On 23rd July 2022, the World Health Organization (WHO) recognized the ongoing monkeypox outbreak as a public medical crisis. Monkeypox virus (MPV), the etiological agent of monkeypox, is a zoonotic, linear, double-stranded DNA virus. In 1970, the Democratic Republic of the Congo reported the first case of MPV infection. Human-to-human transmission can happen through sexual contact, inhaled droplets, or skin-to-skin contact. Once inoculated, the viruses multiply rapidly and spread into the bloodstream to cause viremia, which then affect multiple organs, including the skin, gastrointestinal tract, genitals, lungs, and liver. By September 9, 2022, more than 57,000 cases had been reported in 103 locations, especially in Europe and the United States. Infected patients are characterized by physical symptoms such as red rash, fatigue, backache, muscle aches, headache, and fever. A variety of medical strategies are available for orthopoxviruses, including monkeypox. Monkeypox prevention following the smallpox vaccine has shown up to 85% efficacy, and several antiviral drugs, such as Cidofovir and Brincidofovir, may slow the viral spread. In this article, we review the origin, pathophysiology, global epidemiology, clinical manifestation, and possible treatments of MPV to prevent the propagation of the virus and provide cues to generate specific drugs.

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Fully understanding traditional Chinese medicines (TCMs) is still challenging because of the extreme complexity of their chemical components and mechanisms of action. The TCM Plant Genome Project aimed to obtain genetic information, determine gene functions, discover regulatory networks of herbal species, and elucidate the molecular mechanisms involved in the disease prevention and treatment, thereby accelerating the modernization of TCMs. A comprehensive database that contains TCM‐related information will provide a vital resource. Here, we present an integrative genome database of TCM plants (IGTCM) that contains 14,711,220 records of 83 annotated TCM‐related herb genomes, including 3,610,350 genes, 3,534,314 proteins and corresponding coding sequences, and 4,032,242 RNAs, as well as 1033 non‐redundant component records for 68 herbs, downloaded and integrated from the GenBank and RefSeq databases. For minimal interconnectivity, each gene, protein, and component was annotated using the eggNOG‐mapper tool and Kyoto Encyclopedia of Genes and Genomes database to acquire pathway information and enzyme classifications. These features can be linked across several species and different components. The IGTCM database also provides visualization and sequence similarity search tools for data analyses. These annotated herb genome sequences in IGTCM database are a necessary resource for systematically exploring genes related to the biosynthesis of compounds that have significant medicinal activities and excellent agronomic traits that can be used to improve TCM‐related varieties through molecular breeding. It also provides valuable data and tools for future research on drug discovery and the protection and rational use of TCM plant resources. The IGTCM database is freely available at http://yeyn.group:96/.

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Essential ncRNA is a type of ncRNA which is indispensable for the survival of organisms. Although essential ncRNAs cannot encode proteins, they are as important as essential coding genes in biology. They have got wide variety of applications such as antimicrobial target discovery, minimal genome construction and evolution analysis. At present, the number of species required for the determination of essential ncRNAs in the whole genome scale is still very few due to the traditional methods are time-consuming, laborious and costly. In addition, traditional experimental methods are limited by the organisms as less than 1% of bacteria can be cultured in the laboratory. Therefore, it is important and necessary to develop theories and methods for the recognition of essential non-coding RNA. In this paper, we present a novel method for predicting essential ncRNA by using both compositional and derivative features calculated by information theory of ncRNA sequences. The method was developed with Support Vector Machine (SVM). The accuracy of the method was evaluated through cross-species cross-validation and found to be between 0.69 and 0.81. It shows that the features we selected have good performance for the prediction of essential ncRNA using SVM. Thus, the method can be applied for discovering essential ncRNAs in bacteria.