METHODOLOGY: In search of high-affinity ayurvedic alternatives, we conducted a pan-proteome in silico exploration of the NiV proteins for their interaction with the best-suited phytoconstituents. The toxicity prediction of thirty phytochemicals based on their LD50 value identified thirteen potential candidates. Molecular docking studies of those thirteen phytochemicals with five important NiV proteins identified Tanshinone I as the potential compound with a high binding affinity.

RESULTS: The pharmacokinetics and pharmacodynamics studies also aided in determining the absorption, distribution, metabolism, excretion, and toxicity of the selected phytoconstituent. Interestingly, docking studies also revealed Rosmariquinone as a potent alternative to the antiviral drug Remdesivir binding the same pocket of RNA-dependent RNA polymerase of the NiV. A molecular dynamics simulation study of the surface glycoprotein of NiV against Tanshinone I showed a stable complex formation and significant allosteric changes in the protein structure, implying that these phytochemicals could be a natural alternative to synthetic drugs against NiV.

CASE PRESENTATION: An army officer who returned from Malaysia in October 2016 was found to be positive for Plasmodium both by microscopy and rapid diagnostic test (RDT) by the Anti Malaria Campaign Sri Lanka (AMC) during his third visit to a health care provider. Microscopy findings were suspicious of P. knowlesi infection as the smears showed parasite stages similar to both Plasmodium malariae and Plasmodium falciparum. Nested PCR at AMC confirmed Plasmodium genus, but not the species. In the absence of species confirmation, the patient was treated as a case of P. falciparum. The presence of P. knowlesi was later confirmed by a semi-nested PCR assay performed at the Environmental Health Institute, National Environmental Agency in Singapore. The parasite strain was also characterized by sequencing the circumsporozoite gene. Extensive case investigation including parasitological and entomological surveillance was carried out.

AIMS: We evaluated the performance of machine learning (ML) and non-patented scores for ruling out SF among NAFLD/MASLD patients.

METHODS: Twenty-one ML models were trained (N = 1153), tested (N = 283), and validated (N = 220) on clinical and biochemical parameters of histologically-proven NAFLD/MASLD patients (N = 1656) collected across 14 centres in 8 Asian countries. Their performance for detecting histological-SF (≥F2fibrosis) were evaluated with APRI, FIB4, NFS, BARD, and SAFE (NPV/F1-score as model-selection criteria).

RESULTS: Patients aged 47 years (median), 54.6% males, 73.7% with metabolic syndrome, and 32.9% with histological-SF were included in the study. Patients with SFvs.no-SF had higher age, aminotransferases, fasting plasma glucose, metabolic syndrome, uncontrolled diabetes, and NAFLD activity score (p  140) was next best in ruling out SF (NPV of 0.757, 0.724 and 0.827 in overall, test and validation set).

METHODOLOGY: One thousand two hundred and sixteen prospectively enrolled patients with ACLF (males 98%, mean age 42.5 ± 9.4 years, mean CTP, MELD and AARC scores of 12 ± 1.4, 29.7 ± 7 and 9.8 ± 2 respectively) from the Asian Pacific Association for the Study of the Liver (APASL) ACLF Research Consortium (AARC) database were analysed retrospectively. Patients with or without metabolic risk factors were compared for severity (CTP, MELD, AARC scores) and day 30 and 90 mortality. Information on overweight/obesity, type 2 diabetes mellitus (T2DM), hypertension and dyslipidaemia were available in 1028 (85%), 1019 (84%), 1017 (84%) and 965 (79%) patients respectively.