Functional prediction of unidentified lipids using supervised classifiers

Screening and characterizing the quality markers of Selaginella tamariscina (P. Beauv.) Spring using metabonomics and molecular networking

Objective: Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesions. Methods: Human DNA methylation detection was performed on 224 cervical exfoliated cell specimens from the Cancer Hospital of the Chinese Academy of Medical Sciences, Tianjin Central Hospital of Gynecology Obstetrics, Xinmi Maternal and Child Health Hospital of Henan Province, West China Second Affiliated Hospital of Sichuan University, and Heping Hospital Affiliated to Changzhi Medical College collected during April 2014 and March 2015. The hypermethylated gene fragments related to cervical cancer were selected by high-density, high-association, and hypermethylated gene fragment screening and the LASSO regression algorithm. Taking cervical intraepithelial neoplasia grade 2 (CIN2) or more severe lesions as the research outcome, machine learning predictive models based on the random forest (RF), naive Bayes (NB), and support vector machine (SVM) algorithm, respectively, were constructed. A total of 144 outpatient specimens were used as the training set and 80 cervical exfoliated cell specimens from women participating in the cervical cancer screening program were used as the test set to verify the predictive models. Using histological diagnosis results as the gold standard, the detection efficacy for CIN2 or more severe lesions of the three machine learning predictive models were compared with that of the human papilloma virus (HPV) detection and cytological diagnosis. Results: In the training set of 144 cases, there were 34 cases of HPV positivity, with a positive rate of 23.61%. Cytologically, there were 37 cases diagnosed as no intraepithelial lesion or malignancy (NILM), and 107 cases diagnosed as atypical squamous cells of undetermined significance (ASC-US) or above. Histologically, there were 28 cases without cervical intraepithelial neoplasia or benign cervical lesions, 31 cases of CIN1, 18 cases of CIN2, 31 cases of CIN3, and 36 cases of squamous cell carcinoma. Seven hypermethylated gene fragments were selected from 45 genes, and three machine learning prediction models based on the RF, NB, and SVM algorithm, respectively, were constructed. In the validation set of 80 cases, there were 28 cases of HPV positivity, with a positive rate of 35.00%. Cytologically, there were 65 cases diagnosed as NILM and 15 cases as ASC-US or above. Histologically, there were 39 cases without cervical intraepithelial neoplasia or benign cervical lesions, 10 cases of CIN1, 10 cases of CIN2, 11 cases of CIN3, and 10 cases of squamous cell carcinoma. In the validation set, the area under the curve (AUC) values of the RF model, NB model, SVM model, HPV detection, and cytological diagnosis of CIN2 or above were 0.90, 0.88, 0.82, 0.68, and 0.45, respectively. The DeLong test showed that there was no statistically significant difference in the AUC values between the RF, NB, and SVM models (all P＞0.05), and the AUC values of the RF and NB models were higher than that of HPV detection (both P＜0.01), and the AUC values of the RF, NB, and SVM models were higher than that of cytological diagnosis (all P＜0.01). Compared with the NB model, the sensitivity of the RF model was similar (80.65% vs. 77.42%), but the specificity of the NB model was much higher than that of the RF model (93.88% vs. 73.47%). Conclusion: Among the machine learning prediction models for cervical cancer and precancerous lesions constructed based on human DNA methylation, the NB model has good predictive performance for CIN2 and above lesions, and may be used for screening of cervical cancer and precancerous lesions.

Objective. This study evaluated the potential of interleukin 12 receptor beta 2 and tumor necrosis factor receptor superfamily member 8 as diagnostic biomarkers of oral lichen planus (OLP). Materials and methods. The mRNA expression of IL12RB2 and TNFRSF8 in FFPE OLP samples (OLP group, n = 38) were investigated with quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) analysis and compared to those of chronic non-specific mucositis (Non-OLP group, n = 25) and normal mucosa (Normal group, n = 18). Predictive modeling of the expression of IL12RB2 and TNFRSF8 was constructed using support vector machine (SVM), random forest (RF), linear discriminant analysis (LDA), neural network (NN) and naive Bayes (NB) methods. Results. Normalized expression of IL12RB2 in the OLP group (3.78 ± 1.67) was significantly higher than the Normal group (1.97 ± 1.12), but lower than the Non-OLP group (6.86 ± 1.67). TNFRSF8 gene expression in the OLP group (7.46 ± 1.51) was significantly higher than the Normal group (2.90 ± 1.61), but no significant difference was found between the OLP and Non-OLP groups. The ratio of IL12RB2/TNFRSF8 in the OLP group (0.52 ± 0.23) was significantly lower than the Normal group (0.74 ± 0.39) and the Non-OLP group (1.07 ± 0.38). In the predictive modeling, the area under receiver operating characteristic (ROC) curves (AUC) ranged from 0.83–0.92 and their accuracy was higher than 0.75 in all methods. Conclusions. The IL12RB2/TNFRSF8 ratio can be a useful diagnostic tool for OLP.

This research conducts Classification of Public Perceptions of Transportation Access Development in Darmo Corridor Surabaya using SVM and Naive Bayes methods. The increasing mobility demands necessitate adequate public transportation facilities, such as the Surabaya bus, which has been operational since 2018. The Darmo corridor has witnessed significant development in public transportation facilities and infrastructure, serving as a transit point between two Surabaya bus routes. The purpose of this study is to evaluate public perception towards the implementation of Transit Oriented Development (TOD) concept in the corridor. In this study, a qualitative descriptive analysis was conducted on various aspects of TOD implementation, such as pedestrian paths, bicycle lanes, and green open spaces. Furthermore, public perception was analyzed using Naive Bayes Classifier and SVM methods by collecting data from Twitter accounts and giving weights based on the frequency of words in the tweets. From the analysis it could be concluded Naïve Bayes is not suitable for classification because it’s not stable enough, the SVM Methods matchs 95% proven when its repeated for 5 more time it. The results showed that the Darmo corridor successfully implemented the TOD concept well, with 90% of the community giving a positive response to the TOD implementation in the area. This finding indicates the success of TOD initiatives in improving transportation facilities and infrastructure in Darmo corridor.

The rapid dissemination of information related to the K-Pop world, facilitated by social media, has made it easier to follow developments and controversies. One notable case that sparked extensive discussion on Twitter was the bullying allegations against Kim Garam of LE SSERAFIM. Researchers, using Twitter data, sought to analyze Indonesian public sentiment regarding this case through sentiment analysis, which classifies opinions as positive or negative. For processing textual data, text mining methods, particularly classification techniques, are employed. Two popular algorithms in text mining are the Naive Bayes classifier and the support vector machine (SVM). The Naive Bayes classifier is favored for its speed, simplicity, and high accuracy, while the SVM excels at identifying a hyperplane that maximizes the margin between classes. In this study, sentiment classification results were labeled as either positive or negative. The comparison between the Naive Bayes classifier and SVM for classifying responses to Kim Garam's bullying case on Twitter showed high accuracy rates: 93% for Naive Bayes and 97% for SVM. The higher accuracy of the SVM algorithm indicates its superiority over the Naive Bayes classifier in this context.

The current investigation studied and verified an accurate Ultra performance liquid chromatography mass spectroscopy/ mass spectroscopy technique for the high-throughput detection of pomalidomide in human plasma with celecoxib as an internal standard. Chromatographic studies were carried out on Hypersil BDS (50 mm x 4.1 mm, 5m) with an isocratic mobile phase of acetonitrile: ammonium formate (80:20, V/V), at a flow rate of 0.5 mL min-1 and a total run time of 3 min. A triple quadruple Tandem Mass spectroscopy with electrospray ionization in positive mode was utilized to identify the analyte. Using multiple reaction monitoring modes and precursor-to-product ion transitions of 274.43 m/z for pomalidomide and 382.12 m/z for standard, respectively, pomalidomide and internal standard were measured. The developed technique was verified as per the regulatory standards for bioanalytical procedures validation. Other validation results also match.

Deep learning analysis of UPLC-MS/MS-based metabolomics data to predict Alzheimer's disease

The aim of this study was to develop a simple, sensitive ultra performance liquid chromatography mass spectrometry (UPLC-MS/MS) method for the determination of syringaresinol, N-trans-feruloyltyramine, chelerythrine chloride, sinomenine, coptisine chloride, sanguinarine, chelidonine, magnolflorine, allocryptopine, protopine, farrerol, stylopine and dihydrosanguin-arine in Tong'an injection (TAI), which could be used for the quality control of TAI. The UPLC analysis was performed on Agilent Zorbax SB-Aq column (2.1 mm×150 mm,3.5 μm), with 0.1% formic acid solution (A) -acetonitrile (B) as the mobile phase for gradient elution (0.01-2 min, 5%B; 2-8 min, 5%-30%B; 8-11 min, 30%-95%B; 11-13 min, 95%B; 13-13.1 min, 95%-5%B; 13.1-14 min, 5%B). The flow rate was 0.5 mL•min⁻¹, and the column temperature was 25 ℃; multiple reaction monitoring (MRM) was performed in electrospray ion source positive ion mode for quantitative determination. The calibration curves for the above thirteen compounds showed good linear relationship in corresponding mass concentration range (r>0.999 0). The average recovery rate of the compounds ranged from 95.70% to 104.8%, with RSD of less than 1.9%. The contents of thirteen active components in 10 batches of TAI were 0.021 2-0.029 0, 0.001 7-0.002 3, 0.000 9-0.001 3, 5.952-6.205 2, 0.195 4-0.240 5, 0.002 0-0.002 9, 0.693-0.798 2, 0.069 3-0.078 2, 0.089 29-0.102 9, 0.386 5-0.420 1, 0.014 3-0.015 9, 0.755 3-0.842 1, and 0.008 2-0.011 2 g•L⁻¹ respectively. Methodology validation proved that this method was simple, rapid, sensitive and accurate, which can be used to provide reference for the comprehensive evaluation of TAI quality. The determination results of 10 batches of TAI showed the content of each batch had no significant difference. The results may provide a basis for the quality control of TAI.

Nonsteroidal anti-inflammatory drugs (NSAIDs), such as naproxen (Nap), are among the most widely consumed medications worldwide. However, wastewater treatments are inefficient at removing them, leading to their persistence in the environment. In this study, bacteria isolated from the contaminated lagoon "La Escondida", Reynosa, Mexico, were initially screened for tolerance to pharmaceutical drugs, including Nap. Ten bacterial isolates showed growth in the presence of both Amp (ampicillin) and Nap (100mg l-1). Isolate P43-LBF was identified as a Bacillus group, showing high genomic similarity (ANI 98.26 %) to Bacillus anthracis but lacking the pOX1 and pOX2 virulence plasmids. It caused a degradation of 67.43 % and 43.78 % (5 and 25mg l-1, respectively) of Nap in glucose-supplemented minimal medium in seven days. Degradation products, such as 6-O-desmethylnaproxen, were detected by Ultra Performance Liquid Chromatography Mass Spectrometry (UPLC-MS), indicating active biotransformation. Genomic analysis revealed genes associated with xenobiotic degradation, including catE, encoding catechol-2,3-dioxygenase. Additionally, genes associated with virulence and resistance were also identified, which support stress tolerance. Phylogenetic analysis of Bacillus species known for their ability to degrade aromatic compounds supports their potential application in pharmaceutical bioremediation. These findings highlight strain P43-LBF as a promising candidate for pharmaceutical bioremediation.

Progress in mass spectrometry for the analysis of set-off phenomena in plastic food packaging materials

Analytical methods for determination of anticoagulant rodenticides in biological samples

The arrival of Rohingya refugees in Indonesia has become a highly controversial topic, eliciting various responses from the public. In this context, public sentiment analysis regarding the arrival of Rohingya refugees is crucial for understanding the dynamics of feelings, opinions, and attitudes of the Indonesian society towards this issue. In conducting public sentiment analysis, the selection of methods is crucial to ensure accurate results. The aim of this research is to conduct sentiment analysis regarding the arrival of Rohingya refugees using the Support Vector Machine (SVM) and Naive Bayes methods. The main focus is to evaluate public sentiment and compare the performance of both methods. Two common methods used in sentiment analysis are Support Vector Machine (SVM) and Naïve Bayes. This research utilized a dataset of 3350 tweets to conduct public sentiment analysis on the arrival of Rohingya refugees in Indonesia. In this study, data was divided using the 70:30 split method, where 70% of the data was used for model training and 30% for model testing. The research findings indicate that the SVM model has an accuracy of 76%, while the Naïve Bayes model has an accuracy of 70%. This suggests that the SVM model is better at predicting sentiments and has lower error rates compared to the Naïve Bayes model.

One of the most important problems of telecommunication companies is the potential transfer of customers between the firms. In order to avoid this problem, it is very important to identify customers who are likely to leave. In this study, the performance of the classification and the clustering algorithms in machine learning techniques has been evaluated and compared on the analysis of potential customer trends, which have been reported as churn analysis. K nearest neighbors, decision trees, random forests, support vector machines and naive bayes methods were tested in scope of classification idea. Additionally, K-Means and hierarchical clustering methods were tested. The performances of the methods have been evaluated according to the accuracy, precision, sensitivity and F-measure performance metrics.

Accuracy of discriminant analysis methods to classify activities of subjects with Parkinson's Disease using wearable sensors

Background and aimsThe birth weight of a newborn is a crucial factor that affects their overall health and future well-being. Low birth weight (LBW) is a widespread global issue, which the World Health Organization defines as weighing less than 2,500 g. LBW can have severe negative consequences on an individual’s health, including neonatal mortality and various health concerns throughout their life. To address this problem, this study has been conducted using BDHS 2017–2018 data to uncover important aspects of LBW using a variety of machine learning (ML) approaches and to determine the best feature selection technique and best predictive ML model.MethodsTo pick out the key features, the Boruta algorithm and wrapper method were used. Logistic Regression (LR) used as traditional method and several machine learning classifiers were then used, including, DT (Decision Tree), SVM (Support Vector Machine), NB (Naïve Bayes), RF (Random Forest), XGBoost (eXtreme Gradient Boosting), and AdaBoost (Adaptive Boosting), to determine the best model for predicting LBW. The model’s performance was evaluated based on the specificity, sensitivity, accuracy, F1 score and AUC value.ResultsResult shows, Boruta algorithm identifies eleven significant features including respondent’s age, highest education level, educational attainment, wealth index, age at first birth, weight, height, BMI, age at first sexual intercourse, birth order number, and whether the child is a twin. Incorporating Boruta algorithm’s significant features, the performance of traditional LR and ML methods including DT, SVM, NB, RF, XGBoost, and AB were evaluated where LR, had a specificity, sensitivity, accuracy and F1 score of 0.85, 0.5, 85.15% and 0.915. While the ML methods DT, SVM, NB, RF, XGBoost, and AB model’s respective accuracy values were 85.35%, 85.15%, 84.54%, 81.18%, and 84.41%. Based on the specificity, sensitivity, accuracy, F1 score and AUC, RF (specificity = 0.99, sensitivity = 0.58, accuracy = 85.86%, F1 score = 0.9243, AUC = 0.549) outperformed the other methods. Both the classical (LR) and machine learning (ML) models’ performance has improved dramatically when important characteristics are extracted using the wrapper method. The LR method identified five significant features with a specificity, sensitivity, accuracy and F1 score of 0.87, 0.33, 87.12% and 0.9309. The region, whether the infant is a twin, and cesarean delivery were the three key features discovered by the DT and RF models, which were implemented using the wrapper technique. All three models had the identical F1 score of 0.9318. However, “child is twin” was recognized as a significant feature by the SVM, NB, and AB models, with an F1 score of 0.9315. Ultimately, with an F1 score of 0.9315, the XGBoost model recognized “child is twin” and “age at first sex” as relevant features. Random Forest again beat the other approaches in this instance.ConclusionsThe study reveals Wrapper method as the optimal feature selection technique. The ML method outperforms traditional methods, with Random Forest (RF) being the most effective predictive model for Low-Birth-Weight prediction. The study suggests that policymakers in Bangladesh can mitigate low birth weight newborns by considering identified risk factors.