Survival Curve Analysis Research Articles

Identification of pyroptosis-associated miRNAs in the immunoscape and prognosis of hepatocellular carcinoma

BackgroundHepatocellular carcinoma is one of the most prevalent types of liver malignancy and poses a severe threat to global health. Despite recent improvements in therapeutic approaches, treatment options for patients with advanced or recurrent HCC are still limited.Materials and methodsOur study analyzed miRNA differential expression using data from hepatocellular carcinoma patients in the Cancer Genome Atlas. Pyroptosis-related genes were identified from gene cards. Differential expression of miRNAs was analyzed using DESeq2 and visualized using ggplot2 and pheatmap. A prognostic risk model for pyroptosis-associated miRNAs was constructed using LASSO regression and validated by principal component analysis, Kaplan-Meier survival and ROC curve analysis. We also performed gene and pathway enrichment analysis. Immune cell infiltration and function in HCC were assessed using single-sample genomic enrichment analysis, and correlations with immune cells and function were explored. Also, CCK-8 assay as well as migration and invasion assays were performed after knockdown of miR-6844.ResultsWe have established and validated a prognostic risk model based on ten DEmiRNAs, which is important for the survival of HCC patients. Significant changes in immune cell infiltration and immune function were also found in high-risk patients. It also demonstrated that knockdown of miR-6844 inhibited HCC cell proliferation, migration and invasion, highlighting its role in HCC progression.ConclusionOur study reveals the implications of pyroptosis-associated differential miRNAs on the prognosis of patients with hepatocellular carcinoma and provides a foundation for novel HCC therapies, especially immunotherapy.

BIRC5 as a prognostic and diagnostic biomarker in pan-cancer: an integrated analysis of expression, immune subtypes, and functional networks

IntroductionBIRC5 (Survivin) is a crucial anti-apoptotic protein overexpressed in various cancers, promoting tumor growth and treatment resistance. This study investigates its expression across 33 cancer types and explores its diagnostic, prognostic, and immune-related significance.MethodsWe analyzed RNA-seq data from TCGA and protein expression data from the Human Protein Atlas. Expression levels were compared between tumor and normal tissues. Correlations with molecular and immune subtypes were explored using TISIDB. Prognostic significance was evaluated through survival analysis, Cox regression, and ROC curve analysis. The PPI network was constructed using STRING.ResultsBIRC5 was significantly overexpressed in tumor tissues across 33 cancer types, with higher expression levels observed in tumors compared to normal tissues. The protein expression analysis revealed a similar trend. BIRC5 expression was significantly correlated with various molecular and immune subtypes in multiple cancer types. Survival analysis indicated that high BIRC5 expression was associated with poor prognosis across multiple cancers, including lung adenocarcinoma (LUAD) and kidney renal clear cell carcinoma (KIRC). ROC analysis showed that BIRC5 exhibited strong diagnostic potential, with high AUC values (>0.9) in several cancers. The PPI network analysis identified key interacting proteins involved in the cell cycle and tumor progression, further supporting BIRC5's role in cancer biology. Functional experiments in lung adenocarcinoma (LUAD) revealed that BIRC5 upregulation enhances cell proliferation, migration, and invasion, while its knockdown suppresses these activities.DiscussionBIRC5 is a promising diagnostic and prognostic biomarker in multiple cancers. Its association with immune subtypes suggests a potential role in the tumor immune microenvironment. These findings support BIRC5 as a therapeutic target for cancer treatment.

Routine blood biochemical biomarkers in amyotrophic lateral sclerosis: Systematic review and cohort analysis

Introduction: Blood biochemical biomarkers, including urate, creatinine, albumin, and creatine kinase, have been shown to be useful in ALS. To provide further information about the roles of these four biomarkers roles we performed a systematic review. In addition, we also performed a new study of the role of these biomarkers in predicting survival, using data from our local ALS cohort. Methods: (1) Using established databases and other sources, we searched for papers about the use of urate, creatinine, albumin, and creatine kinase as biomarkers in ALS. Included articles were reviewed for information about biomarker levels in ALS and controls, association with markers of functional decline, and survival. (2) For our local ALS cohort, we performed survival analysis, Cox-proportionate-hazard ratio and ROC curves to investigate the use of these biomarkers in predicting survival. Results: (1) For systematic review, 104 papers were included. There was some variability in the findings. For urate, there was evidence of decreased levels in ALS, with higher levels associated ith longer survival. For creatinine, there was evidence of decreased levels in ALS, and higher levels correlated with longer survival. For albumin, some reports of reduced levels in ALS, but no consistent association with survival. For creatine kinase, some reports of increased levels in ALS, with inconsistent association with survival. (2) For the local ALS cohort there was evidence that urate and creatinine were associated with survival, but no significant association with survival. There was less evidence for albumin and CK. Discussion: This study provides support for further studies of these readily available biochemical measurement as bioamerkers in ALS.

Antibacterial Effect of Lime Juice Against Streptococcus suis and Other Bacteria in Minced Pork.

Streptococcus suis is a significant zoonotic pathogen, capable of causing severe illnesses such as septicemia and meningitis in both swine and humans. Its transmission through pork consumption necessitates effective food safety measures. Lime juice, known for its antimicrobial properties, presents a potential alternative to reduce S. suis contamination in pork products. This study investigated the antibacterial efficacy of lime juice specifically against S. suis and its potential to reduce bacterial contamination in minced pork, aiming to determine optimal treatment parameters for mitigating S. suis transmission through pork consumption. Seven strains of S. suis representing serotypes known to cause zoonotic disease were cultured, and lime juice was prepared. The minimum inhibitory concentration and minimum bactericidal concentration tests consistently demonstrated the antibacterial effect of lime juice against S. suis. Survival curve analyses showed significant bacterial reduction within 15 min at 25% (v/v) lime juice concentration. In minced pork, lime juice caused a notable decrease in total bacteria and S. suis counts after 15 min. This study demonstrates the potential of lime juice as an antibacterial agent against a representative strain of S. suis in pork. However, the results also highlight that lime juice alone may not eliminate all viable bacteria. Therefore, incorporating lime juice treatment together with proper cooking practices remains crucial to ensure safe consumption of pork products.

Traditional Chinese medicine application and evidence analysis of 1426 cases of long COVID: A retrospective study

BackgroundIn recent years, with the increase in COVID-19 infection cases, the focus of treatment and rehabilitation has shifted to symptoms and complications that arise during the recovery period. Traditional Chinese medicine (TCM) has certain advantages in treatment and rehabilitation, but there is currently a lack of systematic clinical research. ObjectiveTo preliminarily evaluate the clinical efficacy of integrated Chinese and Western medical treatment plans and to describe the characteristics of "long COVID'' syndromes. MethodsEligible "long COVID'' patients diagnosed between December 2022 and June 2023 at the Third Affiliated Hospital of Zhejiang Chinese Medical University were identified through electronic medical records. Baseline demographic information was collected, and patients were divided into a TCM group and a Western medicine group based on their treatment methods. The primary outcome for efficacy evaluation was TCM syndrome scores and symptom improvement time, with secondary outcomes including symptom improvement rates. Survival curve analysis was used to assess symptom improvement time, and a Cox regression model was employed to identify potential risk factors affecting symptom prognosis. Chi-square tests were used to compare binary data between or among groups. The Jaccard method was applied for systematic clustering of TCM syndromes, and clinical characteristics at the start and end of treatment were reported. ResultsAnalysis showed that younger patients, those with underweight, and those receiving TCM treatment had shorter recovery times (P < 0.0001). Systematic clustering of the main symptom elements (33 elements with a frequency ≥ 5 %) identified six core combinations. The main symptoms of long COVID included cough, excessive sputum, nasal congestion, chest tightness, sore throat, and fatigue. Notably, symptoms such as fatigue, shortness of breath, and loss of appetite, classified as deficiency syndromes in TCM, had longer recovery times. ConclusionElderly, obesity, underlying diseases, and exclusive Western medicine treatment were associated with poorer prognosis. The early recovery period is characterized by a mix of deficiency and excess syndromes, whereas the later period is dominated by deficiency syndromes. TCM treatment can effectively improve clinical symptoms and shorten symptom duration, but further research is needed.

Onset of age, site and respiratory symptoms are strongly associated with respiratory decline in sporadic amyotrophic lateral sclerosis: a long-term longitudinal study

ObjectiveThe objective of this study is to identify factors influencing progression of respiratory decline from the onset of neurological symptoms to respiratory failure in patients with amyotrophic lateral sclerosis (ALS).MethodsIn...

Evaluating KLHDC8A as a biomarker for glioma: impact on survival and proliferation through the Wnt/β-catenin pathway

Glioma is one of the most common malignant tumors and shows a high metastasis rate and poor prognosis. KLHDC8A has been implicated in several cancers, but its role in glioma and its potential as a biomarker remain unclear. We conducted a pan-cancer analysis of KLHDC8A using multiple databases and assessed its expression levels, survival associations, and diagnostic utility. Kaplan–Meier survival analysis and Receiver Operating Characteristic (ROC) curves were utilized to evaluate KLHDC8A’s prognostic and diagnostic potential. Additionally, we investigated KLHDC8A's role in glioma cell proliferation and its involvement in the Wnt/β-catenin signaling pathway. Our pan-cancer analysis revealed significant dysregulation of KLHDC8A across various tumors. Elevated KLHDC8A expression was associated with poor overall survival (OS) in cancers including ACC, KIRP, LGG, and STAD. Disease-specific survival (DSS) analysis indicated worse outcomes in ACC, KIRP, and LGG with high KLHDC8A levels. ROC curve analysis demonstrated KLHDC8A's strong diagnostic potential for glioblastoma multiforme (GBM) and lower-grade gliomas (LGG), with AUC values of 0.920 and 0.748, respectively. In glioma patients, high KLHDC8A expression correlated with shorter OS and DSS in LGG, while no significant correlation was observed in GBM. Multivariate Cox regression confirmed KLHDC8A as an independent prognostic factor for OS and DSS in LGG. Knockdown experiments in glioma cell lines U251 and U87 showed that KLHDC8A silencing led to reduced cell proliferation and colony formation, which was associated with downregulation of key proteins in the Wnt/β-catenin pathway. Overall, KLHDC8A is a promising biomarker for the diagnosis and prognosis of LGG, and its high expression is linked to poor survival outcomes. The gene's involvement in the Wnt/β-catenin signaling pathway underscores its role in glioma progression, highlighting KLHDC8A’s potential as a therapeutic target and diagnostic marker, particularly for LGG.

Integrated multi-omics and machine learning reveal a gefitinib resistance signature for prognosis and treatment response in lung adenocarcinoma.

Gefitinib resistance (GR) presents a significant challenge in treating lung adenocarcinoma (LUAD), highlighting the need for alternative therapies. This study explores the genetic basis of GR to improve prediction, prevention, and treatment strategies. We utilized public databases to obtain GR gene sets, single-cell data, and transcriptome data, applying univariate and multivariate regression analyses alongside machine learning to identify key genes and develop a predictive signature. The signature's performance was evaluated using survival analysis and time-dependent ROC curves on internal and external datasets. Enrichment and tumor immune microenvironment analyses were conducted to understand the mechanistic roles of the signature genes in GR. Our analysis identified a robust 22-gene signature with strong predictive performance across validation datasets. This signature was significantly associated with chromosomal processes, DNA replication, immune cell infiltration, and various immune scores based on enrichment and tumor microenvironment analyses. Importantly, the signature also showed potential in predicting the efficacy of immunotherapy in LUAD patients. Moreover, we identified alternative agents to gefitinib that could offer improved therapeutic outcomes for high-risk and low-risk patient groups, thereby guiding treatment strategies for gefitinib-resistant patients. In conclusion, the 22-gene signature not only predicts prognosis and immunotherapy efficacy in gefitinib-resistant LUAD patients but also provides novel insights into non-immunotherapy treatment options.

Integrating multi-omics techniques and in vitro experiments reveals that GLRX3 regulates the immune microenvironment and promotes hepatocellular carcinoma cell proliferation and invasion through iron metabolism pathways.

Hepatocellular carcinoma (HCC) is a common malignancy worldwide, and its development is closely related to abnormalities in iron metabolism. This study aims to systematically analyze changes in iron metabolism in the tumor microenvironment of HCC using single-cell sequencing technology, and investigate the potential mechanisms by which iron metabolism regulation affects the survival of liver cancer patients. Single-cell sequencing data from hepatocellular carcinoma patients were obtained from the GEO database. By iron metabolism genomic scoring, we assessed differences in iron metabolism levels in hepatocellular carcinoma samples. By cell communication analysis as well as GO and KEGG enrichment analysis, we determined the functional role of iron metabolism in different cell types. We used survival analysis and Kaplan-Meier curves to assess the impact of iron metabolism levels on patient prognosis. In addition, we identified and analyzed the expression profile of the GLRX3 gene, investigated its key regulatory role in iron metabolism, and validated its clinical value as a prognostic marker. Finally, we explored the effect of GLRX3 on hepatocellular carcinoma phenotype by in vitro experiments such as PCR, transwell, CCK8, and wound healing assay. Bioinformatics results and experimental validation confirmed the dysregulation of iron metabolism in the development of hepatocellular carcinoma, revealing iron's regulatory influence across various cell types. Additionally, GLRX3 was identified as a key regulatory factor in iron metabolism, and the mechanism by which GLRX3 regulates tumor cell proliferation and immune evasion was determined. Furthermore, experiments verified GLRX3's role in facilitating tumor cell proliferation and invasion. This study highlights the critical role of iron metabolism in the progression of hepatocellular carcinoma, particularly the regulatory mechanism of the GLRX3 gene in tumor cell proliferation and immune evasion. Iron metabolism abnormalities are not only drivers of liver cancer development but also key indicators of patient prognosis.

The systemic immune-inflammation index and systemic inflammation response index are useful for predicting mortality in patients with diabetic nephropathy

BackgroundThis study investigated the correlation between the systemic immune-inflammation index (SII) and the systemic inflammation response index (SIRI) and all-cause, cardiovascular, and kidney disease mortality in patients with diabetic nephropathy (DN). It aimed to provide a new predictive assessment tool for the clinic and a scientific basis for managing inflammation in DN.MethodsThe data utilized in this study were obtained from the National Health and Nutrition Examination Survey (NHANES) database, spanning 1999 to 2018. A total of 2641 patients diagnosed with DN were included in the analysis. The association between SII and SIRI levels and mortality in patients with DN was investigated using multivariate Cox proportional risk regression models. These relationships were further validated by Kaplan-Meier survival curves and restricted cubic spline (RCS) modeling, and subgroup analyses were performed to explore the heterogeneity among different characteristic subgroups.ResultsThe multivariate Cox regression analysis indicated that SII and SIRI levels were independently associated with all-cause mortality and cardiovascular mortality in patients with DN. SIRI levels were found to be an independently associated factor with kidney disease mortality in patients with DN. Patients in the highest quartile of SII and SIRI exhibited a 1.49-fold and 1.62-fold increased risk of all-cause mortality, respectively, compared to patients in the lowest quartile. The risk of cardiovascular mortality was 1.31 and 1.73 times higher than that in patients in the lowest quartile, respectively. The risk of kidney disease mortality in patients in the highest quartile of SIRI was 2.74 times higher than that in patients in the lowest quartile. Kaplan-Meier survival curve and RCS analyses further confirmed the positive association between SII and SIRI and mortality and a significant nonlinear relationship between SII and all-cause mortality. The SII and SIRI indices offer incremental value in model predictive power for mortality in patients with DN. Subgroup analyses demonstrated that the correlation between SII and SIRI and mortality risk was stable but heterogeneous across different subgroups.ConclusionSII and SIRI can be utilized as biomarkers for forecasting the likelihood of all-cause and cardiovascular mortality in patients with DN.

Predicting survival in bladder cancer with a novel apoptotic gene-related prognostic model

BackgroundApoptosis and apoptotic genes play a critical role in the carcinogenesis and progression of bladder cancer. However, there is no prognostic model established by apoptotic genes.MethodsMessenger RNA (mRNA), Expression data, and related clinical data were obtained from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. After extracting the apoptosis-related genes, the survival-related apoptosis genes were screened by univariate Cox regression analysis in the TCGA cohort. Following the Least Absolute Shrinkage and Selection Operator (LASSO) regression method, these genes were modeled by multivariate Cox analysis. The predictive abilities of the Apoptosis-Related Gene Model (ARGM) for overall survival (OS) rate, disease-specific survival (DSS) measures, and progression-free survival (PFS) were verified by the Kaplan–Meier(K-M)survival analysis and time-dependent Receiver Operating Characteristic (ROC) curve. Functional enrichment analyses were performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG). CIBERSORT and Single-Sample Gene Set Enrichment Analysis (ssGSEA) were used to calculate the type of immune cell infiltration and immune functions. The model’s predictive ability for immunotherapy were evaluated using Tumor Immune Dysfunction and Exclusion (TIDE) and the Imvigor210 study.The single-cell sequencing was used to display the expression level of the ARGM.Finally,qRT-PCR was executed to validate the expression level of ARGM.ResultsSeveral apoptosis genes were identified through the model, including ANXA1, CASP6, CD2, F2, PDGFRB, SATB1, and TSPO. The prognostic value of the model for OS, DSS, and PFS were verified using the TCGA and GEO cohort. The model can predict patient response to immunotherapy treatment as established through the model’s score which was linked to different types of immune cell infiltration and identified significant differences in the signal pathways between high-risk and low-risk groups. Nomogram variables, prompted from ARGM and clinical parameters, also generate a high predictive value for patient survival.ConclusionOurestablished apoptosis-related gene model (ARGM) has a substantial predictive value for prognosis and immunotherapy of bladder cancer. It may help with clinical consultation, clinical stratification, and treatment selection. The immune infiltration status and signal pathway of different risk groups also provide direction for further research.

Deciphering molecular landscape of breast cancer progression and insights from functional genomics and therapeutic explorations followed by in vitro validation

Breast cancer is caused by aberrant breast cells that proliferate and develop into tumors. Tumors have the potential to spread throughout the body and become lethal if ignored. Metastasis is the process by which invasive tumors move to neighboring lymph nodes or other organs. Metastasis can be lethal and perhaps fatal. The objective of our study was to elucidate the molecular mechanisms underlying the transition of Ductal Carcinoma In Situ (DCIS) to Invasive Ductal Carcinoma (IDC), with a particular focus on hub genes and potential therapeutic agents. Using Weighted Gene Co-expression Network Analysis (WGCNA), we built a comprehensive network combining clinical and phenotypic data from both DCIS and IDC. Modules within this network, correlated with specific phenotypic traits, were identified, and hub genes were identified as critical markers. Receiver Operating Characteristic (ROC) analysis assessed their potential as biomarkers, while survival curve analysis gauged their prognostic value. Furthermore, molecular docking predicted interactions with potential therapeutic agents. Ten hub genes—CDK1, KIF11, NUF2, ASPM, CDCA8, CENPF, DTL, EXO1, KIF2C, and ZWINT—emerged as pivotal fibroblast-specific genes potentially involved in the DCIS to IDC transition. These genes exhibited pronounced positive correlations with key pathways like the cell cycle and DNA repair, Molecular docking revealed Fisetin, an anti-inflammatory compound, effectively binding to both CDK1 and DTL underscoring their role in orchestrating cellular transformation. CDK1 and DTL were selected for molecular docking with CDK1 inhibitors, revealing effective binding of Fisetin, an anti-inflammatory compound, to both. Of the identified hub genes, DTL—an E3 ubiquitin ligase linked to the CRL4 complex—plays a central role in cancer progression, impacting tumor growth, invasion, and metastasis, as well as cell cycle regulation and epithelial-mesenchymal transition (EMT). CDK1, another hub gene, is pivotal in cell cycle progression and associated with various biological processes. In conclusion, our study offers insights into the complex mechanisms driving the transition from DCIS to IDC. It underscores the importance of hub genes and their potential interactions with therapeutic agents, particularly Fisetin. By shedding light on the interplay between CDK1 and DTL expression, our findings contribute to understanding the regulatory landscape of invasive ductal carcinoma and pave the way for future investigations and novel therapeutic avenues.

Risk factors of surgical compliance and impact on survival in patients with kidney cancer: a population-based, propensity score matching study.

To identify the impact of surgical compliance on survival in patients with kidney cancer and to explore the factors that influence surgical compliance. Clinical date of kidney cancer patients were identified from the SEER databases, and the patients were divided into surgical compliance group and surgical noncompliance group. Cox survival analysis and Kaplan-Meier curves were used to evaluate the effect of surgical compliance on overall survival (OS) and cancer-specific survival (CSS). A diagnostic nomogram was constructed to quantify individual differences in compliance, and receiver operating characteristics (ROC) curves and calibration curves were used to assess the accuracy of the nomogram. Propensity score matching (PSM) was performed to balance potential baseline confounding factors. Of the 133,950 patients eligible for surgical resection, 2,814 (2.1%) patients did not opt for surgery ultimately. Surgical noncompliance was associated with poor prognosis. In all patients, Cox regression analysis showed that surgical noncompliance was an independent predictor for OS [before: HR = 2.490, 95% CI 2.374-2.612, p < 0.001; after: HR = 2.380, 95% CI 2.202-2.573, p < 0.001] and CSS [before: HR = 2.490, 95% CI 2.318-2.675, p < 0.001; after: HR = 2.035, 95% CI 1.813-2.285, p < 0.001] of kidney cancer patients before and after PSM. Multivariable logistic regression revealed that older age, afro-american origin, lower household income, and advanced tumor grade were associated with surgical noncompliance. The ROC and calibration curves showed that the diagnostic nomogram had high predictive accuracy. Surgical compliance was an independent prognostic factor for OS and CSS in patients with kidney cancer, and surgical noncompliance was associated with poor survival.

Abstract 4136289: Aortic Valve Calcium as a Predictor of Chronic Kidney Disease in a Multi-Ethnic Cohort: The MESA Study

Background: Aortic valve calcium (AVC) is associated with an increased risk of cardiovascular disease, non-cardiovascular disease such as dementia, and all-cause mortality. Traditional atherosclerotic cardiovascular disease risk factors are associated with both AVC and chronic kidney disease (CKD), but whether there is an association between AVC and CKD is unknown. Objectives: To ascertain whether AVC quantified by cardiac CT scanning is independently associated with the long-term risk of incident CKD among individuals without a previous history of cardiovascular disease. Methods: We examined 6,346 Multi-Ethnic Study of Atherosclerosis (MESA) participants who underwent cardiac CT scanning at Visit 1 (2000-02) and had an eGFR of ≥ 60 mL/min/1.73 m 2 . AVC was quantified using the Agatston method and categorized as 0, 1-99, and ≥100. Incident CKD was defined as an eGFR &lt; 60 mL/min/1.73 m 2 accompanied with an at least 40% decline in eGFR from baseline, and/or a diagnosis of CKD and indicators of end stage renal disease extracted from hospital records using the International Classification of Disease (ICD) codes. We performed Kaplan-Meier survival curve analyses along with multivariable Cox proportional hazard regression models, adjusted for age, gender, race/ethnicity, highest level of education and traditional cardiovascular risk factors along with coronary artery calcium (CAC), lipoprotein (a) (Lp[a]), and the APOE-ε4 genotype to examine the association between AVC (categorical and log-transformed) and incident CKD. Results: Participants had a mean age 62.2±10.1 years, 53% were women, and AVC &gt;0 was present in 795 (12%) participants. During a median follow-up time of 16.9 years, 982 (15%) participants developed incident CKD. AVC examined as a continuous variable was associated with a significantly increased risk of developing CKD (per log-unit [AVC+1] HR 1.06 [95% CI: 1.02-1.10]; p = 0.005). There was a stepwise increased risk for CKD with higher AVC levels ( Figure ). Similarly, in the multivariable adjusted Cox models, participants with AVC ≥100 had a higher risk of incident CKD, compared with the AVC=0 group (HR 1.48 [95% CI: 1.15-1.89]; p = 0.002). The observed associations remained after further adjusting for CAC score ( p = 0.024), Lp (a) ( p = 0.004), and the APOE-ε4 genotype ( p = 0.004). Conclusions: In a multi-ethnic cohort of participants free of CKD at baseline, AVC was independently associated with a higher risk of incident CKD.

PATH-33. THE IMPACT OF THE MOLECULAR CLASSIFICATION IN HISTOLOGICALLY DIAGNOSED GLIOBLASTOMAS

Abstract OBJECTIVE Glioblastomas have been diagnosed based on traditional histological findings (necrosis, microvascular proliferation) and molecular genetic findings (IDH-wildtype, H3-wildtype, TERT promoter mutation, EGFR amplification, +7/−10 chromosome copy-number changes). Thus, IDH-wildtype and H3-wildtype glioblastomas can be classified into those with molecular genetic findings (defined as molecular glioblastoma) and those with only histological findings (defined as non-molecular glioblastoma). We re-evaluated the molecular genetics of patients diagnosed histologically with glioblastoma at our institution and examined their characteristics. METHODS A total of 275 cases diagnosed histologically as glioblastoma between December 2000 and March 2022 were included. Sanger sequencing (IDH1/2, Histone H3, TERTp) and MLPA for copy number analysis (PDGFRA, EGFR, CDKN2A, PTEN, MDM2, CDK4, TP53) were performed. The genetic analysis results and survival periods were analyzed. RESULTS Among the 275 cases, 151 initial glioblastomas met the criteria of IDH wild-type and H3 wild-type. Genetic analysis results that could be evaluated were obtained in 89 of these cases. TERT promoter mutation was present in 43 cases (48%), with 37% (16 cases) showing EGFR amplification and 19% (8 cases) showing EGFR gain/PTEN loss (+7/-10) simultaneously. On the other hand, TERT promoter wild-type was found in 45 cases (51%), with EGFR amplification in only 1 case (2%) and EGFR ain/PTEN loss in 4 cases (9%), and these 5 cases were judged to correspond to molecular glioblastoma. Adding one case with TERT promoter evaluation failure but EGFR amplification, the integrated molecular/non-molecular glioblastoma cases were 49 (55%)/40 (45%), revealing that both tumor types exist in nearly equal proportions. Survival curve analysis showed that median overall survivals of molecular and non-molecular glioblastoma were 17 and 29 months, respectively, showing a two-fold difference, and non-molecular glioblastoma had significantly better prognosis (P=0.0136). CONCLUSION It was revealed that tumors diagnosed as glioblastoma based on conventional histological findings can be broadly classified into molecular and non-molecular glioblastoma, with significant differences in prognosis.

QTc interval prolongation and risk of atrial fibrillation recurrence: a meta-analysis and observational cohort study.

Corrected QT interval (QTc) is a ventricular repolarization marker on electrocardiography. Previous studies evaluated its value in predicting atrial fibrillation (AF) occurrence. However, its predictive efficacy for AF recurrence remains controversial. We searched PubMed and Google databases for studies before January 2024 evaluating the association between QTc interval and AF incidence. A meta-analysis of the eligible datasets was conducted using Bazett's formula, with subgroup analysis to explore the heterogeneity. Additionally, thirty-eight patients with AF who underwent radiofrequency catheter ablation were enrolled and followed-up for 3-36 months. Univariate and multivariate Cox models were used to calculate the hazard ratios (HRs) and determine the relationship between clinical factors and AF recurrence. Kaplan-Meier survival analysis and ROC curve were conducted to assess the impact and predictive efficacy of individual factors. Eleven datasets from nine eligible studies were enrolled and meta-analysed. We found that patients with prolonged QTc interval was associated with a significantly higher AF incidence risk, and the risk increased with every 10-ms prolongation. However, this association was not significant in the AF recurrence subgroup. In our prospective cohort, the preoperative body mass index, QTc, left atrial diameter (LAD), and uric acid levels influenced AF recurrence. Multivariate Cox regression analysis identified LAD as an independent factor affecting AF recurrence in patients with a high predictive efficiency. Kaplan-Meier survival analysis showed that increased LAD (>4.5 cm) was associated with postoperative AF recurrence. Therefore, LAD has better predictive power and can be an indicator for predicting postoperative AF recurrence.

Interpretable prediction of 30-day mortality in patients with acute pancreatitis based on machine learning and SHAP.

Severe acute pancreatitis (SAP) can be fatal if left unrecognized and untreated. The purpose was to develop a machine learning (ML) model for predicting the 30-day all-cause mortality risk in SAP patients and to explain the most important predictors. This research utilized six ML methods, including logistic regression (LR), k-nearest neighbors(KNN), support vector machines (SVM), naive Bayes (NB), random forests(RF), and extreme gradient boosting(XGBoost), to construct six predictive models for SAP. An extensive evaluation was conducted to determine the most effective model and then the Shapley Additive exPlanations (SHAP) method was applied to visualize key variables. Utilizing the optimized model, stratified predictions were made for patients with SAP. Further, the study employed multivariable Cox regression analysis and Kaplan-Meier survival curves, along with subgroup analysis, to explore the relationship between the machine learning-based score and 30-day mortality. Through LASSO regression and recursive feature elimination (RFE), 25 optimal feature variables are selected. The XGBoost model performed best, with an area under the curve (AUC) of 0.881, a sensitivity of 0.5714, a specificity of 0.9651 and an F1 score of 0.64. The first six most important feature variables were the use of vasopressor, high Charlson comorbidity index, low blood oxygen saturation, history of malignant tumor, hyperglycemia and high APSIII score. Based on the optimal threshold of 0.62, patients were divided into high and low-risk groups, and the 30-day survival rate in the high-risk group decreased significantly. COX regression analysis further confirmed the positive correlation between high-risk scores and 30-day mortality. In the subgroup analysis, the model showed good risk stratification ability in patients with different gender, renal replacement therapy and with or without a history of malignant tumor, but it was not effective in predicting peripheral vascular disease. the XGBoost model effectively predicts the severity of SAP, serving as a valuable tool for clinicians to identify SAP early.

Analysis of treatment-free remission outcomes in patients with chronic myeloid leukemia who received sequential nilotinib therapy after achieving deep molecular response to imatinib

Objective: To analyze the treatment-free remission (TFR) outcomes in patients with chronic myeloid leukemia (CML) treated sequentially with nilotinib (NIL) after achieving deep molecular response (DMR) to imatinib (IM). Methods: Retrospectively enrolled 103 CML patients from 6 hematological centers in Henan Province who chose sequential NIL therapy or continued IM therapy after achieving DMR to first-line IM from June 2, 2013 to August 30, 2022. Among them, 42 cases were treated with sequential NIL and 61 cases continued IM therapy. The 42 patients in the sequential NIL group were further divided into 3 subgroups based on the duration of DMR at switching to sequential NIL therapy: Group 1 (17 cases): DMR duration<12 months at switching to sequential NIL therapy; Group 2 (8 cases): DMR duration≥12 months to<24 months at switching to sequential NIL therapy; Group 3 (17 cases): DMR duration≥24 months at switching to sequential NIL therapy. Follow-up ended on January 9, 2024, with a median follow-up of 40 (16, 91) months for the sequential NIL group and 49 (21, 123) months for the continuous IM group. Survival curves were plotted using the Kaplan-Meier method and the log-rank test was performed to compare the TFR rates between groups. Results: There were 19 males and 23 females with a median age [M (Q1, Q3)] of 43 (31, 50) years in the sequential NIL group. There were 32 males and 29 females with a median age of 41 (31, 50) years in the continuous IM group. Kaplan-Meier survival curve analysis showed that the TFR rate was higher in the sequential NIL group than in the continuous IM group (88.1% vs 63.9%, P=0.005). The results of subgroup analysis showed that the TFR rates in Group 1, Group 2 and Group 3 were 94.1%, 87.5% and 82.4%, respectively, with no statistically significant differences (all P>0.05).The TFR rate in Group 1 was higher than in the continued IM group (P=0.017), and there were no statistically significant differences in Group 2 and Group 3 compared with the continuous IM group(all P>0.05). Conclusion: Sequential NIL therapy after achieving DMR with IM therapy can improve the TFR rate in CML patients, especially in those with DMR duration<12 months before switching to sequential NIL therapy.

In vitro killing effect of berberine and niclosamide on ocular Demodex folliculorum

PurposeTo explore the in vitro killing effect of water-soluble berberine and lipid-soluble niclosamide against ocular Demodex folliculorum. MethodsDemodex with good vigor were collected from patients’ eyelashes. These mites were randomly distributed into different groups with 20 mites in each group. Saline, Double Distilled Water (DDW), Polysorbate 80 (TWEEN 80), Polyethylene glycol 300 (PEG 300) and Castor Oil were used to screen solvents and cosolvents. 20 % Tea Tree Oil (TTO) and Anhydrous Ethanol (EtOH) were used as positive controls. 0.2 % Berberine, 0.25 % Niclosamide and 0.5 % Niclosamide, were designated as experimental groups. Following treatment, the analysis of Kaplan-Meier survival curves and survival time of mites and safety of drugs were then performed. ResultsThe survival of Demodex in vitro in Saline and DDW, was not significant different. Therefore, DDW, which was more conducive to the dissolution of berberine, was chosen as the solvent for berberine. 0.2 % Berberine significantly inhibited the survival distribution and survival time (P < 0.001) of Demodex in vitro compared with the DDW group. Through the evaluation of several cosolvents, PEG300 had milder effects on Demodex. Hence, the proportion of PEG300 in the niclosamide solvent group was increased to reduce the irritability of the vehicle. Furthermore, niclosamide could significantly inhibit the survival of Demodex compared with the vehicle group, and the effect of 0.5 % Niclosamide was more obvious (P < 0.001), and was better than 20 %TTO (P < 0.001). In addition, after niclosamide administration, Demodex bodies exhibited gradual distortion along with increased transparency and the presence of blurred dark particles compared to those in the vehicle group. Moreover, both drugs showed good subjective tolerability and safety in a mouse model. Conclusion0.2 % berberine and 0.5 % niclosamide effectively inhibited Demodex survival in vitro, with 0.5 % niclosamide superior to 20 % TTO. These two drugs, with anti-Demodex, anti-bacterial, and anti-inflammatory properties, may offer alternative treatment for Demodex blepharitis.

Identification and Validation of a Novel PANoptosis-related Gene Signatured for Osteosarcoma as Prognostic Model.

To construct and validate a prognostic model for osteosarcoma prognostication and therapeutic potential of PANoptosis- related genes. Observational study. Place and Duration of the Study: Department of Orthopaedics, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China, from August 2021 to January 2024. Transcriptomic data from the GEO and TARGET databases were utilised to construct and validate a prognostic model for osteosarcoma. The analysis involved the use of the LASSO Cox-regression method with the Glmnet R package to identify key PANoptosis-related genes. Differential gene expression analysis was conducted using the Limma R package, and model validation was performed using Kaplan-Meier survival analysis and time-dependent ROC curves. This model, derived from five key PANoptosis-related genes, demonstrated significant predictive capability for patient survival across training and validation cohorts. Further analysis confirmed the model's effectiveness and identified metastasis stage and risk scores as the robust independent prognostic indicators. The prognostic model offers a novel tool for osteosarcoma prognostication and underscores the therapeutic potential of targeting PANoptosis-related pathways. PANoptosis-related genes, Osteosarcoma, Prognosis, Bioinformatics, Tumour micro-environment.