BFLIDS: Blockchain-Driven Federated Learning for Intrusion Detection in IoMT Networks.

The Internet of Medical Things (IoMT) has significantly advanced healthcare, but it has also brought about critical security challenges. Traditional security solutions struggle to keep pace with the dynamic and interconnected nature of IoMT systems. Machine learning (ML)-based Intrusion Detection Systems (IDS) have been increasingly adopted to counter cyberattacks, but centralized ML approaches pose privacy risks due to the single points of failure (SPoFs). Federated Learning (FL) emerges as a promising solution, enabling model updates directly on end devices without sharing private data with a central server. This study introduces the BFLIDS, a Blockchain-empowered Federated Learning-based IDS designed to enhance security and intrusion detection in IoMT networks. Our approach leverages blockchain to secure transaction records, FL to maintain data privacy by training models locally, IPFS for decentralized storage, and MongoDB for efficient data management. Ethereum smart contracts (SCs) oversee and secure all interactions and transactions within the system. We modified the FedAvg algorithm with the Kullback-Leibler divergence estimation and adaptive weight calculation to boost model accuracy and robustness against adversarial attacks. For classification, we implemented an Adaptive Max Pooling-based Convolutional Neural Network (CNN) and a modified Bidirectional Long Short-Term Memory (BiLSTM) with attention and residual connections on Edge-IIoTSet and TON-IoT datasets. We achieved accuracies of 97.43% (for CNNs and Edge-IIoTSet), 96.02% (for BiLSTM and Edge-IIoTSet), 98.21% (for CNNs and TON-IoT), and 97.42% (for BiLSTM and TON-IoT) in FL scenarios, which are competitive with centralized methods. The proposed BFLIDS effectively detects intrusions, enhancing the security and privacy of IoMT networks.

Star Shaped Quivers in Four Dimensions.

We discuss 4D Lagrangian descriptions, across dimensions IR duals, of compactifications of the 6D (D, D) minimal conformal matter theory on a sphere with arbitrary number of punctures and a particular value of flux as a gauge theory with a simple gauge group. The Lagrangian has the form of a "star shaped quiver" with the rank of the central node depending on the 6D theory and the number and type of punctures. Using this Lagrangian one can construct across dimensions duals for arbitrary compactifications (any, genus, any number and type of USp punctures, and any flux) of the (D, D) minimal conformal matter gauging only symmetries which are manifest in the ultraviolet.

Metaverse in Healthcare Integrated with Explainable AI and Blockchain: Enabling Immersiveness, Ensuring Trust, and Providing Patient Data Security.

Digitization and automation have always had an immense impact on healthcare. It embraces every new and advanced technology. Recently the world has witnessed the prominence of the metaverse which is an emerging technology in digital space. The metaverse has huge potential to provide a plethora of health services seamlessly to patients and medical professionals with an immersive experience. This paper proposes the amalgamation of artificial intelligence and blockchain in the metaverse to provide better, faster, and more secure healthcare facilities in digital space with a realistic experience. Our proposed architecture can be summarized as follows. It consists of three environments, namely the doctor's environment, the patient's environment, and the metaverse environment. The doctors and patients interact in a metaverse environment assisted by blockchain technology which ensures the safety, security, and privacy of data. The metaverse environment is the main part of our proposed architecture. The doctors, patients, and nurses enter this environment by registering on the blockchain and they are represented by avatars in the metaverse environment. All the consultation activities between the doctor and the patient will be recorded and the data, i.e., images, speech, text, videos, clinical data, etc., will be gathered, transferred, and stored on the blockchain. These data are used for disease prediction and diagnosis by explainable artificial intelligence (XAI) models. The GradCAM and LIME approaches of XAI provide logical reasoning for the prediction of diseases and ensure trust, explainability, interpretability, and transparency regarding the diagnosis and prediction of diseases. Blockchain technology provides data security for patients while enabling transparency, traceability, and immutability regarding their data. These features of blockchain ensure trust among the patients regarding their data. Consequently, this proposed architecture ensures transparency and trust regarding both the diagnosis of diseases and the data security of the patient. We also explored the building block technologies of the metaverse. Furthermore, we also investigated the advantages and challenges of a metaverse in healthcare.

Detection of COVID-19 in X-ray Images Using Densely Connected Squeeze Convolutional Neural Network (DCSCNN): Focusing on Interpretability and Explainability of the Black Box Model.

The novel coronavirus (COVID-19), which emerged as a pandemic, has engulfed so many lives and affected millions of people across the world since December 2019. Although this disease is under control nowadays, yet it is still affecting people in many countries. The traditional way of diagnosis is time taking, less efficient, and has a low rate of detection of this disease. Therefore, there is a need for an automatic system that expedites the diagnosis process while retaining its performance and accuracy. Artificial intelligence (AI) technologies such as machine learning (ML) and deep learning (DL) potentially provide powerful solutions to address this problem. In this study, a state-of-the-art CNN model densely connected squeeze convolutional neural network (DCSCNN) has been developed for the classification of X-ray images of COVID-19, pneumonia, normal, and lung opacity patients. Data were collected from different sources. We applied different preprocessing techniques to enhance the quality of images so that our model could learn accurately and give optimal performance. Moreover, the attention regions and decisions of the AI model were visualized using the Grad-CAM and LIME methods. The DCSCNN combines the strength of the Dense and Squeeze networks. In our experiment, seven kinds of classification have been performed, in which six are binary classifications (COVID vs. normal, COVID vs. lung opacity, lung opacity vs. normal, COVID vs. pneumonia, pneumonia vs. lung opacity, pneumonia vs. normal) and one is multiclass classification (COVID vs. pneumonia vs. lung opacity vs. normal). The main contributions of this paper are as follows. First, the development of the DCSNN model which is capable of performing binary classification as well as multiclass classification with excellent classification accuracy. Second, to ensure trust, transparency, and explainability of the model, we applied two popular Explainable AI techniques (XAI). i.e., Grad-CAM and LIME. These techniques helped to address the black-box nature of the model while improving the trust, transparency, and explainability of the model. Our proposed DCSCNN model achieved an accuracy of 98.8% for the classification of COVID-19 vs normal, followed by COVID-19 vs. lung opacity: 98.2%, lung opacity vs. normal: 97.2%, COVID-19 vs. pneumonia: 96.4%, pneumonia vs. lung opacity: 95.8%, pneumonia vs. normal: 97.4%, and lastly for multiclass classification of all the four classes i.e., COVID vs. pneumonia vs. lung opacity vs. normal: 94.7%, respectively. The DCSCNN model provides excellent classification performance consequently, helping doctors to diagnose diseases quickly and efficiently.

High-Efficiency Cu(In,Ga)Se2 Thin Film Solar Cells Using ZnS and CdS Buffer Layers.

Most of the existing copper indium gallium diselenide (CIGS) thin film solar cells are based on a cadmium sulfide (CdS) buffer layer fabricated using a chemical bath deposition (CBD) process. However, due to environmental pollution caused by material toxicity and the unique wet process's incompatibility with the vacuum process, many studies are now being actively carried out on nontoxic buffer layers. In this study, to replace CdS buffer layers, zinc sulfide (ZnS) buffer layers with a big band gap and a low optical loss at a short wavelength were fabricated using a magnetron sputtering system. For comparative analysis, this study also fabricated CdS buffer layers using the CBD process. Then, the conversion efficiency of CIGS thin film solar cells deposited with ZnS and CdS thin film as buffer layers was measured. The light conversion efficiency of ZnS buffer layer-based CIGS was measured at 14.44%, while that of the CdS buffer layer-based CIGS was measured at 15.71%. Given that both are higher than the minimum conversion efficiency required for commercialization (10%), ZnS buffer layer-based solar cells could have a competitive edge over the existing CdS buffer layer-based solar cells.

Preparation and Characterization of Celecoxib Nanosuspension Using Bead Milling.

The aim of this study is to develop nanosuspension for improved dissolution of poorly water-soluble celecoxib. We first prepared coarse suspension of celecoxib with Tween 80 and hydroxypropyl methylcellulose as stabilizers, and then fabricated nanosuspension using the bead milling technique. Depending on milling time, the physical properties of nanosuspension were evaluated by photon correlation spectroscopy (e.g., particle size and distribution) and scanning electron microscopy (SEM) (e.g., morphology). As results, the mean size of crystalline celecoxib particles was highly reduced (368.1±14.5 nm) as milling process proceeded comparing to celecoxib powder (6.5±1.0 µm). Morphology of milled celecoxib particles has changed considerably from bar-shape or plate-shape to needle-shape due to a high energy caused by milling. In the dissolution test, the celecoxib nanosuspension showed an improved dissolution profile at pH 1.2 compared to celecoxib powder (less than 1%). In contrast, 53.4% of celecoxib in nanosuspension was dissolved up to 30 minutes, demonstrating improved dissolution of celecoxib. Taken together, bead-milled nanosuspension could be an effective strategy that can improve the dissolution and bioavailability.

Preparation of Silica Microparticles as Volatile Material Carrier and Their Sustained-Release Property.

The aim of this study is to develop an effective delivery system (silica microparticles) encapsulating volatile essential oil (EO) by multiple emulsification process and sol-gel method. Depending on critical materials (Pluronic P123 and HPC) and process parameter (drying temperature), silica microparticles were prepared and evaluated. As results, the amount of EO inside microparticles increased in polymer-dependent manners. On the other hand, the amount of EO was reduced as drying temperature increased. Based on these data, the condition fabricating silica microparticles was optimized: drying temperature (25 °C), Pluronic P123 (1.2%) and HPC (1.2%). The size and morphology of microparticles were observed by scanning electron microscopy. Also, the loadings and release profiles of EO in these particles were quantitatively analyzed by HPLC. Optimized silica microparticles showed the high encapsulation efficiency (32.7%) and sustained-release profiles of EO for 3 days. Taken together, silica microparticles are effective carrier for encapsulating volatile materials and providing sustained-release.

Comparative Assessment of Biological Activities of Mistletoes for Cosmetic Applications: Viscum Album Var. Coloratum (Kom.) Ohwi and Loranthus Tanakae Franch. & Sav.

Mistletoes, hemiparasites, contain many components with various biological activities and have been used in cosmetics industry. Loranthacease (1,000 species) and Viscaceae (550 species) have the most dominant species in mistletoes (nearly 1,600 species). It can be expected that the biological activities vary from species to species; therefore, we have tested Viscum album var. coloratum (Kom.) Ohwi (belonging to Santalaceae) and Loranthus tanakae Franch. & Sav. (belonging to Loranthacease) for a comparative study of their cosmetic properties, including antioxidant, antimelanogenic, and antiwrinkle activities. As results, the ethanol extract of L. tanakae had higher phenolic content and showed effective antioxidant activity and elastase inhibition. Meanwhile, the ethanol extract of V. album more effectively inhibited tyrosinase. Comparing with ethanol extracts, the water extracts of both mistletoes showed lower biological efficacy than the ethanol extracts or no significant effect. Thus, these results show that different extracts of mistletoe have different levels of biological activities, presumably because of the differences in their phytochemical profiles and because of the different extraction methods used.

Characteristics of High-Intent Suicide Attempters Admitted to Emergency Departments.

BACKGROUND: The suicide rate in Korea has been the highest among the Organization for Economic Cooperation and Development countries since 2003. However, there is a lack of in-depth data regarding the characteristics of suicide attempters. Understanding the intent of suicide attempters will help improve the effectiveness of suicide prevention strategies. Therefore, to provide a resource for developing the necessary interventions, this study aimed to examine the differences in suicide-related and clinical variables according to the strength of suicidal intent. METHODS: The subjects were 328 suicide attempters admitted to emergency departments at 5 university hospitals in Daegu-Gyeongbuk province between 2011 and 2014. We used various scales to examine suicide-related and clinical variables and a structured questionnaire to explore psychosocial characteristics. We evaluated suicidal intent using the Pierce Suicide Intent Scale and a clinician-rated scale that measured suicidal authenticity. RESULTS: Individuals with high suicidal intent were significantly older, had higher Hamilton Depression Rating Scale (HDRS) scores, higher rates of premeditation, and sustained suicidal ideation. Furthermore, suicide methods, timing, and psychiatric treatment histories differed by the strength of subjects' suicidal intent. Moreover, multiple logistic regression showed that depressed mood as a reason for attempting suicide, premeditation, and higher HDRS scores were significantly associated with higher suicidal intent. CONCLUSION: Depression, premeditation, older age, and sustained suicidal ideation were characteristics of individuals with high suicidal intent, and it is necessary to evaluate and monitor these factors to prevent repeated suicide attempts.

A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer.

One of the most common symptoms observed among most of the Parkinson's disease patients that affects movement pattern and is also related to the risk of fall, is usually termed as "freezing of gait (FoG)". To allow systematic assessment of FoG, objective quantification of gait parameters and automatic detection of FoG are needed. This will help in personalizing the treatment. In this paper, the objectives of the study are (1) quantification of gait parameters in an objective manner by using the data collected from wearable accelerometers; (2) comparison of five estimated gait parameters from the proposed algorithm with their counterparts obtained from the 3D motion capture system in terms of mean error rate and Pearson's correlation coefficient (PCC); (3) automatic discrimination of FoG patients from no FoG patients using machine learning techniques. It was found that the five gait parameters have a high level of agreement with PCC ranging from 0.961 to 0.984. The mean error rate between the estimated gait parameters from accelerometer-based approach and 3D motion capture system was found to be less than 10%. The performances of the classifiers are compared on the basis of accuracy. The best result was accomplished with the SVM classifier with an accuracy of approximately 88%. The proposed approach shows enough evidence that makes it applicable in a real-life scenario where the wearable accelerometer-based system would be recommended to assess and monitor the FoG.

Analytical characteristics and comparative evaluation of Aptima HIV-1 Quant Dx assay with Ampliprep/COBAS TaqMan HIV-1 test v2.0.

BACKGROUND: Quantitation of HIV-RNA is critically important for diagnosis, prognosis, treatment, monitoring and assessment of infectivity in HIV-1 infection. The objective of this study was to assess performance characteristics of the Aptima HIV-1 Quant Dx assay (Aptima), a new transcription mediated amplification (TMA), fully integrated and automated assay from Hologic Inc., San Diego, CA, USA. The analytical sensitivity, analytical specificity, precision and detection of HIV-1 subtypes were tested based on commercially available international standards or panels. A selected group of 244 anti-HIV-1 (+) plasma samples was used for comparison with Roche COBAS Ampliprep/COBAS TaqMan HIV- 1 test v2.0 (Roche CAP/CTM), (Roche Molecular Systems, Pleasanton, CA). RESULTS: The 50 and 95 % limit of detection were estimated at 4.9 (95 % CI 3.9-5.7) and 17.6 (15.2-21.2) IU/mL respectively. The specificity was found 99.83 (99.06-99.97) %. The standard deviations and coefficient of variations for panels with 50 and 100 copies/mL (1.7 and 2 log copies/mL) were 0.14 log copies/mL (8.67 %CV) and 0.18 log copies/mL (9.91 %CV) respectively. The detection rate for Aptima and Roche assays was 220/244 (90.2 %) and 217/244 (88.9 %) respectively. CONCLUSION: The Aptima assay is a sensitive, specific, precise and accurate test for measuring HIV-1 viral loads and for the detection of HIV-1 infections.

Impact of COVID-19 on the development of major mental disorders in patients visiting a university hospital: a retrospective observational study.

BACKGROUND: This study aimed to investigate the impact of coronavirus disease 2019 (COVID-19) on the development of major mental disorders in patients visiting a university hospital. METHODS: The study participants were patients with COVID-19 (n=5,006) and those without COVID-19 (n=367,162) registered in the database of Keimyung University Dongsan Hospital and standardized with the Observational Medical Outcomes Partnership Common Data Model. Data on major mental disorders that developed in both groups over the 5-year follow-up period were extracted using the FeederNet computer program. A multivariate Cox proportional hazards model was used to estimate the hazard ratio (HR) and 95% confidence interval (CI) for the incidence of major mental disorders. RESULTS: The incidences of dementia and sleep, anxiety, and depressive disorders were significantly higher in the COVID-19 group than in the control group. The incidence rates per 1,000 patient-years in the COVID-19 group vs. the control group were 12.71 vs. 3.76 for dementia, 17.42 vs. 7.91 for sleep disorders, 6.15 vs. 3.41 for anxiety disorders, and 8.30 vs. 5.78 for depressive disorders. There was no significant difference in the incidence of schizophrenia or bipolar disorder between the two groups. COVID-19 infection increased the risk of mental disorders in the following order: dementia (HR, 3.49; 95% CI, 2.45-4.98), sleep disorders (HR, 2.27; 95% CI, 1.76-2.91), anxiety disorders (HR, 1.90; 95% CI, 1.25-2.84), and depressive disorders (HR, 1.54; 95% CI, 1.09-2.15). CONCLUSION: This study showed that the major mental disorders associated with COVID-19 were dementia and sleep, anxiety, and depressive disorders.

Digital Anti-Aging Healthcare: An Overview of the Applications of Digital Technologies in Diet Management.

Diet management has long been an important practice in healthcare, enabling individuals to get an insight into their nutrient intake, prevent diseases, and stay healthy. Traditional methods based on self-reporting, food diaries, and periodic assessments have been used for a long time to control dietary habits. These methods have shown limitations in accuracy, compliance, and real-time analysis. The rapid advancement of digital technologies has revolutionized healthcare, including the diet control landscape, allowing for innovative solutions to control dietary patterns and generate accurate and personalized recommendations. This study examines the potential of digital technologies in diet management and their effectiveness in anti-aging healthcare. After underlining the importance of nutrition in the aging process, we explored the applications of mobile apps, web-based platforms, wearables devices, sensors, the Internet of Things, artificial intelligence, blockchain, and other technologies in managing dietary patterns and improving health outcomes. The research further examines the effects of digital dietary control on anti-aging healthcare, including improved nutritional monitoring, personalized recommendations, and behavioral and sustainable changes in habits, leading to an expansion of longevity and health span. The challenges and limitations of digital diet monitoring are discussed, and some future directions are provided. Although many digital tools are used in diet control, their accuracy, effectiveness, and impact on health outcomes are not discussed much. This review consolidates the existing literature on digital diet management using emerging digital technologies to analyze their practical implications, guiding researchers, healthcare professionals, and policy makers toward personalized dietary management and healthy aging.

Adversarial Training Based Domain Adaptation of Skin Cancer Images.

Skin lesion datasets used in the research are highly imbalanced; Generative Adversarial Networks can generate synthetic skin lesion images to solve the class imbalance problem, but it can result in bias and domain shift. Domain shifts in skin lesion datasets can also occur if different instruments or imaging resolutions are used to capture skin lesion images. The deep learning models may not perform well in the presence of bias and domain shift in skin lesion datasets. This work presents a domain adaptation algorithm-based methodology for mitigating the effects of domain shift and bias in skin lesion datasets. Six experiments were performed using two different domain adaptation architectures. The domain adversarial neural network with two gradient reversal layers and VGG13 as a feature extractor achieved the highest accuracy and F1 score of 0.7567 and 0.75, respectively, representing an 18.47% improvement in accuracy over the baseline model.

Effectiveness and Tolerability of Combination Pharmacotherapy With Stimulant and Non-Stimulant in Children With Attention Deficit Hyperactivity Disorder.

Objectives: This study aimed to investigate the effectiveness and safety of combining psychostimulants and nonstimulants for patients under treatment for attention deficit hyperactivity disorder (ADHD). Methods: The study included 96 patients aged 6-12 years who were diagnosed with ADHD, among whom 34 received combination pharmacotherapy, 32 received methylphenidate monotherapy, and 30 received atomoxetine monotherapy. Statistical analysis was conducted to compare treatment and adverse effects among groups and to analyze changes before and after combination pharmacotherapy. The difference between combination pharmacotherapy and monotherapy was investigated. Logistic regression analysis was used to identify the predictors of combination pharmacotherapy. Results: No significant differences were observed between the groups in terms of age or pretreatment scores. The most common adverse effect experienced by 32% of patients in the combination pharmacotherapy group was decreased appetite. Clinical global impression- severity score decreased significantly after combination pharmacotherapy. All three groups showed significant clinical global impression- severity score improvements over time, with no significant differences among them. The predictive factors for combination pharmacotherapy included the Child Behavior Checklist total score internalizing subscale. Conclusion: Combination pharmacotherapy with methylphenidate and atomoxetine is a relatively effective and safe option for patients with ADHD who do not respond to monotherapy.

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review.

In industry 4.0, where the automation and digitalization of entities and processes are fundamental, artificial intelligence (AI) is increasingly becoming a pivotal tool offering innovative solutions in various domains. In this context, nutrition, a critical aspect of public health, is no exception to the fields influenced by the integration of AI technology. This study aims to comprehensively investigate the current landscape of AI in nutrition, providing a deep understanding of the potential of AI, machine learning (ML), and deep learning (DL) in nutrition sciences and highlighting eventual challenges and futuristic directions. A hybrid approach from the systematic literature review (SLR) guidelines and the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines was adopted to systematically analyze the scientific literature from a search of major databases on artificial intelligence in nutrition sciences. A rigorous study selection was conducted using the most appropriate eligibility criteria, followed by a methodological quality assessment ensuring the robustness of the included studies. This review identifies several AI applications in nutrition, spanning smart and personalized nutrition, dietary assessment, food recognition and tracking, predictive modeling for disease prevention, and disease diagnosis and monitoring. The selected studies demonstrated the versatility of machine learning and deep learning techniques in handling complex relationships within nutritional datasets. This study provides a comprehensive overview of the current state of AI applications in nutrition sciences and identifies challenges and opportunities. With the rapid advancement in AI, its integration into nutrition holds significant promise to enhance individual nutritional outcomes and optimize dietary recommendations. Researchers, policymakers, and healthcare professionals can utilize this research to design future projects and support evidence-based decision-making in AI for nutrition and dietary guidance.

Risk of Neurodegenerative Diseases in Elderly Koreans with an Initial Diagnosis of Type 2 Diabetes: A Nationwide Retrospective Cohort Study.

Type 2 diabetes (T2D) and neurodegenerative diseases (NDs) are common among elderly individuals. Growing evidence has indicated a strong link between T2D and NDs, such as Alzheimer's disease. However, previous studies have limitations in exploring the epidemiological relationship among these diseases as a group of NDs rather than as a specific type of ND. We aimed to investigate the risk of NDs in elderly Koreans who were first diagnosed with T2D and determine the association between T2D and NDs. We conducted a retrospective longitudinal cohort study of patients with who were initially diagnosed with T2D using the Korean National Health Information Database. The study participants were categorized into a T2D group (n = 155,459) and a control group (n = 155,459), aged 60-84 years, that were matched for age, sex, and comorbidities. We followed the participants for 10 years to investigate the incidence of NDs. The Cox proportional hazards regression model was used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for NDs. The numbers of patients diagnosed with ND at the end of follow-up were as follows: 51,096/155,459 (32.9%) in the T2D group and 44,673/155,459 (28.7%) in the control group (χ2 = 622.53, p < 0.001). The incidences of NDs in the T2D and control groups were 44.68 (95% CI: 44.29, 45.07) and 36.89 (95% CI: 36.55, 37.24) cases per 1,000 person-years at risk, respectively. The overall incidence of NDs was higher in the T2D group than that in the control group (HR: 1.23, 95% CI: 1.22, 1.25, p < 0.001). This study revealed a higher incidence of NDs in elderly Koreans who were initially diagnosed with T2D. This suggests that T2D is a risk factor for NDs in elderly Koreans.

Skin Cancer Detection Using Deep Learning-A Review.

Skin cancer is one the most dangerous types of cancer and is one of the primary causes of death worldwide. The number of deaths can be reduced if skin cancer is diagnosed early. Skin cancer is mostly diagnosed using visual inspection, which is less accurate. Deep-learning-based methods have been proposed to assist dermatologists in the early and accurate diagnosis of skin cancers. This survey reviewed the most recent research articles on skin cancer classification using deep learning methods. We also provided an overview of the most common deep-learning models and datasets used for skin cancer classification.

Developing a Low-Cost IoT-Based Remote Cardiovascular Patient Monitoring System in Cameroon.

(1) Background: Cameroonians are exposed to poor health services, more so citizens with cardiovascular-related diseases. The global high cost of acquiring healthcare-related technologies has prompted the government and individuals to promote the need for local research and the development of the health system. (2) Objectives: The main goal of this study is to design and develop a low-cost cardiovascular patient monitoring system (RPM) with wireless capabilities that could be used in any region of Cameroon, accessible, and very inexpensive, that are able to capture important factors, well reflecting the patient's condition and provide alerting mechanisms. (3) Method: Using the lean UX process from the Gothelf and Seiden framework, the implemented IoT-based application measures the patients' systolic, diastolic, and heart rates using various sensors, that are automated to record directly to the application database for analysis. The validity of the heuristic evaluation was examined in an ethnographic study of paramedics using a prototype of the system in their work environment. (4) Results: We obtained a system that was pre-tested on demo patients and later deployed and tested on seven real human test subjects. The users' task performances partially verified the heuristic evaluation results. (5) Conclusions: The data acquired by the sensors have a high level of accuracy and effectively help specialists to properly monitor their patients at a low cost. The proposed system maintains a user-friendliness as no expertise is required for its effective utilization.

Discovery of orally active sulfonylphenyl thieno[3,2-d]pyrimidine derivatives as GPR119 agonists.

G-protein-coupled receptor 119 (GPR119) has great potential as a therapeutic target for the treatment of type II diabetes. Novel thieno[3,2-d]pyrimidine derivatives were discovered as GPR119 agonists through a bioisosteric replacement strategy. The sulfonylphenyl thieno[3,2-d] pyrimidine scaffold was introduced, and its derivatives exhibited potent agonistic activity for GPR119 in cell-based assays. The representative derivative 43 displayed excellent pharmacokinetic profiles in rodents and significantly improved glucose tolerance in vivo. In OGTT study, compound 43 reduced significantly blood glucose levels in both mice and rats.