Premature rupture of membranes (PROM) and preterm PROM (PPROM) are significant obstetric complications, affecting 10-20% and 3% of pregnancies globally and constituting a major cause of preterm birth and neonatal morbidity. While intrauterine infection is a key underlying driver, timely identification of high-risk cases remains a clinical challenge. We developed a noninvasive artificial intelligence (AI) predictive framework using a large-scale dataset of 114,601 antenatal laboratory and ultrasound measurements. Our multimodal model integrated longitudinal clinical measures, including maternal demographics, laboratory results, and ultrasound data. The optimized XGBoost algorithm achieved high performance (AUC: 0.952) and identified elevated C-reactive protein (CRP) and decreased albumin levels in late pregnancy as top features, underscoring the critical roles of inflammation and oxidative stress. SHAP analysis ensured interpretability, unveiling dynamic risk patterns across gestation. This AI-driven approach provides a precise, noninvasive tool for late-gestation PPROM risk stratification, paving the way for translation into precision obstetric care.

Recurrent spontaneous abortion (RSA) affects female reproduction worldwide, yet its pathological mechanisms are still unclear. It has been reported that cellular metabolism reprogramming is a critical step for trophoblasts during embryo implantation. Herein, MTHFD2 was recognized as a key metabolic checkpoint attributed to RSA occurrence. This work figured out that the expression level of MTHFD2 was significantly inhibited in villus tissues from RSA patients, suggesting the potential role of MTHFD2 in RSA occurrence. Moreover, MTHFD2 knockdown impaired cellular folate-nucleotide metabolism, induced the accumulation of AICAR, and thereby impairing the EMT process to inhibit the invasion and migration of trophoblasts Besides, the AICAR accumulation further activated the downstream AMPK which deactivated the JAK/STAT/Slug pathway and ultimately deactivated the EMT process. Using a mouse model, MTHFD2 inhibition was observed to induce embryo implantation failure in vivo. Our results highlighted MTHFD2 as a metabolic checkpoint that remodeled folate-nucleotide metabolism to regulate the EMT process and ultimately altered the migration and invasion of trophoblasts in RSA occurrence. Our findings suggested that MTHFD2 was a promising therapeutic target in recurrent spontaneous abortion treatment.

Refined control of intrinsic and extrinsic signals is critical for specific neuronal differentiation. Here, we differentiated human induced pluripotent stem cells (hiPSCs) from three different healthy donors into neural stem cells (NSCs) and floor plate progenitors (FPPs; progenitors of dopaminergic neurons) and further performed intracellular and extracellular vesicles' (EVs) miRNA profiling. While NSC and FPP cells differed significantly in levels of only 8 intracellular miRNAs, their differences were more evident in the EV miRNAs with 27 differentially expressed miRNAs. Target validation of intracellular miRNAs revealed that FPPs expressed more EXOC5 mRNA than NSCs, which is implicated in the function of primary cilia, an essential signaling organelle in FPPs. Moreover, we found a group of 5 miRNAs consistently enriched in EVs from these three cell types. This study presents a foundation for the field of miRNA regulation in neural development and provides new insights for disease modeling and regenerative medicine.

Accelerated aging and environmental factors are increasingly recognized as significant contributors to hypertension. This study aims to assess the associations between urinary metal and hypertension risk while also exploring the mediating effects of biological aging. The study encompassed 3389 individuals from a Chinese cohort and 5965 individuals from the National Health and Nutrition Examination Survey (NHANES) cohort. Urinary metal concentrations were measured in a Chinese cohort, while those for NHANES were obtained directly from the database. Biological age was calculated using the Klemera and Doubal method. Our results showed that urinary barium (Ba), copper (Cu), manganese (Mn) and zinc (Zn) in the Chinese population, as well as antimony (Sb) and tungsten (W) in the United States (US) population were associated with an increased risk of hypertension. Interestingly, Ba and lead (Pb) appeared to reduce the risk of hypertension in the US population. Mediation analysis demonstrated that accelerated biological aging significantly mediated the relationship between Cu, Zn, Ba and Pb and hypertension, accounting for 12.39 %, 23.04 %, 17.62 %, and 18.71 % of the total effect, respectively. This study suggested that exposure to metals accelerates aging, ultimately exacerbating the progression of hypertension despite variations in the relative contributions of different metals to this condition. Moreover, it underscored the potential benefits of moderate physical activity and tea consumption as interventions to mitigate hypertension risk and slow biological aging.

The COVID-19 pandemic underlined the need to focus on women's health, particularly during pregnancy. Recent studies have shown that maternal SARS-CoV-2 infection during pregnancy may increase obstetrical risks. However, intraamniotic SARS-CoV-2 infection has been understudied. Here, we conduct inclusive autopsies on 18 fetuses following maternal SARS-CoV-2 infection to determine whether and which fetal organs can be infected by SARS-CoV-2 in utero. We analyze a total of 538 samples from 32 tissue types to comprehensively map and quantify the distribution and replication patterns of SARS-CoV-2 across fetal organs. Our observations reveal that SARS-CoV-2 can be widely distributed in fetal organs through vertical transmission. As the length of time from SARS-CoV-2 infection to termination of pregnancy increases, the prevalence of infection in fetal organs decrease. Further, proteomic profiling reveals DNA damage and immune imbalance in infected organs.

Nanoplastics (NPs) are emerging environmental pollutants that pose growing concerns due to their potential health risks. However, the effects of inhaled NP exposure during pregnancy on fetal brain development remain poorly understood. In this study, we investigated the impact of maternal exposure to polypropylene nanoplastics (PP-NPs) on fetal brain development and neurobehavioral outcomes in a mouse model and further explored its mechanism in human cerebral organoids. Maternal exposure to PP-NPs significantly impaired neuronal differentiation and proliferation in the fetal cortex. Neurobehavioral assessments revealed significant deficits in offspring following maternal exposure, including impaired spatial memory, reduced motor coordination, and heightened anxiety-like behavior. Furthermore, human brain organoids exposed to PP-NPs exhibited reduced growth and neuronal differentiation, with significant downregulation of key neuronal markers such as TUJ1, MAP2, and PAX6. Transcriptomic analysis identified alterations in gene expression, particularly in neuroactive ligand-receptor interaction pathway. Molecular docking and fluorescence co-localization analysis further suggested CYSLTR1 and PTH1R as key molecular targets of PP-NPs. These findings provide novel insights into the toxicological effects of NPs on the developing brain and emphasize the need for preventive measures to protect fetal neurodevelopment during pregnancy.

BackgroundInfertility affects approximately 15% of couples globally, with assisted reproductive technologies (ARTs) becoming the primary interventions. Despite the growing use of ARTs, success rates have plateaued at around 30%, highlighting the need for improved predictive models to enhance outcomes. This study aimed to develop a machine learning-based predictive model for live birth outcomes following fresh embryo transfer.MethodsA total of 51,047 ART records were collected from 2016 to 2023 at the Shanghai First Maternity and Infant Hospital. After data preprocessing, 11,728 records and 55 pre-pregnancy features were analyzed. Six machine learning models-Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Gradient Boosting Machines (GBM), Adaptive Boosting (AdaBoost), Light Gradient Boosting Machine (LightGBM), and Artificial Neural Network (ANN)-were employed to construct the prediction model.ResultsAmong the models, RF demonstrated the best predictive performance, achieving an area under the curve (AUC) value exceeding 0.8. Key predictive features included female age, grades of transferred embryos, number of usable embryos, and endometrial thickness. A web tool was developed to assist clinicians in predicting outcomes and individualizing treatments based on patient data.ConclusionsThis study presents a significant advancement in predicting live birth outcomes prior to embryo transfer, moving beyond traditional assessments. The findings underscore the potential of machine learning to improve clinical decision-making and enhance patient counseling in ARTs.

Background Familial hypercholesterolemia (FH) is an autosomal dominant disease of lipid metabolism mainly caused by mutations in the low-density lipoprotein receptor (LDLR) gene. Genetic detection of patients with FH help with precise diagnosis and treatment, thus reducing the risk of coronary heart disease (CHD) and other related diseases. The study aimed to identify the causative gene mutations in a Chinese FH family and reveal the pathogenicity and the mechanism of these mutations. Methods and results Whole exome sequencing was performed in a patient with severe lipid metabolism dysfunction seeking fertility guidance from a Chinese FH family. Two LDLR variants c.1875 C > G (p.N625K; novel variant) and c.1448G > A (p.W483*) were identified in the family. Wildtype and mutant LDLR constructs were established by the site-direct mutagenesis technique. Functional studies were carried out by cell transfection to evaluate the impact of detected variants on LDLR activity. The two variants were proven to affect LDL uptake and binding, resulting in cholesterol clearance reduction to different degrees. According to The American College of Medical Genetics and Genomics (ACMG) Standards and Guidelines, the W483* variant was classified as “Pathogenic”, while the N625K variant as “VUS”. Conclusions Our results provide novel experimental evidence of functional alteration by LDLR variants identified in our study and expand the mutational spectrum of LDLR mutation induced FH.

In preconception counseling, there has been a lack of quantitative approaches to predict the outcome of an upcoming pregnancy, which would greatly benefit women and society. By applying state-of-the-art artificial intelligence algorithms to clinical and metabolome data from 481 women, we have, for the first time, proposed a pre-pregnancy classifier that predicts miscarriage with a high precision rate of 87%. Our embedded feature engineering revealed the critical impact of serum histidine level, further supported by its elevation in recurrent spontaneous miscarriage (RSM). Mechanistically, elevated histidine level, combined with compromised diamine oxidase (DAO) expression, led to a fatal accumulation of histamine at the maternal-fetal interface (157 specimens from 113 donors). Additionally, a high-histidine diet induced significant embryo loss in mice without causing malabsorption of other amino acids. This pilot study shows promise in predicting pregnancy outcomes prior to conception, opening an important window for early warning that is particularly meaningful given the global decline in birth rates.

Introduction: Intrauterine adhesions (IUA), also known as Asherman's syndrome, is caused by trauma to the pregnant or non-pregnant uterus, which leads to damaged endometrial basal lining and partial or total occlusion of the uterine chambers, resulting in abnormal menstruation, infertility, or recurrent miscarriage. The essence of this syndrome is endometrial fibrosis. And there is no effective treatment for IUA to stimulate endometrial regeneration currently. Recently, menstrual blood-derived stem cells (MenSCs) have been proved to hold therapeutic promise in various diseases, such as myocardial infarction, stroke, diabetes, and liver cirrhosis.Methods: In this study, we examined the effects of MenSCs on the repair of uterine adhesions in a rat model, and more importantly, promoted such therapeutic effects via a xeno-free VitroGel MMP carrier.Results: This combined treatment reduced the expression of inflammatory factors, increased the expression of anti-inflammatory factors, restricted the area of endometrial fibrosis, diminished uterine adhesions, and partially restored fertility, showing stronger effectiveness than each component alone and almost resembling the sham group.Discussion: Our findings suggest a highly promising strategy for IUA treatment.

MicroRNA-10b (miR-10b) is an essential glioma driver and one of the top candidates for targeted therapies for glioblastoma and other cancers. This unique miRNA controls glioma cell cycle and viability via an array of established conventional and unconventional mechanisms. Previously reported CRISPR-Cas9-mediated miR-10b gene editing of glioma cells in vitro and established orthotopic glioblastoma in mouse models demonstrated the efficacy of this approach and its promise for therapy development. However, therapeutic gene editing in patients’ brain tumors may be hampered, among other factors, by the imperfect delivery and distribution of targeting vectors. Here, we demonstrate that miR-10b gene editing in glioma cells triggers a potent bystander effect that leads to the selective cell death of the unedited glioma cells without affecting the normal neuroglial cells. The effect is mediated by the secreted miR-10b targets phosphoglycerate kinase 1 (PGK1) and insulin-like growth factor binding protein 2 (IGFBP2) that block cell-cycle progression and induce glioma cell death. These findings further support the feasibility of therapeutic miR-10b editing without the need to target every cell of the tumor.

Developmental coordination disorder (DCD) is a developmental disorder characterized by impaired motor coordination, often co-occurring with attention deficit disorder, autism spectrum disorders, and other psychological and behavioural conditions. The aetiology of DCD is believed to involve brain changes and environmental factors, with genetics also playing a role in its pathogenesis. Recent research has identified several candidate genes and genetic factors associated with motor impairment, including deletions, copy number variations, single nucleotide polymorphisms, and epigenetic modifications. This review provides an overview of the current knowledge in genetic research on DCD, highlighting the importance of continued research into the underlying genetic mechanisms. While evidence suggests a genetic contribution to DCD, the evidence is still in its early stages, and much of the current evidence is based on studies of co-occurring conditions. Further research to better understand the genetic basis of DCD could have important implications for diagnosis, treatment, and our understanding of the condition's aetiology.

Deep learning has proven promising in biological and chemical applications, aiding in accurate predictions of properties such as atomic forces, energies, and material band gaps. Traditional methods with rotational invariance, one of the most crucial physical laws for predictions made by machine learning, have relied on Fourier transforms or specialized convolution filters, leading to complex model design and reduced accuracy and efficiency. However, models without rotational invariance exhibit poor generalization ability across datasets. Addressing this contradiction, this work proposes a rotationally invariant graph neural network, named RotNet, for accurate and accelerated quantum mechanical calculations that can overcome the generalization deficiency caused by rotations of molecules. RotNet ensures rotational invariance through an effective transformation and learns distance and angular information from atomic coordinates. Benchmark experiments on three datasets (protein fragments, electronic materials, and QM9) demonstrate that the proposed RotNet framework outperforms popular baselines and generalizes well to spatial data with varying rotations. The high accuracy, efficiency, and fast convergence of RotNet suggest that it has tremendous potential to significantly facilitate studies of protein dynamics simulation and materials engineering while maintaining physical plausibility.

Effective full quantum mechanics (FQM) calculation of protein remains a grand challenge and of great interest in computational biology with substantial applications in drug discovery, protein dynamic simulation and protein folding. However, the huge computational complexity of the existing QM methods impends their applications in large systems. Here, we design a transfer-learning -based deep learning (TDL) protocol for effective FQM calculations (TDL-FQM) on proteins. By incorporating a transfer-learning algorithm into deep neural network (DNN), the TDL-FQM protocol is capable of performing calculations at any given accuracy using models trained from small datasets with high -precision and knowledge learned from large amount of low-level calculations. The high-level double -hybrid DFT functional and high-level quality of basis set is used in this work as a case study to evaluate the performance of TDL-FQM, where the selected 15 proteins are predicted to have a mean absolute error of 0.01 kcal/mol/atom for potential energy and an average root mean square error of 1.47 kcal/mol/A for atomic forces. The proposed TDL-FQM approach accelerates the FQM calculation more than thirty thousand times faster in average and presents more significant benefits in efficiency as the size of protein increases. The ability to learn knowledge from one task to solve related problems demonstrates that the proposed TDL-FQM overcomes the limitation of standard DNN and has a strong power to predict proteins with high precision, which solves the challenge of high precision prediction in large chemical and biological systems.

We previously reported that prolonged exposure to an enriched environment (EE) enhances hippocampal synaptic plasticity, with one of the significant mechanistic pathways being activation of β 2 -adrenergic receptor (β 2 -AR) signaling, thereby mitigating the synaptotoxic effects of soluble oligomers of amyloid β-protein (oAβ). However, the detailed mechanism remained elusive. In this work, we recorded field excitatory postsynaptic potentials (fEPSP) in the CA1 region of mouse hippocampal slices treated with or without toxic Aβ-species. We found that pharmacological activation of β 2 -AR, but not β 1 -AR, selectively mimicked the effects of EE in enhancing LTP and preventing oAβ-induced synaptic dysfunction. Mechanistic analyses showed that certain histone deacetylase (HDAC) inhibitors mimicked the benefits of EE, but this was not seen in β 2 -AR knockout mice, suggesting that activating β 2 -AR prevents oAβ-mediated synaptic dysfunction via changes in histone acetylation. EE or activation of β-ARs each decreased HDAC2, whereas Aβ oligomers increased HDAC2 levels in the hippocampus. Further, oAβ-induced inflammatory effects and neurite degeneration were prevented by either β 2 -AR agonists or certain specific HDAC inhibitors. These preclinical results suggest that activation of β 2 -AR is a novel potential therapeutic strategy to mitigate oAβ-mediated features of AD.

Depression, a mental disorder that plagues the world, is a burden on many families. There is a great need for new, fast-acting antidepressants to be developed. N-methyl-D-aspartic acid (NMDA) is an ionotropic glutamate receptor that plays an important role in learning and memory processes and its TMD region is considered as a potential target to treat depression. However, due to the unclear binding sites and pathways, the mechanism of drug binding lacks basic explanation, which brings great complexity to the development of new drugs. In this study, we investigated the binding affinity and mechanisms of an FDA-approved antidepressant (S-ketamine) and seven potential antidepressants (R-ketamine, memantine, lanicemine, dextromethorphan, Ro 25-6981, ifenprodil, and traxoprodil) targeting the NMDA receptor by ligand–protein docking and molecular dynamics simulations. The results indicated that Ro 25-6981 has the strongest binding affinity to the TMD region of the NMDA receptor among the eight selected drugs, suggesting its potential effective inhibitory effect. We also calculated the critical binding-site residues at the active site and found that residues Leu124 and Met63 contributed the most to the binding energy by decomposing the free energy contributions on a per-residue basis. We further compared S-ketamine and its chiral molecule, R-ketamine, and found that R-ketamine had a stronger binding capacity to the NMDA receptor. This study provides a computational reference for the treatment of depression targeting NMDA receptors, and the proposed results will provide potential strategies for further antidepressant development and is a useful resource for the future discovery of fast-acting antidepressant candidates.

MicroRNAs (miRNAs) regulate fundamental biological processes by silencing mRNA targets and are dysregulated in many diseases. Therefore, miRNA replacement or inhibition can be harnessed as potential therapeutics. However, existing strategies for miRNA modulation using oligonucleotides and gene therapies are challenging, especially for neurological diseases, and none have yet gained clinical approval. We explore a different approach by screening a biodiverse library of small molecule compounds for their ability to modulate hundreds of miRNAs in human induced pluripotent stem cell-derived neurons. We demonstrate the utility of the screen by identifying cardiac glycosides as potent inducers of miR-132, a key neuroprotective miRNA downregulated in Alzheimer’s disease and other tauopathies. Coordinately, cardiac glycosides downregulate known miR-132 targets, including Tau, and protect rodent and human neurons against various toxic insults. More generally, our dataset of 1370 drug-like compounds and their effects on the miRNome provides a valuable resource for further miRNA-based drug discovery.

Triclabendazole is an effective medication to treat fascioliasis and paragonimiasis parasitic infections. We implemented a reliable quantum mechanical method which is density functional theory at the level of ωB97XD/6-31G* along with embedded fragments to elucidate stability and phase transition between two forms of triclabendazole. We calculated crystal structure parameters, volumes, Gibbs free energies, and vibrational spectra of two polymorphic forms of triclabendazole under different pressures and temperatures. We confirmed form I was more stable than form II at atmospheric pressure and room temperature. From high-pressure Gibbs free energy computations, we found a pressure-induced phase transformation between form I (triclinic unit cell) and form II (monoclinic unit cell). The phase transition between forms I and II was found at a pressure and temperature of 5.5 GPa and ≈350 K, respectively. In addition, we also studied the high-pressure polymorphic behavior of two forms of triclabendazole. At the pressure of 5.5 GPa and temperature from ≈350 K to 500 K, form II was more stable than form I. However, at temperatures lower than ≈350 K, form I was more stable than form II. We also studied the effects of pressures on volumes and Raman spectra. To the best of our knowledge, no such research has been conducted to determine the presence of phase transformation between two forms of triclabendazole. This is a case study that can be applied to various polymorphic crystals to study their structures, stabilities, spectra, and phase transformations. This research can assist scientists, chemists, and pharmacologists in selecting the desired polymorph and better drug design.

Glioblastoma multiforme (GBM), a highly invasive and incurable tumor, is the humans' foremost, commonest, and deadliest brain cancer. As in other cancers, distinct combinations of genetic alterations (GA) in GBM induce a diversity of metabolic phenotypes resulting in enhanced malignancy and altered sensitivity to current therapies. Furthermore, GA as a hallmark of cancer, dysregulated cell metabolism in GBM has been recently linked to the acquired GA. Indeed, Numerous point mutations and copy number variations have been shown to drive glioma cells' metabolic state, affecting tumor growth and patient outcomes. Among the most common, IDH mutations, EGFR amplification, mutation, PTEN loss, and MGMT promoter mutation have emerged as key patterns associated with upregulated glycolysis and OXPHOS glutamine addiction and altered lipid metabolism in GBM. Therefore, current Advances in cancer genetic and metabolic profiling have yielded mechanistic insights into the metabolism rewiring of GBM and provided potential avenues for improved therapeutic modalities. Accordingly, actionable metabolic dependencies are currently used to design new treatments for patients with glioblastoma. Herein, we capture the current knowledge of genetic alterations in GBM, provide a detailed understanding of the alterations in metabolic pathways, and discuss their relevance in GBM therapy.