Systematic Studies on the Anti-SARS-CoV-2 Mechanisms of Tea Polyphenol-Related Natural Products.

The causative pathogen of COVID-19, severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), utilizes the receptor-binding domain (RBD) of the spike protein to bind to human receptor angiotensin-converting enzyme 2 (ACE2). Further cleavage of spike by human proteases furin, TMPRSS2, and/or cathepsin L facilitates viral entry into the host cells for replication, where the maturation of polyproteins by 3C-like protease (3CLpro) and papain-like protease (PLpro) yields functional nonstructural proteins (NSPs) such as RNA-dependent RNA polymerase (RdRp) to synthesize mRNA of structural proteins. By testing the tea polyphenol-related natural products through various assays, we found that the active antivirals prevented SARS-CoV-2 entry by blocking the RBD/ACE2 interaction and inhibiting the relevant human proteases, although some also inhibited the viral enzymes essential for replication. Due to their multitargeting properties, these compounds were often misinterpreted for their antiviral mechanisms. In this study, we provide a systematic protocol to check and clarify their anti-SARS-CoV-2 mechanisms, which should be applicable for all of the antivirals.

Deep learning to assess microsatellite instability directly from histopathological whole slide images in endometrial cancer.

Molecular classification, particularly microsatellite instability-high (MSI-H), has gained attention for immunotherapy in endometrial cancer (EC). MSI-H is associated with DNA mismatch repair defects and is a crucial treatment predictor. The NCCN guidelines recommend pembrolizumab and nivolumab for advanced or recurrent MSI-H/mismatch repair deficient (dMMR) EC. However, evaluating MSI in all cases is impractical due to time and cost constraints. To overcome this challenge, we present an effective and efficient deep learning-based model designed to accurately and rapidly assess MSI status of EC using H&E-stained whole slide images. Our framework was evaluated on a comprehensive dataset of gigapixel histopathology images of 529 patients from the Cancer Genome Atlas (TCGA). The experimental results have shown that the proposed method achieved excellent performances in assessing MSI status, obtaining remarkably high results with 96%, 94%, 93% and 100% for endometrioid carcinoma G1G2, respectively, and 87%, 84%, 81% and 94% for endometrioid carcinoma G3, in terms of F-measure, accuracy, precision and sensitivity, respectively. Furthermore, the proposed deep learning framework outperforms four state-of-the-art benchmarked methods by a significant margin (p < 0.001) in terms of accuracy, precision, sensitivity and F-measure, respectively. Additionally, a run time analysis demonstrates that the proposed method achieves excellent quantitative results with high efficiency in AI inference time (1.03 seconds per slide), making the proposed framework viable for practical clinical usage. These results highlight the efficacy and efficiency of the proposed model to assess MSI status of EC directly from histopathological slides.

Development of nanoparticles incorporated with quercetin and ACE2-membrane as a novel therapy for COVID-19.

INTRODUCTION: Angiotensin-converting enzyme 2 (ACE2) and AXL tyrosine kinase receptor are known to be involved in the SARS-CoV-2 entry of the host cell. Therefore, targeting ACE2 and AXL should be an effective strategy to inhibit virus entry into cells. However, developing agents that can simultaneously target ACE2 and AXL remains a formidable task. The natural compound quercetin has been shown to inhibit AXL expression. MATERIALS AND METHODS: In this study, we employed PLGA nanoparticles to prepare nanoparticles encapsulated with quercetin, coated with ACE2-containing cell membranes, or encapsulated with quercetin and then coated with ACE-2-containing cell membranes. These nanoparticles were tested for their abilities to neutralize or inhibit viral infection. RESULTS: Our data showed that nanoparticles encapsulated with quercetin and then coated with ACE2-containing cell membrane inhibited the expression of AXL without causing cytotoxic activity. Nanoparticles incorporated with both quercetin and ACE2-containing cell membrane were found to be able to neutralize pseudo virus infection and were more effective than free quercetin and nanoparticles encapsulated with quercetin at inhibition of pseudo virus and SARS-CoV-2 infection. CONCLUSIONS: We have shown that the biomimetic nanoparticles incorporated with both ACE-2 membrane and quercetin showed the most antiviral activity and may be further explored for clinical application.

Chronic infective arthritis with osteomyelitis of the ankle due to Pseudomonas aeruginosa infection in a middle-age woman: A rare causative pathogen requiring vigilance.

RATIONALE: Pseudomonas aeruginosa-induced septic arthritis is a relatively uncommon phenomenon. It has been documented in children with traumatic wounds, young adults with a history of intravenous drug use, and elderly patients with recent urinary tract infections or surgical procedures. PATIENT CONCERNS: Fifty-nine year-old female had no reported risk factors. The patient sought medical attention due to a 6-month history of persistent pain and swelling in her right ankle. DIAGNOSES: Magnetic resonance imaging and a 3-phase bone scan revealed findings suggestive of infectious arthritis with concurrent osteomyelitis. Histopathological examination of the synovium suggested chronic synovitis, and synovial tissue culture confirmed the presence of P aeruginosa. INTERVENTION: Arthroscopic synovectomy and debridement, followed by 6 weeks of targeted antibiotic therapy for P aeruginosa. OUTCOMES: Following treatment, the patient experienced successful recovery with no symptom recurrence, although she retained a mild limitation in the range of motion of her ankle. LESSONS: To our knowledge, this is the first reported case of chronic arthritis and osteomyelitis caused by P aeruginosa in a patient without conventional risk factors. This serves as a crucial reminder for clinicians to consider rare causative organisms in patients with chronic arthritis. Targeted therapy is imperative for preventing further irreversible bone damage and long-term morbidity.

Dual Effects of 3-epi-betulin from Daphniphyllum glaucescens in Suppressing SARS-CoV-2-Induced Inflammation and Inhibiting Virus Entry.

The continuous emergence of SARS-CoV-2 variants has led to a protracted global COVID-19 pandemic with significant impacts on public health and global economy. While there are currently available SARS-CoV-2 vaccines and therapeutics, most of the FDA-approved antiviral agents directly target viral proteins. However, inflammation is the initial immune pathogenesis induced by SARS-CoV-2 infection, there is still a need to find additional agents that can control the virus in the early stages of infection to alleviate disease progression for the next pandemic. Here, we find that both the spike protein and its receptor CD147 are crucial for inducing inflammation by SARS-CoV-2 in THP-1 monocytic cells. Moreover, we find that 3-epi-betulin, isolated from Daphniphyllum glaucescens, reduces the level of proinflammatory cytokines induced by SARS-CoV-2, consequently resulting in a decreased viral RNA accumulation and plaque formation. In addition, 3-epi-betulin displays a broad-spectrum inhibition of entry of SARS-CoV-2 pseudoviruses, including Alpha (B.1.1.7), Eplison (B.1.429), Gamma (P1), Delta (B.1.617.2) and Omicron (BA.1). Moreover, 3-epi-betulin potently inhibits SARS-CoV-2 infection with an EC50 of <20 µM in Calu-3 lung epithelial cells. Bioinformatic analysis reveals the chemical interaction between the 3-epi-betulin and the spike protein, along with the critical amino acid residues in the spike protein that contribute to the inhibitory activity of 3-epi-betulin against virus entry. Taken together, our results suggest that 3-epi-betulin exhibits dual effect: it reduces SARS-CoV-2-induced inflammation and inhibits virus entry, positioning it as a potential antiviral agent against SARS-CoV-2.

Synthesis, evaluation, and mechanism of 1-(4-(arylethylenylcarbonyl)phenyl)-4-carboxy-2-pyrrolidinones as potent reversible SARS-CoV-2 entry inhibitors.

A class of 1-(4-(arylethylenylcarbonyl)phenyl)-4-carboxy-2-pyrrolidinones were designed and synthesized via Michael addition, cyclization, aldol condensation, and deprotonation to inhibit the human transmembrane protease serine 2 (TMPRSS2) and Furin, which are involved in priming the SARS-CoV-2 Spike for virus entry. The most potent inhibitor 2f (81) was found to efficiently inhibit the replication of various SARS-CoV-2 delta and omicron variants in VeroE6 and Calu-3 cells, with EC50 range of 0.001-0.026 µM by pre-incubation with the virus to avoid the virus entry. The more potent antiviral activities than the proteases inhibitory activities led to discovery that the synthesized compounds also inhibited Spike's receptor binding domain (RBD):angiotensin converting enzyme 2 (ACE2) interaction as a main target, and their antiviral activities were enhanced by inhibiting TMPRSS2 and/or Furin. To further confirm the blocking effect of 2f (81) on virus entry, SARS-CoV-2 Spike pseudovirus was used in the entry assay and the results showed that the compound inhibited the pseudovirus entry in a ACE2-dependent pathway, via mainly inhibiting RBD:ACE2 interaction and TMPRSS2 activity in Calu-3 cells. Finally, in the in vivo animal model of SARS-CoV-2 infection, the oral administration of 25 mg/kg 2f (81) in hamsters resulted in reduced bodyweight loss and 5-fold lower viral RNA levels in nasal turbinate three days post-infection. Our findings demonstrated the potential of the lead compound for further preclinical investigation as a potential treatment for SARS-CoV-2.

Deep Learning Can Predict Bevacizumab Therapeutic Effect and Microsatellite Instability Directly from Histology in Epithelial Ovarian Cancer.

Epithelial ovarian cancer (EOC) remains a significant cause of mortality among gynecologic cancers, with the majority of cases being diagnosed at an advanced stage. Before targeted therapies were available, EOC treatment relied largely on debulking surgery and platinum-based chemotherapy. Vascular endothelial growth factors have been identified as inducing tumor angiogenesis. According to several clinical trials, anti-vascular endothelial growth factor-targeted therapy with bevacizumab was effective in all phases of EOC treatment. However, there are currently no biomarkers accessible for regular therapeutic use despite the importance of patient selection. Microsatellite instability (MSI), caused by a deficiency of the DNA mismatch repair system, is a molecular abnormality observed in EOC associated with Lynch syndrome. Recent evidence suggests that angiogenesis and MSI are interconnected. Developing predictive biomarkers, which enable the selection of patients who might benefit from bevacizumab-targeted therapy or immunotherapy, is critical for realizing personalized precision medicine. In this study, we developed 2 improved deep learning methods that eliminate the need for laborious detailed image-wise annotations by pathologists and compared them with 3 state-of-the-art methods to not only predict the efficacy of bevacizumab in patients with EOC using mismatch repair protein immunostained tissue microarrays but also predict MSI status directly from histopathologic images. In prediction of therapeutic outcomes, the 2 proposed methods achieved excellent performance by obtaining the highest mean sensitivity and specificity score using MSH2 or MSH6 markers and outperformed 3 state-of-the-art deep learning methods. Moreover, both statistical analysis results, using Cox proportional hazards model analysis and Kaplan-Meier progression-free survival analysis, confirm that the 2 proposed methods successfully differentiate patients with positive therapeutic effects and lower cancer recurrence rates from patients experiencing disease progression after treatment (P < .01). In prediction of MSI status directly from histopathology images, our proposed method also achieved a decent performance in terms of mean sensitivity and specificity score even for imbalanced data sets for both internal validation using tissue microarrays from the local hospital and external validation using whole section slides from The Cancer Genome Atlas archive.

Weakly supervised bilayer convolutional network in segmentation of HER2 related cells to guide HER2 targeted therapies.

Overexpression of human epidermal growth factor receptor 2 (HER2/ERBB2) is identified as a prognostic marker in metastatic breast cancer and a predictor to determine the effects of ERBB2-targeted drugs. Accurate ERBB2 testing is essential in determining the optimal treatment for metastatic breast cancer patients. Brightfield dual in situ hybridization (DISH) was recently authorized by the United States Food and Drug Administration for the assessment of ERRB2 overexpression, which however is a challenging task due to a variety of reasons. Firstly, the presence of touching clustered and overlapping cells render it difficult for segmentation of individual HER2 related cells, which must contain both ERBB2 and CEN17 signals. Secondly, the fuzzy cell boundaries make the localization of each HER2 related cell challenging. Thirdly, variation in the appearance of HER2 related cells is large. Fourthly, as manual annotations are usually made on targets with high confidence, causing sparsely labeled data with some unlabeled HER2 related cells defined as background, this will seriously confuse fully supervised AI learning and cause poor model outcomes. To deal with all issues mentioned above, we propose a two-stage weakly supervised deep learning framework for accurate and robust assessment of ERBB2 overexpression. The effectiveness and robustness of the proposed deep learning framework is evaluated on two DISH datasets acquired at two different magnifications. The experimental results demonstrate that the proposed deep learning framework achieves an accuracy of 96.78 ± 1.25, precision of 97.77 ± 3.09, recall of 84.86 ± 5.83 and Dice Index of 90.77 ± 4.1 and an accuracy of 96.43 ± 2.67, precision of 97.82 ± 3.99, recall of 87.14 ± 10.17 and Dice Index of 91.87 ± 6.51 for segmentation of ERBB2 overexpression on the two experimental datasets, respectively. Furthermore, the proposed deep learning framework outperforms 15 state-of-the-art benchmarked methods by a significant margin (P<0.05) with respect to IoU on both datasets.

Efficient Convolution Network to Assist Breast Cancer Diagnosis and Target Therapy.

Breast cancer is the leading cause of cancer-related deaths among women worldwide, and early detection and treatment has been shown to significantly reduce fatality rates from severe illness. Moreover, determination of the human epidermal growth factor receptor-2 (HER2) gene amplification by Fluorescence in situ hybridization (FISH) and Dual in situ hybridization (DISH) is critical for the selection of appropriate breast cancer patients for HER2-targeted therapy. However, visual examination of microscopy is time-consuming, subjective and poorly reproducible due to high inter-observer variability among pathologists and cytopathologists. The lack of consistency in identifying carcinoma-like nuclei has led to divergences in the calculation of sensitivity and specificity. This manuscript introduces a highly efficient deep learning method with low computing cost. The experimental results demonstrate that the proposed framework achieves high precision and recall on three essential clinical applications, including breast cancer diagnosis and human epidermal receptor factor 2 (HER2) amplification detection on FISH and DISH slides for HER2 target therapy. Furthermore, the proposed method outperforms the majority of the benchmark methods in terms of IoU by a significant margin (p<0.001) on three essential clinical applications. Importantly, run time analysis shows that the proposed method obtains excellent segmentation results with notably reduced time for Artificial intelligence (AI) training (16.93%), AI inference (17.25%) and memory usage (18.52%), making the proposed framework feasible for practical clinical usage.

Natural fucoidans inhibit coronaviruses by targeting viral spike protein and host cell furin.

Fucoidans are a class of long chain sulfated polysaccharides and have multiple biological functions. Herein, four natural fucoidans extracted from Fucus vesiculosus, F. serratus, Laminaria japonica and Undaria pinnatifida, were tested for their HCoV-OC43 inhibition and found to demonstrate EC50 values ranging from 0.15 to 0.61 µg/mL. That from U. pinnatifida exhibited the most potent anti-HCoV-OC43 activity with an EC50 value of 0.15 ± 0.02 µg/mL, a potency largely independent of its sulfate content. Comparison of the gene expression profiles of fucoidan-treated and untreated cells infected with HCoV-OC43 revealed that fucoidan treatment effectively diminished HCoV-OC43 gene expressions associated with induced chemokines, cytokines and viral activities. Further studies using a highly fucoidan-resistant HCoV-OC43 determined that fucoidan inhibited HCoV-OC43 infection via interfering with viral entry and led to the identification of the specific site on the N-terminal region of spike protein, that located adjacent to the host cell receptor binding domain, targeted by the virus. Furthermore, in a SARS-CoV-2 pseudovirus neutralization assay, fucoidan also blocked SARS-CoV-2 entry. In vitro and in vivo, fucoidan decreased SARS-CoV-2 viral loads and inhibited viral infection in Calu-3 or Vero E6 cells and SARS-CoV-2 infected hamsters, respectively. Fucoidan was also found to inhibit furin activity, and reported furin inhibitors were found to inhibit viral infection by wild type HCoV-OC43 or SARS-CoV-2. Accordingly, we conclude that fucoidans inhibit coronaviral infection by targeting viral spike protein and host cell furin to interfere with viral entry.

Detection of ERBB2 and CEN17 signals in fluorescent in situ hybridization and dual in situ hybridization for guiding breast cancer HER2 target therapy.

The overexpression of the human epidermal growth factor receptor 2 (HER2) is a predictive biomarker in therapeutic effects for metastatic breast cancer. Accurate HER2 testing is critical for determining the most suitable treatment for patients. Fluorescent in situ hybridization (FISH) and dual in situ hybridization (DISH) have been recognized as FDA-approved methods to determine HER2 overexpression. However, analysis of HER2 overexpression is challenging. Firstly, the boundaries of cells are often unclear and blurry, with large variations in cell shapes and signals, making it challenging to identify the precise areas of HER2-related cells. Secondly, the use of sparsely labeled data, where some unlabeled HER2-related cells are classified as background, can significantly confuse fully supervised AI learning and result in unsatisfactory model outcomes. In this study, we present a weakly supervised Cascade R-CNN (W-CRCNN) model to automatically detect HER2 overexpression in HER2 DISH and FISH images acquired from clinical breast cancer samples. The experimental results demonstrate that the proposed W-CRCNN achieves excellent results in identification of HER2 amplification in three datasets, including two DISH datasets and a FISH dataset. For the FISH dataset, the proposed W-CRCNN achieves an accuracy of 0.970±0.022, precision of 0.974±0.028, recall of 0.917±0.065, F1-score of 0.943±0.042 and Jaccard Index of 0.899±0.073. For DISH datasets, the proposed W-CRCNN achieves an accuracy of 0.971±0.024, precision of 0.969±0.015, recall of 0.925±0.020, F1-score of 0.947±0.036 and Jaccard Index of 0.884±0.103 for dataset 1, and an accuracy of 0.978±0.011, precision of 0.975±0.011, recall of 0.918±0.038, F1-score of 0.946±0.030 and Jaccard Index of 0.884±0.052 for dataset 2, respectively. In comparison with the benchmark methods, the proposed W-CRCNN significantly outperforms all the benchmark approaches in identification of HER2 overexpression in FISH and DISH datasets (p<0.05). With the high degree of accuracy, precision and recall , the results show that the proposed method in DISH analysis for assessment of HER2 overexpression in breast cancer patients has significant potential to assist precision medicine.

Isolation of Anti-SARS-CoV-2 Natural Products Extracted from Mentha canadensis and the Semi-synthesis of Antiviral Derivatives.

Traditional herbal medicine offers opportunities to discover novel therapeutics against SARS-CoV-2 mutation. The dried aerial part of mint (Mentha canadensis L.) was chosen for bioactivity-guided extraction. Seven constituents were isolated and characterized by nuclear magnetic resonance (NMR) and mass spectrometry (MS). Syringic acid and methyl rosmarinate were evaluated in drug combination treatment. Ten amide derivatives of methyl rosmarinate were synthesized, and the dodecyl (13) and 3-ethylphenyl (19) derivatives demonstrated significant improvement in the anti-SARS-CoV-2 plaque reduction assay, achieving IC50 of 0.77 and 2.70 µM, respectively, against Omicron BA.1 as compared to methyl rosmarinate's IC50 of 57.0 µM. Spike protein binding and 3CLpro inhibition assays were performed to explore the viral inhibition mechanism. Molecular docking of compounds 13 and 19 to 3CLpro was performed to reveal potential interaction. In summary, natural products with anti-Omicron BA.1 activity were isolated from Mentha canadensis and derivatives of methyl rosmarinate were synthesized, showing 21- to 74-fold improvement in antiviral activity against Omicron BA.1.

Robotic Hysterectomy with Ureter Identification and Uterine Artery Ligation for Benign Gynecological Conditions: An Early-Year Single-Center Experience.

The use and application of robotic systems with a high-definition, three-dimensional vision system and advanced EndoWrist technology have become widespread. We sought to share our clinical experience with ureter identification and preventive uterine artery ligation in robotic hysterectomy. The records of patients undergoing robotic hysterectomy between May 2014 and December 2015, including patient preoperative characteristics, operative time, and postoperative outcomes, were analyzed. We evaluated the feasibility and safety of using early ureteral identification and preventive uterine artery ligation in robotic hysterectomy in patients with benign gynecological conditions. Overall, 49 patients diagnosed with benign gynecological conditions were evaluated. The mean age of the patients and mean uterine weight were 46.2 ± 5.3 years and 348.7 ± 311.8 g, respectively. Robotic hysterectomy achieved satisfactory results, including a short postoperative hospital stay (2.7 ± 0.8 days), low conversion rate (n = 0), and low complication rate (n = 1; 2%). The average estimated blood loss was 109 ± 107.2 mL. Our results suggest that robotic hysterectomy using early ureteral identification and preventive uterine artery ligation is feasible and safe in patients with benign gynecological conditions.

The Src-ZNRF1 axis controls TLR3 trafficking and interferon responses to limit lung barrier damage.

Type I interferons are important antiviral cytokines, but prolonged interferon production is detrimental to the host. The TLR3-driven immune response is crucial for mammalian antiviral immunity, and its intracellular localization determines induction of type I interferons; however, the mechanism terminating TLR3 signaling remains obscure. Here, we show that the E3 ubiquitin ligase ZNRF1 controls TLR3 sorting into multivesicular bodies/lysosomes to terminate signaling and type I interferon production. Mechanistically, c-Src kinase activated by TLR3 engagement phosphorylates ZNRF1 at tyrosine 103, which mediates K63-linked ubiquitination of TLR3 at lysine 813 and promotes TLR3 lysosomal trafficking and degradation. ZNRF1-deficient mice and cells are resistant to infection by encephalomyocarditis virus and SARS-CoV-2 because of enhanced type I interferon production. However, Znrf1-/- mice have exacerbated lung barrier damage triggered by antiviral immunity, leading to enhanced susceptibility to respiratory bacterial superinfections. Our study highlights the c-Src-ZNRF1 axis as a negative feedback mechanism controlling TLR3 trafficking and the termination of TLR3 signaling.

Interpretable attention-based deep learning ensemble for personalized ovarian cancer treatment without manual annotations.

Inhibition of pathological angiogenesis has become one of the first FDA approved targeted therapies widely tested in anti-cancer treatment, i.e. VEGF-targeting monoclonal antibody bevacizumab, in combination with chemotherapy for frontline and maintenance therapy for women with newly diagnosed ovarian cancer. Identification of the best predictive biomarkers of bevacizumab response is necessary in order to select patients most likely to benefit from this therapy. Hence, this study investigates the protein expression patterns on immunohistochemical whole slide images of three angiogenesis related proteins, including Vascular endothelial growth factor, Angiopoietin 2 and Pyruvate kinase isoform M2, and develops an interpretable and annotation-free attention based deep learning ensemble framework to predict the bevacizumab therapeutic effect on patients with epithelial ovarian cancer or peritoneal serous papillary carcinoma using tissue microarrays (TMAs). In evaluation with five-fold cross validation, the proposed ensemble model using the protein expressions of both Pyruvate kinase isoform M2 and Angiopoietin 2 achieves a notably high F-score (0.99±0.02), accuracy (0.99±0.03), precision (0.99±0.02), recall (0.99±0.02) and AUC (1.00±0). Kaplan-Meier progression free survival analysis confirms that the proposed ensemble is able to identify patients in the predictive therapeutic sensitive group with low cancer recurrence (p<0.001), and the Cox proportional hazards model analysis further confirms the above statement (p=0.012). In conclusion, the experimental results demonstrate that the proposed ensemble model using the protein expressions of both Pyruvate kinase isoform M2 and Angiopoietin 2 can assist treatment planning of bevacizumab targeted therapy for patients with ovarian cancer.

Elicitation of potent neutralizing antibodies in obese mice by ISA 51-adjuvanted SARS-CoV-2 spike RBD-Fc vaccine.

Vaccination is considered to be the most effective countermeasure to prevent and combat the global health threats of COVID-19. People with obesity are at a greater risk of hospitalization, life-threatening illness, and adverse outcomes after having COVID-19. Therefore, a safe and effective COVID-19 vaccine for obese individuals is urgently needed. In the study, the vaccine composed of the ISA 51 adjuvant and the SARS-CoV-2 spike (S) receptor-binding domain (RBD) in conjugation with the human IgG1 Fc fragment (named as ISA 51-adjuvanted RBD-Fc vaccine) was developed and inoculated in the regular chow diet (RCD) lean mice and the high-fat diet (HFD)-induced obese mice. The S protein-specific IgG titers were largely induced in an increasing manner along with three doses of ISA 51-adjuvanted RBD-Fc vaccine without causing any harmful side effect. In the HFD mice, the S protein-specific IgG titers can be quickly observed 2 weeks post the first inoculation. The antisera elicited by the ISA 51-adjuvanted RBD-Fc vaccine in the RCD and HFD mice exhibited potent SARS-CoV-2 neutralizing activities in the plaque reduction neutralization test (PRNT) assays and showed similar specificity for recognizing the key residues in the RBD which were involved in interacting with angiotensin-converting enzyme 2 (ACE2) receptor. The immune efficacy of the ISA 51-adjuvanted RBD-Fc vaccine in the HFD mice can be sustainably maintained with the PRNT50 values of 1.80-1.91×10-3 for at least 8 weeks post the third inoculation. Collectively, the RBD-Fc-based immunogen and the ISA 51-adjuvanted formulation can be developed as an effective COVID-19 vaccine for obese individuals. KEY POINTS: • The ISA 51-adjuvanted RBD-Fc vaccine can induce potent SARS-CoV-2 neutralizing antibodies in the obese mouse • The antibodies elicited by the ISA 51-adjuvanted RBD-Fc vaccine can bind to the key RBD residues involved in interacting with ACE2 • The immune efficacy of the ISA 51-adjuvanted RBD-Fc vaccine can be sustainably maintained for at least 8 weeks post the third inoculation.

Correction: Lee et al. The Synergistic Inhibition of Coronavirus Replication and Induced Cytokine Production by Ciclesonide and the Tylophorine-Based Compound Dbq33b. Pharmaceutics 2022, 14, 1511.

In the original publication [...].

Annotation-Free Deep Learning-Based Prediction of Thyroid Molecular Cancer Biomarker BRAF (V600E) from Cytological Slides.

Thyroid cancer is the most common endocrine cancer. Papillary thyroid cancer (PTC) is the most prevalent form of malignancy among all thyroid cancers arising from follicular cells. Fine needle aspiration cytology (FNAC) is a non-invasive method regarded as the most cost-effective and accurate diagnostic method of choice in diagnosing PTC. Identification of BRAF (V600E) mutation in thyroid neoplasia may be beneficial because it is specific for malignancy, implies a worse prognosis, and is the target for selective BRAF inhibitors. To the authors' best knowledge, this is the first automated precision oncology framework effectively predict BRAF (V600E) immunostaining result in thyroidectomy specimen directly from Papanicolaou-stained thyroid fine-needle aspiration cytology and ThinPrep cytological slides, which is helpful for novel targeted therapies and prognosis prediction. The proposed deep learning (DL) framework is evaluated on a dataset of 118 whole slide images. The results show that the proposed DL-based technique achieves an accuracy of 87%, a precision of 94%, a sensitivity of 91%, a specificity of 71% and a mean of sensitivity and specificity at 81% and outperformed three state-of-the-art deep learning approaches. This study demonstrates the feasibility of DL-based prediction of critical molecular features in cytological slides, which not only aid in accurate diagnosis but also provide useful information in guiding clinical decision-making in patients with thyroid cancer. With the accumulation of data and the continuous advancement of technology, the performance of DL systems is expected to be improved in the near future. Therefore, we expect that DL can provide a cost-effective and time-effective alternative tool for patients in the era of precision oncology.

IFN-stimulated metabolite transporter ENT3 facilitates viral genome release.

An increasing amount of evidence emphasizes the role of metabolic reprogramming in immune cells to fight infections. However, little is known about the regulation of metabolite transporters that facilitate and support metabolic demands. In this study, we found that the expression of equilibrative nucleoside transporter 3 (ENT3, encoded by solute carrier family 29 member 3, Slc29a3) is part of the innate immune response, which is rapidly upregulated upon pathogen invasion. The transcription of Slc29a3 is directly regulated by type I interferon-induced signaling, demonstrating that this metabolite transporter is an interferon-stimulated gene (ISG). Suprisingly, we unveil that several viruses, including SARS-CoV-2, require ENT3 to facilitate their entry into the cytoplasm. The removal or suppression of Slc29a3 expression is sufficient to significantly decrease viral replication in vitro and in vivo. Our study reveals that ENT3 is a pro-viral ISG co-opted by some viruses to gain a survival advantage.

Clinical assessment of SARS-CoV-2 infectivity by rapid antigen test compared with virus isolation.

Although real-time reverse transcriptase polymerase chain reaction (real-time RT-PCR) remains as a golden standard for detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, it can not be easily expanded to large-scaled screening during outbreaks, and the positive results do not necessarily correlate with infectious status of the identified subjects. In this study, the performance of Vstrip® RV2 COVID-19 Antigen Rapid Test (RAT) and its correlation with virus infectivity was examined by virus culture using 163 sequential respiratory specimens collected from 26 SARS-CoV-2 infected patients. When the presence of cytopathic effects (CPE) in cell culture was used as a reference method for virus infectivity, the sensitivity, specificity and accuracy of Vstrip® RV2 COVID-19 Antigen Rapid Test was 96.43%, 89.63%, and 90.8%, respectively. The highest Ct value was 27.7 for RdRp gene and 25.79 for E gene within CPE-positive samples, and the highest Ct value was 31.9 for RdRp gene and 29.1 for E gene within RAT positive samples. When the Ct values of specimens were below 25, the CPE and RAT results had high degree of consistency. We concluded that the RAT could be a great alternative method for determining the infectious potential of individuals with high viral load.