Blood serum lipid profiling may improve the management of recurrent miscarriage: a combination of machine learning of mid-infrared spectra and biochemical assays

Predicting risk of the subsequent early pregnancy loss in women with recurrent pregnancy loss based on preconception data

BACKGROUND

For women who have experienced recurrent pregnancy loss (RPL), it is crucial not only to treat them but also to evaluate the risk of recurrence. The study aimed to develop a risk predictive model to predict the subsequent early pregnancy loss (EPL) in women with RPL based on preconception data.

METHODS

A prospective, dynamic population cohort study was carried out at the Second Hospital of Lanzhou University. From September 2019 to December 2022, a total of 1050 non-pregnant women with RPL were participated. By December 2023, 605 women had subsequent pregnancy outcomes and were randomly divided into training and validation group by 3:1 ratio. In the training group, univariable screening was performed on RPL patients with subsequent EPL outcome. The least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were utilized to select variables, respectively. Subsequent EPL prediction model was constructed using generalize linear model (GLM), gradient boosting machine (GBM), random forest (RF), and deep learning (DP). The variables selected by LASSO regression and multivariate logistic regression were then established and compared using the best prediction model. The AUC, calibration curve, and decision curve (DCA) were performed to assess the prediction performances of the best model. The best model was validated using the validation group. Finally, a nomogram was established based on the best predictive features.

RESULTS

In the training group, the GBM model achieved the best performance with the highest AUC (0.805). The AUC between the variables screened by the LASSO regression (16-variables) and logistic regression (9-variables) models showed no significant difference (AUC: 0.805 vs. 0.777, P = 0.1498). Meanwhile, the 9-variable model displayed a well discrimination performance in the validation group, with an AUC value of 0.781 (95%CI 0.702, 0.843). The DCA showed the model performed well and was feasible for making beneficial clinical decisions. Calibration curves revealed the goodness of fit between the predicted values by the model and the actual values, the Hosmer-Lemeshow test was 7.427, and P = 0.505.

CONCLUSIONS

Predicting subsequent EPL in RPL patients using the GBM model has important clinical implications. Future prospective studies are needed to verify the clinical applicability.

TRIAL REGISTRATION

This study was registered in the Chinese Clinical Trial Registry with the registration number of ChiCTR2000039414 (27/10/2020).

Immunological parameters of maternal peripheral blood as predictors of future pregnancy outcomes in patients with unexplained recurrent pregnancy loss

INTRODUCTION

Unexplained recurrent pregnancy loss (URPL), affecting approximately 1%-5% of women, exhibits a strong association with various maternal factors, particularly immune disorders. However, accurately predicting pregnancy outcomes based on the complex interactions and synergistic effects of various immune parameters without an automated algorithm remains challenging.

MATERIAL AND METHODS

In this historical cohort study, we analyzed the medical records of URPL patients treated at Xiangya Hospital, Changsha, China, between January 2020 and October 2022. The primary outcomes included clinical pregnancy and miscarriage. Predictors included complement, autoantibodies, peripheral lymphocytes, immunoglobulins, thromboelastography findings, and serum lipids. Least absolute shrinkage and selection operator (LASSO) analysis and logistic regression analysis was performed for model development. The model's performance, discriminatory, and clinical applicability were assessed using area under the curve (AUC), calibration curve, and decision curve analysis, respectively. Additionally, models were visualized by constructing dynamic and static nomograms.

RESULTS

In total, 502 patients with URPL were enrolled, of whom 291 (58%) achieved clinical pregnancy and 211 (42%) experienced miscarriage. Notable differences in complement, peripheral lymphocytes, and serum lipids were observed between the two outcome groups. Moreover, URPL patients with elevated peripheral NK cells (absolute counts and proportion), decreased complement levels, and dyslipidemia demonstrated a significantly increased risk of miscarriage. Four models were developed in this study, of which Model 2 demonstrated superior performance with only seven predictors, achieving an AUC of 0.96 (95% CI: 0.93-0.99) and an accuracy of 0.92. A web-based platform was established to visually present model 2 and to facilitate its utilization by clinicians in outpatient settings (available from: https://yingrongli.shinyapps.io/liyingrong/).

CONCLUSIONS

Our findings suggest that the implementation of such prediction models could serve as valuable tools for providing comprehensive information and facilitating clinicians in their decision-making processes.

Oxidative Stress and Redox-Dependent Pathways in Cholangiocarcinoma

Cholangiocarcinoma (CCA) is a primary liver tumor that accounts for 2% of all cancer-related deaths worldwide yearly. It can arise from cholangiocytes of biliary tracts, peribiliary glands, and possibly from progenitor cells or even hepatocytes. CCA is characterized by high chemoresistance, aggressiveness, and poor prognosis. Potentially curative surgical therapy is restricted to a small number of patients with early-stage disease (up to 35%). Accumulating evidence indicates that CCA is an oxidative stress-driven carcinoma resulting from chronic inflammation. Oxidative stress, due to enhanced reactive oxygen species (ROS) production and/or decreased antioxidants, has been recently suggested as a key factor in cholangiocyte oncogenesis through gene expression alterations and molecular damage. However, due to different experimental models and conditions, contradictory results regarding oxidative stress in cholangiocarcinoma have been reported. The role of ROS and antioxidants in cancer is controversial due to their context-dependent ability to stimulate tumorigenesis and support cancer cell proliferation or promote cell death. On these bases, the present narrative review is focused on illustrating the role of oxidative stress in cholangiocarcinoma and the main ROS-driven intracellular pathways. Heterogeneous data about antioxidant effects on cancer development are also discussed.

Optical Screening and Classification of Drug Binding to Proteins in Human Blood Serum

Protein-drug interactions in the human bloodstream are important factors in applications ranging from drug design, where protein binding influences efficacy and dose delivery, to biomedical diagnostics, where rapid, quantitative measurements could guide optimized treatment regimes. Current measurement approaches use multistep assays, which probe the protein-bound drug fraction indirectly and do not provide fundamental structural or dynamic information about the in vivo protein-drug interaction. We demonstrate that ultrafast 2D-IR spectroscopy can overcome these issues by providing a direct, label-free optical measurement of protein-drug binding in blood serum samples. Four commonly prescribed drugs, known to bind to human serum albumin (HSA), were added to pooled human serum at physiologically relevant concentrations. In each case, spectral changes to the amide I band of the serum sample were observed, consistent with binding to HSA, but were distinct for each of the four drugs. A machine-learning-based classification of the serum samples achieved a total cross-validation prediction accuracy of 92% when differentiating serum-only samples from those with a drug present. Identification on a per-drug basis achieved correct drug identification in 75% of cases. These unique spectroscopic signatures of the drug-protein interaction thus enable the detection and differentiation of drug containing samples and give structural insight into the binding process as well as quantitative information on protein-drug binding. Using currently available instrumentation, the 2D-IR data acquisition required just 1 min and 10 μL of serum per sample, and so these results pave the way to fast, specific, and quantitative measurements of protein-drug binding in vivo with potentially invaluable applications for the development of novel therapies and personalized medicine.

A Review of Machine Learning Methods Recently Applied to FTIR Spectroscopy Data for the Analysis of Human Blood Cells

Machine learning (ML) is a broad term encompassing several methods that allow us to learn from data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision-making. This paper presents a review of articles that discuss the use of Fourier transform infrared (FTIR) spectroscopy and ML for human blood analysis between the years 2019-2023. The literature review was conducted to identify published research of employed ML linked with FTIR for distinction between pathological and healthy human blood cells. The articles' search strategy was implemented and studies meeting the eligibility criteria were evaluated. Relevant data related to the study design, statistical methods, and strengths and limitations were identified. A total of 39 publications in the last 5 years (2019-2023) were identified and evaluated for this review. Diverse methods, statistical packages, and approaches were used across the identified studies. The most common methods included support vector machine (SVM) and principal component analysis (PCA) approaches. Most studies applied internal validation and employed more than one algorithm, while only four studies applied one ML algorithm to the data. A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of ML methods. There is a need to ensure that multiple ML approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that the discrimination of human blood cells is being made with the highest efficient evidence.