Confounder adjustment is critical for accurate causal inference in observational studies. However, the appropriateness of methods for confounder adjustment in studies investigating multiple risk factors, where the factors are not simply mutually confounded, is often overlooked. This study aims to summarise the methods for confounder adjustment and the related issues in studies investigating multiple risk factors. A methodological study was performed. We searched PubMed from January 2018 to March 2023 to identify cohort and case–control studies investigating multiple risk factors for three chronic diseases (cardiovascular disease, diabetes and dementia). Study selection and data extraction were conducted independently by two reviewers. The study objectives were grouped into two categories: widely exploring potential risk factors and examining specific risk factors. The methods for confounder adjustment were classified based on a summarisation of the included studies, identifying six categories: (1) each risk factor was adjusted for potential confounders separately (the recommended method); (2) all risk factors were mutually adjusted (i.e. including all factors in a multivariable model); (3) all risk factors were adjusted for the same confounders separately; (4) all risk factors were adjusted for the same confounders with some factors being mutually adjusted; (5) all risk factors were adjusted for the same confounders with mutual adjustment among them being unclear; and (6) unable to judge. All data were descriptively analysed. A total of 162 studies were included, with 88 (54.3%) exploring potential risk factors and 74 (45.7%) examining specific risk factors. The current status of confounder adjustment was unsatisfactory: only ten studies (6.2%) used the recommended method, all of which aimed at examining several specific risk factors; in contrast, mutual adjustment was adopted in over 70% of the studies. The remaining studies either adjusted for the same confounders across all risk factors, or unable to judge. There is substantial variation in the methods for confounder adjustment among studies investigating multiple risk factors. Mutual adjustment was the most commonly adopted method, which might lead to overadjustment bias and misleading effect estimates. Future research should avoid indiscriminately including all risk factors in a multivariable model to prevent inappropriate adjustment.

Background: Given the limited access to breast cancer (BC) screening, the authors developed and validated a mobile phone-artificial intelligence-based infrared thermography (AI-IRT) system for BC screening. Materials and methods: This large prospective clinical trial assessed the diagnostic performance of the AI-IRT system. The authors constructed two datasets and two models, performed internal and external validation, and compared the diagnostic accuracy of the AI models and clinicians. Dataset A included 2100 patients recruited from 19 medical centres in nine regions of China. Dataset B was used for independent external validation and included 102 patients recruited from Langfang People’s Hospital. Results: The area under the receiver operating characteristic curve of the binary model for identifying low-risk and intermediate/high-risk patients was 0.9487 (95% CI: 0.9231–0.9744) internally and 0.9120 (95% CI: 0.8460–0.9790) externally. The accuracy of the binary model was higher than that of human readers (0.8627 vs. 0.8088, respectively). In addition, the binary model was better than the multinomial model and used different diagnostic thresholds based on BC risk to achieve specific goals. Conclusions: The accuracy of AI-IRT was high across populations with different demographic characteristics and less reliant on manual interpretations, demonstrating that this model can improve pre-clinical screening and increase screening rates.

Lung cancer continues to be the primary cause of cancer-related deaths globally, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of all instances. Recently, immune checkpoint inhibitors (ICIs) have transformed the treatment approach for NSCLC, however, only a subset of patients experiences significant benefits. Therefore, identifying reliable biomarkers to forecast the efficacy of ICIs is crucial for ensuring the safety and effectiveness of treatments, becoming a major focus of current research efforts. This review highlights the recent advances in predictive biomarkers for the efficacy of ICIs in the treatment of NSCLC, including PD-L1 expression, tertiary lymphoid structures (TLS), tumor-infiltrating lymphocytes (TILs), tumor genomic alterations, transcriptional signatures, circulating biomarkers, and the microbiome. Furthermore, it underscores the pivotal roles of liquid biopsy, sequencing technologies, and digital pathology in biomarker discovery. Special attention is given to the predictive value of TLS, circulating biomarkers, and transcriptional signatures. The review concludes that the integration of multiple biomarkers holds promise for achieving more accurate efficacy predictions and optimizing personalized immunotherapy strategies. By providing a comprehensive overview of the current progress, this review offers valuable insights into biomarker-based precision medicine for NSCLC and outlines future research directions.

Background: Small cell carcinoma of the esophagus (SCCE) is an extremely rare and highly aggressive neuroendocrine malignancy with a strikingly poor prognosis. Given the great clinical successes of checkpoint immunotherapies, we explored the expression profile and clinical significance of programmed cell death-ligand 1 (PD-L1) and CD8⁺ T cell in SCCE for the first time. Materials and methods: Tumor-infiltrating immune cells (TIICs) and tumor cells in postoperative, whole tumor sections from 147 SCCE patients were stained for PD-LI expression. We also evaluated each patient’s Combined Positive Score (CPS). Multiplex immunofluorescence staining (CD3, CD20, CD68, and PD-L1) was introduced to clarify the location of PD-L1. CD8 density was analyzed by digital imaging and analysis of entire slides. Clinical outcomes were tested for correlations with both PD-L1 expression and CD8 density. Results: No patients had PD-L1 expressed in their tumor cells. PD-L1⁺ expression in TIICs was detected in 65 patients (44.2%) and 42 (28.6%) exhibited CPS positivity. Multiplex immunofluorescence staining demonstrated that most of the PD-L1 was expressed on the CD68⁺ monocytes/macrophages. PD-L1 expression in the TIICs and CPS was found to be correlated with paraffin block age, tumor length, macroscopic type, T stage, and increased overall survival (OS). Expression of PD-L1 in TIICs showed significantly prolonged relapse-free survival (RFS). Increasing CD8 densities were associated with increased PD-L1 expression (P _trend<0.0001). Multivariate regression confirmed that PD-L1 in TIICs and CD8 states were independent predictors of OS, and CD8 status were found to be independently predictive of RFS. A stratification based on PD-L1 and CD8 status was also significantly associated with both OS and RFS. Conclusion: Expression of PD-L1 was only detected in TIICs from approximately half of the patients with SCCEs. In SCCEs, PD-L1 and CD8 status are novel prognostic biomarkers and may inform the implementation of risk-related therapeutic strategies. SCCEs with higher CD8 infiltration also had higher expression of PD-L1, suggesting the development of resistance against adaptive immunity. These findings support the assertion that PD-L1/programmed cell death 1 inhibitors should be investigated in this rare malignancy.

The influence of Open Access (OA) on the citation impact of scholarly articles remains a topic of considerable debate. This study aims to elucidate the relationship between OA publication and citation metrics, as well as article visibility, within the context of the Postgraduate Medical Journal (PMJ). We conducted a retrospective analysis of 373 articles published in PMJ between 2020 and 2021. Data on OA status, citations, page views, PDF downloads, and other relevant variables were extracted from Journal Citation Reports and PMJ's official website. Multivariable linear regression and other statistical analyses were used to assess the impact of OA on these metrics. OA articles (n = 78) demonstrated significantly higher citation counts, page views, and PDF downloads compared with subscription-based articles (n = 295). Specifically, OA articles showed a significant increase in citation frequency with a β coefficient of 25.08 and a 95% CI of 17.168–32.992 (P < .001). Similarly, OA status was independently associated with increases in page views [β = 288.636, 95%CI: 177.749–399.524, P < .001] and PDF downloads [β = 118.966, 95%CI: 86.357–151.575, P < .001]. Strong correlations among total citations, page views, and PDF downloads were observed in both OA and subscription articles. The study highlights a significant and independent association of OA publishing with increased citation counts, page views, and PDF downloads in PMJ, suggesting that OA articles have broader reach and greater visibility. Further research, including randomized controlled studies across various journals, is needed to confirm these findings and explore the full impact of OA publishing. 1. What is already known on this topic: Prior research on the impact of Open Access (OA) on scholarly articles has produced mixed results, with some studies indicating a citation advantage for OA articles in various fields, while others found no significant difference. This inconsistency in findings across disciplines highlighted the need for a focused study on the citation impact of OA in specific journals such as the Postgraduate Medical Journal (PMJ). 2. What this study adds: Our study uniquely demonstrates that OA articles in PMJ are associated with significantly higher citation counts, page views, and PDF downloads compared with subscription-based articles. It highlights a notable correlation between OA article visibility (as measured by page views and downloads) and citation counts, thereby enriching the current understanding of the benefits of OA publishing. 3. How this study might affect research, practice, or policy: These findings suggest a considerable advantage of OA publishing in increasing the visibility and impact of research. They highlight the potential benefits for journals and policymakers in advocating OA publications and underscore the strategic advantage for researchers in opting for OA to achieve wider dissemination and impact. This study may also serve as a catalyst for further research into the varied effects of OA across different disciplines and journal types.

Background: Acyl-CoA thioesterase 7 (ACOT7) is of great significance in regulating cell cycle, cell proliferation, and glucose metabolism. The function of ACOT7 in pan-cancer and its capacity as a prognostic indicator in lung adenocarcinoma (LUAD) remains unknown. We intended to perform a comprehensive pan-cancer analysis of ACOT7 and to validate its value in LUAD. Methods: The expression levels, prognostic significance, molecular function, signaling pathways, and immune infiltration pattern of ACOT7 in 33 cancers were explored via systematic bioinformatics analysis. Multivariate Cox regression was applied to construct nomograms to predict patients’ prognoses. Moreover, we conducted in vitro experiments including CCK8, scratch, Transwell, and Matrigel assays to further explore the function of ACOT7 in LUAD. Results: Patients with high ACOT7 expression have notably poorer long-term survival in many cancer types, including LUAD. Further enrichment analyses reveal that ACOT7 is involved in immune cells’ infiltration and is substantially related to the cancer–immune microenvironment. ACOT7 could influence drug sensitivities, including afatinib, gefitinib, ibrutinib, lapatinib, osimertinib, sapitinib, taselisib, and PLX-4720 (all p < 0.01). A nomogram demonstrated a fair predictive value of ACOT7 in LUAD (C-index: 0.613, 95% CI: 0.568–0.658). The proliferation and migration of PC9 cells were significantly repressed when ACOT7 expression was downregulated. Conclusion: As an oncogene, ACOT7 is critical in the tumor microenvironment of pan-cancer and might be a novel therapeutic target for LUAD.