Hypertensive disorders during the postpartum period are a major contributor to maternal morbidity and mortality. The most recent statistics suggest that 16% of pregnancies are complicated by hypertension, and that number is increasing. The majority of complications occur in the postpartum period, and new publications have revolutionized the way we manage postpartum hypertension. Recent studies have shown that tight blood pressure (BP) control in the postpartum period may decrease adverse maternal outcomes. Several studies have demonstrated that nifedipine lowers BP more effectively than labetalol, resulting in fewer readmissions. The use of diuretics is a topic of controversy, with mixed evidence regarding their effectiveness. A remote patient monitoring system may improve postpartum BP follow-up in low-resource settings. Patients with hypertension during pregnancy have an increased lifetime risk of cardiovascular diseases, and establishing care for long-term follow-up is an essential part of postpartum care. Contrary to historical teaching, not all hypertensive disorders are cured by delivery. Hypertension requires close follow-up during the fourth trimester, and these patients may benefit from tighter BP control. Further research should be done to establish guideline-based treatment and monitoring throughout the lifetime.

Healthcare inequality remains a significant challenge among tribal communities. This problem is mainly caused by geographical isolation, shortage of medical professionals, inadequate infrastructure, and low income levels. These barriers limit access to timely and quality healthcare services. Recent advancements in Artificial Intelligence (AI) have introduced virtual health technologies such as chatbots, virtual health assistants, and remote monitoring systems. This study examines the role of AI-powered virtual health technologies in promoting healthcare equity for tribal communities. They are capable of providing basic medical guidance, monitoring health conditions, and connecting patients with doctors through online platforms. Additionally, they help reduce the need for long-distance travel to hospitals. With proper training, strong community participation, and active government support, AI-based solutions have the potential to build a more effective, accessible, and equitable healthcare system for tribal populations.

Waste management through open burning is still widely practiced by communities; however, the process is often inefficient due to unstable temperature fluctuations and the production of dense black smoke caused by incomplete combustion. This study aims to design an automatic temperature control and remote monitoring system for a waste incineration furnace based on the Internet of Things (IoT) to improve combustion efficiency. The research employed an experimental approach involving hardware design using a NodeMCU ESP8266 microcontroller, a DS18B20 temperature sensor, and an AC Light Dimmer module based on Pulse Width Modulation (PWM) to dynamically regulate fan speed. The system was also integrated with a Telegram-based interface for real-time temperature monitoring. Experimental results over a 75-minute testing period showed that the automatic mode achieved a maximum temperature of 132.45°C, which is higher than the manual mode that only reached 126.94°C. The implementation of PWM control enabled adaptive oxygen supply regulation, increasing the combustion temperature by 5.51°C and accelerating the reduction of black smoke. Therefore, the proposed IoT-based temperature control system successfully improves combustion efficiency while enabling convenient remote monitoring for operational supervision.

Chronic heart failure (CHF) represents a major global health burden. This review explores the potential of artificial intelligence (AI) in improving its diagnosis, treatment, and management. This study conducted a comprehensive literature review to evaluate the current and emerging applications of AI in CHF. Databases, such as PubMed, Scopus, and IEEE Xplore, were searched for peer-reviewed articles published between 2015 and 2025, focusing on AIbased diagnostic tools, predictive modeling, treatment personalization, and remote monitoring systems. Significant advancements were identified in AI-enhanced diagnostics, predictive models for hospital readmissions, personalized treatment optimization, and AI-driven remote monitoring systems. These technologies have demonstrated improvements in diagnostic accuracy, risk stratification, and real-time patient management. AI offers substantial benefits for CHF management by enabling data-driven, individualized care. Nonetheless, challenges remain, including variability in data quality, lack of algorithm transparency, and ethical considerations regarding patient privacy and accountability. AI holds transformative potential for CHF management. Its successful integration can enhance diagnostic precision, personalize treatment, and support proactive patient care- ultimately improving outcomes and reducing the global burden of CHF.

Accurate detection of trace NO2 at room temperature is crucial for air quality control and the early diagnosis of respiratory diseases. High-precision detection of low-concentration gases has long been a research focus, with material optimization and deep learning algorithms emerging as effective strategies to enhance sensor accuracy. Herein, a remote NO2 monitoring system for low-concentration detection is proposed, based on Bi2S3/WO3 heterostructures, wireless communication modules, and deep learning techniques. The sensor exhibits a high response of 17.9 to 5 ppm NO2, a sensitivity of 3.84/ppm, rapid response/recovery times (27/110 s), excellent selectivity, and reliable stability. These superior performances are attributed to the enhanced charge transfer, carrier separation, and the generation of active oxygen species of the formed heterostructures. To overcome the limitations posed by data scarcity, a 1D-CNN/LSTM deep learning model is introduced, achieving accurate ppb-level regression with an R2 value of 0.9826 after data augmentation. This model significantly improves detection accuracy at low concentrations. Furthermore, by integrating wireless communication modules, the system supports real-time monitoring, multichannel operation, and intelligent alarming, offering a novel strategy for high-precision gas detection at room temperature.

STRIDE II guidelines highlight the importance of closely monitoring patients with inflammatory bowel disease (IBD) to assess therapy effectiveness and predict or manage flares. However, with a growing patient population, the outpatient clinic capacity is strained, and many patients, especially those in longterm remission, may not require frequent in-person visits. This study aims to assess the interest for optimizing resources through remote monitoring for patients with IBD in a high-volume referral centre. An anonymous survey was conducted in 281 adult IBD patients, either untreated or on stable subcutaneous or oral maintenance therapy for more than one year. We assessed interest in a remote monitoring program and insights into their preferences for its implementation as well as eventual cost and time savings. Of the 281 patients (52% female, 67% Crohn's disease, 32% ulcerative colitis, 1% IBD type unclassified), 76% expressed interest in reducing their outpatient visits in favour of remote monitoring. Of note, 79% of these 214 patients were willing to attend outpatient clinic visits every two years. However, patients emphasized the importance of personal contact in establishing a trustworthy and safe remote monitoring system. Additionally, the study identified cost and time savings for patients, as a visit to the outpatient visit took a median (interquartile range) of 3 (2-4) hours. Remote monitoring is a promising program for IBD patients in stable remission, offering potential financial and time savings for employers, patients, and society. However, further research is required to evaluate the safety and feasibility of this approach.

ABSTRACT Sedentary nature of office work contributes to a range of physical health issues, including obesity, which can result from prolonged inactivity, and cardiovascular diseases, linked to heightened risk factors associated with a lack of movement. Furthermore, extended periods of sitting can lead to musculoskeletal disorders, causing discomfort and injuries related to poor posture and ergonomics. Collectively, these factors underscore the profound negative impact of sedentary behavior in the workplace on overall well-being. To address these issues, this study proposes a multi-layered digital twin (DT) system for remote healthcare monitoring in a smart office setting. The suggested approach thoroughly investigates various office-related actions in a DT environment, rating their criticality to estimate potential health consequences. By mining temporal instances of these events, a Physiological Risk Index (PRI) is derived, supporting a predictive healthcare framework capable of generating automated alerts during health emergencies. Furthermore, the time-based data module is designed to assist healthcare practitioners in making better decisions by providing precise information about significant occurrences. The system’s usability and efficacy are demonstrated by testing it against two challenging datasets obtained from internet repositories. The findings indicate that the proposed approach is both efficient and effective in creating a comprehensive medical system.

Sickle cell disease (SCD) remains a challenging hematologic disorder, characterized by chronic hemolysis, vaso-occlusive events, and multi-organ complications. Hypertension, a prevalent comorbidity in SCD, poses significant clinical implications, exacerbating the complexities of disease management and impacting patient outcomes. Understanding the intricate interplay between SCD and hypertension is pivotal. Mechanistic insights uncover a landscape characterized by chronic hemolysis, endothelial dysfunction, altered nitric oxide bioavailability, and increased oxidative stress, contributing to elevated blood pressure and heightened cardiovascular risks in individuals with SCD. The diagnostic challenges inherent in identifying and monitoring hypertension in SCD patients necessitate novel approaches. Current treatment paradigms encompass a spectrum of lifestyle modifications, pharmacological interventions, and multidisciplinary care models. However, the limitations and complexities inherent in managing hypertension in SCD call for innovative strategies. Tailored approaches, personalized treatments, and emerging therapeutic avenues geared explicitly toward SCD patients mark a shift toward more effective management. Advancements in technology, including wearable devices and remote monitoring systems, present opportunities to revolutionize blood pressure monitoring, enhancing patient engagement and compliance while providing more accurate and frequent measurements. Moreover, the review underscores the importance of integrated care models and multidisciplinary collaborations. Collaborative frameworks involving hematologists, cardiologists, nephrologists, and primary care physicians are integral in optimizing hypertension management and addressing the specific needs of individuals with SCD.

Advancements in wireless communication technologies have facilitated the widespread adoption of remote health monitoring systems. Especially with the aging population, the monitoring of increasing chronic diseases has become an important requirement in healthcare services. This study aims to develop a wireless health monitoring system to enable real-time tracking of patients' vital signals such as electrocardiography (ECG), electrooculography (EOG), electromyography (EMG), pulse rate and body temperature through Wi-Fi-based communication. The system transfers the obtained health data to the ThingSpeak platform through an Internet of Things (IoT) supported communication network, where real-time and historical health data is analyzed. Additionally, ECG, EOG and EMG data is recorded and visualized graphically. Thus, patients can interact remotely with their doctors about their health conditions without going to the hospital, and quick interventions can be provided. This system offers a low-cost and accessible solution for individuals living in rural areas where hospitals are not available. The study's results indicate that wireless health monitoring technologies are an effective and sustainable method for remote patient monitoring. Also, the accuracy of the proposed system was validated against a commercial medical device, yielding low measurement errors of 3.03% for pulse and 0.81% for body temperature, demonstrating its reliability for real-time remote monitoring.

BACKGROUND: There has been an increasing focus on the use of digital systems for remote monitoring (RM) of patient health recently. AIM: To evaluate the clinical effectiveness of the RM system in patients with type 1 and type 2 diabetes (T1D and T2D) compared to traditional outpatient care. MATERIALS AND METHODS: a non-randomized prospective open comparative multicenter study with parallel groups was conducted in 7 regions of the Russian Federation from March to September 2024. The study included patients with T1D, T2D on non-insulin therapy, and T2D on insulin therapy. The intervention group used a glycaemia RM system, which included a glucometer with a data transmission set, a mobile application that received data from the glucometer via Bluetooth technology, and a data transmission system for the doctor. In the control group, glycemia was assessed as part of routine clinical practice (in-person visits with a self-monitoring diary). RESULTS: A total of 1,572 patients were included in the study. After a 180-day follow-up, the overall completion rate was 48% (754 patients). The primary endpoint, HbA1c levels, decreased comparably in the RM and control groups in patients with T1DM and in both cohorts of patients with T2DM. The proportion of individuals who achieved HbA1c target values was higher in the RM group compared to the control group in patients with T1DM (26.06% vs. 10.91%, respectively, p=0.023) and T2DM on non-insulin therapy (51.5% vs. 33%, respectively, p=0.003). RM use was associated with a reduction in unscheduled medical interventions. CONCLUSION: RM has shown clinical efficacy in increasing the proportion of patients achieving HbA1c target values in the group of patients with T1D and T2D on non-insulin antidiabetic therapy.

Remote monitoring (RM) of patients involves the continuous or periodic monitoring of a patient’s condition outside the hospital, utilizing modern digital and telecommunication technologies. RM is actively integrated into oncology practice. Modern medical systems offer several basic methods of RM. Modern RM systems enable the real-time recording of various clinically significant complications, automatically notifying medical personnel of critical changes to promptly adjust therapy and reduce the need for emergency medical care. The purpose of the paper is to review the literature data on RM in patients with malignancies. We reviewed publications in the leading scientific databases (PubMed, Google Scholar, and the National Library of Medicine) over the past decade. Data on the most common commercial and applied systems with effectiveness confirmed in several studies and described in open sources are summarized. The characteristics of the included studies – follow-up periods, methods of data collection and analysis, as well as the organization of interaction between doctors and patients – differed significantly, covering periods from 3 to 12 months. The use of Navigating Cancer, ОНКОНЕТ, eRAPID, SCH, Kaiku Health, and other platforms reduced the rate of unscheduled hospitalizations from 32.5% to 20.0% (relative reduction ~38%) and increased patient adherence to therapy to 73–79% (and up to ~92% in the PRO-TECT protocol). According to Kaiku Health, the average response time of the doctor to an alarming event was 19.6 hours. The Russian ONCONET study noted a decrease in the incidence of complications of antitumor treatment, as well as the risk of postponing the dates of the next course of chemotherapy, which significantly improved patient survival. Symptom Care at HOME (PRO-TECT) automated data collection systems significantly improved symptom control and quality of life scores. RM technologies demonstrate convincing efficacy, reducing the need for emergency medical care, improving the control of complications, and, according to some studies, are associated with an increase in the overall survival of cancer patients. Their widespread introduction into the oncological care system is justified, provided that the standardized notification protocols, sustainable funding, and organizational support are available. However, the systematization of the available data presents a significant methodological challenge due to the lack of unified approaches in implementing RM projects, the heterogeneity of methods for assessing effectiveness, and the wide range of conditions and organizational models studied.

Many developing countries still predominantly rely on conventional monitoring of groundwater quality parameters. Emerging technologies have shown significant potential for advancing automated water quality monitoring in recent years. This study developed the Groundwater Live Observation for Water-quality (GLOW) system, which leverages Internet of Things (IoT) technologies combined with water quality sensors. In future applications, this remote sensing-based groundwater monitoring system holds strong potential for detecting pollutant intrusion in water bodies. The GLOW system was tested during two periods, namely from June 2023 to November 2023 and from January 2024 to March 2024, in Bojong District, Sukabumi Regency, Indonesia. The system employed Aqua TROLL 500 sensors capable of measuring water temperature, electrical conductivity (EC), pH, salinity, and total dissolved solids (TDS). The data generated by the GLOW system were transmitted to a website server and subsequently evaluated against laboratory-based data using statistical analyses. The Wilcoxon Signed-Rank Test was applied to assess differences between the two approaches. Most parameters showed no statistically significant differences (p>0.05), except for TDS and salinity (p=0.02). The Bland-Altman analysis confirmed good overall agreement between the two methods, with small mean differences for pH (0.19), EC (8.45 μS/cm), water temperature (-0.34°C), salinity (0.02 PSU), and TDS (0.01 ppm). Future research should expand monitoring by including nitrogen and phosphorus compounds.

Abstract Background Wearable sensors offer a scalable alternative to cardiopulmonary exercise testing for assessing cardiorespiratory fitness, and there is growing evidence to support their use for remote VO 2 max estimation. This study investigated whether heart rate variability (HRV) measures derived from wearable ECG sensors improve VO 2 max estimations in a preoperative cohort and compared the relative contributions of short- and long-term HRV features. ECG and accelerometer data from 198 participants scheduled for major abdominal surgery (REMOTES study, ClinicalTrials.gov: ID NCT06042023) were collected over 72 h. Measures including physical activity, steps, heart rate, and HRV were extracted. Short-term (5-minutes) and long-term (24-hour) heart rate variability features were extracted from free-living ECG data. Two LASSO regression models with five-fold cross-validation were developed: a baseline model (excluding HRV) and a HRV model. Results After exclusions, 163 participants were included in analyses. The HRV model outperformed the baseline across all metrics, achieving a higher R 2 (0.47 ± 0.12 vs. 0.42 ± 0.13) and lower mean absolute error (2.63 ± 0.34 vs. 2.77 ± 0.38 ml/kg/min), root mean square error (3.38 ± 0.53 vs. 3.54 ± 0.57 ml/kg/min) and absolute percentage error (15.55 ± 2.19% vs. 16.22 ± 2.45%). Analysis of feature contributions identified long-term HRV (SDANN HR 24), age, gender, and step-counts as key contributors to model performance. Conclusion HRV features from wearable data, especially long-term measures, can improve remote VO 2 max predictions in a clinical cohort. While performance gains were small, these findings support the integration of HRV features into remote monitoring systems in real-world settings. Long-term HRV measures derived from heart rate signals offer a practical option for cardiorespiratory fitness assessment, requiring minimal additional processing. Trail registration This study was registered at ClinicalTrials.gov (Clinical trial number: NCT06042023) and was registered retrospectively on 11/09/2023.

The integration of artificial intelligence (AI) into plastic surgery and anatomic education represents a transformative shift in modern medicine. In clinical practice, AI enhances preoperative planning through data-driven 3D simulations, augments intraoperative precision with real-time anatomic overlays, and supports postoperative care through remote monitoring systems. These innovations foster greater surgical accuracy, personalization, and patient safety. In anatomic education, AI-driven platforms enable immersive, adaptive learning experiences, democratizing access to high-quality resources and improving spatial understanding. Intelligent tutoring systems, augmented reality (AR), and mixed reality (MR) bridge theoretical knowledge with hands-on skills, equipping future surgeons with enhanced anatomic literacy. Despite these advances, ethical challenges remain, including data privacy, algorithmic bias, and regulatory gaps. Addressing these issues requires diverse data sets, clinician education in AI literacy, and robust oversight. Ultimately, AI is not a replacement for human expertise, but a synergistic tool that amplifies surgical precision, educational accessibility, and individualized care. As AI continues to evolve, it promises to redefine standards in aesthetic and reconstructive surgery while reshaping the educational paradigm for future medical professionals.

An ultra-efficient edge-based wearable system for real-time and remote blood pressure monitoring

The global healthcare landscape is entering the data-driven era of Industry 4.0, challenging nursing with profound structural transformations within increasingly complex clinical environments. The nursing profession in Taiwan is now confronting a "dual crisis" marked by a critical workforce shortage and unsustainable clinical workloads. This crisis permeates both clinical practice and nursing education, necessitating proactive and effective reform strategies. Digital technologies, including artificial intelligence (AI), AI-driven virtual avatars, digital twins, and virtual reality offer significant potential to augment clinical decision-making, enhance educational outcomes, and optimize patient self-management. Therefore, digital empowerment, more than simply the integration of technologies, represents a strategic pathway forward to reshaping nursing values and promoting professional dignity. Topol (2019) emphasized that alleviating clinical dilemmas is the primary aim of smart technology integration in the field of healthcare. By leveraging technology to solve practical issues, clinicians can reclaim time for direct patient care, facilitating humanistic, compassionate care and elevating the intrinsic value of nursing. In clinical practice, several healthcare institutions have already implemented virtual agents and remote monitoring systems to assist in clinical problem-solving, disease progression monitoring, and the provision of individualized health education and psychological support. Furthermore, digital twin technology is emerging as a cornerstone of this transformation. Integrating electronic health records with real-time data streams to create "virtual personas" is allowing nurses to detect subtle physiological changes prior to the onset of clinical deterioration, achieving precision care and optimizing healthcare operations. Carroll and Garcia-Dia (2025) argued that digital twins are not only predictive tools but also catalysts for redefining care delivery processes. In the realm of nursing education, academic institutions are actively developing digital curricula that incorporate metaverse elements such as virtual reality and augmented reality to bolster student engagement and learning efficacy. AI virtual humans and immersive virtual reality are at the forefront of pioneering new pedagogical models designed to enable students to cultivate core competencies within engaging and targeted learning environments. Using no-code platforms, educators are increasingly able to independently develop simulation scenarios that incorporate interactive voice response capabilities. These virtual environments allow students to practice nurse-patient communication and clinical decision-making in low-stakes, repeatable settings that effectively mitigate the "reality shock" traditionally experienced during the transition from academic to actual clinical environments. Ultimately, the goal of digital empowerment is to support professional judgment and return time to nursing professionals, ensuring the core of nursing remains centered on care and compassion, aspects that technology remains largely unable to handle. We hope this column will inspire readers to reflect on how technology can be harnessed to construct a sustainable and professionally valuable future for nursing amidst the smart healthcare wave.

Artificial Intelligence for Knee Osteoarthritis Care and Rehabilitation: A Systematic Review.

Artificial intelligence (AI) and digital technologies are increasingly influencing family life, parenting practices, and pediatric health and mental health services. Applications include AI-enabled parenting support tools, mobile health interventions, digital therapeutics, remote monitoring systems, and algorithm-based platforms for neurodevelopmental and psychiatric assessment. While these technologies offer opportunities to enhance parental support, personalize care, and improve access to services, they also raise concerns related to ethics, equity, data privacy, and parent–child relationships. This narrative review offers an integrative overview of AI and digital technologies in family and parenting contexts, focusing on key domains such as AI-supported parenting interventions, digital health literacy, pediatric and perinatal care, neurodevelopmental and mental health assessment, digital media use, and family communication. Reported benefits, emerging risks, and unintended consequences are discussed with particular attention to parental roles, family-centered design, and implementation challenges. Overall, evidence suggests that AI-enabled tools are most effective when they are transparent, co-designed with parents and clinicians, and integrated into existing health and social care systems. Nonetheless, gaps remain regarding long-term outcomes, equitable access, ethical governance, and sustainable implementation. AI in digital parenting should therefore be viewed as a complementary resource that augments, rather than replaces, parental judgment and professional care.

Abstract With the rapid advancement of Internet of things (IoT) technologies, smart and connected healthcare systems have emerge as a promising solution for continuous and remote patient monitoring. This is particularly critical for elderly populations and patients with chronic conditions, where frequent hospital visits are costly and hectic. In this paper, we propose an experimentally validated Low-cost, low-power IoT-based remote health monitoring system designed for continuous acquisition of vital physiological parameters, including electrocardiogram (ECG), heart rate, blood Oxygen saturation (Sp ), and body temperature. The proposed architecture integrates wearable wireless sensors, energy-efficient clustering mechanisms, secure data transmission, and cloud-based storage and analytics. To address the limitations of existing systems, our methodology combines a hardware prototype with network-level simulations conducted using NS-3 and MATLAB to evaluate latency, energy consumption, packet delivery ratio, and scalability. Security and privacy of patient data are guaranteed through a lightweight encryption framework suitable for resource-constrained IoT devices, with comparative analysis against computationally expensive homomorphic encryption schemes. Experimental results demonstrates that the proposed systems achieve reduced latency, improved energy efficiency and reliable data confidentiality. The findings confirm the suitability of the architecture for real-time remote healthcare monitoring in smart city and rural healthcare environments. Keywords: Internet of Things, Remote Healthcare Monitoring, Wireless Sensor Networks, security, Energy Efficiency, Wearable sensors.

The proliferation of Internet of Things (IoT) devices in healthcare, such as wearable sensors, smart infusion pumps, and remote monitoring systems, has transformed patient care by enabling real-time data collection and analysis. However, this integration has exponentially increased cybersecurity vulnerabilities, making healthcare a prime target for cyber threats including ransomware, Distributed Denial of Service (DDoS) attacks, and data breaches. According to recent statistics, healthcare data breaches affected over 276 million individuals in 2024 alone, with an average cost of $11.45 million per incident, marking the highest across all sectors. Projections for 2025 indicate a continued rise, with global cyber attacks increasing by 30% quarterly, and healthcare organizations facing an average of 1,636 weekly attacks. This paper presents a comprehensive AI-driven framework employing a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN- LSTM) model for real-time threat detection in IoT-enabled healthcare environments (H-IoT). Utilizing the CICIDS2017 dataset—comprising 79 network traffic features and labeled with 15 attack types including DDoS, PortScan, and Botnet— augmented with simulated H-IoT traffic generated via Kali Linux, the model achieves 95.2% accuracy, 94.8% precision, 95.7% recall, and 95.2% F1-score, surpassing baselines like Random Forest (88.5% accuracy) and Support Vector Machines (SVM) (87.3% accuracy). Reinforcement learning via Q-learning enhances adaptability to emerging threats, while Shapley Additive exPlanations (SHAP) provides explainability, identifying key features such as flow duration (contributing 25% to predictions) and packet length (18%). Sandboxed simulations demonstrate detection latency under 50ms for DDoS attacks, with false positive rates below 2%. The framework ensures GDPR compliance through data anonymization and offers modular deployment for scalability. Case studies simulate real-world H-IoT scenarios, showing over 90% detection accuracy. Policy recommendations include stakeholder training and alignment with NHS cybersecurity standards, contributing to enhanced cyber resilience in healthcare.