Highlights Pneumothorax may be a frequent and fatal complication in critically ill patients with COVID-19. Pneumothorax was likely to occur 2 weeks after the beginning of dyspnea in senile male patients. Mechanical ventilation, recruitment maneuver, forced inhalation, severely coughing, and changes of lung structure and function, contribute to the occurrence of pneumothorax. Lung recruitment maneuver should be cautiously considered.

Background Although TP53 co-mutation with KRAS/ATM/EGFR/STK11 have been proved to have predictive value for response to immune checkpoint inhibitors (ICIs), not all TP53 mutations are equal in this context. As the main part of TP53 mutant types, Missense and Nonsense alternations in TP53 as independent factors to predict the response to ICIs within Lung Adenocarcinoma (LUAD) patients have not yet been reported. Methods An integrated analysis based on multiple-dimensional data types including genomic, transcriptomic, proteomic and clinical data from published lung adenocarcinoma data and local database of LUAD taking immune checkpoint inhibitors. Gene set enrichment analysis (GSEA) was used to determine potentially relevant gene expression signatures between specific subgroups. Single-sample GSEA (GSVA) is conducted to calculate the score for enrichment of a set of genes regulating DNA damage repair (DDR) pathway. Findings The TP53-missense-mutation group showed increased PD-L1 (CD274) level and enriched IFN-γ signatures compared with the TP53-wild-type subgroup, but no differences were noted in patients with nonsense-mutant vs wild-type p53. Furthermore, a group of suppressor Immune cells like M2 Macrophage and Neutrophils are found enriched in nonsense group. On the other-side, both TP53 missense and nonsense mutations are associated with elevated TMB and neoantigen levels and contribute equally to DNA damage repair deficiency. The distribution regarding to multi-dimensional factors determining the efficacy of ICIs finally transformed into diverse clinical benefits for LUAD. TP53 missense but not -nonsense Mutants are associated with better clinical benefits taking antiPD-1/1L. However, all such TP53 subgroups responds well to nivolumab (antiPD-L1) plus ipilimumab (antiCTLA-4) therapy. Interpretation Our study demonstrated that not all TP53 mutations are equal in predicting efficacy in patients with LUAD treated with ICIs. Multi-center data showed that TP53 missense and nonsense mutations were significantly different in terms of associations with PD-L1 expression, IFN-γ signatures and TME composition. Special attention should be paid to potential TP53 mutation heterogeneity when evaluating TP53 status as biomarker for ICIs. Funding The study was supported by Key Lab System Project of Guangdong Science and Technology Department – Guangdong Provincial Key Lab of Translational Medicine in Lung Cancer (Grant No. 2017B030314120, to Yi-Long WU)

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After new human transmissible H1N1 (swine flu) viruses were reported in Mexico and the United States in April 2009, the World Health Organization (WHO) announced the emergence of a novel influenza A virus. Most governments in the world have been alerted and are monitoring the situation closely. As one of the official responses to the H1N1 pandemic, the Chinese government has released three editions of a document entitled ''Recommended Schemes for Pandemic Influenza A Diagnoses and Treatments''. The third edition recommended the use of not only two targeted anti-flu drugs, oseltamivir and zanamivir, but also four anti-flu TCM (Traditional Chinese Medicine) prescriptions. Since then, TCM has played a significant role in fighting the pandemic. TCM drugs comprise multiple compounds regulating multiple targets for a given class of medical indications, and are tunable to the symptoms of the individual. This review summarizes anti-influenza agents from TCM, including compounds, herbs, and TCM prescriptions, and suggests that, by further investigating TCM theory and mining TCM databases, a better drug discovery paradigm may arise -one that can be beneficial to both TCM and modern medicine.

To study traditional Chinese medicines and exchange related information through the worldwide Web, we developed a traditional Chinese medicine database and a program for searching and displaying data in the database on the Web. In this paper, the traditional Chinese medicine database is briefly introduced; the methods used in developing the program, including ISAPI (Microsoft Internet Server Application Programming Interface), VRML (Virtual Reality Model Language), and JavaScript are described; and three application examples are also given.

Based on Walters' GERM (Genetic Evolved Receptor Model) algorithm, an improved algorithm PARM (Pseudo Atomic Receptor Model) was put forward. PARM uses a combination of a genetic algorithm and a cross-validation technique to produce an atomic-level pseudoreceptor model, based on a set of known structure-activity relationships. During the genetic process, an artificial interfering method, which is based on a complementary principle of ligand-receptor interaction, was used to accelerate the search speed. The evolved models show a high correlation between intermolecular energy and bioactivity and can predict the bioactivity of an unknown molecule by interpolating in the regression equation of the structure-activity relationship. This algorithm was applied to two systems and produced reasonable results.

A database of marine natural products has been developed. The database contains approximately 6000 chemical compounds derived from over 10,000 marine-derived materials. For each compound, the structure, physical and chemical properties, marine source, and biological activities are given. A computer program for searching this database has also been developed and is described.

Two natural products databases, the marine natural products database (MNPD) and the traditional Chinese medicines database (TCMD), were used to find novel structures of potent SARS-CoV protease inhibitors through virtual screening. Before the procedure, the databases were filtered by Lipinski's ROF and Xu's extension rules. The results were analyzed by statistic methods to eliminate the bias in target-based database screening toward higher molecular weight compounds for enhancing the hit rate. Eighteen lead compounds were recommended by the screening procedure. They were useful for experimental scientists in prioritizing drug candidates and studying the interaction mechanism. The binding mechanism was also analyzed between the best screening compound and the SARS protein.