Exploring melatonin's multifaceted role in female reproductive health: From follicular development to lactation and its therapeutic potential in obstetric syndromes.

BACKGROUND: Melatonin is mainly secreted by the pineal gland during darkness and regulates biological rhythms through its receptors in the suprachiasmatic nucleus of the hypothalamus. In addition, it also plays a role in the reproductive system by affecting the function of the hypothalamic-pituitary-gonadal axis, and by acting as a free radical scavenger thus contributing to the maintenance of the optimal physiological state of the gonads. Besides, melatonin can freely cross the placenta to influence fetal development. However, there is still a lack of overall understanding of the role of melatonin in the reproductive cycle of female mammals. AIM OF REVIEW: Here we focus the role of melatonin in female reproduction from follicular development to delivery as well as the relationship between melatonin and lactation. We further summarize the potential role of melatonin in the treatment of preeclampsia, polycystic ovary syndrome, endometriosis, and ovarian aging. KEY SCIENTIFIC CONCEPTS OF REVIEW: Understanding the physiological role of melatonin in female reproductive processes will contribute to the advancement of human fertility and reproductive medicine research.

Nutritional strategies to reduce intestinal cell apoptosis by alleviating oxidative stress.

The gut barrier is the first line of defense against harmful substances and pathogens in the intestinal tract. The balance of proliferation and apoptosis of intestinal epithelial cells (IECs) is crucial for maintaining the integrity of the intestinal mucosa and its function. However, oxidative stress and inflammation can cause DNA damage and abnormal apoptosis of the IECs, leading to the disruption of the intestinal epithelial barrier. This, in turn, can directly or indirectly cause various acute and chronic intestinal diseases. In recent years, there has been a growing understanding of the vital role of dietary ingredients in gut health. Studies have shown that certain amino acids, fibers, vitamins, and polyphenols in the diet can protect IECs from excessive apoptosis caused by oxidative stress, and limit intestinal inflammation. This review aims to describe the molecular mechanism of apoptosis and its relationship with intestinal function, and to discuss the modulation of IECs' physiological function, the intestinal epithelial barrier, and gut health by various nutrients. The findings of this review may provide a theoretical basis for the use of nutritional interventions in clinical intestinal disease research and animal production, ultimately leading to improved human and animal intestinal health.

Sodium acetate regulates milk fat synthesis through the activation of GPR41/GPR43 signaling pathway.

Background: Fat is a critical component in milk, which provided energy for the early growth and development of mammals. Milk fat is positively related to the concentration of acetate in the blood, while the underlying mechanism is still unclear. Objective: This study is to investigate the effects of sodium acetate (NaAc) on milk fat synthesis in the mammary gland, and explored the underlying mechanism. Methods: In vitro experiments were carried out in mouse mammary epithelial cell line (HC11) cells cultured with NaAc to explore the potential pathway of NaAc on milk fat synthesis. Furthermore, 24 pregnant mice (from d 18.5 of gestation to d 7 of lactation, exposed to 200 mM NaAc drinking water) were used as an in vivo model to verify the results. Results: In this study, we found that NaAc promoted milk fat synthesis and the expression of related genes and proteins in HC11 mammary epithelial cells with the activation of GPCR and mTORC1 signaling pathways (p < 0.05). Pretreatment with the mTORC1 inhibitors and G protein inhibitors attenuated the NaAc-induced milk fat synthesis in HC11 mammary epithelial cells (p < 0.05). Importantly, the effect of NaAc on milk synthesis was attenuated in GPR41 and GPR43 knockdown HC11 mammary epithelial cells (p < 0.05). This evidence indicates that NaAc might regulate milk fat synthesis through the GPR41/GPR43-mTORC1 pathway. Consistently, in in vivo experiment, dietary supplementation with NaAc significantly increased milk fat content and fat synthesis-related proteins in mice mammary glands with the activation of mTORC1 and GPCR signaling pathways at peak lactation (p < 0.05). Conclusion: The addition of NaAc promoted the increase of milk fat synthesis in HC11 mammary epithelial cells and mice mammary glands at peak lactation. Mechanistically, NaAc activates GPR41 and GPR43 receptors, leading to the activation of the mTORC1 signaling pathway to promote the synthesis of milk fat.Graphical abstract.

Causal association between inflammatory bowel disease and IgA nephropathy: A bidirectional two-sample Mendelian randomization study.

Background: An association between inflammatory bowel disease (IBD) [which includes ulcerative colitis (UC) and Crohn's disease (CD)] and IgA nephropathy (IgAN) has been discovered in observational studies, but the causal relationship is still unknown. The aim of this study was to clarify the causal link between IBD (which includes UC and CD) and IgAN via a two-sample Mendelian randomization (MR) analysis. Methods: Eligible single-nucleotide polymorphisms (SNPs) were selected as instrumental variables (IVs) for analyses and were obtained from the publicly available genome-wide association study (GWAS) summary statistics. Inverse-variance weighting (IVW), Mendelian randomization-Egger (MR-Egger) regression, the Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) test, and the weighted median were utilized to obtain the results. The MR-PRESSO test and MR-Egger regression were also performed to detect and correct horizontal pleiotropy. The Cochran's Q test and "leave-one-out" analysis were also conducted to assess the stability and reliability of the MR results. Results: This study found that IBD, UC, and CD all had significant positive causal effects on IgAN risk (IBD: OR = 1.58, 95% CI 1.15-2.16, p = 4.53 × 10-3; UC: OR = 1.55, 95% CI 1.14-2.11, p = 4.88 × 10-3; CD: OR = 1.57, 95% CI 1.21-2.03, p = 5.97 × 10-4). No significant horizontal pleiotropic effect was found for the causal association between IBD, UC, CD, and the risk of IgAN. Cochran's Q test identified no evidence of heterogeneity for the IV estimates. The "leave-one-out" sensitivity analysis also revealed that the MR results were robust. Conclusion: The results of this two-sample MR analysis supported that IBD, UC, and CD were causally associated with the risk of IgAN, while there was no sufficient evidence for the causal effect of IgAN on IBD, UC, or CD. Our findings provide theoretical support and a new perspective for the diagnosis and treatment of these two diseases.

Classification prediction of pancreatic cystic neoplasms based on radiomics deep learning models.

BACKGROUND: Preoperative prediction of pancreatic cystic neoplasm (PCN) differentiation has significant value for the implementation of personalized diagnosis and treatment plans. This study aimed to build radiomics deep learning (DL) models using computed tomography (CT) data for the preoperative differential diagnosis of common cystic tumors of the pancreas. METHODS: Clinical and CT data of 193 patients with PCN were collected for this study. Among these patients, 99 were pathologically diagnosed with pancreatic serous cystadenoma (SCA), 55 were diagnosed with mucinous cystadenoma (MCA) and 39 were diagnosed with intraductal papillary mucinous neoplasm (IPMN). The regions of interest (ROIs) were obtained based on manual image segmentation of CT slices. The radiomics and radiomics-DL models were constructed using support vector machines (SVMs). Moreover, based on the fusion of clinical and radiological features, the best combined feature set was obtained according to the Akaike information criterion (AIC) analysis. Then the fused model was constructed using logistic regression. RESULTS: For the SCA differential diagnosis, the fused model performed the best and obtained an average area under the curve (AUC) of 0.916. It had a best feature set including position, polycystic features (≥6), cystic wall calcification, pancreatic duct dilatation and radiomics-DL score. For the MCA and IPMN differential diagnosis, the fused model with AUC of 0.973 had a best feature set including age, communication with the pancreatic duct and radiomics score. CONCLUSIONS: The radiomics, radiomics-DL and fused models based on CT images have a favorable differential diagnostic performance for SCA, MCA and IPMN. These findings may be beneficial for the exploration of individualized management strategies.

A critical role of AMP-activated protein kinase in regulating intestinal nutrient absorption, barrier function, and intestinal diseases.

As one of the most important organs in animals, the intestine is responsible for nutrient absorption and acts as a barrier between the body and the environment. Intestinal physiology and function require the participation of energy. 5'-adenosine monophosphate-activated protein kinase (AMPK), a classical and highly expressed energy regulator in intestinal cells, regulates the process of nutrient absorption and barrier function and is also involved in the therapy of intestinal diseases. Studies have yielded findings that AMPK regulates the absorption of glucose, amino acids, and fatty acids in the intestine primarily by regulating transportation systems, as we detailed here. Moreover, AMPK is involved in the regulation of the intestinal mechanical barrier and immune barrier through manipulating the expression of tight junctions, antimicrobial peptides, and secretory immunoglobulins. In addition, AMPK also participates in the regulation of intestinal diseases, which indicates that AMPK is a promising therapeutic target for intestinal diseases and cancer. In this review, we summarized the current understanding regarding how AMPK regulates intestinal nutrient absorption, barrier function, and intestinal diseases.

Energy deprivation-induced AMPK activation inhibits milk synthesis by targeting PrlR and PGC-1α.

BACKGROUND: The mammary gland is responsible for milk production and secretion, which is critical for neonatal health during lactation. Lactation efficiency is largely affected by energy status with unclear mechanism. RESULTS: In the current study, we found that synthesis of milk fat and protein was significantly inhibited under energy-deficient conditions, which is accompanied with AMP-activated protein kinase (AMPK) activation. Modulating the AMPK signaling pathway directly or indirectly affects the synthesis of milk fat and protein. Besides mammalian target of rapamycin complex 1 (mTORC1) signaling in the regulation of milk synthesis, we discovered that AMPK mainly regulates the synthesis of milk protein through prolactin signaling. Mechanistically, AMPK triggers the ubiquitination of prolactin receptor (PrlR) through regulating the activity of ß-transducin repeat-containing protein (ß-TrCP, an E3 ligase). Subsequently, PrlR is degraded by the endocytosis process of lysosomes, which further attenuates prolactin signaling. In addition, our results revealed that AMPK activation inhibits milk fat synthesis through decreasing and accelerating de novo synthesis and ß-oxidation of fatty acids, respectively. To be precise, AMPK activation inhibits rate limiting enzymes and transcriptional regulatory factors involved in de novo fatty acid synthesis and decreases the acetylation process of peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1α) to strengthen the oxidation of fatty acids. CONCLUSIONS: Taken together, AMPK regulates the synthesis of milk not only depends on canonical mTORC1 signaling and key rate-limiting enzymes, but also through manipulating the degradation of PrlR and the acetylation of PGC-1α. Video Abstract.

Deep Learning Radiomics to Predict Regional Lymph Node Staging for Hilar Cholangiocarcinoma.

BACKGROUND: Our aim was to establish a deep learning radiomics method to preoperatively evaluate regional lymph node (LN) staging for hilar cholangiocarcinoma (HC) patients. METHODS AND MATERIALS: Of the 179 enrolled HC patients, 90 were pathologically diagnosed with lymph node metastasis. Quantitative radiomic features and deep learning features were extracted. An LN metastasis status classifier was developed through integrating support vector machine, high-performance deep learning radiomics signature, and three clinical characteristics. An LN metastasis stratification classifier (N1 vs. N2) was also proposed with subgroup analysis. RESULTS: The average areas under the receiver operating characteristic curve (AUCs) of the LN metastasis status classifier reached 0.866 in the training cohort and 0.870 in the external test cohorts. Meanwhile, the LN metastasis stratification classifier performed well in predicting the risk of LN metastasis, with an average AUC of 0.946. CONCLUSIONS: Two classifiers derived from computed tomography images performed well in predicting LN staging in HC and will be reliable evaluation tools to improve decision-making.

Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models.

Background: We conduct a study in developing and validating two radiomics-based models to preoperatively distinguish hepatic epithelioid angiomyolipoma (HEAML) from hepatic carcinoma (HCC) as well as focal nodular hyperplasia (FNH). Methods: Totally, preoperative contrast-enhanced computed tomography (CT) data of 170 patients and preoperative contrast-enhanced magnetic resonance imaging (MRI) data of 137 patients were enrolled in this study. Quantitative texture features and wavelet features were extracted from the regions of interest (ROIs) of each patient imaging data. Then two radiomics signatures were constructed based on CT and MRI radiomics features, respectively, using the random forest (RF) algorithm. By integrating radiomics signatures with clinical characteristics, two radiomics-based fusion models were established through multivariate linear regression and 10-fold cross-validation. Finally, two diagnostic nomograms were built to facilitate the clinical application of the fusion models. Results: The radiomics signatures based on the RF algorithm achieved the optimal predictive performance in both CT and MRI data. The area under the receiver operating characteristic curves (AUCs) reached 0.996, 0.879, 0.999, and 0.925 for the training as well as test cohort from CT and MRI data, respectively. Then, two fusion models simultaneously integrated clinical characteristics achieved average AUCs of 0.966 (CT data) and 0.971 (MRI data) with 10-fold cross-validation. Through decision curve analysis, the fusion models were proved to be excellent models to distinguish HEAML from HCC and FNH in comparison between the clinical models and radiomics signatures. Conclusions: Two radiomics-based models derived from CT and MRI images, respectively, performed well in distinguishing HEAML from HCC and FNH and might be potential diagnostic tools to formulate individualized treatment strategies.

Contrast-enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: A two-center study.

BACKGROUND: The present study constructed and validated the use of contrast-enhanced computed tomography (CT)-based radiomics to preoperatively predict microvascular invasion (MVI) status (positive vs negative) and risk (low vs high) in patients with hepatocellular carcinoma (HCC). METHODS: We enrolled 637 patients from two independent institutions. Patients from Institution I were randomly divided into a training cohort of 451 patients and a test cohort of 111 patients. Patients from Institution II served as an independent validation set. The LASSO algorithm was used for the selection of 798 radiomics features. Two classifiers for predicting MVI status and MVI risk were developed using multivariable logistic regression. We also performed a survival analysis to investigate the potentially prognostic value of the proposed MVI classifiers. RESULTS: The developed radiomics signature predicted MVI status with an area under the receiver operating characteristic curve (AUC) of .780, .776, and .743 in the training, test, and independent validation cohorts, respectively. The final MVI status classifier that integrated two clinical factors (age and α-fetoprotein level) achieved AUC of .806, .803, and .796 in the training, test, and independent validation cohorts, respectively. For MVI risk stratification, the AUCs of the radiomics signature were .746, .664, and .700 in the training, test, and independent validation cohorts, respectively, and the AUCs of the final MVI risk classifier-integrated clinical stage were .783, .778, and .740, respectively. Survival analysis showed that our MVI status classifier significantly stratified patients for short overall survival or early tumor recurrence. CONCLUSIONS: Our CT radiomics-based models were able to predict MVI status and MVI risk of HCC and might serve as a reliable preoperative evaluation tool.