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During the past decade, immunotherapy targeting immune checkpoints has become an important component of the treatment paradigm for numerous malignancies, especially PD-1/PD-L1 blockade which was demonstrated to rejuvenate disabled T cells in cancer patients to achieve long-term remissions. However, the clinical outcome of PD-1/PD-L1 targeted monotherapy against solid malignancies is not satisfactory which may be related with the intricate tumor microenvironment. As a vital suppressive immunocyte in tumor microenvironment, Tregs are characterized by PD-1 and PD-L1 and demonstrated to contribute to the tumor progression. The latest studies have suggested that Tregs might be involved in the treatment of PD-1/PD-L1 blockade and PD-1/PD-L1 axis could influence Treg differentiation and function. However, the complicated relationship between PD-1/PD-L1 pathway and Tregs has not been fully clarified. Here, we explored the role of PD-1/PD-L1 axis in Treg development and function, as well as the potential mechanisms of PD-1/PD-L1 blockade resistance related with Tregs. Meanwhile, we discussed the combination therapy aimed at targeting PD-1/PD-L1 axis and Tregs, hoping to provide novel insights for the future cancer treatment.

With a sample of 651 university students experiencing emergency online learning during COVID-19, this study constructed a structural equation modelling to examine the effects of teacher support on students’ academic achievement, with a particular focus on the mediating roles of academic self-efficacy and student engagement. The results show that teacher support had significant total influences on university students’ academic achievement. Furthermore, academic self-efficacy and student engagement, respectively, mediated the effects of teacher support on students’ academic achievement. In addition, academic self-efficacy and student engagement sequentially mediated the effects of teacher support on students’ academic achievement. Research implications are also discussed.

Recently, pre‐trained language models (PLM), such as Roberta and SimCSE, demonstrate strengths in many natural language understanding (NLU) tasks. However, there are few works on applying PLM to neural machine translation (NMT). Motivated by alleviating the data scarcity issue of low‐resource NMT, here incorporating sentence embeddings from SimCSE into the Transformer network is explored and SE‐Former model is proposed. In this model an embed‐fusion module is designed to utilize the output of SimCSE for NMT. Specifically, the outputs of encoder and SimCSE are fed into embed‐fusion module, attention network learns the relationship of sentence embedding and corresponding word embeddings. After addition, concatenation and linear transformation operations, the tensor fused with sentence embedding is obtained. The size of tensor output by embed‐fusion module is the same as original encoder. Finally, embed‐fusion module is connected to Transformer decoder. On IWSLT En‐Es, En‐Zh and En‐Fr tasks, SE‐Former obtains 42.13, 29.32 and 39.21 bilingual evaluation understudy (BLEU) points, respectively. Experimental results show the superiority of this method. To leverage SimCSE for low‐resource NMT, in this work incorporating sentence embeddings from SimCSE into the Transformer network is explored and SE‐Former model is proposed. In this model an embed‐fusion module is designed to utilize the output of SimCSE for NMT.

The current prognostic tools for esophageal squamous cell carcinoma (ESCC) lack the necessary accuracy to facilitate individualized patient management strategies. To address this issue, this study was conducted to develop a machine learning (ML) prediction model for ESCC patients' survival management. Six ML approaches, including Rpart, Elastic Net, GBM, Random Forest, GLMboost, and the machine learning-extended CoxPH method, were employed to develop risk prediction models. The model was trained on a dataset of 1954 ESCC patients with 27 clinical features and validated on a dataset of 487 ESCC patients. The discriminative performance of the models was assessed using the concordance index (C-index). The best performing model was used for risk stratification and clinical evaluation. The study found that N stage, T stage, surgical margin, tumor grade, tumor length, sex, MPV, AST, FIB, and Mg are the important feature for ESCC patients’ survival. The machine learning-extended CoxPH model, Elastic Net, and Random Forest had similar performance in predicting the mortality risk of ESCC patients, and outperformed GBM, GLMboost, and Rpart. The risk scores derived from the CoxPH model effectively stratified ESCC patients into low-, intermediate-, and high-risk groups with distinctly different 3-year overall survival (OS) probabilities of 80.8%, 58.2%, and 29.5%, respectively. This risk stratification was also observed in the validation cohort. Furthermore, the risk model demonstrated greater discriminative ability and net benefit than the AJCC8th stage, suggesting its potential as a prognostic tool for predicting survival events and guiding clinical decision-making. The classical algorithm of the CoxPH method was also found to be sufficiently good for interpretive studies.

The impact of inflammatory factors on the development of Ankylosing Spondylitis (AS) is widely recognized, but the exact causal relationship remains unclear. The bidirectional mendelian-randomization study utilized genetic data from a genome-wide association study (GWAS) of 186 AS cases and 456,162 controls of European ancestry. Inflammatory cytokines were obtained from a GWAS summary of 8,293 healthy participants. Causal associations were primarily investigated using the inverse variance-weighted method, supplemented by MR Egger, weighted median and weighted mode analyses. Heterogeneity in the results was assessed using the Cochrane Q test. Horizontal pleiotropy was evaluated through the MR-Egger intercept test and the MR pleiotropy residual sum and outliers (MR-PRESSO) test. Sensitivity analysis was conducted through leave-one-out analysis. The results suggest a genetically predicted potential association between beta-nerve growth factor (βNGF), Interleukin-1-beta (IL-1β), and TNF-related apoptosis inducing ligand (TRAIL) with the risk of AS (OR: 2.17, 95% CI: 1.13-4.16; OR: 0.41, 95% CI: 0.18-0.95,; OR: 1.47, 95% CI: 1.02-2.13).Additionally, Interleukin-12p70 (IL-12p70), Interleukin-17 (IL-17), Interleukin-6 (IL-6), Interleukin-4 (IL-4), Stromal-cell-derived factor 1 alpha (SDF-1α), Macrophage inflammatory protein 1β (MIP1β), Monocyte chemoattractant protein-3 (MCP-3), Platelet-derived growth factor bb (PDGFbb), Granulocyte-colony stimulating factor (GCSF), Fibroblast growth factor basic (bFGF), TNF-related apoptosis inducing ligand (TRAIL), and Interferon-gamma (IFN -γ) are suggested as consequences of AS in genetically prediction.No evidence of horizontal pleiotropy or heterogeneity between the genetic variants was found (P>0.05), and a leave-one-out test confirmed the stability and robustness of this association. These findings suggest that βNGF, IL-1β, and TRAIL may play a crucial role in the pathogenesis of AS. Additionally, AS may impact the expression of cytokines such as IL-12p70, IL-17, IL-6, IL-4, SDF-1α, MIP1β, MCP-3, PDGFbb,GCSF, bFGF,TRAIL,and IFN-γ. Further investigations are warranted to determine whether these biomarkers can be utilized for the prevention or treatment of AS.

Job burnout among university teachers has become an increasing concern in the context of artificial intelligence integration in higher education. As core contributors to teaching quality and academic innovation, university teachers may perceive artificial intelligence as a potential threat to their professional roles, which can heighten burnout and affect educational outcomes. This study investigates the impact of artificial intelligence awareness on job burnout among university teachers and examines the chain mediating roles of perceived organizational support and organizational commitment. A total of 434 university teachers completed validated scales for artificial intelligence awareness, job burnout, perceived organizational support, and organizational commitment. Data were analyzed using SPSS Statistics 26 and the PROCESS macro version 4.0 (Model 6). The results showed that artificial intelligence awareness positively predicted job burnout, while perceived organizational support and organizational commitment were negatively associated with it. Both perceived organizational support and organizational commitment mediated the relationship between artificial intelligence awareness and burnout, and a significant chain mediation effect was observed. These findings indicate that artificial intelligence awareness may increase job burnout among university teachers, but strong perceived organizational support can enhance organizational commitment, jointly reducing this effect. The study clarifies the mechanism linking artificial intelligence awareness to job burnout and provides guidance for mitigating AI-related burnout in higher education.

Development of effective strategies to manage the inevitable acquired resistance to osimertinib, a third-generation EGFR inhibitor for the treatment of EGFR-mutant (EGFRm) non–small cell lung cancer (NSCLC), is urgently needed. This study reports that DNA topoisomerase II (Topo II) inhibitors, doxorubicin and etoposide, synergistically decreased cell survival, with enhanced induction of DNA damage and apoptosis in osimertinib-resistant cells; suppressed the growth of osimertinib-resistant tumors; and delayed the emergence of osimertinib-acquired resistance. Mechanistically, osimertinib decreased Topo IIα levels in EGFRm NSCLC cells by facilitating FBXW7-mediated proteasomal degradation, resulting in induction of DNA damage; these effects were lost in osimertinib-resistant cell lines that possess elevated levels of Topo IIα. Increased Topo IIα levels were also detected in the majority of tissue samples from patients with NSCLC after relapse from EGFR tyrosine kinase inhibitor treatment. Enforced expression of an ectopic TOP2A gene in sensitive EGFRm NSCLC cells conferred resistance to osimertinib, whereas knockdown of TOP2A in osimertinib-resistant cell lines restored their susceptibility to osimertinib-induced DNA damage and apoptosis. Together, these results reveal an essential role of Topo IIα inhibition in mediating the therapeutic efficacy of osimertinib against EGFRm NSCLC, providing scientific rationale for targeting Topo II to manage acquired resistance to osimertinib.

Gold nanorods (AuNRs)-assisted plasmonic photothermal therapy (AuNRs-PPTT) is a promising strategy for combating cancer in which AuNRs absorb near-infrared light and convert it into heat, causing cell death mainly by apoptosis and/or necrosis. Developing a valid PPTT that induces cancer cell apoptosis and avoids necrosis in vivo and exploring its molecular mechanism of action is of great importance. Furthermore, assessment of the long-term fate of the AuNRs after treatment is critical for clinical use. We first optimized the size, surface modification [rifampicin (RF) conjugation], and concentration (2.5 nM) of AuNRs and the PPTT laser power (2 W/cm²) to achieve maximal induction of apoptosis. Second, we studied the potential mechanism of action of AuNRs-PPTT using quantitative proteomic analysis in mouse tumor tissues. Several death pathways were identified, mainly involving apoptosis and cell death by releasing neutrophil extracellular traps (NETs) (NETosis), which were more obvious upon PPTT using RF-conjugated AuNRs (AuNRs@RF) than with polyethylene glycol thiol-conjugated AuNRs. Cytochrome c and p53-related apoptosis mechanisms were identified as contributing to the enhanced effect of PPTT with AuNRs@RF. Furthermore, Pin1 and IL18-related signaling contributed to the observed perturbation of the NETosis pathway by PPTT with AuNRs@RF. Third, we report a 15-month toxicity study that showed no long-term toxicity of AuNRs in vivo. Together, these data demonstrate that our AuNRs-PPTT platform is effective and safe for cancer therapy in mouse models. These findings provide a strong framework for the translation of PPTT to the clinic.

Tetrachlorantraniliprole (TCAP) is a novel anthranilic diamide insecticide that specifically targets the ryanodine receptors of lepidopteran insect species with excellent insecticidal activity. Previous studies have reported the sublethal effects of multiple diamides on several lepidopteran species, whereas the sublethal and non-target effects of TCAP remain largely unknown. We assessed the sublethal effects of TCAP on Spodoptera exigua . We also investigated the effects of TCAP on non-target Harmonia axyridis and Eisenia fetida , S . exigua was more sensitive to TCAP than to chlorantraniliprole, as the LC 50 (10.371 μg L -1 at 72 h) of TCAP was relatively lower. Compared with those of the control, sublethal concentrations of TCAP (LC 10 and LC 30 ) not only prolonged the duration of the larval and pupal stages as well as the mean generation time but also reduced certain population parameters. On the other hand, TCAP exposure, even at the highest concentration, did not induce toxic effects in H . axyridis ladybugs (1 st instar larvae and adults) or E . fetida earthworms. Taken together, our results suggest that TCAP can be used as a novel and promising component of the integrated pest management (IPM) program against S . exigua due to its robust target effects and negligible non-target risks.

The recent global focus on big data in medicine has been associated with the rise of artificial intelligence (AI) in diagnosis and decision-making following recent advances in computer technology. Up to now, AI has been applied to various aspects of medicine, including disease diagnosis, surveillance, treatment, predicting future risk, targeted interventions and understanding of the disease. There have been plenty of successful examples in medicine of using big data, such as radiology and pathology, ophthalmology cardiology and surgery. Combining medicine and AI has become a powerful tool to change health care, and even to change the nature of disease screening in clinical diagnosis. As all we know, clinical laboratories produce large amounts of testing data every day and the clinical laboratory data combined with AI may establish a new diagnosis and treatment has attracted wide attention. At present, a new concept of radiomics has been created for imaging data combined with AI, but a new definition of clinical laboratory data combined with AI has lacked so that many studies in this field cannot be accurately classified. Therefore, we propose a new concept of clinical laboratory omics (Clinlabomics) by combining clinical laboratory medicine and AI. Clinlabomics can use high-throughput methods to extract large amounts of feature data from blood, body fluids, secretions, excreta, and cast clinical laboratory test data. Then using the data statistics, machine learning, and other methods to read more undiscovered information. In this review, we have summarized the application of clinical laboratory data combined with AI in medical fields. Undeniable, the application of Clinlabomics is a method that can assist many fields of medicine but still requires further validation in a multi-center environment and laboratory.