Whole genome analysis of 26 bacterial strains reveals aromatic and hydrocarbon degrading enzymes from diverse environmental soil samples.

Polycyclic aromatic compounds and petroleum hydrocarbons (PHs) are hazardous pollutants and seriously threaten the environment and human health. However, native microbial communities can adapt to these toxic pollutants, utilize these compounds as a carbon source, and eventually evolve to degrade these toxic contaminants. With this in mind, we isolated 26 bacterial strains from various environmental soil samples.

A Model for Decoding Resistance in Precision Oncology: Acquired Resistance to FGFR inhibitors in Cholangiocarcinoma.

Background: Fibroblast growth factor receptor (FGFR) inhibitors have significantly improved outcomes for patients with FGFR-altered cholangiocarcinoma, leading to their regulatory approval in multiple countries. However, as with many targeted therapies, acquired resistance limits their efficacy. A comprehensive, multimodal approach is crucial to characterizing resistance patterns to FGFR inhibitors.

SWOG S1815: A Phase III Randomized Trial of Gemcitabine, Cisplatin, and Nab-Paclitaxel Versus Gemcitabine and Cisplatin in Newly Diagnosed, Advanced Biliary Tract Cancers.

Purpose: SWOG S1815 was a randomized, open label phase III trial, evaluating gemcitabine, nab-paclitaxel, and cisplatin (GAP) versus gemcitabine and cisplatin (GC) in patients with newly diagnosed advanced biliary tract cancers (BTCs).

Methods: Patients with newly diagnosed locally advanced unresectable or metastatic BTC, including intrahepatic cholangiocarcinoma (ICC) and extrahepatic cholangiocarcinoma (ECC) and gallbladder carcinoma (GBC), were randomly assigned 2:1 to either GAP (gemcitabine 800 mg/m, cisplatin 25 mg/m, and nab-paclitaxel 100 mg/m intravenously once per day on days 1 and 8 of a 21-day cycle) or GC (gemcitabine 1,000 mg/m and cisplatin 25 mg/m intravenously once per day on days 1 and 8 of a 21-day cycle).

Results: Among 452 randomly assigned participants, 441 were eligible and analyzable, 67% with ICC, 16% with GBC, and 17% with ECC.

Using Artificial Intelligence to Detect Risk of Family Violence: Protocol for a Systematic Review and Meta-Analysis.

Background: Despite the implementation of prevention strategies, family violence continues to be a prevalent issue worldwide. Current strategies to reduce family violence have demonstrated mixed success and innovative approaches are needed urgently to prevent the occurrence of family violence. Incorporating artificial intelligence (AI) into prevention strategies is gaining research attention, particularly the use of textual or voice signal data to detect individuals at risk of perpetrating family violence.