Automated Pipeline for Robust Cat Activity Detection Based on Deep Learning and Wearable Sensor Data.

The health, safety, and well-being of household pets such as cats has become a challenging task in previous years. To estimate a cat's behavior, objective observations of both the frequency and variability of specific behavior traits are required, which might be difficult to come by in a cat's ordinary life. There is very little research on cat activity and cat disease analysis based on real-time data.

Deep learning-based anomaly detection using one-dimensional convolutional neural networks (1D CNN) in machine centers (MCT) and computer numerical control (CNC) machines.

Computer numerical control (CNC) and machine center (MCT) machines are mechanical devices that manipulate different tools using computer programming as inputs. Predicting failures in CNC and MCT machines before their actual failure time is crucial to reduce maintenance costs and increase productivity. This study is centered around a novel deep learning-based model using a 1D convolutional neural network (CNN) for early fault detection in MCT machines.

Short-Term Risk of Type 2 Diabetes in Patients Using Various Antidepressants Compared with Patients Using Fluoxetine.

Background: The objective is to compare the risk of developing type 2 diabetes (T2D) within a year in patients prescribed various antidepressants (ADs) and those prescribed fluoxetine as a control group.

Methods: This study used standardized data from the Health Insurance Review and Assessment Service claims database (n=1,456,489). Patients aged ≥10 years with no previous use of ADs and no history of diabetes mellitus, regardless of whether they were diagnosed with any depressive disorder, were eligible for this study.

Risk of dementia in patients with age-related medical conditions: a retrospective cohort study.

Objective: This retrospective cohort study aimed to investigate the impact of age-related medical conditions on the incidence of dementia, considering factors such as hypertension, diabetes mellitus, cerebrovascular disease, cardiovascular disease, chronic kidney disease, osteoarthritis, osteoporosis, chronic obstructive pulmonary disease, and hearing difficulties.

Methods: Data from 513 640 patients at Keimyung University Dongsan Hospital were analyzed using the Observational Medical Outcomes Partnership Common Data Model. Patients with and without age-related medical conditions were assigned to experimental and control groups, respectively, with propensity score matching.

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.

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).

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.

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.

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.

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.

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.

Densely Convolutional Spatial Attention Network for nuclei segmentation of histological images for computational pathology.

Introduction: Automatic nuclear segmentation in digital microscopic tissue images can aid pathologists to extract high-quality features for nuclear morphometrics and other analyses. However, image segmentation is a challenging task in medical image processing and analysis. This study aimed to develop a deep learning-based method for nuclei segmentation of histological images for computational pathology.

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.

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.

Region Segmentation of Whole-Slide Images for Analyzing Histological Differentiation of Prostate Adenocarcinoma Using Ensemble EfficientNetB2 U-Net with Transfer Learning Mechanism.

Recent advances in computer-aided detection via deep learning (DL) now allow for prostate cancer to be detected automatically and recognized with extremely high accuracy, much like other medical diagnoses and prognoses. However, researchers are still limited by the Gleason scoring system. The histopathological analysis involved in assigning the appropriate score is a rigorous, time-consuming manual process that is constrained by the quality of the material and the pathologist's level of expertise.

The Derivatives of Cromolyn Ameliorate the Abnormal Misfolding of Amyloid Proteins and Neuroinflammation in the Neural Cells.

Background: The representative symptom of Alzheimer's Disease (AD) has mainly been mentioned to be misfolding of amyloid proteins, such as amyloid-beta (Aβ) and tau protein. In addition, the neurological pathology related to neuroinflammatory signaling has recently been raised as an important feature in AD. Currently, numerous drug candidates continue to be investigated to reduce symptoms of AD, including amyloid proteins misfolding and neuroinflammation.

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.

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.

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.

Texture, Morphology, and Statistical Analysis to Differentiate Primary Brain Tumors on Two-Dimensional Magnetic Resonance Imaging Scans Using Artificial Intelligence Techniques.

Objective: A primary brain tumor starts to grow from brain cells, and it occurs as a result of errors in the DNA of normal cells. Therefore, this study was carried out to analyze the two-dimensional (2D) texture, morphology, and statistical features of brain tumors and to perform a classification using artificial intelligence (AI) techniques.

Methods: AI techniques can help radiologists to diagnose primary brain tumors without using any invasive measurement techniques.

Artificial Intelligence Is Reshaping Healthcare amid COVID-19: A Review in the Context of Diagnosis & Prognosis.

Preventing respiratory failure is crucial in a large proportion of COVID-19 patients infected with SARS-CoV-2 virus pneumonia termed as Novel Coronavirus Pneumonia (NCP). Rapid diagnosis and detection of high-risk patients for effective interventions have been shown to be troublesome. Using a large, computed tomography (CT) database, we developed an artificial intelligence (AI) parameter to diagnose NCP and distinguish it from other kinds of pneumonia and traditional controls.

Development of a System for Storing and Executing Bio-Signal Analysis Algorithms Developed in Different Languages.

With the development of mobile and wearable devices with biosensors, various healthcare services in our life have been recently introduced. A significant issue that arises supports the smart interface among bio-signals developed by different vendors and different languages. Despite its importance for convenient and effective development, however, it has been nearly unexplored.

Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques.

Preventing exacerbation and seeking to determine the severity of the disease during the hospitalization of chronic obstructive pulmonary disease (COPD) patients is a crucial global initiative for chronic obstructive lung disease (GOLD); this option is available only for stable-phase patients. Recently, the assessment and prediction techniques that are used have been determined to be inadequate for acute exacerbation of chronic obstructive pulmonary disease patients. To magnify the monitoring and treatment of acute exacerbation COPD patients, we need to rely on the AI system, because traditional methods take a long time for the prognosis of the disease.