Objective:To analyze the risk factors for 6-month prognosis of traumatic spinal cord injury (TSCI).Methods:A retrospective analysis was conducted of the 133 patients with TSCI who had been admitted to Department of Orthopaedics, The First Hospital Affiliated to Jinan University from January 2017 to August 2021. The patients with TSCI were categorized into an improved group ( n=82) and a non-improved group ( n=51) according to the improvements in the American Spinal Injury Association (ASIA) grading between admission and 6 months after injury. To identify the risk factors that might affect the prognosis of TSCI patients at 6 months after injury, univariate and logistic regression analyses were conducted of indicators such as gender, age, length of MRI spinal cord signal change, maximum canal compromise (MCC), maximum spinal cord compression (MSCC), brain and spinal cord injury center(BASIC)score, neutrophil-to-lymphocyte ratio (NLR) within 3 days after injury, ASIA grading within 3 days after injury, mean arterial pressure (MAP) at 3 days after operation, and presence of complications. Results:The univariate analysis showed significant differences between the improved and non-improved groups in length of MRI spinal cord signal change, MCC, MSCC, BASIC, NLR within 3 days after injury, ASIA grading within 3 days after injury, MAP at 3 days after operation, and presence of complications (all P<0.05). The logistic regression analysis showed that NLR ( OR=0.463, 95% CI: 0.287 to 0.748, P=0.002) and ASIA grading ( OR=11.684, 95% CI: 1.684 to 81.086, P=0.013) within 3 days after injury, as well as MAP at 3 days after operation ( OR=2.224, 95% CI: 1.306 to 3.787, P=0.003), were risk factors affecting the 6-month prognosis in TSCI patients. Conclusion:The NLR and ASIA grading within 3 days after injury, and MAP at 3 days after operation are risk factors that may affect the prognosis of TSCI patients at 6 months after injury.

Sex difference has shown in the arthritis diseases in human population and animal models. We investigate how the sex and symmetry vary among mouse models with different genomic backgrounds. Disease data of sex and limbs accumulated in the past more than two decades from four unique populations of murine arthritis models were analyzed. They are (1) interleukin-1 receptor antagonist (IL-1ra) deficient mice under Balb/c background (Balb/c KO); (2) Mice with collagen II induced arthritis under DBA/1 background; (3) Mice with collagen II induced arthritis under C57BL/6 (B6) background and (4) A F2 generation population created by Balb/c KO X DBA/1 KO.Our data shows that there is a great variation in sexual dimorphism for arthritis incidence and severity of arthritis in mice harboring specific genetic modifications. For a F2 population, the incidence of arthritis was 57.1% in female mice and 75.6% in male mice. There was a difference in severity related to sex in two populations: B6.DR1/ B6.DR4 (P < 0.001) and F2 (P = 0.023) There was no difference Balb/c parental strain or in collagen-induced arthritis (CIA) in DBA/1 mice. Among these populations, the right hindlimbs are significantly higher than the scores for the left hindlimbs in males (P < 0.05). However, when examining disease expression using the collagen induced arthritis model with DBA/1 mice, sex-dimorphism did not reach statistical significance, while left hindlimbs showed a tendency toward greater disease expression over the right. Sexual dimorphism in disease expression in mouse models is strain and genomic background dependent. It sets an alarm that potential variation in sexual dimorphism among different racial and ethnic groups in human populations may exist. It is important to not only include both sexes and but also pay attention to possible variations caused by disease expression and response to treatment in all the studies of arthritis in animal models and human populations.

Objective:To analyze the global epidemic data of Corona virus disease 2019 (COVID-19) and the prevention and control measures, learn about the epidemic characteristics, development trend and the main factors affecting the prevention and control effect, and provide reference for scientific prevention and control of COVID-19.Methods:The data of COVID-19 mainly came from the WHO website and the websites of the United States, European and other Centers for Disease Control and Prevention (the statistical time was from the beginning of the epidemic in each country to March 31, 2022). The epidemiological characteristics and trends in the world and major countries were analyzed, and the main factors affecting the prevention and control of the epidemic were studied. SPSS19.0 software was used to collate data and statistical analysis.Results:The worldwide cumulative confirmed cases of COVID-19 reached 1 million on April 2, 2020, 10 million cases on June 28, 2020, 100 million cases on January 25, 2021, 200 million cases on August 3, 2021, 300 million cases on January 6, 2022, 400 million cases on February 8, 2022, 489 million cases on March 31, 2022. From January 2020 to March 31, 2022, the interval between each additional 100 million cases was gradually shortened (about 360 days from the beginning of the epidemic to the increase to 100 million, the average time to increase from 100 million to 200 million, from 200 million to 300 million was 170 days, and the number of confirmed cases increased from 300 million to 400 million was only 33 days), the epidemic had accelerated. The worldwide cumulative number of death case was 100 000 on April 9, 2020, 1 million on September 19, 2020, 5 million on October 31, 2021, and 6.14 million on March 31, 2022. From January to October 2021, the average time interval for an increase of 1 million deaths was 97 days. After October, the growth rate decreased, averaging 121 days. At the end of 2021, affected by the Omicron mutation, the number of infected people worldwide increased sharply. By March 31, 2022, the cumulative number of confirmed cases in all continents was Europe (181 million), Asia (141 million), North America (94.67 million), South America (56.09 million), Africa (11.55 million) and Oceania (5.58 million) from high to low. The cumulative deaths from high to low was Europe (1.77 million), North America (1.42 million), Asia (1.41 million), South America (1.28 million), Africa (0.25 million) and Oceania (8 900). The top 5 countries with cumulative confirmed cases of COVID-19 were the United States (80.14 million), India (43.03 million), Brazil (29.98 million), France (25.82 million) and the United Kingdom (21.28 million). The top five countries with accumulated deaths were the United States (980 000), Brazil (660 000), India (520 000), the United Kingdom (160 000) and France (140 000).Conclusions:COVID-19 is a global public health emergency. The epidemic has spread worldwide with strong infectivity, rapid transmission and great harm. It is suggested to focus on the prevention and control of key links, strengthen the early warning mechanism, continue to take scientific public health prevention and control measures such as vaccination, reduce severe case and death and deal with an ongoing challenge.

OBJECTIVE： To study the mechanism of Cistanche deserticola in the treatment of osteoporosis by network pharmacology. METHODS： The active components of C. deserticola were retrieved and obtained by TCM system platform （TCMSP）. Reverse molecular docking server DRAR-CPI and related databases GeneCards and OMIM were used to screen the target of C. deserticola active ingredients in the treatment of osteoporosis. The “component-target”network of C. deserticola was constructed by Cytoscape software， and the interaction between targets was plotted by String database and Cytoscape software. The combination activity of target and active ingredient was evaluated via molecular docking with Systems Dock WebSite server. GO classification and enrichment analysis and KEGG pathway enrichment analysis were conducted for target genes using DAVID database. RESULTS： Totally 13 active ingredients were screened out from C. deserticola， such as verbascoside， leonurine， geniposidic acid. There were 43 active ingredient-treated potential targets， such as RUNX2， VEGF， IL-6， BGP， TNF. Multiple signaling pathways were involved in target action， such as WNT （Wingless/Integrated）， VEGF， TNF. CONCLUSIONS： This study preliminarily explores and validates the main targets and pathways of C. deserticola in the treatment of osteoporosis， which lay the foundation for further study of its mechanism.