Update on the genetic profile of mitral valve development and prolapse.

The purpose of this review is to present a comprehensive overview of the literature published up to February 2024 on the PubMed database regarding the development of mitral valve disease, with detailed reference to mitral valve prolapse, from embryology to a genetic profile. Out of the 3291 publications that deal with mitral valve embryology, 215 refer to mitral valve genetics and 83 were selected for further analysis. After reviewing these data, we advocate for the importance of a gene-based therapy that should be available soon, to prevent or treat non-invasively the valvular degeneration.

Effect of high-calorie formula on weight, height increment, IGF-1 and TLC in growth faltering children: A quasi-experimental study.

High-calorie formulas have been used to promote catch-up growth in undernourished children. The level of insulin-like growth factor 1 (IGF-1) is closely related to weight and nutritional intake, whereas low a total lymphocyte count (TLC) is associated with impaired immune system function in undernourished children. This study was conducted to investigate the effect of high-calorie formula as an intervention on weight, height increment, IGF-1 and TLC in children with growth faltering or undernutrition.

Persistent hypernatremia secondary to adipsic central diabetes insipidus in a patient with herpes-induced meningoencephalitis and COVID-19 infection: a case report.

Central diabetes insipidus (CDI) typically manifests as a polyuria-polydipsia syndrome, in which normonatremia is generally maintained through the polydipsia. A 53-year-old woman presented with diabetic ketosis and hyperosmolar hyperglycemic syndrome. Her medical history included herpes meningoencephalitis, which was associated with confusion and amnesia.

Accessibility crisis of essential medicines at Sudanese primary healthcare facilities: a cross-sectional drugs' dispensaries assessment and patients' perspectives.

Background: Access to essential medicines is a critical component of universal health coverage. However, the availability of essential medicines in Sudan isn't well studied. As well, most Sudanese people lack health insurance, making out-of-pocket spending the primary source of drug financing.

Ventilation and thermal conditions in secondary schools in the Netherlands: Effects of COVID-19 pandemic control and prevention measures.

During the COVID-19 pandemic, the importance of ventilation was widely stressed and new protocols of ventilation were implemented in school buildings worldwide. In the Netherlands, schools were recommended to keep the windows and doors open, and after a national lockdown more stringent measures such as reduction of occupancy were introduced. In this study, the actual effects of such measures on ventilation and thermal conditions were investigated in 31 classrooms of 11 Dutch secondary schools, by monitoring the indoor and outdoor CO concentration and air temperature, both before and after the lockdown.

New Contrast Enhancement Method for Multiple Sclerosis Lesion Detection.

Multiple sclerosis (MS) is one of the most serious neurological diseases. It is the most frequent reason of non-traumatic disability among young adults. MS is an autoimmune disease wherein the central nervous system wrongly destructs the myelin sheath surrounding and protecting axons of nerve cells of the brain and the spinal cord which results in presence of lesions called plaques.

Weakly Supervised Learning using Attention gates for colon cancer histopathological image segmentation.

Recently, Artificial Intelligence namely Deep Learning methods have revolutionized a wide range of domains and applications. Besides, Digital Pathology has so far played a major role in the diagnosis and the prognosis of tumors. However, the characteristics of the Whole Slide Images namely the gigapixel size, high resolution and the shortage of richly labeled samples have hindered the efficiency of classical Machine Learning methods.

U.S. CDC support to international seroprevalence surveys, May 2020-February 2022.

seroprevalence surveys provide critical information to assess the burden of COVID-19, describe population immunity, and guide public health strategies. Early in the pandemic, most of these surveys were conducted within high-income countries, leaving significant knowledge gaps in low-and middle-income (LMI) countries. To address this gap, the U.

Microvasculopathy in spinal muscular atrophy is driven by a reversible autonomous endothelial cell defect.

Spinal muscular atrophy (SMA) is a neuromuscular disorder due to degeneration of spinal cord motor neurons caused by deficiency of the ubiquitously expressed SMN protein. Here, we present a retinal vascular defect in patients, recapitulated in SMA transgenic mice, driven by failure of angiogenesis and maturation of blood vessels. Importantly, the retinal vascular phenotype was rescued by early, systemic SMN restoration therapy in SMA mice.

SARS-CoV-2 Breakthrough Infections among US Embassy Staff Members, Uganda, May-June 2021.

The SARS-CoV-2 Delta variant emerged shortly after COVID-19 vaccines became available in 2021. We describe SARS-CoV-2 breakthrough infections in a highly vaccinated, well-monitored US Embassy community in Kampala, Uganda. Defining breakthrough infection rates in highly vaccinated populations can help determine public health messaging, guidance, and policy globally.

Detection of COVID-19 from CT and Chest X-ray Images Using Deep Learning Models.

Coronavirus 2019 (COVID-19) is a highly transmissible and pathogenic virus caused by severe respiratory syndrome coronavirus 2 (SARS-CoV-2), which first appeared in Wuhan, China, and has since spread in the whole world. This pathology has caused a major health crisis in the world. However, the early detection of this anomaly is a key task to minimize their spread.

COVID-19 detection in CT and CXR images using deep learning models.

Infectious diseases pose a threat to human life and could affect the whole world in a very short time. Corona-2019 virus disease (COVID-19) is an example of such harmful diseases. COVID-19 is a pandemic of an emerging infectious disease, called coronavirus disease 2019 or COVID-19, caused by the coronavirus SARS-CoV-2, which first appeared in December 2019 in Wuhan, China, before spreading around the world on a very large scale.

Deep learning for colon cancer histopathological images analysis.

Nowadays, digital pathology plays a major role in the diagnosis and prognosis of tumours. Unfortunately, existing methods remain limited when faced with the high resolution and size of Whole Slide Images (WSIs) coupled with the lack of richly annotated datasets. Regarding the ability of the Deep Learning (DL) methods to cope with the large scale applications, such models seem like an appealing solution for tissue classification and segmentation in histopathological images.

Development and implementation of the Ebola Exposure Window Calculator: A tool for Ebola virus disease outbreak field investigations.

During an Ebola virus disease (EVD) outbreak, calculating the exposure window of a confirmed case can assist field investigators in identifying the source of infection and establishing chains of transmission. However, field investigators often have difficulty calculating this window. We developed a bilingual (English/French), smartphone-based field application to assist field investigators in determining the exposure window of an EVD case.

Deep Convolutional Encoder-Decoder algorithm for MRI brain reconstruction.

Compressed Sensing Magnetic Resonance Imaging (CS-MRI) could be considered a challenged task since it could be designed as an efficient technique for fast MRI acquisition which could be highly beneficial for several clinical routines. In fact, it could grant better scan quality by reducing motion artifacts amount as well as the contrast washout effect. It offers also the possibility to reduce the exploration cost and the patient's anxiety.

Using Nonpolio Enterovirus Detection to Assess the Integrity of Stool Specimens Collected From Acute Flaccid Paralysis Cases in Somalia During 2014-2017.

Background: Despite insecurity challenges in Somalia, key indicators for acute flaccid paralysis (AFP) surveillance have met recommended targets. However, recent outbreaks of vaccine-derived polioviruses have raised concerns about possible gaps. We analyzed nonpolio enterovirus (NPEV) and Sabin poliovirus isolation rates to investigate whether comparing these rates can inform about the integrity of stool specimens from inaccessible areas and the likelihood of detecting circulating polioviruses.

Trends and Characteristics of CDC Global Rapid Response Team Deployments-A 6-Month Report, October 2018-March 2019.

The Centers for Disease Control and Prevention (CDC) Global Rapid Response Team (GRRT) was launched in June 2015 to strengthen the capacity for international response and to provide an agency-wide roster of qualified surge-staff members who can deploy on short notice and for long durations. To assess GRRT performance and inform future needs for CDC and partners using rapid response teams, we analyzed trends and characteristics of GRRT responses and responders, for deployments of at least 1 day during October 1, 2018, through March 31, 2019. One hundred twenty deployments occurred during the study period, corresponding to 2645 person-days.

Problem solving learning in emergency medicine: Effects and student's perception.

Introduction: the Problem solving Learning (PSL) is an interactive method of teaching that allows students to learn at once clinical reasoning and acquisition of knowledge. The PSL is used to assess learner's competencies.

Aim: To was to assess the educational relevance of PSL in emergency medicine and the perception of learners.

Denoising and contrast-enhancement approach of magnetic resonance imaging glioblastoma brain tumors.

We investigate a new preprocessing approach for MRI glioblastoma brain tumors. Based on combined denoising technique (bilateral filter) and contrast-enhancement technique (automatic contrast stretching based on image statistical information), the proposed approach offers competitive results while preserving the tumor region's edges and original image's brightness. In order to evaluate the proposed approach's performance, quantitative evaluation has been realized through the Multimodal Brain Tumor Segmentation (BraTS 2015) dataset.

Rank-Two NMF Clustering for Glioblastoma Characterization.

This study investigates a novel classification method for 3D multimodal MRI glioblastomas tumor characterization. We formulate our segmentation problem as a linear mixture model (LMM). Thus, we provide a nonnegative matrix from every MRI slice in every segmentation process' step.

Kernel Generalized Likelihood Ratio Test for Fault Detection of Biological Systems.

In this paper, we develop an improved fault detection (FD) technique in order to enhance the monitoring abilities of nonlinear biological processes. Generalized likelihood ratio test (GLRT)-based kernel principal component analysis (KPCA) (called also kernel GLRT) is an effective data-driven technique for monitoring nonlinear processes. However, it is well known that the data collected from complex and multivariate processes are multiscale due to the variety of changes that could occur in process with different localization in time and frequency.

The Influence of Preprocessing Steps on Graph Theory Measures Derived from Resting State fMRI.

Resting state functional MRI (rs-fMRI) is an imaging technique that allows the spontaneous activity of the brain to be measured. Measures of functional connectivity highly depend on the quality of the BOLD signal data processing. In this study, our aim was to study the influence of preprocessing steps and their order of application on small-world topology and their efficiency in resting state fMRI data analysis using graph theory.

A New Adaptive Gamma Correction Based Algorithm Using DWT-SVD for Non-Contrast CT Image Enhancement.

The performances of medical image processing techniques, in particular CT scans, are usually affected by poor contrast quality introduced by some medical imaging devices. This suggests the use of contrast enhancement methods as a solution to adjust the intensity distribution of the dark image. In this paper, an advanced adaptive and simple algorithm for dark medical image enhancement is proposed.

An Advanced MRI Multi-Modalities Segmentation Methodology Dedicated to Multiple Sclerosis Lesions Exploration and Differentiation.

Multiple sclerosis (MS) is one of the most common neurological diseases in young people. This paper dealt with an automatic biomedical aided tool involving volumetric segmentation of multiple sclerosis lesions. To meet this challenge, our proposed methodology requires one preliminary cerebral zones segmentation performed using a new Gaussian mixture model based on various databases atlases.

Non-injection Drug Use and Injection Initiation Assistance among People Who Inject Drugs in Tijuana, Mexico.

Although most people who inject drugs (PWID) report receiving assistance during injection initiation events, little research has focused on risk factors among PWID for providing injection initiation assistance. We therefore sought to determine the influence of non-injection drug use among PWID on their risk to initiate others. We used generalized estimating equation (GEE) models on longitudinal data among a prospective cohort of PWID in Tijuana, Mexico (Proyecto El Cuete IV), while controlling for potential confounders.