Establish Prediction Models Research Articles

Establishment of nomogram prediction model for distant metastasis of nasopharyngeal carcinoma after radiotherapy

Establishment of nomogram prediction model for distant metastasis of nasopharyngeal carcinoma after radiotherapy

Construction and experimental validation of a novel ferroptosis-related gene signature for myelodysplastic syndromes.

Myelodysplastic syndromes (MDS) are clonal hematopoietic disorders characterized by morphological abnormalities and peripheral blood cytopenias, carrying a risk of progression to acute myeloid leukemia. Although ferroptosis is a promising target for MDS treatment, the specific roles of ferroptosis-related genes (FRGs) in MDS diagnosis have not been elucidated. MDS-related microarray data were obtained from the Gene Expression Omnibus database. A comprehensive analysis of FRG expression levels in patients with MDS and controls was conducted, followed by the use of multiple machine learning methods to establish prediction models. The predictive ability of the optimal model was evaluated using nomogram analysis and an external data set. Functional analysis was applied to explore the underlying mechanisms. The mRNA levels of the model genes were verified in MDS clinical samples by quantitative real-time polymerase chain reaction (qRT-PCR). The extreme gradient boosting model demonstrated the best performance, leading to the identification of a panel of six signature genes: SREBF1, PTPN6, PARP9, MAP3K11, MDM4, and EZH2. Receiver operating characteristic curves indicated that the model exhibited high accuracy in predicting MDS diagnosis, with area under the curve values of 0.989 and 0.962 for the training and validation cohorts, respectively. Functional analysis revealed significant associations between these genes and the infiltrating immune cells. The expression levels of these genes were successfully verified in MDS clinical samples. Our study is the first to identify a novel model using FRGs to predict the risk of developing MDS. FRGs may be implicated in MDS pathogenesis through immune-related pathways. These findings highlight the intricate correlation between ferroptosis and MDS, offering insights that may aid in identifying potential therapeutic targets for this debilitating disorder.

Establishment and verification of invasion syndrome prediction model in patients with diabetes complicated with Klebsiella pneumoniae liver abscess

Objective: To analyze the correlative factors of invasion syndrome in patients with diabetes complicated with Klebsiella pneumoniae liver abscess, and to construct and verify the online nomographic prediction model. Methods: A case control study. The clinical data of 213 diabetic patients with Klebsiella pneumoniae liver abscess admitted to the Third Affiliated Hospital of Soochow University from January 1, 2015 to December 31, 2021 were retrospectively analyzed. The patients were divided into the training set (149 cases) and the test set (64 cases) by stratified random sampling method at a ratio of 7∶3. Synthetic minority over-sampling technique(SMOTE) was used to process the imbalanced data, then Lasso regression was used to screen out the optimal feature variables in the training set and multivariate logistic regression model was used to construct the prediction model of invasion syndrome in patients with diabetes complicated with Klebsiella pneumoniae liver abscess, and verify it in the training set and test set. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were used to evaluate the prediction efficiency of the model, and the simple and online interactive dynamic web page column graph was constructed. Results: Among the 213 patients, 60 were males and 153 were females, aged of (61.4±12.0) years. A total of 25(11.74%) diabetic patients with Klebsiella pneumoniae liver abscess developed invasion syndrome, which were included in divided into invasive K.pneumoniae liver abscesses syndrome (IKPLAS) group, and the other 188 cases were in without invasive K.pneumoniae liver abscesses syndrome (NIKPLAS) group. SMOTE algorithm was used for oversampling processing, so that the ratio of positive and negative samples was 1∶1. In the oversampling training set, 5 main risk factors were screened based on Lasso regression, namely fasting blood glucose (λ=0.063), hemoglobin (λ=-0.042), blood urea nitrogen (λ=-0.050), abscess size (λ=-0.025) and sequential organ failure assessment (SOFA) score (λ=0.450), respectively. Multivariate logistic regression model showed that fasting blood glucose (OR=1.20, 95%CI: 0.98-1.48, P=0.006), hemoglobin (OR=0.90, 95%CI: 0.86-0.95, P<0.001), blood urea nitrogen (OR=1.22, 95%CI: 1.03-1.43, P=0.017), abscess diameter (OR=0.76, 95%CI: 0.61-0.94, P=0.010), SOFA score (OR=3.08, 95%CI: 2.18-4.36, P<0.001) were associated with invasion syndrome in patients with diabetes complicated with Klebsiella pneumoniae liver abscess. The area under the curve of ROC in the training set was 0.966 (95%CI: 0.943-0.989), the sensitivity was 90.5%, and the specificity was 91.3%. The area under the curve of the validation set ROC was 0.946 (95%CI: 0.902-0.991), with a sensitivity of 79.6% and a specificity of 88.9%. The calibration curves drawn in the training set and the test set fit well with the ideal curve. DCA showed that the neomorph prediction model had a good clinical net benefit when predicting the risk of IKPLAS in patients with diabetes complicated with Klebsiella pneumoniae liver abscess was 0.10-0.40. Conclusions: Fasting blood glucose, hemoglobin, urea nitrogen, abscess size and SOFA score are the related factors for invasion syndrome in patients with diabetes complicated with Klebsiella pneumoniae liver abscess. The constructed column graph can effectively predict the risk of invasion syndrome in patients with diabetes complicated with Klebsiae pneumoniae liver abscess.

Grinding Force Model and Experimental Study on Ultrasonic Assisted Spiral Grinding of Silicon Carbide Ceramic Materials

In order to reveal the mechanical properties of silicon carbide ceramic materials during the drilling process, based on the indentation fracture mechanics theory, a grinding force model of ultrasonic assisted spiral grinding for hole making was established, and the experimental results of ultrasonic assisted spiral grinding for hole making verified the mechanical model. The results showed that the experimental results had the same change trend as the predicted results of the established mechanical model, and the values were close to each other, indicating the accuracy of the established prediction model. The influence of different process parameters on the size accuracy of the hole inlet and outlet aperture after machining is analyzed. It is found that it should be selected as large rotation speed, moderate axial feed speed and small feed pitch to ensure the accuracy of the aperture and effectively suppress the aperture defects during ultrasonic assisted spiral grinding.

Non-Destructive Near-Infrared Moisture Detection of Dried Goji (Lycium barbarum L.) Berry

To detect the moisture of dried Goji (Lycium barbarum L.) berries nondestructively, a near-infrared (NIR) hyperspectral imager was used for experiments. NIR hyperspectral data were obtained and processed by standard normal variate (SNV) calculation using the MATLAB software v.R2016a. On the basis of the actual moisture of dried Goji berries, the predicted moisture was obtained based on the partial least squares (PLS) algorithm and a prediction model for the moisture of dried goji berries was established. It was found that the moisture of dried Goji berries was responsive to the NIR hyperspectral imager. The established prediction model could accurately predict the moisture of dried goji berries, and its R2-value was 0.9981. The results provide a theoretical basis for the design of non-destructive moisture-detecting equipment for dried Goji berries.

Establishment and Application of Steel Composition Prediction Model Based on t-Distributed Stochastic Neighbor Embedding (t-SNE) Dimensionality Reduction Algorithm

Establishment and Application of Steel Composition Prediction Model Based on t-Distributed Stochastic Neighbor Embedding (t-SNE) Dimensionality Reduction Algorithm

Establishment and validation of prediction models for human leukocyte antigen haplotypes and human leukocyte antigen genotypes

Objective: To establish prediction models for human leukocyte antigen (HLA) haplotypes and HLA genotypes, and verify the prediction accuracy. Methods: The prediction models were established based on the characteristic of HLA haplotype inheritance and linkage disequilibrium (LD), as well as the invention patents and software copyrights obtained. The models include algorithm and reference databases such as HLA A-C-B-DRB1-DQB1 high-resolution haplotypes database, B-C and DRB1-DQB1 LD database, G group alleles table, and NMDP Code alleles table. The prediction algorithm involves data processing, comparison with reference data, filtering results, probability calculation and ranking, confidence degree estimation, and output of prediction results. The accuracy of the predictions was verified by comparing them with the correct results, and the relationship between prediction accuracy and the probability distribution and confidence degree of the predicted results was analyzed. Results: The HLA haplotypes and genotypes prediction models were established. The prediction algorithm included the prediction of A-C-B-DRB1-DQB1 haplotypes according to HLA-A, B, DRB1, C, DQB1 genotypes, the prediction of C and DQB1 high-resolution results according to A, B and DRB1 high-resolution results, and the prediction of A, B, DRB1, C and DQB1 high resolution results according to the A, B and DRB1 intermediate or low resolution results. Validation results of "Predicting A-C-B-DRB1-DQB1 haplotypes basing on HLA-A, B, DRB1, C, DQB1 genotypes" model: for 787 data, the accuracy was 94.0% (740/787) with 740 correct predictions, 34 incorrect predictions, and 13 instances with no predicted results. For 847 data, the accuracy was 100% (847/847). The 2 411 and 2 594 haplotype combinations predicted from 787 and 847 data were grouped according to confidence degree, the accuracy was 100% (48/48, 114/114) for a confidence degree of 1, 96.2% (303/315) and 97.8% (409/418) for a confidence degree of 2 respectively. Validation results of "Predicting A, B, DRB1 and C, DQB1 high-resolution genotypes basing on HLA-A, B, DRB1 high, intermediate, or low resolution genotypes" model: when predicting C and DQB1 high resolution genotypes basing on A, B, and DRB1 high resolution genotypes, 89.3% (1 459/1 634) of the predictions were correct. The accuracy for the top 2 predicted probability (GPP) ranking was 79.2% (1 156/1 459), and for the top 10, it was 95.0% (1 386/1 459). Furthermore, when GPP≥90% and GPP 50%-90%, the prediction accuracy was 81.3% (209/257) and 72.8% (447/614) respectively. The accuracy of predicting C and DQB1 high resolution genotypes basing on the results of A, B, and DRB1 high resolution genotypes from the China Marrow Donor Program was 87.0% (20/23). The accuracy of predicting A, B, DRB1, C, and DQB1 high resolution genotypes basing on the results of A, B, and DRB1 intermediate or low-resolution genotypes was 70.0% (7/10) and 52.5% (21/40) respectively. When predicting whether the patient is likely to have a HLA 10/10 matched donor, the accuracy of the top 2 GPP combinations with a proportion of ≥50% was 85.7% (6/7). Conclusions: When using A, B, DRB1, C, DQB1 genotypes to predict A-C-B-DRB1-DQB1 haplotype combinations, the results with a confidence degree of 1 and 2 are reliable. When predicting C and DQB1 genotypes according to A, B and DRB1 genotypes, the top 10 results ranked by GPP are reliable, and the top 2 results with GPP≥50% are more reliable.

Enhancing Multi-species Liver Microsomal Stability Prediction through Artificial Intelligence.

Liver microsomal stability, a crucial aspect of metabolic stability, significantly impacts practical drug discovery. However, current models for predicting liver microsomal stability are based on limited molecular information from a single species. To address this limitation, we constructed the largest public database of compounds from three common species: human, rat, and mouse. Subsequently, we developed a series of classification models using both traditional descriptor-based and classic graph-based machine learning (ML) algorithms. Remarkably, the best-performing models for the three species achieved Matthews correlation coefficients (MCCs) of 0.616, 0.603, and 0.574, respectively, on the test set. Furthermore, through the construction of consensus models based on these individual models, we have demonstrated their superior predictive performance in comparison with the existing models of the same type. To explore the similarities and differences in the properties of liver microsomal stability among multispecies molecules, we conducted preliminary interpretative explorations using the Shapley additive explanations (SHAP) and atom heatmap approaches for the models and misclassified molecules. Additionally, we further investigated representative structural modifications and substructures that decrease the liver microsomal stability in different species using the matched molecule pair analysis (MMPA) method and substructure extraction techniques. The established prediction models, along with insightful interpretation information regarding liver microsomal stability, will significantly contribute to enhancing the efficiency of exploring practical drugs for development.

Study of different cutting fluids effect on the coupling characteristics of milling noise-vibration and surface roughness of TA2 pure titanium

With the increasing use of titanium alloys, it is essential to improve cutting efficiency. However, there are various problems such as high noise, strong vibration and difficulty to control the surface finish quality in actual cutting process, which can be more serious with the increase of machining parameters. In order to obtain better machining quality and higher efficiency, the influence of cutting parameters and cutting fluid on the milling process of TA2 pure titanium under two cutting conditions that use different cutting fluid is firstly studied in this paper. The correlation between the surface roughness (SR) of the workpiece and multiple metrics which milling noise (MN) and milling vibration (MV) is further studied on the basis of experimental data. In this paper, a series of parameters that have a strong correlation with SR are selected. Then, prediction models based on Least Squares Method (LSM) and Response Surface Methodology (RSM) are established. Finally, established models are compared to find ways to improve prediction accuracy. The experimental results indicate that compared with water-based cutting fluid, oil-based cutting fluid can make TA2 workpiece obtain better and more stable cutting performance most of the time; MV has a more significant correlation with SR and is therefore more suitable for establishing prediction model; The accuracy of the prediction model that takes the multi-factor coupling effect into consideration has been generally improved and the average optimization degree of the Improved Particle Swarm Optimization (IPSO) algorithm for LSM is 2.91 %, which has good optimization performance.

Risk factors analysis and the establishment of nomogram prediction model for PICC-related venous thrombosis in patients with lymphoma: a double-center cohort-based case-control study.

The objective of this study is to examine the risk factors associated with the occurrence of PICC-Related Venous Thrombosis (PICC-RVTE) in individuals diagnosed with lymphoma, as well as to develop a predictive risk nomogram model. A total of 215 patients with lymphoma treated at Yunnan Provincial Tumor Hospital from January 2017 to December 2020 were retrospectively evaluated as the training cohort; 90 patients with lymphoma treated at the Department of Oncology of the First People's Hospital of Anning, Affiliated to Kunming University of Science and Technology during the January 2021 to September 2023 were evaluated as the validation cohort. Independent influencing factors were analyzed by logistic regression, a nomogram was developed and validated, and the model was evaluated using internal and external data cohorts for validation. A total of 305 lymphoma patients were selected and 35 (11.48%) PICC-RVTE occurred, the median time was 13 days. The incidence within 1-2week was 65.71%. Multivariate analysis suggested that the activity amount, thrombosis history(within the last 12 months), ATIII, Total cholesterol and D-dimer levels were independently associated with PICC-RVTE, and a nomogram was constructed based on the multivariate analysis. ROC analysis indicated good discrimination in the training set (area under the curve [AUC] = 0.907, 95%CI:0.850-0.964) and the testing set (AUC = 0.896, 95%CI: 0.782-1.000) for the PICC-RVTE nomogram. The calibration curves showed good calibration abilities, and the decision curves indicated the clinical usefulness of the prediction nomograms. Patients should be advised to undergo color Doppler ultrasound system testing within two week after the implantation of a PICC catheter to detect PICC-RVTE at an early stage. The validated nomogram can be used to predict the risk of catheter-related thrombosis (CRT) in patients with lymphoma who received at least one chemotherapy after PICC catheterization, no bleeding tendency, no recent history of anticoagulant exposure and no severe heart, lung, renal insufficiency. This model has the potential to assist clinicians in formulating individualized treatment strategies for each patient.

Establishment of a risk prediction model for bowel preparation failure prior to colonoscopy

BackgroundThis study aimed to determine the factors that contribute to the failure of bowel preparation in patients undergoing colonoscopy and to develop a risk prediction model.MethodsA total of 1115 outpatients were included. Patients were randomly divided into two groups: the modeling group (669 patients) and the validation group (446 patients). In the modeling group, patients were further divided into two groups based on their success and failure in bowel preparation using the Boston Bowel Preparation Scale. A logistic regression analysis model was used to determine the risk factors of bowel preparation failure, which was subsequently visualized using an alignment diagram.ResultsAfter controlling for relevant confounders, multifactorial logistic regression results showed that age ≥ 60 years (OR = 2.246), male (OR = 2.449), body mass index ≥ 24 (OR = 2.311), smoking (OR = 2.467), chronic constipation (OR = 5.199), diabetes mellitus (OR = 5.396) and history of colorectal surgery (OR = 5.170) were influencing factors of bowel preparation failure. The area under the ROC curve was 0.732 in the modeling group and 0.713 in the validation group. According to the calibration plot, the predictive effect of the model and the actual results were in good agreement.ConclusionsAge ≥ 60 years, male, body mass index ≥ 24, smoking, chronic constipation, diabetes mellitus, and history of colorectal surgery are independent risk factors for bowel preparation failure. The established prediction model has a good predictive efficacy and can be used as a simple and effective tool for screening patients at high risk for bowel preparation failure.

Establishment of Prediction Model for Severe Hypertriglyceridemic Acute Pancreatitis Based on Early Laboratory Indicators

Abstract Background Compared with other types of acute pancreatitis (AP), hypertriglyceridemic acute pancreatitis (HTG-AP) is younger, recurrent and more prone to exacerbation. Severe HTG-AP has a high fatality rate. Early and accurate prediction of the severity is crucial. However, there is currently a lack of a specific scoring system for the severity of HTG-AP. Aim/Purpose To construct a risk prediction model that can accurately predict severe HTG-AP in the early stage and evaluate its clinical value. Methods The clinical data of 1768 patients with AP admitted to the Second Affiliated Hospital of Anhui Medical University from January 2020 to May 2023 were analyzed retrospectively, and 136 HTG-AP patients were finally selected. Univariate and multivariate analysis were performed for the early onset indicators to identify the independent risk factors for developing SAP in the patients of HTG-AP. Logistic regression was then utilized to establish a risk prediction model for the severity of HTG-AP, which was subsequently evaluated for its performance through discrimination and calibration analysis. Results Of the 136 patients with HTG-AP, 39 patients (28.7%) progressed to severe acute pancreatitis (SAP). Multivariate analysis revealed that CRP, RDW/SC, and D-dimer were independent risk factors for developing SAP in the patients of HTG-AP. The logistic regression analysis to establish prediction model was: Logit P = − 8.101 + 0.008 × CRP + 0.425 × D-dimer + 0.743 × RDW/SC. The receiver-operating characteristics (ROC) curve showed that area under curve (AUC) value of CRP, RDW/SC, D-dimer, and the prediction model were 0.831, 0.843, 0.874, and 0.915, respectively. Moreover, the AUC value of the prediction model and commonly used scoring systems of AP were compared: prediction model (AUC = 0.915) &gt; Ranson (AUC = 0.900) &gt; SOFA (AUC = 0.899) &gt; CTSI (AUC = 0.889) &gt; BISAP (AUC = 0.887). Conclusion CRP, RDW/SC and D-dimer were independent risk factors for SAP in the patients of HTG-AP. Compared with commonly used scoring systems of AP, the prediction model had good clinical prediction ability, providing reference for early identification of the patients developing severe HTG-AP and active intervention.

Establishment and Diagnostic Value of an Early Prediction Model for Acute Pancreatitis Complicated With Acute Kidney Injury.

To establish an early prediction model for acute pancreatitis (AP) complicated with acute kidney injury (AKI) and evaluate its diagnostic value. AP patients were recruited from the Emergency Department at Peking University People's Hospital in 2021 and stratified into AKI and control (no AKI) groups. Their clinical data were analyzed. The risk for AKI development was determined using logistic analyses to establish a risk prediction model, whose diagnostic value was analyzed using a receiver operating characteristic curve. There was no significant difference in the basic renal function between the AKI (n = 79) and control (n = 179) groups. The increased triglyceride glucose index (odds ratio [OR], 2.613; 95% confidence interval [CI], 1.324-5.158; P = 0.006), age (OR, 1.076; 95% CI, 1.016-1.140; P = 0.013), and procalcitonin (OR, 1.377; 95% CI, 1.096-1.730, P = 0.006) were associated with AKI development. A model was established for prediction of AKI (sensitivity 79.75%, specificity 96.65%). The area under the receiver operating characteristic curve was 0.856 which was superior to the Ranson, Bedside Index for Severity in AP, and Acute Physiology and Chronic Health Evaluation II scores (0.856 vs 0.691 vs 0.745 vs 0.705). The prediction model based on age, triglyceride glucose, and procalcitonin is valuable for the prediction of AP-related AKI.

Flare prediction after tapering the dose of tumour necrosis factor inhibitors in patients with axial spondyloarthritis: A nationwide cohort study.

To develop a model for predicting flares after tapering the dose of tumour necrosis factor inhibitors (TNFi) in patients with axial spondyloarthritis (axSpA). Data were obtained from the Korean College of Rheumatology Biologics and Targeted Therapy Registry. In total, 526 patients who received the standard-dose TNFi for at least 1 year and tapered their dose were included in the derivation cohort. The main outcome was a flare occurrence defined as an Ankylosing Spondylitis Disease Activity Score with C-reactive protein (ASDAS-CRP) score of ≥ 2.1 after 1 year of TNFi tapering. The final prediction model was validated using an independent cohort. Among 526 patients, 127 (24.1%) experienced flares. The final prediction model included negative human leucocyte antigen B27 (β = 1.088), inflammatory back pain (β = 1.072), psoriasis (β = 1.567), family history of SpA (β = 0.623), diabetes mellitus (β = 1.092), TNFi tapering by ≥ 50% of the standard-dose (β = 0.435), ASDAS-CRP at tapering (β = 1.029), and Bath Ankylosing Spondylitis Functional Index score at tapering (β = 0.194) as covariates. It showed an excellent discrimination performance (AUC = 0.828). According to the predictive risk, patients were classified into three groups (low-, intermediate-, and high-risk). The probabilities of flares in these groups were 4.5%, 18.1%, and 61.8%, respectively. The performance of the model in the validation cohort was also comparable. The established prediction model accurately predicted the risk of flares after TNFi dose tapering in patients with axSpA using eight simple clinical parameters, which could be helpful to select appropriate patients for tapering their TNFi without flare in daily clinical practice.

APPLICATION OF ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF THYROID CANCER WITH ENHANCED COMPUTED TOMOGRAPHY

Thyroid Cancer (TC) is a common malignant tumor, head and neck in the incidence of malignant tumors in the seventh, ranked fourth in the incidence in women. There are several methods to diagnose and recognize TC, such as ultrasonic, computed tomography (CT) and other means. While, it is an important role for CT examination in the diagnosis of TC, because it has the characteristics of objectivity, repeatability, and multi-dimensional imaging, and can clearly understand the scope and spatial characteristics of the lesion. CT has unique advantages in showing lymph nodes and distant metastases, such as coarse-walled or thick-walled ring calcification. The early diagnosis of TC mainly relies on manual labor, which is very inefficient. With the continuous development of information science, the construction of TC diagnosis and recognition models based on artificial intelligence (AI) has gradually become a research hotspot. At present, research on AI-based thyroid screening models mainly focuses on four aspects: first, thyroid region segmentation and image denoising based on image-omics; second, the establishment of a high-precision TC risk prediction model based on multi-omics data; third, screening of biomarkers of TC for clinical diagnosis; fourth, establish the early screening model of TC based on AI. This paper reviews the research status of the AI-based thyroid screening model based on the above four aspects. In addition, this paper also summarizes the main challenges faced by the current AI -based TC recognition and detection model and it proposes a new research idea for the future TC early screening research based on enhanced CT.

Optimization of mix proportions for hybrid fiber engineered cementitious composites based on Box-Behnken design response surface model

In this paper, basalt fiber (BF) and polyethylene fiber (PE) were mixed into cement matrix to prepare a new hybrid fiber Engineered Cementitious Composite. The Box-Behnken experimental design method within the Response Surface Methodology (RSM) was employed, the effects of water-binder ratio, sand-binder ratio and fiber content on compressive strength, flexural strength and equivalent bending toughness of hybrid fiber ECC were studied. Analysis of variance (ANOVA) was used to verify and optimize the model equation, and the ratio of water-binder ratio 0.2, sand-binder ratio 0.35, BF content 1.25%, PE content 1% was obtained. The results show that the synergistic effect of PE and BF has a favorable effect on the mechanical properties of hybrid fiber ECC. PE can achieve high energy absorption and maintain high strain capacity of matrix. BF increases the strength of the specimen. When the BF content increases from 0.9% to 1.2%, the compressive strength increases by 9.6%. The fiber content is highly sensitive to flexural strength and equivalent bending toughness, and the significance level of each factor is more than 95%. The significance level of each factor is BF content > sand-binder ratio > water-binder ratio. The experiment verified that the established prediction model has high accuracy, and the relative error between the optimized RSM value and the experimental value is less than 5%. To provide a certain reference for the design of ECC mix proportions for preparing multi strength and multi toughness series.

Establishment and multicenter external validation of a risk prediction model for de novo intracranial aneurysms based on a systematic review and meta-analysis of 19 cohorts.

A de novo intracranial aneurysm (IA) is a second, new IA that develops in patients with IAs distant from where the initial IA was detected. This study aimed to identify risk factors for de novo IA formation and establish and externally validate a multicenter risk prediction model for de novo IAs. A systematic review and meta-analysis of existing de novo IA cohorts was conducted to form the derivation cohort. The risk ratios and 95% CIs of each risk factor were calculated. In addition, risk scores included in the model were calculated based on the statistically significant risk factors with their weightings. Then the model was validated in a multicenter external cohort of Chinese patients, and receiver operating characteristic and calibration curves, decision curve analysis, and Kaplan-Meier curves were used to evaluate the model. Nineteen studies with 9351 patients, of whom 304 patients (3.25%) developed de novo IAs, were included in the derivation cohort. These patients developed de novo IAs at 2.5-18.5 years during a total follow-up of 3.3-18.8 years. The statistically significant risk factors were age < 60 years, female sex, smoking history, family history of IAs, multiple IAs at initial diagnosis, and initial IAs in the middle cerebral artery, with risk scores of 4, 5, 2, 6, 3, and 3, respectively. Then, a multicenter external cohort comprising 776 patients, of whom 45 patients (5.80%) developed de novo IAs, was included in the validation cohort. De novo IAs formed in these patients at a mean of 5.25 years during a mean follow-up of 6.19 years. The area under the curve of the model was 0.804, with a sensitivity of 0.667 and specificity of 0.900, at a cutoff value of 13. The calibration curve, decision curve analysis, and Kaplan-Meier curves also indicated good performance of the model. This prediction model is a convenient and intuitive tool for identifying high-risk patients with de novo IAs. Reasonable use of the model can not only aid in clinical decision-making but also play a positive role in the prevention of aneurysmal subarachnoid hemorrhage to a certain extent.

Ignition of Forest Fires by Cigarette Butts: Using Pinus massoniana Needles as an Example

As a cigarette butt falls onto the forest surface fuel, it first smolders the fuel, then ignites into flames, and spreads as forest fire under certain conditions. In this study, the needles under a typical stand of P. massoniana were used as the research object. Needle beds with different moisture content and packing ratios were constructed indoors. Cigarette butt-ignition experiments were conducted under different wind velocities, and 30 experiment cycles were conducted under different conditions. There was a total of 5 (packing ratio) × 4 (moisture content) × 6 (wind velocity) = 120 sets of conditions, and a total of 3600 ignition experiments were conducted. The results showed that (1) the total ignition probability of the cigarette butts was 2.36%, which only occurred when the fuelbed moisture content was &lt;10% and the wind velocity was &gt;1 m/s. The ignition time of cigarette butts ranged from 2.73 to 7.25 min. (2) The fuelbed moisture content and wind velocity significantly influenced the ignition probability and time. With an increase in moisture content, the ignition probability of cigarette butts decreased, while the time required for ignition showed an increasing trend. Wind velocity had a dual effect on ignition. The ignition effect was optimal at a wind velocity of 4 m/s. With an increase in wind velocity, the ignition probability first increased and then decreased, and the ignition time first decreased and then increased. (3) The packing ratio had no significant effect on the ignition probability; however, the ignition time significantly decreased as the packing ratio increased. (4) The logistic regression method (LRM), general linear method (GLM), and nonlinear regression method (NLM) were used to establish a prediction model of ignition probability. The prediction effect of GLM was the worst, followed by LRM, and the NLM had the best prediction effect. The GLM was selected to establish the ignition time model, and the error was also within the allowance range. This study elucidated the underlying mechanism of factors affecting cigarette butt-based fuel ignition. In addition, the established prediction model provides a reference for human-caused forest fires and is highly significant for forest fire prevention.

Real-Time Authentication of Camellia Oil by Rapid Evaporative Ionization Mass Spectrometry

Camellia oil is a high-value product with rich nutrients. Recently, the adulteration of camellia oil has become an increasingly concerning issue related to human health. In this study, electric soldering iron coupled with rapid evaporative ionization mass spectrometry (REIMS) was employed for the identification and analysis of camellia oil without any sample preparation. REIMS technology coupled with chemometrics was applied to develop an analysis model for the authentication of camellia oil adulterated with soybean oil, peanut oil, rapeseed oil, sunflower oil, and corn oil (5–40%, v/v). The results showed that different types of vegetable oils could be classified using principal component analysis-linear discriminant analysis (PCA-LDA) with a correct classification of 93.8% in leave-20%-out cross-validation and 100% correctly identified in real-time recognition. The established prediction models were found to be particularly sensitive when the camellia oil samples were adulterated with 5–40% of other oils, indicating that REIMS could be a powerful tool for the authentication and adulteration analysis of camellia oil, particularly for cases where the adulteration levels are relatively high. In conclusion, the results provide valuable insights into the potential of REIMS for the rapid, accurate, and real-time authentication and adulteration analysis of camellia oil.

Risk prediction model construction for asthma after allergic rhinitis by blood immune T effector cells.

Allergic rhinitis (AR) and asthma (AS) are prevalent and frequently co-occurring respiratory diseases, with mutual influence on each other. They share similar etiology, pathogenesis, and pathological changes. Due to the anatomical continuity between the upper and lower respiratory tracts, allergic inflammation in the nasal cavity can readily propagate downwards, leading to bronchial inflammation and asthma. AR serves as a significant risk factor for AS by potentially inducing airway hyperresponsiveness in patients. Currently, there is a lack of reliable predictors for the progression from AR to AS. In this exhaustive investigation, we reexamined peripheral blood single cell RNA sequencing datasets from patients with AS following AR and healthy individuals. In addition, we used the bulk RNA sequencing dataset as a validation lineup, which included AS, AR, and healthy controls. Using marker genes of related cell subtype, signatures predicting the progression of AR to AS were generated. We identified a subtype of immune-activating effector T cells that can distinguish patients with AS after AR. By combining specific marker genes of effector T cell subtype, we established prediction models of 16 markers. The model holds great promise for assessing AS risk in individuals with AR, providing innovative avenues for clinical diagnosis and treatment strategies. Subcluster T effector cells may play a key role in post-AR AS. Notably, ACTR3 and HSPA8 genes were significantly upregulated in the blood of AS patients compared to healthy patients.