Of the 5 human malarial parasites, Plasmodium falciparum and Plasmodium vivax are the most prevalent species globally, while Plasmodium malariae, Plasmodium ovale curtisi and Plasmodium ovale wallikeri are less prevalent and typically occur as mixed-infections. Plasmodium knowlesi, previously considered a non-human primate (NHP) infecting species, is now a cause of human malaria in Malaysia. The other NHP Plasmodium species, Plasmodium cynomolgi, Plasmodium brasilianum, Plasmodium inui, Plasmodium simium, Plasmodium coatneyi and Plasmodium fieldi cause malaria in primates, which are mainly reported in southeast Asia and South America. The non-knowlesi NHP Plasmodium species also emerged and were found to cross-transmit from their natural hosts (NHP) – to human hosts in natural settings. Here we have reviewed and collated data from the literature on the NHPs-to-human-transmitting non-knowlesi Plasmodium species. It was observed that the natural transmission of these NHP parasites to humans had been reported from 2010 onwards. This study shows that: (1) the majority of the non-knowlesi NHP Plasmodium mixed species infecting human cases were from Yala province of Thailand; (2) mono/mixed P. cynomolgi infections with other human-infecting Plasmodium species were prevalent in Malaysia and Thailand and (3) P. brasilianum and P. simium were found in Central and South America.
In the third millennium, developing countries will confront significant environmental problems such as ozone depletion, global warming, the shortage of fossil resources, and greenhouse gas emissions. This research looked at a multigenerational system that can generate clean hydrogen, fresh water, electricity, heat, and cooling. The system's components include Rankine and Brayton cycles, an Organic Rankine Cycle (ORC), flash desalination, an Alkaline electrolyzer, and a solar heliostat. The proposed process has been compared for two different start-up modes with a combustion chamber and solar heliostat to compare renewable and fossil fuel sources. This research evaluated various characteristics, including turbine pressure, system efficiency, solar radiation, and isentropic efficiency. The energy and exergy efficiency of the proposed system were obtained at around 78.93% and 47.56%, respectively. Exergy study revealed that heat exchangers and alkaline electrolyzers had the greatest exergy destruction rates, at 78.93% and 47.56%, respectively. The suggested system produces 0.04663 kg/s of hydrogen. Results indicate that at the best operational conditions, the exergetic efficiency, power, and hydrogen generation of 56%, 6000 kW, and 1.28 kg/s is reached, respectively. Also, With a 15% improvement in the Brayton cycle's isentropic efficacy, the quantity of hydrogen produced increases from 0.040 kg/s to 0.0520 kg/s.
Coronavirus disease 2019 (COVID-19), a recent viral pandemic that first began in December 2019, in Hunan wildlife market, Wuhan, China. The infection is caused by a coronavirus, SARS-CoV-2 and clinically characterized by common symptoms including fever, dry cough, loss of taste/smell, myalgia and pneumonia in severe cases. With overwhelming spikes in infection and death, its pathogenesis yet remains elusive. Since the infection spread rapidly, its healthcare demands are overwhelming with uncontrollable emergencies. Although laboratory testing and analysis are developing at an enormous pace, the high momentum of severe cases demand more rapid strategies for initial screening and patient stratification. Several molecular biomarkers like C-reactive protein, interleukin-6 (IL6), eosinophils and cytokines, and artificial intelligence (AI) based screening approaches have been developed by various studies to assist this vast medical demand. This review is an attempt to collate the outcomes of such studies, thus highlighting the utility of AI in rapid screening of molecular markers along with chest X-rays and other COVID-19 symptoms to enable faster diagnosis and patient stratification. By doing so, we also found that molecular markers such as C-reactive protein, IL-6 eosinophils, etc. showed significant differences between severe and non-severe cases of COVID-19 patients. CT findings in the lungs also showed different patterns like lung consolidation significantly higher in patients with poor recovery and lung lesions and fibrosis being higher in patients with good recovery. Thus, from these evidences we perceive that an initial rapid screening using integrated AI approach could be a way forward in efficient patient stratification.