QBox: Partial Transfer Learning With Active Querying for Object Detection.

Object detection requires plentiful data annotated with bounding boxes for model training. However, in many applications, it is difficult or even impossible to acquire a large set of labeled examples for the target task due to the privacy concern or lack of reliable annotators. On the other hand, due to the high-quality image search engines, such as Flickr and Google, it is relatively easy to obtain resource-rich unlabeled datasets, whose categories are a superset of those of target data. In this article, to improve the target model with cost-effective supervision from source data, we propose a partial transfer learning approach QBox to actively query labels for bounding boxes of source images. Specifically, we design two criteria, i.e., informativeness and transferability, to measure the potential utility of a bounding box for improving the target model. Based on these criteria, QBox actively queries the labels of the most useful boxes from the source domain and, thus, requires fewer training examples to save the labeling cost. Furthermore, the proposed query strategy allows annotators to simply labeling a specific region, instead of the whole image, and, thus, significantly reduces the labeling difficulty. Extensive experiments are performed on various partial transfer benchmarks and a real COVID-19 detection task. The results validate that QBox improves the detection accuracy with lower labeling cost compared to state-of-the-art query strategies for object detection.

Peimine inhibits variants of SARS‐CoV‐2 cell entry via blocking the interaction between viral spike protein and ACE2

Coronavirus disease 2019 (COVID‐19) is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Several vaccines against SARS‐CoV‐2 have been approved;however, variants of concern (VOCs) can evade vaccine protection. Therefore, developing small compound drugs that directly block the interaction between the viral spike glycoprotein and ACE2 is urgently needed to provide a complementary or alternative treatment for COVID‐19 patients. We developed a viral infection assay to screen a library of approximately 126 small molecules and showed that peimine inhibits VOCs viral infections. In addition, a fluorescence resonance energy transfer (FRET) assay showed that peimine suppresses the interaction of spike and ACE2. Molecular docking analysis revealed that peimine exhibits a higher binding affinity for variant spike proteins and is able to form hydrogen bonds with N501Y in the spike protein. These results suggest that peimine, a compound isolated from Fritillaria, may be a potent inhibitor of SARS‐CoV‐2 variant infection. PRACTICAL APPLICATIONS: In this study, we identified a naturally derived compound of peimine, a major bioactive alkaloid extracted from Fritillaria, that could inhibit SARS‐CoV‐2 variants of concern (VOCs) viral infection in 293T/ACE2 and Calu‐3 lung cells. In addition, peimine blocks viral entry through interruption of spike and ACE2 interaction. Moreover, molecular docking analysis demonstrates that peimine has a higher binding affinity on N501Y in the spike protein. Furthermore, we found that Fritillaria significantly inhibits SARS‐CoV‐2 viral infection. These results suggested that peimine and Fritillaria could be a potential functional drug and food for COVID‐19 patients.

Serological responses triggered by different SARS-CoV-2 vaccines against SARS-CoV-2 variants in Taiwan.

Broadly neutralizing ability is critical for developing the next-generation SARS-CoV-2 vaccine. We collected sera samples between December 2021-January 2022 from 113 Taiwan naïve participants after their second dose of homologous vaccine (AZD1222, mRNA-1273, BNT162-b2, and MVC-COV1901) and compared the differences in serological responses of various SARS-CoV-2 vaccines. Compared to AZD1222, the two mRNA vaccines could elicit a higher level of anti-S1-RBD binding antibodies with higher broadly neutralizing ability evaluated using pseudoviruses of various SARS-CoV-2 lineages. The antigenic maps produced from the neutralization data implied that Omicron represents very different antigenic characteristics from the ancestral lineage. These results suggested that constantly administering the vaccine with ancestral Wuhan spike is insufficient for the Omicron outbreak. In addition, we found that anti-ACE2 autoantibodies were significantly increased in all four vaccinated groups compared to the unvaccinated pre-pandemic group, which needed to be investigated in the future.

GSAL: Geometric structure adversarial learning for robust medical image segmentation

• A new learning mechanism for medical image segmentation. We introduce a novel Geometric Structure Learning Mechanism (GSLM) that enhances model learning "focus, path, and difficulty". It enables geometric structure attention learning to bridge image features with large differences, thus capturing the contextual dependencies of images. The image features maintain consistency and continuity along the internal and external geometry structure, which improves the integrity and boundary accuracy of the segmentation results. To the best of our knowledge, we are the first attempt to explicitly establish the target's geometric structure, which has been successfully applied to medical image segmentation. • A novel geometric structure adversarial learning for robust medical image segmentation. We present the geometric structure adversarial learning model (GSAL) that consists of a geometric structure generator, skeleton-like and boundary discriminators, and a geometric structure fusion sub-network. The generator yields the geometric structure that preserves interior characteristics consistency and external boundary structure continuity. The dual discriminators are trained simultaneously to enhance and correct the characterization of interior structure and boundary structure, respectively. The fusion sub-network aims to fuse the geometric structure that optimized by adversarial learning to refine the final segmentation results with higher credibility. • State-of-art results on widely-used benchmarks. Our GSAL achieves SOTA performance on a variety of benchmarks, including Kvasir&CVC-612 dataset, COVID-19 dataset, and LIDC-IDRI dataset. It confirms the robustness and generalizability of our framework. In addition, our method has great advantages in terms of the integrity and boundary accuracy of the segmentation target compared to other competitive methods. GSAL can also achieve a considerable trade-off in terms of accuracy, inference speed, and model complexity, which helps deploy in clinical practice systems. Automatic medical image segmentation plays a crucial role in clinical diagnosis and treatment. However, it is still a challenging task due to the complex interior characteristics (e.g. , inconsistent intensity, low contrast, texture heterogeneity) and ambiguous external boundary structures. In this paper, we introduce a novel geometric structure learning mechanism (GSLM) to overcome the limitations of existing segmentation models that lack learning "focus, path, and difficulty." The geometric structure in this mechanism is jointly characterized by the skeleton-like structure extracted by the mask distance transform (MDT) and the boundary structure extracted by the mask distance inverse transform (MDIT). Among them, the skeleton-like and boundary pay attention to the trend of interior characteristics consistency and external structure continuity, respectively. With this idea, we design GSAL, a novel end-to-end geometric structure adversarial learning for robust medical image segmentation. GSAL has four components: a geometric structure generator, which yields the geometric structure to learn the most discriminative features that preserve interior characteristics consistency and external boundary structure continuity, skeleton-like and boundary structure discriminators, which enhance and correct the characterization of internal and external geometry to mutually promote the capture of global contextual dependencies, and a geometric structure fusion sub-network, which fuses the two complementary and refined skeleton-like and boundary structures to generate the high-quality segmentation results. The proposed approach has been successfully applied to three different challenging medical image segmentation tasks, including polyp segmentation, COVID-19 lung infection segmentation, and lung nodule segmentation. Extensive experimental results demonstrate that the proposed GSAL achieves favorably against most state-of-the-art methods under different evaluation metrics. The code is available at: https://github.com/DLWK/GSAL. [ BSTRACT FROM AUTHOR] Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Electrochemical sensing of dual biomolecules in live cells and whole blood samples: A flexible gold wire-modified copper-organic framework-based hybrid composite.

For clinical research, the precise measurement of hydrogen peroxide (H2O2) and glucose (Glu) is of paramount importance, due to their imbalanced concentrations in blood glucose, and reactive oxygen species (ROS) play a huge role in COVID-19 viral disease. It is critical to construct and develop a simple, rapid, flexible, long-term, and sensitive detection of H2O2 and glucose. In this paper, we have developed a unique morphological structure of MOF(Cu) on a single-walled carbon nanotube-modified gold wire (swnt@gw). Highly designed frameworks with nanotube composites enhance electron rate-transfer behavior while extending conductance and electroactive surface area.The composite sensing system delivers wide linear-range concentrations, low detection limit, and interference-free performance in co-existence with other biomolecules and metal ions. Endogenous quantitative tracking of H2O2 was performed in macrophage live-cells with the help of a strong stimulator lipopolysaccharide.The composite device was effectively utilized for the measurement of H2O2 and glucose in turbid samples of whole blood and milk samples without a pretreatment process. The practical results of biofluids showed favorable voltammetric results and acceptance recovery percentage levels between 97.49 and 98.88%. Finally, a flexible MOF-based hybrid system may provide a suitable detection platform in the construction of electro-biosensors and hold potential promise for clinical-sensory applications.

Machine Learning-Assisted Sensor Based on CsPbBr3@ZnO Nanocrystals for Identifying Methanol in Mixed Environments.

Methanol is a respiratory biomarker for pulmonary diseases, including COVID-19, and is a common chemical that may harm people if they are accidentally exposed to it. It is significant to effectively identify methanol in complex environments, yet few sensors can do so. In this work, the strategy of coating perovskites with metal oxides is proposed to synthesize core-shell CsPbBr3@ZnO nanocrystals. The CsPbBr3@ZnO sensor displays a response/recovery time of 3.27/3.11 s to 10 ppm methanol at room temperature, with a detection limit of 1 ppm. Using machine learning algorithms, the sensor can effectively identify methanol from an unknown gas mixture with 94% accuracy. Meanwhile, density functional theory is used to reveal the formation process of the core-shell structure and the target gas identification mechanism. The strong adsorption between CsPbBr3 and the ligand zinc acetylacetonate lays the foundation for the formation of the core-shell structure. The crystal structure, density of states, and band structure were influenced by different gases, which results in different response/recovery behaviors and makes it possible to identify methanol from mixed environments. Furthermore, due to the formation of type II band alignment, the gas response performance of the sensor is further improved under UV light irradiation.

Quinazolinone-Peptido-Nitrophenyl-Derivatives as Potential Inhibitors of SARS-CoV-2 Main Protease.

The severe acute respiratory syndrome coronavirus 2 main protease (SARS-CoV-2-Mpro) plays an essential role in viral replication, transcription, maturation, and entry into host cells. Furthermore, its cleavage specificity for viruses, but not humans, makes it a promising drug target for the treatment of coronavirus disease 2019 (COVID-19). In this study, a fragment-based strategy including potential antiviral quinazolinone moiety and glutamine- or glutamate-derived peptidomimetic backbone and positioned nitro functional groups was used to synthesize putative Mpro inhibitors. Two compounds, G1 and G4, exhibited anti-Mpro enzymatic activity in a dose-dependent manner, with the calculated IC50 values of 22.47 ± 8.93 µM and 24.04 ± 0.67 µM, respectively. The bio-layer interferometer measured real-time binding. The dissociation kinetics of G1/Mpro and G4/Mpro also showed similar equilibrium dissociation constants (KD) of 2.60 × 10-5 M and 2.55 × 10-5 M, respectively, but exhibited distinct association/dissociation curves. Molecular docking of the two compounds revealed a similar binding cavity to the well-known Mpro inhibitor GC376, supporting a structure-function relationship. These findings may open a new avenue for developing new scaffolds for Mpro inhibition and advance anti-coronavirus drug research.

Post-acute care for frail older people decreases 90-day emergency room visits, readmissions and mortality: An interventional study.

BACKGROUND: To evaluate the effects of post-acute care (PAC) on frail older adults after acute hospitalization in Taiwan. METHODS: This was a multicenter interventional study. Frail patients aged ≥ 75 were recruited and divided into PAC or control group. The PAC group received comprehensive geriatric assessment (CGA) and multifactorial intervention including exercise, nutrition education, and medicinal adjustments for two to four weeks, while the control group received only CGA. Outcome measures included emergency room (ER) visits, readmissions, and mortality within 90 days after PAC. RESULTS: Among 254 participants, 205 (87.6±6.0 years) were in the PAC and 49 (85.2±6.0 years) in the control group. PAC for more than two weeks significantly decreased 90-day ER visits (odds ratio [OR] 0.21, 95% confidence interval [CI] 0.10-0.43; p = 0.024), readmissions (OR 0.30, 95% CI 0.16-0.56; p < 0.001), and mortality (OR 0.20, 95% CI 0.04-0.87; p = 0.032). Having problems in self-care was an independent risk factor for 90-day ER visits (OR 2.11, 95% CI 1.17-3.78; p = 0.012), and having problems in usual activities was an independent risk factor for 90-day readmissions (OR 2.69, 95% CI 1.53-4.72; p = 0.001) and mortality (OR 3.16, 95% CI 1.16-8.63; p = 0.024). CONCLUSION: PAC program for more than two weeks could have beneficial effects on decreasing ER visits, readmissions, and mortality after an acute illness in frail older patients. Those who perceived severe problems in self-care and usual activities had a higher risk of subsequent adverse outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT Identifier: NCT05452395.

Integrated Chinese herbal medicine and Western medicine successfully resolves spontaneous subcutaneous emphysema and pneumomediastinum in a patient with severe COVID-19 in Taiwan: A case report.

CASE: Serious complications of severe coronavirus disease 2019 (COVID-19) include subcutaneous emphysema (SE) and pneumomediastinum, which are complicated to treat with conventional Western medicine. We report how combining Chinese herbal medicine (CHM) with Western medicine quickly resolved a patient's COVID-19-associated pulmonary complications, shortened hospital stay and improved quality of life. CLINICAL FEATURES AND OUTCOME: A 59-year-old male with a history of smoking and tumors was diagnosed with COVID-19 in May 2021. At hospitalization, his oxygen saturation (SpO2) was 80%, he had a continuous severe cough, rapid shallow breathing, spontaneous SE and pneumomediastinum. By Day 4 of hospitalization, his condition was worsening despite standard care, so CHM was added. After 3-5 days, his coughing had lessened and supplementary oxygen therapy was de-escalated. Nine days after starting CHM, the SE had completely resolved and the patient avoided intubation. His WHO OS 10-point Scale score had fallen from 6 to 3 points and the modified Medical Research Council Dyspnea Scale score from 4 to 2 points. He was hospitalized for 19 days. At 1 week post-discharge, the patient could handle most of his daily activities and experienced minor shortness of breath only when performing labor-intensive tasks. At 1 month, his work output was restored to pre-COVID-19 levels. CONCLUSION: CHM combined with standard Western medicine improved pulmonary function, respiratory rate, blood oxygen saturation and shortened the hospital stay of a patient with severe COVID-19 complicated by SE and pneumomediastinum.

Factors influencing neutralizing antibody titers elicited by coronavirus disease 2019 vaccines.

The World Health Organization has highlighted the importance of an international standard (IS) for severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) neutralizing antibody titer detection to calibrate diagnostic techniques. We applied an IS to calibrate neutralizing antibody titers (NTs) (international units/mL) in response to coronavirus disease 2019 (COVID-19) vaccination. Moreover, the association between different factors and neutralizing antibodies was analyzed. A total of 1,667 serum samples were collected from participants receiving different COVID-19 vaccines. Antibody titers were determined by a microneutralization assay using live viruses in a biosafety level 3 (BSL-3) laboratory and a commercial serological MeDiPro kit. The titer determined using the MeDiPro kit was highly correlated with the NT determined using live viruses and calibrated using IS. Fever and antipyretic analgesic treatment were related to neutralizing antibody responses in ChAdOx1-S and BNT162b2 vaccinations. Individuals with diabetes showed a low NT elicited by MVC-COV1901. Individuals with hypertension receiving the BNT162b2 vaccine had lower NTs than those without hypertension. Our study provided the international unit (IU) values of NTs in vaccinated individuals for the development of vaccines and implementation of non-inferiority trials. Correlation of the influencing factors with NTs can provide an indicator for selecting COVID-19 vaccines based on personal attributes.

Out-of-hospital cardiac arrest and in-hospital mortality among COVID-19 patients: A population-based retrospective cohort study.

BACKGROUND/PURPOSE: Predictors for out-of-hospital cardiac arrest (OHCA) in COVID-19 patients remain unclear. We identified the predictors for OHCA and in-hospital mortality among such patients in community isolation centers. METHODS: From May 15 to June 20, 2021, this cohort study recruited 2555 laboratory-confirmed COVID-19 patients admitted to isolation centers in Taiwan. All patients were followed up until death, discharge from the isolation center or hospital, or July 16, 2021. OHCA was defined as cardiac arrest confirmed by the absence of circulation signs and occurring outside the hospital. Multinomial logistic regressions were used to determine factors associated with OHCA and in-hospital mortality. RESULTS: Of the 37 deceased patients, 7 (18.9%) had OHCA and 30 (81.1%) showed in-hospital mortality. The mean (SD) time to OHCA was 6.6 (3.3) days from the symptom onset. After adjusting for demographics and comorbidities, independent predictors for OHCA included age ≥65 years (adjusted odds ratio [AOR]: 13.24, 95% confidence interval [CI]: 1.85-94.82), fever on admission to the isolation center (AOR: 12.53, 95% CI: 1.68-93.34), and hypoxemia (an oxygen saturation level below 95% on room air) (AOR: 26.54, 95% CI: 3.18-221.73). Predictors for in-hospital mortality included age ≥65 years (AOR: 10.28, 95% CI: 2.95-35.90), fever on admission to the isolation centers (AOR: 7.27, 95% CI: 1.90-27.83), and hypoxemia (AOR: 29.87, 95% CI: 10.17-87.76). CONCLUSIONS: Time to OHCA occurrence is rapid in COVID-19 patients. Close monitoring of patients' vital signs and disease severity during isolation is important, particularly for those with older age, fever, and hypoxemia.

Gross hematuria and IgA nephropathy flare-up following the first dose of Moderna vaccine: A case report.

BACKGROUND: Since the mass vaccination for COVID-19, several case reports indicated the risk of autoimmune disease flare-ups after the vaccination. Among them, COVID-19 vaccine-induced glomerular diseases have drawn attention worldwide. The cases demonstrating the association between the mRNA vaccine and IgA nephropathy (IgAN) exacerbation had been noticed. Mostly mentioned, the flare-ups usually occurred after the second dose. METHODS: We present a Taiwanese female with IgAN who developed gross hematuria within only six hours after the first dose of the Moderna vaccine. RESULTS: Six hours after the first dose of Moderna vaccine on 8 June 2021, the patient developed gross hematuria and significantly decreased urine output. All symptoms resolved spontaneously on the fifth day after the vaccination without any intervention. On the fourth day after the vaccination, the patient were able to back to her original condition. CONCLUSION: This was an intriguing case of IgAN flare-up following the first dose of mRNA-based COVID-19 vaccination.

Serological responses triggered by different SARS-CoV-2 vaccines against SARS-CoV-2 variants in Taiwan

Broadly neutralizing ability is critical for developing the next-generation SARS-CoV-2 vaccine. We collected sera samples between December 2021-January 2022 from 113 Taiwan naïve participants after their second dose of homologous vaccine (AZD1222, mRNA-1273, BNT162-b2, and MVC-COV1901) and compared the differences in serological responses of various SARS-CoV-2 vaccines. Compared to AZD1222, the two mRNA vaccines could elicit a higher level of anti-S1-RBD binding antibodies with higher broadly neutralizing ability evaluated using pseudoviruses of various SARS-CoV-2 lineages. The antigenic maps produced from the neutralization data implied that Omicron represents very different antigenic characteristics from the ancestral lineage. These results suggested that constantly administering the vaccine with ancestral Wuhan spike is insufficient for the Omicron outbreak. In addition, we found that anti-ACE2 autoantibodies were significantly increased in all four vaccinated groups compared to the unvaccinated pre-pandemic group, which needed to be investigated in the future.

Immunotherapy for Sepsis Induced by Infections: Clinical Evidence and Potential Targets.

Sepsis is a life-threatening organ dysfunction caused by the maladjustment of the body's response to infection. Abnormal immune response plays an important role in the progression of sepsis, and immunomodulatory therapy is a promising therapeutic strategy for sepsis. Great efforts have been made recently to elucidate the mechanism by which immune dysfunction contributes to sepsis, and identify potential biomarkers and targets for the diagnosis and therapy of sepsis induced by emerging pathogens, especially for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)that causes COVID-19. In this review, we summarize recent progress on the understanding of immune dysregulation involved in sepsis, and highlight potential biomarkers and targets to evaluate immune status of the patients with sepsis for individualized and precise immunotherapy.

Peimine inhibits variants of SARS-CoV-2 cell entry via blocking the interaction between viral spike protein and ACE2.

Coronavirus disease 2019 (COVID-19) is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several vaccines against SARS-CoV-2 have been approved; however, variants of concern (VOCs) can evade vaccine protection. Therefore, developing small compound drugs that directly block the interaction between the viral spike glycoprotein and ACE2 is urgently needed to provide a complementary or alternative treatment for COVID-19 patients. We developed a viral infection assay to screen a library of approximately 126 small molecules and showed that peimine inhibits VOCs viral infections. In addition, a fluorescence resonance energy transfer (FRET) assay showed that peimine suppresses the interaction of spike and ACE2. Molecular docking analysis revealed that peimine exhibits a higher binding affinity for variant spike proteins and is able to form hydrogen bonds with N501Y in the spike protein. These results suggest that peimine, a compound isolated from Fritillaria, may be a potent inhibitor of SARS-CoV-2 variant infection. PRACTICAL APPLICATIONS: In this study, we identified a naturally derived compound of peimine, a major bioactive alkaloid extracted from Fritillaria, that could inhibit SARS-CoV-2 variants of concern (VOCs) viral infection in 293T/ACE2 and Calu-3 lung cells. In addition, peimine blocks viral entry through interruption of spike and ACE2 interaction. Moreover, molecular docking analysis demonstrates that peimine has a higher binding affinity on N501Y in the spike protein. Furthermore, we found that Fritillaria significantly inhibits SARS-CoV-2 viral infection. These results suggested that peimine and Fritillaria could be a potential functional drug and food for COVID-19 patients.

Microwave resonant absorption of SARS-CoV-2 viruses.

Low power microwave can effectively deactivate influenza type A virus through the nonthermal structure-resonant energy transfer effect, at a frequency matching the confined-acoustic dipolar mode frequency of the virus. Currently, aerosol is considered the major route for SARS-CoV-2 transmission. For the potential microwave-based sterilization, the microwave-resonant frequency of SARS-CoV-2 must be unraveled. Here we report a microwave absorption spectroscopy study of the SARS-CoV-2 and HCoV-229E viruses through devising a coplanar-waveguide-based sensor. Noticeable microwave absorption can be observed, while we identified the resonant frequencies of the 1st and 2nd dipolar modes of SARS-CoV-2 virus as 4 and 7.5 GHz respectively. We further found that the resonant frequencies are invariant to the virus titer, and we also studied the microwave absorption of HCoV-229E in weak acidity medium to simulate the common pH value in fluid secretion. Our results suggest the possible radiation frequency for the recently proposed microwave sterilization devices to inactivate SARS-CoV-2 virus through a nonthermal mechanism so as to control the disease transmission in the post-pandemic era.

Characterization of Altered Oropharyngeal Microbiota in Hospitalized Patients With Mild SARS-CoV-2 Infection.

Coronavirus disease 2019 (COVID-19) remains a serious emerging global health problem, and little is known about the role of oropharynx commensal microbes in infection susceptibility and severity. Here, we present the oropharyngeal microbiota characteristics identified by full-length 16S rRNA gene sequencing through the NANOPORE platform of oropharynx swab specimens from 10 mild COVID-19 patients and 10 healthy controls. Our results revealed a distinct oropharyngeal microbiota composition in mild COVID-19 patients, characterized by enrichment of opportunistic pathogens such as Peptostreptococcus anaerobius and Pseudomonas stutzeri and depletion of Sphingomonas yabuuchiae, Agrobacterium sullae, and Pseudomonas veronii. Based on the relative abundance of the oropharyngeal microbiota at the species level, we built a microbial classifier to distinguish COVID-19 patients from healthy controls, in which P. veronii, Pseudomonas fragi, and S. yabuuchiae were identified as the most prominent signatures for their depletion in the COVID-19 group. Several members of the genus Campylobacter, especially Campylobacter fetus and Campylobacter rectus, which were highly enriched in COVID-19 patients with higher severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load and showed a significant correlation with disease status and several routine clinical blood indicators, indicate that several bacteria may transform into opportunistic pathogen in COVID-19 patients when facing the challenges of viral infection. We also found the diver taxa Streptococcus anginosus and Streptococcus alactolyticus in the network of disease patients, suggesting that these oropharynx microbiota alterations may impact COVID-19 severity by influencing the microbial association patterns. In conclusion, the low sample size of SARS-CoV-2 infection patients (n = 10) here makes these results tentative; however, we have provided the overall characterization that oropharyngeal microbiota alterations and microbial correlation patterns were associated with COVID-19 severity in Anhui Province.

Molecular Virology of SARS-CoV-2 and Related Coronaviruses.

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The global COVID-19 pandemic continues to threaten the lives of hundreds of millions of people, with a severe negative impact on the global economy. Although several COVID-19 vaccines are currently being administered, none of them is 100% effective. Moreover, SARS-CoV-2 variants remain an important worldwide public health issue. Hence, the accelerated development of efficacious antiviral agents is urgently needed. Coronavirus depends on various host cell factors for replication. An ongoing research objective is the identification of host factors that could be exploited as targets for drugs and compounds effective against SARS-CoV-2. In the present review, we discuss the molecular mechanisms of SARS-CoV-2 and related coronaviruses, focusing on the host factors or pathways involved in SARS-CoV-2 replication that have been identified by genome-wide CRISPR screening.

EANet: Iterative Edge Attention Network for Medical Image Segmentation

Accurate and automatic segmentation of medical images can greatly assist the clinical diagnosis and analysis. However, it remains a challenging task due to (1) the diversity of scale in the medical image targets and (2) the complex context environments of medical images, including ambiguity of structural boundaries, complexity of shapes, and the heterogeneity of textures. To comprehensively tackle these challenges, we propose a novel and effective iterative edge attention network (EANet) for medical image segmentation with steps as follows. First, we propose a dynamic scale-aware context (DSC) module, which dynamically adjusts the receptive fields to extract multi-scale contextual information efficiently. Second, an edge-attention preservation (EAP) module is employed to effectively remove noise and help the edge stream focus on processing only the boundary-related information. Finally, a multi-level pairwise regression (MPR) module is designed to combine the complementary edge and region information for refining the ambiguous structure. This iterative optimization helps to learn better representations and more accurate saliency maps. Extensive experimental results demonstrate that the proposed network achieves superior segmentation performance to state-of-the-art methods in four different challenging medical segmentation tasks, including lung nodule segmentation, COVID-19 infection segmentation, lung segmentation, and thyroid nodule segmentation. The source code of our method is available at https://github.com/DLWK/EANet