Antibiotic prescription in pediatric patients with influenza in outpatient and emergency departments: A cross-sectional study.

Background: Influenza is a common viral respiratory infection, and inappropriate antibiotic use may lead to increased drug resistance and unnecessary waste of healthcare resources. However, real-world antibiotic prescribing in pediatric influenza patients remains largely unknown in China.

Methods: We performed a cross-sectional study of outpatient and emergency department prescriptions in a tertiary children's hospital for pediatric patients diagnosed with influenza between January 1, 2021, and July 31, 2023.

Paradigms of intraoperative neuromonitoring in paediatric thyroid surgery.

The larynx of children and adolescents is still in the developmental phase and the anatomical structure is still very small and sensitive. The higher malignancy and faster progression of some paediatric thyroid cancers make surgery more difficult. Intraoperative neuromonitoring (IONM) is frequently used in thyroid surgery as an effective means of securing the recurrent laryngeal nerve (RLN).

Machine Learning Approach to Identifying Wrong-Site Surgeries Using Centers for Medicare and Medicaid Services Dataset: Development and Validation Study.

Background: Wrong-site surgery (WSS) is a critical but preventable medical error, often resulting in severe patient harm and substantial financial costs. While protocols exist to reduce wrong-site surgery, underreporting and inconsistent documentation continue to contribute to its persistence. Machine learning (ML) models, which have shown success in detecting medication errors, may offer a solution by identifying unusual procedure-diagnosis combinations.

Host plants selection of Centranthera grandiflora Benth. and nontargeted metabolomics analysis of its parasitic and non-parasitic samples.

According to the previous investigation and research of our group, it was found that Centranthera grandiflora Benth. (C. grandiflora for short) might be a root hemiparasitic plant.

Effect of Drying Temperature on Sensory Quality, Flavor Components, and Bioactivity of Lichuan Black Tea Processed by Echa No. 10.

Lichuan black tea (LBT) is a well-known congou black tea in China, but there is relatively little research on its processing technology. Echa No. 10 is the main tea tree variety for producing LBT.

Integrated Transcriptomic and Metabolomic Analyses Reveal Changes in Aroma- and Taste-Related Substances During the Withering Process of Black Tea.

Withering is one of the major processing steps critical for the quality of black tea. In this study, we investigated the mechanisms underlying the physicochemical changes in metabolites and gene expression during the withering process of black tea using metabolomic and transcriptomic approaches, respectively. Based on gas chromatography/mass spectrometry non-targeted metabolomic approaches (GC-MS) and ultra-high performance liquid chromatograph-tandem mass spectrometry (UHPLC-MS/MS), a total of 76 volatile compounds and 160 non-volatile compounds were identified from tea leaves, respectively.

Constructing Nitrogen-Rich Fused [5,6,5]-Tricyclic Frameworks Through Rearrangement: Heat-Resistant Zwitterionic Salt Energetic Materials.

This study synthesized nitrogen-rich [5,6,5] fused compounds through a novel rearrangement reaction. Owing to its unique zwitterionic salt structure, rearrangement product - exhibits high thermal stability ( > 250 °C), low sensitivity (IS > 40 J, FS > 360 N), and acceptable detonation velocities and pressures (ν = 8520 m s and = 32.53 GPa, respectively), which are better than those of TNT and TATB.

Machine Learning Models for Predicting Significant Liver Fibrosis in Patients with Severe Obesity and Nonalcoholic Fatty Liver Disease.

Purpose: Although noninvasive tests can be used to predict liver fibrosis, their accuracy is limited for patients with severe obesity and nonalcoholic fatty liver disease (NAFLD). We developed machine learning (ML) models to predict significant liver fibrosis in patients with severe obesity through noninvasive tests.

Materials And Methods: This prospective study included 194 patients with severe obesity who underwent wedge liver biopsy and metabolic bariatric surgery at Taipei Medical University Hospital between September 2016 and December 2020.

Enhancing the detonation performance of azobistriazole energetic derivatives inducing -oxide groups.

The -oxide strategy plays a crucial role in regulating the performance and safety of energetic materials. This study mainly addresses the question of how the -oxide group affects the properties of azobistriazole and its derivatives. Our findings indicate that the -oxide group can increase the density of the system, and its effect on the enthalpy of formation depends on the specific situation.

Biological characterization of emerging fungal pathogen Colletotrichum associated with mango (Mangifera indica L.) post-harvest anthracnose from Vietnam.

Background: Post-harvest anthracnose (PHA) of mango is a devastating disease, which results in huge loss to mango producers and importers. Various species of PHA, diverse pathogenicity, and different resistance towards fungicides make it essential to evaluate the pathogen taxonomic status and biological characterization.

Methods And Results: Two strains DM-1 and DM-2 isolated from the fruit of DaQing mango from Vietnam were identified as Colletotrichum fructicola and C.

NR1H4 disease: rapidly progressing neonatal intrahepatic cholestasis and early death.

Background: Clinical studies on progressive familial intrahepatic cholestasis (PFIC) type 5 caused by mutations in NR1H4 are limited.

Methods: New patients with biallelic NR1H4 variants from our center and all patients from literature were retrospectively analyzed.

Results: Three new patients were identified to be carrying five new variants.

Can large language models provide secondary reliable opinion on treatment options for dermatological diseases?

Objective: To investigate the consistency and reliability of medication recommendations provided by ChatGPT for common dermatological conditions, highlighting the potential for ChatGPT to offer second opinions in patient treatment while also delineating possible limitations.

Materials And Methods: In this mixed-methods study, we used survey questions in April 2023 for drug recommendations generated by ChatGPT with data from secondary databases, that is, Taiwan's National Health Insurance Research Database and an US medical center database, and validated by dermatologists. The methodology included preprocessing queries, executing them multiple times, and evaluating ChatGPT responses against the databases and dermatologists.

Regulation of lipid droplets and cholesterol metabolism in adrenal cortical cells.

The adrenal gland is composed of two distinctly different endocrine moieties. The interior medulla consists of neuroendocrine chromaffin cells that secrete catecholamines like adrenaline and noradrenaline, while the exterior cortex consists of steroidogenic cortical cells that produce steroid hormones, such as mineralocorticoids (aldosterone), glucocorticoids (cortisone and cortisol) and androgens. Synthesis of steroid hormones in cortical cells requires substantial amounts of cholesterol, which is the common precursor for steroidogenesis.

Facial Mimicry and Doctor-Patient Satisfaction: The Feasibility of Artificial Empathy in a Clinical Video Data.

Good nonverbal communication between doctor and patient is essential for achieving a successful and therapeutic doctor-patient relationship. Increasing evidence has shown that nonverbal communication mimicry, particularly facial mimicry, where one mirrors another's facial expressions, is linked to empathy and emotion recognition. Empathy is also the key driver of patient satisfaction.

Machine-Learning Based Risk Assessment for Cancer Therapy-Related Cardiac Adverse Events Among Breast Cancer Patients.

The study aims to develop machine-learning models to predict cardiac adverse events in female breast cancer patients who receive adjuvant therapy. We selected breast cancer patients from a retrospective dataset of the Taipei Medical University Clinical Research Database and Taiwan Cancer Registry between January 2004 and December 2020. Patients were monitored at the date of prescribed chemo- and/or -target therapies until cardiac adverse events occurred during a year.

Artificial Intelligence Approach for Severe Dengue Early Warning System.

Dengue fever is a viral infectious disease transmitted through mosquito bites, and has symptoms ranging from mild flu-like symptoms to deadly complications. Dengue fever is one of the global burden diseases which annually have 50-100 million cases with 500,000 cases of severe dengue fever, of which 22,000 deaths occur mostly in children. Despite the discovery of vaccines, vector control is still the main approach for prevention efforts.

Adjustable Cuffless Smartphone Attachment (ACSA+) for Estimation of Blood Pressure Trends: A Pilot Study.

Among the elderly, hypertension remains one of the prevalent health conditions, which requires monitoring and intervention strategies. Nevertheless, regular reporting of blood pressure (BP) from these individuals still poses multiple challenges. However, most people own cell phone and are engaged in phone conversations daily.

Molecular characterization of a novel mycovirus from binucleate Rhizoctonia AG-A strain A46.

The complete genome sequence of a positive-sense single-stranded RNA (+ ssRNA) virus, Rhizoctonia beny-like virus 1 (RBLV1), isolated from binucleate Rhizoctonia AG-A strain A46, was determined. The RBLV1 genome is 10,280 nt in length and contains a short stretch of adenines at the 3' terminus. It contains a single open reading frame (ORF) encoding a 376.

Call for the responsible artificial intelligence in the healthcare.

The integration of artificial intelligence (AI) into healthcare is progressively becoming pivotal, especially with its potential to enhance patient care and operational workflows. This paper navigates through the complexities and potentials of AI in healthcare, emphasising the necessity of explainability, trustworthiness, usability, transparency and fairness in developing and implementing AI models. It underscores the 'black box' challenge, highlighting the gap between algorithmic outputs and human interpretability, and articulates the pivotal role of explainable AI in enhancing the transparency and accountability of AI applications in healthcare.