Research in contextEvidence before this study Several prognostic models for predicting mortality risk, progression to severe disease, or length of hospital stay in COVID-19 have been published. 1 Commonly reported predictors of severe prognosis in patients with COVID-19 include age, sex, computed tomography scan features, C-reactive protein (CRP), lactic dehydrogenase, and lymphocyte count. Symptoms (notably dyspnoea) and comorbidities (e.g. chronic lung disease, cardiovascular disease and hypertension) are also reported to have associations with poor prognosis. 2 However, most studies have not described the study population or intended use of prediction models, and external validation is rare and to date done using datasets originating from different Wuhan hospitals. 3 Given different patterns of testing and organisation of healthcare pathways, external validation in datasets from other countries is required. Added value of this studyThis study used data from Wuhan, China to derive and internally validate multivariable models to predict poor outcome and death in COVID-19 patients after hospital admission, with external validation using data from King's College Hospital, London, UK. Mortality and poor outcome occurred in 4.3% and 9.7% of patients in Wuhan, compared to 34.1% and 42.9% of patients in London. Models based on age, sex and simple routinely available laboratory tests (lymphocyte count, neutrophil count, platelet count, CRP and creatinine) had good discrimination and calibration in internal validation, but performed only moderately well in external validation. Models based on age, sex, symptoms and comorbidity were adequate in internal validation for poor outcome (ICU admission or death) but had poor performance for death alone. Implications of all the available evidenceThis study and others find that relatively simple risk prediction models using demographic, clinical and laboratory data perform well in internal validation but at best moderately in external validation, either because derivation and external validation populations are small (Xie et al 3 ) and/or because they vary greatly in casemix and severity (our study). There are three decision points where risk prediction may be most useful: (1) deciding who to test; (2) deciding which patients in the community are at high-risk of poor outcomes; and (3) identifying patients at high-risk at the point of hospital admission. Larger studies focusing on particular decision points, with rapid external validation in multiple datasets are needed. A key gap is risk prediction tools for use in community triage (decisions to admit, or to keep at home with varying intensities of follow-up including telemonitoring) or in low income settings where laboratory tests may not be routinely available at the point of decision-making. This requires systematic data collection in community and low-income settings to derive and evaluate appropriate models. AbstractBackground

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Objectives. Bronchopleural fistula (BPF) is a serious and life-threatening complication. Following the advent of interventional radiology, subsequent treatment methods for BPF have gradually diversified. Therefore, this article provides an overview of the present scenario of interventional treatment and research advancements pertaining to BPF. Methods. Relevant published studies on the interventional treatment of BPF were identified from the PubMed, Sci-Hub, Google Scholar, CNKI, VIP, and Wanfang databases. The included studies better reflect the current status of and progress in interventional treatments for BPF with representativeness, reliability, and timeliness. Studies with similar and repetitive conclusions were excluded. Results. There are many different interventional treatments for BPF that can be applied in cases of BPF with different fistula diameters. Conclusion. The application of interventional procedures for bronchopleural fistula has proven to be safe, efficacious, and minimally invasive. However, the establishment of comprehensive, standardized treatment guidelines necessitates further pertinent research to attain consensus within the medical community. The evolution of novel technologies, tools, techniques, and materials specifically tailored to the interventional management of bronchopleural fistula is anticipated to be the focal point of forthcoming investigations. These advancements present promising prospects for seamless translation into clinical practice and application, thereby potentially revolutionizing patient care in this field.

Purpose Neoantigens produced from mutations in tumors are important targets of T-cell-based immunotherapy and immune checkpoint blockade has been approved for treating multiple solid tumors. We investigated the potential benefit of adoptive neoantigen-reactive T (NRT) cells in combination with programmed cell death protein 1 inhibitor (anti-PD1) for treating lung cancer in a mouse model. Methods NRT cells were prepared by co-culturing T cells and neoantigen-RNA vaccine-induced dendritic cells. Then, adoptive NRT cells in combination with anti-PD1 were administered to tumor-bearing mice. Pre- and post-therapy cytokine secretion, antitumor efficacy, and tumor microenvironment (TME) changes were determined both in vitro and in vivo. Results We successfully generated NRT cells based on the 5 neoantigen epitopes identified in this study. NRT cells exhibited an enhanced cytotoxic phenotype in vitro and the combination therapy led to attenuated tumor growth. In addition, this combination strategy downregulated the expression of the inhibitory marker PD‐1 on tumor-infiltrating T cells and promoted the trafficking of tumor-specific T cells to the tumor sites. Conclusion The adoptive transfer of NRT cells in association with anti-PD1 therapy can exert an antitumor effect on lung cancer, and is a feasible, effective, and novel immunotherapy regimen for treating solid tumors. Supplementary Information The online version contains supplementary material available at 10.1007/s00432-023-04683-5.