Pulmonary tuberculosis (TB) is a known risk factor for lung cancer. However, a detailed analysis of lung cancer type, age, sex, smoking, and TB burden associated with geographic and socioeconomic status has not been performed previously. We systematically appraised relevant observational studies reporting an association between pulmonary TB and lung cancer. All studies were included in the primary analysis, and studies that used robust TB diagnostic methods, such as validated medical diagnostic codes, were included in the secondary analysis. Thirty-two articles were included. The association between the history of pulmonary TB and diagnosis of lung cancer was statistically significant (OR 2.09, 95% CI: 1.62–2.69, p < 0.001). There was a high heterogeneity (I² = 95%), without any publication bias. The analysis indicated a high association in advanced articles describing stringent pulmonary TB diagnosis (OR 2.26, 95% CI: 1.29–3.94, p = 0.004). The subgroup analyses suggested a significant association in countries with medium or high TB burdens, from East Asia and the Pacific region, and upper-middle income countries. Heterogeneity within the subgroups remained high in a majority of the subgroup analyses. A meta-regression analysis revealed that younger patients showed a significantly higher association between TB and lung cancer (regression coefficient = 0.949, p < 0.001). The history of pulmonary TB is an independent risk factor for lung cancer, especially in younger patients diagnosed with pulmonary TB. Clinicians should be aware of this association while treating young patients with a history of pulmonary TB.

Early detection of bacteremia is important to prevent antibiotic abuse. Therefore, we aimed to develop a clinically applicable bacteremia prediction model using machine learning technology. Data from two tertiary medical centers’ electronic medical records during a 12-year-period were extracted. Multi-layer perceptron (MLP), random forest, and gradient boosting algorithms were applied for machine learning analysis. Clinical data within 12 and 24 hours of blood culture were analyzed and compared. Out of 622,771 blood cultures, 38,752 episodes of bacteremia were identified. In MLP with 128 hidden layer nodes, the area under the receiver operating characteristic curve (AUROC) of the prediction performance in 12- and 24-h data models was 0.762 (95% confidence interval (CI); 0.7617–0.7623) and 0.753 (95% CI; 0.7520–0.7529), respectively. AUROC of causative-pathogen subgroup analysis predictive value for Acinetobacter baumannii bacteremia was the highest at 0.839 (95% CI; 0.8388–0.8394). Compared to primary bacteremia, AUROC of sepsis caused by pneumonia was highest. Predictive performance of bacteremia was superior in younger age groups. Bacteremia prediction using machine learning technology appeared possible for acute infectious diseases. This model was more suitable especially to pneumonia caused by Acinetobacter baumannii. From the 24-h blood culture data, bacteremia was predictable by substituting only the continuously variable values.

Consolidated infection control measures imposed by the government and hospitals during COVID-19 pandemic resulted in a sharp decline of respiratory viruses. Based on the issue of whether Pneumocystis jirovecii could be transmitted by airborne and acquired from the environment, we assessed changes in P. jirovecii pneumonia (PCP) cases in a hospital setting before and after COVID-19. We retrospectively collected data of PCP-confirmed inpatients aged ≥18 years (N = 2922) in four university-affiliated hospitals between January 2015 and June 2021. The index and intervention dates were defined as the first time of P. jirovecii diagnosis and January 2020, respectively. We predicted PCP cases for post-COVID-19 and obtained the difference (residuals) between forecasted and observed cases using the autoregressive integrated moving average (ARIMA) and the Bayesian structural time-series (BSTS) models. Overall, the average of observed PCP cases per month in each year were 36.1 and 47.3 for pre- and post-COVID-19, respectively. The estimate for residuals in the ARIMA model was not significantly different in the total PCP-confirmed inpatients (7.4%, p = 0.765). The forecasted PCP cases by the BSTS model were not significantly different from the observed cases in the post-COVID-19 (−0.6%, 95% credible interval; −9.6~9.1%, p = 0.450). The unprecedented strict non-pharmacological interventions did not affect PCP cases.

Attenuating the expression of immediate early (IE) proteins is essential for controlling the lytic replication of human cytomegalovirus (HCMV). The human microRNAs (hsa-miRs), miR-200b-3p and miR-200c-3p, have been identified to bind the 3′-untranslated region (3′-UTR) of the mRNA encoding IE proteins. However, whether hsa-miRs can reduce IE72 expression and HCMV viral load or exhibit a crosstalk with the host cellular signaling machinery, most importantly the NF-κB cascade, has not been evaluated. In this study, argonaute-crosslinking and immunoprecipitation-seq revealed that miR-200b-3p and miR-200c-3p bind the 3′-UTR of UL123, which is a gene that encodes IE72. The binding of these miRNAs to the 3′-UTR of UL123 was verified in transfected cells stably expressing GFP. We used miR-200b-3p/miR-200c-3p mimics to counteract the downregulation of these miRNA after acute HCMV infection. This resulted in reduced IE72/IE86 expression and HCMV VL during lytic infection. We determined that IE72/IE86 alone can inhibit the phosphorylation of RelA/p65 at the Ser⁵³⁶ residue and that p-Ser⁵³⁶ RelA/p65 binds to the major IE promoter/enhancer (MIEP). The upregulation of miR-200b-3p and miR-200c-3p resulted in the phosphorylation of RelA/p65 at Ser⁵³⁶ through the downregulation of IE, and the binding of the resultant p-Ser⁵³⁶ RelA/p65 to MIEP resulted in a decreased production of pro-inflammatory cytokines. Overall, miR-200b-3p and miR-200c-3p—together with p-Ser⁵³⁶ RelA/p65—can prevent lytic HCMV replication during acute and latent infection.