Enhancing Patient Selection in Sepsis Clinical Trials Design Through an AI Enrichment Strategy: Algorithm Development and Validation.

Background: Sepsis is a heterogeneous syndrome, and enrollment of more homogeneous patients is essential to improve the efficiency of clinical trials. Artificial intelligence (AI) has facilitated the identification of homogeneous subgroups, but how to estimate the uncertainty of the model outputs when applying AI to clinical decision-making remains unknown.

Objective: We aimed to design an AI-based model for purposeful patient enrollment, ensuring that a patient with sepsis recruited into a trial would still be persistently ill by the time the proposed therapy could impact patient outcome.

Continuous prediction and clinical alarm management of late-onset sepsis in preterm infants using vital signs from a patient monitor.

Background And Objective: Continuous prediction of late-onset sepsis (LOS) could be helpful for improving clinical outcomes in neonatal intensive care units (NICU). This study aimed to develop an artificial intelligence (AI) model for assisting the bedside clinicians in successfully identifying infants at risk for LOS using non-invasive vital signs monitoring.

Methods: In a retrospective study from the NICU of the Máxima Medical Center in Veldhoven, the Netherlands, a total of 492 preterm infants less than 32 weeks gestation were included between July 2016 and December 2018.

MIST: A microbial identification and source tracking system for next-generation sequencing data.

The Professional Committee of Microbiology of the National Pharmacopoeia Commission organized the drafting of the Technical Guidelines for Microbial Whole Genome Sequencing (WGS), aiming to standardize the method process and technical indicators of microbial WGS and ensure the accuracy of sequencing and identification. On the basis of the Guidelines, we developed an integrated microbial identification and source tracking (MIST) system, which could meet the needs of microbial identification and contamination investigation in food and drug quality control. MIST integrates three analysis pipelines: 16S/18S/internal transcribed spacer amplicon-based microbial identification, WGS-based microbial identification, and single-nucleotide polymorphism-based microbial source tracking.

A pretrain-finetune approach for improving model generalizability in outcome prediction of acute respiratory distress syndrome patients.

Background: Early prediction of acute respiratory distress syndrome (ARDS) of critically ill patients in intensive care units (ICUs) has been intensively studied in the past years. Yet a prediction model trained on data from one hospital might not be well generalized to other hospitals. It is therefore essential to develop an accurate and generalizable ARDS prediction model adaptive to different hospital or medical centers.

Development and Validation of an Interpretable Conformal Predictor to Predict Sepsis Mortality Risk: Retrospective Cohort Study.

Background: Early and reliable identification of patients with sepsis who are at high risk of mortality is important to improve clinical outcomes. However, 3 major barriers to artificial intelligence (AI) models, including the lack of interpretability, the difficulty in generalizability, and the risk of automation bias, hinder the widespread adoption of AI models for use in clinical practice.

Objective: This study aimed to develop and validate (internally and externally) a conformal predictor of sepsis mortality risk in patients who are critically ill, leveraging AI-assisted prediction modeling.

Development and validation of a deep interpretable network for continuous acute kidney injury prediction in critically ill patients.

Early detection of acute kidney injury (AKI) may provide a crucial window of opportunity to prevent further injury, which helps improve clinical outcomes. This study aimed to develop a deep interpretable network for continuously predicting the 24-hour AKI risk in real-time and evaluate its performance internally and externally in critically ill patients. A total of 21,163 patients' electronic health records sourced from Beth Israel Deaconess Medical Center (BIDMC) were first included in building the model.

A comprehensive technology strategy for microbial identification and contamination investigation in the sterile drug manufacturing facility-a case study.

Objective: A comprehensive strategy for microbial identification and contamination investigation during sterile drug manufacturing was innovatively established in this study, mainly based on MALDI-TOF MS for the identification and complemented by sequencing technology on strain typing.

Methods: It was implemented to monitor the bacterial contamination of a sterile drug manufacturing facility, including its bacterial distribution features and patterns. In three months, two hundred ninety-two samples were collected covering multiple critical components of raw materials, personnel, environment, and production water.

Specific biomarker mining and rapid detection of complex by recombinase polymerase amplification.

Objective: To mine specific proteins and their protein-coding genes as suitable molecular biomarkers for the Complex (BCC) bacteria detection based on mega analysis of microbial proteomic and genomic data comparisons and to develop a real-time recombinase polymerase amplification (rt-RPA) assay for rapid isothermal screening for pharmaceutical and personal care products.

Methods: We constructed an automatic screening framework based on Python to compare the microbial proteomes of 78 BCC strains and 263 non-BCC strains to identify BCC-specific protein sequences. In addition, the specific protein-coding gene and its core DNA sequence were validated with a self-built genome database containing 158 thousand bacteria.

Identification of indications for albumin administration in septic patients with liver cirrhosis.

Background: Albumin infusion is the primary therapeutic strategy for septic patients with liver cirrhosis. Although recent studies have investigated the efficacy of albumin in the resuscitation stage of septic patients with liver cirrhosis, it remains unclear whether daily albumin administration can improve outcomes. Furthermore, the indications for initiating albumin therapy are not well defined.

Ordered intermetallic compounds combining precious metals and transition metals for electrocatalysis.

Ordered intermetallic alloys with significantly improved activity and stability have attracted extensive attention as advanced electrocatalysts for reactions in polymer electrolyte membrane fuel cells (PEMFCs). Here, recent advances in tuning intermetallic Pt- and Pd-based nanocrystals with tunable morphology and structure in PEMFCs to catalyze the cathodic reduction of oxygen and the anodic oxidation of fuels are highlighted. The fabrication/tuning of ordered noble metal-transition metal-bonded intermetallic PtM and PdM (M = Fe, Co) nanocrystals by using high temperature annealing treatments to promote the activity and stability of electrocatalytic reactions are discussed.

Entropy Analysis of Heart Rate Variability in Different Sleep Stages.

How the complexity or irregularity of heart rate variability (HRV) changes across different sleep stages and the importance of these features in sleep staging are not fully understood. This study aimed to investigate the complexity or irregularity of the RR interval time series in different sleep stages and explore their values in sleep staging. We performed approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), distribution entropy (DistEn), conditional entropy (CE), and permutation entropy (PermEn) analyses on RR interval time series extracted from epochs that were constructed based on two methods: (1) 270-s epoch length and (2) 300-s epoch length.

fIDBAC: A Platform for Fast Bacterial Genome Identification and Typing.

To study the contamination of microorganisms in the food industry, pharmaceutical industry, clinical diagnosis, or bacterial taxonomy, accurate identification of species is a key starting point of further investigation. The conventional method of identification by the 16S rDNA gene or other marker gene comparison is not accurate, because it uses a tiny part of the genomic information. The average nucleotide identity calculated between two whole bacterial genomes was proven to be consistent with DNA-DNA hybridization and adopted as the gold standard of bacterial species delineation.

Highly efficient enrichment and identification of pathogens using a herringbone microfluidic chip and by MALDI-TOF mass spectrometry.

Bacterial infections cause considerable morbidity and expensive healthcare costs. The prescription of broad-spectrum antimicrobial drugs results in failure of treatment or overtreatment and exacerbates the spread of multidrug-resistant pathogens. There is an emergent demand for rapid and accurate methods to identify pathogens and conduct personalized therapy.

An Explainable Artificial Intelligence Predictor for Early Detection of Sepsis.

Objectives: Early detection of sepsis is critical in clinical practice since each hour of delayed treatment has been associated with an increase in mortality due to irreversible organ damage. This study aimed to develop an explainable artificial intelligence model for early predicting sepsis by analyzing the electronic health record data from ICU provided by the PhysioNet/Computing in Cardiology Challenge 2019.

Design: Retrospective observational study.

A Comprehensive Multilocus Sequence Typing Scheme for Identification and Genotyping of Strains.

Increasing clinical significance of coagulase-negative staphylococci requires effective methods for species identification and genotyping. In this study, six housekeeping genes (, , , , , and ) with extensive allelic polymorphisms were identified and evaluated to develop a comprehensive multilocus sequence typing (MLST) scheme. Selected primers were capable of amplification of the six loci from all of the 180 strains belonging to 18 different species.

Genomic and transcriptomic analyses reveal distinct biological functions for cold shock proteins (VpaCspA and VpaCspD) in Vibrio parahaemolyticus CHN25 during low-temperature survival.

Background: Vibrio parahaemolyticus causes serious seafood-borne gastroenteritis and death in humans. Raw seafood is often subjected to post-harvest processing and low-temperature storage. To date, very little information is available regarding the biological functions of cold shock proteins (CSPs) in the low-temperature survival of the bacterium.

[Impurity profile study of cefalotin sodium by two-dimensional liquid chromatography-quadrupole time-of-flight mass spectrometry].

A two-dimensional liquid chromatography-quadrupole time-of-flight mass spectrometry (2D-LC-QTOF MS) method to profile the impurities of cefalotin sodium was developed. A Symmetry C18 column (250 mm x 4.6 mm, 5 μm) was used in the first dimensional chromatography, with gradient elution using pH 2.

Characterization of Fe3O4/SiO2/Gd2O(CO3)2 core/shell/shell nanoparticles as T1 and T2 dual mode MRI contrast agent.

Core/shell/shell structured Fe3O4/SiO2/Gd2O(CO3)2 nanoparticles were successfully synthesized. Their properties as a new type of T1-T2 dual model contrast agent for magnetic resonance imaging were investigated. Due to the introduce of a separating SiO2 layer, the magnetic coupling between Gd2O(CO3)2 and Fe3O4 could be modulated by the thickness of SiO2 layer and produce appropriate T1 and T2 signal.

[Study on the proficiency testing program of pharmaceutical preparation analysis].

This proficiency testing program is established to evaluate the pharmaceutical preparation analysis capacity of laboratories recommended by 18 countries and economies. It was authorized by Asia Pacific Laboratory Accreditation Cooperation (APLAC), and organized by Shanghai Institute for Food and Drug Control (SIFDC) and China National Accreditation Service for Conformity Assessment (CNAS). The 0.

A photoelectrochemical immunosensor based on Au-doped TiO2 nanotube arrays for the detection of α-synuclein.

α-Synuclein (α-SYN) is a very important neuronal protein that is associated with Parkinson's disease. In this paper, we utilized Au-doped TiO(2) nanotube arrays to design a photoelectrochemical immunosensor for the detection of α-SYN. The highly ordered TiO(2) nanotubes were fabricated by using an electrochemical anodization technique on pure Ti foil.