As the world recovers from the COVID-19 pandemic, a resurgence in MPXV cases is causing serious concern. The early clinical similarity of MPXV to common ailments like the flu and cold, coupled with the resemblances of its progressing rash to other infections, underscores the importance of prompt and accurate diagnosis. Among the infections, smallpox is clinically closest to MPXV, and rashes similar to MPXV stages also appear in syphilis and varicella zoster. A comprehensive review of MPXV, herpes, and syphilis was carried out, including structural and morphological features, origins, transmission modes, and computational studies. PubMed literature search on MPXV, using MeSH key terms, yielded 1904 results, with the analysis revealing prominent links to sexually transmitted diseases. More in-depth exploration of MPXV, Herpes Simplex Virus (HSV), and Syphilis revealed further disease interconnections and geographical correlations. These findings emphasize the need for a holistic understanding of these interconnected infectious agents for better control and management.

Wrapping of DNA into nucleosomes restricts accessibility to DNA and may affect the recognition of binding motifs by transcription factors. A certain class of transcription factors, the pioneer transcription factors, can specifically recognize their DNA binding sites on nucleosomes, initiate local chromatin opening, and facilitate the binding of co-factors in a cell-type-specific manner. For the majority of human pioneer transcription factors, the locations of their binding sites, mechanisms of binding, and regulation remain unknown. We have developed a computational method to predict the cell-type-specific ability of transcription factors to bind nucleosomes by integrating ChIP-seq, MNase-seq, and DNase-seq data with details of nucleosome structure. We have demonstrated the ability of our approach in discriminating pioneer from canonical transcription factors and predicted new potential pioneer transcription factors in H1, K562, HepG2, and HeLa-S3 cell lines. Last, we systematically analyzed the interaction modes between various pioneer transcription factors and detected several clusters of distinctive binding sites on nucleosomal DNA.

The San-Ao Decoction (SAD) is a well-known Traditional Chinese Medicine (TCM) formula used to alleviate respiratory symptoms, including asthma. However, its precise mechanisms of action have remained largely unknown. In this study, we utilized computer-aided approaches to explore these mechanisms. Firstly, we conducted a comprehensive analysis of the chemical composition of SAD, which allowed us to identify the 28 main ingredients. Then, we employed computer simulations to investigate the potential active ingredients of SAD and the corresponding binding sites of transient receptor potential vanilloid 1 (TRPV1). The simulations revealed that D509 and D647 were the potential binding sites for TRPV1. Notably, molecular dynamics (MD) studies indicated that site D509 may function as an allosteric site of TRPV1. Furthermore, to validate the computer-aided predictions, we performed experimental studies, including in vitro and in vivo assays. The results of these experiments confirmed the predictions made by our computational models, providing further evidence for the mechanisms of action of San-Ao Decoction in asthma treatment. Our findings demonstrated that: i) D509 and D647 of TRPV1 are the key binding sites for the main ingredients of SAD; ii) SAD or its main ingredients significantly reduce the influx of Ca2+ through TRPV1, following the TCM principle of \"Jun, Chen, Zuo, Shi\"; iii) SAD shows efficiency in comprehensive in vivo validation. In conclusion, our computer-aided investigation of San-Ao Decoction in asthma treatment has provided valuable insights into the therapeutic mechanisms of this TCM formula. The combination of computational analysis and experimental validation has proven effective in enhancing our understanding of TCM and may pave the way for future discoveries in the field.

In early December 2019, a large pneumonia epidemic occurred in Wuhan, China. The World Health Organization is concerned about the outbreak of another coronavirus with the powerful, rapid, and contagious transmission. Anyone with minor symptoms like fever and cough or travel history to contaminated places might be suspected of having COVID-19. COVID-19 therapy focuses on treating the disease\'s symptoms. So far, no such therapeutic molecule has been shown effective in treating this condition. So the treatment is mostly supportive and plasma. Globally, numerous studies and researchers have recently started fighting this virus. Vaccines and chemical compounds are also being investigated against infection. COVID-19 was successfully diagnosed using RNA detection and very sensitive RT-PCR (reverse transcription-polymerase chain reaction). The evolution of particular vaccinations is required to reduce illness severity and spread. Numerous computational analyses and molecular docking have predicted various target compounds that might stop this condition. This paper examines the main characteristics of coronavirus and the computational analyses necessary to avoid infection.

A systematic investigation to arrange the typical anti-aromatic porphyrinoids in sequence was performed. Based on density functional theory calculations, six rules are summarized to obtain the high-performance anti-aromatic porphyrinoids: (1) when two atoms are deleted/added on the 18π electron current ring flowing pipe, we will immediately obtain a 16π/20π electron anti-aromatic system; (2) it is a good idea to increase the number of pyrrole/thiophen/furan units on the π-electron current ring flowing pipe; (3) the heteroatom selecting order is -O- (optimal choice), -NH- (second choice), and -S- (last choice); (4) it is worth noting that the C-C=C-C unit is not beneficial for the anti-aromatic properties; (5) it is very significant to avoid the crowded environment in the core space of an anti-aromatic molecule. In this view, -O- is much better than -S- and -NH-; (6) the \"circular\" skeleton is much better than an \"ellipse-like\", \"rectangular\", or \"parallelogram-like\" one.

The nano structures (CNT(8, 0) and C78) are doped with two Co and Sc metals in order to obtain the suitable catalysts to process the ORR. The effects of different solvents on ORR on Co-C-C-Co-CNT(8, 0) and Sc-C78 are examined in benzene, methanol and water by polarizable continuum model. The ORR on Co-C-C-Co-CNT(8, 0) and Sc-C-C-Sc-C78 are processed by two paths as follow: *OOH-Co-C-C-Co-CNT(8, 0) → *OH-Co-C-C-Co-CNT(8, 0)-*OH → *OH-Co-C-C-Co-CNT(8, 0)→ *Co-C-C-Co-CNT(8, 0) reactions and *OOH-Co-C-C-Co-CNT(8, 0) → *OH-Co-C-C-Co-CNT(8, 0)-*OH → *OH-Co-C-C-Co-CNT(8, 0)→ *Co-C-C-Co-CNT(8, 0) reactions. Results shown that the Co-C-C-Co-CNT(8, 0) has higher potential than Sc-C-C-Sc-C78 for ORR in benzene, methanol and water. Over potential on Co-C-C-Co-CNT(8, 0) and Sc-C-C-Sc-C78 are lower than various metals based nano catalysts.

Open online forums like Reddit provide an opportunity to quantitatively examine COVID-19 vaccine perceptions early in the vaccine timeline. We examine COVID-19 misinformation on Reddit following vaccine scientific announcements, in the initial phases of the vaccine timeline.
We collected all posts on Reddit (reddit.com) from January 1 2020 - December 14 2020 (n=266,840) that contained both COVID-19 and vaccine-related keywords. We used topic modeling to understand changes in word prevalence within topics after the release of vaccine trial data. Social network analysis was also conducted to determine the relationship between Reddit communities (subreddits) that shared COVID-19 vaccine posts, and the movement of posts between subreddits.
There was an association between a Pfizer press release reporting 90% efficacy and increased discussion on vaccine misinformation. We observed an association between Johnson and Johnson temporarily halting its vaccine trials and reduced misinformation. We found that information skeptical of vaccination was first posted in a subreddit (r/Coronavirus) which favored accurate information and then reposted in subreddits associated with antivaccine beliefs and conspiracy theories (e.g. conspiracy, NoNewNormal).
Our findings can inform the development of interventions where individuals determine the accuracy of vaccine information, and communications campaigns to improve COVID-19 vaccine perceptions, early in the vaccine timeline. Such efforts can increase individual- and population-level awareness of accurate and scientifically sound information regarding vaccines and thereby improve attitudes about vaccines, especially in the early phases of vaccine roll-out. Further research is needed to understand how social media can contribute to COVID-19 vaccination services.

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Computer aided optimization of lead compounds is of great significance to the design and discovery of new agrochemicals. A series of 2,6-dimethyl-4-aminopyrimidine acylhydrazones 6 was rationally designed as pyruvate dehydrogenase complex component E1 (PDHc-E1) inhibitors using computer aided drug design. Compounds in series 6 showed excellent inhibitory activity against Escherichia coli PDHc-E1, which was considerably higher than that of the lead compound A2. Compound 6l showed the best inhibitory activity (IC50 = 95 nM). Molecular docking, site-directed mutagenesis, and enzymatic assays revealed that the compounds bound in a \"straight\" conformation in the active site of E. coli PDHc-E1. Compounds 6b, 6e, and 6l showed negligible inhibition against porcine PDHc-E1. The in vitro antibacterial activity indicated that 6a, 6d, 6e, 6g, 6h, 6i, 6m, and 6n exhibited 61%-94% inhibition against Ralstonia solanacearum at 100 μg/mL, which was better than commercial thiodiazole‑copper (29%) and bismerthiazol (55%). These results demonstrated that a lead structure for a highly selective PDHc-E1 inhibitor as a bactericide could be obtained using computer aided drug design.

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects millions of people worldwide. However, there are currently no reliable biomarkers for ASD diagnosis. Materials and Methods: The strategy of computational prediction combined with experimental verification was used to identify blood protein biomarkers for ASD. First, brain tissue-based transcriptome data of ASD were collected from Gene Expression Omnibus database and analyzed to find ASD-related genes by bioinformatics method of significance analysis of microarrays. Then, a prediction program of blood-secretory proteins was applied on these genes to predict ASD-related proteins in blood. Furthermore, ELISA was used to verify these proteins in plasma samples of ASD patients. Results: A total of 364 genes were identified differentially expressed in brain tissue of ASD, among which 59 genes were predicted to encode ASD-related blood-secretory proteins. After functional analysis and literature survey, six proteins were chosen for experimental verification and five were successfully validated. Receiver operating characteristic curve analyses showed that the area under the curve of SLC25A12, LIMK1, and RARS was larger than 0.85, indicating that they are more powerful in discriminating ASD cases from controls. Conclusion: SLC25A12, LIMK1, and RARS might serve as new potential blood protein biomarkers for ASD. Our findings provide new insights into the pathogenesis and diagnosis of ASD.