Minimal sourced and lightweight federated transfer learning models for skin cancer detection.

One of the most fatal diseases that affect people is skin cancer. Because nevus and melanoma lesions are so similar and there is a high likelihood of false negative diagnoses challenges in hospitals. The aim of this paper is to propose and develop a technique to classify type of skin cancer with high accuracy using minimal resources and lightweight federated transfer learning models.

Molecular Targets and Nano-Technological Approaches in the Treatment of Hepatic Carcinoma.

Liver cancer is a leading cause of cancer-related mortality, with about one million people losing their lives each year. The disease becomes even more dangerous when tumors cannot be removed through surgery. Globally, hepatocellular carcinoma (HCC) ranks third in terms of fatality rates among liver cancers.

The proteomic landscape of sperm surface deciphers its maturational and functional aspects in buffalo.

Buffalo is a dominant dairy animal in many agriculture-based economies. However, the poor reproductive efficiency (low conception rate) of the buffalo bulls constrains the realization of its full production potential. This in turn leads to economic and welfare issues, especially for the marginal farmers in such economies.

A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review.

Background: Lung diseases, both infectious and non-infectious, are the most prevalent cause of mortality overall in the world. Medical research has identified pneumonia, lung cancer, and Corona Virus Disease 2019 (COVID-19) as prominent lung diseases prioritized over others. Imaging modalities, including X-rays, computer tomography (CT) scans, magnetic resonance imaging (MRIs), positron emission tomography (PET) scans, and others, are primarily employed in medical assessments because they provide computed data that can be utilized as input datasets for computer-assisted diagnostic systems.

Medical Data Analysis Meets Artificial Intelligence (AI) and Internet of Medical Things (IoMT).

AI is a contemporary methodology rooted in the field of computer science [...

A new efficient referential genome compression technique for FastQ files.

Hospitals and medical laboratories create a tremendous amount of genome sequence data every day for use in research, surgery, and illness diagnosis. To make storage comprehensible, compression is therefore essential for the storage, monitoring, and distribution of all these data. A novel data compression technique is required to reduce the time as well as the cost of storage, transmission, and data processing.

Antioxidant peptide nanohybrid: a new perspective to immobilize bioactive peptides from milk industry wastewater.

In this study, dairy industry wastewater was collected and used as a protein source. The proteins were converted into powder form using lyophilization. The proteins were digested using Bacillus subtilis (B.

Diabetic Foot Ulcer Identification: A Review.

Diabetes is a chronic condition caused by an uncontrolled blood sugar levels in the human body. Its early diagnosis may prevent severe complications such as diabetic foot ulcers (DFUs). A DFU is a critical condition that can lead to the amputation of a diabetic patient's lower limb.

Multimodality Medical Image Fusion Using Clustered Dictionary Learning in Non-Subsampled Shearlet Transform.

Imaging data fusion is becoming a bottleneck in clinical applications and translational research in medical imaging. This study aims to incorporate a novel multimodality medical image fusion technique into the shearlet domain. The proposed method uses the non-subsampled shearlet transform (NSST) to extract both low- and high-frequency image components.

Bayesian Adaptive Beamformer for Robust Electromagnetic Brain Imaging of Correlated Sources in High Spatial Resolution.

Reconstructing complex brain source activity at a high spatiotemporal resolution from magnetoencephalography (MEG) or electroencephalography (EEG) remains a challenging problem. Adaptive beamformers are routinely deployed for this imaging domain using the sample data covariance. However adaptive beamformers have long been hindered by 1) high degree of correlation between multiple brain sources, and 2) interference and noise embedded in sensor measurements.

A Non-Conventional Review on Multi-Modality-Based Medical Image Fusion.

Today, medical images play a crucial role in obtaining relevant medical information for clinical purposes. However, the quality of medical images must be analyzed and improved. Various factors affect the quality of medical images at the time of medical image reconstruction.

Accuracy of Prenatal Ultrasound Scans for Screening Cleft Lip and Palate: A Systematic Review.

Children born with clefts encounter various postnatal issues which have a negative impact and long-term psychological effects on both the parents and themselves. This systematic review aims to find the accuracy of two-dimensional (2D)/3D scans for screening cleft lip and palate which would help the parents to be mentally and psychologically prepared to plan for future surgeries. To identify relevant literature, an electronic search was performed using PubMed, Trip database, Cochrane, and Google Scholar database.

Noise Suppression and Edge Preservation for Low-Dose COVID-19 CT Images Using NLM and Method Noise Thresholding in Shearlet Domain.

In the COVID-19 era, it may be possible to detect COVID-19 by detecting lesions in scans, i.e., ground-glass opacity, consolidation, nodules, reticulation, or thickened interlobular septa, and lesion distribution, but it becomes difficult at the early stages due to embryonic lesion growth and the restricted use of high dose X-ray detection.

Bayesian Algorithms for Joint Estimation of Brain Activity and Noise in Electromagnetic Imaging.

Simultaneously estimating brain source activity and noise has long been a challenging task in electromagnetic brain imaging using magneto- and electroencephalography. The problem is challenging not only in terms of solving the NP-hard inverse problem of reconstructing unknown brain activity across thousands of voxels from a limited number of sensors, but also for the need to simultaneously estimate the noise and interference. We present a generative model with an augmented leadfield matrix to simultaneously estimate brain source activity and sensor noise statistics in electromagnetic brain imaging (EBI).

Impact of Educational Intervention on Knowledge, Attitudes, and Practices of Laboratory Technicians regarding Universal Work Precautions at a Tertiary Health Care Center in Central India.

Laboratory technicians are routinely exposed to occupational health hazards that can be a serious threat to their health. To safeguard themselves against laboratory-acquired infections, they must be aware of universal work precautions. A targeted educational intervention to increase awareness about universal precautions was designed to result in behavioral changes in attitudes and practices to help reduce the incidence of laboratory-acquired infections.

RST-DE: Rough Sets-Based New Differential Evolution Algorithm for Scalable Big Data Feature Selection in Distributed Computing Platforms.

In data analysis, data scientists usually focus on the size of data instead of features selection. Owing to the extreme growth of internet resources data are growing exponentially with more features, which leads to big data dimensionality problems. The high volume of features contains much of redundant data, which may affect the feature classification in terms of accuracy.

A Hybrid Deep Learning Approach for ECG-Based Arrhythmia Classification.

Arrhythmias are defined as irregularities in the heartbeat rhythm, which may infrequently occur in a human's life. These arrhythmias may cause potentially fatal complications, which may lead to an immediate risk of life. Thus, the detection and classification of arrhythmias is a pertinent issue for cardiac diagnosis.

Extra-pulmonary Manifestations of COVID-19 in Western India.

Unlabelled: SARS-CoV-2 is well known disorder to affect respiratory system, although it can also influence several extrapulmonary organs through variety of pathological mechanism. In this study, we aimed to discuss the prevalence of atypical and/or extrapulmonary manifestations in COVID-19, therefor action for early isolation and diagnosis can be initiated to prevent spread of infection.

Methods: This retrospective observational study included 4200 admitted COVID-19 patients.

Low-dose COVID-19 CT Image Denoising Using CNN and its Method Noise Thresholding.

Unlabelled: Noise in computed tomography (CT) images may occur due to low radiation doses. Hence, the main aim of this paper is to reduce the noise from low-dose CT images so that the risk of high radiation dose can be reduced.

Background: The novel coronavirus outbreak has ushered in different new areas of research in medical instrumentation and technology.

Rate of head ultrasound abnormalities at one month in very premature and extremely premature infants with normal initial screening ultrasound.

Background: Premature infants are at risk for multiple types of intracranial injury with potentially significant long-term neurological impact. The number of screening head ultrasounds needed to detect such injuries remains controversial.

Objective: To determine the rate of abnormal findings on routine follow-up head ultrasound (US) performed in infants born at ≤ 32 weeks' gestational age (GA) after initial normal screening US.

Determining Association Between Cognitive Function and Oral Health Status among Rural Community Dwelling Geriatrics.

Objective: Numerous prospective studies worldwide investigated the association between oral health status and dementia or cognitive decline. No clear agreement has emerged on the association. This study aimed to determine the association of cognitive function and oral health status among community dwelling geriatrics in rural South India.

Robust estimation of noise for electromagnetic brain imaging with the champagne algorithm.

Robust estimation of the number, location, and activity of multiple correlated brain sources has long been a challenging task in electromagnetic brain imaging from M/EEG data, one that is significantly impacted by interference from spontaneous brain activity, sensor noise, and other sources of artifacts. Recently, we introduced the Champagne algorithm, a novel Bayesian inference algorithm that has shown tremendous success in M/EEG source reconstruction. Inherent to Champagne and most other related Bayesian reconstruction algorithms is the assumption that the noise covariance in sensor data can be estimated from "baseline" or "control" measurements.

Predicting the time period of extension of lockdown due to increase in rate of COVID-19 cases in India using machine learning.

The research paper proposes a methodology to predict the extension of lockdown in order to eradicate COVID-19 from India. All the concepts related to Coronavirus, its history, prevention and cure is explained in the research paper. Concept used to predict the number of active cases, deaths and recovery is Linear Regression which is an application of machine learning.

Robust Empirical Bayesian Reconstruction of Distributed Sources for Electromagnetic Brain Imaging.

Electromagnetic brain imaging is the reconstruction of brain activity from non-invasive recordings of the magnetic fields and electric potentials. An enduring challenge in this imaging modality is estimating the number, location, and time course of sources, especially for the reconstruction of distributed brain sources with complex spatial extent. Here, we introduce a novel robust empirical Bayesian algorithm that enables better reconstruction of distributed brain source activity with two key ideas: kernel smoothing and hyperparameter tiling.