The Characteristics and Prognosis of Acute Hearing Loss in Military Personnel.

The military population is one of the high-risk groups for acute hearing loss. This retrospective study aims to examine acute acoustic trauma (AAT) and idiopathic sudden sensorineural hearing loss (ISSNHL) among military personnel. A total of 111 cases of acute hearing loss from a tertiary hospital between 2009 and 2021 were divided into AAT (53 cases) and ISSNHL (58 cases) groups.

Notoginsenoside R1 Attenuates Cisplatin-Induced Ototoxicity by Inducing Heme Oxygenase-1 Expression and Suppressing Oxidative Stress.

Cisplatin-induced ototoxicity occurs in approximately half of patients treated with cisplatin, and pediatric patients are more likely to be affected than adults. The oxidative stress elicited by cisplatin is a key contributor to the pathogenesis of ototoxicity. Notoginsenoside R1 (NGR1), the main bioactive compound of saponins, has antioxidant and antiapoptotic effects.

Large language model application in emergency medicine and critical care.

In the rapidly evolving healthcare landscape, artificial intelligence (AI), particularly the large language models (LLMs), like OpenAI's Chat Generative Pretrained Transformer (ChatGPT), has shown transformative potential in emergency medicine and critical care. This review article highlights the advancement and applications of ChatGPT, from diagnostic assistance to clinical documentation and patient communication, demonstrating its ability to perform comparably to human professionals in medical examinations. ChatGPT could assist clinical decision-making and medication selection in critical care, showcasing its potential to optimize patient care management.

Optimal methods of vitamin D supplementation to prevent acute respiratory infections: a systematic review, dose-response and pairwise meta-analysis of randomized controlled trials.

Background: Vitamin D supplementation may prevent acute respiratory infections (ARIs). This study aimed to identify the optimal methods of vitamin D supplementation.

Methods: PubMed, Embase, Cochrane Central Register of Controlled Trials, Web of Science, and the ClinicalTrials.

A Deep-Learning-Enabled Electrocardiogram and Chest X-Ray for Detecting Pulmonary Arterial Hypertension.

The diagnosis and treatment of pulmonary hypertension have changed dramatically through the re-defined diagnostic criteria and advanced drug development in the past decade. The application of Artificial Intelligence for the detection of elevated pulmonary arterial pressure (ePAP) was reported recently. Artificial Intelligence (AI) has demonstrated the capability to identify ePAP and its association with hospitalization due to heart failure when analyzing chest X-rays (CXR).

Artificial Intelligence-Enabled Electrocardiography Predicts Future Pacemaker Implantation and Adverse Cardiovascular Events.

Medical advances prolonging life have led to more permanent pacemaker implants. When pacemaker implantation (PMI) is commonly caused by sick sinus syndrome or conduction disorders, predicting PMI is challenging, as patients often experience related symptoms. This study was designed to create a deep learning model (DLM) for predicting future PMI from ECG data and assess its ability to predict future cardiovascular events.

End-tidal Carbon Dioxide Trajectory-based Prognostication of Out-of-hospital Cardiac Arrest.

Background: During cardiopulmonary resuscitation (CPR), end-tidal carbon dioxide (EtCO) is primarily determined by pulmonary blood flow, thereby reflecting the blood flow generated by CPR. We aimed to develop an EtCO trajectory-based prediction model for prognostication at specific time points during CPR in patients with out-of-hospital cardiac arrest (OHCA).

Methods: We screened patients receiving CPR between 2015-2021 from a prospectively collected database of a tertiary-care medical center.

Ultrasound-Mediated Lysozyme Microbubbles Targeting NOX4 Knockdown Alleviate Cisplatin-Exposed Cochlear Hair Cell Ototoxicity.

The nicotinamide adenine dinucleotide phosphate (NADPH) oxidase 4 (NOX4) protein plays an essential role in the cisplatin (CDDP)-induced generation of reactive oxygen species (ROS). In this study, we evaluated the suitability of ultrasound-mediated lysozyme microbubble (USMB) cavitation to enhance NOX4 siRNA transfection in vitro and ex vivo. Lysozyme-shelled microbubbles (LyzMBs) were constructed and designed for siNOX4 loading as siNOX4/LyzMBs.

Deep Learning-Based Localization and Detection of Malpositioned Nasogastric Tubes on Portable Supine Chest X-Rays in Intensive Care and Emergency Medicine: A Multi-center Retrospective Study.

Malposition of a nasogastric tube (NGT) can lead to severe complications. We aimed to develop a computer-aided detection (CAD) system to localize NGTs and detect NGT malposition on portable chest X-rays (CXRs). A total of 7378 portable CXRs were retrospectively retrieved from two hospitals between 2015 and 2020.

The Risk of Aircraft-Acquired SARS-CoV-2 Transmission during Commercial Flights: A Systematic Review.

The aircraft-acquired transmission of SARS-CoV-2 poses a public health risk. Following PRISMA guidelines, we conducted a systematic review and analysis of articles, published prior to vaccines being available, from 24 January 2020 to 20 April 2021 to identify factors important for transmission. Articles were included if they mentioned index cases and identifiable flight duration, and excluded if they discussed non-commercial aircraft, airflow or transmission models, cases without flight data, or that were unable to determine in-flight transmission.

C-Phycocyanin Attenuates Noise-Induced Cochlear Synaptopathy via the Inhibition of Oxidative Stress and Intercellular Adhesion Molecule-1 in the Cochlea.

The synapses between inner hair cells (IHCs) and spiral ganglion neurons (SGNs) are the most vulnerable structures in the noise-exposed cochlea. Cochlear synaptopathy results from the disruption of these synapses following noise exposure and is considered the main cause of poor speech understanding in noisy environments, even when audiogram results are normal. Cochlear synaptopathy leads to the degeneration of SGNs if damaged IHC-SGN synapses are not promptly recovered.

Prognostic Impact of Heart Rhythm Shockability Trajectory in Out-of-Hospital Cardiac Arrest: A Multicenter Retrospective Study.

Background: This study aimed to investigate the association between the temporal transitions in heart rhythms during cardiopulmonary resuscitation (CPR) and outcomes after out-of-hospital cardiac arrest.

Methods: This was an analysis of the prospectively collected databases in 3 academic hospitals in northern and central Taiwan. Adult patients with out-of-hospital cardiac arrest transported by emergency medical service between 2015 and 2022 were included.

AI-enabled electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial.

The early identification of vulnerable patients has the potential to improve outcomes but poses a substantial challenge in clinical practice. This study evaluated the ability of an artificial intelligence (AI)-enabled electrocardiogram (ECG) to identify hospitalized patients with a high risk of mortality in a multisite randomized controlled trial involving 39 physicians and 15,965 patients. The AI-ECG alert intervention included an AI report and warning messages delivered to the physicians, flagging patients predicted to be at high risk of mortality.

Outcome prediction of cardiac arrest with automatically computed gray-white matter ratio on computed tomography images.

Background: This study aimed to develop an automated method to measure the gray-white matter ratio (GWR) from brain computed tomography (CT) scans of patients with out-of-hospital cardiac arrest (OHCA) and assess its significance in predicting early-stage neurological outcomes.

Methods: Patients with OHCA who underwent brain CT imaging within 12 h of return of spontaneous circulation were enrolled in this retrospective study. The primary outcome endpoint measure was a favorable neurological outcome, defined as cerebral performance category 1 or 2 at hospital discharge.

Prognostic implication of heart failure stage and left ventricular ejection fraction for patients with in-hospital cardiac arrest: a 16-year retrospective cohort study.

Background: The 2022 AHA/ACC/HFSA guidelines for the management of heart failure (HF) makes therapeutic recommendations based on HF status. We investigated whether the prognosis of in-hospital cardiac arrest (IHCA) could be stratified by HF stage and left ventricular ejection fraction (LVEF).

Methods: This single-center retrospective study analyzed the data of patients who experienced IHCA between 2005 and 2020.

Host-Pathogen Interactions in Urinary Tract Infections: Investigating Genetic Risk Factors in the Taiwanese Population.

Background: () urinary tract infections pose a significant challenge in Taiwan. The significance of this issue arises because of the growing concerns about the antibiotic resistance of . Therefore, this study aimed to uncover potential genomic risk factors in Taiwanese patients with urinary tract infections through genome-wide association studies (GWAS).

Nanoscale sorting of extracellular vesicles optically-induced dielectrophoresis on an integrated microfluidic system.

We reported a microfluidic system for sorting of extracellular vesicles (EVs), which can house DNAs, RNAs, lipids, proteins, and metabolites that are important in intercellular communication. Their presence within bodily fluids has demonstrated potential in both clinical diagnostic and therapeutic applications. Furthermore, EVs exhibit distinct subtypes categorized by their sizes, each endowed with unique biophysical properties.

Application of Machine Learning to Ultrasonography in Identifying Anatomical Landmarks for Cricothyroidotomy Among Female Adults: A Multi-center Prospective Observational Study.

We aimed to develop machine learning (ML)-based algorithms to assist physicians in ultrasound-guided localization of cricoid cartilage (CC) and thyroid cartilage (TC) in cricothyroidotomy. Adult female volunteers were prospectively recruited from two hospitals between September and December, 2020. Ultrasonographic images were collected via a modified longitudinal technique.

Artificial Intelligence-enabled Chest X-ray Classifies Osteoporosis and Identifies Mortality Risk.

A deep learning model was developed to identify osteoporosis from chest X-ray (CXR) features with high accuracy in internal and external validation. It has significant prognostic implications, identifying individuals at higher risk of all-cause mortality. This Artificial Intelligence (AI)-enabled CXR strategy may function as an early detection screening tool for osteoporosis.