Access to -Diindolylcyclopropanes via Double Indolylation of -Difluorocyclopropanes.

gem-Difluorocyclopropanes (-DFCPs) have gained significant attention as versatile fluorinated synthons in organic synthesis due to their unique structural and electronic properties. Recent advancements have demonstrated the utility of -DFCPs in transition-metal-catalyzed cross-coupling reactions with indoles, enabling the synthesis of monofluoroallylic indole derivatives. In this study, a transition-metal-free double indolylation of -DFCPs was developed, preserving the cyclopropane core and facilitating the direct incorporation of two indole units.

Development and validation of risk prediction models for acute kidney disease in gout patients: a retrospective study using machine learning.

Background: Limited research has been conducted on the prevalence of acute kidney injury (AKI) and acute kidney disease (AKD) in gout patients, as well as the impact of these renal complications on patient outcomes. This study aims to develop machine learning models to predict AKI and AKD in gout patients, with the goal of deploying web-based applications to support clinicians in making informed, real-time decisions for high-risk patients.

Methods: A total of 1260 gout patients admitted to a tertiary hospital between January 2020 and January 2024 were included.

Urinary phthalate metabolites associate with blood levels of estrogen quinone-derived hemoglobin adducts in Taiwanese pregnant women.

In this study, we aimed to analyze the background levels of estrogen quinone-derived adducts in hemoglobin (Hb) obtained from pregnant women in Taiwan (n = 394) and to investigate the associations of these adducts with levels of urine metabolites of phthalates. Both 17β-estradiol-2,3-quinone (E-2,3-Q) and 17β-estradiol-3,4-quinone (E-3,4-Q) are reactive metabolites of estrogen that are thought to be responsible for the estrogen-induced genotoxicity and carcinogenicity. Results confirmed that levels of estrogen quinone-derived adducts in pregnant women, including E-3,4-Q-2-S-Hb and E-2,3-Q-4-S-Hb, were detected at concentrations comparable with those of non-pregnant women with mean levels at 165 (range 108-428) and 97.

Establishing identifiable characteristic fingerprints of mulberry leaves: Integrating chemical composition and bioactivity through machine learning.

Ethnopharmacological Relevance: Mulberry leaves (Morus alba L.) are used in traditional Chinese medicine to clear the lungs and dispel wind-heat. Despite their common use, chemical reference substance rely solely on rutin, which may not reflect their full pharmacological potential.

Integrated network toxicology and population-based analysis uncovers organophosphate flame retardant exposure as a risk factor for hepatic steatosis and fibrosis: mechanistic and clinical insights.

Background: Laboratory evidence has recently shown that exposure to organophosphate flame retardants (OPFRs) can cause adverse liver outcomes, which lacks further validation.

Objective: The present study investigated the correlation and toxicological mechanism between OPFRs exposure and hepatic steatosis or fibrosis.

Method: To explore the association of OPFRs exposure with hepatic steatosis and liver fibrosis, we conducted the population analysis using the data of urinary OPFRs monitoring and liver vibration-controlled transient elastography (VCTE) examinations from the National Health and Nutrition Examination Survey (NHANES) during 2017-2018.

Erratum: miR-17-3p Contributes to Exercise-Induced Cardiac Growth and Protects against Myocardial Ischemia-Reperfusion Injury: Erratum.

[This corrects the article DOI: 10.7150/thno.15162.

Interpretable Independent Recurrent Networks for Forecasting Stroke in Atrial Fibrillation.

Background: Atrial fibrillation (AF) is a major risk factor for transient ischemic attack (TIA)/ischemic stroke (IS).

Objectives: Given the dynamic nature of IS risk, this study aimed to predict IS risk in AF patients using a high-dimensional time-series model.

Methods: We conducted a cohort study at the National Taiwan University Hospital from 2014 to 2019, including 7,710 AF patients, with external validation in 6,822 patients from the National Taiwan University Hospital Yunlin Branch.

Palmitoylated COX-2 reprogrammed mitochondrial metabolism in pyroptotic inflammatory injury in patients with post-acute COVID-19 syndrome.

Introduction: The complex interplay between protein palmitoylation, mitochondrial dynamics, and inflammatory responses plays a pivotal role in respiratory diseases. One significant feature of post-acute coronavirus disease 2019 (COVID-19) syndrome (PACS) is the occurrence of a storm of inflammatory cytokines related to the NOD-like receptor protein 3 (NLRP3). However, the specific mechanisms via which palmitoylation affects mitochondrial function and its impact on the NLRP3 inflammasome under pathological respiratory conditions remain to be elucidated.

Construction and validation of prognostic models for acute kidney disease and mortality in patients at risk of malnutrition: an interpretable machine learning approach.

Background: Acute kidney injury (AKI) is a prevalent complication in patients at risk of malnutrition, elevating the risks of acute kidney disease (AKD) and mortality. AKD reflects the adverse events developing after AKI. This study aimed to develop and validate machine learning (ML) models for predicting the occurrence of AKD, AKI and mortality in patients at risk of malnutrition.

A comparative analysis between ChatGPT versus NASS clinical guidelines for adult isthmic spondylolisthesis.

Background: Isthmic spondylolisthesis is a prevalent condition often diagnosed in adults, especially those with low back pain. The main objective of this study was to evaluate the clinical viability of ChatGPT 3.5 and 4.

Risk prediction for acute kidney disease and adverse outcomes in patients with chronic obstructive pulmonary disease: an interpretable machine learning approach.

Background: Little is known about acute kidney injury (AKI) and acute kidney disease (AKD) in patients with chronic obstructive pulmonary disease (COPD) and COPD mortality based on the acute/subacute renal injury. This study develops machine learning models to predict AKI, AKD, and mortality in COPD patients, utilizing web applications for clinical decisions.

Methods: We included 2,829 inpatients from January 2016 to December 2018.

A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging.

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages.

Deep STI: Deep Stochastic Time-series Imputation on Electronic Health Records.

Electronic Health Records (EHRs) are a cornerstone of modern healthcare analytics, offering rich datasets for various disease analyses through advanced deep learning algorithms. However, the pervasive issue of missing values in EHRs significantly hampers the development and performance of these models. Addressing this challenge is crucial for enhancing clinical decision-making and patient care.

Multi-task Learning Graph Neural Networks for Cancer Prognosis Prediction with Genomic Data.

Providing robust prognosis predictions for cancers with limited data samples remains a challenge for precision oncology. In this study, we propose a novel approach that combines multi-task learning (MTL) and graph neural networks (GNNs) to address this issue. By representing gene-gene interactions as a graph network, our approach leverages multi-task learning to effectively capture the relationships of genes relevant to the oncogenesis and progression of breast, lung, and colon cancer.

Fibroblast growth factor receptor 3 mutation promotes HSPB6-mediated cuproptosis in hypochondroplasia by impairing chondrocyte autophagy.

Background: Hypochondroplasia (HCH) is a prevalent form of dwarfism linked to mutations in the fibroblast growth factor receptor 3 () gene, causing missense alterations. We previous report was the first to identify (G382D) gain-of-function variants with a positive family history as a novel cause of HCH. However, the precise contribution of to the pathogenesis of HCH remains elusive.

FGF1 alleviates polystyrene nanoplastics-induced neuroinflammation through the suppression of lipophagy.

Global contamination with nanoplastics (NPs) has raised public concern regarding their adverse effects on human health. However, little is known about the toxic effects of NPs on the nervous system. This study explored the neurotoxicity of polystyrene nanoplastics (PS-NPs) under the exposure model in vitro and in vivo.

High Steatosis-Associated Fibrosis Estimator scores predict hepatocellular carcinoma in viral and non-viral hepatitis and metabolic dysfunction-associated steatotic liver disease.

Background/aims: There are no hepatocellular carcinoma (HCC) surveillance recommendations for non-viral chronic liver diseases (CLD), such as metabolic dysfunction-associated steatotic liver disease (MASLD). We explored the Steatosis-Associated Fibrosis Estimator (SAFE) score to predict HCC in MASLD and other CLD etiologies.

Methods: Patients with various CLDs were included from medical centers in Taiwan.

Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach.

Background: Acute kidney injury (AKI) and acute kidney disease (AKD) are prevalent among pediatric patients, both linked to increased mortality and extended hospital stays. Early detection of kidney injury is crucial for improving outcomes. This study presents a machine learning-based risk prediction model for AKI and AKD in pediatric patients, enabling personalized risk predictions.

Signatures of lower respiratory tract microbiome in children with severe community-acquired pneumonia using shotgun metagenomic sequencing.

Background: Severe community-acquired pneumonia was associated with high morbidity and mortality in children. However, species-level microbiome of lower airway was sparse, and we used shotgun metagenomic next-generation sequencing to explore microbial signatures.

Methods: We conducted a prospective cohort study to recruit children under 18 who required admission to an intensive care unit for community-acquired pneumonia between December 2019 and February 2022.

Analysis of the Abasic Sites in Breast Cancer Patients With 5 Year Postoperative Treatment Without Recurrence in Taiwan.

Purpose: This prospective study aimed to investigate estrogen-induced carcinogenesis by assessing the background levels of abasic sites (apurinic/apyrimidinic sites, AP sites) in Taiwanese breast cancer patients following 5 years of postoperative treatment without recurrence (5-year survivors) (n = 70). The study also sought to compare the extent of these DNA lesions with those found in healthy controls and in breast cancer patients prior to treatment.

Methods: Abasic sites were measured using an aldehyde reactive probe and quantified as the total number of abasic sites per total nucleotides.

Machine learning-based risk prediction of acute kidney disease and hospital mortality in older patients.

Introduction: Acute kidney injury (AKI) is a prevalent complication in older people, elevating the risks of acute kidney disease (AKD) and mortality. AKD reflects the adverse events developing after AKI. We aimed to develop and validate machine learning models for predicting the occurrence of AKD, AKI and mortality in older patients.

Formononetin Alleviates Ischemic Acute Kidney Injury by Regulating Macrophage Polarization through KLF6/STAT3 Pathway.

Recent research has indicated that formononetin demonstrates a potent anti-inflammatory effect in various diseases. However, its impact on sterile inflammation kidney injury, specifically acute kidney injury (AKI), remains unclear. In this study, we utilized an ischemia/reperfusion-induced AKI (IRI-AKI) mouse model and bone marrow-derived macrophages (BMDMs) to investigate the effects of formononetin on sterile inflammation of AKI and to explore the underlying mechanism.

Multimodal Attention Network for Dementia Prediction.

The early identification of an individual's dementia risk is crucial for disease prevention and the design of insurance products in an aging society. This study aims to accurately predict the future incidence risk of dementia in individuals by leveraging the advantages of neural networks. This is, however, complicated by the high dimensionality and sparsity of the International Classification of Diseases (ICD) codes when utilizing data from Taiwan's National Health Insurance, which includes individual profiles and medical records.

New insight into the effects of p-benzoquinone on photocatalytic reduction of Cr(VI) over Fe-doped g-CN.

It is of great significance to establish an effective method for removing Cr(VI) from wastewater. Herein, Fe-doped g-CN (namely Fe-g-CN-2) was synthesized and then employed as photocatalyst to conduct the test of Cr(VI) reduction. Notably, the embedding of Fe ion in g-CN can offer the Fe/Fe redox couples, so reducing the interfacial resistance of charge transfer and suppressing the recombination of photogenerated electrons and holes.