Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Objective To compare the prediction effect of combined model and single model in HFRS incidence fitting and prediction, and to provide a reference for optimizing HFRS prediction model. Methods The province with the highest incidence in China (Heilongjiang Province) in recent years was selected as the research site. The monthly incidence data of HFRS in Heilongjiang Province from 2004 to 2017 were collected. The data from 2004 to 2016 was used as training data, and the data from January to December 2017 was used as test data. The training data was used to train SARIMA , ETS and NNAR models, respectively. The reciprocal variance method and particle swarm optimization algorithm (PSO) were used to calculate the model coefficients of SARIMA, ETS and NNAR, respectively, to construct combined model A and combined model B. The established models were used to predict the incidence of HFRS from January to December 2017. The fitted and predicted values of the five models were compared with the training data and test data, respectively. Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Root Mean Standard Deviation (RMSE), and Mean Error Rate (MER) were used to evaluate the model fitting and prediction effects. Results The optimal SARIMA model was SARIMA(1,0,2)(2,1,1)₁₂. The optimal ETS model was ETS(M, N, M), and the smoothing parameter =0.738，=1*10. The optimal NNAR model was NNAR(13,1,7)₁₂. The residuals of the three single models were white noise (P>0.05). The expression of combined model A was ŷ=0.134*y_SARIMA+0.162*y_ETS+0.704*y_NNAR; the expression of combined model B was ŷ=0.246*y_SARIMA+0.435*y_ETS+0.319*y_NNAR. The MAPE, MAE, RMSE, and MER fitted by SARIMA, ETS, NNAR, combined model A and combined model B were 24.10%, 0.11, 0.17, 23.29%; 17.14%, 0.08, 0.14, 17.96%; 6.33%, 0.02, 0.03, 4.25%; 9.03%, 0.03, 0.05, 7.51%; 13.16%, 0.06, 0.09, 12.33%, respectively. The MAPE, MAE, RMSE, and MER predicted by the five models were 18.70%, 0.05, 0.06, 19.62%; 23.83%, 0.06, 0.07, 24.49%; 28.30%, 0.07, 0.10, 29.21%; 21.69%, 0.06, 0.08, 22.63%; 17.39%, 0.05, 0.07, 18.76%, respectively. Conclusion The fitting and prediction effects of the combined models are better than the single models. The combined model based on PSO to calculate the weight of the single model is the optimal model.

Objective:To explore the active components, targets and mechanism of Guizhi Fuling Pills in the treatment of atherosclerosis (AS) based on network pharmacology and molecular docking technology.Methods:The active components and potential target information of Guizhi Fuling Pills in the treatment of AS was obtained using Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), SwissTargetPrediction database and Genecards database. The target protein interaction network was constructed by using STRING database. The DAVID database was used to perform the Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment on potential targets. AutoDockVina and PyMOL software were used to verify the molecular docking of the main active components and key targets of Guizhi Fuling Pills.Results:A total of 74 active components, 239 potential targets and 4 710 AS-related disease targets were screened, and 182 intersection targets were obtained. A total of 484 biological process items, 132 molecular function items and 74 cellular component items were obtained by GO functional enrichment analysis, and 116 signal pathways were screened by KEGG enrichment analysis. The results of molecular docking suggested that the active components of Guizhi Fuling Pills have good binding activity to the key intersection targets.Conclusion:The active components of Guizhi Fuling Pills, such as sitosterol and paeoniflorin, mainly treat AS by regulating estrogen signal pathway and inflammatory signal pathway through TNF, VEGFA and other targets.

Objective To explore the applicability of the TBATS in predicting the incidence of mumps. Methods The incidence of mumps of Jiangxi Province from 2004 to 2017 was used as the demonstration data. The incidence of mumps in Jiangxi Province from July to December 2017 was used as test data. The training data from January 2004 to June 2017 were used to train the TBATS and the SARIMA, and predict the value from July to December 2017. The fitted and predicted values were compared with the test data. The MAPE, RMSE, MAE and MER were used to evaluate model fitting and prediction effects. Results SARIMA (1,0,0)(1,1,0)₁₂ with drift was the optimal SARIMA. The MAPE, MAE, RMSE and MER fitted by the TBATS and the SARIMA were 15.06%, 0.21, 0.29, 13.57% and 21.93%, 0.29, 0.41, 18.73%, respectively. The MAPE, MAE, RMSE and MER predicted by the TBATS and the SARIMA were 7.95%, 0.08, 0.11, 7.12% and 15.33%, 0.17, 0.18, 14.93%. Conclusion The TBATS has high accuracy in predicting the incidence of mumps and is worthy of popularization and application.

The efficient production of lignocellulolytic enzyme systems is an important support for large-scale biorefinery of plant biomass. On-site production of lignocellulolytic enzymes could increase the economic benefits of the process by lowering the cost of enzyme usage. Penicillium species are commonly found lignocellulose-degrading fungi in nature, and have been used for industrial production of cellulase preparations due to their abilities to secrete complete and well-balanced lignocellulolytic enzyme systems. Here, we introduce the reported Penicillium species for cellulase production, summarize the characteristics of their enzymes, and describe the strategies of strain engineering for improving the production and performance of lignocellulolytic enzymes. We also review the progress in fermentation process optimization regarding the on-site production of lignocellulolytic enzymes using Penicillium species, and suggest prospect of future work from the perspective of building a "sugar platform" for the biorefinery of lignocellulosic biomass.
Biomass ; Cellulase/metabolism* ; Fermentation ; Fungi/metabolism* ; Lignin/metabolism* ; Penicillium

Objective:To retrospectively analyze the setup errors of thermoplastic head and shoulder molds alone or combined with vacuum pad in hypofractionated stereotactic radiotherapy (HFSRT) for non-small cell lung cancer (NSCLC) with brain metastases.Methods:Fifty-four NSCLC patients with brain metastases who received HFSRT from 2017 to 2019 were enrolled in this study. Twenty-four patients were fixed with thermoplastic head and shoulder molds (group A), and 30 patients were fixed with thermoplastic head and shoulder molds plus vacuum pad (group B). The interfraction and intrafraction setup errors were acquired from cone-beam CT online image registration before and after the HFSRT. Optical surface system was applied in monitoring the intrafraction setup errors. The setup errors in each direction between two groups were analyzed by independent samples t-test. Results:For the interfraction setup errors of the whole group, the proportion of the horizontal setup errors of ≥3mm was 7.0% to 15.4% and 7.0% to 12.6% for the rotation setup errors of ≥2°. In group A, the anteroposterior setup error was (1.035±1.180)mm, significantly less than (1.512±0.955)mm in group B ( P=0.009). In group A, the sagittal rotation setup error was 0.665°±0.582°, significantly less than 0.921°±0.682° in group B ( P=0.021). For the intrafraction setup errors of the whole group, the proportion of horizontal setup errors of ≥1mm was 0% to 0.7%, whereas no rotation setup error of ≥1° were observed. In group B, bilateral, anteroposterior and sagittal rotation setup errors were (0.047±0.212)mm, (0.023±0.152)mm and 0.091°±0.090°, significantly less compared with (0.246±0.474)mm, (0.140±0.350)mm and 0.181°±0.210° in group A ( P=0.004, P=0.020, P=0.001), respectively. Optical surface monitoring data were consistent with the obtained results. Conclusions:Thermoplastic head and shoulder molds (with or without vacuum pad) combined with online image registration and six-dimensional robotic couch correction can be applied in HFSRT for brain metastases from NSCLC. The intrafraction setup errors in group B are smaller than those in group A. Optical surface system has certain value in monitoring the intrafractional movement.

Since the outbreak of the COVID-19 at the end of December 2019, Hubei province has actively adopted a series of prevention and control measures such as ^“quarantine, treatment, testing, and containment^”, and have basically blocked the spread of COVID-19. However, with the development of overseas epidemics and the occurrence of case clusters in local areas, we not only face the threat of imported cases, but also face the urgent need to resume normal work and daily life. This puts forward higher requirements for regular prevention and control of COVID-19. Therefore, we should more deeply understand the significance of regular prevention and control as well as the epidemic situation in our province, summarize experience and lessons, and adhere to the prevention and control strategy of ^“government-led, group-specialist combination, and specialized-oriented^”. Meanwhile, it is necessary to implement the working requirement that combines regular prevention and control surveillance with rapid emergency response to local COVID-19 outbreaks. Furthermore, we should establish a regular multi-point trigger early warning mechanism for COVID-19, strengthen the reserve of emergency supplies and carry out training and drills on epidemic prevention and control across the province to make full preparations for the coming autumn and winter epidemics. The most important is to reform the system of disease prevention and control and public health, comprehensively improve the ability of prevention and treatment, and promote the modernization of public health governance.

Objective To understand the epidemiological characteristics of the novel coronavirus diseases 2019 (COVID-19), and to scientifically guide the prevention and control of COVID-19 in Hubei Province. Methods All COVID-19 cases reported online in Hubei Province as of March 31, 2020 were extracted from Hubei^'s Infectious Disease Information System. The epidemic curve, age and sex characteristics, and spatiotemporal distribution characteristics of the COVID-19 cases were analyzed. Results As of March 31, 2020, a total of 70 764 cases were reported in Hubei Province, including 49 195 confirmed cases. A total of 4 579 deaths occurred among the confirmed cases, and the reported case fatality rate was 6.47%. The peak of the onset of symptoms occurred from January 20 to February 14, 2020. The sex ratio of male to female of the confirmed cases was 0.99: 1, and most were 30-69 years old. The cases diagnosed before January 5 were mainly reported by Wuhan City. From January 6 to January 31, all counties and districts in the province reported that the incidence of confirmed COVID-19 cases began to rise, and about 50% counties reported that the morbidity rate of confirmed COVID-19 cases was over 10 cases per 100 000. The morbidity rate of COVID-19 cases rose rapidly between February 1-15, and then gradually reached its peak after February 16. Conclusion Wuhan City of Hubei Province first discovered and reported the COVID-19 outbreak. The onset of symptoms peaked in January 20 to February 14, and the 30-69 years old group was the key population. Many measures such as restricting personnel movement, reducing contact, and strengthening health education played an important role in controlling the outbreak of COVID-19 in Hubei.