A Risk Model for 28-Day in-Hospital Mortality in 173 COVID-19 Patients Admission to ICU: A Retrospective Study.

Background: The surge in the number of patients diagnosed with COVID-19 since China's open-door policy has placed a huge burden on the public healthcare system, especially the intensive care system. This study's objective was to discover possible clinical outcome predictors in COVID-19 patients treated in intensive care units (ICUs) and to provide useful information for future preventative efforts and therapies. Methods: This retrospective study included 173 COVID-19 critically ill patients and reviewed the 28-day survival outcome in the First Affiliated Hospital of Nanjing Medical University. Competing risk analysis was performed to predict the cumulative incidence function (CIF) of mortality in hospital. The independent prognostic factors were identified by applying the Fine-Gray proportional subdistribution hazard model. Receiver operating characteristic (ROC) curves were used to evaluate model efficacy, and calibration curves were used to validate the model. Finally, we compared the competing risk model with the traditional proportional hazards model (Cox regression model) using CIF. Results: Of these 173 patients, 66 (38.2%) survived, 55 (31.8%) died, and 52 (30.0%) discharged. In univariate analysis, 12 variables were significantly correlated with mortality. In multivariate analysis, Age, Neutrophil ratio, Direct Bilirubin (DBIL) and Renal disease were independent predictors of 28-day outcome. The ROC curve of the multivariate prediction model showed an AUC (area under the curve) of 0.790. The results of the calibration curve and the concordance index (C-index) show that the model has good discriminatory power. The competing risk model we applied was more accurate than the Cox model. Conclusion: We presented a more accurate multivariate prediction model for 28-day in-hospital mortality for ICU COVID-19 patients using a competing risk model.

Cellular crosstalk of macrophages and therapeutic implications in non-small cell lung cancer revealed by integrative inference of single-cell transcriptomics.

Introduction: Non-small cell lung cancer (NSCLC) exhibits heterogeneity with diverse immune cell infiltration patterns that can influence tumor cell behavior and immunotherapy. A comprehensive characterization of the tumor microenvironment can guide precision medicine. Methods: Here, we generated a single-cell atlas of 398170 cells from 52 NSCLC patients, and investigated the imprinted genes and cellular crosstalk for macrophages. Subsequently, we evaluated the effect of tumor cells on macrophages and verified the expression of marker genes using co-culture experiments, flow cytometry and RT-qPCR assays. Results: Remarkable macrophage adaptability to NSCLC environment was observed, which contributed to generating tumor-associated macrophages (TAMs). We identified 5 distinct functional TAM subtypes, of which the majority were SELENOP-positive macrophages, with high levels of SLC40A1 and CCL13. The TAMs were also involved in mediating CD8+ T cell activity and form intercellular interaction with cancer cells, as indicated by receptor-ligand binding. Indirect coculture of tumor cells SPC-A1 and THP-1 monocytes, produced M2-like TAMs that highly expressed several markers of SELENOP-positive macrophages. The abundance of this type TAMs seemed to be associated with poorer overall survival rates [hazard ratio (HR) = 1.34, 95% confidence interval (CI) = 0.98-1.83, p = 0.068] based on deconvolution of TCGA-LUAD dataset. Discussion: In summary, we provided a high-resolution molecular resource of TAMs, and displayed the acquired properties in the tumor microenvironment. Dynamic crosstalk between TAMs and tumor cells via multiple ligand-receptor pairs were revealed, emphasizing its role in sustaining the pro-tumoral microenvironment and its implications for cancer therapy.

Self-management challenges and support needs among patients with primary glaucoma: a qualitative study.

BACKGROUND: Self-management plays an important role in the disease management of glaucoma patients. The effectiveness of the program can be improved by assessing the patient's perspective and needs to tailor self-management support. Most studies have focused on assessing one of these self-management behaviours, such as medication adherence, and there is a lack of systematic assessment of the support needs and challenges of self-management for patients with glaucoma. Therefore, in this study, we conducted an in-depth investigation into the self-management challenges and support needs of patients with primary glaucoma, providing a basis for nursing staff to implement self-management support. METHOD: The phenomenological method and semistructured interviews were used in this study. A total of 20 patients with primary glaucoma were recruited between June and December 2022. Colaizzi's analysis method was used to analyse the interview data. RESULTS: Challenges for patients include becoming an expert in glaucoma, managing negative emotions, adapting to daily life changes and resuming social activities. To address these challenges, four themes of patient self-management support needs were identified: (1) health information support, (2) social support, (3) psychological support, and (4) daily living support. CONCLUSION: Patients with primary glaucoma experience varying degrees of challenge in dealing with medical, emotional, and social aspects. Comprehending the support needs of patients, healthcare professionals should deliver targeted, personalized and comprehensive self-management interventions to enhance their capacity of patients to perform self-management and improve their quality of life.

PD-1+ IFN-γ+ subset of CD8+ T cell in circulation predicts response to anti-PD-1 therapy in NSCLC.

Background: Treatment with programmed cell death protein-1 (PD-1) antibodies has minimal response rates in patients with non-small cell lung cancer (NSCLC), and, actually, they are treated with chemotherapy combined with anti-PD-1 therapy clinically. Reliable markers based on circulating immune cell subsets to predict curative effect are still scarce. Methods: We included 30 patients with NSCLC treated with nivolumab or atezolizumab plus platinum drugs between 2021 and 2022. Whole blood was collected at baseline (before treatment with nivolumab or atezolizumab). The percentage of circulating PD-1+ Interferon-γ (IFN-γ+) subset of CD8+ T cell was determined by flow cytometry. The proportion of PD-1+ IFN-γ+ was calculated after gating on CD8+ T cells. Neutrophil/lymphocyte ratio (NLR), relative eosinophil count (%), and Lactate dehydrogenase (LDH) concentration at baseline of included patients were extracted from electronic medical records. Results: The percentage of circulating PD-1+ IFN-γ+ subset of CD8+ T cell at baseline in responders was significantly higher than those in non-responders (P < 0.05). Relative eosinophil count (%) and LDH concentration in responders showed no significance between non-responders and responders. NLR in responders was significantly lower than those in non-responders (P < 0.05). Receiver operation characteristic (ROC) analysis found that the areas under the ROC curve for PD-1+ IFN-γ+ subset of CD8+ T cell and NLR were 0.7781 (95% CI, 0.5937-0.9526) and 0.7315 (95% CI, 0.5169-0.9461). Moreover, high percentage of PD-1+ IFN-γ+ subset in CD8+ T cells was relevant to long progression-free survival in patients with NSCLC treated with chemotherapy combined with anti-PD-1 therapy. Conclusion: The percentage of circulating PD-1+ IFN-γ+ subset of CD8+ T cell could be a potential marker at baseline to predict early response or progression in patients with NSCLC receiving chemotherapy combined with anti-PD-1 therapy.

Survey on deep learning for pulmonary medical imaging.

As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.