Constructing a predictive model for live birth following fresh embryo transfer in antagonist protocol for polycystic ovary syndrome.

OBJECTIVE: The present research aims to assess the factors that influence live birth outcomes following fresh embryo transfers using antagonist protocols in individuals diagnosed with polycystic ovary syndrome (PCOS). Furthermore, it seeks to develop a predictive nomogram model to facilitate clinical decision-making and provide personalized treatment strategies. METHODS: This retrospective cohort research analyzed the clinical data of 1242 individuals having PCOS who went through fresh embryo transfers employing antagonist protocols and in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) at Fujian Provincial Maternal and Child Health Hospital between January 2018 and December 2022. Individuals were assigned randomly to a modeling group (869 cases) and a validation group (373 cases) in a 7:3 ratio. The Boruta algorithm and multivariable logistic regression were utilized to identify independent risk factors linked to live births after transfer. A predictive nomogram was subsequently developed. The discriminatory power of the model and its accuracy were monitored by utilizing receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. RESULTS: Multivariable logistic regression analysis identified several independent factors that influence live birth rates in fresh embryo transfer cycles for individuals having PCOS using antagonist protocols, including female age, body mass index (BMI), infertility duration, serum testosterone levels, progesterone levels at the time of human chorionic gonadotropin (hCG) injection, number of high-quality cleavage-stage embryos, type of embryo transferred, and the total number of embryos transferred. Based on these findings, a predictive nomogram was developed. The area under the ROC curve stood at 0.804 (95% confidence interval (CI), 0.775-0.833) for the modeling group and 0.807 (95% CI, 0.762-0.851) for the validation group. Calibration curves confirmed that the predictions of the nomogram closely matched the actual live birth outcomes. Decision curve analysis highlighted that the model provides significant net benefits for predicting live birth rates, with optimal performance across a probability range of 16.5 to 88.6%. CONCLUSION: Independent factors, including female age, infertility duration, BMI, serum testosterone levels, progesterone levels on the day of hCG injection, and the number and type of high-quality cleavage-stage embryos transferred are pivotal in influencing live birth outcomes in fresh embryo transfer cycles under antagonist protocols in individuals with PCOS undergoing IVF/ICSI treatments. The predictive nomogram developed from these factors offers substantial predictive accuracy and clinical utility, providing a reliable basis for clinical prognosis, targeted interventions, and the development of personalized treatment plans.

Nomogram to predict the probability of clinical pregnancy in women with poor ovarian response undergoing in vitro fertilization/ intracytoplasmic sperm injection cycles.

BACKGROUND: Poor ovarian response (POR) is associated with decreased clinical pregnancy rates, emphasizing the need for developing clinical prediction models. Such models can improve prognostic accuracy, personalize medical interventions, and ultimately enhance live birth rates among patients with POR. OBJECTIVE: This study aims to develop and validate a prognostic model for predicting clinical pregnancy outcomes in individuals with POR undergoing in vitro fertilization/ intracytoplasmic sperm injection (IVF/ICSI) cycles. METHODS: A retrospective cohort of 969 patients with POR undergoing fresh embryo transfer cycles at the Reproductive Center of Fujian Maternal and Child Health Center from January 2018 to January 2022 was included. The cohort was randomly divided into model (n = 678) and validation (n = 291) groups in a 7:3 ratio. A single-factor analysis was performed on the model group to identify variables influencing clinical pregnancy. Optimal variables were selected using LASSO regression, and a clinical prediction model was constructed using multivariate logistic regression analysis. The model's calibration and discrimination were assessed using receiver operating characteristic (ROC) and calibration curves, while the clinical utility was evaluated using decision curve analysis. RESULTS: Multivariate logistic regression analysis revealed that the age of the women (odds ratio [OR] 0.936, 95% confidence interval [CI] 0.898-0.976, P = 0.002), body mass index (BMI) ≤ 24 (OR 2.748, 95% CI 1.724-4.492, P < 0.001), antral follicle count (AFC) (OR 1.232, 95% CI 1.073-1.416, P = 0.003), anti-Müllerian hormone (AMH) (OR 1.67, 95% CI 1.178-2.376, P = 0.004), number of mature oocytes (OR 1.227, 95% CI 1.075-1.403, P = 0.003), number of embryos transferred (OR 1.692, 95% CI 1.132-2.545, P = 0.011), and transfer of high-quality embryos (OR 3.452, 95% CI 1.548-8.842, P = 0.005) were independent predictors of clinical pregnancy in patients with POR. According to the receiver operating characteristic (ROC) analysis, the prediction model exhibited an area under the curve (AUC) of 0.752 (0.714, 0.789) in the model group and 0.765 (0.708, 0.821) in the validation group. The clinical decision curve demonstrated that the model held maximum clinical utility in both cohorts when the threshold probability of clinical pregnancy ranged from 6-81% to 12-82%, respectively. CONCLUSION: Clinical pregnancy outcomes in patients with POR who underwent IVF/ICSI treatment were influenced by several independent factors, including the age of the women, BMI, AFC, AMH, number of mature oocytes, number of embryos transferred, and transfer of high-quality embryos. A clinical prediction model based on these factors exhibited favorable clinical predictive and applicative value. Therefore, this model can serve as a valuable tool for clinical prognosis, intervention, and facilitating personalized medical treatment.

ß-Hydroxybutyrate Protects Against Cisplatin-Induced Renal Damage via Regulating Ferroptosis.

Cisplatin is a particularly potent antineoplastic drug. However, its usefulness is restricted due to the induction of nephrotoxicity. More recent research has indicated that ß-hydroxybutyrate (ß-HB) protects against acute or chronic organ damage as an efficient healing agent. Nonetheless, the therapeutic mechanisms of ß-HB in acute kidney damage caused by chemotherapeutic drugs remain unclear. Our study developed a model of cisplatin-induced acute kidney injury (AKI), which involved the administration of a ketogenic diet or ß-HB. We analyzed blood urea nitrogen (BUN) and creatinine (Cr) levels in serum, and used western blotting and immunohistochemical staining to assess ferroptosis and the calcium/calmodulin-dependent kinase kinase 2 (Camkk2)/AMPK pathway. The mitochondrial morphology and function were examined. Additionally, we conducted in vivo and in vitro experiments using selective Camkk2 inhibitor or activator to investigate the protective mechanism of ß-HB on cisplatin-induced AKI. Exogenous or endogenous ß-HB effectively alleviated cisplatin-induced abnormally elevated levels of BUN and Cr and renal tubular necrosis in vivo. Additionally, ß-HB reduced ferroptosis biomarkers and increased the levels of anti-ferroptosis biomarkers in the kidney. ß-HB also improved mitochondrial morphology and function. Moreover, ß-HB significantly attenuated cisplatin-induced cell ferroptosis and damage in vitro. Furthermore, western blotting and immunohistochemical staining indicated that ß-HB may prevent kidney injury by regulating the Camkk2-AMPK pathway. The use of the Camkk2 inhibitor or activator verified the involvement of Camkk2 in the renal protection by ß-HB. This study provided evidence of the protective effects of ß-HB against cisplatin-induced nephrotoxicity and identified inhibited ferroptosis and Camkk2 as potential molecular mechanisms.

ß-HB protects against cisplatin-induced renal damage both in vivo and in vitro.Moreover, ß-HB is effective in attenuating cisplatin-induced lipid peroxidation and ferroptosis.The regulation of energy metabolism, as well as the treatment involving ß-HB, is associated with Camkk2.

Safety assessment, whole genome sequence, and metabolome analysis of Streptococcus thermophilus CICC 20372 for bone cement fermentation.

Bone is a kind of meat processing by-product with high nutritional value but low in calorie, which is a typical food in China and parts of East Asian countries. Microbial fermentation by lactic acid bacteria showed remarkable advantages to increase the absorption of nutrients from bone cement by human body. Streptococcus thermophilus CICC 20372 is proven to be a good starter for bone cement fermentation. No genes encoding virulence traits or virulence factors were found in the genome of S. thermophilus CICC 20372 by a thorough genomic analysis. Its notable absence of antibiotic resistance further solidifies the safety. Furthermore, the genomic analysis identified four types of gene clusters responsible for the synthesis of antimicrobial metabolites. A comparative metabolomic analysis was performed by cultivating the strain in bone cement at 37 °C for 72 h, with the culture in de Man, Rogosa, and Sharpe (MRS) medium as control. Metabolome analysis results highlighted the upregulation of pathways involved in 2-oxocarboxylic acid metabolism, ATP-binding cassette (ABC) transporters, amino acid synthesis, and nucleotide metabolism during bone cement fermentation. S. thermophilus CICC 20372 produces several metabolites with health-promoting function during bone cement fermentation, including indole-3-lactic acid, which is demonstrated ameliorative effects on intestinal inflammation, tumor growth, and gut dysbiosis. In addition, lots of nucleotide and organic acids were accumulated at higher levels, which enriched the fermented bone cement with a variety of nutrients. Collectively, these features endow S. thermophilus CICC 20372 a great potential strain for bone food processing.

Acteoside protects podocyte against apoptosis through regulating AKT/GSK-3ß signaling pathway in db/db mice.

BACKGROUND: Podocyte apoptosis is one of the important pathological mechanisms of diabetic kidney disease (DKD). Acteoside (Act), a major active component of Rehmannia glutinosa leaves total glycoside, has a strong renoprotective action. Our study aims to demonstrate Act's renoprotective actions in db/db mice. METHODS: We adopted C57BLKS/J db/db mice as DKD animal models. After 8 weeks of Act administration, the 24-hour urine albumin, renal function index, and blood lipid levels were quantified using matching kits. Renal pathology was evaluated by HE and PAS staining. The podocyte damage and apoptosis-related signaling pathway were observed by using immunohistochemistry, western blot, and TUNEL staining. RESULTS: The albuminuria of db/db mice was reduced from 391 ug/24 h to 152 ug/24 h, and renal pathology changes were alleviated after Act administration. The western blot and immunohistochemistry showed that Act treatment upregulated the synaptopodin and podocin expression compared with db/db mice, while the TUNEL staining indicated podocyte apoptosis was inhibited. The B-cell lymphoma-2 (Bcl-2) level was upregulated in the Act group, but cleaved caspase-3 and Bcl-2 associated X protein (Bax) expression declined, while the protein kinase B/glycogen synthase kinase-3ß (AKT/GSK-3ß) signaling pathway was repressed. CONCLUSIONS: By inhibiting the AKT/GSK-3ß signaling pathway, Act protected podocytes from apoptosis, decreasing the urine albumin of db/db mice and delaying the course of DKD.

Exploratory metabolomic analysis based on UHPLC-Q-TOF-MS/MS to study hypoxia-reoxygenation energy metabolic alterations in HK-2 cells.

PURPOSE: Renal ischemia-reperfusion injury(IRI)is a major cause of acute kidney injury(AKI), the injury and repair of renal tubular epithelial cells play an important role in the pathological process of IR-AKI. Metabolomics was used to detect cell metabolism alterations and metabolic reprogramming in the initial injury, peak injury, and recovery stage of human renal proximal tubular cells (HK-2 cells) to provide insights into clinical prevention and treatment of IRI-induced AKI. METHODS: An in vitro ischemia-reperfusion (H/R) injury and the recovery model of HK-2 cells were established at different times of hypoxia/reoxygenation. Comprehensive detection of metabolic alterations in HK-2 cells after H/R induction by nontarget metabolomics. Interconversion of glycolysis and fatty acid oxidation (FAO) in HK-2 cells after H/R induction was examined by western blotting and qRT-PCR. RESULTS: Multivariate data analysis found significant differences among the groups, with significant changes in metabolites such as glutamate, malate, aspartate, and L-palmitoylcarnitine. Hypoxia-reoxygenated HK-2 cells are accompanied by altered metabolisms such as disturbance of amino acid and nucleotide metabolism, dysregulation of lipid metabolism, increased glycolysis, and metabolic reprogramming, which manifests as a shift in energy metabolism from FAO to glycolysis. CONCLUSION: The development of IRI-induced AKI in HK-2 cells is accompanied by the disturbance of amino acid, nucleotide, and tricarboxylic acid cycle metabolism and specifically metabolic reprogramming of FAO to glycolytic conversion. The timely recovery of energy metabolism in HK-2 cells is of great significance for treating and prognosis IRI-induced AKI.

Prognostic prediction of idiopathic membranous nephropathy using interpretable machine learning.

BACKGROUND: Established prognostic models of idiopathic membranous nephropathy (IMN) were limited to traditional modeling methods and did not comprehensively consider clinical and pathological patient data. Based on the electronic medical record (EMR) system, machine learning (ML) was used to construct a risk prediction model for the prognosis of IMN. METHODS: Data from 418 patients with IMN were diagnosed by renal biopsy at the Fifth Clinical Medical College of Shanxi Medical University. Fifty-nine medical features of the patients could be obtained from EMR, and prediction models were established based on five ML algorithms. The area under the curve, recall rate, accuracy, and F1 were used to evaluate and compare the performances of the models. Shapley additive explanation (SHAP) was used to explain the results of the best-performing model. RESULTS: One hundred and seventeen patients (28.0%) with IMN experienced adverse events, 28 of them had compound outcomes (ESRD or double serum creatinine (SCr)), and 89 had relapsed. The gradient boosting machine (LightGBM) model had the best performance, with the highest AUC (0.892 ± 0.052, 95% CI 0.840-0.945), accuracy (0.909 ± 0.016), recall (0.741 ± 0.092), precision (0.906 ± 0.027), and F1 (0.905 ± 0.020). Recursive feature elimination with random forest and SHAP plots based on LightGBM showed that anti-phospholipase A2 receptor (anti-PLA2R), immunohistochemical immunoglobulin G4 (IHC IgG4), D-dimer (D-DIMER), triglyceride (TG), serum albumin (ALB), aspartate transaminase (AST), ß2-microglobulin (BMG), SCr, and fasting plasma glucose (FPG) were important risk factors for the prognosis of IMN. Increased risk of adverse events in IMN patients was correlated with high anti-PLA2R and low IHC IgG4. CONCLUSIONS: This study established a risk prediction model for the prognosis of IMN using ML based on clinical and pathological patient data. The LightGBM model may become a tool for personalized management of IMN patients.

Recent Progresses in Non-Dialysis Chronic Kidney Disease Patients with Hyperkalemia: Outcomes and Therapeutic Strategies.

Chronic kidney disease (CKD) affects about 10% of the world's population. Hyperkalemia is a life-threatening complication in patients with CKD, as it is associated with adverse cardiovascular and kidney outcomes. There are still many challenges and questions to address to improve the currently available therapeutic strategies to treat hyperkalemia, such as how to approach the emergency management of hyperkalemia. In recent years, in addition to novel oral potassium binders, great progress has been made in the application of novel kidney protective strategies, such as mineralocorticoid receptor antagonists and sodium-glucose cotransporter 2 inhibitors (SGLT2i) in hyperkalemia therapy. This review will discuss the recent advances from clinical trials in the effective management of hyperkalemia in non-dialysis CKD patients, enhancing the knowledge of physicians and internists concerning these newer agents and providing a helpful reference for clinical practice.

Bidirectional crosstalk between dysbiotic gut microbiota and systemic lupus erythematosus: What is new in therapeutic approaches?

Systemic lupus erythematosus is an autoimmune disease characterized by chronic inflammation and multiple organs damage. Its pathogenesis is complex and involves multiple factors including gut microbiota. Accumulating evidence indicates the interaction of microbial communities with the host immune system to maintain a state of homeostasis. Imbalances within the gut microbial composition and function may contribute to the development of many autoimmune diseases including SLE. In this review, we aim to highlight the dysregulation of commensal bacteria and their metabolites in the gastrointestinal tract and the resulting autoimmune responses in lupus and to decrypt the cross-link between the altered gut microbiota and the immune system in the SLE condition. We also provide new insights into targeting gut microbiota as a promising therapeutic approach to treat and manage SLE.

Plasma metabolomics-based reveals the treatment mechanism of ShenGui capsule for application to coronary heart disease in a rat model.

Shen Gui capsule (SGC) has been demonstrated to have a significant treatment effect for coronary heart disease (CHD). Nevertheless, the holistic therapeutic mechanism of SGC in vivo remain poorly interpreted. We aimed to systematically explore the preventive effect and mechanism of SGC on CHD rats using plasma metabolomics strategy. Rat CHD model was established by left anterior descending coronary artery ligation (LAD). Echocardiography, histological analyses of the myocardium and biochemical assays on serum were used to confirm the successful establishment of the CHD model and therapeutic effects of SGC. Then, UHPLC-MS/MS-based plasma metabolomics was combined with multivariate data analysis to screen potential pharmaco biomarkers associated with SGC treatment in the LAD-induced rat CHD model. After SGC treatment, 12 abnormal metabolites considered as potiential pharmaco biomarkers recovered to near normal levels. These biomarkers were involved in several metabolic pathways, including fat and protein metabolism, phenylalanine metabolism, neuroactive ligand-receptor interaction, androgen receptor signaling pathway, and estrone metabolism.These results suggested that SGC achieves therapeutic action on CHD via regulating various aspects of the body such as energy metabolism, neurological disturbances and inflammation, and thus plays a significant role in the treatment of CHD and its complications. The study is useful to systematically understand and analyze the mechanism of SGC's "multipie pathways, multiple levels, multiple targets" prevention and treatment of CHD.