Introduction: Malaysia has one of the highest diabetes prevalence rates in Asia (1 in 5), with >50% fail to achieve optimal glycemic control. Continuous glucose monitoring (CGM) provides detailed insights beyond hemoglobin A1c (HbA1c), capturing daily glucose fluctuations and variability. Realworld data describing CGM patterns and their clinical associations in Malaysian adults with type 2 diabetes mellitus (T2DM) are limited. Methodology: This cross-sectional study analyzed CGM data from 25 Malaysian adults with T2DM, standardized over 14 days. CGM metrics included time in range (TIR), time above range (TAR), time below range (TBR), mean glucose, and coefficient of variation (CV). Weekday–weekend comparisons were performed, and correlations with age, diabetes duration, HbA1c, body mass index (BMI), treatment regimen, and complications were assessed. Results: Participants had a mean age of 56.2 ± 13.2 years (52% male), mean HbA1c 9.4 ± 2.7%, diabetes duration 10.7 ± 9.0 years, and BMI 29.8 ± 9.4 kg/m². Mean TIR, TAR, TBR, mean glucose, and CV were similar between weekdays and weekends (p >0.34 for all). TIR >70% was achieved by 52% of participants on weekdays and 48% on weekends, with no statistically significant difference (p = 0.75). Longer diabetes duration correlated with lower TIR (r = −0.62, p <0.001), higher mean glucose (r = 0.58, p = 0.002), and greater variability (r = 0.54, p = 0.004). Older age was associated with lower TIR (r = −0.48, p = 0.014) and higher mean glucose (r = 0.44, p = 0.025). Higher HbA1c correlated with lower TIR (r = −0.36, p = 0.04), higher TAR (r = 0.35, p = 0.05), greater variability (r = 0.51, p = 0.006), and increased target organ damage (ρ = 0.55, p = 0.004). BMI was not associated with TIR (r = −0.18, p = 0.38). Participants with ≥2 complications had lower TIR (55.8 ± 28.4% vs 76.7 ± 19.2%, p = 0.073), while insulin the Conclusion In Malaysian adults with T2DM, CGM metrics were similar between weekdays and weekends, indicating lifestyle differences had minimal impact on glycemic control. Longer diabetes duration, older age, higher HbA1c, multiple complications, and insulin therapy identified patients at highest risk for poor glycemic control, greater variability, and hypoglycemia. These findings support the use of CGM for risk stratification, individualized monitoring, and therapy optimization to reduce complications and hypoglycemia risk.
Adult ; Blood Glucose ; Blood Glucose Self-Monitoring ; Continuous Glucose Monitoring ; Diabetes Mellitus, Type 2

Introduction: ISPAD guidelines recommend continuous glucose monitoring (CGM) as the standard of care for paediatric type 1 diabetes (T1DM). However, a “real-world” adoption gap persists, particularly in resource-limited settings. The Introductions of the study were to evaluate CGM adoption prevalence, identify documented barriers, and compare glycemic outcomes between active and non-active users. Methodology: This retrospective review analyzed electronic medical records (EMR) of 125 paediatric T1DM patients at Hospital Putrajaya (2025). Data included CGM status, insulin delivery method, and documented barriers. Independent T-tests compared mean hemoglobin A1c (HbA1c) between groups, and multivariable logistic regression identified independent predictors of adoption. Results: Cohort mean age was 11.8 ± 3.8 years. Active CGM users were 32.8% (n = 41), of whom 29.3% (n = 12) utilized predominantly automated insulin delivery (AID) systems. The remaining 67.2% (n = 84) were classified as non-active users, comprising both never-users and ex-users (discontinued use). Active users achieved significantly lower mean HbA1c than non-active users (8.73% vs 9.86%; p <0.001), with no significant difference in rates of DKA (p = 0.564) or severe hypoglycemia (p = 0.250). Among never-users, 42.9% lacked documented technology counselling (p = 0.001). Multivariable analysis identified funding source as the sole independent predictor of CGM adoption (adjusted OR = 7.76, p <0.001). While the primary documented barrier was financial (31.0%), a lack of documented barriers was noted in 61.9% of non-active users. Conclusion A substantial technology gap exists, primarily driven by financial access rather than clinical demographics. The difference of 1.13% in HbA1c between groups underscores the need to address financial setbacks to improve technology access in Malaysia and prevent diabetes complications.
Child ; Blood Glucose ; Blood Glucose Self-Monitoring ; Continuous Glucose Monitoring ; Diabetes Mellitus, Type 1

Cardiotocography (CTG) is a non-invasive and important tool for diagnosing fetal distress during pregnancy. To meet the needs of intelligent fetal heart monitoring based on deep learning, this paper proposes a TWD-MOAL deep active learning algorithm based on the three-way decision (TWD) theory and multi-objective optimization Active Learning (MOAL). During the training process of a convolutional neural network (CNN) classification model, the algorithm incorporates the TWD theory to select high-confidence samples as pseudo-labeled samples in a fine-grained batch processing mode, meanwhile low-confidence samples annotated by obstetrics experts were also considered. The TWD-MOAL algorithm proposed in this paper was validated on a dataset of 16 355 prenatal CTG records collected by our group. Experimental results showed that the algorithm proposed in this paper achieved an accuracy of 80.63% using only 40% of the labeled samples, and in terms of various indicators, it performed better than the existing active learning algorithms under other frameworks. The study has shown that the intelligent fetal heart monitoring model based on TWD-MOAL proposed in this paper is reasonable and feasible. The algorithm significantly reduces the time and cost of labeling by obstetric experts and effectively solves the problem of data imbalance in CTG signal data in clinic, which is of great significance for assisting obstetrician in interpretations CTG signals and realizing intelligence fetal monitoring.
Humans ; Pregnancy ; Female ; Cardiotocography/methods* ; Deep Learning ; Neural Networks, Computer ; Algorithms ; Fetal Monitoring/methods* ; Heart Rate, Fetal ; Fetal Distress/diagnosis* ; Fetal Heart/physiology*

Heart rate is a crucial indicator of human health with significant physiological importance. Traditional contact methods for measuring heart rate, such as electrocardiograph or wristbands, may not always meet the need for convenient health monitoring. Remote photoplethysmography (rPPG) provides a non-contact method for measuring heart rate and other physiological indicators by analyzing blood volume pulse signals. This approach is non-invasive, does not require direct contact, and allows for long-term healthcare monitoring. Deep learning has emerged as a powerful tool for processing complex image and video data, and has been increasingly employed to extract heart rate signals remotely. This article reviewed the latest research advancements in rPPG-based heart rate measurement using deep learning, summarized available public datasets, and explored future research directions and potential advancements in non-contact heart rate measurement.
Humans ; Deep Learning ; Heart Rate/physiology* ; Photoplethysmography/methods* ; Video Recording ; Face ; Monitoring, Physiologic/methods* ; Signal Processing, Computer-Assisted

In order to accurately capture the respiratory muscle movement and extract the synchronization signals corresponding to the breathing phases, a comprehensive signal sensing system for sensing the movement of the respiratory muscle was developed with applying the thin-film varistor FSR402 IMS-C07A in this paper. The system integrated a sensor, a signal processing circuit, and an application program to collect, amplify and denoise electronic signals. Based on the respiratory muscle movement sensor and a STM32F107 development board, an experimental platform was designed to conduct experiments. The respiratory muscle movement data and respiratory airflow data were collected from 3 healthy adults for comparative analysis. In this paper, the results demonstrated that the method for determining respiratory phase based on the sensing the respiratory muscle movement exhibited strong real-time performance. Compared to traditional airflow-based respiratory phase detection, the proposed method showed a lead times ranging from 33 to 210 ms [(88.3 ± 47.9) ms] for expiration switched into inspiration and 17 to 222 ms [(92.9 ± 63.8) ms] for inspiration switched into expiration, respectively. When this system is applied to trigger the output of the ventilator, it will effectively improve the patient-ventilator synchrony and facilitate the ventilation treatment for patients with respiratory diseases.
Humans ; Respiratory Muscles/physiology* ; Signal Processing, Computer-Assisted ; Movement/physiology* ; Respiration ; Monitoring, Physiologic/methods* ; Adult

Heart failure (HF) is the end-stage of all cardiac diseases, characterized by high prevalence, high mortality, and heavy social and economic burden. Early warning of HF exacerbation is of great value for outpatient management and reducing readmission rates. Currently, remote dynamic monitoring technology, which captures changes in hemodynamic and physiological parameters of HF patients, has become the primary method for early warning and is a hot research topic in clinical studies. This paper systematically reviews the progress in this field, which was categorized into invasive monitoring based on implanted devices, non-invasive monitoring based on wearable devices, and other monitoring technologies based on audio and video. Invasive monitoring primarily involves direct hemodynamic parameters such as left atrial pressure and pulmonary artery pressure, while non-invasive monitoring covers parameters such as thoracic impedance, electrocardiogram, respiration, and activity levels. These parameters exhibit characteristic changes in the early stages of HF exacerbation. Given the clinical heterogeneity of HF patients, multi-source information fusion analysis can significantly improve the prediction accuracy of early warning models. The results of this study suggest that, compared with invasive monitoring, non-invasive monitoring technology, with its advantages of good patient compliance, ease of operation, and cost-effectiveness, combined with AI-driven multimodal data analysis methods, shows significant clinical application potential in establishing an outpatient management system for HF.
Humans ; Heart Failure/physiopathology* ; Monitoring, Physiologic/methods* ; Wearable Electronic Devices ; Remote Sensing Technology ; Early Diagnosis ; Electrocardiography ; Hemodynamics

OBJECTIVE: To examine the mechanistic of organochlorine-associated changes in lung function. METHODS: This study investigated 76 healthy older adults in Jinan, Shandong Province, over a five-month period. Personal exposure to organochlorines was quantified using wearable passive samplers, while inflammatory factors and thyroid hormones were analyzed from blood samples. Participants' lung function was evaluated. After stratifying participants according to their thyroid hormone levels, we analyzed the differential effects of organochlorine exposure on lung function and inflammatory factors across the low and high thyroid hormone groups. Mediation analysis was further conducted to elucidate the relationships among organochlorine exposures, inflammatory factors, and lung function. RESULTS: Bis (2-chloro-1-methylethyl) ether (BCIE), was negatively associated with forced vital capacity (FVC, -2.05%, 95% CI: -3.11% to -0.97%), and associated with changes in inflammatory factors such as interleukin (IL)-2, IL-7, IL-8, and IL-13 in the low thyroid hormone group. The mediation analysis indicated a mediating effect of IL-2 (15.63%, 95% CI: 0.91% to 44.64%) and IL-13 (13.94%, 95% CI: 0.52% to 41.07%) in the association between BCIE exposure and FVC. CONCLUSION Lung function and inflammatory factors exhibited an increased sensitivity to organochlorine exposure at lower thyroid hormone levels, with inflammatory factors potentially mediating the adverse effects of organochlorines on lung function.
Environmental Exposure ; Hydrocarbons, Chlorinated/metabolism* ; China ; Ethyl Ethers/metabolism* ; Environmental Monitoring ; Thyroid Hormones/blood* ; Lung/physiology* ; Inhalation Exposure/statistics & numerical data* ; Air Pollution/statistics & numerical data* ; Air Pollutants/metabolism* ; Humans ; Male ; Female ; Middle Aged ; Aged

Wastewater-based epidemiology has emerged as a transformative surveillance tool for estimating substance consumption and monitoring disease prevalence, particularly during the COVID-19 pandemic. It enables the population-level monitoring of illicit drug use, pathogen prevalence, and environmental pollutant exposure. In this perspective, we summarize the key challenges specific to the Chinese context: (1) Sampling inconsistencies, necessitating standardized 24-hour composite protocols with high-frequency autosamplers (≤ 15 min/event) to improve the representativeness of samples; (2) Biomarker validation, requiring rigorous assessment of excretion profiles and in-sewer stability; (3) Analytical method disparities, demanding inter-laboratory proficiency testing and the development of automated pretreatment instruments; (4) Catchment population dynamics, reducing estimation uncertainties through mobile phone data, flow-based models, or hydrochemical parameters; and (5) Ethical and data management concerns, including privacy risks for small communities, mitigated through data de-identification and tiered reporting platforms. To address these challenges, we propose an integrated framework that features adaptive sampling networks, multi-scale wastewater sample banks, biomarker databases with multidimensional metadata, and intelligent data dashboards. In summary, wastewater-based epidemiology offers unparalleled scalability for equitable health surveillance and can improve the health of the entire population by providing timely and objective information to guide the development of targeted policies.
China/epidemiology* ; Humans ; Wastewater/analysis* ; COVID-19/epidemiology* ; Public Health ; Wastewater-Based Epidemiological Monitoring ; SARS-CoV-2

Objective To assess the blood pressure control and its influencing factors among hypertensive patients in communities in different time periods by 24 h ambulatory blood pressure monitoring(24 h ABPM)and provide reference for optimizing the health management services for hypertension in communities. Methods A total of 765 hypertensive patients registered in the hypertension management project of national essential public health services in Sanxiang Town,Zhongshan City from October 2022 to September 2023 were identified as target subjects.The 24 h ABPM devices were distributed for blood pressure monitoring and a questionnaire survey was conducted to analyze the influencing factors of blood pressure control. Results Of all the participants,16.5% did not monitor blood pressure regularly,and 59.2% monitored blood pressure 1-2 times per week.The patients who were not on night shifts/staying up late had higher mean rates of achieving the target blood pressure and the circadian rhythm of blood pressure during 24 h,nighttime,and early morning than those who were on night shifts/staying up late(all P<0.05).The patients who never drank alcohol had higher rate of achieving the target blood pressure in early morning than those who drank alcohol(P=0.012).The average blood pressure during daytime,nighttime,and 24 h were different by sex(all P<0.05).The average blood pressure during nighttime was different by age and job types(all P<0.05).The average blood pressure during daytime,nighttime,and 24 h were different in patients with different body weight types(all P<0.05).The results of the multivariate logistic regression analysis showed that uncontrolled blood pressure during daytime was more likely to occur in male patients(OR=1.394,95%CI=1.045-1.858,P=0.024),and that during nighttime was more likely to be associated with male patients(OR=1.573,95%CI=1.088-2.275,P=0.016)and night shifts(OR=2.467,95%CI=1.198-5.077,P=0.014).It was difficult to achieve blood pressure control in early morning for the patients who drank alcohol for more than three times per week(OR=4.567,95%CI=1.629-12.807,P=0.004),woke up at night(OR=1.800,95%CI=1.125-2.878,P=0.014),and had night shifts(OR=1.579,95%CI=1.102-2.465,P=0.044).The patients on night shifts were more likely to have abnormal circadian rhythm of blood pressure(OR=1.753,95%CI:1.018-3.018,P=0.043). Conclusions The personal characteristics and lifestyle of hypertensive patients significantly affect the blood pressure control in different time periods(daytime,nighttime,and early morning)and the circadian rhythm of blood pressure.The family doctor team of community healthcare institutions can implement targeted and precise intervention measures for hypertensive patients according to the influencing factors of blood pressure control in different time periods,so as to achieve better management effects.
Humans ; Blood Pressure Monitoring, Ambulatory ; Hypertension/physiopathology* ; Male ; Female ; Middle Aged ; Circadian Rhythm ; Blood Pressure ; Surveys and Questionnaires ; Adult ; Aged ; Time Factors

Humans ; Drug Monitoring/methods* ; Child ; Inflammatory Bowel Diseases/drug therapy* ; Immunosuppressive Agents/therapeutic use* ; China ; Biological Products/therapeutic use*