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Novel spam with rapidly evolving content faces a scarcity of labeled data in its early stages. Yet, current detection models rely heavily on large datasets and high-dimensional features, leading to poor generalization and opaque decisions when data is scarce. This opacity hinders error tracing and limits their use in early threat detection and response. The belief rule base (BRB), as an expert system, demonstrates effective learning under small-sample conditions, and its rule-based reasoning mechanism provides decision interpretability. However, high-dimensional features may cause combination explosion. To address these issues, a BRB spam detection model based on the Discriminative term frequency-inverse document frequency (TF-IDF) method (DTI-BRB) is proposed in this paper. By discriminating whether terms are more indicative of ham or spam, the Discriminative TF-IDF method converts raw text into low-dimensional features, thereby effectively resolving the combination explosion problem inherent in the traditional BRB model. Through two case studies under small-sample conditions, the effectiveness of the proposed model is validated. With only 200 samples, it achieves accuracies of 91.5% and 95.5% in the two cases, respectively, exhibiting excellent predictive performance and interpretability.

Tumor progression is closely related to tumor tissue metabolism and reshaping of the microenvironment. Oral squamous cell carcinoma (OSCC), a representative hypoxic tumor, has a heterogeneous internal metabolic environment. To clarify the relationship between different metabolic regions and the tumor immune microenvironment (TME) in OSCC, Single cell (SC) and spatial transcriptomics (ST) sequencing of OSCC tissues were performed. The proportion of TME in the ST data was obtained through SPOTlight deconvolution using SC and GSE103322 data. The metabolic activity of each spot was calculated using scMetabolism, and k-means clustering was used to classify all spots into hyper-, normal-, or hypometabolic regions. CD4T cell infiltration and TGF-β expression is higher in the hypermetabolic regions than in the others. Through CellPhoneDB and NicheNet cell-cell communication analysis, it was found that in the hypermetabolic region, fibroblasts can utilize the lactate produced by glycolysis of epithelial cells to transform into inflammatory cancer-associated fibroblasts (iCAFs), and the increased expression of HIF1A in iCAFs promotes the transcriptional expression of CXCL12. The secretion of CXCL12 recruits regulatory T cells (Tregs), leading to Treg infiltration and increased TGF-β secretion in the microenvironment and promotes the formation of a tumor immunosuppressive microenvironment. This study delineates the coordinate work axis of epithelial cells-iCAFs-Tregs in OSCC using SC, ST and TCGA bulk data, and highlights potential targets for therapy.

Background Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder that is accompanied by muscle weakness and muscle atrophy, typically resulting in death within 3–5 years from the disease occurrence. Though the cause of ALS remains unclear, increasing evidence has suggested that inflammation is involved in the pathogenesis of ALS. Thus, we performed two-sample Mendelian randomization (MR) analyses to estimate the associations of circulating levels of cytokines and growth factors with the risk of ALS. Methods Genetic instrumental variables for circulating cytokines and growth factors were identified from a genome-wide association study (GWAS) of 8293 European participants. Summary statistics of ALS were obtained from a GWAS including 20,806 ALS cases and 59,804 controls of European ancestry. We used the inverse-variance weighted (IVW) method as the primary analysis. To test the robustness of our results, we further performed the simple-median method, weighted-median method, MR-Egger regression, and MR pleiotropy residual sum and outlier test. Finally, a reverse MR analysis was performed to assess the possibility of reverse causation between ALS and the cytokines that we identified. Results After Bonferroni correction, genetically predicted circulating level of basic fibroblast growth factor (FGF-basic) was suggestively associated with a lower risk of ALS [odds ratio (OR): 0.74, 95% confidence interval (95% CI): 0.60–0.92, P = 0.007]. We also observed suggestive evidence that interferon gamma-induced protein 10 (IP-10) was associated with a 10% higher risk of ALS (OR: 1.10, 95% CI: 1.03–1.17, P = 0.005) in the primary study. The results of sensitivity analyses were consistent. Conclusions Our systematic MR analyses provided suggestive evidence to support causal associations of circulating FGF-basic and IP-10 with the risk of ALS. More studies are warranted to explore how these cytokines may affect the development of ALS.

Predicting student performance is essential for making informed teaching decisions, customizing learning, and ensuring educational equity. When developing student performance prediction models, it is crucial to provide high prediction accuracy, a clear and logical prediction process, as well as easily understandable and traceable prediction outputs. The Belief Rule Base (BRB) combines expert knowledge to ensure accuracy while also having a certain degree of interpretability. However, the following problems still exist: When there are too many attributes, BRB will encounter the problem of rule combination explosion; After the optimization stage of the BRB model is completed, its interpretability may decline. Furthermore, when experts have limited knowledge, the reference values they cite may weaken the prediction accuracy of the model. In response to the above problems, this paper presents an interpretable student performance prediction model based on a multi-parameter optimized belief rule base(IBRB-m). Firstly, an attribute selection method based on random forest was introduced to screen out the important features that affect students’ academic performance; Secondly, The criteria for interpretability in the model optimization process have been defined. Finally, a student performance prediction model is constructed and a model parameter optimization method with multi-parameter optimization and interpretable constraints is proposed. The effectiveness of this method was verified through a case study of the performance of students in a certain school.

Student performance prediction (SPP) constitutes one of the pivotal tasks in educational data analysis. Outcomes from the prediction enables educators to implement targeted interventions for students. Therefore, developing an effective SPP model is of critical importance. The belief rule base (BRB) is a rule-based modeling approach that integrates expert knowledge and effectively manages uncertain information. Nevertheless, when employing traditional BRB to construct a prediction model, excessive input attributes and reference points may result in a combination explosion. Furthermore, in practical scenarios, the configuration of the model’s parameters may be restricted by the limitations of expert knowledge. To overcome these challenges, an SPP model using an interval BRB structure based on the random forest (RF) attribute selection method (IBRB-C) is proposed. The parameters of the IBRB-C model are determined by combining the expert knowledge and the Kmeans++ algorithm. Subsequently, the P-CMA-ES algorithm is applied to optimize the initial model. Ablation experiment is conducted to validate the rationality of the IBRB-C. Finally, case studies on graduate applications and GPA of students demonstrate that the mean squared error (MSE) of the IBRB-C is 0.0024 and 0.1014, respectively. The results of comparative experiments confirm the superiority of the IBRB-C model in predicting student performance.

Background Tumor cells develop multiple mechanisms to facilitate their immune evasion. Identifying tumor-intrinsic factors that support immune evasion may provide new strategies for cancer immunotherapy. We aimed to explore the function and the mechanism of the tumor-intrinsic factor NPM1, a multifunctional nucleolar phosphoprotein, in cancer immune evasion and progression. Methods The roles of NPM1 in tumor progression and tumor microenvironment (TME) reprogramming were examined by subcutaneous inoculation of Npm1 -deficient tumor cells into syngeneic mice, and then explored by CyTOF, flow cytometry, immunohistochemistry staining, and RNA-seq. The in-vitro T-cell killing of OVA-presenting tumor cells by OT-1 transgenic T cells was observed. The interaction of NPM1 and IRF1 was verified by Co-IP. The regulation of NPM1 in IRF1 DNA binding to Nlrc5 , Ciita promoter was determined by dual-luciferase reporter assay and ChIP-qPCR. Results High levels of NPM1 expression predict low survival rates in various human tumors. Loss of NPM1 inhibited tumor progression and enhanced the survival of tumor-bearing mice. Npm1 -deficient tumors showed increased CD8 + T cell infiltration and activation alongside the reduced presence of immunosuppressive cells. Npm1  deficiency increased MHC-I and MHC-II molecules and specific T-cell killing. Mechanistically, NPM1 associates with the transcription factor IRF1 and then sequesters IRF1 from binding to the Nlrc5 and Ciita promoters to suppress IRF1-mediated expression of MHC-I and MHC-II molecules in tumor cells. Conclusions Tumor-intrinsic NPM1 promotes tumor immune evasion via suppressing IRF1-mediated antigen presentation to impair tumor immunogenicity and reprogram the immunosuppressive TME. Our study identifies NPM1 as a potential target for improving cancer immunotherapy.

Objective Tongue and mouth floor squamous cell carcinoma (T/MF SCC) exhibits a high rate of local recurrence and cervical lymph node metastasis. The effect of the tumor microenvironment on T/MF SCC remains unclear. Materials and methods Transcriptome and somatic mutation data of patients with T/MF SCC were obtained from HNSC projects of the Cancer Genome Atlas. Immune infiltration quantification in early- (clinical stage I–II) and advanced-stage (clinical stage III–IV) T/MF SCC was performed using single sample Gene Set Enrichment Analysis and MCPcounter. Differentially expressed gene data were filtered, and their function was assessed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Kaplan–Meier survival curve analysis and Cox regression model were conducted to evaluate the survival of patients with the CCL22 signature. Maftools was used to present the overview of somatic mutations. Results In T/MF SCC, T helper (Th)2 cell counts were significantly increased in patients with early-stage disease compared to those with advanced-stage disease. Expression of the Th2 cell-related chemokine, CCL22, was downregulated in patients with advanced-stage T/MF SCC. Univariate and multivariate Cox analyses revealed that CCL22 was a good prognostic factor in T/MF SCC. A nomogram based on the expression of CCL22 was constructed to serve as a prognostic indicator for T/MF SCC. NOTCH1 mutations were found at a higher rate in patients with advanced-stage T/MF SCC than in those with early-stage T/MF SCC, resulting in the inhibition of the activation of the NOTCH1-Th2 cell differentiation pathway. The expression levels of CCL22, GATA-3, and IL4 were higher in patients with early-stage T/MF SCC than in those with advanced-stage T/MF SCC. Conclusion In T/MF SCC, high expression of CCL22 may promote the recruitment of Th2 cells and help predict a better survival. Mutations in NOTCH1 inhibit the differentiation of Th2 cells, facilitating tumor progression through a decrease in Th2 cell recruitment and differentiation.

Purpose To evaluate the effects of patient age and sex on the early visual outcomes of small-incision lenticule extraction (SMILE) for myopia correction. Methods This retrospective study enrolled 706 consecutive right eyes that underwent SMILE to correct myopia with or without astigmatism. All eyes were evaluated preoperatively and at 1 day, 1 week and 1 month postoperatively. At each follow-up visit, UDVA was transformed into a qualitative outcome on the basis of whether the postoperative UDVA had reached the preoperative CDVA. Thus, a logistic regression model with multiple variables was used to evaluate the effects of possible predictors of vision recovery. Results One month postsurgery, 96.6% of the eyes had a UDVA of 20/20 or better. Furthermore, the corrected distance visual acuity (CDVA) of 68.6% of the eyes remained unchanged, 26.2% gained one line of CDVA, and 3.3% lost one line of CDVA. Additionally, our data revealed that 90.5% of the eyes were within 0.5 diopters (D) and that 38.1% were within ± 0.13 D at 1 month after surgery. Multivariate logistic regression analysis showed that age hindered postoperative UDVA from reaching preoperative CDVA at 1 day (OR = 0.954, P  < 0.001) and 1 week (OR = 0.932, P  < 0.001) but had no significant effect at 1 month (OR = 0.981, P  = 0.418) postoperatively. In addition, female gender hindered postoperative UDVA from reaching preoperative CDVA at 1 day (OR = 0.592, P  = 0.006) and 1 week (OR = 0.631, P  = 0.031) but had no significant effect at 1 month (OR = 0.669, P  = 0.317) postoperatively. Conclusions The refractive outcomes of SMILE are affected by age and sex in the early postoperative period. This study revealed that age and female sex hindered postoperative UDVA from reaching preoperative CDVA at 1 day and 1 week after surgery, but had no effect at 1 month after surgery.

Background Malnutrition is related to impaired oral health and function that causes poor dietary intake, declining the general health of older adults. The role of dietary intake in the association between oral function and nutritional status of Chinese older adults (aged 75 and above) was examined in this cross-sectional study. Methods Through the randomized cluster sampling method, 267 older adults living in rural areas of Qingdao, Shandong (aged 81.4 ± 4.3, 75–94 years) were chosen as the primary research participants. A Mini Nutritional Assessment - Short Form was used to determine nutritional status, and Food Frequency Questionnaire and 24-hour Food Intake Recall were used to assess dietary intake. The oral function was evaluated by analyzing the teeth, oral problems, bite force, tongue pressure, lip sealing pressure, chewing function questionnaire, whole saliva flow rate, 10-Item Eating Assessment Tool, and water swallow test. Results Based on the MNA-SF score, it was divided into a well-nourished group and a malnutrition group, with the malnutrition group comprising 40.6% of participants. The participants in the malnutrition group showed a higher rate of xerostomia, lower bite force, tongue pressure, and lip sealing pressure, and higher Chewing Function Questionnaire and 10-Item Eating Assessment Tool scores. Furthermore, their plant fat, iron, cereals and potatoes, vegetables, fruits, and seafood intake were relatively low. The regression model indicated that exercise frequency, stroke, chewing and swallowing function, intake of vegetables and fruits were risk factors for nutritional status of older adults. Conclusion Malnutrition was relatively common among the Chinese older adults aged 75 and above, and it was significantly correlated with exercise frequency, stroke, chewing and swallowing function, and intake of vegetables and fruits. Therefore, nutrition management should be carried out under the understanding and guidance of the oral function and dietary intake of the older adults.

The electric and temperature fields formed in the furnace water system by electrodes of different shapes differ to some extent when the immersed high-voltage electrode boiler is in operation. To obtain the distribution of electric and temperature fields in the furnace water when different shapes of electrodes are used in a high-voltage electrode boiler, simulation models of spherical electrodes, planar electrodes, and tangential electrodes are established, respectively. The effects of the electrode structure on the electric and temperature fields in the furnace water were investigated. The simulation results show that the electric field distribution and temperature field distribution of the spherical electrode in the furnace water are the best among the three electrode structures. Meanwhile, in order to verify the accuracy of the adopted simulation method, simulation and temperature rise tests were carried out on a small spherical electrode model under 400 V AC, and the simulation calculation results and the temperature rise test results at special points were compared. The difference between the simulation and test results is less than 3%, which proves the reasonableness of the method. The method can be used as a reference for the design of electrodes for immersed high-voltage electrode boilers, as well as the analysis of electric and temperature fields around the electrodes.