Immune checkpoints (ICPs) play a crucial role in regulating the immune response. In the tumor, malignant cells can hijack the immunosuppressive effects of inhibitory ICPs to promote tumor progression. Extracellular vesicles (EVs) are produced by a variety of cells and contain bioactive molecules on their surface or within their lumen. The expression of ICPs has also been detected on EVs. In vitro and in vivo studies have shown that extracellular vesicle immune checkpoints (EV ICPs) have immunomodulatory effects and are involved in tumor immunity. EV ICPs isolated from the peripheral blood of cancer patients are closely associated with the tumor progression and the prognosis of cancer patients. Blocking inhibitory ICPs has been recognized as an effective strategy in cancer treatment. However, the efficacy of immune checkpoint inhibitors (ICIs) in cancer treatment is hindered by the emergence of therapeutic resistance, which limit their widespread use. Researchers have demonstrated that EV ICPs are correlated with clinical response to ICIs therapy and were involved in therapeutic resistance. Therefore, it’s essential to investigate the immunomodulatory effects, underlying mechanisms, and clinical significance of EV ICPs in cancer. This review aims to comprehensively explore these aspects. We have provided a comprehensive description of the cellular origins, immunomodulatory effects, and clinical significance of EV ICPs in cancer, based on relevant studies.

The inactivated whole-virion vaccine, CoronaVac, is one of the most widely used coronavirus disease 2019 (COVID-19) vaccines worldwide. There is a paucity of data indicating the durability of the immune response and the impact of immune imprinting induced by CoronaVac upon Omicron infection. In this prospective cohort study, 41 recipients of triple-dose CoronaVac and 14 unvaccinated individuals were recruited. We comprehensively profiled adaptive immune parameters in both groups, including spike-specific immunoglobulin (Ig) G and IgA titers, neutralizing activity, B cells, circulating follicular helper T (cTfh) cells, CD4 ⁺ and CD8 ⁺ T cells, and their memory subpopulations at 12 months after the third booster dose and at 4 and 20 weeks after Omicron BA.5 infection. Twelve months after the third CoronaVac vaccination, spike-specific antibodies and cellular responses were detectable in most vaccinated individuals. BA.5 infection significantly augmented the magnitude, cross-reactivity, and durability of serum neutralization activities, Fc-mediated phagocytosis, nasal spike-specific IgA responses, memory B cells, activated cTfh cells, memory CD4 ⁺ T cells, and memory CD8 ⁺ T cells for both the ancestral strain and Omicron subvariants, compared to unvaccinated individuals. Notably, the increase in BA.5-specific immunity after breakthrough infection was consistently comparable to or higher than that of the ancestral strain, suggesting no evidence of immune imprinting. Immune landscape analyses showed that vaccinated individuals have better synchronization of multiple immune components than unvaccinated individuals upon heterologous infection. Our data provide detailed insight into the protective role of the inactivated COVID-19 vaccine in shaping humoral and cellular immunity to Omicron infection. IMPORTANCE: There is a paucity of data indicating the durability of the immune response and the impact of immune imprinting induced by CoronaVac upon Omicron breakthrough infection. In this prospective cohort study, the anti-severe acute respiratory syndrome coronavirus 2 adaptive responses were analyzed before and after the Omicron BA.5 infection. Our data provide detailed insight into the protective role of the inactivated COVID-19 vaccine in shaping humoral and cellular immune responses to heterologous Omicron infection. CLINICAL TRIAL: This study is registered with ClinicalTrials.gov as NCT05680896 .

Large language models (LLMs) have established a presence in providing medical services to patients and supporting clinical practice for doctors. To explore the ability of LLMs in answering clinical questions related to autoimmune diseases, this study was designed with 65 questions related to autoimmune diseases, covering five domains: concepts, report interpretation, diagnosis, prevention and treatment, and prognosis. Types of diseases include Sjögren’s syndrome, systemic lupus erythematosus, rheumatoid arthritis, systemic sclerosis, and others. These questions were answered by three LLMs: ChatGPT 4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro. The responses were then evaluated by 8 clinicians based on criteria including relevance, completeness, accuracy, safety, readability, and simplicity. We analyzed the scores of the three LLMs across five domains and six dimensions and compared their accuracy in answering the report interpretation section with that of two senior doctors and two junior doctors. The results showed that the performance of the three LLMs in the evaluation of autoimmune diseases significantly surpassed that of both junior and senior doctors. Notably, Claude 3.5 Sonnet excelled in providing comprehensive and accurate responses to clinical questions on autoimmune diseases, demonstrating the great potential of LLMs in assisting doctors with the diagnosis, treatment, and management of autoimmune diseases.

Background: Artificial intelligence (AI) has made great strides. To explore the potential of Large Language Models (LLMs) in providing medical services to patients and assisting physicians in clinical practice, our study evaluated the performance in delivering clinical questions related to autoimmune diseases.Methods: 46 questions related to autoimmune diseases were input into ChatGPT 3.5, ChatGPT 4.0, and Gemini. The responses were then evaluated by rheumatologists based on five quality dimensions: relevance, correctness, completeness, helpfulness, and safety. Simultaneously, the responses were assessed by laboratory specialists across six medical fields: concept, clinical features, report interpretation, diagnosis, prevention and treatment, and prognosis. Finally, statistical analysis and comparisons were performed on the performance of the three chatbots in the five quality dimensions and six medical fields.Results: ChatGPT 4.0 outperformed both ChatGPT 3.5 and Gemini across all five quality dimensions, with an average score of 199.8 ± 10.4, significantly higher than ChatGPT 3.5 (175.7 ± 16.6) and Gemini (179.1 ± 11.8) (p = 0.009 and p = 0.001, respectively). The average performance differences between ChatGPT 3.5 and Gemini across these five dimensions were not statistically significant. Specifically, ChatGPT 4.0 demonstrated superior performance in relevance (p < 0.0001, p < 0.0001), completeness (p < 0.0001, p = 0.0006), correctness (p = 0.0001, p = 0.0002), helpfulness (p < 0.0001, p < 0.0001), and safety (p < 0.0001, p = 0.0025) compared to both ChatGPT 3.5 and Gemini. Furthermore, ChatGPT 4.0 scored significantly higher than both ChatGPT 3.5 and Gemini in medical fields such as report interpretation (p < 0.0001, p = 0.0025), prevention and treatment (p < 0.0001, p = 0.0103), prognosis (p = 0.0458, p = 0.0458).Conclusions: This study demonstrates that ChatGPT 4.0 significantly outperforms ChatGPT 3.5 and Gemini in addressing clinical questions related to autoimmune diseases, showing notable advantages across all five quality dimensions and six clinical domains. These findings further highlight the potential of large language models in enhancing healthcare services.

Objectives: Virus infection closely associated with autoimmune disease. The study aimed to explore the autoantibody profiles and the correlation of autoantibodies with the disease severity and the prognosis of the coronavirus disease 2019 (COVID-19) patients.Methods: Three hundred thirty-seven hospitalized COVID-19 patients from 6th to 23rd January 2023 were enrolled. Logistic and Cox regression analyses were used to analyze the risk factors for the patient’s disease severity and outcome. The association between Anti-extractable nuclear antigen antibody (ENA) positivity and the prognosis of COVID-19 patients was analyzed using Kaplan–Meier survival curves.Results: 137 of COVID-19 patients were detected positive for antinuclear antibody (ANA), 61 had positive results for ENA, and 38 were positive for ANA and ENA. ANA positivity rate was higher in non-severe illness group (p = 0.032). COVID-19 patients who died during hospitalization had a high rate of ENA positivity than convalescent patients (p = 0.002). Multivariate logistic regression showed that ANA positivity was a protective factor for the disease severity of COVID-19. Multivariate Cox regression analysis revealed that ENA positivity, white blood cells count (WBC), aspartate aminotransferase (AST), Creatinine (CREA), and CRP were independent risk factors for the outcome of COVID-19 patients, and that COVID-19 patients with ENA positivity had a lower cumulative survival rate (p = 0.002).Conclusion: A spectrum of autoantibodies were expressed in COVID-19 patients, among which ANA and ENA positivity was associated with the severity and prognosis of COVID-19. Therefore, autoantibodies may help to assess the disease severity and prognosis of COVID-19 patients.

Background: The inactivated whole-virion vaccine, CoronaVac, is one of the most widely used coronavirus disease 2019 (COVID-19) vaccines worldwide. There is a paucity of data indicating the durability of the immune response and the impact of immune imprinting induced by CoronaVac upon Omicron breakthrough infection. Methods: In this prospective cohort study, 41 recipients of triple-dose CoronaVac and 14 unvaccinated individuals were recruited. We comprehensively profiled adaptive immune parameters in both groups, including spike-specific immunoglobulin (Ig) G and IgA titers, neutralizing activity, B cells, follicular helper T (Tfh) cells, CD4 ⁺ and CD8 ⁺ T cells, and their memory subpopulations at 12 months after the third booster dose and at 4 weeks and 20 weeks after Omicron BA.5 infection. Results: Twelve months after the third CoronaVac vaccination, spike-specific antibody and cellular responses were detectable in most vaccinated individuals. BA.5 infection significantly augmented the magnitude, cross-reactivity and durability of serum neutralization activities, Fc-mediated phagocytosis, and nasal spike-specific IgA responses, memory B cells, activated Tfh cells memory CD4+ T cells, and memory CD8+ T cells for both the ancestral strain and Omicron subvariants, compared to unvaccinated individuals. Notably, the increase in BA.5-specific immunity after breakthrough infection was consistently higher than for the ancestral strain, suggesting no evidence of immune imprinting. Immune landscape analyses showed vaccinated individuals have better synchronization of multiple immune components than unvaccinated individuals upon heterologous SARS-CoV-2 infection. Conclusion: Our data provides detailed insight into the protective role of inactivated COVID-19 vaccine in shaping humoral and cellular immune responses to heterologous Omicron infection. Trial registration: ClinicalTrials.gov NCT05680896 Funding: This study was supported by the National Natural Science Foundation of China (92269118, 92269205), Nanjing Important Science & Technology Specific Projects (2021-11005), Scientific Research Project of Jiangsu Health Commission (M2022013), Clinical Trials from the Affiliated Drum Tower Hospital, Medical School of Nanjing University (2021-LCYJ-PY-9), and Jiangsu graduate practice innovation project (JX22013929).