Early detection of Alzheimer's disease in structural and functional MRI.

Objectives: To implement state-of-the-art deep learning architectures such as Deep-Residual-U-Net and DeepLabV3+ for precise segmentation of hippocampus and ventricles, in functional magnetic resonance imaging (fMRI). Integrate VGG-16 with Random Forest (VGG-16-RF) and VGG-16 with Support Vector Machine (VGG-16-SVM) to enhance the binary classification accuracy of Alzheimer's disease, comparing their performance against traditional classifiers.

Method: OpenNeuro and Harvard's Data verse provides Alzheimer's coronal functional MRI data.

Pustular and crusted lesions in systemic lupus erythematosus: A case report.

Systemic lupus erythematosus (SLE) is the prototype of an autoimmune disease with various manifestations in the skin and several other organs. Subacute cutaneous lupus erythematosus may present with annular and psoriasiform lesions. There have been case reports of pustular lesions in SLE.

COPD Unveiled: Insights from Dwellers along a Busy Highway in Kattankulathur Block.

Background: Chronic obstructive pulmonary disease (COPD) is a serious lung condition that imposes a significant financial burden on individuals and society, straining the medical system. According to the World Health Organization, it was the third leading cause of death worldwide in 2020. In India, it ranks as the second leading cause of death, and in Tamil Nadu, it is the tenth.

Association of Abdominal Obesity and Diabetes among the Adult Tribal Population in Tamil Nadu.

Background: Previously overweight and obesity are considered to be an issue of high-income countries but now increasingly becoming more prevalent in low-income and middle-income countries. Obesity and overweight are growing health issues in India. It is important to determine the quantum of the risk contribution by individual risk factor siuch as abdominal obesity.

Aging and cancer: Clinical role of tumor markers in the geriatric population (Review).

Aging, with the progressive deterioration and functional decline of several organ systems, is highly heterogeneous for both between and within individuals. Tumor markers are widely used in clinical practice as a screening test for individuals >50 years of age. More specifically, caring for elderly patients is a public health concern, given the incidence of cancer and its related mortality and morbidity.

A Robust Malaria Cell Detection Framework Using Adaptive and Atrous Convolution-Based Recurrent Mobilenetv2 with Trans-MobileUNet + + -Based Abnormality Segmentation.

The highly contagious malaria disease is spread by the female Anopheles mosquito. This disease results in a patient's death or incapacity to move their muscles, if it is not appropriately identified in the early stages. A Rapid Diagnostic Test (RDT) is a frequently used approach to find malaria cells in red blood cells.

Performance evaluation of a low-cost Ti-Mo-Fe (TMF8) as a replacement for Ti-6Al-4V for internal fixation implants used in mandibular angular fractures: a finite element analysis study.

Stainless steel and titanium-based alloys have been the gold standard when it comes to permanent implants and magnesium-based alloys have been the best option for bioresorbable alloys. Ti-6Al-4V, Ti-64, with its 110 GPa Young's Modulus is the most commonly employed alloy to manufacture biomedical implants used for treatment of fractures of skeleton. Recently, researchers have developed a new low-cost and toxic Vanadium-free alternative to this alloy, Ti-3Mo-0.

Enhancing image-based diagnosis of gastrointestinal tract diseases through deep learning with EfficientNet and advanced data augmentation techniques.

The early detection and diagnosis of gastrointestinal tract diseases, such as ulcerative colitis, polyps, and esophagitis, are crucial for timely treatment. Traditional imaging techniques often rely on manual interpretation, which is subject to variability and may lack precision. Current methodologies leverage conventional deep learning models that, while effective to an extent, often suffer from overfitting and generalization issues on medical image datasets due to the intricate and subtle variations in disease manifestations.

Neurocognitive effects of proanthocyanidin in Alzheimer's disease: a systematic review of preclinical evidence.

Cognitive disorders and dementia largely influence individual independence and orientation. Based on the Alzheimer's Disease International (ADI) estimation, approximately 75% of individuals with dementia are undiagnosed. In fact, in some low- and middle-income countries, the percentage is as high as 90%.

An Integrated Approach using YOLOv8 and ResNet, SeResNet & Vision Transformer (ViT) Algorithms based on ROI Fracture Prediction in X-ray Images of the Elbow.

Introduction: In this study, we harnessed three cutting-edge algorithms' capabilities to refine the elbow fracture prediction process through X-ray image analysis. Employing the YOLOv8 (You only look once) algorithm, we first identified Regions of Interest (ROI) within the X-ray images, significantly augmenting fracture prediction accuracy.

Methods: Subsequently, we integrated and compared the ResNet, the SeResNet (Squeeze-and-Excitation Residual Network) ViT (Vision Transformer) algorithms to refine our predictive capabilities.

Breast Cancer Detection and Analytics Using Hybrid CNN and Extreme Learning Machine.

Early detection of breast cancer is essential for increasing survival rates, as it is one of the primary causes of death for women globally. Mammograms are extensively used by physicians for diagnosis, but selecting appropriate algorithms for image enhancement, segmentation, feature extraction, and classification remains a significant research challenge. This paper presents a computer-aided diagnosis (CAD)-based hybrid model combining convolutional neural networks (CNN) with a pruned ensembled extreme learning machine (HCPELM) to enhance breast cancer detection, segmentation, feature extraction, and classification.

Shared decision-making and disease management in advanced cancer and chronic kidney disease using patient-reported outcome dashboards.

Objectives: To assess the use of a co-designed patient-reported outcome (PRO) clinical dashboard and estimate its impact on shared decision-making (SDM) and symptomatology in adults with advanced cancer or chronic kidney disease (CKD).

Materials And Methods: We developed a clinical PRO dashboard within the Northwestern Medicine Patient-Reported Outcomes system, enhanced through co-design involving 20 diverse constituents. Using a single-group, pretest-posttest design, we evaluated the dashboard's use among patients with advanced cancer or CKD between June 2020 and January 2022.

Generative adversarial network for Multimodal Contrastive Domain Sharing based on efficient invariant feature-centric growth analysis improved brain tumor classification.

Efficient and accurate classification of brain tumor categories remains a critical challenge in medical imaging. While existing techniques have made strides, their reliance on generic features often leads to suboptimal results. To overcome these issues, Multimodal Contrastive Domain Sharing Generative Adversarial Network for Improved Brain Tumor Classification Based on Efficient Invariant Feature Centric Growth Analysis (MCDS-GNN-IBTC-CGA) is proposed in this manuscript.

Comparing the influence of cuspal angulation, occlusal loading, and connector widths between tooth- and implant-supported zirconia fixed dental prosthesis.

Background: Few studies have established the relationship between connector widths, cuspal angulation, loading forces, and supporting structures of zirconia fixed dental prosthesis (FDP). The objective of the study was to compare the stress distribution in implant- and tooth-supported zirconia FDP with different connector designs, and cuspal angulations of replaced teeth under diverse angulations of forces.

Methods: Finite element (FE) analysis was done by simulating a 3-unit implant- and tooth-supported FDP.

Assessment of differences in stress markers of edentulous patients rehabilitated with complete dentures.

Background: Biomarkers can aid in determining the success of complete denture. Fewer studies evaluated the biomarkers in edentulous patients. This can aid in planning of better qualitative care for edentulous patients.

Trends and future perspectives of 3D printing in prosthodontics.

The three-dimensional (3D) printing technology has led to transformative shift in prosthodontics. This review summarizes the evolution, processing techniques, materials, integration of digital plan, challenges, clinical applications and future directions of 3D printing in prosthodontics. It appraises from the launch of 3D printing to its current applications in prosthodontics.

Meta-analysis of renin angiotensin aldosterone modulators mitigating atrial fibrillation risk in hypertensive patients.

Introduction: Hypertension is associated with left ventricular hypertrophy/enlargement/fibrosis and atrial ectopic rhythm, leading to an increased risk of Atrial Fibrillation (AF). We aimed to stratify the effect of Angiotensin Converting Enzyme Inhibitors (ACEi) and Angiotensin Receptor Blockers (ARB) on the risk of AF.

Methods: PubMed, Scopus, and Google Scholar databases were screened, and cross-citation was conducted for studies reporting AF in hypertensive patients on ACEi and ARB.

Maternal Experiences during Pregnancy, Delivery, and Breastfeeding Practices: A Community-based Analytical Cross-sectional Study.

Background: Evidence on variation in the information provided to mothers during antenatal and postnatal periods, its influence on breastfeeding awareness, and practice in urban and rural settings of India is scarce. The aim of the study was to assess the variation in mothers experience during pregnancy, delivery, and maternity period across settings and its influence on breastfeeding practices in the first six months of infants' life.

Methods: A community-based analytical cross-sectional study was carried out in urban and rural settings of Coimbatore, Tamil Nadu, among 800 mothers who had delivered between one year and six months before the date of the survey using simple random sampling.

State-of-art technologies, challenges, and emerging trends of computer vision in dental images.

Computer vision falls under the broad umbrella of artificial intelligence that mimics human vision and plays a vital role in dental imaging. Dental practitioners visualize and interpret teeth, and the structure surrounding the teeth and detect abnormalities by manually examining various dental imaging modalities. Due to the complexity and cognitive difficulty of comprehending medical data, human error makes correct diagnosis difficult.