Low-dose radiation research insights in experimental animals: A gateway to therapeutic implications.

In recent years, a significant research effort has been underway to explore the effects of low-dose radiation (LDR). Animal models play a key role in various fields of research, including biomedicine, pharmaceutical, environmental, and behavioral studies. The use of animal models has been an invaluable tool in radiation research for understanding radiation biology, assessing radiation risks, and developing strategies for radiation protection and medical management.

The impact of lifetime intimate partner violence on abortion method choice.

Objective(s): To evaluate if the type of abortion patients prefer differs for those with a history of intimate partner violence (IPV) compared to those without a history of IPV.

Study Design: We compared choice of medication versus procedural abortion between those with a history of lifetime IPV and those without a history of IPV among patients seeking abortion at 11 weeks' gestation or less. Secondarily, we compared preferred abortion characteristics and assessed reproductive autonomy.

Novel techniques for quantifying oxygen pulse curve characteristics during cardiopulmonary exercise testing in tetralogy of fallot.

Background: Cardiopulmonary exercise testing (CPET) is used in evaluation of repaired tetralogy of Fallot (rTOF), particularly for pulmonary valve replacement need. Oxygen pulse (OP) is the CPET surrogate for stroke volume and peripheral oxygen extraction.

Objectives: This study assessed OP curve properties against non-invasive cardiac output monitoring (NICOM) and clinical testing.

A rare synchronous presentation of primary malignant melanoma in cervix and vagina.

Malignant melanoma, primarily a cutaneous malignancy, can also involve mucosal surfaces and constitutes 2% to 7% of all gynecological malignancies. Primary melanoma of the uterine cervix is an uncommon tumor and has poor prognosis. In the female genital tract, the synchronous occurrence of primary malignant melanoma in the cervix and vagina is rare.

EpilepIndex: A novel feature engineering tool to detect epilepsy using EEG signals.

Epilepsy is a common neurological disease characterized by seizures. A person with a seizure onset can lose consciousness which in turn can lead to fatal accidents. Electroencephalogram (EEG) is a recording of the electrical signals from the brain which is used to analyse the epileptic seizures.

Quantum Logic Enhanced Sensing in Solid-State Spin Ensembles.

We demonstrate quantum logic enhanced sensitivity for a macroscopic ensemble of solid-state, hybrid two-qubit sensors. We achieve over a factor of 30 improvement in the single-shot signal-to-noise ratio, translating to an ac magnetic field sensitivity enhancement exceeding an order of magnitude for time-averaged measurements. Using the electronic spins of nitrogen vacancy (NV) centers in diamond as sensors, we leverage the on-site nitrogen nuclear spins of the NV centers as memory qubits, in combination with homogeneous and stable bias and control fields, ensuring that all of the ∼10^{9} two-qubit sensors are sufficiently identical to permit global control of the NV ensemble spin states.

ViVGG19: Novel exemplar deep feature extraction-based shoulder rotator cuff tear and biceps tendinosis detection using magnetic resonance images.

Background And Purpose: Rotator cuff tear (RCT) and biceps tendinosis (BT) are the two most common shoulder disorders worldwide. These disorders can be diagnosed using magnetic resonance imaging (MRI), but the expert interpretation is manual, time-consuming, and subjected to human errors. Therefore, a fixed-size feature extraction model was created to objectively and accurately perform automated binary classification of RCT vs.

RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights.

Myocarditis is heart muscle inflammation that is becoming more prevalent these days, especially with the prevalence of COVID-19. Noninvasive imaging cardiac magnetic resonance (CMR) can be used to diagnose myocarditis, but the interpretation is time-consuming and requires expert physicians. Computer-aided diagnostic systems can facilitate the automatic screening of CMR images for triage.

Automated Diagnosis and Assessment of Cardiac Structural Alteration in Hypertension Ultrasound Images.

Hypertension (HTN) is a major risk factor for cardiovascular diseases. At least 45% of deaths due to heart disease and 51% of deaths due to stroke are the result of hypertension. According to research on the prevalence and absolute burden of HTN in India, HTN positively correlated with age and was present in 20.

PFP-LHCINCA: Pyramidal Fixed-Size Patch-Based Feature Extraction and Chi-Square Iterative Neighborhood Component Analysis for Automated Fetal Sex Classification on Ultrasound Images.

Objectives: Fetal sex determination with ultrasound (US) examination is indicated in pregnancies at risk of X-linked genetic disorders or ambiguous genitalia. However, misdiagnoses often arise due to operator inexperience and technical difficulties while acquiring diagnostic images. We aimed to develop an efficient automated US-based fetal sex classification model that can facilitate efficient screening and reduce misclassification.

EMD-based analysis of complexity with dissociated EEG amplitude and frequency information: a data-driven robust tool -for Autism diagnosis- compared to multi-scale entropy approach.

Objective: Autism spectrum disorder (ASD) is usually characterised by altered social skills, repetitive behaviours, and difficulties in verbal/nonverbal communication. It has been reported that electroencephalograms (EEGs) in ASD are characterised by atypical complexity. The most commonly applied method in studies of ASD EEG complexity is multiscale entropy (MSE), where the sample entropy is evaluated across several scales.

A Novel Intelligent Hybrid Optimized Analytics and Streaming Engine for Medical Big Data.

Medical data processing is exponentially increasing day by day due to the frequent demand for many applications. Healthcare data is one such field, which is dynamically growing day by day. In today's scenario, an enormous amount of sensing devices and data collection units have been employed to generate and collect medical data all over the world.

A Versatile and Ubiquitous IoT-Based Smart Metabolic and Immune Monitoring System.

In the present medical age, the focus on prevention and prediction is achieved using the medical internet of things. With a broad and complete framework, effective behavioral, environmental, and physiological criteria are necessary to govern the major healthcare sectors. Wearables play an essential role in personal health monitoring data measurement and processing.

Classification of Electroencephalogram Signal for Developing Brain-Computer Interface Using Bioinspired Machine Learning Approach.

Transforming human intentions into patterns to direct the devices connected externally without any body movements is called Brain-Computer Interface (BCI). It is specially designed for rehabilitation patients to overcome their disabilities. Electroencephalogram (EEG) signal is one of the famous tools to operate such devices.

Classification of Breast Cancer Images by Implementing Improved DCNN with Artificial Fish School Model.

Breast cancer is an important factor affecting human health. This issue has various diagnosis process which were evolved such as mammography, fine needle aspirate, and surgical biopsy. These techniques use pathological breast cancer images for diagnosis.

Polymethyl Methacrylate-Based Smart Microfluidic Point-of-Care Testing of Prothrombin Time and International Normalized Ratio through Optical Detection.

The mechanical heart valve is a crucial solution for many patients. However, it cannot function on the state of blood as human tissue valves. Thus, people with mechanical valves are put under anticoagulant therapy.

Algorithm for the Comparison of Human Periodic Movements Using Wearable Devices.

In the context of teaching-learning of motor skills in a virtual environment, videos are generally used. The person who wants to learn a certain movement watches a video and tries to perform the activity. In this sense, feedback is rarely thought of.

Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram.

Human-computer interfaces (HCI) allow people to control electronic devices, such as computers, mouses, wheelchairs, and keyboards, by bypassing the biochannel without using motor nervous system signals. These signals permit communication between people and electronic-controllable devices. This communication is due to HCI, which facilitates lives of paralyzed patients who do not have any problems with their cognitive functioning.

Diagnosis of Mucopolysaccharidoses and Mucolipidosis by Assaying Multiplex Enzymes and Glycosaminoglycans.

Mucopolysaccharidoses (MPS) and mucolipidosis (ML II/III) are a group of lysosomal storage disorders (LSDs) that occur due to a dysfunction of the lysosomal hydrolases responsible for the catabolism of glycosaminoglycans (GAGs). However, ML is caused by a deficiency of the enzyme uridine-diphosphate N-acetylglucosamine:lysosomal-enzyme-N-acetylglucosamine-1-phosphotransferase (GlcNAc-1-phosphotransferase, EC2.7.

SARS-CoV 2 spike protein S1 subunit as an ideal target for stable vaccines: A bioinformatic study.

The Covid-19 a pandemic infectious disease and affected life across the world resulting in over 188.65 million confirmed cases across 223 countries, territories and areas with 4.06 million deaths.

Automated ASD detection using hybrid deep lightweight features extracted from EEG signals.

Background: Autism spectrum disorder is a common group of conditions affecting about one in 54 children. Electroencephalogram (EEG) signals from children with autism have a common morphological pattern which makes them distinguishable from normal EEG. We have used this type of signal to design and implement an automated autism detection model.

A Study on Diagnostic Evaluation of Two Different Rapid DNA Polymerase Chain Reaction Techniques Namely Gene Xpert Mycobacterium Tuberculosis/Rifampin (MTB/RIF) and Mycoreal Polymerase Chain Reaction in the Diagnosis of Endometrial Tuberculosis Considering Culture as Gold Standard.

Context: The study involves the evaluation of two polymerase chain reaction (PCR) techniques one of which has been endorsed by the WHO for their diagnostic capabilities.

Aims: The aim of this study is to evaluate the diagnostic accuracy of GeneXpert mycobacterium tuberculosis/Rifampin (MTB/RIF) and mycoreal PCR techniques in the diagnosis of endometrial tuberculosis (TB) considering culture as the gold standard.

Settings And Design: A retrospective study conducted at Gunasheela surgical and maternity hospital.

Newborn screening of mucopolysaccharidoses: past, present, and future.

Mucopolysaccharidoses (MPS) are a subtype of lysosomal storage disorders (LSDs) characterized by the deficiency of the enzyme involved in the breakdown of glycosaminoglycans (GAGs). Mucopolysaccharidosis type I (MPS I, Hurler Syndrome) was endorsed by the U.S.

Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals.

Autistic individuals often have difficulties expressing or controlling emotions and have poor eye contact, among other symptoms. The prevalence of autism is increasing globally, posing a need to address this concern. Current diagnostic systems have particular limitations; hence, some individuals go undiagnosed or the diagnosis is delayed.