Objective To evaluate viral loads at different stages of disease progression in patients infected with the 2019 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the first four months of the epidemic in Zhejiang province, China. Design Retrospective cohort study. Setting A designated hospital for patients with covid-19 in Zhejiang province, China. Participants 96 consecutively admitted patients with laboratory confirmed SARS-CoV-2 infection: 22 with mild disease and 74 with severe disease. Data were collected from 19 January 2020 to 20 March 2020. Main outcome measures Ribonucleic acid (RNA) viral load measured in respiratory, stool, serum, and urine samples. Cycle threshold values, a measure of nucleic acid concentration, were plotted onto the standard curve constructed on the basis of the standard product. Epidemiological, clinical, and laboratory characteristics and treatment and outcomes data were obtained through data collection forms from electronic medical records, and the relation between clinical data and disease severity was analysed. Results 3497 respiratory, stool, serum, and urine samples were collected from patients after admission and evaluated for SARS-CoV-2 RNA viral load. Infection was confirmed in all patients by testing sputum and saliva samples. RNA was detected in the stool of 55 (59%) patients and in the serum of 39 (41%) patients. The urine sample from one patient was positive for SARS-CoV-2. The median duration of virus in stool (22 days, interquartile range 17-31 days) was significantly longer than in respiratory (18 days, 13-29 days; P=0.02) and serum samples (16 days, 11-21 days; P<0.001). The median duration of virus in the respiratory samples of patients with severe disease (21 days, 14-30 days) was significantly longer than in patients with mild disease (14 days, 10-21 days; P=0.04). In the mild group, the viral loads peaked in respiratory samples in the second week from disease onset, whereas viral load continued to be high during the third week in the severe group. Virus duration was longer in patients older than 60 years and in male patients. Conclusion The duration of SARS-CoV-2 is significantly longer in stool samples than in respiratory and serum samples, highlighting the need to strengthen the management of stool samples in the prevention and control of the epidemic, and the virus persists longer with higher load and peaks later in the respiratory tissue of patients with severe disease.

Despite great success in cancer immunotherapy, immune checkpoint-targeting drugs are not the most popular weapon in the armory of cancer therapy. Accumulating evidence suggests that the tumor immune microenvironment plays a critical role in anti-cancer immunity, which may result in immune checkpoint blockade therapy being ineffective, in addition to other novel immunotherapies in cancer patients. In the present review, we discuss the deficiencies of current cancer immunotherapies. More importantly, we highlight the critical role of tumor immune microenvironment regulators in tumor immune surveillance, immunological evasion, and the potential for their further translation into clinical practice. Based on their general targetability in clinical therapy, we believe that tumor immune microenvironment regulators are promising cancer immunotherapeutic targets. Targeting the tumor immune microenvironment, alone or in combination with immune checkpoint-targeting drugs, might benefit cancer patients in the future.

Oncolytic viruses (OVs) have attracted growing awareness in the twenty-first century, as they are generally considered to have direct oncolysis and cancer immune effects. With the progress in genetic engineering technology, OVs have been adopted as versatile platforms for developing novel antitumor strategies, used alone or in combination with other therapies. Recent studies have yielded eye-catching results that delineate the promising clinical outcomes that OVs would bring about in the future. In this review, we summarized the basic principles of OVs in terms of their classifications, as well as the recent advances in OV-modification strategies based on their characteristics, biofunctions, and cancer hallmarks. Candidate OVs are expected to be designed as “qualified soldiers” first by improving target fidelity and safety, and then equipped with “cold weapons” for a proper cytocidal effect, “hot weapons” capable of activating cancer immunotherapy, or “auxiliary weapons” by harnessing tactics such as anti-angiogenesis, reversed metabolic reprogramming and decomposing extracellular matrix around tumors. Combinations with other cancer therapeutic agents have also been elaborated to show encouraging antitumor effects. Robust results from clinical trials using OV as a treatment congruously suggested its significance in future application directions and challenges in developing OVs as novel weapons for tactical decisions in cancer treatment.

Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely performed for clinical indications, offers the potential for large-scale screening, however, identification of PDAC using non-contrast CT has long been considered impossible. Here, we develop a deep learning approach, pancreatic cancer detection with artificial intelligence (PANDA), that can detect and classify pancreatic lesions with high accuracy via non-contrast CT. PANDA is trained on a dataset of 3,208 patients from a single center. PANDA achieves an area under the receiver operating characteristic curve (AUC) of 0.986–0.996 for lesion detection in a multicenter validation involving 6,239 patients across 10 centers, outperforms the mean radiologist performance by 34.1% in sensitivity and 6.3% in specificity for PDAC identification, and achieves a sensitivity of 92.9% and specificity of 99.9% for lesion detection in a real-world multi-scenario validation consisting of 20,530 consecutive patients. Notably, PANDA utilized with non-contrast CT shows non-inferiority to radiology reports (using contrast-enhanced CT) in the differentiation of common pancreatic lesion subtypes. PANDA could potentially serve as a new tool for large-scale pancreatic cancer screening.

Objective Hepatocellular carcinoma (HCC) is heterogeneous, especially in multifocal tumours, which decreases the efficacy of clinical treatments. Understanding tumour heterogeneity is critical when developing novel treatment strategies. However, a comprehensive investigation of tumour heterogeneity in HCC is lacking, and the available evidence regarding tumour heterogeneity has not led to improvements in clinical practice. Design We harvested 42 samples from eight HCC patients and evaluated tumour heterogeneity using whole-exome sequencing, RNA sequencing, mass spectrometry-based proteomics and metabolomics, cytometry by time-of-flight, and single-cell analysis. Immunohistochemistry and quantitative polymerase chain reactions were performed to confirm the expression levels of genes. Three independent cohorts were further used to validate the findings. Results Tumour heterogeneity is considerable with regard to the genomes, transcriptomes, proteomes, and metabolomes of lesions and tumours. The immune status of the HCC microenvironment was relatively less heterogenous. Targeting local immunity could be a suitable intervention with balanced precision and practicability. By clustering immune cells in the HCC microenvironment, we identified three distinctive HCC subtypes with immunocompetent, immunodeficient, and immunosuppressive features. We further revealed the specific metabolic features and cytokine/chemokine expression levels of the different subtypes. Determining the expression levels of CD45 and Foxp3 using immunohistochemistry facilitated the correct classification of HCC patients and the prediction of their prognosis. Conclusion There is comprehensive intratumoral and intertumoral heterogeneity in all dimensions of HCC. Based on the results, we propose a novel immunophenotypic classification of HCCs that facilitates prognostic prediction and may support decision making with regard to the choice of therapy.