Self-flocculation behaviour of cellulose-based bioflocculant synthesized from sewage water grown Chlorella sorokiniana and Scenedesmus abundans.

The global energy crisis has spurred a shift from conventional to clean and sustainable energy sources. Biomass derived from microalgae is emerging as an alternative energy source with diverse applications. Despite the numerous advantages of microalgae, large-scale biomass harvesting is not economical and convenient.

Comparison of Fecal Calprotectin with Different Endoscopic Scores in the Assessment of Ulcerative Colitis (UC) Activity and Its Utility in Differentiating IBS from IBD.

Background: Ulcerative colitis (UC), a chronic inflammatory disease of gastrointestinal tract, can have initial presentation which is clinically difficult to differentiate from functional bowel disorders [irritable bowel syndrome (IBS) and irritable bowel disease (IBD)]. Conventional laboratory tests, such as erythrocyte sedimentation rate (ESR), C-reactive protein, and albumin express systemic patient responses instead of intestinal inflammation. In the last decade, fecal calprotectin, a calcium-binding protein, has been suggested as a sensitive marker of intestinal inflammation.

Advances in the fabrication, modification, and performance of biochar, red mud, calcium oxide, and bentonite catalysts in waste-to-fuel conversion.

Various catalysts are being used in fuel production from biomass and polymeric waste for the obtention of an alternative energy source with both environmental friendliness and economic viability. Biochar, red mud bentonite, and calcium oxide have been shown to play a pertinent role as catalysts in waste-to-fuel conversion processes, such as transesterification and pyrolysis. In this line of thought, this paper has provided a compendium of the fabrication and modification technologies of bentonite, red mud calcium oxide, and biochar, together with their various performances in their application in the waste-to-fuel processes.

Ultrasound placental image texture analysis using artificial intelligence to predict hypertension in pregnancy.

Background: The placental pathological changes in hypertensive disorders of pregnancy (HDP) starts early in pregnancy, the deep convolutional neural networks (CNN) can identify these changes before its clinical manifestation.

Objective: To compare the placental quantitative ultrasound image texture of women with HDP to those with the normal outcome.

Methods: The cases were enrolled in the first trimester of pregnancy, good quality images of the placenta were taken serially in the first, second, and third trimester of pregnancy.