Hospital mortality due to infective endocarditis: Analysis of risk factors in a developing country.

Introduction: Infective endocarditis (IE) is a disease with high mortality that, in recent decades, has experienced changes in its epidemiology, pathogenesis, and microbiology. Therefore, understanding its landscape and risk factors for mortality is essential.

Objective: To identify the epidemiological, clinical, laboratory, etiological, and echocardiographic profile of patients hospitalized with IE and to determine predictors for in-hospital mortality.

Have We Solved Glottis Segmentation? Review and Commentary.

Quantification of voice physiology has been a key research goal. Segmenting the glottal area to describe the vocal fold motion has seen increased attention in the last two decades. However, researchers struggled to fully automatize the segmentation task.

Implicit neural representations in light microscopy.

Three-dimensional stacks acquired with confocal or two-photon microscopy are crucial for studying neuroanatomy. However, high-resolution image stacks acquired at multiple depths are time-consuming and susceptible to photobleaching. In vivo microscopy is further prone to motion artifacts.

DeepD3, an open framework for automated quantification of dendritic spines.

Dendritic spines are the seat of most excitatory synapses in the brain, and a cellular structure considered central to learning, memory, and activity-dependent plasticity. The quantification of dendritic spines from light microscopy data is usually performed by humans in a painstaking and error-prone process. We found that human-to-human variability is substantial (inter-rater reliability 82.

A Generative Method for a Laryngeal Biosignal.

The Glottal Area Waveform (GAW) is an important component in quantitative clinical voice assessment, providing valuable insights into vocal fold function. In this study, we introduce a novel method employing Variational Autoencoders (VAEs) to generate synthetic GAWs. Our approach enables the creation of synthetic GAWs that closely replicate real-world data, offering a versatile tool for researchers and clinicians.

Machine Learning-assisted immunophenotyping of peripheral blood identifies innate immune cells as best predictor of response to induction chemo-immunotherapy in head and neck squamous cell carcinoma - knowledge obtained from the CheckRad-CD8 trial.

Purpose: Individual prediction of treatment response is crucial for personalized treatment in multimodal approaches against head-and-neck squamous cell carcinoma (HNSCC). So far, no reliable predictive parameters for treatment schemes containing immunotherapy have been identified. This study aims to predict treatment response to induction chemo-immunotherapy based on the peripheral blood immune status in patients with locally advanced HNSCC.

The modified Polsby-Popper score, a novel quantitative histomorphological biomarker and its potential to predict lymph node positivity and cancer-specific survival in oral tongue squamous cell carcinoma.

Background: The significance of different histological spreading patterns of tumor tissue in oral tongue squamous cell carcinoma (TSCC) is well known. Our aim was to construct a numeric parameter on a continuous scale, that is, the modified Polsby-Popper (MPP) score, to describe the aggressiveness of tumor growth and infiltration, with the potential to analyze hematoxylin and eosin-stained whole slide images (WSIs) in an automated manner. We investigated the application of the MPP score in predicting survival and cervical lymph node metastases as well as in determining patients at risk in the context of different surgical margin scenarios.

Case report: Maintaining altered states of consciousness over repeated ketamine infusions may be key to facilitate long-lasting antidepressant effects: some initial lessons from a personalized-dosing single-case study.

Background: The interest in psychoactive agents for treating mental disorders has gathered a growing body of scientific interest. However, research on the relationship between altered states of consciousness (ASCs) and ketamine's antidepressant properties is still limited. Likewise, approaches to sustain early treatment success for the long-term are needed.

Explainable convolutional neural networks for assessing head and neck cancer histopathology.

Purpose: Although neural networks have shown remarkable performance in medical image analysis, their translation into clinical practice remains difficult due to their lack of interpretability. An emerging field that addresses this problem is Explainable AI.

Methods: Here, we aimed to investigate the ability of Convolutional Neural Networks (CNNs) to classify head and neck cancer histopathology.

Wearable Prophylaxis Tool for AI-Driven Identification of Early Warning Patterns of Pressure Ulcers.

As today's society ages, age-related diseases become more frequent. One very common but yet preventable disease is the development of pressure ulcers (PUs). PUs can occur if tissue is exposed to a long-lasting pressure load, e.

GlottisNetV2: Temporal Glottal Midline Detection Using Deep Convolutional Neural Networks.

High-speed videoendoscopy is a major tool for quantitative laryngology. Glottis segmentation and glottal midline detection are crucial for computing vocal fold-specific, quantitative parameters. However, fully automated solutions show limited clinical applicability.

Pixel-Based Swallow Measurements: Correcting Nonsquare Pixels.

Purpose: This research note illustrates the effects of video data with nonsquare pixels on the pixel-based measures obtained from videofluoroscopic swallow studies (VFSS).

Method: Six pixel-based distance and area measures were obtained from two different videoflouroscopic study units; both yielding videos with nonsquare pixels with different pixel aspect ratios (PARs). The swallowing measures were obtained from the original VFSS videos and from the videos after their pixels were squared.

Norovirus and rotavirus in surface, malacoculture, and human consumption water in Santa Catarina State, Brazil.

This study evaluated the results recorded at the Central Public Health Laboratory of Santa Catarina state (Brazil) concerning the investigation of Rotavirus (RVA) and Norovirus (NoVs) - genogroups GI and GII. Samples were taken from seawater, river water, estuary water, lagoon water, and treated water samples, from 2018 to 2021. The aim was to correlate them with each other and evaluate their association with the type of water, presence of shellfish farming, population density, and sewage treatment.

Long-term performance assessment of fully automatic biomedical glottis segmentation at the point of care.

Deep Learning has a large impact on medical image analysis and lately has been adopted for clinical use at the point of care. However, there is only a small number of reports of long-term studies that show the performance of deep neural networks (DNNs) in such an environment. In this study, we measured the long-term performance of a clinically optimized DNN for laryngeal glottis segmentation.

A single latent channel is sufficient for biomedical glottis segmentation.

Glottis segmentation is a crucial step to quantify endoscopic footage in laryngeal high-speed videoendoscopy. Recent advances in deep neural networks for glottis segmentation allow for a fully automatic workflow. However, exact knowledge of integral parts of these deep segmentation networks remains unknown, and understanding the inner workings is crucial for acceptance in clinical practice.

Buddhist-like opposite diminishing and non-judging during ketamine infusion are associated with antidepressant response: an open-label personalized-dosing study.

Cognition that is not dominated by thinking in terms of opposites (opposite diminishing) or by making judgments (non-judging) can be found both in Buddhist/mindfulness contexts and in mental states that are fostered by dissociative psychedelics (-methyl-D-aspartate antagonists) such as ketamine. Especially for the Buddhist/mindfulness case, both opposite diminishing and non-judging have been proposed to relate to mental well-being. Whether ketamine-occasioned opposite diminishing and/or non-judging relate to increased mental well-being in the form of antidepressant response is unknown, and was investigated in the present study.

A cerebellar internal model calibrates a feedback controller involved in sensorimotor control.

Animals must adapt their behavior to survive in a changing environment. Behavioral adaptations can be evoked by two mechanisms: feedback control and internal-model-based control. Feedback controllers can maintain the sensory state of the animal at a desired level under different environmental conditions.

Effects of surgical masks on aerosol dispersion in professional singing.

Background: In the CoVID-19 pandemic, singing came into focus as a high-risk activity for the infection with airborne viruses and was therefore forbidden by many governmental administrations.

Objective: The aim of this study is to investigate the effectiveness of surgical masks regarding the spatial and temporal dispersion of aerosol and droplets during professional singing.

Methods: Ten professional singers performed a passage of the Ludwig van Beethoven's "Ode of Joy" in two experimental setups-each with and without surgical masks.

OpenHSV: an open platform for laryngeal high-speed videoendoscopy.

High-speed videoendoscopy is an important tool to study laryngeal dynamics, to quantify vocal fold oscillations, to diagnose voice impairments at laryngeal level and to monitor treatment progress. However, there is a significant lack of an open source, expandable research tool that features latest hardware and data analysis. In this work, we propose an open research platform termed OpenHSV that is based on state-of-the-art, commercially available equipment and features a fully automatic data analysis pipeline.

A Deep Learning Enhanced Novel Software Tool for Laryngeal Dynamics Analysis.

Purpose High-speed videoendoscopy (HSV) is an emerging, but barely used, endoscopy technique in the clinic to assess and diagnose voice disorders because of the lack of dedicated software to analyze the data. HSV allows to quantify the vocal fold oscillations by segmenting the glottal area. This challenging task has been tackled by various studies; however, the proposed approaches are mostly limited and not suitable for daily clinical routine.

Interdependencies between acoustic and high-speed videoendoscopy parameters.

In voice research, uncovering relations between the oscillating vocal folds, being the sound source of phonation, and the resulting perceived acoustic signal are of great interest. This is especially the case in the context of voice disorders, such as functional dysphonia (FD). We investigated 250 high-speed videoendoscopy (HSV) recordings with simultaneously recorded acoustic signals (124 healthy females, 60 FD females, 44 healthy males, 22 FD males).

Rethinking glottal midline detection.

A healthy voice is crucial for verbal communication and hence in daily as well as professional life. The basis for a healthy voice are the sound producing vocal folds in the larynx. A hallmark of healthy vocal fold oscillation is the symmetric motion of the left and right vocal fold.

BAGLS, a multihospital Benchmark for Automatic Glottis Segmentation.

Laryngeal videoendoscopy is one of the main tools in clinical examinations for voice disorders and voice research. Using high-speed videoendoscopy, it is possible to fully capture the vocal fold oscillations, however, processing the recordings typically involves a time-consuming segmentation of the glottal area by trained experts. Even though automatic methods have been proposed and the task is particularly suited for deep learning methods, there are no public datasets and benchmarks available to compare methods and to allow training of generalizing deep learning models.

Determination of Clinical Parameters Sensitive to Functional Voice Disorders Applying Boosted Decision Stumps.

Background: Various voice assessment tools, such as questionnaires and aerodynamic voice characteristics, can be used to assess vocal function of individuals. However, not much is known about the best combinations of these parameters in identification of functional dysphonia in clinical settings.

Methods: This study investigated six scores from clinically commonly used questionnaires and seven acoustic parameters.