Heterogeneity of the tumor immune microenvironment and clinical interventions / 医学前沿

The tumor immune microenvironment (TIME) is broadly composed of various immune cells, and its heterogeneity is characterized by both immune cells and stromal cells. During the course of tumor formation and progression and anti-tumor treatment, the composition of the TIME becomes heterogeneous. Such immunological heterogeneity is not only present between populations but also exists on temporal and spatial scales. Owing to the existence of TIME, clinical outcomes can differ when a similar treatment strategy is provided to patients. Therefore, a comprehensive assessment of TIME heterogeneity is essential for developing precise and effective therapies. Facilitated by advanced technologies, it is possible to understand the complexity and diversity of the TIME and its influence on therapy responses. In this review, we discuss the potential reasons for TIME heterogeneity and the current approaches used to explore it. We also summarize clinical intervention strategies based on associated mechanisms or targets to control immunological heterogeneity.

The impact of vaccination on patients with COVID-19 during the wave of Omicron in Shanghai.

Background: The global health has been affected by the COVID-19 pandemic persistently, of which Omicron is currently the predominant variant. However, the impact of vaccination on Omicron remained uncertain. Objective: This study sought to explore the effect of vaccination on patients infected with Omicron. Methods: A retrospective observational cohort was conducted in the largest Fangcang shelter hospital in Shanghai from April 1 to May 30, 2022. The demographics, length of hospital stay, clinical symptoms, the comorbidities and vaccination status were recorded. Clinical outcomes of the vaccinated and non-vaccinated groups were compared and analyzed. Results: Of the 3,119 patients who fulfilled the eligibility criteria and were enrolled in the study, 2,226 (71.4%) patients had received nCoV-19 vaccine while 893 (28.6%) patients had not received it before admission. Patients in the vaccinated group had significantly shorter length of hospital stay than those in the unvaccinated group (15.48 ± 2.708 vs. 15.85 ± 3.102, p < 0.001). More asymptomatic patients were observed in the vaccinated group than the non-vaccinated (70.4 vs. 64.5%, p < 0.001). Further subgroup analysis demonstrated that the older the age, the more significant the difference was (p < 0.005). Conclusions: Vaccination was associated with a significant reduction in the severity of Omicron infection compared with no vaccination. Vaccination appears to make Omicron-infected people with milder symptoms than unvaccinated people. This suggests the potential effectiveness of current vaccines against Omicron.

Immune phenotyping based on neutrophil-to-lymphocyte ratio and IgG predicts disease severity and outcome for patients with COVID-19

BackgroundA recently emerging respiratory disease named coronavirus disease 2019 (COVID-19) has quickly spread across the world. This disease is initiated by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and uncontrolled cytokine storm, but it remains unknown as to whether a robust antibody response is related to clinical deterioration and poor outcome in laboratory-confirmed COVID-19 patients. MethodsAnti-SARS-CoV-2 IgG and IgM antibodies were determined by chemiluminescence analysis (CLIA) in COVID-19 patients from a single center in Wuhan. Median IgG and IgM levels in acute and convalescent-phase sera (within 35 days) for all included patients were calculated and compared among severe and nonsevere patients. Immune response phenotyping based on late IgG levels and neutrophil-to-lymphocyte ratio (NLR) was characterized to stratify patients with different disease severities and outcome. Laboratory parameters in patients with different immune response phenotypes and disease severities were analyzed. FindingsA total of 222 patients were included in this study. IgG was first detected on day 4 of illness, and its peak levels occurred in the fourth week. Severe cases were more frequently found in patients with high IgG levels, compared to those who with low IgG levels (51.8% versus 32.3%; p=0.008). Severity rates for patients with NLRhiIgGhi, NLRhiIgGlo, NLRloIgGhi, and NLRloIgGlo phenotype was 72.3%, 48.5%, 33.3%, and 15.6%, respectively (p<0.0001). Furthermore, severe patients with NLRhiIgGhi, NLRhiIgGlo had higher proinflammatory cytokines levels including IL-2, IL-6 and IL-10, and decreased CD4+ T cell count compared to those with NLRloIgGlo phenotype (p<0.05). Recovery rate for severe patients with NLRhiIgGhi, NLRhiIgGlo, NLRloIgGhi, and NLRloIgGlo phenotype was 58.8% (20/34), 68.8% (11/16), 80.0% (4/5), and 100% (12/12), respectively (p=0.0592). Dead cases only occurred in NLRhiIgGhi and NLRhiIgGlo phenotypes. InterpretationCOVID-19 severity is associated with increased IgG response, and an immune response phenotyping based on late IgG response and NLR could act as a simple complementary tool to discriminate between severe and nonsevere COVID-19 patients, and further predict their clinical outcome. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSFollowing SARS-CoV-2 infection, a high viral load and overexuberant host immune response involving innate and acquired immunity, simultaneously contributes to the pathogenesis of COVID-19 and organ injury. Through searching PubMed and the China National knowledge infrastructure databases up to March 12, 2020, no published article focusing on anti-SARS-CoV-2 IgG-mediated immune response was identified. Added value of this studyWe evaluated antibody response within 35 days after symptom onset in laboratory-confirmed case with COVID-19 as one component of an overall exaggerated immune activation in severe SARS-CoV-2 infection, and developed an immune phenotyping based on late IgG response and NLR that could help determine disease severity and clinical outcome of COVID-19 patients. Severe cases were more frequently found in patients with high IgG levels, compared to those who with low IgG levels (51.8% versus 32.3%). Severity rates for patients with NLRhiIgGhi, NLRhiIgGlo, NLRloIgGhi, and NLRloIgGlo phenotype was 72.3%, 48.5%, 33.3%, and 15.6%, respectively. Furthermore, severe patients with NLRhiIgGhi, NLRhiIgGlo had higher proinflammatory cytokines levels including IL-2, IL-6 and IL-10, and decreased CD4+ T cell count compared to those with NLRloIgGlo phenotype. Recovery rate for severe patients with NLRhiIgGhi, NLRhiIgGlo, NLRloIgGhi, and NLRloIgGlo phenotype was 58.8% (20/34), 68.8% (11/16), 80.0% (4/5), and 100% (12/12), respectively. Implications of all the available evidenceCOVID-19 severity is associated with a high viral load and overexuberant IgG response. We developed an immune response phenotyping based on NLR and IgG that could act as a simple complementary tool to discriminate between severe and nonsevere COVID-19 patients and would be helpful in guiding clinical decision.

Application of medical related WeChat mini program in internet plus continuing medical education / 中华医学教育探索杂志

Utilizing of the WeChat Mini Programs (WMPs) in optimizing the paradigm of Continuing Medical Education (CME) is a promising avenue to improve training mode for physician. Here, we introduce the development status of WMP, and classify them into professional societies, hospital departments, academic institutions, pharmaceutical technology companies and individuals according to the operation subjects. This paper also analyzes the advantages and disadvantages of WMPs, and puts forward the establishment of authoritative and highly recognized mini programs that can be awarded CME credits. Through the "Internet plus medical care" strategy, we should integrate network resources to enhance the overall level of continuing medical education, so as to provide a good platform for related medical workers.

Practice of cultivating the ability of big data mining in graduates working for professional degree in medical oncology / 中华医学教育探索杂志

At present, the cultivation of exploratory thinking and the ability to use medical public big data is easy to be ignored, which leads to the obvious lack of the ability of graduate students in oncology to use public big data to discover and confirm new clinical phenomena and new laws in scientific research. This study introduces the access and download tools of common medical big databases such as TCGA, introduces the learning method of repeated classic analysis cases, and proposes research methods to verify clinical hypothesis using public big data. It is expected to provide useful enlightenment and reference for improving the research ability of graduate students in oncology specialty to find clinical problems and summarize scientific laws.