Multivariate Regression Algorithm Research Articles

The mitochondria-related gene risk mode revealed p66Shc as a prognostic mitochondria-related gene of glioblastoma.

Numerous studies have highlighted the pivotal role of mitochondria-related genes (MRGs) in the initiation and progression of glioblastoma (GBM). However, the specific contributions of MRGs coding proteins to GBM pathology remain incompletely elucidated. The identification of prognostic MRGs in GBM holds promise for the development of personalized targeted therapies and the enhancement of patient prognosis. We combined differential expression with univariate Cox regression analysis to screen prognosis-associated MRGs in GBM. Based on the nine MRGs, the hazard ratio model was conducted using a multivariate Cox regression algorithm. SHC-related survival, pathway, and immune analyses in GBM cohorts were obtained from the Biomarker Exploration of the Solid Tumor database. The proliferation and migration of U87 cells were measured by CCK-8 and transwell assay. Apoptosis in U87 cells was evaluated using flow cytometry. Confocal microscopy was employed to measure mitochondrial reactive oxygen species (ROS) levels and morphology. The expression levels of SHC1 and other relevant proteins were examined via western blotting. We screened 15 prognosis-associated MRGs and constructed a 9 MRGs-based model. Validation of the model's risk score confirmed its efficacy in predicting the prognosis of patients with GBM. Furthermore, analysis revealed that SHC1, a constituent MRG of the prognostic model, was upregulated and implicated in the progression, migration, and immune infiltration of GBM. In vitro experiments elucidated that p66Shc, the longest isoform of SHC1, modulates mitochondrial ROS production and morphology, consequently promoting the proliferation and migration of U87 cells. The 9 MRGs-based prognostic model could predict the prognosis of GBM. SHC1 was upregulated and correlated with the prognosis of patients by involvement in immune infiltration. Furthermore, in vitro experiments demonstrated that p66Shc promotes U87 cell proliferation and migration by mediating mitochondrial ROS production. Thus, p66Shc may serve as a promising biomarker and therapeutic target for GBM.

Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods?

ABSTRACT Objectives To evaluate the performance of an artificial intelligence (AI) model in predicting orthognathic surgical outcomes compared to conventional prediction methods. Materials and Methods Preoperative and posttreatment lateral cephalograms from 705 patients who underwent combined surgical-orthodontic treatment were collected. Predictors included 254 input variables, including preoperative skeletal and soft-tissue characteristics, as well as the extent of orthognathic surgical repositioning. Outcomes were 64 Cartesian coordinate variables of 32 soft-tissue landmarks after surgery. Conventional prediction models were built applying two linear regression methods: multivariate multiple linear regression (MLR) and multivariate partial least squares algorithm (PLS). The AI-based prediction model was based on the TabNet deep neural network. The prediction accuracy was compared, and the influencing factors were analyzed. Results In general, MLR demonstrated the poorest predictive performance. Among 32 soft-tissue landmarks, PLS showed more accurate prediction results in 16 soft-tissue landmarks above the upper lip, whereas AI outperformed in six landmarks located in the lower border of the mandible and neck area. The remaining 10 landmarks presented no significant difference between AI and PLS prediction models. Conclusions AI predictions did not always outperform conventional methods. A combination of both methods may be more effective in predicting orthognathic surgical outcomes.

Integrating Tehran metropolitan air pollution into the current transport system and sprawl growth: an emphasis on urban performance and accessibility

Air pollution is one of the most challenging issues for urban environment and environmental management. The goal of this study was to determine the impact of Tehran metropolitan's performance and accessibility on urban transportation and air pollution as sprawl grows. Tehran, with a population of 15.98 million people, has many environmental issues, including air pollution. Secondary data were collected from the Tehran Air Quality Control Company as well as Landsat satellite imagery (OLI). The raw data of intra-city and suburban traffic counts for spatial analysis of movements, combined with the raw data of measuring stations, were then used as a sample in the ArcGIS software environment for three selected days in 2013, 2014, and 2016. Following geometric and radiometric correction, programming methods and a multivariate regression algorithm were applied to the images, yielding results in the form of additional stations. According to the results obtained (about 3.29 m) root-mean-square error (RMSE), the Inverse Distance Weighting (IDW) model was used in air pollution maps for better assessment. The findings suggest that Tehran is not the only source of air pollution and that TMA performance and accessibility play a significant role in the amount of air pollution. Furthermore, there is a strong correlation (more than 75%) between air pollution maps and transportation flow maps on specific days (February 23, 2013, February 26, 2015, and December 28, 2016). Finally, the analysis of this situation in three circles showed that the highest volume of traffic was done in the city of Tehran (CBD), suburban area (Suburban), and suburban area (Exurban), respectively, and the pattern of distribution and spatial accumulation of pollution has also been a function of this situation.

Abstract 3679: Development and validation of prognostic signatures comprising neutrophil-specific long non-coding RNAs (LncRNAs) for predicting survival in urothelial carcinoma

Abstract Background: Tumor-associated neutrophils profoundly influence the tumor immune microenvironment (TME), impacting urothelial carcinoma (UC) progression, metastasis, and survival. Long non-coding RNAs (lncRNAs) have emerged as critical players in cancer biology. Recognizing neutrophils as influential contributors to tumor biology, our study aimed to explore neutrophil-specific lncRNAs and evaluate their prognostic significance in UC. Methods: In the GSE24890 dataset, 46 lncRNAs were identified with elevated expression in neutrophils compared to other immune cells, thus designating them as neutrophil-specific lncRNAs. To establish a tailored prognostic model, the TCGA BLCA dataset served as the internal training set. Candidate lncRNAs underwent filtration and selection through univariate Cox regression analysis, followed by the construction of a risk score employing LASSO and multivariate Cox regression algorithms. We used IMVigor210 dataset as external validation for this lncRNA model. Results: Ten lncRNAs exhibiting correlation with overall survival (OS) were initially identified through univariate Cox regression. Following LASSO regression and subsequent multivariate Cox regression analysis, a refined set of 9 lncRNAs was selected to construct the risk model, represented by the formula: Risk score = (-0.096 × FAM13A-AS1 expression level) + (0.157 × FAM27C expression level) + (-0.294 × HCG27 expression level) + (-0.037 × HOTAIRM1 expression level) + (-0.137 × LINC-PINT expression level) + (-0.279 × LINC00612 expression level) + (-0.058 × LINC00967 expression level) + (0.187 × LINC01018 expression level) + (-0.288 × LINC01138 expression level). Kaplan-Meier survival analysis of the BLCA cohort revealed that patients in the high-risk group experienced significantly worse OS compared to those in the low-risk group (p < 0.001). The area under the curve (AUC) values at 1, 3, and 5 years were 0.66, 0.67, and 0.66, respectively. In the multivariate Cox regression model, the risk score remained an independent prognostic factor (p < 0.001; HR 2.5; 95% CI 1.91-3.30). The validity of the risk score was further confirmed in the IMVigor210 dataset, demonstrating discriminative predictive ability for OS between high- and low-risk groups (p = 0.027). Conclusions: This study underscores the interplay between neutrophils and lncRNAs in UC. Neutrophil-related lncRNAs identified may serve as promising therapeutic targets and prognostic markers for UC. Citation Format: Harvey Yu-Li Su, Chang-Ting Lin, Shih-Yu Huang, Yi-Hua Chen, Chung-Wen Kuo. Development and validation of prognostic signatures comprising neutrophil-specific long non-coding RNAs (LncRNAs) for predicting survival in urothelial carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3679.

Predicting the malignancy of extremity soft-tissue tumors by an ultrasound-based radiomics signature.

Accurate differentiation of extremity soft-tissue tumors (ESTTs) is important for treatment planning. To develop and validate an ultrasound (US) image-based radiomics signature to predict ESTTs malignancy. A dataset of US images from 108 ESTTs were retrospectively enrolled and divided into the training cohort (78 ESTTs) and validation cohort (30 ESTTs). A total of 1037 radiomics features were extracted from each US image. The most useful predictive radiomics features were selected by the maximum relevance and minimum redundancy method, least absolute shrinkage, and selection operator algorithm in the training cohort. A US-based radiomics signature was built based on these selected radiomics features. In addition, a conventional radiologic model based on the US features from the interpretation of two experienced radiologists was developed by a multivariate logistic regression algorithm. The diagnostic performances of the selected radiomics features, the US-based radiomics signature, and the conventional radiologic model for differentiating ESTTs were evaluated and compared in the validation cohort. In the validation cohort, the area under the curve (AUC), sensitivity, and specificity of the US-based radiomics signature for predicting ESTTs malignancy were 0.866, 84.2%, and 81.8%, respectively. The US-based radiomics signature had better diagnostic predictability for predicting ESTT malignancy than the best single radiomics feature and the conventional radiologic model (AUC = 0.866 vs. 0.719 vs. 0.681 for the validation cohort, all P <0.05). The US-based radiomics signature could provide a potential imaging biomarker to accurately predict ESTT malignancy.

Combination of Artificial Neural Networks and Principal Component Analysis for the Simultaneous Quantification of Dyes in Multi-Component Aqueous Mixtures

Dyes are organic compounds capable of transmitting their color to materials, which is why they are widely used, for example, in textile fibers, leather, paper, plastic, and the food industry. In the dying process, measuring the dye’s content is extremely important to evaluate the process efficiency and minimize the dye’s discharge in wastewater, but most of the time, dyes are present in multi-component mixtures; hence, quantification by spectrophotometric methods presents a great challenge because the signal obtained in the measurement overlaps the components in the mixture. In order to overcome this issue, the use of the high-performance liquid chromatography (HPLC) method is recommended; however, it has the disadvantage of being an expensive technique, complex, and requiring excessive sample preparation. In recent years, some direct spectrophotometric methods based on multivariate regression algorithms for the quantification of dyes in bicomponent mixtures have been reported. This study presents a new framework that uses a combined ANN and principal component analysis (PCA) model for the determination of the concentration of three dyes in aqueous mixtures: Tartrazine (TZ), Amaranth Red (AR), and Blue 1 CFC (B1) dyes. The PCA–ANN model was trained and validated with ternary mixture samples of TZ, AR, and B1, and with known different compositions, spectra absorbance samples were measured in a UV-Vis spectrophotometer at wavelengths between 350–700 nm with intervals of 1 nm. The PCA–ANN model showed a mean absolute prediction error and correlation coefficient (r2) of less than 1% and greater than 0.99, respectively. The results demonstrate that the PCA–ANN model is a quick and highly accurate alternative in the simultaneous determination of dyes in ternary aqueous mixtures.

Accurate prediction of myopic progression and high myopia by machine learning.

Myopia is a leading cause of visual impairment in Asia and worldwide. However, accurately predicting the progression of myopia and the high risk of myopia remains a challenge. This study aims to develop a predictive model for the development of myopia. We first retrospectively gathered 612 530 medical records from five independent cohorts, encompassing 227 543 patients ranging from infants to young adults. Subsequently, we developed a multivariate linear regression algorithm model to predict the progression of myopia and the risk of high myopia. The model to predict the progression of myopia achieved an R2 value of 0.964 vs a mean absolute error (MAE) of 0.119D [95% confidence interval (CI): 0.119, 1.146] in the internal validation set. It demonstrated strong generalizability, maintaining consistent performance across external validation sets: R2 = 0.950 vs MAE = 0.119D (95% CI: 0.119, 1.136) in validation study 1, R2 = 0.950 vs MAE = 0.121D (95% CI: 0.121, 1.144) in validation study 2, and R2 = 0.806 vs MAE = -0.066D (95% CI: -0.066, 0.569) in the Shanghai Children Myopia Study. In the Beijing Children Eye Study, the model achieved an R2 of 0.749 vs a MAE of 0.178D (95% CI: 0.178, 1.557). The model to predict the risk of high myopia achieved an area under the curve (AUC) of 0.99 in the internal validation set and consistently high area under the curve values of 0.99, 0.99, 0.96 and 0.99 in the respective external validation sets. Our study demonstrates accurate prediction of myopia progression and risk of high myopia providing valuable insights for tailoring strategies to personalize and optimize the clinical management of myopia in children.

Identification of risk factors for disseminated cryptococcosis in non-hiv patients: a retrospective analysis

ObjectiveThis study aimed to investigate the potential risk factors associated with disseminated cryptococcosis in HIV-negative individuals.MethodsA total of 106 HIV-negative patients with cryptococcal disease were enrolled. The observation group consisted of patients with disseminated cryptococcosis (DC), whereas the control groups included patients with pulmonary cryptococcosis (PC) and cryptococcal meningitis (CM). Univariate and multivariate logistic regression algorithms were used to explore the significant clinical and laboratory characteristics that affect the progression of cryptococcal infections. Finally, receiver operating characteristics (ROC) curves are applied to assess the diagnostic value of identified risk factors.LE: Kindly check the edit made in the title.I agreeResultsOf the 106 patients, 57 were diagnosed with pulmonary cryptococcosis, 22 with cryptococcal meningitis, and 27 with disseminated cryptococcosis. The logistic regression equation included five variables: diabetes, decompensated liver cirrhosis, long-term use of immunosuppressive agents, decreased serum albumin level, and elevated plasma cytokine IL-10 level. The ROC curves showed that albumin (AUC > 0.7), IL-10 (AUC > 0.7) and decompensated liver cirrhosis (AUC > 0.6) have relatively high diagnostic capacity in predicting the progression of Cryptococcus.ConclusionThis study identified elevated IL-10 levels as an independent risk factor for developing disseminated cryptococcosis in the control groups. Furthermore, decompensated liver cirrhosis and decreased serum albumin independently affected the progression of cryptococcosis in the CM and PC groups, respectively.

Smoothness-Driven Consensus Based on Compact Representation for Robust Feature Matching.

For robust feature matching, a popular and particularly effective method is to recover smooth functions from the data to differentiate the true correspondences (inliers) from false correspondences (outliers). In the existing works, the well-established regularization theory has been extensively studied and exploited to estimate the functions while controlling its complexity to enforce the smoothness constraint, which has shown prominent advantages in this task. However, despite the theoretical optimality properties, the high complexities in both time and space are induced and become the main obstacle of their application. In this article, we propose a novel method for multivariate regression and point matching, which exploits the sparsity structure of smooth functions. Specifically, we use compact Fourier bases for constructing the function, which inherently allows a coarse-to-fine representation. The smoothness constraint can be explicitly imposed by adopting a few low-frequency bases for representation, resulting in reduced computational complexities of the induced multivariate regression algorithm. To cope with potential gross outliers, we formulate the learning problem into a Bayesian framework with latent variables indicating the inliers and outliers and a mixture model accounting for the distribution of data, where a fast expectation-maximization solution can be derived. Extensive experiments are conducted on synthetic data and real-world image matching, and point set registration datasets, which demonstrates the advantages of our method against the current state-of-the-art methods in terms of both scalability and robustness.

Radiomics Based on Single-Phase CTA for Distinguishing Left Atrial Appendage Thrombus from Circulatory Stasis in Patients with Atrial Fibrillation before Ablation.

Differentiation of left atrial appendage thrombus (LAAT) and left atrial appendage (LAA) circulatory stasis is difficult when based only on single-phase computed tomography angiography (CTA) in routine clinical practice. Radiomics provides a promising tool for their identification. We retrospectively enrolled 204 (training set: 144; test set: 60) atrial fibrillation patients before ablation, including 102 LAAT and 102 circulatory stasis patients. Radiomics software was used to segment whole LAA on single-phase CTA images and extract features. Models were built and compared via a multivariable logistic regression algorithm and area under of the receiver operating characteristic curves (AUCs), respectively. For the radiomics model, radiomics clinical model, radiomics radiological model, and combined model, the AUCs were 0.82, 0.86, 0.90, 0.93 and 0.82, 0.82, 0.84, 0.85 in the training set and the test set, respectively (p < 0.05). One clinical feature (rheumatic heart disease) and four radiological features (transverse diameter of left atrium, volume of left atrium, location of LAA, shape of LAA) were added to the combined model. The combined model exhibited excellent differential diagnostic performances between LAAT and circulatory stasis without increasing extra radiation exposure. The single-phase, CTA-based radiomics analysis shows potential as an effective tool for accurately detecting LAAT in patients with atrial fibrillation before ablation.

Value of radiomics in differentiating synchronous double primary lung adenocarcinomas from intrapulmonary metastasis.

Distinguishing synchronous double primary lung adenocarcinoma (SDPLA) from intrapulmonary metastasis (IPM) of lung cancer has significant therapeutic and prognostic values. This study aimed to develop and validate a CT-based radiomics model to differentiate SDPLA from IPM. A total of 153 patients (93 SDPLA and 60 IPM) with 306 pathologically confirmed lesions were retrospectively studied. CT morphological features were also recorded. Region of interest (ROI) segmentation was performed semiautomatically, and 1,037 radiomics features were extracted from every segmented lesion The differences of radiomics features were defined as the relative net difference in radiomics features between the two lesions on CT. Those low reliable (ICC <0.75) and redundant (r>0.9) features were excluded by intraclass correlation coefficients (ICC) and Pearson's correlation. Multivariate logistic regression (LR) algorithm was used to establish the classification model according to the selected features. The radiomics model was based on the four most contributing differences of radiomics features. Clinical-CT model and MixModel were based on selected clinical and CT features only and the combination of clinical-CT and Rad-score, respectively. In both the training and testing cohorts, the area under the curves (AUCs) of the radiomics model were larger than those of the clinical-CT model (0.944 vs. 0.793 and 0.886 vs. 0.735 on training and testing cohorts, respectively), and statistically significant differences between the two models in the testing set were found (P<0.001). Meanwhile, three radiologists had sensitivities of 84.2%, 63.9%, and 68.4%, and specificities of 76.9%, 69.2%, and 76.9% in differentiating 19 SDPLA cases from 13 cases of IPM in the testing set. Compared with the performance of the three radiologists, the radiomics model showed better accuracy to the patients in both the training and testing cohorts. Among the three models, the radiomics model showed the best net benefits. The differences of radiomics features showed excellent diagnostic performance for preoperative differentiation between synchronous double primary lung adenocarcinoma from interpulmonary metastasis, superior to the clinical model and decisions made by radiologists.

The Evaluation of the Grade of Leaf Disease in Apple Trees Based on PCA-Logistic Regression Analysis

Extensive research suggested that the core of how to use pesticides scientifically is the careful and accurate determination of the severity of crop diseases. The existing grading standards of plant leaf diseases have been excessively singular. Thus, the diseases roughly fall into general and severe grades. To address the above problems, this study considered the effect of the distribution of disease spots, and two evaluation indicators (termed the imbalance degree and main vein distance) were newly added to optimize the grading criteria of apple leaf diseases. Combined with other factors, the grade evaluation indicator was determined through PCA principal component analysis. A gradual multivariate logistic regression algorithm was proposed to evaluate apple leaf disease grade and an optimized apple leaf disease grade evaluation model was built through PCA-logistic regression analysis. In addition, three common apple leaf diseases with a total of 4500 pictures (i.e., black rot, scab, and rust) were selected from several open-source datasets as the subjects of this paper. The object detection algorithm was then used to verify the effectiveness of the new model. As indicated by the results, it can be seen from the loss curve that the loss rate reaches a stable range of around 70 at the epoch. Compared with Faster R-CNN, the average accuracy of Mask R-CNN for the type and grade recognition of apple leaf disease was optimized by 4.91%, and the average recall rate was increased by 5.19%. The average accuracy of the optimized apple leaf disease grade evaluation model was 90.12%, marking an overall increase of 20.48%. Thus, the effectiveness of the new model was confirmed.

Identification Invasion-Related Long Non-Coding RNAs in Lung Adenocarcinoma and Analysis of Competitive Endogenous RNA Regulatory Networks.

Cell invasion plays a vital role in cancer development and progression. Aberrant expression of long non-coding RNAs (lncRNAs) is also critical in carcinogenesis. However, the prognostic value of invasion-related lncRNAs in lung adenocarcinoma (LUAD) remains unknown. Differentially expressed mRNAs (DEmRNAs), lncRNAs (DElncRNAs), and microRNAs (DEmiRNAs) were between LUAD and control samples. Pearson correlation analyses were performed to screen for invasion-related DElncRNAs (DEIRLs). Univariate and multivariate Cox regression algorithms were applied to identify key genes and construct the risk score model, which was evaluated using receiver operating characteristic (ROC) curves. Gene set enrichment analysis (GSEA) was used to explore the underlying pathways of the risk model. Moreover, an invasion-related competitive endogenous RNA (ceRNA) regulatory network was constructed. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed to detect the expression of prognostic lncRNAs in the LUAD and control samples. A total of 45 DElncRNAs were identified as DEIRLs. RP3-525N10.2, LINC00857, EP300-AS1, PDZRN3-AS1, and RP5-1102E8.3 were potential prognostic lncRNAs, the expression of which was verified by RT-qPCR in LUAD samples. Both the risk score model and nomogram used the prognostic lncRNAs. ROC curves showed the risk score model had moderate accuracy and the nomogram had high accuracy in predicting patient prognosis. GSEA results indicated that the risk score model was associated with many biological processes and pathways relevant to cell proliferation. A ceRNA regulatory network was constructed in which PDZRN3-miR-96-5p-CPEB1, EP300-AS1-miR-93-5p-CORO2B, and RP3-525N10.2-miR-130a-5p-GHR may be key invasion-related regulatory pathways in LUAD. Our study identified five novel invasion-related prognostic lncRNAs (RP3-525N10.2, LINC00857, EP300-AS1, PDZRN3-AS1, and RP5-1102E8.3) and established an accurate model for predicting the prognosis of patients with LUAD. These findings enrich our understanding of the relationships between cell invasion, lncRNAs, and LUAD and may provide novel treatment directions.

Student semester marks prediction using linear regression algorithms in machine learning

Student Mark prediction using the previous semester's internal marks and semester marks project using a multivariate linear regression algorithm. Using the real-time data sets gathered from department, and analyze the data. The project's goal is to forecast the grade for the upcoming semester so that, by taking the necessary steps, for forecast the grade and enhance the outcomes in the upcoming semester. Designer developed three machine learning models for this project using the multivariate linear regression algorithm. Among these 3 models, two models are used to predict semester marks and another model is to predict the final internal or model exam marks. With the treatment of the data points and its specifies, to increase the machine learning model's effectiveness in this project.

Trip budget prediction using multivariate linear regression algorithm in machine learning

Trip budget prediction based on the number of people, distance and duration of a local travel agency using a multivariate linear regression algorithm. Real-time data sets will be used to analyse the data, and a multivariate linear regression machine learning technique will be used to train the machine. Using factors like the number of days, the destination city, and the number of travellers, to determine the most accurate budget for the planned trip. The proposed project uses multiple models to predict the budget based on food, travel and stay expenses. This approach works even on large data set efficiently. Also, this project can be added as an additional feature to already existing platforms such as Trip Advisor, Make My Trip, Goibibo, Airbnb, Agoda etc…

A Dummy-Variable Model for Humidity-Influenced DC Film Capacitors Lifetime Estimation

Film capacitors are used in power electronic converters due to their higher reliability compared to aluminum electrolytic capacitors. Humidity is one of the critical factors affecting the aging of film capacitors. However, the existing methodologies for the lifetime estimation of film capacitors rarely consider humidity. Besides, sometimes it is hard to obtain a large amount of data in the accelerated aging test of the device. To solve these problems, this article proposes a lifetime estimation methodology that requires fewer data and testing time. In the proposed methodology, humidity dummy variables are introduced into the degradation model based on the multivariate linear regression (MLR) algorithm. The impact of introduced dummy variables with different structures on the degradation model is studied and the effect of the proposed lifetime estimation method is verified. The results show that the proposed scheme achieves a comparable performance and can be valid in reality.

Multivariate Regression in Conjunction with GA-BP for Optimization of Data Processing of Trace NO Gas Flow in Active Pumping Electronic Nose.

Exhaled nitric oxide trace gas at the ppb level is a biomarker of human airway inflammation. To detect this, we developed a method for the collection of active pumping electronic nose bionic chamber gas. An optimization algorithm based on multivariate regression (MR) and genetic algorithm-back propagation (GA-BP) was proposed to improve the accuracy of trace-level gas detection. An electronic nose was used to detect NO gas at the ppb level by substituting breathing gas with a sample gas. The impact of the pump suction flow capacity variation on the response of the electronic nose system was determined using an ANOVA. Further, the optimization algorithm based on MR and GA-BP was studied for flow correction. The results of this study demonstrate an increase in the detection accuracy of the system by more than twofold, from 17.40%FS before correction to 6.86%FS after correction. The findings of this research lay the technical groundwork for the practical application of electronic nose systems in the daily monitoring of FeNO.

P276 Development and validation of a computed tomography-based radiomic nomogram to predict early-onset surgery risk in patients with Crohn’s disease

Abstract Background Identifying patients with aggressive Crohn’s disease (CD) threatened by a high risk of early onset surgery is challenging. We aimed to establish and validate a radiomic nomogram to predict one-year surgery risk after the diagnosis of CD, thereby facilitating therapeutic strategies making. Methods Patients with CD who had undergone computed tomography enterography (CTE) examination at diagnosis were recruited and randomly divided into training and test cohorts at a ratio of 7:3. Arterial phase CTE images were obtained. Inflamed segments and mesenteric fat were semiautomatically segmented, followed by feature selection and signature building. A radiomic nomogram was constructed and validated using a multivariate logistic regression algorithm. (Figure 1) Results A total of 268 eligible patients were retrospectively included, 69 of whom underwent surgery one-year after diagnosis. A total of 1208 features from inflamed segments and 1100 features from peripheral mesenteric fat were extracted, and reduced to 11 and 16 potential predictors, respectively, to construct two radiomic signatures. By incorporating the radiomics signatures and clinical factors, the radiomic-clinical nomogram showed favorable calibration and discrimination in the training cohort, with an area under the curve (AUC) of 0.957, which was confirmed in the test set (AUC, 0.917). Decision curve analysis and net reclassification improvement index demonstrated the clinical usefulness of the nomogram. (Figure 2) Conclusion We successfully established and validated a CTE-based radiomic nomogram with both inflamed segment and mesenteric fat simultaneously evaluated to predict one-year surgery risk in CD patients, which assisted in clinical decision-making and individualized management.

Effects of immune inflammation in head and neck squamous cell carcinoma: Tumor microenvironment, drug resistance, and clinical outcomes.

Background: Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor with a very high mortality rate, and a large number of studies have confirmed the correlation between inflammation and malignant tumors and the involvement of inflammation-related regulators in the progression of HNSCC. However, a prognostic model for HNSCC based on genes involved in inflammatory factors has not been established. Methods: First, we downloaded transcriptome data and clinical information from patients with head and neck squamous cell carcinoma from TCGA and GEO (GSE41613) for data analysis, model construction, and differential gene expression analysis, respectively. Genes associated with inflammatory factors were screened from published papers and intersected with differentially expressed genes to identify differentially expressed inflammatory factor-related genes. Subgroups were then typed according to differentially expressed inflammatory factor-related genes. Univariate, LASSO and multivariate Cox regression algorithms were subsequently applied to identify prognostic genes associated with inflammatory factors and to construct prognostic prediction models. The predictive performance of the model was evaluated by Kaplan-Meier survival analysis and receiver operating characteristic curve (ROC). Subsequently, we analyzed differences in immune composition between patients in the high and low risk groups by immune infiltration. The correlation between model genes and drug sensitivity (GSDC and CTRP) was also analyzed based on the GSCALite database. Finally, we examined the expression of prognostic genes in pathological tissues, verifying that these genes can be used to predict prognosis. Results: Using univariate, LASSO, and multivariate cox regression analyses, we developed a prognostic risk model for HNSCC based on 13 genes associated with inflammatory factors (ITGA5, OLR1, CCL5, CXCL8, IL1A, SLC7A2, SCN1B, RGS16, TNFRSF9, PDE4B, NPFFR2, OSM, ROS1). Overall survival (OS) of HNSCC patients in the low-risk group was significantly better than that in the high-risk group in both the training and validation sets. By clustering, we identified three molecular subtypes of HNSCC carcinoma (C1, C2, and C3), with C1 subtype having significantly better OS than C2 and C3 subtypes. ROC analysis suggests that our model has precise predictive power for patients with HNSCC. Enrichment analysis showed that the high-risk and low-risk groups showed strong immune function differences. CIBERSORT immune infiltration score showed that 25 related and differentially expressed inflammatory factor genes were all associated with immune function. As the risk score increases, specific immune function activation decreases in tumor tissue, which is associated with poor prognosis. We also screened for susceptibility between the high-risk and low-risk groups and showed that patients in the high-risk group were more sensitive to talazoparib-1259, camptothecin-1003, vincristine-1818, Azd5991-1720, Teniposide-1809, and Nutlin-3a (-) -1047.Finally, we examined the expression of OLR1, SCN1B, and PDE4B genes in HNSCC pathological tissues and validated that these genes could be used to predict the prognosis of HNSCC. Conclusion: In this experiment, we propose a prognostic model for HNSCC based on inflammation-related factors. It is a non-invasive genomic characterization prediction method that has shown satisfactory and effective performance in predicting patient survival outcomes and treatment response. More interdisciplinary areas combining medicine and electronics will be explored in the future.

Robust statistical analysis to predict and estimate the concentration of the cannabidiolic acid in Cannabis sativa L.: A comparative study

Growing awareness of the medicinal and therapeutic benefits of cannabis has resulted in extensive research, increasing numbers of commercial growers, and the provision of commercial testing. For commercial production, the importance of monitoring for consistent cannabinoid composition is essential. Gas chromatography and/or liquid chromatography methods are effective methods. Unfortunately, they are not easily scaled up for frequent or large-scale testing. Precision cultivation for medicinal cannabis requires the industry to test large volumes of plants frequently during different stages of the growth cycle. This cannot be achieved with destructive testing alone. A statistical method with 1H nuclear magnetic resonance (NMR) is proposed and investigated to explore its potential for a cost-effective mass-screening method of plant material samples. In addition, hyperspectral imaging (HSI) is employed for frequent non-destructive measurements. This allows increasing the frequency of observations during the growth of the plant. The cannabidiolic acid (CBDA) concentration is determined by the application of both HSI and NMR spectroscopy and validated against liquid chromatography-mass spectrometry (LCMS). The paper proposes a multivariate statistical regression algorithm that automatically determines the CBDA concentration directly from bucket integration of the NMR spectrum. The algorithm successfully predicted the CBDA concentration of 7 unknown samples from data selected from 4 known samples, with an average R2 value of 0.98. The proposed statistical method was applied to the data collected for HSI. It showed that while the hyperspectral dataset can be correlated with CBDA concentration, it was subject to high variance. However, HSI retains the spatial information of the actual plant structure, allowing the CBDA prediction to be mapped back to the original spatial location in the plant while providing visual information on CBDA concentration within a flower or leaf without requiring the plant component to be destroyed in the process.