Metastasis or recurrence following curative surgery is the main indicator of tumor progress and is the main cause of patient death. For more than three decades, the potential for general anesthesia to affect cancer outcomes has been a subject of concern with considerable research interest. Here, we conducted this systematic review and meta-analysis to summarize the effect of inhalational anesthesia (IHNA) vs. propofol-based total intravenous anesthesia (TIVA) on metastasis and recurrence after cancer surgery from clinical and pre-clinical studies. The relative risk for metastasis/recurrence in TIVA is 0.61 (95% confidence interval (95% CI) 0.46 to 0.82, p = 0.0009) compared to IHNA. Inflammatory cytokines have been implicated in cancer metastasis following cancer surgery, thus we analyzed inflammatory cytokines levels after surgery under IHNA or TIVA. Based on pooled analysis, a lower IL-6 level was noticed in TIVA in comparison to IHNA (standardized mean difference (SMD) = 0.77, 95% CI = 0.097 to 1.44, I² = 92%, p = 0.02) but not TNF-α or IL-10. Preclinical animal model studies show that inhalational anesthetics increase the risk of breast cancer metastasis compared to propofol. In conclusion, the current evidence suggests intravenous anesthetic propofol is associated with less metastasis/recurrence and lower postoperative IL-6 level over inhaled anesthetics in the oncological surgery. We urge more well-designed clinical and preclinical studies in this field.

This study aims to evaluate plasma S100A9 levels in tuberculosis (TB) patients with admission to the ICU as a marker to predict the risk of death for pulmonary severe TB. This study enrolled 256 severe TB patients admitted to Beijing Chest Hospital from Jan to Dec 2019. The S100A9 levels were measured by ELISA. Standard clinical parameters were collected. The non-parametric Mann-Whitney test, t-test, and chi-square test were applied to statistical comparison. A multivariable analysis was performed to identify risk factors for death. The plasma S100A9 levels were higher in non-survivors (25.88, 16.77–44.64) compared to survivors (15.51, 13.67–19.94). S100A9 performed better than Acute Physiology and Chronic Health Evaluation (APACHE II) score in predicting death, with AUC of 0.725, sensitivity of 65.5%, and specificity of 80.3%. By combining APACHE II score together with the S100A9 levels we got an AUC of 0.754 (95% CI 0.68 to 0.82) in predicting death. Lastly, S100A9 levels were significantly higher in patients with APACHE II score >17.5, sputum smear-positive, early death, and high cavitary lesions numbers, all of which were related to TB progression. Measurement and monitoring levels of plasma S100A9 in severe TB patients could facilitate the evaluation of patients with high risk at the early stage, which may help to improve the treatment outcome for TB patients.

Background: The progression of tuberculosis to severe disease is the main cause of death of tuberculosis patients. Early identification of severe tuberculosis is very important. LncRNA can be used as a potential marker in the diagnosis of tuberculosis, but there is a lack of research on lncRNA in the field of severe tuberculosis. Methods: Peripheral blood samples of severe pulmonary tuberculosis patients, mild pulmonary tuberculosis patients and healthy controls were collected to extract peripheral blood monocytes. The RNA was then extracted and sent to the SBC human ceRNA V1.0 analysis. The results were verified by qPCR and analyzed by KEGG and GO analyses. Differentially expressed lncRNAs were measured by ROC curves. Results: Four lncRNAs exhibited statistically distinct expression patterns in STB versus MTB groups (NR_033909: p=0.0097; lnc-MYCBPAP-2:4: p=0.0053; lnc-PRDM7-1:2: p< 0.0001; NR_033841: p=0.0279). The areas under the curve (AUC) value are respectively 0.7280(lnc-PRDM7-1:2), 0.7288(lnc-MYCBPAP-2:4), 0.6647(NR_033909) and 0.6615(NR_033841). Conclusion: LncRNAs NR_033909, lnc-MYCBPAP-2:4, lnc-PRDM7-1:2 and NR_033841 may demonstrate diagnostic potential for differentiating severe from mild pulmonary tuberculosis cases. These results create a platform for monitoring TB progression and open new avenues for studying disease pathogenesis.

The World Health Organization encourages the development of novel diagnostic tools based on 'non-sputum' samples to meet global goals for tuberculosis (TB) control. We aimed to develop a machine learning-driven model for TB diagnosis, using long non-coding RNAs (lncRNAs) as biomarkers. Peripheral blood mononuclear cells (PBMCs) from 10 TB patients, 10 latent TB infection individuals (LTBI), and 10 healthy controls (HCs) underwent microarray analysis, and the TB-related lncRNA modules were identified by weighted gene co-expression network analysis (WGCNA). Key lncRNAs were validated by qPCR and selected using LASSO regression. Five machine learning algorithms were employed to construct a diagnostic model, with the ROC analysis assessing its performance. Based on the differential lncRNA profile, WGCNA identified 12 key modules associated with TB. From the most significant modules, 45 candidate lncRNAs were validated by qPCR, with 14 showing differential expression among TB (n = 192), LTBI (n = 55), HC (n = 66), and NTB (n = 78) groups. Five lncRNAs demonstrating the greatest contribution to TB diagnosis were further selected by LASSO analysis. AdaBoost algorithm incorporating these five lncRNAs achieved optimal diagnostic performance, with an area under the curve (AUC) of 0.97 (95%CI: 0.95-0.98) in the training cohort (n = 272) and 0.91 (95%CI: 0.86-0.96) in the validation cohort (n = 119). Independent validation of the model in another cohort (n = 206) showed an AUC of 0.92 (95%CI: 0.88-0.95). This study established a novel, blood-based diagnostic model incorporating five host-derived lncRNAs with an AdaBoost algorithm, offering a non-sputum approach to enhance TB diagnosis.

Background/Objectives: To develop and temporally validate staged models for predicting acute respiratory distress syndrome (ARDS) after thymic epithelial tumour resection. Methods: This single-centre retrospective study included 596 adults with pathologically confirmed thymic epithelial tumours. Patients treated in 2005–2020 formed the development cohort (n = 395), and those treated in 2021–2026 formed the temporal validation cohort (n = 201). Postoperative ARDS was adjudicated using the 2024 global definition. A preoperative model and a postoperative landmark model incorporating intraoperative, pathological, and early postoperative variables were developed using least absolute shrinkage and selection operator and multivariable logistic regression. Performance was assessed using discrimination, calibration, Brier score, decision curve analysis, bootstrap internal validation, and temporal validation. Results: ARDS occurred in 116 development patients (29.4%) and 58 validation patients (28.9%). The postoperative landmark model included the preoperative linear predictor, one-lung ventilation duration, Masaoka–Koga stage, perioperative transfusion, and early postoperative glucose, albumin, and C-reactive protein. It achieved higher AUCs than the preoperative model in the development (0.908 vs. 0.693; p < 0.001) and validation cohorts (0.928 vs. 0.655; p < 0.001), with lower Brier scores (0.107 vs. 0.187 and 0.094 vs. 0.192, respectively; both p < 0.001). The optimism-corrected development AUC was 0.898. Calibration and decision curve analyses also favoured the postoperative landmark model. Conclusions: Incorporating perioperative information improved ARDS prediction after thymic epithelial tumour surgery. The staged models may support sequential perioperative risk assessment; however, prospective evaluation and independent multicentre validation are required before implementation.