Amino acid metabolomics and machine learning for assessment of post-hepatectomy liver regeneration.

Objective: Amino acid (AA) metabolism plays a vital role in liver regeneration. However, its measuring utility for post-hepatectomy liver regeneration under different conditions remains unclear. We aimed to combine machine learning (ML) models with AA metabolomics to assess liver regeneration in health and non-alcoholic steatohepatitis (NASH). Methods: The liver index (liver weight/body weight) was calculated following 70% hepatectomy in healthy and NASH mice. The serum levels of 39 amino acids were measured using ultra-high performance liquid chromatography-tandem mass spectrometry analysis. We used orthogonal partial least squares discriminant analysis to determine differential AAs and disturbed metabolic pathways during liver regeneration. The SHapley Additive exPlanations algorithm was performed to identify potential AA signatures, and five ML models including least absolute shrinkage and selection operator, random forest, K-nearest neighbor (KNN), support vector regression, and extreme gradient boosting were utilized to assess the liver index. Results: Eleven and twenty-two differential AAs were identified in the healthy and NASH groups, respectively. Among these metabolites, arginine and proline metabolism were commonly disturbed metabolic pathways related to liver regeneration in both groups. Five AA signatures were identified, including hydroxylysine, L-serine, 3-methylhistidine, L-tyrosine, and homocitrulline in healthy group, and L-arginine, 2-aminobutyric acid, sarcosine, beta-alanine, and L-cysteine in NASH group. The KNN model demonstrated the best evaluation performance with mean absolute error, root mean square error, and coefficient of determination values of 0.0037, 0.0047, 0.79 and 0.0028, 0.0034, 0.71 for the healthy and NASH groups, respectively. Conclusion: The KNN model based on five AA signatures performed best, which suggests that it may be a valuable tool for assessing post-hepatectomy liver regeneration in health and NASH.

An adhesion-based method for rapid and low-cost isolation of circulating tumor cells.

BACKGROUND: Noninvasive monitoring of cancer through circulating tumor cells (CTCs) is hampered long by unsatisfactory CTCs testing techniques. Efficient isolation of CTCs in a rapid and price-favorable way from billions of leukocytes is crucial for testing. METHODS: We developed a new method based on the stronger adhesive power of CTCs versus leukocytes to sensitively isolate CTCs. Using a BSA-coated microplate and low-speed centrifuge, this method could easily separate cancer cells within 20 min at a very low cost. RESULT: The capture ratio can reach 70.7-86.6% in various cancer cell lines (breast/lung/liver/cervical/colorectal cancer) covering different epithelial-mesenchymal transformation (EMT) phenotypes and cell sizes, demonstrating the potential for efficient pan-cancer CTCs detection. Moreover, the label-free process can well preserve cell viability (∼99%) to fit downstream DNA/RNA sequencing. CONCLUSIONS: A novel technique for non-destructive and rapid enrichment of CTCs has been devised. It has enabled the successful isolation of rare tumor cells in the patient blood sample and pleural effusion, highlighting a promising future of this method in clinical translation.

An optimization for postpartum depression risk assessment and preventive intervention strategy based machine learning approaches.

BACKGROUND: Postpartum depression (PPD) is one of the most common psychiatric disorders for women after delivery. The establishment of an effective PPD prediction model helps to distinguish high-risk groups, and verifying whether such high-risk groups can benefit from drug intervention is very important for clinical guidance. METHODS: We collected data of parturients that underwent a cesarean delivery. The Control group was divided into a training cohort and a testing cohort. Six different ML models were constructed and we compared their prediction performance in the testing cohort. For model interpretation, we introduced SHapley Additive exPlanations (SHAP). Then, training cohort, ketamine group and dexmedetomidine (DEX) group were classified as high or low risk for PPD by the model. A 1:1 propensity score matching (PSM) was performed to compare the incidence of PPD between two groups in different risk cohorts. RESULTS: Extreme gradient enhancement (XGB) had the best recognition effect, with an area under the receiver operating characteristic curve (AUROC) of 0.789 (95 % CI 0.742-0.836) in the training cohort and 0.744 (95 % CI 0.655-0.823) in the testing cohort, respectively. A threshold of 21.5 % PPD risk probability was determined. After PSM, the results showed that the incidence of PPD in the two intervention groups was significantly different from the control group in the high-risk cohort (P < 0.001) but not in the low-risk cohort (P > 0.001). CONCLUSION: Our study demonstrated that the XGB algorithm provided a more accurate in prediction of PPD risk, and it was beneficial to receive early intervention for the high-risk groups distinguished by the model.

Development and External Validation of a Dynamic Nomogram With Potential for Risk Assessment of Ruptured Multiple Intracranial Aneurysms.

BACKGROUND AND PURPOSE: About 20.1% of intracranial aneurysms (IAs) carriers are multiple intracranial aneurysms (MIAs) patients with higher rupture risk and worse prognosis. A prediction model may bring some potential benefits. This study attempted to develop and externally validate a dynamic nomogram to assess the rupture risk of each IA among patients with MIA. METHOD: We retrospectively analyzed the data of 262 patients with 611 IAs admitted to the Hunan Provincial People's Hospital between November 2015 and November 2021. Multivariable logistic regression (MLR) was applied to select the risk factors and derive a nomogram model for the assessment of IA rupture risk in MIA patients. To externally validate the nomogram, data of 35 patients with 78 IAs were collected from another independent center between December 2009 and May 2021. The performance of the nomogram was assessed in terms of discrimination, calibration, and clinical utility. RESULT: Size, location, irregular shape, diabetes history, and neck width were independently associated with IA rupture. The nomogram showed a good discriminative ability for ruptured and unruptured IAs in the derivation cohort (AUC = 0.81; 95% CI, 0.774-0.847) and was successfully generalized in the external validation cohort (AUC = 0.744; 95% CI, 0.627-0.862). The nomogram was calibrated well, and the decision curve analysis showed that it would generate more net benefit in identifying IA rupture than the "treat all" or "treat none" strategies at the threshold probabilities ranging from 10 to 60% both in the derivation and external validation set. The web-based dynamic nomogram calculator was accessible on https://wfs666.shinyapps.io/onlinecalculator/. CONCLUSION: External validation has shown that the model was the potential to assist clinical identification of dangerous aneurysms after longitudinal data evaluation. Size, neck width, and location are the primary risk factors for ruptured IAs.

Development and assessment of machine learning models for predicting recurrence risk after endovascular treatment in patients with intracranial aneurysms.

Intracranial aneurysms (IAs) remain a major public health concern and endovascular treatment (EVT) has become a major tool for managing IAs. However, the recurrence rate of IAs after EVT is relatively high, which may lead to the risk for aneurysm re-rupture and re-bleed. Thus, we aimed to develop and assess prediction models based on machine learning (ML) algorithms to predict recurrence risk among patients with IAs after EVT in 6 months. Patient population included patients with IAs after EVT between January 2016 and August 2019 in Hunan Provincial People's Hospital, and an adaptive synthetic (ADASYN) sampling approach was applied for the entire imbalanced dataset. We developed five ML models and assessed the models. In addition, we used SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models. A total of 425 IAs were enrolled into this study, and 66 (15.5%) of which recurred in 6 months. Among the five ML models, gradient boosting decision tree (GBDT) model performed best. The area under curve (AUC) of the GBDT model on the testing set was 0.842 (sensitivity: 81.2%; specificity: 70.4%). Our study firstly demonstrated that ML-based models can serve as a reliable tool for predicting recurrence risk in patients with IAs after EVT in 6 months, and the GBDT model showed the optimal prediction performance.

Assessment of anti-PD-(L)1 for patients with coexisting malignant tumor and tuberculosis classified by active, latent, and obsolete stage.

BACKGROUND: It is not a rare clinical scenario to have patients presenting with coexisting malignant tumor and tuberculosis. Whether it is feasible to conduct programmed death-(ligand) 1 [PD-(L)1] inhibitors to these patients, especially those with active tuberculosis treated with concurrent anti-tuberculosis, is still unknown. METHODS: This study enrolled patients with coexisting malignancy and tuberculosis and treated with anti-PD-(L)1 from Jan 2018 to July 2021 in 2 institutions. The progression-free survival (PFS), objective response rate (ORR), and safety of anti-PD-(L)1 therapy, as well as response to anti-tuberculosis treatment, were evaluated. RESULTS: A total of 98 patients were screened from this cohort study, with 45 (45.9%), 21 (21.4%), and 32 (32.7%) patients diagnosed with active, latent, and obsolete tuberculosis, respectively. The overall ORR was 36.0% for anti-PD-(L)1 therapy, with 34.2%, 35.5%, and 41.2% for each subgroup. Median PFS was 8.0 vs 6.0 vs 6.0 months (P=0.685) for each subgroup at the time of this analysis. For patients with active tuberculosis treated with concurrent anti-tuberculosis, median duration of anti-tuberculosis therapy was 10.0 (95% CI, 8.01-11.99) months. There were 83.3% (20/24) and 93.3% (42/45) patients showing sputum conversion and radiographic response, respectively, after anti-tuberculosis therapy, and two patients experienced tuberculosis relapse. Notably, none of the patients in latent and only one patient in obsolete subgroups showed tuberculosis induction or relapse after anti-PD-(L)1 therapy. Treatment-related adverse events (TRAEs) occurred in 33 patients (73.3%) when treated with concurrent anti-PD-(L)1 and anti-tuberculosis. Grade 3 or higher TRAEs were hematotoxicity (n = 5, 11.1%), and one patient suffered grade 3 pneumonitis leading to the discontinuation of immunotherapy. CONCLUSIONS: This study demonstrated that patients with coexisting malignant tumor and tuberculosis benefited equally from anti-PD-(L)1 therapy, and anti-tuberculosis response was unimpaired for those with active tuberculosis. Notably, the combination of anti-PD-(L)1 and anti-tuberculosis therapy was well-tolerated without significant unexpected toxic effects.

Measurement of fexofenadine concentration in micro-sample human plasma by a rapid and sensitive LC-MS/MS employing protein precipitation: application to a clinical pharmacokinetic study.

A simple, rapid and sensitive liquid chromatography/positive ion electro-spray tandem mass spectrometry method (LC-MS/MS) was developed and validated for the quantification of fexofenadine with 100 microL human plasma employing glipizide as internal standard (IS). Protein precipitation was used in the sample preparation procedure. Chromatographic separation was achieved on a reversed-phase C(18 )column (5 microm, 100 x 2.1 mm) with methanol : buffer (containing 10 mmol/L ammonium acetate and 0.1% formic acid; 70 : 30, v/v) as mobile phase. The total chromatographic runtime was approximately 3.0 min with retention time for fexofenadine and IS at approximately 1.9 and 2.1 min, respectively. Detection of fexofenadine and IS was achieved by LC-MS/MS in positive ion mode using 502.1 --> 466.2 and 446.0 --> 321.1 transitions, respectively. The method was proved to be accurate and precise at linearity range of 1-600 ng/mL with a correlation coefficient (r) of > or =0.9976. The validated method was applied to a pharmacokinetic study in human volunteers following oral administration of 60 or 120 mg fexofenadine formulations, successfully.

A simple and sensitive HPLC-ESI-MS/MS method for the determination of andrographolide in human plasma.

A liquid chromatography-electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS) method for the determination of andrographolide in human plasma was established. Dehydroandrographolide was used as the internal standard (I.S.). The plasma samples were deproteinized with methanol and separated on a Hanbon C(18) column with a mobile phase of methanol-water (70:30, v/v). HPLC-ESI-MS/MS was performed in the selected ion monitoring (SIM) mode using target ions at [M-H(2)O-H](-), m/z 331.1 for andrographolide and [M-H](-), m/z 331.1 for the I.S. Calibration curve was linear over the range of 1.0-150.0ng/mL. The chromatographic separation was achieved in less than 6.5min. The lower limits of quantification (LLOQ) was 1.0ng/mL. The intra and inter-run precisions were less than 6.95 and 7.22%, respectively. The method was successfully applied to determine the plasma concentrations of andrographolide in Chinese volunteers.