Prognostic model for predicting overall survival in patients with glioblastoma: an analysis based on the SEER database.

Predicting the prognosis of glioblastoma (GBM) has always been important for improving survival. An understanding of the prognostic factors for patients with GBM can help guide treatment. Herein, we aimed to construct a prognostic model for predicting overall survival (OS) for patients with GBM. We identified 11,375 patients with pathologically confirmed GBM from the Surveillance, Epidemiology, and End Results database between 2004 and 2015. The 1-, 2-, and 3-year survival probabilities were 48.8%, 22.5%, and 13.1%, respectively. The patients were randomly divided into the training cohort (n = 8531) and the validation cohort (n = 2844). A Cox proportional risk regression model was used to analyze the prognostic factors of patients in the training cohort, and a nomogram was constructed. Then concordance indexes (C-indexes), calibration curves, and receiver operating characteristic (ROC) curves were used to assess the performance of the nomograms by internal (training cohort) and external validation (validation cohort). Log-rank test and univariate analysis showed that age, race, marital status, extent of surgical resection, chemotherapy, and radiation were the prognostic factors for patients with GBM (p < 0.05), which were used to construct nomogram. The C-index of the nomogram was 0.717 (95% confidence interval (CI), 0.710-0.724) in the training cohort, and 0.724 (95% CI, 0.713-0.735) in the validation cohort. The nomogram had a higher areas under the ROC curve value. The nomogram was well validated, which can effectively predict the OS of patients with GBM. Thus, this nomogram could be applied in clinical practice.

Development of a prognostic prediction model based on a combined multi-omics analysis of head and neck squamous cell carcinoma cell pyroptosis-related genes.

This study aimed to understand the prognosis of patients with head and neck squamous cell carcinoma (HNSCC) and to develop and validate a prognostic model for HNSCC based on pyroptosis-associated genes (PAGs) in nasopharyngeal carcinoma. The Cancer Genome Atlas database was used to identify differentially expressed PAGs. These genes were analyzed using the Kyoto Encyclopedia of Genes and Genomes functional annotation analyses and Gene Ontology analyses. The NLR family pyrin domain containing 1 (NLRP1) gene, charged multivesicular body protein 7 (CHMP7) gene, and cytochrome C (CYCS) gene were used to create a prognostic model for HNSCC. The results of the Kaplan-Meier (K-M) and Cox regression analyses indicated that the developed model served as an independent risk factor for HNSCC. According to the K-M analysis, the overall survival of high-risk patients was lower than that of low-risk patients. The hazard ratios corresponding to the risk scores determined using the multivariate and univariate Cox regression analyses were 1.646 (95% confidence interval (CI): 1.189-2.278) and 1.724 (95% CI: 1.294-2.298), respectively, and the area under the receiver operator characteristic curve was 0.621. The potential mechanisms associated with the functions of the identified genes were then identified, and the tumor microenvironment and levels of immune cell infiltration achieved were analyzed. The immune infiltration analysis revealed differences in the distribution of Th cells, tumor-infiltrating lymphocytes, regulatory T cells, follicular helper T cells, adipose-derived cells, interdigitating dendritic cells, CD8+ T cells, and B cells. However, validating bioinformatics analyses through biological experiments is still recommended. This study developed a prognostic model for HNSCC that included NLRP1, CHMP7, and CYCS.

High cerebrospinal fluid lactate concentration at 48 h of hospital admission predicts poor outcomes in patients with tuberculous meningitis: A multicenter retrospective cohort study.

Objective: This study aimed to analyze the cerebrospinal fluid (CSF) parameters affecting the outcomes of patients with tuberculous meningitis (TBM). Methods: This is a multi-center, retrospective, cohort study involving 81 patients who were diagnosed with TBM and treated in Haihe Clinical College of Tianjin Medical University, Tianjin Medical University General Hospital, and General Hospital of Air Force PLA from January 2016 to December 2019. Baseline data, Glasgow Coma Scale (GCS) score, and clinical presentations of all patients were collected at admission. CSF samples were collected at 48 h, 1, 2, and 3 weeks after admission. CSF lactate, adenosine deaminase, chloride, protein, glucose levels and intracranial pressure were measured. After a follow-up of 16.14 ± 3.03 months, all patients were assessed using the modified Rankin Scale (mRS) and divided into good (mRS scores of 0-2 points) and poor outcome groups (mRS scores of 3-6 points). The differences in patients' baseline data, GCS score, clinical presentations, and levels of CSF parameters detected at 48 h, 1, 2, and 3 weeks after admission between two groups were compared. Statistically significant variables were added to the binary logistic regression model to identify the factors impacting the outcomes of patients with TBM. Receiver operating characteristic (ROC) curve was used to assess the predictive ability of the model. Results: The CSF lactate level exhibited a decreasing trend within 3 weeks of admission in the two groups. For the within-group comparison, statistically significant differences in the lactate level was found in both groups between four different time points. A binary logistic regression model revealed that CSF lactate level at 48 h after admission, age, and GSC score on admission were independently associated with the outcomes of patients with TBM. ROC curve analysis showed that the area under the ROC curve (AUC) was 0.786 for the CSF lactate level (48 h), 0.814 for GCS score, and 0.764 for age. Conclusion: High CSF lactate level at 48 h after admission is one of the important factors for poor outcomes in patients with TBM.

Based on Network Pharmacology and Molecular Dynamics Simulations, Baicalein, an Active Ingredient of Yiqi Qingre Ziyin Method, Potentially Protects Patients With Atrophic Rhinitis From Cognitive Impairment.

Cognition may be improved by the active ingredients of the Yiqi Qingre Ziyin method in patients with atrophic rhinitis (AR). This study aimed to identify potential targets of the Yiqi Qingre Ziyin method for the treatment of patients with cognitive impairment. Nasal mucosal tissue samples from patients with AR were subjected to proteomic assays, and differentially expressed proteins were obtained. To explore the mechanism of AR leading to mild cognitive impairment (MCI), a differential analysis of AR related differential proteins in the MCI related GSE140831 dataset was performed. Most AR-related differential proteins are also differentially expressed in peripheral blood tissues of MCI, have similar biological functions and are enriched in similar pathways. These co-expressed differential factors in AR and MCI are known as common differential proteins of AR and MCI (CDPAM). Based on the analysis and validation of the random forest, support vector machine and neural network models, CDPAM acted as a diagnostic marker for MCI risk. Cytochrome C (CYCS) was significantly upregulated in the peripheral blood of patients with MCI. The active ingredients in the Yiqi Qingre Ziqin method were obtained and targeted 137 proteins. Among these targeted proteins, CYCS belong to the CDPAM set. Molecular docking and molecular dynamics analysis revealed that baicalein, an active ingredient in the Yiqi Qingre Ziyin method, stably targeted the CYCS protein. Results of the enrichment analysis revealed that the up-regulation of CYCS expression may have a defensive effect on the cells to resist foreign stimuli. Therefore, baicalein, an active ingredient in the Yiqi Qingre Ziyin method, may prevent the development and progression of MCI by targeting the CYCS protein.

Exploring the Mechanism of Yiqi Qingre Ziyin Method in Regulating Neuropeptide Expression for the Treatment of Atrophic Rhinitis.

Atrophic rhinitis (AR) is a chronic disease that causes severe structural changes to the nasal mucosa leading to squamous epithelial metaplasia. However, treatment regarding AR remains a major challenge. We used network pharmacology and molecular docking methods to explore the potential mechanisms of the Yiqi Qingre Ziyin method to modulate neuropeptides in the treatment of AR. The active ingredients of the Yiqi Qingre Ziyin method and their targets of action were obtained from the Traditional Chinese Medicine Systematic Pharmacology Database Analysis Platform (TCMSP). Disease targets for AR were obtained from four databases: GeneCards, PharmGKB, DrugBank, and Online Mendelian Inheritance in Man (OMIM). A total of 59 active ingredients, 39 potential targets, and 76 relevant neuropeptides were obtained after deduplication. We constructed target interaction networks with the STRING database. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the 14 potential target proteins. We used Cytoscape software to construct the "drug-active ingredient-potential target" and "ingredient-target-pathway" networks of the Yiqi Qingre Ziyin method for treating AR. Molecular docking results suggest that dipeptidyl peptidase 4 (DPP4), opioid receptor gene d1 (OPRD1), and opioid receptor m1 (OPRM1) are key targets for the Yiqi Qingre Ziyin method. Therefore, this study proposed a potential mechanism for the treatment of AR by affecting the expression of neuropeptide-related genes (including DPP4, OPRD1, and OPRM1), which may potentially improve the immune microenvironment of the nasal mucosa.

Differential diagnosis of pancreatic cystic neoplasms through a radiomics-assisted system.

Pancreatic cystic neoplasms (PCNs) are a group of heterogeneous diseases with distinct prognosis. Existing differential diagnosis methods require invasive biopsy or prolonged monitoring. We sought to develop an inexpensive, non-invasive differential diagnosis system for PCNs based on radiomics features and clinical characteristics for a higher total PCN screening rate. We retrospectively analyzed computed tomography images and clinical data from 129 patients with PCN, including 47 patients with intraductal papillary mucinous neoplasms (IPMNs), 49 patients with serous cystadenomas (SCNs), and 33 patients with mucinous cystic neoplasms (MCNs). Six clinical characteristics and 944 radiomics features were tested, and nine features were finally selected for model construction using DXScore algorithm. A five-fold cross-validation algorithm and a test group were applied to verify the results. In the five-fold cross-validation section, the AUC value of our model was 0.8687, and the total accuracy rate was 74.23%, wherein the accuracy rates of IPMNs, SCNs, and MCNs were 74.26%, 78.37%, and 68.00%, respectively. In the test group, the AUC value was 0.8462 and the total accuracy rate was 73.61%. In conclusion, our research constructed an end-to-end powerful PCN differential diagnosis system based on radiomics method, which could assist decision-making in clinical practice.

Post-Surgery Subcutaneous Seeding of Laryngeal Squamous Cell Carcinoma: A Rare Case.

Laryngeal squamous cell carcinoma (LSCC) is the most common malignant head and neck cancer, with a 40% recurrence rate in the first 3 years after radical treatment. Recurrence of LSCC mostly comprises lymphogenous metastasis, hematogenic metastasis, and locoregional recurrence, while LSCC seeding is rarest: there are only 4 cases reported in PubMed, and none of them is one of subcutaneous seeding. We report a case with post-surgery subcutaneous seeding of LSCC. The final biopsy demonstrated that the subcutaneous seeding of the LSCC was 2 cm away from the primary lesion, with no recurrent foci observed in the larynx and tracheostoma and little relation to the primary lesion. Thus, we drew the conclusion that LSCC surgeries should stick to the principle of the non-tumor technique to prevent subcutaneous seeding.

Fingolimod administration improves neurological functions of mice with subarachnoid hemorrhage.

PURPOSE: To investigate the brain protective effects of fingolimod on inflammatory response of SAH mice. METHODS: We utilized an endovascular mouse perforation model of SAH. Mice were divided into three groups: sham group, SAH group and SAH + Fingolimod group. Mice received either saline or fingolimod (1 mg/kg) intraperitoneally 2 h after sham surgery or SAH. The modified neurological severity score (mNSS) and Morris water maze were respectively used to evaluate the influence of nerve function. Evens blue (EB) extravasation was used to detect the permeability of blood-brain barrier, and water content in brain tissue was also detected. Flow cytometry, ELISA kits and western blotting were used to detect inflammatory factors in brain tissue. RESULTS: The results showed that compared with SAH group, after treatment, the delay time of locating the hidden platform was shorter. The mNSS results showed that fingolimod improved the behavior of SAH mice. In addition, fingolimod could reduce the water content in brain. Flow cytometry results showed that after 3 d of treatment, fingolimod significantly increased Treg cells and down-regulated NK cells. Western blotting results showed fingolimod inhibited the expression of inflammatory cytokines in brain tissue. ELISA kit results showed that fingolimod could down-regulate IL-6 and TNF-α and up-regulate IL-10 and TGF-ß1 in serum. CONCLUSIONS: Fingolimod could regulate the inflammatory response to alleviate SAH-induced brain damage and promote neurological recovery, which provides a new therapeutic strategy for SAH treatment.