Antimicrobial effect of herbal and conventional root canal endodontic irrigants against persistent pathogens.

Evaluation and comparison of natural products like triphala, eucalyptus and carvacol with conventional root canal irrigant such as sodium hypochlorite (NaOCL) and Chlorhexidine against persistent root canal pathogens like is of interest. Samples were taken both before irrigation as well as after irrigation. CFU was counted after the plates had been incubated overnight at temperature of 37°C overnight.

Advances and Challenges in Meta-Learning: A Technical Review.

Meta-learning empowers learning systems with the ability to acquire knowledge from multiple tasks, enabling faster adaptation and generalization to new tasks. This review provides a comprehensive technical overview of meta-learning, emphasizing its importance in real-world applications where data may be scarce or expensive to obtain. The article covers the state-of-the-art meta-learning approaches and explores the relationship between meta-learning and multi-task learning, transfer learning, domain adaptation and generalization, self-supervised learning, personalized federated learning, and continual learning.

A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.

The emergence of the COVID-19 pandemic in Wuhan in 2019 led to the discovery of a novel coronavirus. The World Health Organization (WHO) designated it as a global pandemic on 11 March 2020 due to its rapid and widespread transmission. Its impact has had profound implications, particularly in the realm of public health.

Dominance and improved survivability of human γδT17 cell subset aggravates the immunopathogenesis of pemphigus vulgaris.

Human γδ T cells are highly enriched in epithelial cell-dominated compartments like skin. Nonetheless, their function in the pathogenesis of pemphigus vulgaris (PV), an autoimmune skin disorder, is lacking. Therefore, we investigated the functional expression of human γδT cell subsets along with their homing chemokine receptor-ligand and inflammatory cytokines in the immunopathogenesis of PV.

AI Tools for Assessing Human Fertility Using Risk Factors: A State-of-the-Art Review.

Infertility has massively disrupted social and marital life, resulting in stressful emotional well-being. Early diagnosis is the utmost need for faster adaption to respond to these changes, which makes possible via AI tools. Our main objective is to comprehend the role of AI in fertility detection since we have primarily worked to find biomarkers and related risk factors associated with infertility.

Novel markers of COVID-19 mortality-A comparative study with patients of acute coronary syndrome.

Background: COVID-19 can cause severe pneumonia that can progress to multiple organ failure. It is believed that dysregulation of inflammation and cytokine storm, contributes to severe COVID-19. As inflammatory mediators play an important role in the pathogenesis of the severe disease, inflammatory markers like fever, leucocytosis, and C-reactive protein are known to predict severe disease.

Unboxing Industry-Standard AI Models for Male Fertility Prediction with SHAP.

Infertility is a social stigma for individuals, and male factors cause approximately 30% of infertility. Despite this, male infertility is underrecognized and underrepresented as a disease. According to the World Health Organization (WHO), changes in lifestyle and environmental factors are the prime reasons for the declining rate of male fertility.

SecureFed: federated learning empowered medical imaging technique to analyze lung abnormalities in chest X-rays.

Machine learning is an effective and accurate technique to diagnose COVID-19 infections using image data, and chest X-Ray (CXR) is no exception. Considering privacy issues, machine learning scientists end up receiving less medical imaging data. Federated Learning (FL) is a privacy-preserving distributed machine learning paradigm that generates an unbiased global model that follows local model (from clients) without exposing their personal data.

Analyzing Overlaid Foreign Objects in Chest X-rays-Clinical Significance and Artificial Intelligence Tools.

The presence of non-biomedical foreign objects (NBFO), such as coins, buttons and jewelry, and biomedical foreign objects (BFO), such as medical tubes and devices in chest X-rays (CXRs), make accurate interpretation difficult, as they do not indicate known biological abnormalities like excess fluids, tuberculosis (TB) or cysts. Such foreign objects need to be detected, localized, categorized as either NBFO or BFO, and removed from CXR or highlighted in CXR for effective abnormality analysis. Very specifically, NBFOs can adversely impact the process, as typical machine learning algorithms would consider these objects to be biological abnormalities producing false-positive cases.

Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseaseX: Covid-19, pneumonia, and tuberculosis.

Chest X-ray (CXR) imaging is a low-cost, easy-to-use imaging alternative that can be used to diagnose/screen pulmonary abnormalities due to infectious diseaseX: Covid-19, Pneumonia and Tuberculosis (TB). Not limited to binary decisions (with respect to healthy cases) that are reported in the state-of-the-art literature, we also consider non-healthy CXR screening using a lightweight deep neural network (DNN) with a reduced number of epochs and parameters. On three diverse publicly accessible and fully categorized datasets, for non-healthy versus healthy CXR screening, the proposed DNN produced the following accuracies: 99.

Advances in Deep Learning for Tuberculosis Screening using Chest X-rays: The Last 5 Years Review.

There has been an explosive growth in research over the last decade exploring machine learning techniques for analyzing chest X-ray (CXR) images for screening cardiopulmonary abnormalities. In particular, we have observed a strong interest in screening for tuberculosis (TB). This interest has coincided with the spectacular advances in deep learning (DL) that is primarily based on convolutional neural networks (CNNs).

A systematic review on cough sound analysis for Covid-19 diagnosis and screening: is my cough sound COVID-19?

For COVID-19, the need for robust, inexpensive, and accessible screening becomes critical. Even though symptoms present differently, cough is still taken as one of the primary symptoms in severe and non-severe infections alike. For mass screening in resource-constrained regions, artificial intelligence (AI)-guided tools have progressively contributed to detect/screen COVID-19 infections using cough sounds.

Socioeconomic impact due to COVID-19: An empirical assessment.

Starting from December 2019, the novel COVID-19 threatens human lives and economies across the world. It was a matter of grave concern for the governments of all the countries as the deadly virus started expanding its paws over neighboring regions of infected areas. The spread got uncontrollable, thereby leaving no choice for the nations but to impose and observe nationwide lockdown.

Machine Learning Techniques for Human Age and Gender Identification Based on Teeth X-Ray Images.

The use of digital medical images is increasing with advanced computational power that has immensely contributed to developing more sophisticated machine learning techniques. Determination of age and gender of individuals was manually performed by forensic experts by their professional skills, which may take a few days to generate results. A fully automated system was developed that identifies the gender of humans and age based on digital images of teeth.

One-year outcomes of novel BioMime Morph tapered stent in long and multiple coronary artery lesions.

Objective: Even with the immense progress achieved in the field of percutaneous coronary interventions (PCIs), treatment of diffuse long atherosclerotic coronary artery disease continues to remain a challenge for durable outcomes. The downstream reduction in diameter along the lesion length of a coronary artery may compel the cardiologist to use either 2 overlapping stents of different diameters or a single long stent leading to stent-vessel mismatch at the edges. Recently, Meril Life Sciences Pvt.

Deep neural network to detect COVID-19: one architecture for both CT Scans and Chest X-rays.

Since December 2019, the novel COVID-19's spread rate is exponential, and AI-driven tools are used to prevent further spreading [1]. They can help predict, screen, and diagnose COVID-19 positive cases. Within this scope, imaging with Computed Tomography (CT) scans and Chest X-rays (CXRs) are widely used in mass triage situations.

Interval timing and midfrontal delta oscillations are impaired in Parkinson's disease patients with freezing of gait.

Gait abnormalities and cognitive dysfunction are common in patients with Parkinson's disease (PD) and get worse with disease progression. Recent evidence has suggested a strong relationship between gait abnormalities and cognitive dysfunction in PD patients and impaired cognitive control could be one of the causes for abnormal gait patterns. However, the pathophysiological mechanisms of cognitive dysfunction in PD patients with gait problems are unclear.

Covid-19 Imaging Tools: How Big Data is Big?

In this paper, considering year 2020 and Covid-19, we analyze medical imaging tools and their performance scores in accordance with the dataset size and their complexity. For this, we mainly consider AI-driven tools that employ two different types of image data, namely chest Computed Tomography (CT) and X-ray. We elaborate on their strengths and weaknesses by taking the following important factors into account: i) dataset size; ii) model fitting criteria (over-fitting and under-fitting); iii) transfer learning in the deep learning era; and iv) data augmentation.

Improved U-Net architecture with VGG-16 for brain tumor segmentation.

Automated assessment and segmentation of Brain MRI images facilitate towards detection of neurological diseases and disorders. In this paper, we propose an improved U-Net with VGG-16 to segment Brain MRI images and identify region-of-interest (tumor cells). We compare results of improved U-Net with a custom-designed U-Net architecture by analyzing the TCGA-LGG dataset (3929 images) from the TCI archive, and achieve pixel accuracies of 0.

Diploma in primary care psychiatry: Do primary care doctors need training in cannabis use disorder module?

Background: Cannabis is one of the most used illicit substances in India but is under-recognized and under-represented in clinical settings of India, especially at primary care. Patients usually do not seek treatment primarily for cannabis use, but it is identified on pro-active questioning by doctors. The aim is to study the clinical profiles of patients with cannabis use disorders (CUD) at primary care and to derive learning points from collaborative consultations to devise an optional module for CUD primarily for primary care doctors (PCDs).