A potent new-scaffold androgen receptor antagonist discovered on the basis of a MIEC-SVM model.

Prostate cancer (PCa) is the second most prevalent malignancy among men worldwide. The aberrant activation of androgen receptor (AR) signaling has been recognized as a crucial oncogenic driver for PCa and AR antagonists are widely used in PCa therapy. To develop novel AR antagonist, a machine-learning MIEC-SVM model was established for the virtual screening and 51 candidates were selected and submitted for bioactivity evaluation. To our surprise, a new-scaffold AR antagonist C2 with comparable bioactivity with Enz was identified at the initial round of screening. C2 showed pronounced inhibition on the transcriptional function (IC50 = 0.63 µM) and nuclear translocation of AR and significant antiproliferative and antimetastatic activity on PCa cell line of LNCaP. In addition, C2 exhibited a stronger ability to block the cell cycle of LNCaP than Enz at lower dose and superior AR specificity. Our study highlights the success of MIEC-SVM in discovering AR antagonists, and compound C2 presents a promising new scaffold for the development of AR-targeted therapeutics.

Deep-Learning Uncovers certain CCM Isoforms as Transcription Factors.

BACKGROUND: Cerebral Cavernous Malformations (CCMs) are brain vascular abnormalities associated with an increased risk of hemorrhagic strokes. Familial CCMs result from autosomal dominant inheritance involving three genes: KRIT1 (CCM1), MGC4607 (CCM2), and PDCD10 (CCM3). CCM1 and CCM3 form the CCM Signal Complex (CSC) by binding to CCM2. Both CCM1 and CCM2 exhibit cellular heterogeneity through multiple alternative spliced isoforms, where exons from the same gene combine in diverse ways, leading to varied mRNA transcripts. Additionally, both demonstrate nucleocytoplasmic shuttling between the nucleus and cytoplasm, suggesting their potential role in gene expression regulation as transcription factors (TFs). Due to the accumulated data indicating the cellular localization of CSC proteins in the nucleus and their interaction with progesterone receptors, which serve dual roles as both cellular signaling components and TFs, a question has arisen regarding whether CCMs could also function in both capacities like progesterone receptors. METHODS: To investigate this potential, we employed our proprietary deep-learning (DL)-based algorithm, specifically utilizing a biased-Support Vector Machine (SVM) model, to explore the plausible cellular function of any of the CSC proteins, particularly focusing on CCM gene isoforms with nucleocytoplasmic shuttling, acting as TFs in gene expression regulation. RESULTS: Through a comparative DL-based predictive analysis, we have effectively discerned a collective of 11 isoforms across all CCM proteins (CCM1-3). Additionally, we have substantiated the TF functionality of 8 isoforms derived from CCM1 and CCM2 proteins, marking the inaugural identification of CCM isoforms in the role of TFs. CONCLUSIONS: This groundbreaking discovery directly challenges the prevailing paradigm, which predominantly emphasizes the involvement of CSC solely in endothelial cellular functions amid various potential cellular signal cascades during angiogenesis.

Prediction of personal default risks based on a sparrow search algorithm with support vector machine model.

Aiming at the personal credit evaluation of commercial banks, this paper constructs a classified prediction model based on machine learning methods to predict the default risk. At the same time, this paper proposes to combine the sparrow search algorithm (SSA) with the support vector machine (SVM) to explore the application of the SSA-SVM model in personal default risk prediction. Therefore, this paper takes the personal credit data as the original data, carries out statistical analysis, normalization and principal factor analysis, and substitutes the obtained variables as independent variables into the SSA-SVM model. Under the premise of the same model, the experimental results show that the evaluation indexes of the experimental data are better than the original data, which shows that it is effective for the data processing operation of the original data in this paper. On the premise of the same data, each evaluation index of the SSA-SVM model is better than the SVM model, which shows that the hybridized model established in this paper is better than the latter one in predicting personal default risk, and has certain practical value.

Experimental and support vector machine predictions of entropy generations and exergy efficiency of Fe3O4-SiO2/Water hybrid nanofluid in a plate heat exchanger.

Several experiments of Fe3O4-SiO2/water hybrid nanofluids with volumetric concentrations ranging from 0.2 % to 1.0 % circulating in the cold-side of a plate heat exchanger at flow rates ranging from 0.05 kg/s to 0.1166 kg/s are performed. Under these ranges of flow rates and volumetric concentrations, the flow of Fe3O4-SiO2/water hybrid nanofluids remains laminar. The results of these experiments are predicted with support vector machine (SVM) algorithm to determine hybrid nanofluid entropy generation thermal, entropy generation frictional, and efficiency of exergy. Fe3O4-SiO2 nanomaterials was synthesized with reduction of chemicals and insitu development techniques, with XRD, FTIR and VSM instruments, characterizations were done. The SVM model gives large precision predictions of the measured data with correlations coefficients of 0.9944, 0.99798, and 0.99428 for frictional entropy generation, thermal entropy generation and exergy efficiency. At a flow rate of 0.1166 kg/s in the cold-side of PHE, the exergy efficiency is found to be 77.96 % for water (Reynolds number of 935.4) and with 1.0 vol% of Fe3O4-SiO2/water hybrid nanofluid in the cold-side of PHE, the efficiency is increased to 82.97 %, respectively. Under similar conditions of 0.1166 kg/s of flow circulation and 1.0 % vol. concentration of hybrid nanofluid, the thermal entropy generation is dropped off to 18.37 %, but the frictional entropy generation is increased by 20.97 %, compared to water, with the results that the total entropy generation drops off by 15.91 %, compared to water data. Preliminary curve-fitting correlations have been developed for the frictional entropy generation, thermal entropy generation, and exergy efficiency.

SVM-Based Model Combining Patients' Reported Outcomes and Lymphocyte Phenotypes of Depression in Systemic Lupus Erythematosus.

BACKGROUND: The incidence of depression in patients with systemic lupus erythematosus (SLE) is high and leads to a lower quality of life than that in undepressed SLE patients and healthy individuals. The causes of SLE depression are still unclear. METHODS: A total of 94 SLE patients were involved in this study. A series of questionnaires (Hospital Depression Scale, Social Support Rate Scale and so on) were applied. Flow cytometry was used to test the different stages and types of T cells and B cells in peripheral blood mononuclear cells. Univariate and multivariate analyses were conducted to explore the key contributors to depression in SLE. Support Vector Machine (SVM) learning was applied to form the prediction model. RESULTS: Depressed SLE patients showed lower objective support, severer fatigue, worse sleep quality and higher percentages of ASC%PBMC, ASC%CD19+, MAIT, TEM%Th, TEMRA%Th, CD45RA+CD27-Th, TEMRA%CD8 than non-depressed patients. A learning-based SVM model combining objective and patient-reported variables showed that fatigue, objective support, ASC%CD19+, TEM%Th and TEMRA%CD8 were the main contributing factors to depression in SLE. With the SVM model, the weight of TEM%Th was 0.17, which is the highest among objective variables, and the weight of fatigue was 0.137, which was the highest among variables of patients' reported outcomes. CONCLUSIONS: Both patient-reported factors and immunological factors could be involved in the occurrence and development of depression in SLE. Scientists can explore the mechanism of depression in SLE or other psychological diseases from the above perspective.

SVM Regression to Assess Meat Characteristics of Bísaro Pig Loins Using NIRS Methodology.

This study evaluates the ability of the near infrared reflectance spectroscopy (NIRS) to estimate the aW, protein, moisture, ash, fat, collagen, texture, pigments, and WHC in the Longissimus thoracis et lumborum (LTL) of Bísaro pig. Samples (n = 40) of the LTL muscle were minced and scanned in an FT-NIR MasterTM N500 (BÜCHI) over a NIR spectral range of 4000-10,000 cm-1 with a resolution of 4 cm-1. The PLS and SVM regression models were developed using the spectra's math treatment, DV1, DV2, MSC, SNV, and SMT (n = 40). PLS models showed acceptable fits (estimation models with RMSE ≤ 0.5% and R2 ≥ 0.95) except for the RT variable (RMSE of 0.891% and R2 of 0.748). The SVM models presented better overall prediction results than those obtained by PLS, where only the variables pigments and WHC presented estimation models (respectively: RMSE of 0.069 and 0.472%; R2 of 0.993 and 0.996; slope of 0.985 ± 0.006 and 0.925 ± 0.006). The results showed NIRs capacity to predict the meat quality traits of Bísaro pig breed in order to guarantee its characterization.

Rapid identification of producing area of wheat using terahertz spectroscopy combined with chemometrics.

Wheat from different producing areas has different flavor and properties, and thus the identification of producing area of wheat is significant to assure the quality of wheat. The traditional method of producing area of wheat determination is time-consuming, complex and needs a lot of pretreatment. The purpose of this research is to develop a new method for the determination of wheat producing areas by terahertz time domain spectroscopy in combination with chemometrics. Firstly, a total of 240 wheat samples from Shandong Province, Shaanxi Province, Henan Province, Hebei Province and Anhui Province of China were collected to analyze and obtain the time-domain spectral signals, frequency-domain spectral signals, and absorption coefficient spectral signals of the samples were obtained. Then, four different preprocessing methods of Savitzky-Golay (S-G), multiplicative scatter correction (MSC), mean centering, and standard normal variate (SNV) were applied to preprocess the absorption coefficient spectral signals, and the uninformative variable elimination (UVE) was used for variable selection of THz spectra data, for developing an effective prediction model. Finally, chemometrics methods, including the partial least squares discriminant analysis (PLS-DA), back propagation neural network (BPNN) and least squares support vector machines (LS-SVM) qualitative models were used for model building and discrimination results obtained through such models were compared. According to the test results, the comprehensive discrimination accuracy of wheat from different origins by the SNV-LS-SVM model reached 96.76%, Furthermore, these results demonstrated that an accurate qualitative analysis of producing area of wheat samples could be achieved by terahertz time-domain spectroscopy combined with chemometrics, which can provide a fast and accurate solution for grain security detection and origin tracing.

Emotional wellbeing in intercity travel: Factors affecting passengers' long-distance travel moods.

The travel mood perception can significantly affect passengers' mental health and their overall emotional wellbeing when taking transport services, especially in long-distance intercity travels. To explore the key factors influencing intercity travel moods, a field survey was conducted in Xi'an to collect passengers' individual habits, travel characteristics, moods, and weather conditions. Travel mood was defined using the 5-Likert scale, based on degrees of happiness, panic, anxiety, and tiredness. A support vector machine (SVM) and ordered logit model were used in tandem for determinant identification and exploring their respective influences on travel moods. The results showed that gender, age, occupation, personal monthly income, car ownership, external temperature, precipitation, relative humidity, air quality index, visibility, travel purposes, intercity travel mode, and intercity travel time were all salient influential variables. Specifically, intercity travel mode ranked the first in affecting panic and anxiety (38 and 39% importance, respectively); whereas occupation was the most important factor affecting happiness (23% importance). Moreover, temperature appeared as the most important influencing factor of tiredness (22% importance). These findings help better understand the emotional health of passengers in long-distance travel in China.

A Five-Genes Based Diagnostic Signature for Sepsis-Induced ARDS.

Background: Acute respiratory distress syndrome (ARDS) is a frequent and serious complication of sepsis without specific and sensitive diagnostic signatures. Methods: The mRNA profiles, including 60 blood samples with sepsis-induced ARDS and 86 blood samples with sepsis alone, were obtained from the Gene Expression Omnibus (GEO). The differently expressed genes (DEGs) were analyzed by limma package of R language. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were carried out using the clusterProfiler package of R. Eventually, multivariate logistic regression model was established through the glm function of R, and support vector machine (SVM) model was constructed via the e1071 package of R. Results: A total of 242 DEGs in GSE32707 and 102 DEGs in GSE66890 were identified. Notably, five genes exhibited significant differences between the two datasets and were considered to be closely associated with the occurrence of ARDS induced by sepsis. Furthermore, functional enrichment analysis based on the DEGs showed there were 80 overlapped GO terms and one KEGG pathway which were significantly enriched in the two datasets. The logistic regression model and SVM model constructed could efficiently distinguish sepsis patients with or without ARDS. Conclusion: In brief, our study suggested that NKG7, SPTA1, FGL2, RGS2, and IFI27 might be potential diagnostic signatures for sepsis-induced ARDS, which contributed to the future exploration in mechanism of ARDS occurrence and development.

IL-8, MSPa, MIF, FGF-9, ANG-2 and AgRP collection were identified for the diagnosis of colorectal cancer based on the support vector machine model.

Colorectal cancer (CRC) is one of the most common cancer, and the early detection of CRC is essential to improve the survival rate of patients. To identify diagnostic markers for colorectal cancer (CRC) by screening differentially expressed proteins (DEPs) in CRC. The DEPs were initially obtained from 12 CRC samples and 12 healthy control samples, and verification analysis was performed in another 34 CRC samples and 34 normal controls. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment with DEPs was analyzed by the R package clusterProfiler (Version 3.2.11), and the DEP-associated protein-protein interaction (PPI) network was created from the STRING database. Additionally, Support Vector Machine (SVM) model prediction and survival analyses were conducted on the key DEPs. Preliminary screening and functional analysis showed that the DEPs mainly overrepresented in pathways such as cytokine-cytokine receptor interaction, chemokine signaling pathway, Rap1, Ras, and MAPK signaling pathways. The key DEPs, including AgRP, ANG-2, Dtk, EOT3, FGF-4, FGF-9, HCC-4, IL-16, IL-8, MIF, MSPa, TECK, TPO, TRAIL R3, and VEGF-D, were used to construct a custom chip. The drug-gene interaction network suggested that TPO was a key drug target. ROC curve showed the SVM diagnostic model with the DEPs IL-8, MSPa, MIF, FGF-9, ANG-2, and AgRP had better diagnostic performance with an AUC of 0.933. Survival analysis showed the expression of FGF9, TPO, TRAIL R3, Dtk, TECK and FGF4 were associated with prognosis. This study revealed the important serum proteins in the pathogenesis of CRC, which might serve as useful and noninvasive predictors for the diagnosis of CRC.

Clinical value of machine learning-based interpretation of I-123 FP-CIT scans to detect Parkinson's disease: a two-center study.

PURPOSE: Our aim was to develop and validate a machine learning (ML)-based approach for interpretation of I-123 FP-CIT SPECT scans to discriminate Parkinson's disease (PD) from non-PD and to determine its generalizability and clinical value in two centers. METHODS: We retrospectively included 210 consecutive patients who underwent I-123 FP-CIT SPECT imaging and had a clinically confirmed diagnosis. Linear support vector machine (SVM) was used to build a classification model to discriminate PD from non-PD based on I-123-FP-CIT striatal uptake ratios, age and gender of 90 patients. The model was validated on unseen data from the same center where the model was developed (n = 40) and consecutively on data from a different center (n = 80). Prediction performance was assessed and compared to the scan interpretation by expert physicians. RESULTS: Testing the derived SVM model on the unseen dataset (n = 40) from the same center resulted in an accuracy of 95.0%, sensitivity of 96.0% and specificity of 93.3%. This was identical to the classification accuracy of nuclear medicine physicians. The model was generalizable towards the other center as prediction performance did not differ thereby obtaining an accuracy of 82.5%, sensitivity of 88.5% and specificity of 71.4% (p = NS). This was comparable to that of nuclear medicine physicians (p = NS). CONCLUSION: ML-based interpretation of I-123-FP-CIT scans results in accurate discrimination of PD from non-PD similar to visual assessment in both centers. The derived SVM model is therefore generalizable towards centers using comparable acquisition and image processing methods and implementation as diagnostic aid in clinical practice is encouraged.

Raman spectroscopy of potential bio-hazards commonly found in bio-aerosols.

Pathogenic bio-aerosols are a threat to public health today, and thus quick detection and identification is of paramount importance. In this study, Raman spectroscopy was used to test 14 types of pollens, one type of fungus and two types of bacteria that are commonly found in bio-aerosols. For bacteria and fungus, surface enhanced Raman spectroscopy (SERS) was used due to their relatively weak signals. Data analysis was performed on the Raman measurement results; principal component analysis was used to reduce the dimension of the data, and support vector machine was used to establish a model for sample identification. The model was able to identify data in the validation set with high (97.3%) accuracy.

Estimation of chlorophyll-a Concentration of lakes based on SVM algorithm and Landsat 8 OLI images.

Chlorophyll-a (Chl-a) is the main component of phytoplankton and an important index of water quality. Pearson correlation analysis is conducted on measured Chl-a concentration and band reflectance to determine the sensitive bands or multiband combinations of the Chl-a to input to a support vector machine (SVM) model. An indicator ß is defined to evaluate the model performance of fitting and prediction. The model performs well with the lowest ß (decision coefficient, (R2) = 0.774; root mean square error (RMSE) = 22.636 µg/L) of the validation set. The model test results prove that the model performs well. We analyze the impact factors of the model. The seasonal factor affects the model performance significantly; thus, samples from different seasons should be combined to train the model and inverse the water quality. Noise points reduce the model accuracy significantly; therefore, obvious outliers must be excluded at first. Additionally, the sampling method affects model accuracy, and systematic sampling in the descending order of Chl-a concentration is recommended. The combination of SVM algorithm and remote sensing technology provides a convenient, scientific, and real-time method to monitor and control water quality.

Identification of potential transcriptomic markers in developing asthma: An integrative analysis of gene expression profiles.

The goal of this study was to identify potential transcriptomic markers in developing asthma by an integrative analysis of multiple public microarray data sets. Using the R software and bioconductor packages, we performed a statistical analysis to identify differentially expressed (DE) genes in asthma, and further performed functional interpretation (enrichment analysis and co-expression network construction) and classification quality evaluation of the DE genes identified. 3 microarray datasets (192 cases and 91 controls in total) were collected for this analysis. 62 DE genes were identified in asthma, among which 43 genes were up-regulated and 19 genes were down-regulated. The up-regulated gene with the highest Log2 Fold Change (LFC) was CLCA1 (LFC=2.81). The down-regulated gene with the highest absolute LFC was BPIFA1 (LFC=-1.45). Enrichment analysis revealed that those DE genes strongly associated with proteolysis, retina homeostasis, humoral immune response, and salivary secretion. A support vector machine classifier (asthma versus healthy control) was also trained based on DE genes. In conclusion, the consistently DE genes identified in this study are suggested as candidate transcriptomic markers for asthma diagnosis, and provide novel insights into the pathogenesis of asthma.

Rapid Detection of K1 Hypervirulent Klebsiella pneumoniae by MALDI-TOF MS.

Hypervirulent strains of Klebsiella pneumoniae (hvKP) are genetic variants of K. pneumoniae which can cause life-threatening community-acquired infection in healthy individuals. Currently, methods for efficient differentiation between classic K. pneumoniae (cKP) and hvKP strains are not available, often causing delay in diagnosis and treatment of hvKP infections. To address this issue, we devised a Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) approach for rapid identification of K1 hvKP strains. Four standard algorithms, genetic algorithm (GA), support vector machine (SVM), supervised neural network (SNN), and quick classifier (QC), were tested for their power to differentiate between K1 and non-K1 strains, among which SVM was the most reliable algorithm. Analysis of the receiver operating characteristic curves of the interest peaks generated by the SVM model was found to confer highly accurate detection sensitivity and specificity, consistently producing distinguishable profiles for K1 hvKP and non-K1 strains. Of the 43 K. pneumoniae modeling strains tested by this approach, all were correctly identified as K1 hvKP and non-K1 capsule type. Of the 20 non-K1 and 17 K1 hvKP validation isolates, the accuracy of K1 hvKP and non-K1 identification was 94.1 and 90.0%, respectively, according to the SVM model. In summary, the MALDI-TOF MS approach can be applied alongside the conventional genotyping techniques to provide rapid and accurate diagnosis, and hence prompt treatment of infections caused by hvKP.

Remifentanil blood concentration forecast model based on support vector machine with particle swarm optimization / 中国药学杂志

OBJECTIVE: To develop a SVM model which is constructed by using particle swarm optimization to a predict the plasma concentration of remifentail. METHODS: This research establishes a PSO-SVM model which is constructed by using particle swarm optimization to a predict the plasma concentration of remifentanil. The model was capable of capturing the nonlinear relationship among plasma concentration, time, and the patient's signs exactly. RESULTS: The average error of PSO-SVM is -1.07%, while that of NONMEM is -2.24%. The absolute average error of PSO-SVM is 9.09%, while that of NONMEM is 19.92%. CONCLUSION: Experimental results indicate that PSO-SVM model could predict the plasma concentration of remifentanil rapidly and stably, with high accuracy and low error. For the characteristic of simple principle and fast computing speed, this method is suitable to data analysis of short-acting anesthesia drug population pharmacokinetics and pharmacodynamics.