Designing an ECG curriculum for residents: Evidence-based approaches to improving resident ECG interpretation skills.

Residents enter their training with variable comfort and competency in electrocardiogram (ECG) interpretation. Accurately interpreting an ECG is a fundamental skill in medicine and resident physicians would benefit from a longitudinal, dedicated ECG curriculum as part of their training to enhance interpretation skills and improve patient outcomes. Educators currently employ a wide array of methodologies to teach their trainees proper ECG interpretation skills, with no single modality established as the gold-standard for teaching this crucial skill.

Implementation of a longitudinal, near-peer ECG didactic curriculum in an internal medicine residency program and impact on ECG interpretation skills.

Background: To determine the impact of a longitudinal, near-peer, dedicated ECG didactic curriculum on internal medicine resident ability to accurately interpret ECGs.

Methods: This study employs a prospective cohort design. Internal medicine residents at University at Buffalo participated in monthly ECG didactic sessions over a 7-month period.

Evaluation of in-service smartphone battery drainage profile for video calling feature in major apps.

Video calling is one of the most energy-intensive features in apps requiring the simultaneous operation of the mobile camera, display screen, audio speaker, and internet services. This feature impacts a smartphone battery's runtime and lifetime. This paper is the first of its kind experimental study, which quantifies the operating profile (discharge current, temperature, and terminal voltage) of video call feature in multiple widely used social media apps, which include WhatsApp, Facebook Messenger, Zoom, Skype, WeChat, Google Hangouts, Imo and Viber.

Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation.

A rapidly spreading epidemic, COVID-19 had a serious effect on millions and took many lives. Therefore, for individuals with COVID-19, early discovery is essential for halting the infection's progress. To quickly and accurately diagnose COVID-19, imaging modalities, including computed tomography (CT) scans and chest X-ray radiographs, are frequently employed.

Corrigendum: The NMT Scalp EEG Dataset: An Open-Source Annotated Dataset of Healthy and Pathological EEG Recordings for Predictive Modeling.

[This corrects the article DOI: 10.3389/fnins.2021.

On Smart Gaze Based Annotation of Histopathology Images for Training of Deep Convolutional Neural Networks.

Unavailability of large training datasets is a bottleneck that needs to be overcome to realize the true potential of deep learning in histopathology applications. Although slide digitization via whole slide imaging scanners has increased the speed of data acquisition, labeling of virtual slides requires a substantial time investment from pathologists. Eye gaze annotations have the potential to speed up the slide labeling process.

The NMT Scalp EEG Dataset: An Open-Source Annotated Dataset of Healthy and Pathological EEG Recordings for Predictive Modeling.

Electroencephalogram (EEG) is widely used for the diagnosis of neurological conditions like epilepsy, neurodegenerative illnesses and sleep related disorders. Proper interpretation of EEG recordings requires the expertise of trained neurologists, a resource which is scarce in the developing world. Neurologists spend a significant portion of their time sifting through EEG recordings looking for abnormalities.

Pakistan Oral Cancer Collaborative: analyzing barriers and obstacles to oral cancer diagnosis, treatment, and prevention in Pakistan.

Oral cancer is a global health problem with increasing case numbers worldwide and no significant improvement in prognosis over the last few decades. It is one of the most common cancers and a leading cause of death in Pakistan, although the number reported is significantly underreported owing to the lack of a national cancer repository, and the true magnitude of this challenge is not known. Bilateral discussions and workshops funded by the Global Challenges Research Fund brought together a number of like-minded researchers and clinicians from the United Kingdom and Pakistan to analyze the status quo and plan the future course.

Multifaceted strategies to improve blood pressure control in a primary care clinic: A quality improvement project.

Background: Approximately 80% of patients with hypertension in the Internal Medicine Clinic were uncontrolled (BP > 130/80 mmHg), according to the 2017 American College of Cardiology (ACC)/American Heart Association (AHA) hypertension guidelines, leading to increased morbidity and mortality. The aim of this quality improvement (QI) was to improve BP control <130/80 from the baseline rates of 20%-30% and <140/90 from the baseline rates of 40%-60% between ages of 18-75 years, within 12 months.

Methods: We used the Plan-Do-Study-Act method.

Brain tumor classification in MRI image using convolutional neural network.

Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Recent progress in the field of deep learning has helped the health industry in Medical Imaging for Medical Diagnostic of many diseases. For Visual learning and Image Recognition, task CNN is the most prevalent and commonly used machine learning algorithm.

Automated feature detection in dental periapical radiographs by using deep learning.

Objective: The aim of this study was to investigate automated feature detection, segmentation, and quantification of common findings in periapical radiographs (PRs) by using deep learning (DL)-based computer vision techniques.

Study Design: Caries, alveolar bone recession, and interradicular radiolucencies were labeled on 206 digital PRs by 3 specialists (2 oral pathologists and 1 endodontist). The PRs were divided into "Training and Validation" and "Test" data sets consisting of 176 and 30 PRs, respectively.

Extensively Drug-Resistant (XDR) Typhoid: Evolution, Prevention, and Its Management.

Typhoid fever is the result of a human host-restricted serotype typhi infection that causes enteric fever. Around 21 million people contract typhoid annually, with Pakistan's inhabitants at most risk amongst Asian countries where typhoid remains prevalent. Decades of indiscriminate antibiotic usage has driven the evolution of multidrug-resistant strains and more recently, extensively drug-resistant (XDR) strains of serotype typhi.

Screening for atrial fibrillation in high-risk nursing home residents.

Background: Various methods have been implemented for screening of patients for atrial fibrillation (AF), but the yield has generally been low. Targeting high-risk patients may improve detection of asymptomatic AF, which could be of value if appropriate treatment could be initiated before a potential thromboembolic event.

Objective: The purpose of this study was to test screening of high-risk nursing home residents having ≥2 risk factors for AF and no previous history of AF using a smartphone-based electrocardiographic (ECG) monitoring device to determine whether it is an accurate, easy-to-use method of screening for asymptomatic AF.

Cancer Image Quantification With And Without, Expensive Whole Slide Imaging Scanners.

Automated analysis of digitized pathology images in tele-health applications can have a transformative impact on under-served communities in the developing world. However, the vast majority of existing image analysis algorithms are trained on slide images acquired via expensive Whole-Slide-Imaging (WSI) scanners. High scanner cost is a key bottleneck preventing large-scale adoption of digital pathology in developing countries.

Synthesis of Symmetric Bridged Bis-Pyrazolone Based Metal Complex Acid Dyes and their Applications on Leather.

Our present investigation aims at the synthesis and application of new, symmetric bridged bis-pyrazolone based acid dyes. The bis-pyrazolone compounds were accomplished from bis- hydrazine of 4,4'-Diaminostilbene-2,2'-disulfonic acid and ethyl acetoacetate. The bis-pyrazolones have been coupled with diazonium salts of o-hydroxyl aromatic amines which resulted in ligand dyes.

Physical activity classification using time-frequency signatures of motion artifacts in multi-channel electrical impedance plethysmographs.

Physical activities are known to introduce motion artifacts in electrical impedance plethysmographic (EIP) sensors. Existing literature considers motion artifacts as a nuisance and generally discards the artifact containing portion of the sensor output. This paper examines the notion of exploiting motion artifacts for detecting the underlying physical activities which give rise to the artifacts in question.

Virtual Spirometry and Activity Monitoring Using Multichannel Electrical Impedance Plethysmographs in Ambulatory Settings.

Continuous monitoring of respiratory patterns and physical activity levels can be useful for remote health management of patients with conditions such as heart disease and chronic obstructive pulmonary disease. In a clinical setting, spirometers serve as the gold standard for monitoring respiratory patterns such as breathing rate and changes in lung volume. However, direct measurements using a spirometer requires placement of a sensor in the patient's airway and is thus infeasible for continuous monitoring in nonclinical, ambulatory settings.