Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning.

Well-annotated medical datasets enable deep neural networks (DNNs) to gain strong power in extracting lesion-related features. Building such large and well-designed medical datasets is costly due to the need for high-level expertise. Model pre-training based on ImageNet is a common practice to gain better generalization when the data amount is limited. However, it suffers from the domain gap between natural and medical images. In this work, we pre-train DNNs on ultrasound (US) domains instead of ImageNet to reduce the domain gap in medical US applications. To learn US image representations based on unlabeled US videos, we propose a novel meta-learning-based contrastive learning method, namely Meta Ultrasound Contrastive Learning (Meta-USCL). To tackle the key challenge of obtaining semantically consistent sample pairs for contrastive learning, we present a positive pair generation module along with an automatic sample weighting module based on meta-learning. Experimental results on multiple computer-aided diagnosis (CAD) problems, including pneumonia detection, breast cancer classification, and breast tumor segmentation, show that the proposed self-supervised method reaches state-of-the-art (SOTA). The codes are available at https://github.com/Schuture/Meta-USCL.

E-Learning during COVID-19: perspectives and experiences of the faculty and students.

BACKGROUND: Aimed to corroborate students' and faculty's experiences with e-learning during the current pandemic. METHODS: A cross-sectional study was conducted from February to June 2020. Seven surveys were distributed electronically to all undergraduate students and the faculty (4 to students and 3 to teachers) at the Southern Medical University (China). Descriptive statistics and t-tests were used to analyze the data. Statistical significance was set at p < .05. RESULTS: Most students had some exposure to e-learning prior to the all e-learning regiment, contrasted with close to 90% of teachers having no or very limited experience. Students' perceptions of the most helpful e-learning activities did not change significantly overall (Week 3 vs. Week 9). Approaching 60% of students (Week 9) did find online discussion/Q&A/forum helpful, an increase from less than 30% (Week 3). Among teachers, gaps emerged (Week 9) between e-teaching activities used and their perceived effectiveness. Despite pre-recorded lectures being the most frequently used method, the least gap was associated with live-stream lectures-the least used. Over time, teacher's perceived effectiveness of e-teaching vs. in-person teaching did not differ significantly overall. When the results among students (Week 7) and teachers (Week 9) were corroborated, a slightly higher percentage of teachers viewed online teaching to be less effective than in-person teaching and a slightly higher percentage of teachers viewed online teaching as far less effective. For preferred learning modes after the resumption of in-person learning, students' preferences did not differ significantly overall (Week 3 vs. week 9). Surveys conducted in Week 9 found that a slightly higher percentage of students (~ 70%) than teachers (~ 60%) preferred some forms of hybrid learning and a lower percentage of students preferred face-to-face learning only. Approximately three quarters of teachers responded that at least 50% of course materials could be mastered by students on their own. CONCLUSIONS: Overall, the perceived effectiveness of e-learning among students and teachers has not changed significantly over time. Nor have students' preferences shifted significantly for various learning modes after the in-person learning resumed. However, informative directional trends have emerged. Our research illustrates empirically the need to corroborate students' and instructors' experiences over time to inform more holistic improvements of e-learning.

Management of clinical trials for anticancer drug during the corona virus disease 2019

Since December 2019, the number of corona virus disease 2019 cases has continued to increase. With the spread of the epidemic, clinical trials of anticancer drugs are facing great challenge. Cancer patients are suffering from the tumor itself, surgery, radiotherapy and chemotherapy under special immune status, they are susceptible to the new coronavirus, and are more susceptible to infection with the coronavirus than the normal population. Therefore, this article will elaborate on the particularity of subjects in clinical trials of anticancer drugs in the context of the corona virus disease 2019, management of clinical trials, existing problems and countermeasures to expect optimizing processes and reasonable arrangements, and to ensure the safety of patients so that the smooth progress of the clinical trial is guaranteed.

Optimization of Triarylpyridinone Inhibitors of the Main Protease of SARS-CoV-2 to Low-Nanomolar Antiviral Potency.

Non-covalent inhibitors of the main protease (Mpro) of SARS-CoV-2 having a pyridinone core were previously reported with IC50 values as low as 0.018 µM for inhibition of enzymatic activity and EC50 values as low as 0.8 µM for inhibition of viral replication in Vero E6 cells. The series has now been further advanced by consideration of placement of substituted five-membered-ring heterocycles in the S4 pocket of Mpro and N-methylation of a uracil ring. Free energy perturbation calculations provided guidance on the choice of the heterocycles, and protein crystallography confirmed the desired S4 placement. Here we report inhibitors with EC50 values as low as 0.080 µM, while remdesivir yields values of 0.5-2 µM in side-by-side testing with infectious SARS-CoV-2. A key factor in the improvement is enhanced cell permeability, as reflected in PAMPA measurements. Compounds 19 and 21 are particularly promising as potential therapies for COVID-19, featuring IC50 values of 0.044-0.061 µM, EC50 values of ca. 0.1 µM, good aqueous solubility, and no cytotoxicity.

Structure-guided design of a perampanel-derived pharmacophore targeting the SARS-CoV-2 main protease.

There is a clinical need for direct-acting antivirals targeting SARS-CoV-2, the coronavirus responsible for the COVID-19 pandemic, to complement current therapeutic strategies. The main protease (Mpro) is an attractive target for antiviral therapy. However, the vast majority of protease inhibitors described thus far are peptidomimetic and bind to the active-site cysteine via a covalent adduct, which is generally pharmacokinetically unfavorable. We have reported the optimization of an existing FDA-approved chemical scaffold, perampanel, to bind to and inhibit Mpro noncovalently with IC50s in the low-nanomolar range and EC50s in the low-micromolar range. Here, we present nine crystal structures of Mpro bound to a series of perampanel analogs, providing detailed structural insights into their mechanism of action and structure-activity relationship. These insights further reveal strategies for pursuing rational inhibitor design efforts in the context of considerable active-site flexibility and potential resistance mechanisms.

Potent Noncovalent Inhibitors of the Main Protease of SARS-CoV-2 from Molecular Sculpting of the Drug Perampanel Guided by Free Energy Perturbation Calculations.

Starting from our previous finding of 14 known drugs as inhibitors of the main protease (Mpro) of SARS-CoV-2, the virus responsible for COVID-19, we have redesigned the weak hit perampanel to yield multiple noncovalent, nonpeptidic inhibitors with ca. 20 nM IC50 values in a kinetic assay. Free-energy perturbation (FEP) calculations for Mpro-ligand complexes provided valuable guidance on beneficial modifications that rapidly delivered the potent analogues. The design efforts were confirmed and augmented by determination of high-resolution X-ray crystal structures for five analogues bound to Mpro. Results of cell-based antiviral assays further demonstrated the potential of the compounds for treatment of COVID-19. In addition to the possible therapeutic significance, the work clearly demonstrates the power of computational chemistry for drug discovery, especially FEP-guided lead optimization.

Identification of 14 Known Drugs as Inhibitors of the Main Protease of SARS-CoV-2.

A consensus virtual screening protocol has been applied to ca. 2000 approved drugs to seek inhibitors of the main protease (Mpro) of SARS-CoV-2, the virus responsible for COVID-19. 42 drugs emerged as top candidates, and after visual analyses of the predicted structures of their complexes with Mpro, 17 were chosen for evaluation in a kinetic assay for Mpro inhibition. Remarkably 14 of the compounds at 100-µM concentration were found to reduce the enzymatic activity and 5 provided IC50 values below 40 µM: manidipine (4.8 µM), boceprevir (5.4 µM), lercanidipine (16.2 µM), bedaquiline (18.7 µM), and efonidipine (38.5 µM). Structural analyses reveal a common cloverleaf pattern for the binding of the active compounds to the P1, P1', and P2 pockets of Mpro. Further study of the most active compounds in the context of COVID-19 therapy is warranted, while all of the active compounds may provide a foundation for lead optimization to deliver valuable chemotherapeutics to combat the pandemic.