Wenjie Jiao

Neoadjuvant immunotherapy plus chemoradiotherapy (nICRT) for esophageal squamous cell carcinoma (ESCC) received increasing attention because of high pathol

Background: Smart healthcare systems and cloud platforms have been widely used in the field of medicine and healthcare, but it is rarely being applied in the field of postoperative follow-up. This study aimed to assess the feasibility and acceptability of a smart healthcare cloud platform based on a standardized discourse form in the postoperative follow-up of patients with non-small cell lung cancer (NSCLC). Methods: A smart healthcare cloud platform was established by designing a standardized discourse form, and constructing a follow-up database and a health education platform. A total of 608 postoperative patients with NSCLC treated between June 2021 and June 2022 were enrolled in the study, including 306 patients in the intervention group and 302 patients in the control group. The patients in the intervention and control groups were followed up by the cloud platform and outpatient department, respectively. At the end of the follow-up period, a satisfaction follow-up survey and the Short-Form (SF)-36 Health Survey were administered. Results: There were no statistically significant differences in the baseline characteristics of the patients between the two groups. After 12 months of follow-up, 74.5%, 24.2%, 1.3% and 0.0% of patients in the intervention group were “strongly satisfied”, “satisfied”, “unsatisfied”, and “strongly unsatisfied”, respectively, compared with 61.3%, 35.1%, 2.6%, and 1.0% of patients in the control group (Pvs. 66.73±14.07, P=0.35), except that the role-emotional score was higher in the intervention group than that in the control group (67.11±27.53 vs. 62.36±27.59, P=0.03). After 36 months of follow-up, 80.1%, 19.3%, 0.6% and 0.0% of patients in the intervention group were “strongly satisfied”, “satisfied”, “unsatisfied”, and “strongly unsatisfied”, respectively, compared with 68.9%, 29.1%, 1.7%, and 0.3% of patients in the control group (P=0.001). The intervention group also showed similar results to the control group across the SF-36 Health Survey domain scores (average: 73.76±13.65 vs. 72.86±14.36, P=0.43). Conclusions: The smart healthcare cloud platform based on a standardized discourse form can achieve positive satisfaction of patients with follow-up and does not affect the quality of life of patients. Thus, its application is feasible and acceptable in the postoperative follow-up of patients with NSCLC.

Lung cancer is one of the most commonly diagnosed types of cancer worldwide, attracting significant attention from researchers due to its high mortality rate and limited treatment options.

Background: Robust prognostic markers for small cell lung cancer (SCLC) are currently lacking, underscoring the need for novel prediction models to optimize individualized treatment and improve patient outcomes. Inflammatory/nutritional indexes have been extensively employed in prognostic investigations of malignant tumors. The study aimed to precisely ascertain the prognosis of SCLC patients undergoing surgery by preoperative serological indexes. Methods: We included patients with SCLC who underwent surgery at The Affiliated Hospital of Qingdao University. Potential predictors included basic clinical characteristics and preoperative serum inflammatory/nutritional indexes. We employed 10 machine learning algorithms and their 101 combinations to select the superior model and establish a novel nomogram. Follow-up involved regular clinic visits or telephone contact, with imaging and laboratory tests conducted at defined intervals to assess overall survival (OS) and progression-free survival (PFS). The cohort was randomly split into training and validation cohorts in a 7:3 ratio. Harrell’s C-index, Kaplan-Meier curves, log-rank tests, and Cox regression analyses were used for model evaluation and prognostic assessment. Results: A total of 219 patients were included in this study. Prognostic nutritional index (PNI), lymphocyte-to-monocyte ratio (LMR), platelet-to-neutrophil ratio (PNR), neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammatory index (SII), pan-immune-inflammation value (PIV), and systemic inflammatory response index (SIRI) were correlated with the prognosis of SCLC patients. Smoking status and the tumor-node-metastasis (TNM) stage were independent prognostic indicators of OS. The Random Forest model achieved the highest mean concordance index (C-index) (0.784). Patients classified as high-risk based on this model exhibited a higher prevalence of smoking and more advanced pathological N stage and TNM stage. No significant differences were observed between risk groups regarding age, gender, body mass index (BMI), alcohol history, tumor site, pathological T stage, Ki-67 index, or visceral pleural invasion (VPI). Nomograms based on risk grouping, smoking status, and TNM stage demonstrated high precision and considerable clinical value. Multivariate Cox analysis identified PNI and NLR as the most valuable prognostic markers, with optimal cut-off values of 50.6 and 1.99, respectively. Conclusions: A machine learning model based on serological inflammatory/nutritional indexes can reasonably estimate the long-term prognosis of SCLC patients and is anticipated to serve as a practical instrument for identifying the ideal candidates for thoracic surgery.

Background: Inflammatory indexes are increasingly being considered to predict treatment response in tumors. This study aimed to investigate the efficacy of serum inflammatory indexes in predicting pathological response in patients with esophageal squamous cell carcinoma (ESCC) receiving anti-PD-1 neoadjuvant immunochemotherapy (NICT). Methods: We retrospectively collected clinical and laboratory data from 116 ESCC patients who received NICT. We set three outcome variables: pathologic complete response (PCR), good response (GR), and response (R). We assessed between-group differences in inflammation indexes and their diagnostic efficacy. Independent diagnostic markers were filtered using least absolute shrinkage and selection operator (LASSO) logistic regression and multivariable analysis, and the corresponding nomograms for PCR and GR were constructed, respectively. Receiver operating characteristic curves (ROC) and calibration curves assessed the efficiency and accuracy of the models. Decision curve analysis (DCA) and clinical impact curves (CIC) evaluated the clinical value. Moreover, we internally validated the predictive model with a random sample of 30% of patients. Results: The prognostic nutritional index (PNI) predicted a cutoff value of 53.585 for PCR with an area under curve (AUC) value of 0.720, a cutoff value of 47.85 for GR with an AUC of 0.723, a cutoff value of 47.85 for R with an AUC of 0.629. Smoking and PNI were independent predictors of PCR, platelet-to-lymphocyte ratio (PLR) and PNI were independent predictors of GR, and PNI was an independent predictor of R. We built PNI-based nomograms to predict PCR and GR with AUC values of 0.795 and 0.763 for the training cohort and 0.907 and 0.757 for the validation cohort, respectively. The predicted and actual results of the calibration curves for both the training and validation groups showed good agreement, with Brier scores below 0.25. Conclusion: High PNI value is a shared independent predictor of achieving PCR, GR, and R in ESCC patients receiving anti-PD1 NICT. PNI-based diagnostic models can be used as a practical tool to identify ideal patients for personalized clinical decisions.

Objective: In NSCLC, the main approach to differentiate between intrapulmonary metastases (IPM) and multiple primary lung cancer (MPLC) is to integrate histopathological and genomic information. Here, we identified viable biomarkers that can distinguish IPM from MPLC by integrating comprehensive genomic profiling (CGP) and targeted RNA sequencing. Methods: We retrospectively collected tissues from at least two lesions in 34 patients. 29 and 5 out of 34 patients determined as pathologic MPLC (pMPLC) and pathologic IPM, respectively, according to Martini-Melamed criteria (M-M criteria). A comprehensive investigation at genomic and transcriptomic level was conducted. Results: Nine of the 29 pMPLCs shared trunk mutations in their lesions and were consequently reclassified as IPM. Survival analyses revealed that classification integrated M-M criteria and mutational profiling could distinguish IPM/MPLC more accurately. Further exploration at the transcriptomic level revealed elevated expression levels of genes related to epithelial-mesenchymal transition and immunomodulatory pathways in IPM. Notably, the expression of CXCL1 and CXCL8 was significantly upregulated in IPM. Conclusions: We found that the expression of CXCL1 and CXCL8 in any tumor lesion within a patient could reliably indicate IPM. Additionally, assessing the transcriptional levels of CXCL1 and CXCL8 also provide a dependable and practical approach to identify IPM from MPLC.

Background: Malignant pleural mesothelioma (MPM) is an aggressive tumor originating from the mesothelial lining of the pleural cavity. It is characterized by extensive nodular pleural thickening and has a propensity to invade the pleural adipose tissue and adjacent chest structures. The prognosis is poor, with a median survival time rarely exceeding 12 months following diagnosis. Methods: This bibliometric analysis systematically assessed global trends in MPM research from 2010 to 2023 using 6,487 publications indexed in PubMed. Quantitative evaluations of publication metrics, international collaboration, and keyword co-occurrence networks were conducted using R software with the bibliometrix package. Network construction and thematic mapping were employed to analyze the temporal evolution of research topics. Results: The United States and Europe have played pivotal roles in this research, while contributions from China and Japan have been steadily increasing. Traditional treatment approaches and etiological studies are relatively well-established. Meanwhile, immunotherapy has emerged as a prominent focus of recent research. Conclusions: Future global collaboration in this field should be enhanced, as precision medicine related to immunology and genetics has the potential to transform the treatment landscape of MPM.

Lung ischemia-reperfusion (I/R) injury represents an inevitable complication in lung transplantation, characterized by the excessive production of oxygen free radicals and toxic substances. Dexmedetomidine (DEX), a widely used anesthetic agent, has been shown to significantly elevate glutathione (GSH) levels, thereby conferring protection against copper influx. This study investigates the protective mechanisms of DEX in lung I/R injury, with a particular focus on cuproptosis. Utilizing a rat I/R model established by clamping the left hilum of lung for 90 min followed by 120 min of reperfusion, we examined the effects of DEX on lung injury scores, GSH content, and the expression of key proteins involved in cuproptosis. In conclusion, cuproptosis is implicated in pulmonary I/R injury, and the protective effect of DEX against lung I/R injury is partly mediated by inhibition of cuproptosis.