Reliable Predictive Tool Research Articles

GraphscoreDTA: optimized graph neural network for protein-ligand binding affinity prediction.

Computational approaches for identifying the protein-ligand binding affinity can greatly facilitate drug discovery and development. At present, many deep learning-based models are proposed to predict the protein-ligand binding affinity and achieve significant performance improvement. However, protein-ligand binding affinity prediction still has fundamental challenges. One challenge is that the mutual information between proteins and ligands is hard to capture. Another challenge is how to find and highlight the important atoms of the ligands and residues of the proteins. To solve these limitations, we develop a novel graph neural network strategy with the Vina distance optimization terms (GraphscoreDTA) for predicting protein-ligand binding affinity, which takes the combination of graph neural network, bitransport information mechanism and physics-based distance terms into account for the first time. Unlike other methods, GraphscoreDTA can not only effectively capture the protein-ligand pairs' mutual information but also highlight the important atoms of the ligands and residues of the proteins. The results show that GraphscoreDTA significantly outperforms existing methods on multiple test sets. Furthermore, the tests of drug-target selectivity on the cyclin-dependent kinase and the homologous protein families demonstrate that GraphscoreDTA is a reliable tool for protein-ligand binding affinity prediction. The resource codes are available at https://github.com/CSUBioGroup/GraphscoreDTA.

Development and validation of prognostic nomogram for malignant pleural mesothelioma

Objective: To development the prognostic nomogram for malignant pleural mesothelioma (MPM). Methods: Two hundred and ten patients pathologically confirmed as MPM were enrolled in this retrospective study from 2007 to 2020 in the People's Hospital of Chuxiong Yi Autonomous Prefecture, the First and Third Affiliated Hospital of Kunming Medical University, and divided into training (n=112) and test (n=98) sets according to the admission time. The observation factors included demography, symptoms, history, clinical score and stage, blood cell and biochemistry, tumor markers, pathology and treatment. The Cox proportional risk model was used to analyze the prognostic factors of 112 patients in the training set. According to the results of multivariate Cox regression analysis, the prognostic prediction nomogram was established. C-Index and calibration curve were used to evaluate the model's discrimination and consistency in raining and test sets, respectively. Patients were stratified according to the median risk score of nomogram in the training set. Log rank test was performed to compare the survival differences between the high and low risk groups in the two sets. Results: The median overall survival (OS) of 210 MPM patients was 384 days (IQR=472 days), and the 6-month, 1-year, 2-year, and 3-year survival rates were 75.7%, 52.6%, 19.7%, and 13.0%, respectively. Cox multivariate regression analysis showed that residence (HR=2.127, 95% CI: 1.154-3.920), serum albumin (HR=1.583, 95% CI: 1.017-2.464), clinical stage (stage Ⅳ: HR=3.073, 95% CI: 1.366-6.910) and the chemotherapy (HR=0.476, 95% CI: 0.292-0.777) were independent prognostic factors for MPM patients. The C-index of the nomogram established based on the results of Cox multivariate regression analysis in the training and test sets were 0.662 and 0.613, respectively. Calibration curves for both the training and test sets showed moderate consistency between the predicted and actual survival probabilities of MPM patients at 6 months, 1 year, and 2 years. The low-risk group had better outcomes than the high-risk group in both training (P=0.001) and test (P=0.003) sets. Conclusion: The survival prediction nomogram established based on routine clinical indicators of MPM patients provides a reliable tool for prognostic prediction and risk stratification.

Abiotic Reduction of Organic and Inorganic Compounds by Fe(II)-Associated Reductants: Comprehensive Data Sets and Machine Learning Modeling.

Iron-associated reductants play a crucial role in providing electrons for various reductive transformations. However, developing reliable predictive tools for estimating abiotic reduction rate constants (logk) in such systems has been impeded by the intricate nature of these systems. Our recent study developed a machine learning (ML) model based on 60 organic compounds toward one soluble Fe(II)-reductant. In this study, we built a comprehensive kinetic data set covering the reactivity of 117 organic and 10 inorganic compounds toward four major types of Fe(II)-associated reductants. Separate ML models were developed for organic and inorganic compounds, and the feature importance analysis demonstrated the significance of resonance structures, reducible functional groups, reductant descriptors, and pH in logk prediction. Mechanistic interpretation validated that the models accurately learned the impact of various factors such as aromatic substituents, complexation, bond dissociation energy, reduction potential, LUMO energy, and dominant reductant species. Finally, we found that 38% of the 850,000 compounds in the Distributed Structure-Searchable Toxicity (DSSTox) database contain at least one reducible functional group, and the logk of 285,184 compounds could be reasonably predicted using our model. Overall, the study is a significant step toward reliable predictive tools for anticipating abiotic reduction rate constants in iron-associated reductant systems.

Strengthening of clay brick masonry wall with spraying polyurea for repeated blast resistance

As one of the main components of a building, the collapse of masonry walls under blast load and the ejection of debris were essential factors that caused injured and damage to internal personnel and facilities. In terrorist bombing attacks, masonry walls will likely undergo multiple blast loads. It is a significant problem to develop new protection technology to enhance the resistance of masonry walls to multiple blast loads. In this study, the anti-blast protection technology of spraying polyurea is proposed, and a numerical model was developed to accurately predict the dynamic response and damage behavior of unreinforced masonry wall (UMW) and polyurea reinforced walls (PRWs) under repeated blast loads. A detailed micro modeling approach was used to establish a numerical model, and the accuracy was verified by the field test results. The failure mechanism and protection mechanism of the polyurea under repeated blast loads were clarified with microscopic detection technology. Finally, through parametric analysis, the damage behavior of PRW under close-in extreme repeated blast loads was studied. According to the calculation results of the numerical model, the damage level diagrams under different scaled distances were established. The results showed that when the scaled distance was no less than 0.25 m/kg1/3, the existence of the polyurea coatings could provide reliable protection for the wall without catastrophic damage. The contribution is to provide a reliable numerical prediction tool and a reference guide for engineering design that can predict the dynamic response of PRW under repeated blast load, and to provide a scientific basis for the practical application of polyurea in building anti-blast.

Development and Validation of a Prediction Model for Future Estimated Glomerular Filtration Rate in People With Type 2 Diabetes and Chronic Kidney Disease

Type 2 diabetes increases the risk of progressive diabetic kidney disease, but reliable prediction tools that can be used in clinical practice and aid in patients' understanding of disease progression are currently lacking. To develop and externally validate a model to predict future trajectories in estimated glomerular filtration rate (eGFR) in adults with type 2 diabetes and chronic kidney disease using data from 3 European multinational cohorts. This prognostic study used baseline and follow-up information collected between February 2010 and December 2019 from 3 prospective multinational cohort studies: PROVALID (Prospective Cohort Study in Patients with Type 2 Diabetes Mellitus for Validation of Biomarkers), GCKD (German Chronic Kidney Disease), and DIACORE (Diabetes Cohorte). A total of 4637 adult participants (aged 18-75 years) with type 2 diabetes and mildly to moderately impaired kidney function (baseline eGFR of ≥30 mL/min/1.73 m2) were included. Data were analyzed between June 30, 2021, and January 31, 2023. Thirteen variables readily available from routine clinical care visits (age, sex, body mass index; smoking status; hemoglobin A1c [mmol/mol and percentage]; hemoglobin, and serum cholesterol levels; mean arterial pressure, urinary albumin-creatinine ratio, and intake of glucose-lowering, blood-pressure lowering, or lipid-lowering medication) were selected as predictors. Repeated eGFR measurements at baseline and follow-up visits were used as the outcome. A linear mixed-effects model for repeated eGFR measurements at study entry up to the last recorded follow-up visit (up to 5 years after baseline) was fit and externally validated. Among 4637 adults with type 2 diabetes and chronic kidney disease (mean [SD] age at baseline, 63.5 [9.1] years; 2680 men [57.8%]; all of White race), 3323 participants from the PROVALID and GCKD studies (mean [SD] age at baseline, 63.2 [9.3] years; 1864 men [56.1%]) were included in the model development cohort, and 1314 participants from the DIACORE study (mean [SD] age at baseline, 64.5 [8.3] years; 816 men [62.1%]) were included in the external validation cohort, with a mean (SD) follow-up of 5.0 (0.6) years. Updating the random coefficient estimates with baseline eGFR values yielded improved predictive performance, which was particularly evident in the visual inspection of the calibration curve (calibration slope at 5 years: 1.09; 95% CI, 1.04-1.15). The prediction model had good discrimination in the validation cohort, with the lowest C statistic at 5 years after baseline (0.79; 95% CI, 0.77-0.80). The model also had predictive accuracy, with an R2 ranging from 0.70 (95% CI, 0.63-0.76) at year 1 to 0.58 (95% CI, 0.53-0.63) at year 5. In this prognostic study, a reliable prediction model was developed and externally validated; the robust model was well calibrated and capable of predicting kidney function decline up to 5 years after baseline. The results and prediction model are publicly available in an accompanying web-based application, which may open the way for improved prediction of individual eGFR trajectories and disease progression.

Morphological variation across space does not predict phenotypic change through time in two Neogene planktonic foraminifera species

IntroductionLines of least resistance, or the direction of maximum phenotypic variation, are reliable predictive tools for directions of evolutionary divergence through time. However, the consistency of trait covariation patterns through space, i.e. in different populations of the same taxa inhabiting different environmental settings, remains poorly established.MethodsTo test whether the predicted direction of evolutionary change is the same through time as it is across space, we compare within- and among-population trait covariation patterns across six Atlantic populations of two planktonic foraminifera species. Our study interval is 600,000 years long, spans the Neogene/Quaternary boundary and includes Earth’s most recent major natural shift in global climate state: the intensification of Northern Hemisphere Glaciation.ResultsWe show that, despite powerful global temporal changes in climate, there is a strong spatial signal in the evolutionary response. Population-specific trait covariation slopes vary among sites, climate phases and core/edge position within the species’ biogeographic range.DiscussionOur results imply that the direction of expected evolutionary change does not align across populations. This suggests that trait covariation patterns in the study species are driven by adaptation to local environmental settings rather than species-wide constraints. Single populations should therefore not be used to predict the response of other populations, even if they are faced with similar environmental conditions. We caution against using individual populations to project future response of other/global populations unless the underlying mechanism for trait covariation is shown to be universal.

Sandstone groundwater salinization modelling using physicochemical variables in Southern Saudi Arabia: Application of novel data intelligent algorithms

Reliable modelling and simulation of groundwater management are crucial for sustainable development. Groundwater salinization is considered challenging and has recently led to the development of several emerging advancements and technologies, which grant a feasible solution to integrated water management and desalination processes. For this purpose, Electrical Conductivity (EC) as the early Salinization sign is modelled using various computational techniques, namely, Least Square-boost (LSQ-Boost), Gaussian Process regression (GPR), support vector regression (SVR) and stepwise linear regression (SWLR). The experiment data from sandstone aquifers include parameters from the physical, chemical and hydrogeochemical aspects. Four different input combinations (C1-C4) were developed using linear and ranking nonlinear feature selection and validated modelling results weres assessed by mean square error (MSE), mean absolute error (MAE), root means square error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (R). The analysis also considers the effect of multicollinearity, and the variables affected, such as TDS (mg/L), were not included in the first three combinations. The novel GPR proved superior to other models, with GPR-C1 justified quantitatively (MSE = 0.0255, MAE = 25260.49 and RMSE = 0.1595) in the verification phase. Other intelligent models (SVR, LSQ-Boost) depicted promising for C3 and C4 combinations with more than 88–90% predictive accuracy. The explored novel GPR algorithm offered an excellent and reliable EC prediction tool. The study also suggested using direct correlated positive variables, including hydrochemical and topographic factors, in modelling groundwater salinization. This would lead to more effective water-resources-related planning and decision making.

Staging Systems and Nomograms for Soft Tissue Sarcoma.

Reliable tools for prognosis prediction are crucially needed by oncologists so they can tailor individual treatments. However, the wide spectrum of histologies and prognostic behaviors of sarcomas challenges their development. In this field, nomograms could definitely better account for their granularity compared to the more widely used AJCC/UICC TNM staging system. Nomograms are predictive tools that incorporate multiple risk factors and return a numerical probability of a clinical event. Since the development of the first nomogram in 2002, several other nomograms have been built, either general, site-specific, histology-specific, or both. Recently, some new "dynamic" nomograms and prognostic tools have been developed, allowing doctors to "recalculate" a patient's prognosis by taking into account the time since primary surgery, the event history, and the potential time-dependent effect of covariates. Due to these new tools, prognosis prediction is no longer limited to the time of the first computation but can be adapted and recalculated based on the occurrence (or not) of any event as time passes from the first computation. In this review, we aimed to give an overview of the available nomograms for STS and to help clinicians in the process of selecting the best tool for each patient.

A DNA methylation signature for the prediction of tumour recurrence in stage II colorectal cancer.

A major challenge in stage II colorectal carcinoma is to identify patients with increased risk of recurrence. Biomarkers that distinguish patients with poor prognosis from patients without recurrence are currently lacking. This study aims to develop a robust DNA methylation classifier that allows the prediction of recurrence and chemotherapy benefit in patients with stage II colorectal cancer. We performed a genome-wide DNA methylation capture sequencing in 243 stage II colorectal carcinoma samples and identified a relapse-specific DNA methylation signature consisting of eight CpG sites. Two hundred and forty-three patients with stage II CRC were enrolled in this study. In order to select differential methylation sites among recurrence and non-recurrence stage II CRC samples, DNA methylation profiles of 62 tumour samples including 31 recurrence and 31 nonrecurrence samples were analysed using the Agilent SureSelectXT Human Methyl-Seq, a comprehensive target enrichment system to analyse CpG methylation. Pyrosequencing was applied to quantify the methylation level of candidate DNA methylation sites in 243 patients. Least absolute shrinkage and selection operator (LASSO) method was employed to build the disease recurrence prediction classifier. We identified a relapse-related DNA methylation signature consisting of eight CpG sites in stage II CRC by DNA methylation capture sequencing. The classifier showed significantly higher prognostic accuracy than any clinicopathological risk factors. The Kaplan-Meier survival curve showed an association of high-risk score with poor prognosis. In multivariate analysis, the signature was the most significant prognosis factor, with an HR of 2.80 (95% CI, 1.71-4.58, P < 0.001). The signature could identify patients who are suitable candidates for adjuvant chemotherapy. An eight-CpG DNA methylation signature is a reliable prognostic and predictive tool for disease recurrence in patients with stage II CRC.

P689 Predictive transcriptional signatures associated to vedolizumab therapy response in patients with ulcerative colitis

Abstract Background Vedolizumab is one of the current treatments for patients with inflammatory bowel disease (IBD). The efficacy and safety, along with its gut specificity, make this drug an appealing therapeutic option for IBD patients with moderate to severe disease. However, as observed for other biologic treatments, a significant proportion of patients do not have an initial response to vedolizumab treatment. Currently, there is a lack of reliable predictive tools for vedolizumab treatment response, although this would help to alleviate the socioeconomic costs derived from this disease. For that reason, the primary aim of this study is to show putative transcriptional signatures associated with vedolizumab treatment response. Methods For the realization of this study, we used RNA-seq datasets from the Gene Expression Omnibus (GEO) database: GSE191328. This dataset includes samples from peripheral blood cells from responder and non-responder ulcerative colitis (UC) patients treated with vedolizumab and infliximab at baseline. Gene Set Enrichment Analysis (GSEA) was performed to compare the differential expression and the enrichment analysis was calculated using ssGSEA of the different groups under study. Results The analysis of the transcriptome of peripheral blood cells from responder (R) and non-responder (NR) vedolizumab-treated patients at baseline revealed interesting enrichment results. We found 20 gene sets positively enriched in NR versus R and only 9 gene sets negatively enriched in the same phenotypes. In order to find vedolizumab specific transcriptional signatures, we performed a similar analysis in samples from infliximab-treated patients. After that second round of analysis, we observed that around 50% of the enriched gene sets were similar for both biological treatments. Nevertheless, several interesting biological functions such as peroxisome function, reactive oxygen function or beta-catenin signalling are specifically enriched in NR patients to vedolizumab treatment. Conclusion Our data suggest that vedolizumab NR patients specifically show a transcriptional enrichment profile, which differs from vedolizumab R patients. Also, the comparative analysis with infliximab-treated patients reveals that those specific transcripts are modulated in response to this α4β7 integrin antibodies in peripheral blood cells. Therefore, we found a solid system to search for vedolizumab therapy predictive response markers using low invasive techniques.

Adiabatic Trajectory Approximation: A New General Method in the Toolbox of Mixed Quantum/Classical Theory for Collisional Energy Transfer

A new version of the MQCT program is presented, which includes an important addition, adiabatic trajectory approximation (AT-MQCT), in which the equations of motion for the classical and quantum parts of the system are decoupled. This method is much faster, which permits calculations for larger molecular systems and at higher collision energies than was possible before. AT-MQCT is general and can be applied to any molecule + molecule inelastic scattering problem. A benchmark study is presented for H2O + H2O rotational energy transfer, an important asymmetric-top rotor + asymmetric-top rotor collision process, a very difficult problem unamenable to the treatment by other codes that exist in the community. Our results indicate that AT-MQCT represents a reliable computational tool for prediction of collisional energy transfer between the individual rotational states of two molecules, and this is valid for all combinations of state symmetries (such as para and ortho states of each collision partner).

PepLab Platform: Database and Software Tools for Analysis of Food-Derived Bioactive Peptides

Research on food-derived bioactive peptides is expanding and the need for a convenient online platform that combines a foolproof and intuitive user interface with a reliable database and tools for prediction and analysis is rising. In this regard, this paper presents an open-access web-based platform PepLab (Peptides Laboratory). The database contains 2764 peptide sequence entries, grouped into sixteen classes according to their biological activity and into seven classes according to the source from which they were derived. Moreover, it includes bioinformatic tools for their processing and analysis. The DMpep tool allows extracting information about a number of physicochemical characteristics of a peptide or a set of peptides, including those that are not recorded in the database. The main advantages of PepLab are a user-friendly interface, a responsive design, and optimized search engines for better visibility on the Internet. Prediction of non-reported activity is available based on amino acid sequence analysis. In addition, users can download data and results in a convenient format (text and/or graphic) that was limited in the existing platforms. In this way, the presented PepLab platform will be helpful for researchers from various fields—bioinformatics, pharmaceuticals, food sciences, dietetics, biotechnology, analytical chemistry, etc.

Prediction for CET-4 Based on Random Forest

This study is targeted to use AI (artificial intelligence) to predict the English proficiency test scores (CET-4) of students in private universities. The study will train and test multiple machine learning models using a dataset of students’ demographic information, English language background, and test scores. These models will use different algorithms such as linear regression, decision trees, and neural networks to identify patterns and relationships in the data and make accurate predictions. The study aims to develop a reliable and effective predictive tool for educators to assess students’ language proficiency levels and provide targeted teaching strategies. Comprehensive evaluation of students’ overall quality will play a crucial role in the prediction model. Factors such as educational resources, teaching methods, student learning habits, and learning atmosphere in each college will affect the accuracy of the prediction. The full-coverage classification prediction of student performance using modern scientific and technological strategies holds great research significance for private universities’ educational tasks and targeted teaching plans.

The Efficacy of Pretreatment 18F-FDG PET-CT-Based Deep Learning Network Structure to Predict Survival in Nasopharyngeal Carcinoma.

Previous studies have shown that the 5-year survival rates of patients with nasopharyngeal carcinoma (NPC) were still not ideal despite great improvement in NPC treatments. To achieve individualized treatment of NPC, we have been looking for novel models to predict the prognosis of patients with NPC. The objective of this study was to use a novel deep learning network structural model to predict the prognosis of patients with NPC and to compare it with the traditional PET-CT model combining metabolic parameters and clinical factors. A total of 173 patients were admitted to 2 institutions between July 2014 and April 2020 for the retrospective study; each received a PET-CT scan before treatment. The least absolute shrinkage and selection operator (LASSO) was employed to select some features, including SUVpeak-P, T3, age, stage II, MTV-P, N1, stage III and pathological type, which were associated with overall survival (OS) of patients. We constructed 2 survival prediction models: an improved optimized adaptive multimodal task (a 3D Coordinate Attention Convolutional Autoencoder and an uncertainty-based jointly Optimizing Cox Model, CACA-UOCM for short) and a clinical model. The predictive power of these models was assessed using the Harrell Consistency Index (C index). Overall survival of patients with NPC was compared by Kaplan-Meier and Log-rank tests. The results showed that CACA-UOCM model could estimate OS (C index, 0.779 for training, 0.774 for validation, and 0.819 for testing) and divide patients into low and high mortality risk groups, which were significantly associated with OS (P < .001). However, the C-index of the model based only on clinical variables was only 0.42. The deep learning network model based on 18F-FDG PET/CT can serve as a reliable and powerful predictive tool for NPC and provide therapeutic strategies for individual treatment.

Machine learning models for fast selection of amino acids as green thermodynamic inhibitors for natural gas hydrate

Natural amino acids are non-toxic thermodynamic hydrate inhibitors without negative environmental impact, but it is difficult to accurately select the appropriate amino acid as a quick response to the operational conditions changes in the natural gas pipeline. The objective of this study was to develop mathematical models to predict the hydrate formation temperature (HFT) in presence of amino acids, capture the relationship between amino acid structure properties and their hydrate inhibition strength, and determine the optimal type and concentration to use. The HFT prediction was evaluated using multiple linear regression (MLR) and three machine learning methods including random forest (RF), M5 Rule (M5R) and support vector machine (SVM). After parameter optimization using the trial-and-error method, the coefficient of determination (R2) of the four models were 0.9328, 0.9793, 0.9795 and 0.9980, respectively. The SVM prediction of HFT outperformed other models as the root mean square error (RMSE) was 83%, 76% and 69% lower than that of the MLR, RF and M5R, respectively. Results also demonstrated that the relative importance of the amino acid concentration to the hydrate phase equilibrium was 5-fold higher than that of the intrinsic properties of the amino acid molecular. The SVM model proposed in this study served an easy-to-use tool for reliable prediction of HFT by just providing a new set of input data. This made it possible to accurately determine the minimum concentration of amino acids to be used during the gas pipeline transportation.

Early Biomarkers of Bronchopulmonary Dysplasia: A Quick Look to the State of the Art.

Bronchopulmonary dysplasia (BPD) is one of the most common pulmonary sequelae of extreme preterm birth, with long-lasting respiratory symptoms and reduced lung function. A reliable predictive tool of BPD development is urgent and its search remains one of the major challenges for neonatologists approaching the upcoming arrival of possible new preventive therapies. Biomarkers, identifying an ongoing pathogenetic pathway, could allow both the selection of preterm infants with an evolving disease and potentially the therapeutic targets of the indicted pathogenesis. The "omic" sciences represent well-known promising tools for this objective. In this review, we resume the current laboratoristic, metabolomic, proteomic, and microbiomic evidence in the prediction of BPD. KEY POINTS: · The early prediction of BPD development would allow the targeted implementation of new preventive therapies.. · BPD is a multifactorial disease consequently it is unlikely to find a single disease biomarker.. · "Omic" sciences offer a promising insight in BPD pathogenesis and its development's fingerprints..

Integrating cell interaction with transcription factors to obtain a robust gene panel for prognostic prediction and therapies in cholangiocarcinoma.

Objective: The efficacy of immunotherapy for cholangiocarcinoma (CCA) is blocked by a high degree of tumor heterogeneity. Cell communication contributes to heterogeneity in the tumor microenvironment. This study aimed to explore critical cell signaling and biomarkers induced via cell communication during immune exhaustion in CCA. Methods: We constructed empirical Bayes and Markov random field models eLBP to determine transcription factors, interacting genes, and associated signaling pathways involved in cell-cell communication using single-cell RNAseq data. We then analyzed the mechanism of immune exhaustion during CCA progression. Results: We found that VEGFA-positive macrophages with high levels of LGALS9 could interact with HAVCR2 to promote the exhaustion of CD8+ T cells in CCA. Transcription factors SPI1 and IRF1 can upregulate the expression of LGALS9 in VEGFA-positive macrophages. Subsequently, we obtained a panel containing 54 genes through the model, which identified subtype S2 with high expression of immune checkpoint genes that are suitable for immunotherapy. Moreover, we found that patients with subtype S2 with a higher mutation ratio of MUC16 had immune-exhausted genes, such as HAVCR2 and TIGIT. Finally, we constructed a nine-gene eLBP-LASSO-COX risk model, which was designated the tumor microenvironment risk score (TMRS). Conclusion: Cell communication-related genes can be used as important markers for predicting patient prognosis and immunotherapy responses. The TMRS panel is a reliable tool for prognostic prediction and chemotherapeutic decision-making in CCA.

A novel nomogram model for clinical outcomes of severe subarachnoid hemorrhage patients.

Systemic responses, especially inflammatory responses, after aneurysmal subarachnoid hemorrhage (SAH) are closely related to clinical outcomes. Our study aimed to explore the correlation between the systemic responses in the acute stage and the mid-term outcomes of severe SAH patients (Hunt-Hess grade III-V). Severe SAH patients admitted to Jinling Hospital from January 2015 to December 2019 were retrospectively analyzed in the study. The univariate and multivariate logistic regression analyses were used to explore the risk factors of 6-month clinical outcomes in severe SAH patients. A predictive model was established based on those risk factors and was visualized by a nomogram. Then, the predictive nomogram model was validated in another severe SAH patient cohort from January 2020 to January 2022. A total of 194 patients were enrolled in this study. 123 (63.4%, 123 of 194) patients achieved good clinical outcomes at the 6-month follow-up. Univariate and multivariate logistic regression analysis revealed that age, Hunt-Hess grade, neutrophil-to-lymphocyte ratio (NLR), and complications not related to operations were independent risk factors for unfavorable outcomes at 6-month follow-up. The areas under the curve (AUC) analysis showed that the predictive model based on the above four variables was significantly better than the Hunt-Hess grade (0.812 vs. 0.685, P = 0.013). In the validation cohort with 44 severe SAH patients from three different clinical centers, the AUC of the prognostic nomogram model was 0.893. The predictive nomogram model could be a reliable predictive tool for the outcome of severe SAH patients. Systemic inflammatory responses after SAH and complications not related to operations, especially hydrocephalus, delayed cerebral ischemia, and pneumonia, might be the important risk factors that lead to poor outcomes in severe SAH patients.

Cancer associated fibroblast derived gene signature determines cancer subtypes and prognostic model construction in head and neck squamous cell carcinomas.

Head and neck squamous cell carcinomas (HNSCC) are the most common type of head and neck cancer with an unimproved prognosis over the past decades. Although the role of cancer-associated-fibroblast (CAF) has been demonstrated in HNSCC, the correlation between CAF-derived gene expression and patient prognosis remains unknown. A total of 528 patients from TCGA database and 270 patients from GSE65858 database were contained in this study. After extracting 66 CAF-related gene expression data from TCGA database, consensus clustering was performed to identify different HNSCC subtypes. Limma package was used to distinguish the differentially expression genes (DEGs) between these subtypes, followed by Lasso regression analysis to construct a prognostic model. The model was validated by performing Kaplan-Meier survival, ROC and risk curve, univariate and multivariate COX regression analysis. GO, KEGG, GSEA, ESTIMATE and ssGSEA analyses was performed to explort the potential mechanism leading to different prognosis. Based on the 66 CAF-related gene expression pattern we stratitied HNSCC patients into two previously unreported subtypes with different clinical outcomes. A prognostic model composed of 15 DEGs was constructed and validated. In addition, bioinformatics analysis showed that the prognostic risk of HNSCC patients was also negatively correlated to immune infiltration, implying the role of tumor immune escape in HNSCC prognosis and treatment option. The study develops a reliable prognostic prediction tool and provides a theoretical treatment guidance for HNSCC patients.

Comprehensive Analysis of the Prognostic Value and Molecular Function of CRNDE in Glioma at Bulk and Single-Cell Levels.

Colorectal neoplasia differentially expressed (CRNDE) is an oncogenic long noncoding RNA (lncRNA) overexpressed in diverse malignancies. Here, we comprehensively analyze the prognostic value and molecular function of CRNDE in glioma. Bulk RNA-sequencing data from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), and single-cell RNA-sequencing data from the Tumor Immune Single-Cell Hub (TISCH) were analyzed. Kaplan-Meier survival analysis was applied to verify the prognostic value of CRNDE. Then, a nomogram based on multivariate Cox regression was established for individualized survival prediction. Subsequently, the expression characteristic and biological function of CRNDE were analyzed at the single-cell level. Lastly, the effects of CRNDE on the proliferation and invasion of glioma cell were explored in vitro. We discovered that CRNDE was a powerful marker for risk stratification of glioma patients. Regardless of the status of IDH and 1p/19q, CRNDE could effectively stratify patients' prognosis. The nomogram that incorporated the CRNDE expression was proved to be a reliable tool for survival prediction. In addition, epithelial-mesenchymal transition may be the most important biological process regulated by CRNDE, which was identified at both the bulk and single-cell levels. Moreover, CRNDE knockdown significantly inhibited the proliferation and invasion of glioma cell. Overall, CRNDE is a vital oncogene and may be a valuable supplement to improve the clinical stratification of glioma.