Predicting Mortality in Sepsis-Associated Acute Respiratory Distress Syndrome: A Machine Learning Approach Using the MIMIC-III Database.

Background: To develop and validate a mortality prediction model for patients with sepsis-associated Acute Respiratory Distress Syndrome (ARDS).

Methods: This retrospective cohort study included 2466 patients diagnosed with sepsis and ARDS within 24 h of ICU admission. Demographic, clinical, and laboratory parameters were extracted from Medical Information Mart for Intensive Care III (MIMIC-III) database.

Concentration Recognition-Based Auto-Dynamic Regulation System (CRUISE) Enabling Efficient Production of Higher Alcohols.

Microbial factories lacking the ability of dynamically regulating the pathway enzymes overexpression, according to in situ metabolite concentrations, are suboptimal, especially when the metabolic intermediates are competed by growth and chemical production. The production of higher alcohols (HAs), which hijacks the amino acids (AAs) from protein biosynthesis, minimizes the intracellular concentration of AAs and thus inhibits the host growth. To balance the resource allocation and maintain stable AA flux, this work utilizes AA-responsive transcriptional attenuator ivbL and HA-responsive transcriptional activator BmoR to establish a concentration recognition-based auto-dynamic regulation system (CRUISE).

[FTA/PCR detection for Cryptosporidium added in beverage].

An established FVA/PCR protocol was applied to detect Cryptosporidium oocysts which were added in beverage, and proved to be as sensitive as 1.0 oocyst/ml detected within 6 hours. The study indicates that this technique is sensitive, time-saving and easy to perform.