Medical image mis-segmentation region refinement framework based on dynamic graph convolution

In medical image segmentation tasks, it is hard for traditional Convolutional Neural Network (CNN) to capture essential information such as spatial structure and global contextual semantic features since it suffers from a limited receptive field. The deficiency weakens the CNN segmentation performance in the lesion boundary regions. To handle the aforementioned problems, a medical image mis-segmentation region refinement framework based on dynamic graph convolution is proposed to refine the boundary and under-segmentation regions. The proposed framework first employs a lightweight dual-path network to detect the boundaries and nearby regions, which can further obtain potentially misclassified pixels from the coarse segmentation results of the CNN. Then, we construct the pixels into the appropriate graphs by CNN-extracted features. Finally, we design a dynamic residual graph convolutional network to reclassify the graph nodes and generate the final refinement results. We chose UNet and its eight representative improved networks as the basic networks and tested them on the COVID, DSB, and BUSI datasets. Experiments demonstrated that the average Dice of our framework is improved by 1.79%, 2.29%, and 2.24%, the average IoU is improved by 2.30%, 3.53%, and 2.39%, and the Se is improved by 5.08%, 4.78%, and 5.31% respectively. The experimental results prove that the proposed framework has the refinement capability to remarkably strengthen the segmentation result of the basic network. Furthermore, the framework has the advantage of high portability and usability, which can be inserted into the end of mainstream medical image segmentation networks as a plug-and-play enhancement block.

Study on the COVID-19 epidemic in mainland China between November 2022 and January 2023, with prediction of its tendency.

The prediction system EpiSIX was used to study the COVID-19 epidemic in mainland China between November 2022 and January 2023, based on reported data from December 9, 2022, to January 30, 2023, released by The Chinese Center for Disease Control and Prevention on February 1, 2023. Three kinds of reported data were used for model fitting: the daily numbers of positive nucleic acid tests and deaths, and the daily number of hospital beds taken by COVID-19 patients. It was estimated that the overall infection rate was 87.54% and the overall case fatality rate was 0.078%-0.116% (median 0.100%). Assuming that a new COVID-19 epidemic outbreak would start in March or April of 2023, induced by a slightly more infectious mutant strain, we predicted a possible large rebound between September and October 2023, with a peak demand of between 800,000 and 900,000 inpatient beds. If no such new outbreak was induced by other variants, then the current COVID-19 epidemic course in mainland China would remain under control until the end of 2023. However, it is suggested that the necessary medical resources be prepared to manage possible COVID-19 epidemic emergencies in the near future, especially for the period between September and October 2023.

Inhibitory activities of alginate phosphate and sulfate derivatives against SARS-CoV-2 in vitro.

Alginate derivatives have been demonstrated remarkable antiviral activities. Here we firstly identified polymannuronate phosphate (PMP) as a highly potential anti-SARS-CoV-2 agent. The structure-activity relationship showed polymannuronate monophosphate (PMPD, Mw: 5.8 kDa, P%: 8.7 %) was the most effective component to block the interaction of spike to ACE2 with an IC50 of 85.5 nM. Surface plasmon resonance study indicated that PMPD could bind to spike receptor binding domain (RBD) with the KD value of 78.59 nM. Molecular docking further suggested that the probable binding site of PMPD to spike RBD protein is the interaction interface between spike and ACE2. PMPD has the potential to inhibit the SARS-CoV-2 infection in an independent manner of heparan sulfate proteoglycans. In addition, polyguluronate sulfate (PGS) and propylene glycol alginate sodium sulfate (PSS) unexpectedly showed 3CLpro inhibition with an IC50 of 1.20 µM and 1.42 µM respectively. The polyguluronate backbone and sulfate group played pivotal roles in the 3CLpro inhibition. Overall, this study revealed the potential of PMPD as a novel agent against SARS-CoV-2. It also provided a theoretical basis for further study on the role of PGS and PSS as 3CLpro inhibitors.

Inhibitory activities of alginate phosphate and sulfate derivatives against SARS-CoV-2 in vitro

Alginate derivatives have been demonstrated remarkable antiviral activities. Here we firstly identified polymannuronate phosphate (PMP) as a highly potential anti-SARS-CoV-2 agent. The structure-activity relationship showed polymannuronate monophosphate (PMPD, Mw: 5.8 kDa, P%: 8.7 %) was the most effective component to block the interaction of spike to ACE2 with an IC50 of 85.5 nM. Surface plasmon resonance study indicated that PMPD could bind to spike receptor binding domain (RBD) with the KD value of 78.59 nM. Molecular docking further suggested that the probable binding site of PMPD to spike RBD protein is the interaction interface between spike and ACE2. PMPD has the potential to inhibit the SARS-CoV-2 infection in an independent manner of heparan sulfate proteoglycans. In addition, polyguluronate sulfate (PGS) and propylene glycol alginate sodium sulfate (PSS) unexpectedly showed 3CLpro inhibition with an IC50 of 1.20 μM and 1.42 μM respectively. The polyguluronate backbone and sulfate group played pivotal roles in the 3CLpro inhibition. Overall, this study revealed the potential of PMPD as a novel agent against SARS-CoV-2. It also provided a theoretical basis for further study on the role of PGS and PSS as 3CLpro inhibitors. Graphical Unlabelled Image

Hydrogen bonding penalty used for virtual screening to discover potent inhibitors for Papain-Like cysteine proteases of SARS-CoV-2.

The Papain-Like proteases (PLpro) of SARS-CoV-2 play a crucial role in viral replication and the formation of nonstructural proteins. To find available inhibitors, the 3D structure of PLpro of SARS2 was obtained by homologous modelling, and we used this structure as a target to search for inhibitors through molecular docking and MM/GBSA binding free energy rescoring. A novel hydrogen bonding penalty was applied to the screening process, which meanwhile took desolvation into account. Finally, 61 compounds were acquired and 4 of them with IC50 at micromolar level tested in vitro enzyme activity assay, which includes clinical drugs tegaserod. Considering the importance of crystal water molecules, the 4 compounds were re-docked and considered bound waters in the active site as a part of PLpro. The binding modes of these 4 compounds were further explored with metadynamics simulations. The hits will provide a starting point for future key interactions identified and lead optimization targetting PLpro.

Anti-SARS-CoV-2 IgG antibodies pre- and post-vaccination behavior in health workers from a Guatemalan cancer center.

INTRODUCTION: The study of anti-SARS-CoV-2 IgG antibodies allows asymptomatic individuals with COVID-19 to be identified, and post-infection and post-vaccination immunity status to be evaluated. OBJECTIVE: To know the behavior of anti-SARS-CoV-2 IgG antibodies before and after vaccination in workers of a cancer center. METHODS: Prior to the application of the vaccine, the presence of anti-SARS-CoV-2 IgG antibodies (n = 171) was analyzed by evaluating anti-N IgG antibodies; post-vaccination, after receiving the second dose, anti-S IgG antibodies were evaluated (n = 60). RESULTS: Prior to vaccination, IgG antibodies were present in 18.71% of participants; they were detected in 65.22% of those with prior history of COVID-19 diagnosis and in 11.49% of those without it. The positions with the highest prevalence were nurses (28.26%), paramedics (27.59%) and administrative workers (27.78%), p < 0.01. Anosmia, ageusia and chest tightness were associated with the presence of IgG (p < 0.05). Post-vaccination, all participants developed IgG antibodies; people with a previous COVID-19 diagnosis had higher titers: 10,277 vs. 6,819 AU/mL, p < 0.001. CONCLUSIONS: The study of anti-SARS-CoV-2 IgG antibodies allowed asymptomatic health workers to be identified. A high percentage of participants with prior COVID-19 diagnosis had antibodies. All participants developed IgG antibodies after vaccination, with higher titers being identified in those with previous infection.

INTRODUCCIÓN: El estudio de anticuerpos IgG anti-SARS-CoV-2 permite identificar individuos asintomáticos con COVID-19 y evaluar la inmunidad posinfección y posvacunación. OBJETIVO: Conocer el comportamiento de los anticuerpos IgG anti-SARS-CoV-2 pre y posvacunación en trabajadores de un centro oncológico. MÉTODOS: Antes de aplicar la vacuna se analizaron los anticuerpos IgG anti-SARS-CoV-2 (n = 171) con la evaluación de IgG anti-N; después de la segunda dosis se evaluó IgG anti-S (n = 60). RESULTADOS: Prevacunación, los anticuerpos IgG estaban presentes en 18.71 % de los participantes; se detectaron en 65.22 % de aquellos con antecedente de diagnóstico de COVID-19 y en 11.49 % de aquellos sin antecedentes. Los profesiones con mayor prevalencia fueron enfermeros (28.26 %), paramédicos (27.59 %) y administrativos (27.78 %), p < 0.01. La anosmia, ageusia y opresión en el pecho se asociaron a la presencia de IgG (p < 0.05). Posvacunación, todos los participantes desarrollaron IgG; las personas con diagnóstico previo de COVID-19 presentaron mayores títulos: 10 277 versus 6819 UA/mL, p < 0.001. CONCLUSIONES: El estudio de anticuerpos IgG anti-SARS-CoV-2 permitió identificar a trabajadores de salud asintomáticos. Un alto porcentaje de los participantes con diagnóstico previo de COVID-19 presentó anticuerpos. Todos los participantes desarrollaron anticuerpos IgG posvacunación; las personas con infección previa presentaron una cuantificación más alta de títulos.

Identify potent SARS-CoV-2 main protease inhibitors via accelerated free energy perturbation-based virtual screening of existing drugs.

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and thus repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a restraint energy distribution (RED) function, making the practical FEP-ABFE-based virtual screening of the existing drug library possible. As a result, out of 25 drugs predicted, 15 were confirmed as potent inhibitors of SARS-CoV-2 Mpro The most potent one is dipyridamole (inhibitory constant Ki = 0.04 µM) which has shown promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki = 0.36 µM) and chloroquine (Ki = 0.56 µM) were also found to potently inhibit SARS-CoV-2 Mpro We anticipate that the FEP-ABFE prediction-based virtual screening approach will be useful in many other drug repurposing or discovery efforts.