Personal Care Research Articles

Background: In research cohorts, pathologic genetic variants have been reported in nearly 30% of children with hypoplastic left heart syndrome (HLHS). However, reports of real-world genetic testing practices and findings are limited. Research Questions We aimed to describe the variation in rates and type of genetic testing performed among academic centers in North America caring for newborns with HLHS. We also aimed to describe the real-world diagnostic yield in this population. Methods: We performed an ancillary study to a multicenter retrospective cohort study of fetuses and infants <2 months of age with HLHS admitted 1/2012-12/2016 to participating Fetal Heart Society institutions in North America. Prenatal and postnatal genetic testing and extracardiac anomalies (EA) were collected from participating centers. Results: Among 11 centers, 521 fetuses and infants were included. Rates of any form of diagnostic genetic testing varied between centers (24% to 96%). Overall, 109 (20%) had prenatal testing (excluding non-invasive prenatal testing, NIPT), and 302 (58%) eventually had some form of diagnostic testing (other than FISH for 22q11.2 deletion). Aneuploidy was evaluated for in 290 (56%); 16 were diagnostic (5.5%, Table), predominantly for Turner syndrome (TS). Comprehensive evaluation for copy number variation by chromosomal microarray (CMA) occurred in 216 patients (41.5%), with 16 positive findings, for a 7.4% yield. Of these, 6 were detected prenatally (of 59 with prenatal CMA, 10.2%). Only 19 patients (3.6%) underwent whole exome sequencing (WES) of which 6 had sequence variants, for a solve rate of 31.6%. Of the 494 in whom presence of EA was characterized, 86 had EA (17.4%). Among those with both genetic testing and information on EA (n=292), the yield of genetic testing was higher in those with EA but still ranged from 3.8-18.2% in those without (Table). Conclusions: Patterns of genetic testing in fetuses and neonates with HLHS vary significantly among academic centers in North America. Only 20% of the cohort had prenatal genetic testing beyond NIPT, and just over half had any testing. During the study period, WES was rarely performed but had the highest yield. Offering consistent genetic testing, including appropriate testing for sequence variants, will likely result in more frequent diagnosis of genetic disorders. In turn, this may improve our understanding of neurodevelopmental variability and inform personalized counseling and medical care in HLHS.

Accumulating data related to prevention, surveillance and treatment of chronic hepatitis B (CHB) provided the impetus for this updated guideline, using the Grading of Recommendation Assessment, Development and Evaluation (GRADE) approach. The guideline was developed in compliance with the National Academy of Medicine standards. The guideline panel developed structured questions following the Population, Intervention Comparison, Outcomes (PICO) framework. The panel addressed 6 PICO questions covering prevention (maternal to infant transmission and horizontal transmission), surveillance for liver cancer (among hepatitis B surface antigen positive (HBsAg) persons co-infected with hepatitis C virus, hepatitis D virus and/or human immunodeficiency viruses and after HBsAg loss) and treatment (HBsAg positive persons in immune-tolerant or indeterminate phases as well as withdrawal of antiviral therapy), providing evidence-based recommendations on these topics. Four systematic reviews of the literature were conducted, and two existing systematic reviews were utilized to support the recommendations in this practice guideline. This evidence-based guideline provides updated recommendations to optimize the care of persons with CHB.

Background: Coronary artery disease (CAD) is the leading cause of mortality globally. As a chronic condition, CAD requires long-term management, and evaluating patients’ health-related quality of life (HRQoL) has become crucial from a patient-centered care perspective. HRQoL is a subjective assessment of health status that reflects an individual’s overall well-being. CAD patients tend to have lower HRQoL compared to the general population due to physical, psychological, and social factors. Objective: This study aimed to develop a machine learning-based model to predict HRQoL in patients with CAD using Wilson and Cleary’s conceptual model. Methods: This study extracted data from the 6th to 8th (2013–2020) Korea National Health and Nutrition Examination Survey (KNHANES). Adult patients diagnosed with angina or myocardial infarction were included. HRQoL was measured using EQ-5D; participants were classified as high-risk (EQ-5D < 0.678, n = 147) or non-risk (n = 1,163). SPSS 29.0 was used for complex sample analyses. Python 3.0 was used for data preprocessing, model development, evaluation, and feature importance analysis. Results: Six machine learning models were tested: logistic regression, decision tree, naive Bayes, random forest, support vector machine (SVM), and extreme gradient boosting (XGBoost). XGBoost showed the best performance (accuracy: 93%, AUC: 0.98). Key predictors included perceived health status, physical activity, discomfort, education, income, occupation, and activity limitation. Conclusions: Machine learning models, particularly XGBoost, demonstrated strong predictive performance for HRQoL in CAD patients. These findings may support personalized care strategies and the development of interventions to enhance quality of life in this population.

Introduction: Personal care products (PCPs) are complex chemical mixtures, which include endocrine disrupting chemicals linked to elevated risk of hormonally-responsive chronic diseases. Blood pressure is influenced by sex hormones; however, the relationship between PCPs and hypertension is unclear. Hypothesis: We investigated whether PCP usage patterns were associated with risk of hypertension in U.S. women. Methods: The Sister Study is a prospective cohort study of 50,884 women recruited in 2003-2009. The participants self-reported their usage frequency of 41 PCPs in the 12-month period before baseline and doctor’s diagnosis of incident hypertension. We analyzed individual PCPs and four product groups (i.e., beauty, everyday hair, hygiene, and skincare products). Among product groups, latent class analyses were used to identify PCP usage patterns (“infrequent”, “moderate”, or “frequent”). Multivariable Cox regression was used to estimate the associations between PCPs and hypertension risk, adjusted for potential confounders. The population attributable risk percentage contrasting “frequent” versus “infrequent” users was calculated using Levin's formula. Results: We found an increasing dose-response relationship between beauty products and incident hypertension (p-trend<0.0001; Table 1), with frequent users having a significantly higher risk compared with infrequent users (Hazard Ratio (HR)=1.11 (95% confidence intervals (CI):1.05,1.16)). A similar dose-response relationship was found for hygiene products (p-trend<0.0001), with elevated risks observed in moderate (HR=1.07 (95%CI:1.01,1.13) and frequent (HR=1.13 (95%CI:1.08,1.19) users. An estimated 6.1% and 5.8% reduction in hypertension incidence would be observed among U.S. women with decreased use of beauty and hygiene products, respectively. Our findings were largely consistent across different subgroups defined by age, menopausal status, race, and socioeconomic status. The use of several individual everyday hair and skincare products was associated with increased risk of hypertension; however, latent classes of either everyday hair or skincare product use were not. Conclusions: We found that use of certain PCPs contribute to future development of hypertension, a strong but modifiable risk factor for most cardiovascular diseases. Our findings support the need to identify the pathogenic constituents of PCPs that drive hypertension risk.

Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous condition with high morbidity and mortality. Risk stratification of patients with HFpEF is important for advancing therapeutic development and improving clinical care. Research Question: Predicting overall mortality and heart failure (HF) Hospitalization in real world HFpEF population Aims : to leverage machine learning model to develop prognostic models based on real-world data, with the potential to support risk stratification in routine clinical practice Methods: CONFIDENT is an observational, multi-cohort study across three centers in Europe and the US. Patients with HFpEF, according to the HFA-PEFF criteria with ≥ 2 years of follow-up, were included from 2013 to 2022. The dataset included multimodal data from electronic health records, lab tests, echocardiography, and electrocardiography, with 82 baseline candidate variables. We developed machine learning-based prognostic models to predict all-cause mortality and HF hospitalization. Model performance was compared to conventional risk score in an external validation cohort. Results: A total of 1208 patients were included in the study. The mean age was 72±12. The 2-year risk of HF hospitalization and all-cause mortality ranged from 13 to 44% and 9 to 19% respectively. The all-cause mortality prognostic model achieved good discrimination with a C-index of 0.67 [95%CI, 0.66-0.68], and 0.68 [95%CI 0.66-0.69] in the training cohorts, and 0.72 [95%CI 0.65-0.78] in the validation cohort, and performed better than the PREDICT-HFpEF score (C-index: 0.66 [95%CI 0.65-0.68], p-value =0.012; 0.60, [95%CI 0.59-0.62],p-value < 0.01 and 0.67 [95%CI 0.58-0.73], p-value =0.013 respectively. Similar results were observed when compared to the Meta-Analysis Global Group In Chronic Heart Failure Risk Score (MAGGIC). Similarly, the model derived for HF hospitalization outperformed PREDICT-HFpEF and MAGGIC + natriuretic peptide. Conclusion: CONFIDENT prognostic models for all-cause mortality and HF hospitalization using routinely collected variables can reliably predict outcomes and facilitate personalized care and trial recruitment strategies in HFpEF.

This paper presents a multi-agent AI system designed to provide accurate diagnostic and personalized treatment recommendations for heart attack, heart failure, cardiac arrhythmia, coronary artery disease, and left ventricular hypertrophy. The system tackles the challenges of integrating various data sources, including electronic health records (EHR), cardiac imaging, genetic information, and electrocardiogram (ECG) data, within a unified multi-agent framework for personalized care related to these conditions. A collaborative network of specialized AI agents, such as the EHR Agent, Cardiac Imaging Agent, Genetic Analysis Agent, and ECG Analysis Agent, work in concert to process and analyze this multi data, identifying potential cardiac conditions and risk factors associated with the above-mentioned target indicators. Research Questions/Hypothesis: This study investigates whether a multi-agent AI system can effectively process patient data, including symptoms, genetic information, and test results, to generate potential conditions and diagnoses. We hypothesize that this integrated approach can potentially improve the speed of assessment for accurate and timely diagnosis, provide relevant diagnostic information and personalized treatment recommendation. Methods/Approach: The multi-agent system comprises several specialized agents responsible for tasks such as symptom analysis, diagnosis, and treatment planning. The system is targeted at processing patient data, including symptom descriptions and test results from labs (biomarkers), ECG, echo, MRI and CT scans, along with genetic variants. The symptom analysis agent identifies potential cardiovascular conditions based on input symptoms. The diagnostic agent then integrates information from potential conditions, patient history, and test results to generate a diagnosis. Results/Data: Analysis of simulated data demonstrates that the symptom analysis agent consistently identifies expected potential conditions with high level of speed and accuracy. Recording 1-2 seconds of diagnosis time with precision level of 98% based on simulated data and programmed logic. We’re only reporting metrics based on the internal consistency of the agent's logic and simulated outcomes. Conclusion(s): The developed multi-agent system demonstrates a functional approach to integrating diverse simulated patient data for cardiovascular assessment and potential diagnosis.

The integration of artificial intelligence (AI) into Australian healthcare promises to improve diagnostic accuracy, workflow efficiency, and personalised care, yet it also introduces critical cybersecurity vulnerabilities that threaten not only data privacy but also patient safety and health system trust. This perspective argues that cybersecurity must be recognised as a core dimension of healthcare quality and formally embedded in Australia's safety governance frameworks. Drawing on recent national incidents, regulatory gaps, and international comparisons, we propose five policy actions to align AI-enabled innovation with secure, ethical, and resilient healthcare delivery. Embedding cybersecurity within clinical governance and system reform agendas is vital to ensure sustainable digital transformation.

Pregnancy induces significant alterations in the maternal microbiome, which are critical for fetal development and maternal health. Gynecological diseases, along with infertility, have increased due to excessive personal care product usage, which contains endocrine-disrupting chemicals (EDCs). Mammalian immune systems develop during pregnancy and after birth owing to crucial inputs from the environment. The growing incidence of autoimmune diseases (AIMDs) emphasizes the need to understand the environmental elements that play a role in their development, with the microbiome emerging as a key player. Exposure to EDCs with oxidative stress (OS) induces microbiome disruptions to promote AIMDs and negatively impacts female reproductive health and fetuses. Because the body changes in a number of ways to provide ideal conditions for fetal growth, pregnancy is a special moment in a woman's life. All microorganisms undergo changes, and their quantity and composition vary over the three trimesters of pregnancy. Recent research suggests a connection between pregnancy issues and the microorganisms present during pregnancy. This review explores the pivotal role of the human microbiome in pregnancy health, emphasizing how microbiome dynamics influence immune development and long-term immunity in offspring. It examines the impact of environmental factors, particularly EDCs, on maternal microbiota and their association with pregnancy complications such as hypertensive disorders and autoimmune diseases. The manuscript highlights current research findings and discusses potential microbiome-targeted interventions to promote maternal and fetal well-being.

BackgroundSaudi Arabia’s Vision 2030 places significant emphasis on the health sector, including dental clinics, as a vital contributor to economic growth and the enhancement of quality healthcare services. Patient satisfaction and loyalty, measured through willingness to revisit, serve as key indicators of success in this evolving healthcare landscape. This study evaluates how service quality dimensions influence patient satisfaction and revisit intentions within dental clinics, Aligning with the goals of improving healthcare standards under Vision 2030.MethodsA primary data was collected using random sampling method among 236 patients visiting dental clinics, with an age range of 10 to 70 years. The SERVQUAL model was employed to evaluate service quality across five dimensions: tangibility, accountability, responsiveness, assurance, and empathy. T-test and regression analysis were used to analyze the influence of these dimensions on patient satisfaction and willingness to revisit.ResultsThe average scores for tangibility, accountability, responsiveness, assurance, and empathy were 3.95/5, 2.93/5, 3.04/5, 3.55/5, and 3.08/5, respectively. Responsiveness, assurance, and empathy significantly influenced both patient satisfaction and willingness to revisit, while accountability had a negative impact. The regression analysis revealed that 42.8% of the variation in patient satisfaction and 42.9% in willingness to revisit could be explained by the service quality dimensions. Empathy emerged as a crucial factor, positively impacting patient satisfaction and willingness to revisit.ConclusionThe study highlights the importance of responsiveness, assurance, and empathy in determining patient satisfaction and loyalty in dental clinics. While tangibility was not a significant factor in patient satisfaction, it influenced the willingness to revisit, suggesting that the physical aspects of the clinic play a role in patient loyalty. The findings underscore the need for dental clinics to focus on improving scheduling systems, providing financial assurances, and enhancing personal care to improve patient experiences. This research contributes to the understanding of service quality in dental clinics and provides insights for enhancing patient satisfaction and loyalty, Aligning with the goals of Vision 2030.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12903-025-06879-2.

Triclocarban (TCC), a synthetic antimicrobial compound prevalent in personal care products, has emerged as a typical contaminant in aquatic ecosystems. Intestinal microbiota maintains the host’s health homeostasis by regulating nutrient metabolism and immunity and is regarded as a sensitive biomarker for the risk assessment of pollutants. Currently, there is still a lack of toxicity assessment of TCC on the intestinal microbiota homeostasis of shrimp. Therefore, this study employed 16S rDNA sequencing to explore intestinal microbiota perturbations in Penaeus monodon following subchronic exposure (14 days) to graded TCC concentrations (1 and 10 μg/L). The results showed that TCC exposure altered intestinal microbiota diversity, marked by increases in the ACE, Chao1, and Shannon indices and a decrease in the Simpson index; however, none of these changes reached statistical significance (p > 0.05). Furthermore, the community composition was also altered, characterized by a significant increase in Bacteroidetes and a significant decrease in Tenericutes (p < 0.05), alongside non-significant increases in Proteobacteria and decreases in Firmicutes (p > 0.05). The abundances of some putative beneficial bacterial genera (Alloprevotella, Bacteroidales S24-7 group_norank, Cetobacterium, Enterococcus and Lactobacillus) and harmful bacteria (Photobacterium and Aeromonas) were decreased (p > 0.05); the abundance of Vibrio was decreased in the T1 group but increased in the T10 group (p > 0.05). Additionally, the predicted functions of the intestinal microbiota, such as glycan biosynthesis and degradation, steroid and isoflavone biosynthesis, and nucleotide metabolism, were enhanced. These results indicated that TCC exposure had a negative effect on the homeostasis of the intestinal microbiota of P. monodon.

Objective This study provides a comprehensive bibliometric review of patientcentered care (PCC) in in vitro fertilization (IVF), a domain integral to improving patient outcomes, satisfaction, and experiences in assisted reproductive technology. By analyzing global research output and trends, this study aims to identify key themes, contributors, and gaps in the literature to inform future research and practice. Methods Data were collected from Scopus and Web of Science databases, covering the period from 2003 to 2024. Bibliometric analyses, conducted using R Studio and Biblioshiny, examined publication trends, keyword frequencies, and collaborative networks among authors, institutions, and countries. A supplementary literature review contextualized bibliometric findings to address limitations in metadata. Results A total of 54 studies from 33 unique sources were analyzed, revealing an annual growth rate of 11.57% in PCC publications up to 2023. The USA and the Netherlands led in research output, contributing 22 and 16 publications, respectively. Mayo Clinic and prominent authors such as Nelen W. and Kremer J. were key contributors. Keywords like “infertility,” “in vitro fertilization,” and “patient care” demonstrated increasing prominence, underscoring growing interest in PCC. Barriers to PCC were identified at institutional, communication, environmental, and personal levels, while facilitators included multidisciplinary approaches and technological integration. Conclusion This review highlights PCC’s critical role in enhancing IVF outcomes and identifies gaps in psychological support, clinicianpatient communication, and access equity. Recommendations include targeted interventions, international collaborations, and integration of personalized care models. Future research should explore innovative strategies to overcome barriers and advance PCC frameworks in fertility care. BJMS, Vol. 24 No. 04 October’25 Page : 1093-1105

Terrestrial inputs of synthetic musks from Chinese mainland to the East China Sea.

Background/Objectives: Artificial Intelligence (AI) is rapidly advancing in medicine, facilitating personalized care by leveraging complex clinical data, imaging, and patient monitoring. This study characterizes current practices in AI use within oncology clinical trials by analyzing completed U.S. trials within the Cancer Control Continuum (CCC), a framework that spans the stages of cancer etiology, prevention, detection, diagnosis, treatment, and survivorship. Methods: This cross-sectional study analyzed U.S.-based oncology trials registered on ClinicalTrials.gov between January 2015 and April 2025. Using AI-related MeSH terms, we identified trials addressing stages of the CCC. Results: Fifty completed oncology trials involving AI were identified; 66% were interventional and 34% observational. Machine Learning was the most common AI application, though specific algorithm details were often lacking. Other AI domains included Natural Language Processing, Computer Vision, and Integrated Systems. Most trials were single-center with limited participant enrollment. Few published results or reported outcomes, indicating notable reporting gaps. Conclusions: This analysis of ClinicalTrials.gov reveals a dynamic and innovative landscape of AI applications transforming oncology care, from cutting-edge Machine Learning models enhancing early cancer detection to intelligent chatbots supporting treatment adherence and personalized survivorship interventions. These trials highlight AI’s growing role in improving outcomes across the CCC in advancing personalized cancer care. Standardized reporting and enhanced data sharing will be important for facilitating the broader application of trial findings, accelerating the development and clinical integration of reliable AI tools to advance cancer care.

Design and rationale of "a pragmatic approach to the investigation of stable chest pain: A UK, multicenter, randomized trial to assess patient outcomes, quality of life and cost effectiveness (CE-MARC 3)".

Understanding lived experiences with KCNQ2 developmental and epileptic encephalopathy.

This study investigates the relationship between personal satisfaction and career decision-making among science, technology, engineering, and mathematics (STEM) students. The research aims to identify how personal fulfillment influences students’ career choices and the extent of their satisfaction with selected paths. Utilizing a structured questionnaire, data were collected from 67 senior high school students at Caraga State University Cabadbaran Campus (CSUCC). Findings reveal that while students view personal fulfillment as an important factor in their career decisions, it does not significantly correlate with overall career satisfaction. The results suggest that personal satisfaction should be complemented by other elements, such as job market conditions and effective career guidance, to enhance students’ decision-making processes. This study provides valuable insights for educational institutions and regional stakeholders, promoting improved career counseling and mentorship programs to foster a skilled and motivated workforce.

From source to stream: CEC source identification and quantification in the Changle River Watershed.

Anti-epidemic pharmaceuticals predominantly contributed to PPCPs flux in the Yangtze River during 2020.

Ageing process characterization of innovative substrates in vertical-flow constructed wetlands after treating greywater.

Hidden costs of beauty: An in vitro study on the ecotoxicological impacts of ultraviolet filters and parabens on the bioindicator species Mytilus galloprovincialis.