Long-term health risk of offspring born from assisted reproductive technologies.

Since the world's first in vitro fertilization baby was born in 1978, there have been more than 8 million children conceived through assisted reproductive technologies (ART) worldwide, and a significant proportion of them have reached puberty or young adulthood. Many studies have found that ART increases the risk of adverse perinatal outcomes, including preterm birth, low birth weight, small size for gestational age, perinatal mortality, and congenital anomalies. However, data regarding the long-term outcomes of ART offspring are limited. According to the developmental origins of health and disease theory, adverse environments during early life stages may induce adaptive changes and subsequently result in an increased risk of diseases in later life. Increasing evidence also suggests that ART offspring are predisposed to an increased risk of non-communicable diseases, such as malignancies, asthma, obesity, metabolic syndrome, diabetes, cardiovascular diseases, and neurodevelopmental and psychiatric disorders. In this review, we summarize the risks for long-term health in ART offspring, discuss the underlying mechanisms, including underlying parental infertility, epigenetic alterations, non-physiological hormone levels, and placental dysfunction, and propose potential strategies to optimize the management of ART and health care of parents and children to eliminate the associated risks. Further ongoing follow-up and research are warranted to determine the effects of ART on the long-term health of ART offspring in later life.

The causal association between smoking initiation, alcohol and coffee consumption, and women's reproductive health: A two-sample Mendelian randomization analysis.

Objective: A number of epidemiological studies have demonstrated that smoking initiation and alcohol and coffee consumption were closely related to women's reproductive health. However, there was still insufficient evidence supporting their direct causality effect. Methods: We utilized two-sample Mendelian randomization (TSMR) analysis with summary datasets from genome-wide association study (GWAS) to investigate the causal relationship between smoking initiation, alcohol and coffee consumption, and women's reproductive health-related traits. Exposure genetic instruments were used as variants significantly related to traits. The inverse-variance weighted (IVW) method was used as the main analysis approach, and we also performed MR-PRESSO, MR-Egger, weighted median, and weighted mode to supplement the sensitivity test. Then, the horizontal pleiotropy was detected by using MRE intercept and MR-PRESSO methods, and the heterogeneity was assessed using Cochran's Q statistics. Results: We found evidence that smoking women showed a significant inverse causal association with the sex hormone-binding globulin (SHBG) levels (corrected ß = -0.033, p = 9.05E-06) and age at menopause (corrected ß = -0.477, p = 6.60E-09) and a potential positive correlation with the total testosterone (TT) levels (corrected ß = 0.033, p = 1.01E-02). In addition, there was suggestive evidence for the alcohol drinking effect on the elevated TT levels (corrected ß = 0.117, p = 5.93E-03) and earlier age at menopause (corrected ß = -0.502, p = 4.14E-02) among women, while coffee consumption might decrease the female SHBG levels (corrected ß = -0.034, p = 1.33E-03). Conclusion: Our findings suggested that smoking in women significantly decreased their SHBG concentration, promoted earlier menopause, and possibly reduced the TT levels. Alcohol drinking had a potential effect on female higher TT levels and earlier menopause, while coffee consumption might lead to lower female SHBG levels.

Understanding risk factors for postoperative mortality in neonates based on explainable machine learning technology.

PURPOSE: We aimed to introduce an explainable machine learning technology to help clinicians understand the risk factors for neonatal postoperative mortality at different levels. METHODS: A total of 1481 neonatal surgeries performed between May 2016 and December 2019 at a children's hospital were included in this study. Perioperative variables, including vital signs during surgery, were collected and used to predict postoperative mortality. Several widely used machine learning methods were trained and evaluated on split datasets. The model with the best performance was explained by SHAP (SHapley Additive exPlanations) at different levels. RESULTS: The random forest model achieved the best performance with an area under the receiver operating characteristic curve of 0.72 in the validation set. TreeExplainer of SHAP was used to identify the risk factors for neonatal postoperative mortality. The explainable machine learning model not only explains the risk factors identified by traditional statistical analysis but also identifies additional risk factors. The visualization of feature contributions at different levels by SHAP makes the "black-box" machine learning model easily understood by clinicians and families. Based on this explanation, vital signs during surgery play an important role in eventual survival. CONCLUSIONS: The explainable machine learning model not only exhibited good performance in predicting neonatal surgical mortality but also helped clinicians understand each risk factor and each individual case.

TREM2 Dictates Antibacterial Defense and Viability of Bone Marrow-derived Macrophages during Bacterial Infection.

Macrophages undergo profound metabolic reprogramming to join key immunoregulatory functions, which can be initiated by pattern recognition receptors. TREM2 (triggering receptor expressed on myeloid cells 2), a macrophage phagocytic receptor, plays pivotal roles in sepsis by enhancing bacterial clearance, which is associated with regulation of reactive oxygen species (ROS) production. However, how intracellular ROS participate in TREM2-mediated bactericidal activity remains unclear. This study was designed to investigate the organelle source and biological activity of ROS in the context of TREM2-mediated immune defense during Escherichiacoli infection. Bone marrow-derived macrophages (BMDMs) were transfected with TREM2-overexpressing adenoviruses or control viruses and challenged with E. coli. The BMDMs were administered to mouse models with local E. coli infection. In addition, monocytic TREM2 expression, NOX2 concentrations, and pyroptosis were detected in patients with bacterial sepsis. General ROS production was found to be comparable between TREM2-overexpressing and control BMDMs upon E. coli challenge. The deficiency of Nox2 led to impaired phagosome degradation and lack of bactericidal ability and abolished TREM2-mediated protective activity against pulmonary E. coli infection. Overexpression of TREM2 suppressed mitochondrial ROS generation, inhibited NLRP3/caspase-1 inflammasome activation, and finally protected BMDMs from gasdermin D-mediated pyroptosis during pulmonary E. coli infection. The protective role of TREM2 was further confirmed in mice with abdominal E. coli infection. Moreover, monocytic TREM2 expression was positively correlated with NOX2 concentrations and negatively correlated with pyroptosis and disease severity in patients with bacterial sepsis. Collectively, TREM2 controls macrophage immune functions by fine-tuning ROS generation and enhances the host defense against bacterial infection. Our data suggest that TREM2 is a promising candidate target for sepsis therapy.