Quality assessment of Astragali Radix based on pseudo-targeted metabolomics and chemometric approach.

Astragali Radix is widely used because of its dual use in medicine and food, and its quality evaluation is of great importance. In this study, a pseudo-targeted metabolomics approach based on scheduled multiple reaction monitoring was developed, and a total of 114 compounds with good linearity, sensitivity, and reproducibility were selected for relative quantification, and the chemical differences between Astragali Radix of different growth patterns were further compared by chemometric analysis. With the help of multivariate and univariate analysis, 26 differential compounds between wild/semi-wild Astragali Radix and cultivated Astragali Radix were determined. Then five marker compounds were screened out by lasso regression, and further verified by systematic clustering, random forest, support vector machine, and logistic regression. In addition, malonyl-substituted flavonoids showed relatively higher content in wild/semi-wild Astragali Radix. Thus, the malonyl substitution was characteristic for flavonoids in wild/semi-wild Astragali Radix. In conclusion, the application of pseudo-targeted metabolomics and various statistical methods could offer multi-dimensional information for the holistic quality evaluation of Astragali Radix.

[Establishment of a fast discriminant model with electronic nose for Polygonati Rhizoma mildew based on odor variation].

The odor fingerprint of Pollygonati Rhizoma samples with different mildewing degrees was analyzed and the relationship between the odor variation and the mildewing degree was explored. A fast discriminant model was established according to the response intensity of electronic nose. The α-FOX3000 electronic nose was applied to analyze the odor fingerprint of Pollygonati Rhizoma samples with different mildewing degrees and the radar map was used to analyze the main contributors among the volatile organic compounds. The feature data were processed and analyzed by partial least squares discriminant analysis(PLS-DA), K-nearest neighbor(KNN), sequential minimal optimization(SMO), random forest(RF) and naive Bayes(NB), respectively. According to the radar map of the electronic nose, the response values of three sensors, namely T70/2, T30/1, and P10/2, increased with the mildewing, indicating that the Pollygonati Rhizoma produced alkanes and aromatic compounds after the mildewing. According to PLS-DA model, Pollygonati Rhizoma samples of three mildewing degrees could be well distinguished in three areas. Afterwards, the variable importance analysis of the sensors was carried out and then five sensors that contributed a lot to the classification were screened out: T70/2, T30/1, PA/2, P10/1 and P40/1. The classification accuracy of all the four models(KNN, SMO, RF, and NB) was above 90%, and KNN was most accurate(accuracy: 97.2%). Different volatile organic compounds were produced after the mildewing of Pollygonati Rhizoma, and they could be detected by electronic nose, which laid a foundation for the establishment of a rapid discrimination model for mildewed Pollygonati Rhizoma. This paper shed lights on further research on change pattern and quick detection of volatile organic compounds in moldy Chinese herbal medicines.

Discriminant models for the prediction of postponed viral shedding time and disease progression in COVID-19.

BACKGROUND: COVID-19 infection can cause life-threatening respiratory disease. This study aimed to fully characterize the clinical features associated with postponed viral shedding time and disease progression, then develop and validate two prognostic discriminant models. METHODS: This study included 125 hospitalized patients with COVID-19, for whom 44 parameters were recorded, including age, gender, underlying comorbidities, epidemiological features, laboratory indexes, imaging characteristics and therapeutic regimen, et al. Fisher's exact test and Mann-Whitney test were used for feature selection. All models were developed with fourfold cross-validation, and the final performances of each model were compared by the Area Under Receiving Operating Curve (AUROC). After optimizing the parameters via L2 regularization, prognostic discriminant models were built to predict postponed viral shedding time and disease progression of COVID-19 infection. The test set was then used to detect the predictive values via assessing models' sensitivity and specificity. RESULTS: Sixty-nine patients had a postponed viral shedding time (> 14 days), and 28 of 125 patients progressed into severe cases. Six and eleven demographic, clinical features and therapeutic regimen were significantly associated with postponed viral shedding time and disease progressing, respectively (p < 0.05). The optimal discriminant models are: y1 (postponed viral shedding time) = - 0.244 + 0.2829x1 (the interval from the onset of symptoms to antiviral treatment) + 0.2306x4 (age) + 0.234x28 (Urea) - 0.2847x34 (Dual-antiviral therapy) + 0.3084x38 (Treatment with antibiotics) + 0.3025x21 (Treatment with Methylprednisolone); y2 (disease progression) = - 0.348-0.099x2 (interval from Jan 1st,2020 to individualized onset of symptoms) + 0.0945x4 (age) + 0.1176x5 (imaging characteristics) + 0.0398x8 (short-term exposure to Wuhan) - 0.1646x19 (lymphocyte counts) + 0.0914x20 (Neutrophil counts) + 0.1254x21 (Neutrphil/lymphocyte ratio) + 0.1397x22 (C-Reactive Protein) + 0.0814x23 (Procalcitonin) + 0.1294x24 (Lactic dehydrogenase) + 0.1099x29 (Creatine kinase).The output ≥ 0 predicted postponed viral shedding time or disease progressing to severe/critical state. These two models yielded the maximum AUROC and faired best in terms of prognostic performance (sensitivity of78.6%, 75%, and specificity of 66.7%, 88.9% for prediction of postponed viral shedding time and disease severity, respectively). CONCLUSION: The two discriminant models could effectively predict the postponed viral shedding time and disease severity and could be used as early-warning tools for COVID-19.

[Differential bioinformational model for diagnostics of inflammatory and tumor diseases of the parotid salivary gland]. / Differentsial'naya bioinformatsionnaya model' diagnostiki vospalitel'nykh i opukholevykh zabolevanii okoloushnoi slyunnoi zhelezy.

THE AIM: Of the work was to develop a diagnostic algorithm for the differentiation of chronic inflammatory, benign and malignant processes in the parotid salivary gland (PSG) by the ratio of pro- and anti-inflammatory cytokines in the oral fluid. MATERIALS AND METHODS: The epidemiological group of patients with cancer of the parotid salivary gland included 140 people from the oncological register of the Rostov region with the date of diagnosis, from 1969 to 2020. The clinical part of the work was performed on 70 patients of both sexes aged 50 to 80 years: 15 patients with chronic nonspecific parenchymal sialadenitis of the PSG (ICD K11.2) (group 1), 19 patients with pleomorphic adenoma of the PSG (ICD D11.0) (2 group), 20 patients with cancer of the PSG (ICD C07) (group 3) and 16 healthy individuals without pathology of the oral cavity (control group). The concentration of interleukin-6 (IL-6) and interleukin-10 (IL-10) was determined in the oral fluid by enzyme immunoassay. RESULTS: It was found that in 58.5% of cases at the initial examination of patients with PSG cancer referred to a tertiary care hospital an erroneous opinion was formed about the inflammatory origin of the process. In inflammatory and tumor lesions of the PSG multidirectional differences are noted in the ratio between the concentrations of pro- and anti-inflammatory mediators in the oral fluid. In chronic sialadenitis of PSG in the oral fluid a moderate increase in the levels of IL-6 and IL-10 occurs, in the presence of adenoma of PSG, the concentration of IL-6 does not change while IL-10 increases threefold, and there is a sharp and unidirectional increase in the concentration of cytokines of the opposite groups in case of a malignant lesion of PSG. CONCLUSION: Comparison of the concentration of IL-6 and IL-10 in saliva and their ratio defined by the developed discriminant models helps to make an individual diagnostic decision in a specific clinical situation.

Application of combined cerebrospinal fluid physicochemical parameters to detect intracranial infection in neurosurgery patients.

Routine test of cerebrospinal fluid (CSF), such as glucose concentrations, chloride ion, protein and leukocyte, as well as color, turbidity and clot, were important indicators for intracranial infection. However, there were no models to predict the intracranial infection with these parameters. We collected data of 221 cases with CSF positive-culture and 50 cases with CSF negative culture from January 1, 2016 to December 31, 2018 in the First Affiliated Hospital of Nanchang University, China. SPSS17.0 software was used to establish the model by adopting seven described indicators, and P < 0.05 was considered as statistically significant. Meanwhile, 40 cases with positive-culture and 10 cases with negative-culture were selected to verify the sensitivity and specificity of the model. The results showed that each parameter was significant in the model establishment (P < 0.05). To extract the above seven parameters, the interpretation model C was established, and C = 0.952-0.183 × glucose value (mmol/L) - 0.024 × chloride ion value (mmol/L)- 0.000122 × protein value (mg/L) - 0.0000859 × number of leukocytes per microliter (× 106/L) + 1.354 × color number code + 0.236 × turbidity number code + 0.691 × clot number code. In addition, the diagnostic sensitivity and specificity of the model were 85.0 and 100%, respectively. The combining application of seven physicochemical parameters of CSF might be of great value in the diagnosis of intracranial infection for adult patients.

Usefulness of milk mid-infrared spectroscopy for predicting lameness score in dairy cows.

The objective of this study was to evaluate the ability of milk infrared spectra to predict cow lameness score (LMS) for use as an indicator of cow health on Australian dairy farms, or as an indicator trait for genetic evaluation purposes. The study involved 3,771 cows from 10 farms in Australia. Milk infrared spectra collected during the monthly herd testing were available in all the farms involved in the study. Lameness score was measured once in each herd, within 72 h from a test day, and merged to the closest spectra records. Lameness score was expressed on a scale from 0 to 3, where 0 is assigned to sound cows and scores 1 to 3 are assigned to cows with increased lameness severity. Partial least squares discriminant analysis was used to develop prediction models for classifying sound (score 0) and not-sound cows (i.e., cows walking unevenly, score greater than 0). Discriminant models were tested in a 10-fold random cross-validation process. Milk infrared spectra correctly classified only 57% of the cows walking unevenly and only 59% of the sound cows. When additional predictors (parity, age at calving, days in milk, and milk yield) were included in the prediction model, the model correctly classified 57% of the cows walking unevenly and 62% of the sound cows. The same model applied only to the cows in the first third of lactation correctly classified 66% of the cows walking unevenly and 57% of the sound cows. When the prediction model was used to identify lame cows (scores 2 and 3), only 49% of them were classified as such. These results are considered to be too poor to envisage a practical application of these models in the near future as on-farm tools to provide an indication of LMS. To investigate whether, at this stage, predictions of the LMS could be useful as large-scale phenotypes for animal breeding purposes, we estimated (co)variance components for actual and predicted LMS using 2,670 and 24,560 records, respectively. As the genetic correlation between actual and predicted LMS was not significantly different from zero, predictions of lameness from milk spectra and additional on-farm variables cannot be used, at this stage, as an indicator trait for actual LMS. More research is needed to find better strategies to predict lameness.

Validation of Breast Cancer Margins by Tissue Spray Mass Spectrometry.

Current methods for the intraoperative determination of breast cancer margins commonly suffer from the insufficient accuracy, specificity and/or low speed of analysis, increasing the time and cost of operation as well the risk of cancer recurrence. The purpose of this study is to develop a method for the rapid and accurate determination of breast cancer margins using direct molecular profiling by mass spectrometry (MS). Direct molecular fingerprinting of tiny pieces of breast tissue (approximately 1 × 1 × 1 mm) is performed using a home-built tissue spray ionization source installed on a Maxis Impact quadrupole time-of-flight mass spectrometer (qTOF MS) (Bruker Daltonics, Hamburg, Germany). Statistical analysis of MS data from 50 samples of both normal and cancer tissue (from 25 patients) was performed using orthogonal projections onto latent structures discriminant analysis (OPLS-DA). Additionally, the results of OPLS classification of new 19 pieces of two tissue samples were compared with the results of histological analysis performed on the same tissues samples. The average time of analysis for one sample was about 5 min. Positive and negative ionization modes are used to provide complementary information and to find out the most informative method for a breast tissue classification. The analysis provides information on 11 lipid classes. OPLS-DA models are created for the classification of normal and cancer tissue based on the various datasets: All mass spectrometric peaks over 300 counts; peaks with a statistically significant difference of intensity determined by the Mann-Whitney U-test (p < 0.05); peaks identified as lipids; both identified and significantly different peaks. The highest values of Q2 have models built on all MS peaks and on significantly different peaks. While such models are useful for classification itself, they are of less value for building explanatory mechanisms of pathophysiology and providing a pathway analysis. Models based on identified peaks are preferable from this point of view. Results obtained by OPLS-DA classification of the tissue spray MS data of a new sample set (n = 19) revealed 100% sensitivity and specificity when compared to histological analysis, the "gold" standard for tissue classification. "All peaks" and "significantly different peaks" datasets in the positive ion mode were ideal for breast cancer tissue classification. Our results indicate the potential of tissue spray mass spectrometry for rapid, accurate and intraoperative diagnostics of breast cancer tissue as a means to reduce surgical intervention.

Distinguishing brain tumors by Label-free confocal micro-Raman spectroscopy.

BACKGROUND: Brain tumors have serious adverse effects on public health and social economy. Accurate detection of brain tumor types is critical for effective and proactive treatment, and thus improve the survival of patients. METHODS: Four types of brain tumor tissue sections were detected by Raman spectroscopy. Principal component analysis (PCA) has been used to reduce the dimensionality of the Raman spectra data. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods were utilized to discriminate different types of brain tumors. RESULTS: Raman spectra were collected from 40 brain tumors. Variations in intensity and shift were observed in the Raman spectra positioned at 721, 854, 1004, 1032, 1128, 1248, 1449 cm-1 for different brain tumor tissues. The PCA results indicated that glioma, pituitary adenoma, and meningioma are difficult to differentiate from each other, whereas acoustic neuroma is clearly distinguished from the other three tumors. Multivariate analysis including QDA and LDA methods showed the classification accuracy rate of the QDA model was 99.47 %, better than the rate of LDA model was 95.07 %. CONCLUSIONS: Raman spectroscopy could be used to extract valuable fingerprint-type molecular and chemical information of biological samples. The demonstrated technique has the potential to be developed to a rapid, label-free, and intelligent approach to distinguish brain tumor types with high accuracy.

Rapid identification model of mine water inrush source using random forest optimized by multi-strategy improved sparrow search algorithm.

Mine water inrush accident is one of the most threatening disasters in coal mine production process. In order to improve the identification accuracy of mine water inrush source, a fast identification method of mine water inrush source based on improved sparrow search (SSA) algorithm coupled with Random Forest algorithm was proposed. Firstly, taking Zhaogezhuang Mine as the research object, six factors were selected as the discriminant index and three principal components were extracted by kernel principal component analysis. Secondly, four strategies are employed to enhance the SSA for achieving the ISSA, while multiple benchmark functions are utilized to validate its performance. The extracted principal components serve as input, and the categories of water inrush sources act as output. Subsequently, the prediction results of Random Forest (RF) algorithm after optimizing hyperparameters through Improve SSA are compared with those obtained from other models. The research findings demonstrate that optimizing the RF model using Improve SSA yields superior predictive performance compared to alternative models. Finally, this model is applied to identify water inrush sources in a mine located in Shandong province. The discrimination results exhibit higher accuracy, precision, recall, and F1 index than other models, thereby confirming the reliability and stability of this approach. The results demonstrate that the kernel principal component analysis-based rapid identification model for mine water outburst source, combined with an improved sparrow search algorithm to optimize Random Forest, exhibits excellent robustness and accuracy. This model effectively fulfills the requirements of identifying mine water outbursts and provides a reliable guarantee for ensuring mining safety production.

Establishment of a differential diagnosis method and an online prediction platform for AOSD and sepsis based on gradient boosting decision trees algorithm.

OBJECTIVE: The differential diagnosis between adult-onset Still's disease (AOSD) and sepsis has always been a challenge. In this study, a machine learning model for differential diagnosis of AOSD and sepsis was developed and an online platform was developed to facilitate the clinical application of the model. METHODS: All data were collected from 42 AOSD patients and 50 sepsis patients admitted to Affiliated Hospital of Xuzhou Medical University from December 2018 to December 2021. In addition, 5 AOSD patients and 10 sepsis patients diagnosed in our hospital after March 2022 were collected for external validation. All models were built using the scikit-learn library (version 1.0.2) in Python (version 3.9.7), and feature selection was performed using the SHAP (Shapley Additive exPlanation) package developed in Python. RESULTS: The results showed that the gradient boosting decision tree(GBDT) optimization model based on arthralgia, ferritin × lymphocyte count, white blood cell count, ferritin × platelet count, and α1-acid glycoprotein/creatine kinase could well identify AOSD and sepsis. The training set interaction test (AUC: 0.9916, ACC: 0.9457, Sens: 0.9556, Spec: 0.9578) and the external validation also achieved satisfactory results (AUC: 0.9800, ACC: 0.9333, Sens: 0.8000, Spec: 1.000). We named this discrimination method AIADSS (AI-assisted discrimination of Still's disease and Sepsis) and created an online service platform for practical operation, the website is http://cppdd.cn/STILL1/ . CONCLUSION: We created a method for the identification of AOSD and sepsis based on machine learning. This method can provide a reference for clinicians to formulate the next diagnosis and treatment plan.

Nondestructive discrimination of analogous density foreign matter inside soy protein meat semi-finished products based on transmission hyperspectral imaging.

Analogous density foreign matter (ADFM) embedded in soy protein meat semi-finished (SFSPM) is hidden by SFSPM and has similar acoustic impedance features to SFSPM, which makes non-destructive testing techniques such as computer vision (CV), reflectance spectroscopy and ultrasound imaging inappropriate for ADFM, which not only seriously affects the quality of soy protein meat (SPM) products but also increases the safety risk to consumers. In this study, to locate and separate ADFM by using transmission hyperspectral imaging (T-HSI) technique which is sensitive to chemical composition and highlight internal contours. The optimal discrimination model SVM + PCA + MSC + SPA was constructed using transmission spectral information with an accuracy of 95.00 %. The visualization results based on the optimal model showed clearer localization results than CV and ultrasound imaging. The study demonstrated that the advantages of T-HSI technology in detecting and locating ADFM inside SFSPM, which provides a basis for improving the production quality and safety of SPM.

Ecofriendly shiitake authentication using bulk and amino acid-specific stable isotope models.

Accurate authentication of ecofriendly shiitake, such as organic and pesticide-free shiitake, is required to improve food safety and to increase reliability of the national agrofood certification system; however, this is a challenging task. Therefore, this study examined the feasibility of bulk and compound-specific isotope analyses to discriminate ecofriendly shiitake against conventional counterparts. Using the compound-specific isotope model, the classification accuracy was greater (100%) than that of the bulk isotope model (74.5%) for each original sample set. In the compound-specific model, a cutting score of -4.42 discriminated organic shiitake from pesticide-free shiitake and a cutting score of 4.87 discriminated organic shiitake from conventional shiitake. The isotope fractionation trend was less influenced by shiitake type and the amino acid synthetic pathway. Thus, the compound-specific isotope model of amino acids may be a good complementary authentication tool to overcome the limitations of bulk stable isotopes or a pesticide residue test.

Remnant Lipoprotein Cholesterol as a Factor Related to Adult Fatty Liver Disease.

CONTEXT: Dyslipidemia is related to fatty liver disease (FLD), whose relationship with remnant lipoprotein cholesterol (RLP-C), a component of blood lipids, remains unclear. OBJECTIVE: To clarify the correlation between RLP-C and the occurrence and severity of FLD and establish an FLD discriminant model based on health check indicators. METHODS: Retrospective study of participants who underwent health check-up in the First Affiliated Hospital of China Medical University (Shenyang, China) between January and December 2019. We categorized participants according to liver ultrasound results and analyzed the correlation between RLP-C and occurrence of FLD (n = 38 885) through logistic regression, restricted cubic spline, and receiver operating characteristic curve. We categorized the severity of FLD according to the control attenuation parameter and analyzed the correlation between RLP-C and FLD severity through multiple logistic regression; only males were included (n = 564). RESULTS: The adjusted OR (aOR) per SD between RLP-C and FLD was 2.33 (95% CI 2.21-2.46, P < .001), indicating a dose-response relationship (P < .0001). The optimal cut-off value of RLP-C was 0.45 mmol/L and the area under the curve (AUC) was 0.79. The AUC of the 8-variable model was 0.89 in both the training and the validation sets. FLD severity was related to the level of RLP-C (aOR per SD = 1.29, 95% CI 1.07-1.55, P = .008). CONCLUSION: RLP-C has a strong positive correlation with FLD occurrence and FLD severity. These results may help clinicians identify and implement interventions in individuals with high FLD risk and reduce FLD prevalence.

Construction of a Nomogram Discriminating Malignancy-Associated Membranous Nephropathy From Idiopathic Membranous Nephropathy: A Retrospective Study.

Background: Based on the etiology, membranous nephropathy (MN) can be categorized into idiopathic membranous nephropathy (IMN) and secondary membranous nephropathy. Malignancy-associated membranous nephropathy (MMN) is a common type of secondary MN. Its incidence is only second to that of lupus nephritis. As the treatment and prognosis of MMN differ significantly from those of other MNs, the identification of MMN is crucial for clinical practice. The purpose of this study was to develop a model that could efficiently discriminate MMN, to guide more precise selection of therapeutic strategies. Methods: A total of 385 with IMN and 62 patients with MMN, who were hospitalized at the First Affiliated Hospital of Zhengzhou University between January 2017 and December 2020 were included in this study. We constructed a discriminant model based on demographic information and laboratory parameters for distinguishing MMN and IMN. To avoid an increased false positivity rate resulting from the large difference in sample numbers between the two groups, we matched MMN and IMN in a 1:3 ratio according to gender. Regression analysis was subsequently performed and a discriminant model was constructed. The calibration ability and clinical utility of the model were assessed via calibration curve and decision curve analysis. Results: We constructed a discriminant model based on age, CD4+ T cell counts, levels of cystatin C, albumin, free triiodothyronine and body mass index, with a diagnostic power of 0.860 and 0.870 in the training and test groups, respectively. The model was validated to demonstrate good calibration capability and clinical utility. Conclusion: In clinical practice, patients demonstrating higher scores after screening with this model should be carefully monitored for the presence of tumors in order to improve their outcome.

Improved accuracy of geographical origin identification of shiitake grown in sawdust medium: A compound-specific isotope model-based pilot study.

In countries like South Korea and the USA, origin labeling of shiitake grown using imported Chinese-inoculated medium is an issue. Therefore, we evaluated the use of compound-specific isotope analysis (CSIA) for the accurate identification of the geographical origin of shiitake (Korean, Chinese-inoculated medium, and Chinese); Chinese-inoculated medium shiitake were cultivated in Korea using inoculated sawdust medium from China. The CSIA-discriminant model showed an overall accuracy of 100% in the geographical classification of the original set and 96.4% for the cross-validated set. Glutamate and aspartate δ15N values were the most important variables for differentiating shiitake based on their origins. Compared to that observed upon using the bulk stable isotope analysis, the CSIA model was associated with significantly improved predictability of origin identification. Our findings elucidate the importance of isotope signatures in developing a reliable origin labeling method for shiitake cultured on the sawdust medium for the global market.

Discriminant Analysis of Pu-Erh Tea of Different Raw Materials Based on Phytochemicals Using Chemometrics.

Pu-erh tea processed from the sun-dried green tea leaves can be divided into ancient tea (AT) and terrace tea (TT) according to the source of raw material. However, their similar appearance makes AT present low market identification, resulting in a disruption in the tea market rules of fair trade. Therefore, this study analyzed the classification by principal component analysis/hierarchical clustering analysis and conducted the discriminant model through stepwise Fisher discriminant analysis and decision tree analysis based on the contents of water extract, phenolic components, alkaloid, and amino acids, aiming to investigate whether phytochemicals coupled with chemometric analyses distinguish AT and TT. Results showed that there were good separations between AT and TT, which was caused by 16 components with significant (p < 0.05) differences. The discriminant model of AT and TT was established based on six discriminant variables including water extract, (+)-catechin, (−)-epicatechin, (−)-epigallocatechin, theacrine, and theanine. Among them, water extract comprised multiple soluble solids, representing the thickness of tea infusion. The model had good generalization capability with 100% of performance indexes according to scores of the training set and model set. In conclusion, phytochemicals coupled with chemometrics analyses are a good approach for the identification of different raw materials.

The atherogenic index of plasma: A novel factor more closely related to non-alcoholic fatty liver disease than other lipid parameters in adults.

Background and aims: The relationship of non-alcoholic fatty liver disease (NAFLD) with the atherogenic index of plasma (AIP) is unclear. This study aims to detect the association between AIP and NAFLD, compare the discriminative power of AIP with other lipid parameters for NAFLD, and establish a discriminant model using physical examination data. Methods: Participants aged over 20 years who underwent routine physical examination in Beijing Chaoyang Hospital from April 2016 to August 2020 were included. We categorized subjects based on hepatic ultrasound results and analyzed the association between NAFLD risk and AIP, conventional plasma lipids, remnant cholesterol (RC), triglyceride and glucose (TyG) index, and other atherogenic indices (n = 112,200) using logistic regression, restricted cubic spline regression, and receiver operating characteristic curve. Results: Out of the 112,200 subjects, 30.4% had NAFLD. The body weight index, plasma glucose, conventional lipids, TyG index, AIP, atherogenic coefficient (AC), and coronary risk index (CRI) were significantly higher, while HDL-C was lower (p < 0.001) in patients with NAFLD than those without NAFLD (all p < 0.001). Compared with conventional lipids, RC, TyG index, AC, and CRI, AIP had a stronger correlation with the risk of NAFLD (OR 6.71, 95% CI 6.23-7.22, p < 0.001) after adjusting confounders and presented a non-linear dose-response relationship (p < 0.0001). The optimal cut-off value of AIP was 0.05 and the area under the curve (AUC) was 0.82 (95% CI: 0.81-0.82) with high sensitivity and specificity. The AUC of the simplified three-variable NAFLD discriminant model was 0.90 in both the training set and the validation set. Conclusion: AIP was significantly associated with NAFLD and showed superior discriminative performance to other lipid parameters. These findings might help screen NAFLD in high-risk individuals and reduce the prevalence of NAFLD.

An online alpha-thalassemia carrier discrimination model based on random forest and red blood cell parameters for low HbA2 cases.

BACKGROUND: Since screening of α-thalassemia carriers by low HbA2 has a low positive predictive value (PPV), the PPV was as low as 40.97% in our laboratory, other more effective screening methods need to be devised. This study aimed at developing a machine learning model by using red blood cell parameters to identify α-thalassemia carriers from low HbA2 patients. METHODS: Laboratory data of 1213 patients with low HbA2 used for modeling was randomly divided into the training set (849 of 1213, 70%) and the internal validation set (364 of 1213, 30%). In addition, an external data set (n = 399) was used for model validation. Fourteen machine learning methods were applied to construct a discriminant model. Performance was evaluated with accuracy, sensitivity, specificity, etc. and compared with 7 previously published discriminant function formulae. RESULTS: The optimal model was based on random forest with 5 clinical features. The PPV of the model was more than twice the PPV of HbA2, and the model had a high negative predictive value (NPV) at the same time. Compared with seven formulae in screening of α-thalassemia carriers, the model had a better accuracy (0.915), specificity (0.967), NPV (0.901), PPV (0.942) and area under the receiver operating characteristic curve (AUC, 0.948) in the independent test set. CONCLUSION: Use of a random forest-based model enables rapid discrimination of α-thalassemia carriers from low HbA2 cases.

A decision support system for primary headache developed through machine learning.

BACKGROUND: Primary headache is a disorder with a high incidence and low diagnostic accuracy; the incidence of migraine and tension-type headache ranks first among primary headaches. Artificial intelligence (AI) decision support systems have shown great potential in the medical field. Therefore, we attempt to use machine learning to build a clinical decision-making system for primary headaches. METHODS: The demographic data and headache characteristics of 173 patients were collected by questionnaires. Decision tree, random forest, gradient boosting algorithm and support vector machine (SVM) models were used to construct a discriminant model and a confusion matrix was used to calculate the evaluation indicators of the models. Furthermore, we have carried out feature selection through univariate statistical analysis and machine learning. RESULTS: In the models, the accuracy, F1 score were calculated through the confusion matrix. The logistic regression model has the best discrimination effect, with the accuracy reaching 0.84 and the area under the ROC curve also being the largest at 0.90. Furthermore, we identified the most important factors for distinguishing the two disorders through statistical analysis and machine learning: nausea/vomiting and photophobia/phonophobia. These two factors represent potential independent factors for the identification of migraines and tension-type headaches, with the accuracy reaching 0.74 and the area under the ROC curve being at 0.74. CONCLUSIONS: Applying machine learning to the decision-making system for primary headaches can achieve a high diagnostic accuracy. Among them, the discrimination effect obtained by the integrated algorithm is significantly better than that of a single learner. Second, nausea/vomiting, photophobia/phonophobia may be the most important factors for distinguishing migraine from tension-type headaches.

An enhanced approach to the robust discriminant analysis and class sparsity based embedding.

In recent times, feature extraction attracted much attention in machine learning and pattern recognition fields. This paper extends and improves a scheme for linear feature extraction that can be used in supervised multi-class classification problems. Inspired by recent frameworks for robust sparse LDA and Inter-class sparsity, we propose a unifying criterion able to retain the advantages of these two powerful linear discriminant methods. We introduce an iterative alternating minimization scheme in order to estimate the linear transformation and the orthogonal matrix. The linear transformation is efficiently updated via the steepest descent gradient technique. The proposed framework is generic in the sense that it allows the combination and tuning of other linear discriminant embedding methods. We used our proposed method to fine tune the linear solutions delivered by two recent linear methods: RSLDA and RDA_FSIS. Experiments have been conducted on public image datasets of different types including objects, faces, and digits. The proposed framework compared favorably with several competing methods.