetic markers that interact with dietary quality, specifically SSBs, in relation to bone health. These findings may be useful to guide early detection and preventative care.

Assessing the Effect of Hyperbaric Oxygen Therapy on Heart Rate and Blood Pressure

Cardiovascular diseases (CVDs) are the leading cause of global mortality, encompassing heart disease, strokes, and related conditions. To combat this public health concern, monitoring vital indicators like heart rate and blood pressure plays a pivotal role in early detection, preventive care, treatment assessment, fitness tracking, and post-surgery care. This study aims to assess the effect of Hyperbaric Oxygen Therapy on heart rate and blood pressure, exploring its potential role as a therapeutic intervention to optimize cardiovascular system health. This study examines the effects of hyperbaric oxygen therapy (HBOT) on heart rate and blood pressure in patients who receive multiple treatments. The research includes 113 participants undergoing HBOT at different frequencies over 2 weeks. This study used specific statistical tests to analyze the effects of Hyperbaric Oxygen Therapy (HBOT) on heart rate and blood pressure. For nonparametric data, the Wilcoxon test will be employed, and for normally distributed data, the Paired T-test will be used. To determine the most influential HBOT frequencies group, the Kruskal- Wallis test will be applied due to the delta heart rate categorized as ordinal. The statistical analysis revealed a significant difference in heart rate before and after HBOT treatment with frequencies of two, three, and five times a week with the details p = 0.008, p &lt; 0.001, and p &lt; 0.001, respectively. As for heart rate alterations, HBOT should be administered at least three or five times a week, which led to notable reductions in heart rate. The correlation values on systole and diastole before and after treatments are 951 and 916 respectively. This data shows that hyperbaric oxygen therapy does improve the blood pressure quality of hypertensive patients. By examining the effect of HBOT on heart rate and blood pressure, this study seeks to contribute valuable insights to the field of cardiovascular system health and provide evidence for the use of HBOT in promoting healing and well-being.

A simple nomogram for predicting aspiration associated with dysphagia in hospitalized patients after stroke.

Aspiration is a common complication of poststroke dysphagia (PSD) and is associated with poor prognosis and mortality. There is no uniform criterion for determining aspiration associated with dysphagia. The aim of this study was to identify early predictors of aspiration, leading to the development of a simple nomogram for identifying aspiration risk associated with dysphagia in hospitalized patients after stroke. Demographic information and clinical characteristics of 330 patients with PSD in the training cohort were utilized to develop a nomogram. The LASSO regression method was used to screen variables, and logistic regression was used to construct the nomogram. Internal validation was performed with bootstrap in the training cohort, and external validation was performed in the validation cohort of another 82 patients. The area under the curve (AUC), calibration curves, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. Seven variables were selected based on LASSO and multivariate logistic regression. The AUC of the nomogram was 0.834 (95% CI, 0.790-0.878) in the training cohort, 0.806 (95% CI, 0.791-0.880) in the internal validation cohort, and 0.882 (95% CI, 0.810-0.954) in the external validation cohort, which indicated that the model had good discrimination. The calibration and DCA curves showed that the nomogram had good accuracy and clinical utility. In this study, we established a nomogram that can be used to identify the risk of aspiration associated with dysphagia after stroke, and patients may benefit from early screening and preventive care.

Virtual Doctor: Enhancing Global Healthcare Accessibility through Image Classification

Virtual Doctor aims to make healthcare more available worldwide by using image classification to detect diseases from pictures. Virtual Doctor represents a ground-breaking initiative aimed at transforming health assessments worldwide, particularly in areas where healthcare resources are scarce. Utilizing a sophisticated machine learning model, this platform allows users to perform real-time health evaluations by simply capturing images using a smartphone or other camera-equipped mobile devices. Virtual Doctor empowers individuals to conduct selfassessments and receive immediate feedback on their health, facilitating early detection and preventive care. This innovative approach not only educates users about proper hygiene practices but also significantly enhances access to healthcare services. By simplifying the process of health monitoring, Virtual Doctor seeks to improve overall health outcomes in underserved populations. This report will focus on the Dental Health module of the platform, illustrating how the underlying technology can be adapted to other health domains such as facial health, each tailored to meet specific diagnostic needs.

The Surprising Connections between Oral Health &amp; Well Being! Routine Oral Checks- A missed Opportunity for Dental, Oral &amp; Overall, Health!

Our teeth don't necessarily hurt until something big is going on. While some health issues show up in the mouth, Poor oral hygiene can lead to negative health outcomes, the problems go both ways: Some studies show that there may even be a connection between poor oral health and worse brain health Brushing at least twice a day, flossing at least once a day, contribute to greater dental, oral and overall health." Studies suggest that oral bacteria and the inflammation associated with a severe form of gum disease (periodontitis) play a role in diseases like oral thrush, loose teeth, carries. Similarly, diseases such as diabetes and HIV/AIDS lower the body's resistance to infection, turning oral health problems more severe. A primary care doctor plays varied roles – a scientist, a healer, and an organized service provider. Healing may not reverse the damage but curtails the illness or its consequences from controlling our lives. Every PCP as they grow into the profession must be driven by competence, newer medical developments &amp; technologies, precision in decision-making / treatment, improving people’s lives, understanding the importance of compassion, and realizing that the science is incomplete, then only a healer emerges in him or her. The system must take responsibility for their appropriate training. Each PCP must make it a practice to have a quick scan of the mouth in every visit of their patients not to miss early detection, treatment, and preventive care opportunity. Materials and Methods: This article is based on identification of two cases of Oral cancers one in early stage, biopsied leading to simple excision and cured from Bhutan and another case from Raichur in November 2023 in late stage currently under chemotherapy, one case of Aphthous ulcer and a few cases of gingivitis due to diabetes, with mixed results.

An enhanced diabetes prediction amidst COVID-19 using ensemble models.

In the contemporary landscape of healthcare, the early and accurate prediction of diabetes has garnered paramount importance, especially in the wake of the COVID-19 pandemic where individuals with diabetes exhibit increased vulnerability. This research embarked on a mission to enhance diabetes prediction by employing state-of-the-art machine learning techniques. Initial evaluations highlighted the Support Vector Machines (SVM) classifier as a promising candidate with an accuracy of 76.62%. To further optimize predictions, the study delved into advanced feature engineering techniques, generating interaction and polynomial features that unearthed hidden patterns in the data. Subsequent correlation analyses, visualized through heatmaps, revealed significant correlations, especially with attributes like Glucose. By integrating the strengths of Decision Trees, Gradient Boosting, and SVM in an ensemble model, we achieved an accuracy of 93.2%, showcasing the potential of harmonizing diverse algorithms. This research offers a robust blueprint for diabetes prediction, holding profound implications for early diagnosis, personalized treatments, and preventive care in the context of global health challenges and with the goal of increasing life expectancy.

Early stage HIV diagnosis using optimized ensemble learning technique

Human immunodeficiency Virus most commonly known as HIV, is a retrovirus that attacks and weakens the immune system, leaving the body vulnerable to opportunistic infections and cancers and can be transmitted through unprotected intercourse or shared syringes. HIV can become AIDS, which is a more deadly form of HIV if left untreated. Individuals and NGOs are working hard to encourage open discussions, decrease the negative feelings around HIV, and make sure more people know and understand it better. Early prevention and proper medical care can help reduce the mortality rate and harmful effects of HIV. Machine learning applications aid in early prediction of the disease. In this work, we have proposed an optimized ensemble machine learning technique using grid search hyper-parameter optimization, to diagnose disease in early stages on the basis of learning received from the HIV dataset from Kaggle used in our experiment. The proposed approach outperformed other individual and existing models with the highest classification accuracy of 97.86%.

Implementing the Bismarck model for dental care in San Diego County

The Bismarck model is a great model that has been used in other countries as a reference. It provides coverage for all, and it is a more-simple model than the American healthcare system. Coverage is required regardless of someone's income, and it has a higher life expectancy because of the early preventative care provided. The Bismarck model was implemented in 1875 and has worked for Germany and Japan. San Diego County would benefit from this Bismarck model because it has proven to have positive outcomes for its citizens. The objective of this paper is to implement the Bismarck model in San Diego County.

Maternal intrapartum fever during epidural labour analgesia: Incidence and influencing factors.

The management and nursing care of women's temperature during delivery is an important part of clinical obstetrics. We aimed to evaluate maternal intrapartum fever during epidural labour analgesia to provide evidence for the management and care of women in labour. This study was conducted and reported according to the STROBE statement. Women in labour undergoing epidural labour analgesia in our hospital from 1 January 2021 to 31 August 2022 were retrospectively selected. The characteristics of women in labour with and without intrapartum fever were compared. Pearson correlation and logistic regression analysis were used to analyse the influencing factors of postpartum fever. A total of 196 women in labour were included, the incidence of maternal intrapartum fever in women in labour undergoing epidural analgesia was 27.5%. Pearson correlation analyses showed that BMI, oxytocin use, labour duration, number of vaginal examinations, time from rupture of the foetal membranes to the end of labour and duration of epidural analgesia were all correlated with the occurrence of intrapartum fever (all P < 0.05). Logistic regression analyses indicated that body mass index ≥28 kg/m2 (OR = 1.825), oxytocin use (OR = 2.082), labour duration ≥9.2h (OR = 2.613), number of vaginal examinations ≥8 (OR = 2.044-3.115), the time from rupture of the foetal membranes to the end of labour ≥250 min (OR = 2.766) and duration of epidural analgesia ≥300 min (OR = 3.106) were risk factors for intrapartum fever in women in labour undergoing epidural analgesia (all P < 0.05). Maternal intrapartum fever in women in labour undergoing epidural analgesia is common and influenced by many factors. Nurses should take early preventive care measures according to these factors during epidural analgesia in labour.

Evaluating the Adipose Tissue Depth as a Predictor Factor for Gestational Diabetes in Later Pregnancy-A Systematic Review.

The increasing prevalence of gestational diabetes mellitus (GDM) requires non-invasive and precise techniques for evaluating the predisposing risk factors such as visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). According to PRISMA, we developed a systematic review and searched after "visceral adipose tissue AND gestational diabetes" and identified 221 articles on the MEDLINE and Word of Science databases. After assessing them for inclusion criteria and two researchers screened them, 11 relevant articles were included. Although evidence is conflicting, more studies favor using US-determined VAT in GDM prediction. VAT may be more valuable than body mass index or SAT in predicting GDM. VAT can represent an additive factor to the prediction tool of the risk of developing GDM when used in conjunction with other anthropometric or biological parameters or maternal risk factors. US measurements are heterogeneous given different evaluation techniques, cut-off values and inter-operator variation. A significant limitation is the lack of a gold standard to identify GDM confidently. Pregnant women may benefit from early monitoring and preventive care if classified as high risk for GDM early in the gestational period. US-measured VAT during the first trimester of pregnancy seems a valuable and inexpensive screening approach to predict GDM development later in pregnancy, either by itself or if used in conjunction with other clinical and biological parameters.