Enhancing anaerobic digestion of fat, oil, and grease (FOG) using electrically conductive and calcium-rich waste materials: A comparative performance analysis.

High-strength organic wastes such as fat, oil, and grease (FOG) are promising co-substrates for anaerobic digestion (AD) due to their high energy content. However, the accumulation of long-chain fatty acids (LCFAs) often leads to microbial inhibition and process instability. This study evaluated the effectiveness of oyster shell biochar (OSB) as a dual-function additive that integrates calcium supplementation and electrical conductivity to improve AD performance.

PCW-A1001, AI-assisted design approach to design a selective inhibitor for FLT-3(D835Y) in acute myeloid leukemia.

Treating acute myeloid leukemia (AML) by targeting FMS-like tyrosine kinase 3 (FLT-3) is considered an effective treatment strategy. By using AI-assisted hit optimization, we discovered a novel and highly selective compound with desired drug-like properties with which to target the FLT-3 (D835Y) mutant. In the current study, we applied an AI-assisted design approach to identify a novel inhibitor of FLT-3 (D835Y).

Central precocious puberty with hypothalamic hamartoma: the first case reports of 2 siblings with different phenotypes of Seckel syndrome 5.

Hypothalamic hamartomas (HHs) are nonneoplastic mass lesions located in the hypothalamus that can cause central precocious puberty (CPP) and/or gelastic seizures. Seckel syndrome 5 (OMIM210600, SCKL5) is a rare autosomal recessive genetic spectrum disorder characterized by intrauterine growth retardation, proportionate osteodysplastic primordial dwarfism, a wide range of intellectual disability, "bird-headed" facial features, and microcephaly with various structural brain abnormalities. Two siblings presented with short stature and small head circumference and were diagnosed with SCKL 5.

Machine-Learning-Based Approach to Differential Diagnosis in Tuberculous and Viral Meningitis.

Background: Tuberculous meningitis (TBM) is the most severe form of tuberculosis, but differentiating between the diagnosis of TBM and viral meningitis (VM) is difficult. Thus, we have developed machine-learning modules for differentiating TBM from VM.

Material And Methods: For the training data, confirmed or probable TBM and confirmed VM cases were retrospectively collected from five teaching hospitals in Korea between January 2000 - July 2018.