Double-nylon purse-string suture in closing postoperative wounds following endoscopic resection of large (≥ 3 cm) gastric submucosal tumors.

BACKGROUND: Endoscopic full-thickness resection (EFTR) of gastric submucosal tumors (SMTs) is safe and effective; however, postoperative wound management is equally important. Literature on suturing following EFTR for large (≥ 3 cm) SMTs is scarce and limited. AIM: To evaluate the efficacy and clinical value of double-nylon purse-string suture in closing postoperative wounds following EFTR of large (≥ 3 cm) SMTs. METHODS: We retrospectively analyzed the data of 85 patients with gastric SMTs in the fundus of the stomach or in the lesser curvature of the gastric body whose wounds were treated with double-nylon purse-string sutures after successful tumor resection at the Endoscopy Center of Renmin Hospital of Wuhan University. The operative, postoperative, and follow-up conditions of the patients were evaluated. RESULTS: All tumors were completely resected using EFTR. 36 (42.35%) patients had tumors located in the fundus of the stomach, and 49 (57.65%) had tumors located in the body of the stomach. All patients underwent suturing with double-nylon sutures after EFTR without laparoscopic assistance or further surgical treatment. Postoperative fever and stomach pain were reported in 13 (15.29%) and 14 (16.47%) patients, respectively. No serious adverse events occurred during the intraoperative or postoperative periods. A postoperative review of all patients revealed no residual or recurrent lesions. CONCLUSION: Double-nylon purse-string sutures can be used to successfully close wounds that cannot be completely closed with a single nylon suture, especially for large (≥ 3 cm) EFTR wounds in SMTs.

AhR Activation Ameliorates Intestinal Barrier Damage in Immunostressed Piglets by Regulating Intestinal Flora and Its Metabolism.

The primary factor leading to elevated rates of diarrhea and decreased performance in piglets is immunological stress. The regulation of immune stress through the intestinal flora is a crucial mechanism to consider. In total, 30 weaned piglets were randomly allocated to five groups: the basal diet group (Control), basal diet + lipopolysaccharides group (LPS), basal diet + 250 µg/kg 6-Formylindolo [3,2-b] carbazole + LPS group (FICZ), basal diet + 3mg/kg Cardamonin + LPS group (LCDN), and basal diet + 6mg/kg Cardamonin + LPS group (HCDN/CDN). The results showed that compared with those of the LPS group, the expression of tight junction proteins (occludin; claudin-1) in the FICZ group was significantly increased, and the mRNA levels of IL-1ß and TNF-α were significantly reduced (p < 0.05). HCDN treatment had a better effect on LPS-induced intestinal barrier damage in this group than it did in the LCDN group. HCDN treatment leads to a higher villus height (VH), a higher ratio of villi height to crypt depth (V/C), higher tight junction proteins (ZO-1; occludin), and higher short-chain fatty acids (SCFAs). In addition, correlation analyses showed that Succinivibrio was positively correlated with several SCFAs and negatively correlated with prostaglandin-related derivatives in the FICZ group and CDN group (p < 0.05). In summary, Cardamonin alleviates intestinal mucosal barrier damage and inflammatory responses by regulating the intestinal microbiota and its metabolism.

Dietary emodin alleviates lipopolysaccharide-induced intestinal mucosal barrier injury by regulating gut microbiota in piglets.

This study was to determine the effects of dietary emodin (ED) on the intestinal mucosal barrier, nuclear factor kappa-B (NF-κB) pathways, and gut microbial flora in lipopolysaccharide (LPS)-induced piglets. Twenty-four weaned piglets were chosen and 4 treatments were created by randomly distributing piglets into CON, ED, LPS, and ED_LPS groups. Experiments were done in a 2 × 2 factorial arrangement and maintained for 21 d. Dietary treatment (a basal diet or 300 mg/kg ED) and immunological challenge (LPS or sterile saline) were 2 major factors. Intraperitoneal injections of LPS or sterilized saline were given to piglets on d 21. Six hours after the LPS challenge, all piglets were euthanized for sample collection and analysis. The results showed that piglets of the ED_LPS group had higher (P < 0.05) villus height to crypt depth ratio (VCR), and lower (P < 0.05) plasma D-lactate and diamine oxidase (DAO) than the LPS group. Furthermore, ED inhibited (P < 0.05) the decrease of glutathione peroxidase (GSH-Px) and catalase (CAT) activities and increase of malonaldehyde level (P < 0.05) in jejunal mucosa induced by LPS. The mRNA levels of pro-inflammatory cytokine genes (IL-6, IL-1ß, and TNF-α) were significantly reduced (P < 0.05), and the mRNA levels of antioxidant enzyme genes (GPX-1, SOD2 and CAT), as well as protein and mRNA levels of tight junction proteins (occludin, claudin-1, and ZO-1), were also significantly increased (P < 0.05) by ED addition in LPS-induced piglets. Meanwhile, ED supplementation significantly decreased the LPS-induced protein levels of cyclooxygenase-2 and phosphorylation levels of NF-κB p65 and IκBα in jejunal mucosa. Emodin had a significant effect on the composition of gut microbial flora at various taxonomic positions as indicated by 16S RNA sequencing. The acetic acid, isobutyric acid, valeric acid, and isovaleric acid concentrations in the cecum were also increased by ED addition in pigs (P < 0.05). Furthermore, the correlation analysis revealed that some intestinal microbiota had a potential relationship with jejunal VCR, plasma D-lactate and DAO, jejunal mucosa GSH-Px and CAT activity, and cecal short-chain fatty acid concentration. These data suggest that ED is effective in alleviating LPS-induced intestinal mucosal barrier injury by modulating gut microbiota in piglets.

Identification of Hub Genes Correlated with the Initiation and Development in Chronic Kidney Disease via Bioinformatics Analysis.

INTRODUCTION: Chronic kidney disease (CKD) is a major public health issue worldwide, which is characterized by irreversible loss of nephron and renal function. However, the molecular mechanism of CKD remains underexplored. METHODS: This study integrated three transcriptional profile datasets to investigate the molecular mechanism of CKD. The differentially expressed genes (DEGs) between Sham control (Con) and unilateral ureteral obstruction (UUO)-operated mice were analyzed by utilizing the limma package in R. The shared DEGs were analyzed by Gene Ontology and functional enrichment. Protein-protein interactions (PPIs) were constructed by utilizing the STRING database. Hub genes were analyzed by MCODE and Cytohubba. We further validated the gene expression by using the other dataset and mouse UUO model. RESULTS: A total of 315 shared DEGs between Con and UUO samples were identified. Gene function and KEGG pathway enrichment revealed that DEGs were mainly enriched in inflammatory response, immune system process, and chemokine signaling pathway. Two modules were clustered based on PPI network analysis. Module 1 contained 13 genes related to macrophage activation, migration, and chemotaxis. Ten hub genes were identified by PPI network analysis. Subsequently, the expression levels of hub genes were validated with the other dataset. Finally, these four validated hub genes were further confirmed by our UUO mice. Three validated hub genes, Gng2, Pf4, and Ccl9, showed significant response to UUO. CONCLUSION: Our study reveals the coordination of genes during UUO and provides a promising gene panel for CKD treatment. GNG2 and PF4 were identified as potential targets for developing CKD drugs.

A New Risk Score to Predict Intensive Care Unit Admission for Patients with Acute Pancreatitis 48 Hours After Admission: Multicenter Study.

AIMS: The objective of this study was to develop and validate an easy-to-use risk score (APRS) to predict which patients with acute pancreatitis (AP) will need intensive care unit (ICU) treatment within 48 h post-hospitalization on the basis of the ubiquitously available clinical records. METHODS: Patients with acute pancreatitis were retrospectively included from three independent institutions (RM cohort, 5280; TJ cohort, 262; SN cohort, 196), with 56 candidate variables collected within 48 h post-hospitalization. The RM cohort was randomly divided into a training set (N = 4220) and a test set (N = 1060). The most predictive features were extracted by LASSO from the RM cohort and entered into multivariate analysis. APRS was constructed using the coefficients of the statistically significant variables weighted by the multivariable logistic regression model. The APRS was validated by RM, TJ, and SN cohorts. The C-statistic was employed to evaluate the APRS's discrimination. DeLong test was used to compare area under the receiver operating characteristic curve (AUC) differences. RESULTS: A total of 5738 patients with AP were enrolled. Eleven variables were selected by LASSO and entered into multivariate analysis. APRS was inferred using the above five factors (pleural effusion, ALT/AST, ALB/GLB, urea, and glucose) weighted by their regression coefficients in the multivariable logistic regression model. The C-statistics of APRS were 0.905 (95% CI 0.82-0.98) and 0.889 (95% CI 0.81-0.96) in RM and TJ validation. An online APRS web-based calculator was constructed to assist the clinician to earlier assess the clinical outcomes of patients with AP. CONCLUSION: APRS could effectively stratify patients with AP into high and low risk of ICU admission within 48 h post-hospitalization, offering clinical value in directing management and personalize therapeutic selection for patients with AP.

Machine learning model identifies aggressive acute pancreatitis within 48 h of admission: a large retrospective study.

BACKGROUND: Acute pancreatitis (AP) with critical illness is linked to increased morbidity and mortality. Current risk scores to identify high-risk AP patients have certain limitations. OBJECTIVE: To develop and validate a machine learning tool within 48 h after admission for predicting which patients with AP will develop critical illness based on ubiquitously available clinical, laboratory, and radiologic variables. METHODS: 5460 AP patients were enrolled. Clinical, laboratory, and imaging variables were collected within 48 h after hospital admission. Least Absolute Shrinkage Selection Operator with bootstrap method was employed to select the most informative variables. Five different machine learning models were constructed to predictive likelihood of critical illness, and the optimal model (APCU) was selected. External cohort was used to validate APCU. APCU and other risk scores were compared using multivariate analysis. Models were evaluated by area under the curve (AUC). The decision curve analysis was employed to evaluate the standardized net benefit. RESULTS: Xgboost was constructed and selected as APCU, involving age, comorbid disease, mental status, pulmonary infiltrates, procalcitonin (PCT), neutrophil percentage (Neu%), ALT/AST, ratio of albumin and globulin, cholinesterase, Urea, Glu, AST and serum total cholesterol. The APCU performed excellently in discriminating AP risk in internal cohort (AUC = 0.95) and external cohort (AUC = 0.873). The APCU was significant for biliogenic AP (OR = 4.25 [2.08-8.72], P < 0.001), alcoholic AP (OR = 3.60 [1.67-7.72], P = 0.001), hyperlipidemic AP (OR = 2.63 [1.28-5.37], P = 0.008) and tumor AP (OR = 4.57 [2.14-9.72], P < 0.001). APCU yielded the highest clinical net benefit, comparatively. CONCLUSION: Machine learning tool based on ubiquitously available clinical variables accurately predicts the development of AP, optimizing the management of AP.

An End-to-End Integrated Clinical and CT-Based Radiomics Nomogram for Predicting Disease Severity and Need for Ventilator Support in COVID-19 Patients: A Large Multisite Retrospective Study.

Objective: The disease COVID-19 has caused a widespread global pandemic with ~3. 93 million deaths worldwide. In this work, we present three models-radiomics (MRM), clinical (MCM), and combined clinical-radiomics (MRCM) nomogram to predict COVID-19-positive patients who will end up needing invasive mechanical ventilation from the baseline CT scans. Methods: We performed a retrospective multicohort study of individuals with COVID-19-positive findings for a total of 897 patients from two different institutions (Renmin Hospital of Wuhan University, D1 = 787, and University Hospitals, US D2 = 110). The patients from institution-1 were divided into 60% training, D 1 T ( N = 473 ) , and 40% test set D 1 V ( N = 314 ) . The patients from institution-2 were used for an independent validation test set D 2 V ( N = 110 ) . A U-Net-based neural network (CNN) was trained to automatically segment out the COVID consolidation regions on the CT scans. The segmented regions from the CT scans were used for extracting first- and higher-order radiomic textural features. The top radiomic and clinical features were selected using the least absolute shrinkage and selection operator (LASSO) with an optimal binomial regression model within D 1 T . Results: The three out of the top five features identified using D 1 T were higher-order textural features (GLCM, GLRLM, GLSZM), whereas the last two features included the total absolute infection size on the CT scan and the total intensity of the COVID consolidations. The radiomics model (MRM) was constructed using the radiomic score built using the coefficients obtained from the LASSO logistic model used within the linear regression (LR) classifier. The MRM yielded an area under the receiver operating characteristic curve (AUC) of 0.754 (0.709-0.799) on D 1 T , 0.836 on D 1 V , and 0.748 D 2 V . The top prognostic clinical factors identified in the analysis were dehydrogenase (LDH), age, and albumin (ALB). The clinical model had an AUC of 0.784 (0.743-0.825) on D 1 T , 0.813 on D 1 V , and 0.688 on D 2 V . Finally, the combined model, MRCM integrating radiomic score, age, LDH and ALB, yielded an AUC of 0.814 (0.774-0.853) on D 1 T , 0.847 on D 1 V , and 0.771 on D 2 V . The MRCM had an overall improvement in the performance of ~5.85% ( D 1 T : p = 0.0031; D 1 V p = 0.0165; D 2 V : p = 0.0369) over MCM. Conclusion: The novel integrated imaging and clinical model (MRCM) outperformed both models (MRM) and (MCM). Our results across multiple sites suggest that the integrated nomogram could help identify COVID-19 patients with more severe disease phenotype and potentially require mechanical ventilation.

Deep-learning-based hepatic fat assessment (DeHFt) on non-contrast chest CT and its association with disease severity in COVID-19 infections: A multi-site retrospective study.

BACKGROUND: Hepatic steatosis (HS) identified on CT may provide an integrated cardiometabolic and COVID-19 risk assessment. This study presents a deep-learning-based hepatic fat assessment (DeHFt) pipeline for (a) more standardised measurements and (b) investigating the association between HS (liver-to-spleen attenuation ratio <1 in CT) and COVID-19 infections severity, wherein severity is defined as requiring invasive mechanical ventilation, extracorporeal membrane oxygenation, death. METHODS: DeHFt comprises two steps. First, a deep-learning-based segmentation model (3D residual-UNet) is trained (N.ß=.ß80) to segment the liver and spleen. Second, CT attenuation is estimated using slice-based and volumetric-based methods. DeHFt-based mean liver and liver-to-spleen attenuation are compared with an expert's ROI-based measurements. We further obtained the liver-to-spleen attenuation ratio in a large multi-site cohort of patients with COVID-19 infections (D1, N.ß=.ß805; D2, N.ß=.ß1917; D3, N.ß=.ß169) using the DeHFt pipeline and investigated the association between HS and COVID-19 infections severity. FINDINGS: The DeHFt pipeline achieved a dice coefficient of 0.95, 95% CI [0.93...0.96] on the independent validation cohort (N.ß=.ß49). The automated slice-based and volumetric-based liver and liver-to-spleen attenuation estimations strongly correlated with expert's measurement. In the COVID-19 cohorts, severe infections had a higher proportion of patients with HS than non-severe infections (pooled OR.ß=.ß1.50, 95% CI [1.20...1.88], P.ß<.ß.001). INTERPRETATION: The DeHFt pipeline enabled accurate segmentation of liver and spleen on non-contrast CTs and automated estimation of liver and liver-to-spleen attenuation ratio. In three cohorts of patients with COVID-19 infections (N.ß=.ß2891), HS was associated with disease severity. Pending validation, DeHFt provides an automated CT-based metabolic risk assessment. FUNDING: For a full list of funding bodies, please see the Acknowledgements.

CircRNA_0040705 promotes the progression of hepatocellular carcinoma.

Circular RNAs (circRNAs) are involved in cancer progression. Nonetheless, the role and mechanism of circ_0040705 in hepatocellular carcinoma (HCC) are unclear. The aberrantly expressed circRNAs and microRNAs (miRNAs) in HCC tissues were screened by bioinformatics. Circ_0040705, miR-557, SRY-box transcription factor 2 (SOX2), E-cadherin, and N-cadherin expressions were determined using quantitative real-time polymerase chain reaction (qRT-PCR) or Western blot. Cell counting kit-8 (CCK-8), 5-ethynyl-2'-deoxyuridine (EdU), and Transwell experiments were utilized to examine the changes in HCC cell growth, migration, and invasion after circ_0040475 was overexpressed or knocked down. Lung metastasis assay was used to validate the effects of circRNA_0040705 on the lung metastasis of HCC cells in vivo. Binding sequences between circ_0040705 and miR-557 and between miR-557 and SOX2 were verified using dual-luciferase reporter gene experiments. The expression levels of circ_0040705 and SOX2 mRNA were markedly increased in HCC tissues, but miR-557 expression was down-regulated. Circ_0040705 overexpression enhanced the growth, migration, invasion, and the expressions of E-cadherin and N-cadherin of HCC cells and promoted lung metastasis in vivo, whereas circ_0040705 knockdown exerted the opposite effects in HCC cells. Circ_0040705 worked as a sponge for miR-557 to down-modulate miR-557 expression, and miR-557 could specifically down-modulate SOX2 expression. Circ_0040705 facilitates HCC cell growth, migration, and invasion by down-modulating miR-557 expression and up-modulating SOX2 expression.

Peptide OPTX-1 From Ornithodoros papillipes Tick Inhibits the pS273R Protease of African Swine Fever Virus.

African swine fever virus (ASFV) is a large double-stranded DNA virus and causes high mortality in swine. ASFV can be transmitted by biological vectors, including soft ticks in genus Ornithodoros but not hard ticks. However, the underlying mechanisms evolved in the vectorial capacity of soft ticks are not well-understood. Here, we found that a defensin-like peptide toxin OPTX-1 identified from Ornithodoros papillipes inhibits the enzyme activity of the ASFV pS273R protease with a Ki =0.821±0.526µM and shows inhibitory activity on the replication of ASFV. The analogs of OPTX-1 from hard ticks show more inhibitory efficient on pS273R protease. Considering that ticks are blood-sucking animals, we tested the effects of OPTX-1 and its analogs on the coagulation system. At last, top 3D structures represented surface analyses of the binding sites of pS273R with different inhibitors that were obtained by molecular docking based on known structural information. In summary, our study provides evidence that different inhibitory efficiencies between soft tick-derived OPTX-1 and hard tick-derived defensin-like peptides may determine the vector and reservoir competence of ticks.

Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study.

BACKGROUND: To reduce the high incidence and mortality of gastric cancer (GC), we aimed to develop deep learning-based models to assist in predicting the diagnosis and overall survival (OS) of GC patients using pathological images. METHODS: 2333 hematoxylin and eosin-stained pathological pictures of 1037 GC patients were collected from two cohorts to develop our algorithms, Renmin Hospital of Wuhan University (RHWU) and the Cancer Genome Atlas (TCGA). Additionally, we gained 175 digital pictures of 91 GC patients from National Human Genetic Resources Sharing Service Platform (NHGRP), served as the independent external validation set. Two models were developed using artificial intelligence (AI), one named GastroMIL for diagnosing GC, and the other named MIL-GC for predicting outcome of GC. FINDINGS: The discriminatory power of GastroMIL achieved accuracy 0.920 in the external validation set, superior to that of the junior pathologist and comparable to that of expert pathologists. In the prognostic model, C-indices for survival prediction of internal and external validation sets were 0.671 and 0.657, respectively. Moreover, the risk score output by MIL-GC in the external validation set was proved to be a strong predictor of OS both in the univariate (HR = 2.414, P < 0.0001) and multivariable (HR = 1.803, P = 0.043) analyses. The predicting process is available at an online website (https://baigao.github.io/Pathologic-Prognostic-Analysis/). INTERPRETATION: Our study developed AI models and contributed to predicting precise diagnosis and prognosis of GC patients, which will offer assistance to choose appropriate treatment to improve the survival status of GC patients. FUNDING: Not applicable.

Integrated Clinical and CT Based Artificial Intelligence Nomogram for Predicting Severity and Need for Ventilator Support in COVID-19 Patients: A Multi-Site Study.

Almost 25% of COVID-19 patients end up in ICU needing critical mechanical ventilation support. There is currently no validated objective way to predict which patients will end up needing ventilator support, when the disease is mild and not progressed. N = 869 patients from two sites (D1: N = 822, D2: N = 47) with baseline clinical characteristics and chest CT scans were considered for this study. The entire dataset was randomly divided into 70% training, D1train (N = 606) and 30% test-set (Dtest: D1test (N = 216) + D2 (N = 47)). An expert radiologist delineated ground-glass-opacities (GGOs) and consolidation regions on a subset of D1train, (D1train_sub, N = 88). These regions were automatically segmented and used along with their corresponding CT volumes to train an imaging AI predictor (AIP) on D1train to predict the need of mechanical ventilators for COVID-19 patients. Finally, top five prognostic clinical factors selected using univariate analysis were integrated with AIP to construct an integrated clinical and AI imaging nomogram (ClAIN). Univariate analysis identified lactate dehydrogenase, prothrombin time, aspartate aminotransferase, %lymphocytes, albumin as top five prognostic clinical features. AIP yielded an AUC of 0.81 on Dtest and was independently prognostic irrespective of other clinical parameters on multivariable analysis (p<0.001). ClAIN improved the performance over AIP yielding an AUC of 0.84 (p = 0.04) on Dtest. ClAIN outperformed AIP in predicting which COVID-19 patients ended up needing a ventilator. Our results across multiple sites suggest that ClAIN could help identify COVID-19 with severe disease more precisely and likely to end up on a life-saving mechanical ventilation.

Ubiquitin-Specific Protease 25 Aggravates Acute Pancreatitis and Acute Pancreatitis-Related Multiple Organ Injury by Destroying Tight Junctions Through Activation of The STAT3 Pathway.

Ubiquitin-specific protease 25 (USP25) plays an important role in inflammation and immunity. However, the role of USP25 in acute pancreatitis (AP) is still unclear. To evaluate the role of USP25 in AP, we conducted research on clinical AP patients, USP25wild-type(WT)/USP25 knockout (USP25-/-) mice, and pancreatic acinar cells. Our results showed that serum USP25 concentration was higher in AP patients than in healthy controls and was positively correlated with disease severity. AP patients' serum USP25 levels after treatment were significantly lower than that at the onset of AP. Moreover, USP25 expression was upregulated in cerulein-induced AP in mice, while USP25 deficiency attenuates AP and AP-related multiple organ injury. In vivo and in vitro studies showed that USP25 exacerbates AP by promoting the release of pro-inflammatory factors and destroying tight junctions of the pancreas. We showed that USP25 aggravates AP and AP-related multiple organ injury by activating the signal transducer and activator of transcription 3 (STAT3) pathway. Targeting the action of USP25 may present a potential therapeutic option for treating AP.

Gastrointestinal Symptoms Onset in COVID-19 Patients in Wuhan, China.

BACKGROUND: Early detection is critical in limiting the spread of 2019 novel coronavirus (COVID-19). Although previous data revealed characteristics of GI symptoms in COVID-19, for patients with only GI symptoms onset, their diagnostic process and potential transmission risk are still unclear. METHODS: We retrospectively reviewed 205 COVID-19 cases from January 16 to March 30, 2020, in Renmin Hospital of Wuhan University. All patients were confirmed by virus nuclei acid tests. The clinical features and laboratory and chest tomographic (CT) data were recorded and analyzed. RESULTS: A total of 171 patients with classic symptoms (group A) and 34 patients with only GI symptoms (group B) were included. In patients with classical COVID-19 symptoms, GI symptoms occurred more frequently in severe cases compared to non-severe cases (20/43 vs. 91/128, respectively, p < 0.05). In group B, 91.2% (31/34) patients were non-severe, while 73.5% (25/34) patients had obvious infiltrates in their first CT scans. Compared to group A, group B patients had a prolonged time to clinic services (5.0 days vs. 2.6 days, p < 0.01) and a longer time to a positive viral swab normalized to the time of admission (6.9 days vs. 3.3 days, respectively, p < 0.01). Two patients in group B had family clusters of SARS-CoV-2 infection. CONCLUSION: Patients with only GI symptoms of COVID-19 may take a longer time to present to healthcare services and receive a confirmed diagnosis. In areas where infection is rampant, physicians must remain vigilant of patients presenting with acute gastrointestinal symptoms and should do appropriate personal protective equipment.

Snake Cathelicidin Derived Peptide Inhibits Zika Virus Infection.

Zika virus (ZIKV) is a mosquito-borne virus belonging to the genus Flavivirus and has reemerged in recent years with epidemic potential. ZIKV infection may result in severe syndromes such as neurological complications and microcephaly in newborns. Therefore, ZIKV has become a global public health threat and currently there is no approved specific drug for its treatment. Animal venoms are important resources of novel drugs. Cathelicidin-BF (BF-30) is a defensive peptide identified from Bungarus fasciatus snake venom and has been shown to be an excellent template for applicable peptide design. In this study, we found that ZY13, one of the peptidic analogs of BF-30, inhibits ZIKV infection in vitro and in vivo. Mechanistic studies revealed that ZY13 can directly inactivate ZIKV and reduce the production of infectious virions. Further studies also indicated that administration of ZY13 strengthen the host antiviral immunity via AXL-SOCS (suppressor of cytokine signaling protein) pathway. Additionally, the results of mouse experiment suggest that ZY13 efficiently restrict ZIKV infection and improve the growth defects of ZIKV-infected mouse pups. Together, our findings not only demonstrate that ZY13 might be a candidate for anti-ZIKV drug, but also indicated the importance of animal venom peptides as templates for antivirals development.

Characteristics of disease progress in patients with coronavirus disease 2019 in Wuhan, China.

Coronavirus disease 2019 (COVID-19) patients were classified into four clinical stages (uncomplicated illness, mild, severe and critical pneumonia) depending on disease severity. We aim to investigate the corresponding clinical, radiological and laboratory characteristics between different clinical stages. A retrospective, single-centre study of 101 confirmed patients with COVID-19 at Renmin Hospital of Wuhan University from 2 January to 28 January 2020 was enrolled; follow-up endpoint was on 8 February 2020. Clinical data were collected and compared during the course of illness. The median age of the 101 patients was 51.0 years and 33.6% were medical staff. Fever (68%), cough (50%) and fatigue (23%) are the most common symptoms. About 26% patients underwent the mechanical ventilation and 98% patients were treated with antibiotics. Thirty-seven per cent patients were cured and 11 died. On admission, the number of patients with uncomplicated illness, mild, severe and critical pneumonia were 2 [2%], 86 [85%], 11 [11%] and 2 [2%]. Forty-four of the 86 mild pneumonia progressed to severe illness within 4 days, with nine patients worsened due to critical pneumonia within 4 days. Two of the 11 severe patients improved to mild condition while three others deteriorated. Significant differences were observed among groups of different clinical stages in numbers of influenced pulmonary segments (6 vs. 12 vs. 17, P < 0.001). A significantly upward trend was witnessed in ground-glass opacities overlapped with striped shadows (33% vs. 42% vs. 55% vs. 80%, P < 0.001), while pure ground-glass opacities gradually decreased as disease progressed (45% vs. 35% vs. 24% vs. 13%, P < 0.001) within 12 days. Lymphocytes, prealbumin and albumin showed a downtrend as disease progressed from mild to severe or critical condition, an uptrend was found in white blood cells, C-reactive protein, neutrophils and lactate dehydrogenase. The proportions of serum amyloid A > 300 mg/l in mild, severe and critical conditions were 18%, 46% and 71%, respectively.

Do underlying cardiovascular diseases have any impact on hospitalised patients with COVID-19?

OBJECTIVES: An outbreak of the highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has sickened thousands of people in China. The purpose of this study was to explore the early clinical characteristics of COVID-19 patients with cardiovascular disease (CVD). METHODS: This is a retrospective analysis of patients with COVID-19 from a single centre. All patients underwent real-time reverse transcription PCR for SARS-CoV-2 on admission. Demographic and clinical factors and laboratory data were reviewed and collected to evaluate for significant associations. RESULTS: The study included 541 patients with COVID-19. A total of 144 (26.6%) patients had a history of CVD. The mortality of patients with CVD reached 22.2%, which was higher than that of the overall population of this study (9.8%). Patients with CVD were also more likely to develop liver function abnormality, elevated blood creatinine and lactic dehydrogenase (p<0.05). Symptoms of sputum production were more common in patients with CVD (p=0.026). Lymphocytes, haemoglobin and albumin below the normal range were pervasive in the CVD group (p<0.05). The proportion of critically ill patients in the CVD group (27.8%) was significantly higher than that in the non-CVD group (8.8%). Multivariable logistic regression analysis revealed that CVD (OR: 2.735 (95% CI 1.495 to 5.003), p=0.001) was associated with critical COVID-19 condition, while patients with coronary heart disease were less likely to reach recovery standards (OR: 0.331 (95% CI 0.125 to 0.880), p=0.027). CONCLUSIONS: Considering the high prevalence of CVD, a thorough CVD assessment at diagnosis and early intervention are recommended in COVID-19 patients with CVD. Patients with CVD are more vulnerable to deterioration.

Glandular orientation and shape determined by computational pathology could identify aggressive tumor for early colon carcinoma: a triple-center study.

BACKGROUND: Identifying the early-stage colon adenocarcinoma (ECA) patients who have lower risk cancer vs. the higher risk cancer could improve disease prognosis. Our study aimed to explore whether the glandular morphological features determined by computational pathology could identify high risk cancer in ECA via H&E images digitally. METHODS: 532 ECA patients retrospectively from 2 independent data centers, as well as 113 from The Cancer Genome Atlas (TCGA), were enrolled in this study. Four tissue microarrays (TMAs) were constructed across ECA hematoxylin and eosin (H&E) stained slides. 797 quantitative glandular morphometric features were extracted and 5 most prognostic features were identified using minimum redundancy maximum relevance to construct an image classifier. The image classifier was evaluated on D2/D3 = 223, D4 = 46, D5 = 113. The expression of Ki67 and serum CEA levels were scored on D3, aiming to explore the correlations between image classifier and immunohistochemistry data and serum CEA levels. The roles of clinicopathological data and ECAHBC were evaluated by univariate and multivariate analyses for prognostic value. RESULTS: The image classifier could predict ECA recurrence (accuracy of 88.1%). ECA histomorphometric-based image classifier (ECAHBC) was an independent prognostic factor for poorer disease-specific survival [DSS, (HR = 9.65, 95% CI 2.15-43.12, P = 0.003)]. Significant correlations were observed between ECAHBC-positive patients and positivity of Ki67 labeling index (Ki67Li) and serum CEA. CONCLUSION: Glandular orientation and shape could predict the high risk cancer in ECA and contribute to precision oncology. Computational pathology is emerging as a viable and objective means of identifying predictive biomarkers for cancer patients.