A Novel Immune Classification for Predicting Immunotherapy Responsiveness in Patients With Adamantinomatous Craniopharyngioma

Adamantinomatous craniopharyngioma (ACP) is the most common tumor of the sellar region in children. The aggressive behavior of ACP challenges the treatment for it. However, immunotherapy is rarely studied in ACP. In this research, we performed unsupervised cluster analysis on the 725 immune-related genes and arrays of 39 patients with ACP patients in GSE60815 and GSE94349 databases. Two novel immune subtypes were identified, namely immune resistance (IR) subtype and immunogenic (IG) subtype. Interestingly, we found that the ACPs with IG subtype (34.78%, 8/23) were more likely to respond to immunotherapy than the ACPs with IR subtype (6.25%, 1/16) via tumor immune dysfunction and exclusion (TIDE) method. Simultaneously, the enrichment analysis indicated that the differentially expressed genes (DEGs) (p < 0.01, FDR < 0.01) of the IG subtype were chiefly involved in inflammatory and immune responses. However, the DEGs of the IR subtype were mainly involved in RNA processing. Next, immune infiltration analysis revealed a higher proportion of M2 macrophage in the IG subtype than that in the IR subtype. Compared with the IR subtype, the expression levels of immune checkpoint molecules (PD1, PDL1, PDL2, TIM3, CTLA4, Galectin9, LAG3, and CD86) were significantly upregulated in the IG subtype. The ssGSEA results demonstrated that the biofunction of carcinogenesis in the IG subtype was significantly enriched, such as lymphocyte infiltration, mesenchymal phenotype, stemness maintenance, and tumorigenic cytokines, compared with the IR subtype. Finally, a WDR89 (the DEG between IG and IR subtype)-based nomogram model was constructed to predict the immune classification of ACPs with excellent performance. This predictive model provided a reliable classification assessment tool for clinicians and aids treatment decision-making in the clinic

Leveraging Prototype Patient Representations with Feature-Missing-Aware Calibration to Mitigate EHR Data Sparsity

Electronic Health Record (EHR) data frequently exhibits sparse characteristics, posing challenges for predictive modeling. Current direct imputation such as matrix imputation approaches hinge on referencing analogous rows or columns to complete raw missing data and do not differentiate between imputed and actual values. As a result, models may inadvertently incorporate irrelevant or deceptive information with respect to the prediction objective, thereby compromising the efficacy of downstream performance. While some methods strive to recalibrate or augment EHR embeddings after direct imputation, they often mistakenly prioritize imputed features. This misprioritization can introduce biases or inaccuracies into the model. To tackle these issues, our work resorts to indirect imputation, where we leverage prototype representations from similar patients to obtain a denser embedding. Recognizing the limitation that missing features are typically treated the same as present ones when measuring similar patients, our approach designs a feature confidence learner module. This module is sensitive to the missing feature status, enabling the model to better judge the reliability of each feature. Moreover, we propose a novel patient similarity metric that takes feature confidence into account, ensuring that evaluations are not based merely on potentially inaccurate imputed values. Consequently, our work captures dense prototype patient representations with feature-missing-aware calibration process. Comprehensive experiments demonstrate that designed model surpasses established EHR-focused models with a statistically significant improvement on MIMIC-III and MIMIC-IV datasets in-hospital mortality outcome prediction task. The code is publicly available at \url{https://github.com/yhzhu99/SparseEHR} to assure the reproducibility

A comprehensive benchmark for COVID-19 predictive modeling using electronic health records in intensive care

The COVID-19 pandemic highlighted the need for predictive deep-learning models in health care. However, practical prediction task design, fair comparison, and model selection for clinical applications remain a challenge. To address this, we introduce and evaluate two new prediction tasks?outcome-specific length-of-stay and early-mortality prediction for COVID-19 patients in intensive care?which better reflect clinical realities. We developed evaluation metrics, model adaptation designs, and open-source data preprocessing pipelines for these tasks while also evaluating 18 predictive models, including clinical scoring methods and traditional machine-learning, basic deep-learning, and advanced deep-learning models, tailored for electronic health record (EHR) data. Benchmarking results from two real-world COVID-19 EHR datasets are provided, and all results and trained models have been released on an online platform for use by clinicians and researchers. Our efforts contribute to the advancement of deep-learning and machine-learning research in pandemic predictive modeling

Early Growth Response Gene-1 Suppresses Foot-and-Mouth Disease Virus Replication by Enhancing Type I Interferon Pathway Signal Transduction

Early growth response gene-1 (EGR1) is a multifunctional transcription factor that is implicated in viral infection. In this study, we observed that foot-and-mouth disease virus (FMDV) infection significantly triggered EGR1 expression. Overexpression of EGR1 suppressed FMDV replication in porcine cells, and knockdown of EGR1 considerably promoted FMDV replication. A previously reported FMDV mutant virus (with two amino acids mutations in SAP domain) that displays a strong type I interferon (IFN) induction activity was used in this study. We found that SAP mutant FMDV infection induced a higher expression of EGR1 than wildtype FMDV infection, and also triggered higher IFN-β and IFN-stimulated genes (ISGs) expression than wildtype FMDV infection. This implied a link between EGR1 and type I IFN signaling. Further study showed that overexpression of EGR1 resulted in Sendai virus (SeV)-induced IFN-stimulated response element (ISRE) and NF-κB promoter activation. In addition, the SeV-induced ISGs expression was impaired in EGR1 knockdown cells. EGR1 upregulation promoted type I IFN signaling activation and suppressed FMDV and Seneca Valley virus replication. Suppression of the transcriptional activity of EGR1 did not affect its antiviral effect against FMDV. This study reveals a new mechanism evolved by EGR1 to enhance type I IFN signaling and suppress FMDV replication

Automated Penetration Testing for PHP Web Applications

Penetration Testing emerged in the mid-1960s as an approach to exploit vulnerabilities of possible attacks of a software application by nefarious users. Traditional penetration testing is done manually, which is not only inefficient but also unstable in terms of reliability. In the recent decade, multiple automated penetration testing approaches have been proposed, including automatically test inputs generation based on genetic algorithms and neural networks learning. However, these black-box testing methods only have limited accuracy, and usually require a large number of data to train the agents before they can be used to do actual tests. To address this issue, we present a novel approach in which program static analysis is exploited. The proposed penetration testing system is able to not only estimate HTTP request data more precisely, but also discover dynamic interfaces exposed by the web applications. This research is focused on PHP web applications only.Undergraduat

Endoscopic endonasal transsphenoidal surgery for the cavernous sinus hemangioma: Surgical application and review of the literature

Aim: Cavernous sinus hemangiomas (CSHs) are hypervascular malformations and the surgical treatment is technically demanding. Although some articles have reported resection of CSHs using endoscopic endonasal transsphenoidal surgery (EETS), most of them were encountered for a lack of preoperative strategy guidance. Herein, we reported gross total resection (GTR) of intrasellar CSHs in two patients undergoing strategical EETS and compared EETS with frontotemporal craniotomy (FC) and stereotactic radiosurgery by literature review. Material and methods: Two patients with CSHs who underwent EETS were reported. The literature review was conducted to exhaust studies that reported surgical treatment for CSHs. The tumor resection rate, and the postoperative short-term and long-term newly-developed or deteriorative cranial-nerve function rates were extracted. Results: GTR was achieved with no postoperative complications in the two cases. Nine articles reported 14 cases undergoing EETS for CSHs and twenty-three articles reported 195 cases undergoing FC for CSHs. The GTR rates of EETS and FC were 57.14% (8/14) and 78.97% (154/195) respectively. The postoperative short-term and long-term newly-developed or deteriorative cranial-nerve function rates were 0% (0/7) and 0% (0/6) for the EETS group, and 57% (57/100) and 18.18% (18/99) for the FC group. According to the previous meta-analysis, stereotactic radiosurgery resulted in remarkable tumor shrinkage in 67.80% (40/59) of patients and partial shrinkage in 25.42% of patients. Discussion: The results showed that the intrasellar type of CSHs could be removed safely by EETS without crossing the nerves in the CS