Advancing intrauterine adhesion severity prediction: Integrative machine learning approach with hysteroscopic cold knife system, clinical characteristics and hematological parameters.

Intrauterine Adhesion (IUA) constitute a significant determinant impacting female fertility, potentially leading to infertility, miscarriage, menstrual irregularities, and placental complications. The precise assessment of the severity of IUA is pivotal for the customization of personalized treatment plans, aimed at enhancing the success rate of treatments and mitigating reproductive health risks. This study proposes bTLSMA-SVM-FS, a novel feature selection machine learning model that integrates an enhanced slime mould algorithm (SMA), termed TLSMA, with support vector machines (SVM), aiming to develop a predictive model for assessing the severity of IUA. Initially, a series of optimization comparative experiments were conducted on the TLSMA using the CEC 2017 benchmark functions. By comparing it with eleven meta-heuristic algorithms as well as eleven SOTA algorithms, the experimental outcomes corroborated the superior performance of the TLSMA. Subsequently, the developed bTLSMA-SVM-FS model was employed to conduct a thorough analysis of the clinical features of 107 IUA patients from Wenzhou People's Hospital, comprising 61 cases of moderate IUA and 46 cases of severe IUA. The evaluation results of the model demonstrated exceptional performance in predicting the severity of IUA, achieving an accuracy of 86.700 % and a specificity of 87.609 %. Moreover, the model successfully identified critical factors influencing the prediction of IUA severity, including the preoperative Chinese IUA score, production times, thrombin time, preoperative endometrial thickness, and menstruation. The identification of these key factors not only further validated the efficacy of the proposed model but also provided vital scientific evidence for a deeper understanding of the pathogenesis of IUA and the enhancement of targeted treatment strategies.

Prediction of recurrent spontaneous abortion using evolutionary machine learning with joint self-adaptive sime mould algorithm.

Recurrent spontaneous abortion (RSA) is a frequent abnormal pregnancy with long-term psychological repercussions that disrupt the peace of the whole family. In the diagnosis and treatment of RSA worsened by thyroid disorders, recurrent spontaneous abortion is also a significant obstacle. The pathogenesis and possible treatment methods for RSA are yet unclear. Using clinical information, vitamin D and thyroid function measurements from normal pregnant women with RSA, we attempt to build a framework for conducting an effective analysis for RSA in this research. The framework is presented by combining the joint self-adaptive sime mould algorithm (JASMA) with the common kernel learning support vector machine with maximum-margin hyperplane theory, abbreviated as JASMA-SVM. The JASMA has a complete set of adaptive parameter change methods, which improves the algorithm's global search and optimization capabilities and guarantees that it speeds convergence and departs from the local optimum. On CEC 2014 benchmarks, the property of JASMA is validated, and then it is utilized to concurrently optimize parameters and select optimal features for SVM on RSA data from VitD, thyroid hormone levels, and thyroid autoantibodies. The statistical results demonstrate that the proposed JASMA-SVM can be treated as a potential tool for RSA with accuracy of 92.998%, MCC of 0.92425, sensitivity of 93.286%, specificity of 93.064%.

The relationship between maternal vitamin D deficiency and glycolipid metabolism and adverse pregnancy outcome.

OBJECTIVE: Maternal vitamin D deficiency is associated with glucose and lipid metabolism in the mother and offspring. Meanwhile, it can also lead to adverse pregnancy outcomes. The aim of this case-control study was to document maternal, umbilical arterial glucose and lipid metabolic levels and correlations in pregnancies with or without vitamin D deficiency, while also investigating adverse pregnancy outcomes. DESIGN/PARTICIPANTS/MEASUREMENTS: A total of 425 pregnant women who received antenatal care and delivered at Wenzhou People's Hospital were enrolled. According to their serum 25-hydroxyvitamin D [25(OH)D] level, the pregnant women were divided into the vitamin D deficiency group [25(OH)D < 20 ng/mL, 185 participants] and the control group [25(OH)D ≥ 20 ng/mL, 240 participants]. Maternal blood samples were collected at 24-28 weeks of gestation and delivery for 75-g oral glucose tolerance test (OGTT), and measurements of glucose and lipid metabolite levels and 25(OH)D levels. Umbilical arterial samples were collected during delivery (33.57-41.43 gestational weeks). RESULTS: Compared with control participants, vitamin D deficiency women had significantly higher concentrations of fasting blood-glucose (P < .01), 1-h OGTT plasma glucose (P < .01), 2-h OGTT plasma glucose (P < .01), insulin (P < .01), HOMA-IR (P < .01), LDL (P < .01) and triglycerides (P = .02) and lower concentrations of HOMA-S (P < .01). Compared with the control group, vitamin D deficiency women had higher concentrations of triglycerides (P < .01) and lower concentrations of HDL-C (P < .01) and HOMA-ß (P = .01) in infant umbilical arterial blood. Pearson's correlation analysis demonstrated that the maternal 25(OH)D level was negatively correlated with maternal plasma glucose, insulin, LDL-C, cholesterol, triglyceride and HOMA-IR (r = -.38, -.27, -.2, -.11, -.11, -.33 and .11; P < .01, <.01, <.01, <.05, <.05 and <.01, respectively), while there was a positive correlation between maternal serum 25(OH)D and HOMA-S (r = .11, P < .05). The triglyceride level in the umbilical artery was negatively correlated with maternal serum 25(OH)D concentration (r = -.286, P < .01), while the HDL-C and HOMA-ß in umbilical artery were positively related (r = .154, .103, P < .01). Compared with the control group, the incidences of pre-eclampsia [4.8% (9/185) vs 1.25% (3/240), P = .03], gestational diabetes mellitus [19.45% (36/185) vs 12.08% (29/240), P = .04] and premature rupture of membranes [15.68% (29/185) vs 5.42% (13/240), P < .01] were higher in the vitamin D deficiency group. CONCLUSION: Vitamin D deficiency during pregnancy is associated with maternal glucose and lipid metabolism and pregnancy outcomes. Therefore, it is worth recommending to maintain vitamin D status at an optimal level in pregnant women to prevent metabolic disorders and pregnancy complications.