Hematoxylin and Eosin-stained whole slide image dataset annotated for skin tissue segmentation.

Skin diseases have a significant impact on the socio-economic landscape as they affect not only the medical health of the patient but also their psychological well-being. Moreover, as the majority of individuals suffering from skin diseases are over the age of 60, these individuals have to also cope with the stress associated to age-related conditions such as diabetes, high blood pressure, and cardiac diseases. To alleviate this burden, it is essential to identify skin diseases at an early stage, which can help prevent disease progression.

Investigation of Phenolic Composition and Antioxidant Capacities in Selected Turkish Indigenous Wheat Varieties.

Indigenous wheat varieties continue to be cultivated and preserved due to their climatic adaptability and distinctive flavor contributions to cultural cuisines. This study covers the research gap by investigating the natural bioactive and antioxidant compounds present in Turkish indigenous wheat varieties. To this aim, the phenolic composition, ash contents, and antioxidant capacity were investigated in eighteen different indigenous Turkish wheat varieties (4 monococcum, 3 dicoccum, 4 durum, and 7 aestivum genotypes).

AI-CADR: Artificial Intelligence Based Risk Stratification of Coronary Artery Disease Using Novel Non-Invasive Biomarkers.

Coronary artery disease (CAD) is one of the most common causes of sudden cardiac arrest, accounting for a large percentage of global mortality. A timely diagnosis and detection may save a person's life. The research suggests a methodological framework for non-invasive risk stratification based on information only possible after invasive coronary angiography.

Insights from EEG analysis of evoked memory recalls using deep learning for emotion charting.

Affect recognition in a real-world, less constrained environment is the principal prerequisite of the industrial-level usefulness of this technology. Monitoring the psychological profile using smart, wearable electroencephalogram (EEG) sensors during daily activities without external stimuli, such as memory-induced emotions, is a challenging research gap in emotion recognition. This paper proposed a deep learning framework for improved memory-induced emotion recognition leveraging a combination of 1D-CNN and LSTM as feature extractors integrated with an Extreme Learning Machine (ELM) classifier.

Transformer-based framework for multi-class segmentation of skin cancer from histopathology images.

Introduction: Non-melanoma skin cancer comprising Basal cell carcinoma (BCC), Squamous cell carcinoma (SCC), and Intraepidermal carcinoma (IEC) has the highest incidence rate among skin cancers. Intelligent decision support systems may address the issue of the limited number of subject experts and help in mitigating the parity of health services between urban centers and remote areas.

Method: In this research, we propose a transformer-based model for the segmentation of histopathology images not only into inflammation and cancers such as BCC, SCC, and IEC but also to identify skin tissues and boundaries that are important in decision-making.

An Enhanced Agile V-Model: Conformance to regulatory bodies and experiences from model's adoption to medical device development.

Context: Medical devices fall under the broad topic encompass everything from basic hardware to integrated software systems. The integration of software into hardware devices is not simple due to requirements of regional regulatory bodies. Therefore, medical businesses need to oversee not only the creation of devices but also the observance of guidelines and standards established by regulatory bodies.

UAV sensor failures dataset: Biomisa arducopter sensory critique (BASiC).

Unmanned aerial vehicles (UAV) rely on a variety of sensors to perceive and navigate their airborne environment with precision. The autopilot software interprets this sensory data, acting as the control mechanism for autonomous flights. As UAVs are exposed to physical environment, they are vulnerable to potential impairments in their sensory mechanism.

Dual contrastive learning based image-to-image translation of unstained skin tissue into virtually stained H&E images.

Staining is a crucial step in histopathology that prepares tissue sections for microscopic examination. Hematoxylin and eosin (H&E) staining, also known as basic or routine staining, is used in 80% of histopathology slides worldwide. To enhance the histopathology workflow, recent research has focused on integrating generative artificial intelligence and deep learning models.

Pleomorphic adenomia of lower lip: A case report.

Pleomorphic adenoma is a benign tumor of the salivary glands. It commonly occurs in the parotid gland, palate, upper lip and cheek. The authors present a rare case of a pleomorphic adenoma of the lower lip in a 30 years old female admitted on 20 of July, 2022 at Akbar Niazi Teaching Hospital, Islamabad with a complaint of painless, slightly itchy swelling on the lower lip for the last four months.

Proliferative Diabetic Retinopathy Diagnosis Using Varying-Scales Filter Banks and Double-Layered Thresholding.

Diabetic retinopathy is one of the abnormalities of the retina in which a diabetic patient suffers from severe vision loss due to an affected retina. Proliferative diabetic retinopathy (PDR) is the final and most critical stage of diabetic retinopathy. Abnormal and fragile blood vessels start to grow on the surface of the retina at this stage.

Effect of Animal Age, Postmortem Calcium Chloride Marination, and Storage Time on Meat Quality Characteristics of of Buffalo Bulls.

This study investigated the effect of animal age, calcium chloride marination, and storage time on meat quality characteristics of buffalo bulls to suggest a cost-effective method of improving buffalo meat quality. The current study was designed considering the importance of buffalo meat and the usage of meat from spent buffalo animals in local markets of South Asian countries. A total of 36 animals comprised of 18 young and 18 spent buffalo bulls were selected.

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks.

Artificial Intelligence (AI) techniques of deep learning have revolutionized the disease diagnosis with their outstanding image classification performance. In spite of the outstanding results, the widespread adoption of these techniques in clinical practice is still taking place at a moderate pace. One of the major hindrance is that a trained Deep Neural Networks (DNN) model provides a prediction, but questions about why and how that prediction was made remain unanswered.

Amide Functionalized Mesoporous MOF LOCOM-1 as a Stable Highly Active Basic Catalyst for Knoevenagel Condensation Reaction.

Acyl-amide is extensively used as functional group and is a superior contender for the design of MOFs with the guest accessible functional organic sites. A novel acyl-amide-containing tetracarboxylate ligand, bis(3,5-dicarboxy-pheny1)terephthalamide, has been successfully synthesized. The HL linker has some fascinating attributes as follows: (i) four carboxylate moieties as the coordination sites confirm affluent coordination approaches to figure a diversity of structure; (ii) two acyl-amide groups as the guest interaction sites can engender guest molecules integrated into the MOF networks through H-bonding interfaces and have a possibility to act as functional organic sites for the condensation reaction.

Automated grading of chest x-ray images for viral pneumonia with convolutional neural networks ensemble and region of interest localization.

Following its initial identification on December 31, 2019, COVID-19 quickly spread around the world as a pandemic claiming more than six million lives. An early diagnosis with appropriate intervention can help prevent deaths and serious illness as the distinguishing symptoms that set COVID-19 apart from pneumonia and influenza frequently don't show up until after the patient has already suffered significant damage. A chest X-ray (CXR), one of many imaging modalities that are useful for detection and one of the most used, offers a non-invasive method of detection.

A Deep Learning Based Approach for Grading of Diabetic Retinopathy Using Large Fundus Image Dataset.

Diabetic Retinopathy affects one-third of all diabetic patients and may cause vision impairment. It has four stages of progression, i.e.

An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals.

Emotion charting using multimodal signals has gained great demand for stroke-affected patients, for psychiatrists while examining patients, and for neuromarketing applications. Multimodal signals for emotion charting include electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, and galvanic skin response (GSR) signals. EEG, ECG, and GSR are also known as physiological signals, which can be used for identification of human emotions.

Tumor micro-environment sensitive release of doxorubicin through chitosan based polymeric nanoparticles: An in-vitro study.

Conventional chemotherapy poses toxic effects to healthy tissues. A therapeutic system is thus required that can administer, distribute, metabolize, and excrete medicine from human body without damaging healthy cells. This is possible by designing a therapeutic system that can release drug at specific target tissue.

EEG-based emotion charting for Parkinson's disease patients using Convolutional Recurrent Neural Networks and cross dataset learning.

Electroencephalogram (EEG) based emotion classification reflects the actual and intrinsic emotional state, resulting in more reliable, natural, and meaningful human-computer interaction with applications in entertainment consumption behavior, interactive brain-computer interface, and monitoring of psychological health of patients in the domain of e-healthcare. Challenges of EEG-based emotion recognition in real-world applications are variations among experimental settings and cognitive health conditions. Parkinson's Disease (PD) is the second most common neurodegenerative disorder, resulting in impaired recognition and expression of emotions.

Continual Learning Objective for Analyzing Complex Knowledge Representations.

Human beings tend to incrementally learn from the rapidly changing environment without comprising or forgetting the already learned representations. Although deep learning also has the potential to mimic such human behaviors to some extent, it suffers from catastrophic forgetting due to which its performance on already learned tasks drastically decreases while learning about newer knowledge. Many researchers have proposed promising solutions to eliminate such catastrophic forgetting during the knowledge distillation process.

Localization and Edge-Based Segmentation of Lumbar Spine Vertebrae to Identify the Deformities Using Deep Learning Models.

The lumbar spine plays a very important role in our load transfer and mobility. Vertebrae localization and segmentation are useful in detecting spinal deformities and fractures. Understanding of automated medical imagery is of main importance to help doctors in handling the time-consuming manual or semi-manual diagnosis.

Intelligent framework for automated failure prediction, detection, and classification of mission critical autonomous flights.

Autonomous flights are the major industry contributors towards next-generation developments in pervasive and ubiquitous computing. Modern aerial vehicles are designed to receive actuator commands from the primary autopilot software as input to regulate their servos for adjusting control surfaces. Due to real-time interaction with the actual physical environment, there exists a high risk of control surface failures for engine, rudder, elevators, and ailerons etc.