Sepsis

Real-time holographic monitoring of insect cell morphology during baculovirus coinfection for adeno-associated virus vector production using machine learning-based classification models.

The baculovirus expression vector system (BEVS) is a scalable platform used to produce recombinant adeno-associated virus vectors (rAAV) in insect cells. A major challenge in this system is reducing the formation of empty rAAV capsids, which do not contain vector DNA and lack therapeutic value. The proportion of empty capsids is influenced by the balance between the two baculovirus constructs that...

Green in-situ photoreduction synthesis of SERS substrates based on violet phosphorus arrays/silver nanoparticles for ultrasensitive detection and accurate identification of UTI bacteria.

Surface-enhanced Raman spectroscopy (SERS) holds significant promise for identifying urinary tract infection (UTI) pathogens due to its exceptional sensitivity and fingerprinting ability. However, traditional SERS substrates suffer from poor stability, high synthesis cost, and reliance on toxic reagents, which limit their applications in detecting low bacterial concentrations and multiple pathogen...

Cross-modal attention deep learning reveals how transformation products inherit life-cycle risks from parent antibiotics: Insights for environmental, ecological, health, and AMR risks.

The environmental, ecological, antimicrobial resistance (AMR), and human health risks of antibiotic transformation products (TPs) in aquatic environme...

Predicting the active sites of quinolone antibiotics interacting with organisms by deep learning and molecular docking.

Quinolones (QNs) antibiotics have become one of the most commonly used antibacterial drugs for human and animals in the world. In this study, we focus...

Sepsis Diagnostics via Biosensors: Engineering Platforms, Artificial Intelligence Integration, and Clinical Translation.

Sepsis remains one of the most diagnostically challenging syndromes due to its clinical heterogeneity, overlapping host-pathogen responses, and lack o...

De novo design of GPCR exoframe modulators.

G-protein-coupled receptors (GPCRs) are important therapeutic targets and have been targeted mainly through their orthosteric site, where the endogeno...

An overview of Parkinson's disease: a narrative review.

Parkinson's disease (PD) is a degenerative neurological condition defined by a wide range of motor and non-motor symptoms that can affect function to ...

The Anti-proliferative and Pro-apoptotic Effects of Iranian Viper Venom (Vipera raddei kurdistanica) on Human Gastric Cancer Cells (C615) are Mediated by PI3K/AKT/mTOR Signalling Pathway Inhibition.

OBJECTIVE: Gastric cancer (GC) is one of the most common malignancies worldwide. Since the PI3K/AKT/mTOR signalling pathway plays a crucial role in tu...

Risk prediction for chronic atrophic gastritis using a random forest model: A multicenter study.

BACKGROUND: Chronic atrophic gastritis (CAG) is a significant precancerous condition of gastric cancer (GC). CAG often lacks typical symptoms in its e...

Artificial intelligence-based prediction of postoperative sepsis in patients undergoing suction mini-percutaneous nephrolithotomy in real world practice: a multicenter analysis from the global Suction Lechnology Utility in Mini-PCNL Study (STUMPS) registry.

Artificial intelligence in combating challenges in antimicrobial resistance: a narrative review.

Antimicrobial resistance (AMR) is a major global health challenge that threatens the effective prevention and treatment of infections. It arises from ...

Marine-Derived bioactive compounds from Aquaculture: Receptor-Mediated neuroprotection in neurodegenerative disorders.

Neurodegenerative diseases, like Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), amyotrophic lateral sclerosis (ALS), a...

Interpretable multimodal machine learning model for predicting health risks of patients with heart failure.

Heart failure (HF) is one of the major causes of morbidity and mortality globally, necessitating accurate tools for health outcome prediction and risk...

Personalized sepsis mortality prediction: An interpretable machine learning nomogram.

PURPOSE: Sepsis remains a major cause of mortality in ICU patients, requiring accurate prognostic tools for optimal management. This study aimed to de...

Development and external validation of a machine learning model for predicting in-hospital mortality in acute liver failure.

BACKGROUND: Acute liver failure (ALF) is a rapidly progressive and life-threatening condition that requires accurate risk stratification. Existing pro...

A Machine Learning-Based Prognostic Model for Sepsis-Associated Liver Injury Using Routine Indicators.

OBJECTIVE: Sepsis-associated liver injury (SALI) occurs in approximately 40% of sepsis cases and is linked to high mortality, a challenge that may ste...

Longitudinal modeling of Post-COVID-19 condition over three years: A machine learning approach using clinical, neuropsychological, and fluid markers.

Post-COVID-19 condition (PCC) manifests with prolonged, heterogeneous symptoms challenging both, diagnosis and therapeutic management. This three-year...

Klebsiella oxytoca: an opportunistic bacterial pathogen that poses challenges for treatment and vaccine development.

Klebsiella oxytoca is a clinically significant opportunistic bacterium that contributes to global morbidity and mortality. Despite its clinical releva...

Interpretable four-factor day-1 nomogram for predicting sepsis-associated encephalopathy in septic ICU patients with AKI: Development and internal validation in MIMIC-IV.

Sepsis-associated encephalopathy (SAE) is common in the intensive care unit (ICU) and portends worse short- and long-term outcomes. To enable real-tim...

Identification of key biomarkers for initial corticosteroid resistance in newly diagnosed primary immune thrombocytopenia.

Corticosteroid resistance is a major obstacle to the management of patients with primary immune thrombocytopenia (ITP), as the factors contributing to...