Therapeutic potential of the secreted Kazal-type serine protease inhibitor SPINK4 in colitis.

Mucus injury associated with goblet cell (GC) depletion constitutes an early event in inflammatory bowel disease (IBD). Using single-cell sequencing to detect critical events in mucus dysfunction, we discover that the Kazal-type serine protease inhibitor SPINK4 is dynamically regulated in colitic intestine in parallel with disease activities. Under chemically induced colitic conditions, the grim status in Spink4-conditional knockout mice is successfully rescued by recombinant murine SPINK4. Notably, its therapeutic potential is synergistic with existing TNF-α inhibitor infliximab in colitis treatment. Mechanistically, SPINK4 promotes GC differentiation using a Kazal-like motif to modulate EGFR-Wnt/ß-catenin and -Hippo pathways. Microbiota-derived diacylated lipoprotein Pam2CSK4 triggers SPINK4 production. We also show that monitoring SPINK4 in circulation is a reliable noninvasive technique to distinguish IBD patients from healthy controls and assess disease activity. Thus, SPINK4 serves as a serologic biomarker of IBD and has therapeutic potential for colitis via intrinsic EGFR activation in intestinal homeostasis.

Low frequency of repetitive trans-spinal magnetic stimulation promotes functional recovery after spinal cord injury in mice through inhibiting TGF-ß1/Smad2/3 signaling pathway.

Spinal cord injury (SCI) remains a worldwide challenge due to limited treatment strategies. Repetitive trans-spinal magnetic stimulation (rTSMS) is among the most cutting-edge treatments for SCI. However, the mechanism underlying rTSMS on functional recovery is still unclear. In this study, 8-week-old C57BL/6J female mice were used to design SCI models followed by treatment with monotherapy (1 Hz rTSMS or LY364947) or combination therapy (rTSMS + LY364947). Our results showed obvious functional recovery after monotherapies compared to untreated mice. Immunofluorescence results demonstrated that rTSMS and LY364947 modulate the lesion scar by decreasing fibrosis and GFAP and possess the effect on neural protection. In addition, rTSMS suppressed inflammation and the activation of TGFß1/Smad2/3 signaling pathway, as evidenced by markedly reduced TGF-ßRⅠ, Smad2/3, and p-Smad2/3 compared with untreated mice. Overall, it was confirmed that 1 Hz rTSMS promotes SCI recovery by suppressing the TGFß1/Smad2/3 signaling, revealing a novel pathological mechanism of 1 Hz rTSMS intervention, and may provide potential targets for clinical treatment.

SaB-Net: Self-attention backward network for gastric tumor segmentation in CT images.

Gastric cancer is a significant contributor to cancer-related fatalities globally. The automated segmentation of gastric tumors has the potential to analyze the medical condition of patients and enhance the likelihood of surgical treatment success. However, the development of an automatic solution is challenged by the heterogeneous intensity distribution of gastric tumors in computed tomography (CT) images, the low-intensity contrast between organs, and the high variability in the stomach shapes and gastric tumors in different patients. To address these challenges, we propose a self-attention backward network (SaB-Net) for gastric tumor segmentation (GTS) in CT images by introducing a self-attention backward layer (SaB-Layer) to feed the self-attention information learned at the deep layer back to the shallow layers. The SaB-Layer efficiently extracts tumor information from CT images and integrates the information into the network, thereby enhancing the network's tumor segmentation ability. We employed datasets from two centers, one for model training and testing and the other for external validation. The model achieved dice scores of 0.8456 on the test set and 0.8068 on the external verification set. Moreover, we validated the model's transfer learning ability on a publicly available liver cancer dataset, achieving results comparable to state-of-the-art liver cancer segmentation models recently developed. SaB-Net has strong potential for assisting in the clinical diagnosis of and therapy for gastric cancer. Our implementation is available at https://github.com/TyrionJ/SaB-Net.

A radiomics based approach using adrenal gland and periadrenal fat CT images to allocate COVID-19 health care resources fairly.

BACKGROUND: The value of radiomics features from the adrenal gland and periadrenal fat CT images for predicting disease progression in patients with COVID-19 has not been studied extensively. We assess the value of radiomics features from the adrenal gland and periadrenal fat CT images in predicting COVID-19 disease exacerbation. METHODS: A total of 1,245 patients (685 moderate and 560 severe patients) were enrolled in a retrospective study. We proposed a 3D V-net to segment adrenal glands in onset CT images automatically, and periadrenal fat was obtained using inflation operation around the adrenal gland. Next, we built a clinical model (CM), three radiomics models (adrenal gland model [AM], periadrenal fat model [PM], and fusion of adrenal gland and periadrenal fat model [FM]), and radiomics nomogram (RN) after radiomics features extracted. RESULTS: The auto-segmentation framework yielded a dice value 0.79 in the training set. CM, AM, PM, FM, and RN obtained AUCs of 0.717, 0.716, 0.736, 0.760, and 0.833 in the validation set. FM and RN had better predictive efficacy than CM (P < 0.0001) in the training set. RN showed that there was no significant difference in the validation set (mean absolute error [MAE] = 0.04) and test set (MAE = 0.075) between predictive and actual results. Decision curve analysis showed that if the threshold probability was between 0.4 and 0.8 in the validation set or between 0.3 and 0.7 in the test set, it could gain more net benefits using RN than FM and CM. CONCLUSIONS: Radiomics features extracted from the adrenal gland and periadrenal fat CT images are related to disease exacerbation in patients with COVID-19.

Recent developments on BMPs and their antagonists in inflammatory bowel diseases.

Inflammatory bowel diseases (IBDs), including ulcerative colitis, and Crohn's disease, are intestinal disorders characterized by chronic relapsing inflammation. A large proportion of patients with IBD will progress to develop colitis-associated colorectal cancer due to the chronic intestinal inflammation. Biologic agents that target tumour necrosis factor-α, integrin α4ß7, and interleukin (IL)12/23p40 have been more successful than conventional therapies in treating IBD. However, drug intolerance and loss of response are serious drawbacks of current biologics, necessitating the development of novel drugs that target specific pathways in IBD pathogenesis. One promising group of candidate molecules are bone morphogenetic proteins (BMPs), members of the TGF-ß family involved in regulating morphogenesis, homeostasis, stemness, and inflammatory responses in the gastrointestinal tract. Also worth examining are BMP antagonists, major regulators of these proteins. Evidence has shown that BMPs (especially BMP4/6/7) and BMP antagonists (especially Gremlin1 and follistatin-like protein 1) play essential roles in IBD pathogenesis. In this review, we provide an updated overview on the involvement of BMPs and BMP antagonists in IBD pathogenesis and in regulating the fate of intestinal stem cells. We also described the expression patterns of BMPs and BMP antagonists along the intestinal crypt-villus axis. Lastly, we synthesized available research on negative regulators of BMP signalling. This review summarizes recent developments on BMPs and BMP antagonists in IBD pathogenesis, which provides novel insights into future therapeutic strategies.

Dietary palmitoleic acid reprograms gut microbiota and improves biological therapy against colitis.

Magnitude and diversity of gut microbiota and metabolic systems are critical in shaping human health and diseases, but it remains largely unclear how complex metabolites may selectively regulate gut microbiota and determine health and diseases. Here, we show that failures or compromised effects of anti-TNF-α therapy in inflammatory bowel diseases (IBD) patients were correlated with intestinal dysbacteriosis with more pro-inflammatory bacteria, extensive unresolved inflammation, failed mucosal repairment, and aberrant lipid metabolism, particularly lower levels of palmitoleic acid (POA). Dietary POA repaired gut mucosal barriers, reduced inflammatory cell infiltrations and expressions of TNF-α and IL-6, and improved efficacy of anti-TNF-α therapy in both acute and chronic IBD mouse models. Ex vivo treatment with POA in cultured inflamed colon tissues derived from Crohn's disease (CD) patients reduced pro-inflammatory signaling/cytokines and conferred appreciable tissue repairment. Mechanistically, POA significantly upregulated the transcriptional signatures of cell division and biosynthetic process of Akkermansia muciniphila, selectively increased the growth and abundance of Akkermansia muciniphila in gut microbiota, and further reprogrammed the composition and structures of gut microbiota. Oral transfer of such POA-reprogrammed, but not control, gut microbiota induced better protection against colitis in anti-TNF-α mAb-treated recipient mice, and co-administration of POA with Akkermansia muciniphila showed significant synergistic protections against colitis in mice. Collectively, this work not only reveals the critical importance of POA as a polyfunctional molecular force to shape the magnitude and diversity of gut microbiota and therefore promote the intestinal homeostasis, but also implicates a new potential therapeutic strategy against intestinal or abenteric inflammatory diseases.

A Novel Nomogram Combining Mucus Barrier Index for Predicting Treatment Failures in Ulcerative Colitis.

Background: Treatment failures (TFs) generally exist in the course of ulcerative colitis (UC), while early reliable predictors of TFs are still lacking. We aimed to generate nomograms for the prediction of TFs. Methods: In this retrospective case-control study, the endpoint was the occurrence of TFs, which included medically associated treatment failures and surgery-associated treatment failures (colectomy). Clinical features and mucus integrity evident by goblet cells (GCs) number, expression levels of MUC2 and SLC26A3 were enrolled in the univariate analysis. Nomogram performance was evaluated by discrimination and calibration. Results: We identified 256 UC patients at our center from January 2010 to June 2022. Fourteen variables for TFs and 9 for colectomy were identified by univariate analysis. Five baseline indices were incorporated into the nomogram for the prediction of TFs: area of GCs, age at diagnosis, disease duration, hemoglobin, and Mayo score. The model was presented with decent discrimination (C index of 0.822) and well calibration. In addition, the colectomy predictive nomogram was built using MUC2 intensity, age at onset, and Mayo score with a good discrimination (C index of 0.92). Conclusion: Nomograms based on comprehensive factors including mucus barrier function were developed to predict TFs in UC patients with great discrimination, which may serve as practical tools aiming to identify high-risk subgroups warrant timely intervention.

COVID-19 imaging, where do we go from here? Bibliometric analysis of medical imaging in COVID-19.

OBJECTIVES: We conducted a systematic and comprehensive bibliometric analysis of COVID-19-related medical imaging to determine the current status and indicate possible future directions. METHODS: This research provides an analysis of Web of Science Core Collection (WoSCC) indexed articles on COVID-19 and medical imaging published between 1 January 2020 and 30 June 2022, using the search terms "COVID-19" and medical imaging terms (such as "X-ray" or "CT"). Publications based solely on COVID-19 themes or medical image themes were excluded. CiteSpace was used to identify the predominant topics and generate a visual map of countries, institutions, authors, and keyword networks. RESULTS: The search included 4444 publications. The journal with the most publications was European Radiology, and the most co-cited journal was Radiology. China was the most frequently cited country in terms of co-authorship, with the Huazhong University of Science and Technology being the institution contributing with the highest number of relevant co-authorships. Research trends and leading topics included: assessment of initial COVID-19-related clinical imaging features, differential diagnosis using artificial intelligence (AI) technology and model interpretability, diagnosis systems construction, COVID-19 vaccination, complications, and predicting prognosis. CONCLUSIONS: This bibliometric analysis of COVID-19-related medical imaging helps clarify the current research situation and developmental trends. Subsequent trends in COVID-19 imaging are likely to shift from lung structure to function, from lung tissue to other related organs, and from COVID-19 to the impact of COVID-19 on the diagnosis and treatment of other diseases. Key Points • We conducted a systematic and comprehensive bibliometric analysis of COVID-19-related medical imaging from 1 January 2020 to 30 June 2022. • Research trends and leading topics included assessment of initial COVID-19-related clinical imaging features, differential diagnosis using AI technology and model interpretability, diagnosis systems construction, COVID-19 vaccination, complications, and predicting prognosis. • Future trends in COVID-19-related imaging are likely to involve a shift from lung structure to function, from lung tissue to other related organs, and from COVID-19 to the impact of COVID-19 on the diagnosis and treatment of other diseases.

Role of Rho GTPases in inflammatory bowel disease.

Rat sarcoma virus homolog (Rho) guanosine triphosphatases (GTPases) function as "molecular switch" in cellular signaling regulation processes and are associated with the pathogenesis of inflammatory bowel disease (IBD). This chronic intestinal tract inflammation primarily encompasses two diseases: Crohn's disease and ulcerative colitis. The pathogenesis of IBD is complex and considered to include four main factors and their interactions: genetics, intestinal microbiota, immune system, and environment. Recently, several novel pathogenic components have been identified. In addition, potential therapies for IBD targeting Rho GTPases have emerged and proven to be clinically effective. This review mainly focuses on Rho GTPases and their possible mechanisms in IBD pathogenesis. The therapeutic possibility of Rho GTPases is also discussed.

Herpes simplex virus type 2 inhibits TNF-α-induced NF-κB activation through viral protein ICP22-mediated interaction with p65.

Herpes simplex virus type 2 (HSV-2) is a prevalent human pathogen and the main cause of genital herpes. After initial infection, HSV-2 can establish lifelong latency within dorsal root ganglia by evading the innate immunity of the host. NF-κB has a crucial role in regulating cell proliferation, inflammation, apoptosis, and immune responses. It is known that inhibition of NF-κB activation by a virus could facilitate it to establish infection in the host. In the current study, we found that HSV-2 inhibited TNF-α-induced activation of NF-κB-responsive promoter in a dose-dependent manner, while UV-inactivated HSV-2 did not have such capability. We further identified the immediate early protein ICP22 of HSV-2 as a vital viral element in inhibiting the activation of NF-κB-responsive promoter. The role of ICP22 was confirmed in human cervical cell line HeLa and primary cervical fibroblasts in the context of HSV-2 infection, showing that ICP22 deficient HSV-2 largely lost the capability in suppressing NF-κB activation. HSV-2 ICP22 was further shown to suppress the activity of TNF receptor-associated factor 2 (TRAF2)-, IκB kinase α (IKK α)-, IKK ß-, IKK γ-, or p65-induced activation of NF-κB-responsive promoter. Mechanistically, HSV-2 ICP22 inhibited the phosphorylation and nuclear translocation of p65 by directly interacting with p65, resulting in the blockade of NF-κB activation. Furthermore, ICP22 from several alpha-herpesviruses could also inhibit NF-κB activation, suggesting the significance of ICP22 in herpesvirus immune evasion. Findings in this study highlight the importance of ICP22 in inhibiting NF-κB activation, revealing a novel mechanism by which HSV-2 evades the host antiviral responses.

CCL28 Enhances HSV-2 gB-Specific Th1-Polarized Immune Responses against Lethal Vaginal Challenge in Mice.

Plasmid DNA (pDNA) represents a promising "genetic vaccine platform" capable of overcoming major histocompatibility complex barriers. We previously demonstrated that low-to-moderate doses of mucosae-associated epithelial chemokine (MEC or CCL28) as an immunomodulatory adjuvant can trigger effective and long-lasting systemic and mucosal HSV-2 gD-specific immune responses, whereas mice immunized with gD in combination with high-dose CCL28 showed toxicity and lost their immunoprotective effects after lethal HSV-2 challenge. The exact causes underlying high-dose, CCL28-induced lesions remain unknown. In an intramuscularly immunized mouse model, we investigated the immune-enhancement mechanisms of low-dose CCL28 as a molecular adjuvant combined with the relatively weak immunogen HSV-2 gB. Compared with the plasmid gB antigen group, we found that a low-dose of plasmid CCL28 (pCCL28) codelivered with pgB induced increased levels of gB-specific serum IgG and vaginal fluid IgA, serum neutralizing antibodies (NAb), Th1-polarized IgG2a, and cytokine IL-2 (>5-fold). Furthermore, low-dose pCCL28 codelivery with pgB enhanced CCL28/CCR10-axis responsive CCR10− plus CCR10+ B-cell (~1.2-fold) and DC pools (~4-fold) in the spleen, CCR10− plus CCR10+ T-cell pools (~2-fold) in mesenteric lymph nodes (MLNs), and the levels of IgA-ASCs in colorectal mucosal tissues, leading to an improved protective effect against a lethal dose of HSV-2 challenge. Findings in this study provide a basis for the development of CCL28-adjuvant vaccines against viral mucosal infections.

StoHisNet: A hybrid multi-classification model with CNN and Transformer for gastric pathology images.

BACKGROUND AND OBJECTIVES: Gastric cancer has high morbidity and mortality compared to other cancers. Accurate histopathological diagnosis has great significance for the treatment of gastric cancer. With the development of artificial intelligence, many researchers have applied deep learning for the classification of gastric cancer pathological images. However, most studies have used binary classification on pathological images of gastric cancer, which is insufficient with respect to the clinical requirements. Therefore, we proposed a multi-classification method based on deep learning with more practical clinical value. METHODS: In this study, we developed a novel multi-scale model called StoHisNet based on Transformer and the convolutional neural network (CNN) for the multi-classification task. StoHisNet adopts Transformer to learn global features to alleviate the inherent limitations of the convolution operation. The proposed StoHisNet can classify the publicly available pathological images of a gastric dataset into four categories -normal tissue, tubular adenocarcinoma, mucinous adenocarcinoma, and papillary adenocarcinoma. RESULTS: The accuracy, F1-score, recall, and precision of the proposed model in the public gastric pathological image dataset were 94.69%, 94.96%, 94.95%, and 94.97%, respectively. We conducted additional experiments using two other public datasets to verify the generalization ability of the model. On the BreakHis dataset, our model performed better compared with other classification models, and the accuracy was 91.64%. Similarly, on the four-classification task on the Endometrium dataset, our model showed better classification ability than others with accuracy of 81.74%. These experiments showed that the proposed model has excellent ability of classification and generalization. CONCLUSION: The StoHisNet model had high performance in the multi-classification on gastric histopathological images and showed strong generalization ability on other pathological datasets. This model may be a potential tool to assist pathologists in the analysis of gastric histopathological images.

Human Norovirus Induces Aquaporin 1 Production by Activating NF-κB Signaling Pathway.

Human norovirus (HuNoV) is one of the major pathogens of acute nonbacterial gastroenteritis. Due to the lack of a robust and reproducible in vitro culture system and an appropriate animal model, the mechanism underlying HuNoV-caused diarrhea remains unknown. In the current study, we found that HuNoV transfection induced the expression of aquaporin 1 (AQP1), which was further confirmed in the context of virus infection, whereas the enterovirus EV71 (enterovirus 71) did not have such an effect. We further revealed that VP1, the major capsid protein of HuNoV, was crucial in promoting AQP1 expression. Mechanistically, HuNoV induces AQP1 production through the NF-κB signaling pathway via inducing the expression, phosphorylation and nuclear translocation of p65. By using a model of human intestinal epithelial barrier (IEB), we demonstrated that HuNoV and VP1-mediated enhancement of small molecule permeability is associated with the AQP1 channel. Collectively, we revealed that HuNoV induced the production of AQP1 by activating the NF-κB signaling pathway. The findings in this study provide a basis for further understanding the significance of HuNoV-induced AQP1 expression and the potential mechanism underlying HuNoV-caused diarrhea.

FaNet: fast assessment network for the novel coronavirus (COVID-19) pneumonia based on 3D CT imaging and clinical symptoms.

The novel coronavirus (COVID-19) pneumonia has become a serious health challenge in countries worldwide. Many radiological findings have shown that X-ray and CT imaging scans are an effective solution to assess disease severity during the early stage of COVID-19. Many artificial intelligence (AI)-assisted diagnosis works have rapidly been proposed to focus on solving this classification problem and determine whether a patient is infected with COVID-19. Most of these works have designed networks and applied a single CT image to perform classification; however, this approach ignores prior information such as the patient's clinical symptoms. Second, making a more specific diagnosis of clinical severity, such as slight or severe, is worthy of attention and is conducive to determining better follow-up treatments. In this paper, we propose a deep learning (DL) based dual-tasks network, named FaNet, that can perform rapid both diagnosis and severity assessments for COVID-19 based on the combination of 3D CT imaging and clinical symptoms. Generally, 3D CT image sequences provide more spatial information than do single CT images. In addition, the clinical symptoms can be considered as prior information to improve the assessment accuracy; these symptoms are typically quickly and easily accessible to radiologists. Therefore, we designed a network that considers both CT image information and existing clinical symptom information and conducted experiments on 416 patient data, including 207 normal chest CT cases and 209 COVID-19 confirmed ones. The experimental results demonstrate the effectiveness of the additional symptom prior information as well as the network architecture designing. The proposed FaNet achieved an accuracy of 98.28% on diagnosis assessment and 94.83% on severity assessment for test datasets. In the future, we will collect more covid-CT patient data and seek further improvement.

Emerging roles of the Hedgehog signalling pathway in inflammatory bowel disease.

The Hedgehog (Hh) signalling pathway plays a critical role in the growth and patterning during embryonic development and maintenance of adult tissue homeostasis. Emerging data indicate that Hh signalling is implicated in the pathogenesis of inflammatory bowel disease (IBD). Current therapeutic treatments for IBD require optimisation, and novel effective drugs are warranted. Targeting the Hh signalling pathway may pave the way for successful IBD treatment. In this review, we introduce the molecular mechanisms underlying the Hh signalling pathway and its role in maintaining intestinal homeostasis. Then, we present interactions between the Hh signalling and other pathways involved in IBD and colitis-associated colorectal cancer (CAC), such as the Wnt and nuclear factor-kappa B (NF-κB) pathways. Furthermore, we summarise the latest research on Hh signalling associated with the occurrence and progression of IBD and CAC. Finally, we discuss the future directions for research on the role of Hh signalling in IBD pathogenesis and provide viewpoints on novel treatment options for IBD by targeting Hh signalling. An in-depth understanding of the complex role of Hh signalling in IBD pathogenesis will contribute to the development of new effective therapies for IBD patients.

An AI-based radiomics nomogram for disease prognosis in patients with COVID-19 pneumonia using initial CT images and clinical indicators.

BACKGROUND: This study utilized a comprehensive nomogram to evaluate the prognosis of patients with COVID-19 pneumonia. METHODS: COVID-19 pneumonia data was divided into training set (256 of 321, 80%), internal validation set (65 of 321, 20%) and independent external validation set (n = 188). After image processing, lesion segmentation, feature extraction and feature selection, radiomics signatures and clinical indicators were used to develop a radiomics model and a clinical model respectively. Combining radiomics signatures and clinical indicators, a radiomics nomogram was built. The performance of proposed models was evaluated by the receiver operating characteristic curve (AUC). Calibration curves and decision curve analysis were used to assess the performance of the radiomics nomogram. RESULTS: Two clinical indicators that were age and chronic lung disease or asthma and 21 radiomics features were selected to build the radiomics nomogram. The radiomics nomogram yielded an Area Under The Curve1 (AUC) of 0.88 and accuracy of 0.80 in the training set, an AUC of 0.85 and accuracy of 0.77 in internal testing validation set and an AUC of 0.84 and accuracy of 0.75 in independent external validation set. The performance of radiomics nomogram was better than clinical model (AUC = 0.77, p < 0.001) and radiomics model (AUC = 0.72, p = 0.025) in independent external validation set. CONCLUSIONS: The radiomics nomogram may be used to assess the deterioration of COVID-19 pneumonia.

Human Norovirus NTPase Antagonizes Interferon-ß Production by Interacting With IkB Kinase ε.

Human norovirus (HuNoV) is the leading cause of epidemic acute gastroenteritis worldwide. Type I interferons (IFN)-α/ß are highly potent cytokines that are initially identified for their essential roles in antiviral defense. It was reported that HuNoV infection did not induce IFN-ß expression but was controlled in the presence of IFN-ß in human intestinal enteroids and a gnotobiotic pig model, suggesting that HuNoV has likely developed evasion countermeasures. In this study, we found that a cDNA clone of GII.4 HuNoV, the predominantly circulating genotype worldwide, inhibits the production of IFN-ß and identified the viral NTPase as a key component responsible for such inhibition. HuNoV NTPase not only inhibits the activity of IFN-ß promoter but also the mRNA and protein production of IFN-ß. Additional studies indicate that NTPase inhibits the phosphorylation and nuclear translocation of interferon-regulatory factor-3 (IRF-3), leading to the suppression of IFN-ß promoter activation. Mechanistically, NTPase interacts with IkB kinase ε (IKKε), an important factor for IRF-3 phosphorylation, and such interaction blocks the association of IKKε with unanchored K48-linked polyubiquitin chains, resulting in the inhibition of IKKε phosphorylation. Further studies demonstrated that the 1-179 aa domain of NTPase which interacts with IKKε is critical for the suppression of IFN-ß production. Our findings highlight the role of HuNoV NTPase in the inhibition of IFN-ß production, providing insights into a novel mechanism underlying how HuNoV evades the host innate immunity.

Advances in Human Norovirus Vaccine Research.

Human norovirus (HuNoV) is the leading cause of acute gastroenteritis (AGE) worldwide, which is highly stable and contagious, with a few virus particles being sufficient to establish infection. Although the World Health Organization in 2016 stated that it should be an absolute priority to develop a HuNoV vaccine, unfortunately, there is currently no licensed HuNoV vaccine available. The major barrier to the development of an effective HuNoV vaccine is the lack of a robust and reproducible in vitro cultivation system. To develop a HuNoV vaccine, HuNoV immunogen alone or in combination with other viral immunogens have been designed to assess whether they can simultaneously induce protective immune responses against different viruses. Additionally, monovalent and multivalent vaccines from different HuNoV genotypes, including GI and GII HuNoV virus-like particles (VLPs), have been assessed in order to induce broad protection. Although there are several HuNoV vaccine candidates based on VLPs that are being tested in clinical trials, the challenges to develop effective HuNoV vaccines remain largely unresolved. In this review, we summarize the advances of the HuNoV cultivation system and HuNoV vaccine research and discuss current challenges and future perspectives in HuNoV vaccine development.

An AI-based auxiliary empirical antibiotic therapy model for children with bacterial pneumonia using low-dose chest CT images.

PURPOSE: To construct an auxiliary empirical antibiotic therapy (EAT) multi-class classification model for children with bacterial pneumonia using radiomics features based on artificial intelligence and low-dose chest CT images. MATERIALS AND METHODS: Data were retrospectively collected from children with pathogen-confirmed bacterial pneumonia including Gram-positive bacterial pneumonia (122/389, 31%), Gram-negative bacterial pneumonia (159/389, 41%) and atypical bacterial pneumonia (108/389, 28%) from January 1 to June 30, 2019. Nine machine-learning models were separately evaluated based on radiomics features extracted from CT images; three optimal submodels were constructed and integrated to form a multi-class classification model. RESULTS: We selected five features to develop three radiomics submodels: a Gram-positive model, a Gram-negative model and an atypical model. The comprehensive radiomics model using support vector machine method yielded an average area under the curve (AUC) of 0.75 [95% confidence interval (CI), 0.65-0.83] and accuracy (ACC) of 0.58 [sensitivity (SEN), 0.57; specificity (SPE), 0.78] in the training set, and an average AUC of 0.73 (95% CI 0.61-0.79) and ACC of 0.54 (SEN, 0.52; SPE, 0.75) in the test set. CONCLUSION: This auxiliary EAT radiomics multi-class classification model was deserved to be researched in differential diagnosing bacterial pneumonias in children.