Ziqiang Zhang

Whether statins, as upstream therapy for atrial fibrillation (AF), can reduce the incidence of AF in elderly individuals remains unclear. This study aimed to examine whether statin use is linked to a lower risk of incident AF in older adults from Shanghai, China. To provide important implications for optimizing the prevention and management strategies of AF in the elderly. Data for this retrospective cohort study were collected from a community health service center in Shanghai. Individuals had no history of AF in 2018 and before were included, and new cases of AF were documented in 2019, 2020, and 2021. A Poisson generalized linear model was used to evaluate the relationship between statin treatment and AF incidence. Both standard covariate adjustment and propensity score matching (PSM) to control for confounding factors. To evaluate the risk factors for new-onset AF among the elderly, univariate logistic analysis was conducted. Variables with statistical significance in univariate analysis (p < 0.05), such as sex, age, SBP, BMI, WHR, Scr and HbA1c were selected as covariates for inclusion in the multivariable logistic regression model. Out of the 5675 individuals in the study group (43.5% male; median age of 68 years), 456 participants (8.0%) received statin treatment. The propensity score unmatched and matched cohorts of 453 participants (stain users and nou-users) were evaluated, in each group. There was no reduction in AF incidence with statin use (95%CI, 0.948 to 1.018) in the unmatched cohort and in the matched cohort (95%CI, 0.459 to 1.512). The multivariate regression analysis results showed that age, SBP, BMI, HbA1c and Scr were factors independently linked to the occurrence of new-onset AF. Despite extensive analysis, our study did not find evidence that statin use reduces the risk of new-onset AF in this population. For older people, taking statins did not reduce risk of new on-set AF. Independent predictors of AF onset including age, SBP, BMI, HbA1c and Scr. This is a retrospective cohort study, making it difficult to completely control for all confounding factors, and the short follow-up period may have resulted in insufficient statistical power to detect a meaningful effect of statins on atrial fibrillation. Future studies may use randomized controlled studies to explore the effects of different types and doses of statins on new-onset atrial fibrillation in older adults.

Long, non-coding RNAs (lncRNAs) are a class of RNAs exceeding 200 nucleotides in length, lacking the ability to be translated into proteins. Over the past few decades, an increasing number of publications have established lncRNAs as potent regulators in a broad spectrum of diseases. They modulate the expression of critical genes by affecting transcription, post-transcription, translation, and protein modification. This regulation frequently involves the interaction of lncRNAs with various molecules, such as proteins, RNA, and DNA. lncRNAs are involved in diseases where stress is a significant factor. In recent years, lncRNAs have been identified as regulators of both innate and adaptive immune responses, playing significant roles in the onset and progression of diseases. Additionally, lncRNAs hold potential as biomarkers or therapeutic targets for numerous stress- and immune-related diseases. lncRNA nuclear-enriched abundant transcript 1 (NEAT1) is a notable example. This review consolidates the latest findings about the role of lncRNA NEAT1 in stress response and immune cell function in non-cancer diseases. It summarizes studies on NEAT1 regulating stress response, both innate and adaptive immunity, and its potential as a biomarker and therapeutic target for stress- and immune-related diseases.

Long non-coding RNAs (lncRNAs) are a class of RNAs exceeding 200 nucleotides in length, lacking the ability to be translated into proteins. Over the past few decades, an increasing number of publications have established lncRNAs as potent regulators in a broad spectrum of diseases. They modulate the expression of critical genes by affecting transcription, post-transcription, translation, and protein modification. This regulation frequently involves interaction of lncRNAs with other different molecules, such as proteins, RNA and DNA. In recent years, lncRNAs have been identified as regulators of both innate and adaptive immune responses, playing significant roles in the onset and progression of diseases. Additionally, lncRNAs hold potential as biomarkers or therapeutic targets for numerous immune-related diseases. lncRNA nuclear enriched abundant transcript 1 (NEAT1) is a notable example. This review consolidates the latest findings about the role of lncRNA NEAT1 in immune cell function and immune-related diseases.

Background: Coronavirus disease 2019 (COVID-19) vaccine has played a major role in ending the pandemic. However, little is known about the influence of COVID-19 vaccine on the efficacy of immunotherapy in patients with non-small cell lung cancer (NSCLC). Objectives: The goal of this study is to explore whether COVID-19 vaccine impacts the efficacy of immune checkpoint inhibitors (ICIs) in NSCLC patients. Methods: We retrospectively analyzed the survival data of ICI-treated 104 patients with stage III–IV NSCLC, who either received COVID-19 vaccination (n = 25) or no vaccination (n = 79). The potential risk factors, in particular roles of COVID-19 vaccination in the efficacy of ICIs in these patients, were evaluated. Results: Our results showed significantly improved ORR (28.0% vs. 11.39%, p = 0.05) and DCR (88.0% vs. 54.43%, p = 0.005) in the COVID-19 vaccinated group compared with the non-vaccinated group. Regarding the long-term survival benefits, COVID-19 vaccine showed profound influence both on the PFS (HR = 0.16, p = 0.021) and OS (HR = 0.168, p = 0.019) in patients with NSCLC under ICIs treatment. The PFS (p < 0.001) or OS (p < 0.001) was significantly improved in the COVID-19 vaccinated group, compared with the non-vaccinated group. Moreover, CD4 T cell (p = 0.047) level was higher in the COVID-19 vaccinated group than in the non-vaccinated group. Conclusions: COVID-19 vaccination enhances anti-PD-1 immunotherapy efficacy in patients with stage III–IV NSCLC, suggesting that COVID-19 vaccination may provide additional benefit to NSCLC patients.

Objective: This study aimed to investigate the longitudinal circulating eosinophil (EOS) data impacted by the COVID-19 vaccine, the predictive role of circulating EOS in the disease severity, and its association with T cell immunity in patients with SARS-CoV-2 Omicron BA.2 variant infection in Shanghai, China.Methods: We collected a cohort of 1,157 patients infected with SARS-CoV-2 Omicron/BA.2 variant in Shanghai, China. These patients were diagnosed or admitted between Feb 20, 2022, and May 10, 2022, and were classified as asymptomatic (n = 705), mild (n = 286) and severe (n = 166) groups. We compiled and analyzed data of patients’ clinical demographic characteristics, laboratory findings, and clinical outcomes.Results: COVID-19 vaccine reduced the incidence of severe cases. Severe patients were shown to have declined peripheral blood EOS. Both the 2 doses and 3 doses of inactivated COVID-19 vaccines promoted the circulating EOS levels. In particular, the 3rd booster shot of inactivated COVID-19 vaccine was shown to have a sustained promoting effect on circulating EOS. Univariate analysis showed that there was a significant difference in age, underlying comorbidities, EOS, lymphocytes, CRP, CD4, and CD8 T cell counts between the mild and the severe patients. Multivariate logistic regression analysis and ROC curve analysis indicate that circulating EOS (AUC = 0.828, p = 0.025), the combination of EOS and CD4 T cell (AUC = 0.920, p = 0.017) can predict the risk of disease severity in patients with SARS-CoV-2 Omicron BA.2 variant infection.Conclusions: COVID-19 vaccine promotes circulating EOS and reduces the risk of severe illness, and particularly the 3rd booster dose of COVID-19 vaccine sustainedly promotes EOS. Circulating EOS, along with T cell immunity, may have a predictive value for the disease severity in SARS-CoV-2 Omicron infected patients.

Although gut microbiota has been linked to cancer, little is known about the crosstalk between gut- and intratumoral-microbiomes. The goal of this study was to determine whether gut Akkermansia muciniphila (Akk) is involved in the regulation of intratumoral microbiome and metabolic contexture, leading to an anticancer effect on lung cancer. We evaluated the effects of gut endogenous or gavaged exogenous Akk on the tumorigenesis using the Lewis lung cancer mouse model. Feces, blood, and tumor tissue samples were collected for 16S rDNA sequencing. We then conducted spatially resolved metabolomics profiling to discover cancer metabolites in situ directly and to characterize the overall Akk-regulated metabolic features, followed by the correlation analysis of intratumoral bacteria with metabolic network. Our results showed that both endogenous and exogenous gavaged Akk significantly inhibited tumorigenesis. Moreover, we detected increased Akk abundance in blood circulation or tumor tissue by 16S rDNA sequencing in the Akk gavaged mice, compared with the control mice. Of great interest, gavaged Akk may migrate into tumor tissue and influence the composition of intratumoral microbiome. Spatially resolved metabolomics analysis revealed that the gut-derived Akk was able to regulate tumor metabolic pathways, from metabolites to enzymes. Finally, our study identified a significant correlation between the gut Akk-regulated intratumoral bacteria and metabolic network. Together, gut-derived Akk may migrate into blood circulation, and subsequently colonize into lung cancer tissue, which contributes to the suppression of tumorigenesis by influencing tumoral symbiotic microbiome and reprogramming tumoral metabolism, although more studies are needed.

Background: Gut microbiome has been linked to a regulatory role in cancer progression. However, whether broad-spectrum antibiotics (ATB) associated gut microbiome dysbiosis contributes to an impaired T cell immune function, and ultimately promotes lung cancer metastasis is not well known. Methods: In this study, a retrospective analysis was performed in a cohort of 263 patients initially diagnosed with non-small cell lung cancer (NSCLC) patients, including the ATB group (patients with broad-spectrum antibiotics treatment) (n = 124), and non-ATB group (n = 139) as control. ATB patients were prescribed ATB for over 5 days within 30 days prior to the collection of blood and fecal specimens and followed surgical treatment or first-line therapy. T cell immune function and metastasis-free survival (MFS) were evaluated between the two groups. Gut microbiota was evaluated by 16S rDNA sequencing. The predictive value of T cell immunity for MFS was evaluated by ROC analysis and Cox regression analysis. Results: Our results suggest that broad-spectrum antibiotics (ATB) impair T cell immune function in patients with either early-stage or advanced NSCLC, which likely contribute to the promotion of lung cancer metastasis. Results of the survival analysis show that metastasis-free survival (MFS) is significantly shorter in the ATB patients than that in the non-ATB patients with stage III NSCLC. The 16S rDNA sequencing shows that ATB administration contributes to a significant dysbiosis of the composition and diversity of gut microbiota. Moreover, ROC analysis results of CD4 (AUC 0.642, p = 0.011), CD8 (AUC was 0.729, p < 0.001), CD16 + 56 + (AUC 0.643, p = 0.003), and the combination of CD4, CD8 and CD16 + 56+ (AUC 0.810, p < 0.001), or Cox regression analysis results of CD4 (HR 0.206, p < 0.001), CD8 (HR 0.555, p = 0.009), which is likely regulated by ATB administration, have significantly predictive values for MFS. Conclusion: These results provide evidence of gut microbiome disturbance due to ATB administration is involved in the regulation of T cell immunity, and their predictive value for the tumor metastasis in lung cancer patients. Thus, gut microbiota may serve as a therapeutic target for lung cancer. Consequently, caution should be exercised before the long-term administration of broad-spectrum antibiotics in cancer patients.

Objective: Pulmonary embolism (PE) is a rare complication in bronchiectasis (BE) patients associated with a high rate of mortality and morbidity. However, data regarding bronchiectasis patients complicated with PE are limited. Early diagnosis of PE in bronchiectasis patients can improve the prognosis, this study aimed to investigate the clinical features and potential risk factors for early diagnosis of PE in bronchiectasis patients. Methods: Data of Patients were collected from Tongji Hospital of Tongji University of China. Bronchiectasis patients complicated with pulmonary embolism were named as BE/PE group (n = 63), as well as contemporaneous aged- and sex-matched bronchiectasis patients without pulmonary embolism named as BE group (n = 189), at a ratio of 1:3(cases to controls). Clinical parameters and risk factors were analyzed. Results: Univariate analysis shows that long-term bed rest, chronic lung disease, autoimmune disease, peripheral artery disease (PAD), tuberculosis history, dyspnea, blood homocysteine, CD4/CD8 ratio, or SIQIIITIII syndrome were closely correlated with the incidence of PE in the bronchiectasis patients (p < 0.05). Multivariate logistic regression analysis of significant variables showed that CD4/CD8 ratio (OR 1.409, 95% CI 1.045–1.901) and autoimmune disease (OR 0.264, 95% CI 0.133–0.524) are independent risk factors for BE/PE patients, compared with the BE patients. 53 out of 189 (28.0%) BE patients had hemoptysis, and 15 out of 63 (23.8%) BE/PE patients had hemoptysis (p > 0.05). Conclusions: The coexistence of pulmonary embolism and bronchiectasis are rarely encountered and easily to be ignored. Early identification of the clinical characteristic and potential risk factors of pulmonary embolism in bronchiectasis patients may help optimize the treatment strategies.

Background: The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. It causes acute respiratory distress syndrome and results in a high mortality rate if pneumonia is involved. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans, which facilitates the spread of the disease at the community level, and contributes to the overwhelming of medical resources in intensive care units. Goal: This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist global frontline doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods: With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan Unversity (approval number B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. These patients had SARS-CoV-2 RT-PCR test results and chest CT scans, both of which were used as the gold standard for the diagnosis of COVID-19 and COVID-19 pneumonia. In particular, the dataset included 137 indeterminate cases who initially did not have RT-PCR tests and subsequently had positive RT-PCR results, 62 suspected cases who initially had false-negative RT-PCR test results and subsequently had positive RT-PCR results, and 122 asymptomatic cases who had positive RT-PCR test results, amongst whom 31 cases were diagnosed. We also integrated the function of a survey in nCapp to collect user feedback from frontline doctors. Findings: We applied the statistical method of a multi-factor regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are fast and accessible: ‘Residing or visiting history in epidemic regions’, ‘Exposure history to COVID-19 patient’, ‘Dry cough’, ‘Fatigue’, ‘Breathlessness’, ‘No body temperature decrease after antibiotic treatment’, ‘Fingertip blood oxygen saturation ≤93%’, ‘Lymphopenia’, and ‘C-reactive protein (CRP) increased’. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). To ensure the sensitivity of the model, we used a cutoff value of 0.09. The sensitivity and specificity of the model were 98.0% (95% CI: 96.9%, 99.1%) and 17.3% (95% CI: 15.0%, 19.6%), respectively, in the training dataset, and 96.5% (95% CI: 95.1%, 98.0%) and 18.8% (95% CI: 16.4%, 21.2%), respectively, in the validation dataset. In the subset of the 137 indeterminate cases who initially did not have RT-PCR tests and subsequently had positive RT-PCR results, the model predicted 132 cases, accounting for 96.4% (95% CI: 91.7%, 98.8%) of the cases. In the subset of the 62 suspected cases who initially had false-negative RT-PCR test results and subsequently had positive RT-PCR results, the model predicted 59 cases, accounting for 95.2% (95% CI: 86.5%, 99.0%) of the cases. Considering the specificity of the model, we used a cutoff value of 0.32. The sensitivity and specificity of the model were 83.5% (95% CI: 80.5%, 86.4%) and 83.2% (95% CI: 80.9%, 85.5%), respectively, in the training dataset, and 79.6% (95% CI: 76.4%, 82.8%) and 81.3% (95% CI: 78.9%, 83.7%), respectively, in the validation dataset, which is very close to the published AI model. The results of the online survey ‘Questionnaire Star’ showed that 90.9% of nCapp users in WeChat mini programs were ‘satisfied’ or ‘very satisfied’ with the tool. The WeChat mini program received a significantly higher satisfaction rate than other platforms, especially for ‘availability and sharing convenience of the App’ and ‘fast speed of log-in and data entry’. Discussion: With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results. These patients require timely isolation or close medical supervision. By applying the model, medical resources can be allocated more reasonably, and missed diagnoses can be reduced. In addition, further education and interaction among medical professionals can improve the diagnostic efficiency for COVID-19, thus avoiding the transmission of the disease from asymptomatic patients at the community level.