Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 5 de 5
Filter
1.
Computer Applications in Engineering Education ; 2023.
Article in English | Scopus | ID: covidwho-2246641

ABSTRACT

Building practical programming competency requires a long-lasting journey of discovery, trial and error, learning and improvement. This article presents essential findings of a case study of a Python programming contest with an automatic judgement system for Competitive Programming training extending the learning experiences for students in an introductory course, computational thinking and problem-solving. The benefits and challenges are discussed. Due to the coronavirus disease 2019 (COVID-19) epidemic, a hybrid model of the contest was adopted, that is, some students participated in the contest on-site, while others participated remotely. To alleviate human effort in judging the submissions, the DOMjudge platform, a web-based automatic judgement system, has been deployed as an online automatic judgement system and contest management in competitive programming. The implementation roadmap and framework were provided. The contest problems and contestants' performances were discussed. Not many junior contestants could solve at least one problem(s), and competitive computing training should be offered if the students are keen on open competitions. An empirical study was conducted to evaluate the student feedback after the contest. Preliminary results revealed that the contest offering the chance to stimulate student learning interests could enhance their independent learning, innovative thinking and problem-solving skills, and could thus lead to the overall benefits of the learning experiences, which further encourage them to participate in future contests to improve their learning and therefore enhance their employability. Employers often treasure student experiences in competitive programming events, like association for computing machinery programming contests, Google Code Jam or Microsoft Imagine Cup. Sharp vision requiring skills to tackle unseen problems within a short period is also instrumental to students planning for graduate school. © 2023 Wiley Periodicals LLC.

2.
Chinese Journal of Pharmacology and Toxicology ; 35(8):561-574, 2021.
Article in Chinese | EMBASE | ID: covidwho-1896941

ABSTRACT

Since the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the number of SARS-CoV-2 infections has been increasing and health care is facing huge challenges. Innovative drug development in emergency and the development of new indications for the treatment of Coronavirus disease 2019 (COVID-19) on the market have become critical to finding effective drugs and optimal treatment options for COVID-19. The angiotensin converting enzyme 2 (ACE2) -inducing mechanism of SARS-CoV-2 invasion into host cells and the potential therapeutic targets based on SARS-CoV-2 and (or) host include RNA-dependent RNA polymerase, 3-chymotrypsin-like protease, papain like protease, Janus kinase, interleukine-6 and immune modulators etc. Based on the pharmacological mechanism mentioned above the clinical research and development of new indications and innovative drugs for the treatment of COVID-19 have achieved great progress, but no specific drugs have been found. Some traditional Chinese medicines can block the SARS-CoV-2 replication cycle, regulate human immune response and play an important role in the treatment of COVID-19. The new drugs for COVID-19 are undergoing Phase I clinical studies worldwide, and biologic drugs are gaining momentum, accounting for 67% of the total. The problems with the research and development of drugs for COVID-19 treatment in China include inadequate of biological safety laboratories, less research on SARS-CoV-2 reacting mechanisms, shortages of non-clinical cells and animal models, imperfect research platforms for quantitative pharmacological research and training systems of professionals and poor levels of informatization of drug clinical trials and sample detection. It is hoped that China can take this opportunity to improve the ability to develop new drugs in emergency and better protect human health.

3.
2020 Ieee International Conference on Bioinformatics and Biomedicine ; : 2306-2312, 2020.
Article in English | Web of Science | ID: covidwho-1354399

ABSTRACT

Traditional Chinese medicine has been used to treat and prevent infectious diseases for thousands of years, and has accumulated a large number of effective prescriptions. Deep learning methods provide powerful applications in calculating interactions between drugs and targets. In this study, we try to use the method of deep learning to reposition molecules of Chinese medicines (CMs) and the targets of syndrome coronavirus 2 (SARS-CoV-2). A deep convolution neural network with residual module (DCNN-Res) is constructed and trained on KIBA dataset. The accuracy of predicting the binding affinity of drug-target pairs is 85.33%. By ranking binding affinity scores of 433 molecules in 35 CMs to 6 targets of SARS-Cov-2, DCNN-Res recommends 30 possible repositioning molecules. The consistency between our result and the latest research is 0.827. The molecules in Gancao and Huangqin have a strong binding affinity to targets of SARS-CoV-2, which is also consistent with the latest research.

4.
Pharmaceutics ; 13(4):14, 2021.
Article in English | MEDLINE | ID: covidwho-1208951

ABSTRACT

Since coronavirus disease 2019 (COVID-19) is a serious new worldwide public health crisis with significant morbidity and mortality, effective therapeutic treatments are urgently needed. Drug repurposing is an efficient and cost-effective strategy with minimum risk for identifying novel potential treatment options by repositioning therapies that were previously approved for other clinical outcomes. Here, we used an integrated network-based pharmacologic and transcriptomic approach to screen drug candidates novel for COVID-19 treatment. Network-based proximity scores were calculated to identify the drug-disease pharmacological effect between drug-target relationship modules and COVID-19 related genes. Gene set enrichment analysis (GSEA) was then performed to determine whether drug candidates influence the expression of COVID-19 related genes and examine the sensitivity of the repurposing drug treatment to peripheral immune cell types. Moreover, we used the complementary exposure model to recommend potential synergistic drug combinations. We identified 18 individual drug candidates including nicardipine, orantinib, tipifarnib and promethazine which have not previously been proposed as possible treatments for COVID-19. Additionally, 30 synergistic drug pairs were ultimately recommended including fostamatinib plus tretinoin and orantinib plus valproic acid. Differential expression genes of most repurposing drugs were enriched significantly in B cells. The findings may potentially accelerate the discovery and establishment of an effective therapeutic treatment plan for COVID-19 patients.

5.
Zhonghua Jie He He Hu Xi Za Zhi ; 44(3): 230-236, 2021 Mar 12.
Article in Chinese | MEDLINE | ID: covidwho-1134266

ABSTRACT

Objective: To explore a modified CT scoring system, its feasibility for disease severity evaluation and its predictive value in coronavirus disease 2019 (COVID-19) patients. Methods: This study was a multi-center retrospective cohort study. Patients confirmed with COVID-19 were recruited in three medical centers located in Beijing, Wuhan and Nanchang from January 27, 2020 to March 8, 2020. Demographics, clinical data, and CT images were collected. CT were analyzed by two emergency physicians of more than ten years' work experience independently through a modified scoring system. Final score was determined by average score from the two reviewers if consensus was not reached. The lung was divided into 6 zones (upper, middle, and lower on both sides) by the level of trachea carina and the level of lower pulmonary veins. The target lesion types included ground-glass opacity (GGO), consolidation, overall lung involvement, and crazy-paving pattern. Bronchiectasis, cavity, pleural effusion, etc., were not included in CT reading and analysis because of low incidence. The reviewers evaluated the extent of the targeted patterns (GGO, consolidation) and overall affected lung parenchyma for each zone, using Likert scale, ranging from 0-4 (0=absent; 1=1%-25%; 2=26%-50%; 3=51%-75%; 4=76%-100%). Thus, GGO score, consolidation score, and overall lung involvement score were sum of 6 zones ranging from 0-24. For crazy-paving pattern, it was only coded as absent or present (0 or 1) for each zone and therefore ranging from 0-6. Results: A total of 197 patients from 3 medical centers and 522 CT scans entered final analysis. The median age of the patients was 64 years, and 54.8% were male. There were 76(38.8%) patients had hypertension and 30(15.3%) patients had diabetes mellitus. There were 75 of the patients classified as moderate cases, as well as 95 severe cases and 27 critical cases. As initial symptom, dry cough occurred in 170 patients, 134 patients had fever, and 125 patients had dyspnea. Reparatory rate, oxygen saturation, lymphocyte count and CURB 65 score on admission day varied among patients with different disease severity scale. There were 50 of the patients suffered from deterioration during hospital stay. The median time consumed for each CT by clinicians was 86.5 seconds. Cronbach's alpha for GGO, consolidation, crazy-paving pattern, and overall lung involvement between two clinicians were 0.809, 0.712, 0.678, and 0.906, respectively, showing good or excellent inter-rater correlation. There were 193 (98.0%) patients had GGO, 147 (74.6%) had consolidation, and 126(64.0%) had crazy-paving pattern throughout clinical course. Bilateral lung involvement was observed in 183(92.9%) patients. Median time of interval for CT scan in our study was 7 days so that the whole clinical course was divided into stages by week for further analysis. From the second week on, the CT scores of various types of lesions in severe or critically patients were higher than those of moderate cases. After the fifth week, the course of disease entered the recovery period. The CT score of the upper lung zones was lower than that of other zones in moderate and severe cases. Similar distribution was not observed in critical patients. For moderate cases, the ground glass opacity score at the second week had predictive value for the escalation of the severity classification during hospitalization. The area under the receiver operating characteristic curve was 0.849, the best cut-off value was 5 points, with sensitivity of 84.2% and specificity of 75.0%. Conclusions: It is feasible for clinicians to use the modified semi-quantitative CT scoring system to evaluate patients with COVID-19. Severe/critical patients had higher scores for ground glass opacity, consolidation, crazy-paving pattern, and overall lung involvement than moderate cases. The ground glass opacity score in the second week had an optimal predictive value for escalation of disease severity during hospitalization in moderate patients on admission. The frequency of CT scan should be reduced after entering the recovery stage.


Subject(s)
COVID-19 , Lung/diagnostic imaging , Radiography, Thoracic/standards , Tomography, X-Ray Computed/methods , China , Female , Humans , Male , Predictive Value of Tests , Radiography, Thoracic/methods , SARS-CoV-2 , Spatial Analysis
SELECTION OF CITATIONS
SEARCH DETAIL