Anticancer Activity and Mechanism of Action of Bark Decoction in Gastric Adenocarcinoma Cancer Cell Line.

, a medicinal plant autochthonal to South America and South India, is widely used in the ethnomedicine of the indigenous peoples of these regions thanks to its alleged antimicrobial, anti-inflammatory, antioxidant and wound-healing properties. The majority of studies have mainly analyzed organic extracts of the Indian plant's flowers and leaves, with limited research on its bark decoction, traditionally used in Amazonian shamanic medicine. In this study, we investigated the anticancer effects of the bark decoction and its main fractions obtained through chromatographic separation, as well as the underlying molecular mechanisms in AGS gastric cancer cells.

Extended nnU-Net for Brain Metastasis Detection and Segmentation in Contrast-Enhanced Magnetic Resonance Imaging With a Large Multi-Institutional Data Set.

Purpose: The purpose of this study was to investigate an extended self-adapting nnU-Net framework for detecting and segmenting brain metastases (BM) on magnetic resonance imaging (MRI).

Methods And Materials: Six different nnU-Net systems with adaptive data sampling, adaptive Dice loss, or different patch/batch sizes were trained and tested for detecting and segmenting intraparenchymal BM with a size ≥2 mm on 3 Dimensional (3D) post-Gd T1-weighted MRI volumes using 2092 patients from 7 institutions (1712, 195, and 185 patients for training, validation, and testing, respectively). Gross tumor volumes of BM delineated by physicians for stereotactic radiosurgery were collected retrospectively and curated at each institute.

Co-ordinate-based positional embedding that captures resolution to enhance transformer's performance in medical image analysis.

Vision transformers (ViTs) have revolutionized computer vision by employing self-attention instead of convolutional neural networks and demonstrated success due to their ability to capture global dependencies and remove spatial biases of locality. In medical imaging, where input data may differ in size and resolution, existing architectures require resampling or resizing during pre-processing, leading to potential spatial resolution loss and information degradation. This study proposes a co-ordinate-based embedding that encodes the geometry of medical images, capturing physical co-ordinate and resolution information without the need for resampling or resizing.

Differential Reinforcement of Low Frequency Behavior as an Interdependent Group Contingency for Children Diagnosed with Autism Spectrum Disorder.

An ABAB reversal design was employed to evaluate the effect of differential reinforcement of low (DRL) frequency behavior as an interdependent group contingency on the frequency of vocal disruptions of five males, 6-14-years old, diagnosed with an autism spectrum disorder. The results showed lower frequencies of vocal disruptions during intervention conditions as compared to baseline conditions; the combination of DRL and interdependent group contingency was effective at reducing the target behavior from baseline levels. Implications of concurrent interventions for the applied setting are discussed.

Couroupita guianensis bark decoction: From Amazonian medicine to the UHPLC-HRMS chemical profile and its role in inflammation processes and re-epithelialization.

Ethnopharmacological Relevance: In the Amazon rainforest, the shamans of the Mayantuyacu site use the healing virtues of decoctions and teas from different parts of the Couroupita guianensis Aubl. (Lecythidaceae) trees as remedies in Ashaninka medicine. However, composition of the remedy and the underlying mechanism remain unclear.

Long-term outcome of thyroid abnormalities in patients with severe Covid-19.

Objective: We have previously observed thyroid dysfunction, i.e. atypical thyroiditis (painless thyrotoxicosis associated with non-thyroidal illness syndrome), in patients with severe acute respiratory syndrome coronavirus 2 disease (Covid-19).

A Cultural Generalization: An Effective Training for Staff Integrity on DTT in the Application of the PEAK in Brazil.

Unlabelled: Promoting the Emergence of Advanced Knowledge (PEAK) direct training is an evidence-based curriculum with extensive research. Treatment integrity is one of the critical components of an effective treatment. In developing countries where training opportunities are scarce, providing precise training poses a challenge.

Meta-Analysis on Associations of Timing of Maternal Smoking Cessation Before and During Pregnancy With Childhood Overweight and Obesity.

Introduction: There is a lack of comprehensive review on associations of maternal smoking cessation (versus nonsmokers) with childhood overweight and obesity.

Aims And Methods: We conducted a systematic review and meta-analysis of existing evidence in this field. Within PubMed, EMBASE, and CENTRAL databases, we identified and screened 1147 abstracts.

The medieval skincare routine according to the formulations of Madgistra Trotula and the Medical School of Salerno and its reflection on cosmetology of the third millennium.

Background: Officinal plants, minerals, animal derivatives, and miscellaneous have always been used to treat and improve appearance despite the different aesthetic canons of a specific historical and cultural context.

Objective: The aim of this work was to make a critical comparison between medieval and modern dermocosmetics analyzing the works of Trotula de Ruggiero, a female doctor of the 11th century teaching and working inside the illustrious "Medical School of Salerno," who devoted particular attention to the promotion of female care, beauty, and well-being.

Methods: We applied the historical-critical method analyzing the Latin text and the nglish translation of the standardized corpus of the main Trotula medieval manuscript De Ornatu Mulierum with a multidisciplinary scientific approach ranging from botany to pharmaceutical chemistry and technology, pharmacology and pathology.

Artificial Intelligence with Statistical Confidence Scores for Detection of Acute or Subacute Hemorrhage on Noncontrast CT Head Scans.

Purpose: To present a method that automatically detects, subtypes, and locates acute or subacute intracranial hemorrhage (ICH) on noncontrast CT (NCCT) head scans; generates detection confidence scores to identify high-confidence data subsets with higher accuracy; and improves radiology worklist prioritization. Such scores may enable clinicians to better use artificial intelligence (AI) tools.

Materials And Methods: This retrospective study included 46 057 studies from seven "internal" centers for development (training, architecture selection, hyperparameter tuning, and operating-point calibration; = 25 946) and evaluation ( = 2947) and three "external" centers for calibration ( = 400) and evaluation ( = 16764).

Dysmetabolism, Diabetes and Clinical Outcomes in Patients Cured of Chronic Hepatitis C: A Real-Life Cohort Study.

The aim of this study was to examine the impact of features of dysmetabolism on liver disease severity, evolution, and clinical outcomes in a real-life cohort of patients treated with direct acting antivirals for chronic hepatitis C virus (HCV) infection. To this end, we considered 7,007 patients treated between 2014 and 2018, 65.3% with advanced fibrosis, of whom 97.

Evaluating deep learning methods in detecting and segmenting different sizes of brain metastases on 3D post-contrast T1-weighted images.

We investigate the impact of various deep-learning-based methods for detecting and segmenting metastases with different lesion volume sizes on 3D brain MR images. A 2.5D U-Net and a 3D U-Net were selected.

Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort.

Objectives: To investigate machine learning classifiers and interpretable models using chest CT for detection of COVID-19 and differentiation from other pneumonias, interstitial lung disease (ILD) and normal CTs.

Methods: Our retrospective multi-institutional study obtained 2446 chest CTs from 16 institutions (including 1161 COVID-19 patients). Training/validation/testing cohorts included 1011/50/100 COVID-19, 388/16/33 ILD, 189/16/33 other pneumonias, and 559/17/34 normal (no pathologies) CTs.

Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT.

Purpose: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations.

Materials And Methods: In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning.

Association Between Added Sugars from Infant Formulas and Rapid Weight Gain in US Infants and Toddlers.

Background: Formulas often contain high amounts of added sugars, though little research has studied their connection to obesity.

Objectives: This study assessed the contribution of added sugars from formulas during complementary feeding on total added sugar intakes, and the association between these sugars and upward weight-for-age percentile (WFA%) crossing (i.e.

Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks.

With the rapid growth and increasing use of brain MRI, there is an interest in automated image classification to aid human interpretation and improve workflow. We aimed to train a deep convolutional neural network and assess its performance in identifying abnormal brain MRIs and critical intracranial findings including acute infarction, acute hemorrhage and mass effect. A total of 13,215 clinical brain MRI studies were categorized to training (74%), validation (9%), internal testing (8%) and external testing (8%) datasets.

Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings.

Objective: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management.

Materials And Methods: All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]).

High rates of sustained virological response despite premature discontinuation of directly acting antivirals in HCV-infected patients treated in a real-life setting.

In routine clinical practice, hepatitis C virus-infected patients can prematurely discontinue the prescribed regimen for several reasons. The aim of our study was to investigate sustained virological response (SVR12) rates in patients who prematurely discontinued directly acting antiviral (DAA) regimens and to assess the shortest effective duration of DAA able to lead to SVR12. We retrospectively collected the SVR rates of patients, registered in the NAVIGATORE-Lombardia Network database from January 2015, who discontinued DAAs before the predefined end of treatment.

Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT.

Purpose: To present a method that automatically segments and quantifies abnormal CT patterns commonly present in coronavirus disease 2019 (COVID-19), namely ground glass opacities and consolidations.

Materials And Methods: In this retrospective study, the proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions, based on a dataset of 9749 chest CT volumes. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities, based on deep learning and deep reinforcement learning.

Sleep Disturbances Rate among Medical and Allied Health Professions Students in Iran: Implications from a Systematic Review and Meta-Analysis of the Literature.

Medicine and healthcare professions are prestigious and valued careers and, at the same time, demanding, challenging, and arduous jobs. Medical and allied health professions students, experiencing a stressful academic and clinical workload, may suffer from sleep disturbances. In Iran, several studies have been conducted to explore the prevalence rate among medical and healthcare professions students.

Trans-cultural Adaptation and Validation of the "Teacher Job Satisfaction Scale" in Arabic Language Among Sports and Physical Education Teachers ("Teacher of Physical Education Job Satisfaction Inventory"-TPEJSI): Insights for Sports, Educational, and Occupational Psychology.

Job satisfaction is largely associated with organizational aspects, including improved working environments, worker's well-being and more effective performance. There are many definitions regarding job satisfaction in the existing scholarly literature: it can be expressed as a positive emotional state, a positive impact of job-related experiences on individuals, and employees' perceptions regarding their jobs. No reliable scales in Arabic language to assess job satisfaction in the sports and physical education field exist.

Cultural Validation of the "Academic Flow Scale" (Flow 4D 16) in Arabic Language: Insights for Occupational and Educational Psychology From an Exploratory Study.

As an optimal psychological state, flow represents those moments when everything comes together for the performer. Flow is often associated with high levels of performance and is a positive psychological experience. Our study aimed to validate the "Academic Flow Scale" (Flow 4D 16) in Arabic language across Tunisian population, and to test its factor structure, in terms of internal consistency/reliability, predictive validity, and sensitivity.

A reliability and content analysis of Italian language anorexia nervosa-related websites.

Objective: Internet represents a major source of information related to health issues, increasingly used by providers. Indeed, there are numerous websites concerning eating and weight disorders, such as anorexia nervosa. The aim of the current investigation was to systematically perform a reliability and content analysis of Italian language anorexia nervosa-related websites.

The efficacy of (Saffron) versus placebo and Fluoxetine in treating depression: a systematic review and meta-analysis.

Depression represents a serious public health concern, imposing a high burden, both in epidemiological and clinical terms. (Saffron) is a herbal remedy that has anti-cancer, anti-oxidant, anti-inflammatory and anti-platelet properties. However, the exact mechanisms of Saffron in treating depression are not yet clear.