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Abstract Background Immune checkpoint inhibitors, such as anti-programmed cell death-1 (PD-1) and PD-1 ligand-1 (PD-L1) antibodies, have achieved breakthrough results in improving long-term survival rates in lung cancer. Although high levels of PD-L1 expression and tumor mutational burden have emerged as pivotal biomarkers, not all patients derive lasting benefits, and resistance to immune checkpoint blockade remains a prevalent issue. Comprehending the immunological intricacies of lung cancer is crucial for uncovering the mechanisms that govern responses and resistance to immunomodulatory treatments. This study aimed to explore the potential of peripheral immune markers in predicting treatment efficiency among lung cancer patients undergoing PD-1/PD-L1 checkpoint inhibitors. Methods This study enrolled 71 lung cancer patients undergoing PD-1/PD-L1 inhibitor therapy and 20 healthy controls. Immune cell subsets (CD4 + T cells, CD8 + T cells, B cells, NK cells, and NKT cells), phenotypic analysis of T cells and B cells, and PMA/Ionomycin-stimulated lymphocyte function assay were conducted. Results Lung cancer patients exhibited significant alterations in immune cell subsets, notably an increased percentage of Treg cells. Post-treatment, there were substantial increases in absolute numbers of CD3 + T cells, CD8 + T cells, and NKT cells, along with heightened HLA-DR expression on CD3 + T and CD8 + T cells. Comparison between complete remission and non-complete remission (NCR) groups showed higher Treg cell percentages and HLA-DR + CD4 + T cells in the NCR group. Conclusion The study findings suggest potential predictive roles for immune cell subsets and phenotypes, particularly Treg cells, HLA-DR + CD4 + T cells, and naïve CD4 + T cells, in evaluating short-term PD-1/PD-L1 therapy efficacy for lung cancer patients. These insights offer valuable prospects for personalized treatment strategies and underscore the importance of immune profiling in lung cancer immunotherapy.

BackgroundHepatocellular carcinoma (HCC) poses a significant global health challenge due to its poor prognosis and limited therapeutic modalities. Anoikis and ErbB signaling pathways are pivotal in cancer cell proliferation and metastasis, but their relevance in HCC remains insufficiently explored.MethodsThis study evaluates the prognostic significance of anoikis and ErbB signaling pathways in HCC by utilizing data from The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), three additional independent validation cohorts, and an in-house cohort. Advanced bioinformatics analyses and 167 machine learning models based on leave-one-out cross-validation (LOOCV) were used to predict HCC prognosis and assess outcomes of immune-targeted therapies. Additionally, key biological processes of the anoikis and ErbB signaling pathways in HCC were further investigated.ResultsThe single sample Gene Set Enrichment Analysis revealed a strong correlation between upregulated ErbB signaling in high anoikis-expressing tumors and poor clinical outcomes. The development of the Anoikis-ErbB Related Signature (AERS) using the LASSO + RSF model demonstrated robust predictive capabilities, as validated across multiple patient cohorts, and proved effective in predicting responses to immune-targeted therapies. Further investigation highlighted activated NOTCH signaling pathways and decreased macrophage infiltration was associated with resistance to sorafenib and immune checkpoint inhibitors, as evidenced by bulk and single-cell RNA sequencing (scRNA-seq).ConclusionAERS provides a novel tool for clinical prognosis and paves the way for immune-targeted therapeutic approaches, underscoring the potential of integrated molecular profiling in enhancing treatment strategies for HCC.

BackgroundRadiotherapy (RT) is a critical component of treatment for locally advanced rectal cancer (LARC), though patient response varies significantly. The variability in treatment outcomes is partly due to the resistance conferred by cancer stem cells (CSCs) and tumor immune microenvironment (TiME). This study investigates the role of EIF5A in radiotherapy response and its impact on the CSCs and TiME.MethodsPredictive models for preoperative radiotherapy (preRT) response were developed using machine learning, identifying EIF5A as a key gene associated with radioresistance. EIF5A expression was analyzed via bulk RNA-seq and single-cell RNA-seq (scRNA-seq). Functional assays and in vivo experiments validated EIF5A’s role in radioresistance and TiME modulation.ResultsEIF5A was significantly upregulated in radioresistant colorectal cancer (CRC) tissues. EIF5A knockdown in CRC cell lines reduced cell viability, migration, and invasion after radiation, and increased radiation-induced apoptosis. Mechanistically, EIF5A promoted cancer stem cell (CSC) characteristics through the Hedgehog signaling pathway. Analysis of the TiME revealed that the radiation-resistant group had an immune-desert phenotype, characterized by low immune cell infiltration. In vivo experiments showed that EIF5A knockdown led to increased infiltration of CD8+ T cells and M1 macrophages, and decreased M2 macrophages and Tregs following radiation therapy, thereby enhancing the radiotherapy response.ConclusionEIF5A contributes to CRC radioresistance by promoting CSC traits via the Hedgehog pathway and modulating the TiME to an immune-suppressive state. Targeting EIF5A could enhance radiation sensitivity and improve immune responses, offering a potential therapeutic strategy to optimize radiotherapy outcomes in CRC patients.

BackgroundTransarterial chemo(embolization) is preferred for treating unresectable hepatocellular carcinoma (uHCC); however, because of emerging immune-targeted therapies, its efficacy is at stake. This systematic review pioneers to evaluate the clinical efficacy and safety of transarterial chemo(embolization) combined with immune-targeted therapy for uHCC patients.MethodsPubMed, Embase, and Cochrane Library were searched for studies comparing immune-targeted therapy with or without transarterial chemo(embolization) until 31 May 2024. The complete response (CR) rate, objective response rate (ORR), and disease control rate (DCR) were considered to be the primary outcomes calculated for the clinical outcomes of transarterial chemo(embolization) combined with immune-targeted therapy, along with progression-free survival (PFS) and overall survival (OS). The incidence of treatment-related severe adverse events was set as the major measure for the safety outcome.ResultsSixteen studies, encompassing 1,789 patients receiving transarterial chemo(embolization) plus immune-targeted therapy and 1,215 patients receiving immune-targeted therapy alone, were considered eligible. The combination of transarterial chemo(embolization) and immune-targeted therapy demonstrated enhanced outcomes in CR (OR = 2.12, 95% CI = 1.35–3.31), ORR (OR = 2.78, 95% CI = 2.15–3.61), DCR (OR = 2.46, 95% CI = 1.72–3.52), PFS (HR = 0.59, 95% CI = 0.50–0.70), and OS (HR = 0.51, 95% CI = 0.44–0.59), albeit accompanied by a surge in ALT (OR = 2.17, 95% CI = 1.28–3.68) and AST (OR = 2.28, 95% CI = 1.42–3.65). The advantages of additional transarterial chemo(embolization) to immune-targeted therapy were also verified in subgroups of first-line treatment, intervention techniques, with or without extrahepatic metastasis, Child–Pugh grade A or B, and with or without tumor thrombus.ConclusionThe combination of transarterial chemo(embolization) and immune-targeted therapy seems to bolster local control and long-term efficacy in uHCC, albeit at the expense of hepatic complications.Systematic review registrationhttp://www.crd.york.ac.uk/PROSPERO/, identifier 474669.

Objective: The purpose of this study was to determine the analgesic effect of a flurbiprofen ester injection via continuous intravenous drip during transvaginal 4-dimensional hysterosalpingography (TVS 4D-HyCoSy). Methods: Two hundred thirty patients who underwent TVS 4D-HyCoSy for infertility from May 2018 to August 2021 at our hospital were selected. The participants were grouped based on tubal patency, flurbiprofen ester use, and uterine cannula diameter, as follows: bilateral tubal patency group; non-bilateral tubal patency group; atropine group; atropine + flurbiprofen ester group; coarse tube group; and fine tube group. The analgesic effect during TVS 4D-HyCoSy and pain relief were compared between groups using visual analog scoring (NRS). Additionally, the incidence of adverse effects was recorded and factors related to the influence of pain were analyzed. Results: 1. Tubal patency reduced pain during ultrasound tubal examination, flurbiprofenate provided significant analgesia after ultrasound tubalography and reduced adverse effects (P < 0.001). 2. The tube diameter thickness had no effect on tubal ultrasonography procedure-related pain. 3. Multivariable analysis of pain relief during imaging suggested that the use of flurbiprofen for bilateral tubal patency had a significant positive effect on pain relief within 30 min after the examination with an AUC of 0.732 (95% CI: 0.665–0.798). Conclusion: A flurbiprofen ester continuous intravenous drip had a good analgesic effect in patients with TVS 4D-HyCoSy. Specifically, the pain relief effect after examination was significant and reduced the incidence of adverse reactions during the contrast examination. Flurbiprofen ester can be administered independently and is worthy of clinical promotion and application.

In order to achieve impact load localization of complex structures such as ships, this paper proposes a multi-scale feature fusion convolutional neural network (MSFF-CNN) method for impact load localization. An end-to-end machine learning model is used, where the raw vibration signals of impact loads are directly fed into the network model to avoid the process of feature extraction. Automatic feature learning and feature concatenation of the signal are achieved through four independent convolutional layers, each using a different size of convolutional kernel. Data normalization and L2 regularization techniques are introduced to enhance the data and prevent overfitting. Classification and localization of impact loads are accomplished using a softmax classification layer. Validation experiments are carried out using a ship’s stern compartment model. Our results show that the classification and localization accuracy of the impact load sample group of MSFF-CNN reaches 94.29% compared with a traditional CNN. The method further improves the ability of the network to extract state features, takes local perception and global vision into account, effectively improves the classification ability of the model, and has good prospects for engineering applications.

In addressing the challenging issue of impact source localization for large-scale anisotropic stiffened compartmental cylindrical shell structures, this paper presents a novel impact localization method. The method is based on a time-reversal virtual focusing triangulation approach and does not rely on prior knowledge of the structure or specific measurements of wave velocity. By employing energy power filtering to select key sensors, wavelet packet decomposition is utilized to extract narrowband Lamb wave signals, which are then synthesized. Further enhancement of signal recognition is achieved through time-reversal amplification techniques. Experimental results demonstrate that under non-motorized operating conditions, this method achieves an average error of 0.89 m. Under motorized operating conditions, the average error is 1.12 m. Although the presence of background noise leads to an increase in error, the overall localization performance is superior to traditional triangulation methods. Additionally, selecting the top three sensors in terms of energy power ranking can more accurately record impact response.

Objective Systemic autoimmune rheumatic diseases (SARDs) encompass a diverse group of complex conditions with overlapping clinical features, making accurate diagnosis challenging. This study aims to develop a multiclass machine learning (ML) model for early-stage SARDs classification using accessible laboratory indicators.Methods A total of 925 SARDs patients were included, categorised into SLE, Sjögren’s syndrome (SS) and inflammatory myositis (IM). Clinical characteristics and laboratory markers were collected and nine key indicators, including anti-dsDNA, anti-SS-A60, anti-Sm/nRNP, antichromatin, anti-dsDNA (indirect immunofluorescence assay), haemoglobin (Hb), platelet, neutrophil percentage and cytoplasmic patterns (AC-19, AC-20), were selected for model building. Various ML algorithms were used to construct a tripartite classification ML model.Results Patients were divided into two cohorts, cohort 1 was used to construct a tripartite classification model. Among models assessed, the random forest (RF) model demonstrated superior performance in distinguishing SLE, IM and SS (with area under curve=0.953, 0.903 and 0.836; accuracy= 0.892, 0.869 and 0.857; sensitivity= 0.890, 0.868 and 0.795; specificity= 0.910, 0.836 and 0.748; positive predictive value=0.922, 0.727 and 0.663; and negative predictive value= 0.854, 0.915 and 0.879). The RF model excelled in classifying SLE (precision=0.930, recall=0.985, F1 score=0.957). For IM and SS, RF model outcomes were (precision=0.793, 0.950; recall=0.920, 0.679; F1 score=0.852, 0.792). Cohort 2 served as an external validation set, achieving an overall accuracy of 87.3%. Individual classification performances for SLE, SS and IM were excellent, with precision, recall and F1 scores specified. SHAP analysis highlighted significant contributions from antibody profiles.Conclusion This pioneering multiclass ML model, using basic laboratory indicators, enhances clinical feasibility and demonstrates promising potential for SARDs classification. The collaboration of clinical expertise and ML offers a nuanced approach to SARDs classification, with potential for enhanced patient care.

Background: This study aimed to assess the diagnostic value of Krebs von den Lungen-6 (KL-6), Surfactant protein-A (SP-A), SP-D and molecular matrixmetalloproteinase-7 (MMP-7) in discriminating patients with interstitial lung diseases (ILDs) from disease control subjects. Methods: Serum levels of KL-6, SP-A, SP-D and MMP-7 were measured in both the ILD and non-ILD (NILD) groups. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the diagnostic potential of these markers and laboratory indices. High-resolution computed tomography (HRCT) fibrosis scores were determined, and their correlation with the serum markers was analyzed. Results: Serum levels of KL-6 and MMP-7 were significantly elevated in the ILD group compared to the control group, while no significant differences were observed for SP-A and SP-D. ROC analysis of KL-6 demonstrated superior diagnostic accuracy, with a sensitivity of 76.36%, specificity of 91.07%, and an area under curve (AUC) of 0.902 (95%CI 0.866–0.945). These findings were consistent across an additional cohort. Correlation analysis revealed a link between KL-6 levels at initial diagnosis and HRCT fibrosis scores, indicating disease severity. Moreover, a negative correlation was found between KL-6 and pulmonary function indices, reflecting disease progression. Patients with increased 12-month HRCT fibrosis score showed higher lactate dehydrogenase (LDH) levels, with LDH exhibiting an AUC of 0.767 (95% CI: 0.520–0.927) as a predictor of progression. Conclusions: Serum KL-6 detection proves to be a valuable tool for accurately distinguishing ILDs from control subjects. While KL-6 shows a correlation with HRCT fibrosis scores and a negative association with pulmonary function indices, its predictive value for ILDs prognosis is limited. Trial registration: This study received retrospective approval from the Ethical Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (institutional review board ID: TJ-IRB20210331, date: 2021.03.30).

Background: Transarterial chemo(embolization) is preferred for treating unresectable hepatocellular carcinoma (uHCC); however, because of emerging immune-targeted therapies, its efficacy is at stake. This systematic review pioneers to evaluate the clinical efficacy and safety of transarterial chemo (embolization) combined with immune-targeted therapy for uHCC patients. Methods: PubMed, Embase, and Cochrane Library were searched for studies comparing immune-targeted therapy with or without transarterial chemo (embolization) until 31 May 2024. The complete response (CR) rate, objective response rate (ORR), and disease control rate (DCR) were considered to be the primary outcomes calculated for the clinical outcomes of transarterial chemo (embolization) combined with immune-targeted therapy, along with progressionfree survival (PFS) and overall survival (OS). The incidence of treatment-related severe adverse events was set as the major measure for the safety outcome. Results: Sixteen studies, encompassing 1,789 patients receiving transarterial chemo(embolization) plus immune-targeted therapy and 1,215 patients receiving immune-targeted therapy alone, were considered eligible. The combination of transarterial chemo(embolization) and immune-targeted therapy demonstrated enhanced outcomes in CR (OR = 2.12, 95% CI = 1.35-3.31), ORR (OR = 2.78, 95% CI = 2.15-3.61), DCR (OR = 2.46, 95% CI = 1.72-3.52), PFS (HR = 0.59, 95% CI = 0.50-0.70), and OS (HR = 0.51, 95% CI = 0.44-0.59), albeit accompanied by a surge in ALT (OR = 2.17, 95% CI = 1.28-3.68) and AST (OR = 2.28, 95% CI = 1.42-3.65). The advantages of additional transarterial chemo(embolization) to immune-targeted therapy were also verified in subgroups of first-line treatment, intervention techniques, with or without extrahepatic metastasis, Child-Pugh grade A or B, and with or without tumor thrombus. Conclusion: The combination of transarterial chemo(embolization) and immune-targeted therapy seems to bolster local control and long-term efficacy in uHCC, albeit at the expense of hepatic complications. [ABSTRACT FROM AUTHOR]

We aimed to analyze and investigate the clinical factors that influence the occurrence of liver metastasis in locally advanced rectal cancer patients, with an attempt to assist patients in devising the optimal imaging-based follow-up nursing. Between June 2011 and May 2021, patients with rectal cancer at our hospital were retrospectively analyzed. A random survival forest model was developed to predict the probability of liver metastasis and provide a practical risk-based approach to surveillance. The results indicated that age, perineural invasion, and tumor deposit were significant factors associated with the liver metastasis and survival. The liver metastasis risk of the low-risk group was higher at 6-21 months, with a peak occurrence time in the 15th month. The liver metastasis risk of the high-risk group was higher at 0-24 months, with a peak occurrence time in the 8th month. In general, our clinical model could predict liver metastasis in rectal cancer patients. It provides a visualization tool that can aid physicians and nurses in making clinical decisions, by detecting the probability of liver metastasis. [ABSTRACT FROM AUTHOR]