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.

Deep efficient-nets with transfer learning assisted detection of COVID-19 using chest X-ray radiology imaging.

Corona Virus (COVID-19) could be considered as one of the most devastating pandemics of the twenty-first century. The effective and the rapid screening of infected patients could reduce the mortality and even the contagion rate. Chest X-ray radiology could be designed as one of the effective screening techniques for COVID-19 exploration.

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.

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.

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.

Relative Ectopic Kidney Function Quantification Using DMSA Tomoscintigraphy Modality.

The ectopic renal function estimation based on a manual region of interest (ROI) extraction could be considered as time consuming. It could also affect the clinical interpretation and thus deviate the therapeutic attitude. For this purpose, we propose an advanced tool to evaluate such function through the dimercaptosuccinic acid (DMSA) kidney scintigraphy scans.

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.

Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification.

Accurate and fully automatic brain tumor grading from volumetric 3D magnetic resonance imaging (MRI) is an essential procedure in the field of medical imaging analysis for full assistance of neuroradiology during clinical diagnosis. We propose, in this paper, an efficient and fully automatic deep multi-scale three-dimensional convolutional neural network (3D CNN) architecture for glioma brain tumor classification into low-grade gliomas (LGG) and high-grade gliomas (HGG) using the whole volumetric T1-Gado MRI sequence. Based on a 3D convolutional layer and a deep network, via small kernels, the proposed architecture has the potential to merge both the local and global contextual information with reduced weights.

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.

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.

Congenital anomalies in Tunisia: Frequency and risk factors.

Background: Despite the high risk of recurrence of congenital malformations, there are no well-accepted preventive measures in developing countries like Tunisia. It is recommended that thorough epidemiological studies of congenital anomalies in this country are needed. The aim of this study is to assess the frequency and types of congenital anomalies in Tunisia and research some risk factors associated with occurrence of these anomalies.

Longitudinal changes in functional connectivity of cortico-basal ganglia networks in manifests and premanifest huntington's disease.

Huntington's disease (HD) is a genetic neurological disorder resulting in cognitive and motor impairments. We evaluated the longitudinal changes of functional connectivity in sensorimotor, associative and limbic cortico-basal ganglia networks. We acquired structural MRI and resting-state fMRI in three visits one year apart, in 18 adult HD patients, 24 asymptomatic mutation carriers (preHD) and 18 gender- and age-matched healthy volunteers from the TRACK-HD study.

[Cigarette smoking initiation among Tunisian adolescents: Risk and protective factors].

Background: Since smoking is a major public health problem among Tunisian teenagers, it is important to identify the underlying risk and protective factors associated with initiation of this behavior.

Methods: A cross-sectional, school-based survey of students was conducted by the Tunisian Ministry of Health among a nationally representative sample of 4172 adolescents aged between 12 and 20 years attending public, private, and professional secondary schools across Tunisia who participated in the Survey of the Health of Tunisian adolescents in 2000. For data analysis, we first calculated crude odds ratios (OR) followed by calculating adjusted OR after using multivariate logistic regression models.

Breast Cancer Ultrasound Images' Sequence Exploration Using BI-RADS Features' Extraction: Towards an Advanced Clinical Aided Tool for Precise Lesion Characterization.

This research concerned a clinical need for precise breast cancer lesion characterization imaged by ultrasound sequences. Using therefore BI-RADS features that would be carefully extracted, the purpose of this study could be mainly to prove and to demonstrate the possibility of surveying precisely the changing characteristics of a breast cancer lesion within a considered ultrasound images' sequence. This was in fact a clinical need of a computer aided diagnosis (CAD) system permitting flexible and convivial clinical analysis of multi-slices' ultrasound breast cancer lesion with greater precision.

Spatial Resolution Improvement of EEG Source Reconstruction Using swLORETA.

Electroencephalography (EEG) and magnetic resonance imaging (MRI) are noninvasive neuro-imaging modalities largely used in neurology explorations. MRI is considered as a static modality and could be so important for anatomy by its high spatial resolution. EEG, on the other hand, is an important tool permitting to image temporal dynamic activities of the human brain.

Relationship between MDM2 and p53 alterations in colorectal cancer and their involvement and prognostic value in the Tunisian population.

Background: The prevalence of p53 mutations in colorectal cancer could reach 90%. The most important regulator of this protein that was identified originally was the Murine Double Minute2 (MDM2) oncoprotein, by which the levels of p53 were fixed through an autoregulatory feedback loop. In cancer cases, the overexpression of MDM2 deregulates this feedback, and the signaling pathway between MDM2 and p53 is blocked.