Identification of an RNA-binding perturbing characteristic for thiopurine drugs and their derivatives to disrupt CELF1-RNA interaction.

RNA-binding proteins (RBPs) are attractive targets in human pathologies. Despite a number of efforts to target RBPs with small molecules, it is still difficult to develop RBP inhibitors, asking for a deeper understanding of how to chemically perturb RNA-binding activity. In this study, we found that the thiopurine drugs (6-mercaptopurine and 6-thioguanine) effectively disrupt CELF1-RNA interaction. The disrupting activity relies on the formation of disulfide bonds between the thiopurine drugs and CELF1. Mutating the cysteine residue proximal to the RNA recognition motifs (RRMs), or adding reducing agents, abolishes the disrupting activity. Furthermore, the 1,2,4-triazole-3-thione, a thiopurine analogue, was identified with 20-fold higher disrupting activity. Based on this analogue, we found that compound 9 disrupts CELF1-RNA interaction in living cells and ameliorates CELF1-mediated myogenesis deficiency. In summary, we identified a thiol-mediated binding mechanism for thiopurine drugs and their derivatives to perturb protein-RNA interaction, which provides novel insight for developing RBP inhibitors. Additionally, this work may benefit the pharmacological and toxicity research of thiopurine drugs.

Identification of DNase I hypersensitive sites in the human genome by multiple sequence descriptors.

DNase I hypersensitive sites (DHSs) are chromatin regions highly sensitive to DNase I enzymes. Studying DHSs is crucial for understanding complex transcriptional regulation mechanisms and localizing cis-regulatory elements (CREs). Numerous studies have indicated that disease-related loci are often enriched in DHSs regions, underscoring the importance of identifying DHSs. Although wet experiments exist for DHSs identification, they are often labor-intensive. Therefore, there is a strong need to develop computational methods for this purpose. In this study, we used experimental data to construct a benchmark dataset. Seven feature extraction methods were employed to capture information about human DHSs. The F-score was applied to filter the features. By comparing the prediction performance of various classification algorithms through five-fold cross-validation, random forest was proposed to perform the final model construction. The model could produce an overall prediction accuracy of 0.859 with an AUC value of 0.837. We hope that this model can assist scholars conducting DNase research in identifying these sites.

Mitochondrial folate metabolism-mediated α-linolenic acid exhaustion masks liver fibrosis resolution.

Sustainable TGF-ß1 signaling drives organ fibrogenesis. However, the cellular adaptation to maintain TGF-ß1 signaling remains unclear. In this study, we revealed that dietary folate restriction promoted the resolution of liver fibrosis in mice with nonalcoholic steatohepatitis. In activated hepatic stellate cells, folate shifted toward mitochondrial metabolism to sustain TGF-ß1 signaling. Mechanistically, nontargeted metabolomics screening identified that α-linolenic acid (ALA) is exhausted by mitochondrial folate metabolism in activated hepatic stellate cells. Knocking down serine hydroxymethyltransferase 2 increases the bioconversion of ALA to docosahexaenoic acid, which inhibits TGF-ß1 signaling. Finally, blocking mitochondrial folate metabolism promoted liver fibrosis resolution in nonalcoholic steatohepatitis mice. In conclusion, mitochondrial folate metabolism/ALA exhaustion/TGF-ßR1 reproduction is a feedforward signaling to sustain profibrotic TGF-ß1 signaling, and targeting mitochondrial folate metabolism is a promising strategy to enforce liver fibrosis resolution.

Tunable Contacts of Bi2 O2 Se Nanosheets MSM Photodetectors by Metal-Assisted Transfer Approach for Self-Powered Near-Infrared Photodetection.

Owing to the Fermi pinning effect arose in the metal electrodes deposition process, metal-semiconductor contact is always independent on the work function, which challenges the next-generation optoelectronic devices. In this work, a metal-assisted transfer approach is developed to transfer Bi2 O2 Se nanosheets onto the pre-deposited metal electrodes, benefiting to the tunable metal-semiconductor contact. The success in Bi2 O2 Se nanosheets transfer is contributed to the stronger van der Waals adhesion of metal electrodes than that of growth substrates. With the pre-deposited asymmetric electrodes, the self-powered near-infrared photodetectors are realized, demonstrating low dark current of 0.04 pA, high Ilight /Idark ratio of 380, fast rise and decay times of 4 and 6 ms, respectively, under the illumination of 1310 nm laser. By pre-depositing the metal electrodes on polyimide and glass, high-performance flexible and omnidirectional self-powered near-infrared photodetectors are achieved successfully. This study opens up new opportunities for low-dimensional semiconductors in next-generation high-performance optoelectronic devices.

Heterogeneous integration of an on-chip Nd:YAG whispering gallery mode laser with a lithium-niobate-on-insulator platform.

The integration of heterogeneous optical components onto an optical platform is crucial for the advancement of photonic chips. To achieve this, efficient coupling of optical signals between components and the platform is essential. Here, we have successfully integrated a Nd:YAG microdisk laser with a lithium-niobate-on-insulator (LNOI) photonic platform by modulating the propagation modes of LNOI. Ridge waveguides are fabricated on the LNOI by carefully adjusting the cross-sectional dimensions to enable the propagation of higher-order propagation modes. This ridge waveguide ensures that the effective refractive index of the higher-order mode closely matches that of the fundamental mode of the Nd:YAG microdisk, ensuring efficient waveguide-microdisk coupling. This on-chip laser, consisting of an Nd:YAG microdisk and LNOI integration, achieves a maximum output power of 23 µW, and a mode suppression ratio of 53.6 dB. This research presents an efficient approach for constructing highly functional heterogeneous integrated optical chips.

Free-space laser emission from Nd:YAG elliptical microdisks.

The utilization of deformed microcavities, such as elliptical microdisks, has been widely acknowledged as an effective solution for achieving free-space emission in microcavity lasers. However, the deformations introduced in the microcavity structure tend to decrease the quality factor (Q factor), resulting in weakened output intensity. To address this issue, one potential approach is to employ highly efficient laser gain media that can compensate for the negative impact of the structure on the output intensity. In this study, we employed the exceptional laser crystal material Nd:YAG as the laser gain medium and successfully fabricated an elliptical microdisk laser with a major semiaxis of 15 µm and an eccentricity ratio of 0.15. By utilizing an 808 nm laser for pumping, we were able to achieve free-space laser emission with a slope efficiency of 1.7% and a remarkable maximum output power of 58 µW. This work contributes toward the advancement of the application of deformation microcavity lasers.

Development and validation of a Radiopathomics model based on CT scans and whole slide images for discriminating between Stage I-II and Stage III gastric cancer.

OBJECTIVE: This study aimed to develop and validate an artificial intelligence radiopathological model using preoperative CT scans and postoperative hematoxylin and eosin (HE) stained slides to predict the pathological staging of gastric cancer (stage I-II and stage III). METHODS: This study included a total of 202 gastric cancer patients with confirmed pathological staging (training cohort: n = 141; validation cohort: n = 61). Pathological histological features were extracted from HE slides, and pathological models were constructed using logistic regression (LR), support vector machine (SVM), and NaiveBayes. The optimal pathological model was selected through receiver operating characteristic (ROC) curve analysis. Machine learnin algorithms were employed to construct radiomic models and radiopathological models using the optimal pathological model. Model performance was evaluated using ROC curve analysis, and clinical utility was estimated using decision curve analysis (DCA). RESULTS: A total of 311 pathological histological features were extracted from the HE images, including 101 Term Frequency-Inverse Document Frequency (TF-IDF) features and 210 deep learning features. A pathological model was constructed using 19 selected pathological features through dimension reduction, with the SVM model demonstrating superior predictive performance (AUC, training cohort: 0.949; validation cohort: 0.777). Radiomic features were constructed using 6 selected features from 1834 radiomic features extracted from CT scans via SVM machine algorithm. Simultaneously, a radiopathomics model was built using 17 non-zero coefficient features obtained through dimension reduction from a total of 2145 features (combining both radiomics and pathomics features). The best discriminative ability was observed in the SVM_radiopathomics model (AUC, training cohort: 0.953; validation cohort: 0.851), and clinical decision curve analysis (DCA) demonstrated excellent clinical utility. CONCLUSION: The radiopathomics model, combining pathological and radiomic features, exhibited superior performance in distinguishing between stage I-II and stage III gastric cancer. This study is based on the prediction of pathological staging using pathological tissue slides from surgical specimens after gastric cancer curative surgery and preoperative CT images, highlighting the feasibility of conducting research on pathological staging using pathological slides and CT images.

Photosensitizer-loaded hydrogels: A new antibacterial dressing.

Bacterial wound infection has emerged as a pivotal threat to human health worldwide, and the situation has worsened owing to the gradual increase in antibiotic-resistant bacteria caused by the improper use of antibiotics. To reduce the use of antibiotics and avoid the increase in antibiotic-resistant bacteria, researchers are increasingly paying attention to  photodynamic therapy, which uses light to produce reactive oxygen species to kill bacteria. Treating bacteria-infected wounds by photodynamic therapy requires fixing the photosensitizer (PS) at the wound site and maintaining a certain level of wound humidity. Hydrogels are materials with a high water content and are well suited for fixing PSs at wound sites for antibacterial photodynamic therapy. Therefore, hydrogels are often loaded with PSs for treating bacteria-infected wounds via antibacterial photodynamic therapy. In this review, we systematically summarised the antibacterial mechanisms and applications of PS-loaded hydrogels for treating bacteria-infected wounds via photodynamic therapy. In addition, the recent  studies and the research status progresses of novel antibacterial hydrogels are discussed. Finally, the challenges and future prospects of PS-loaded hydrogels are reviewed.

Protein Engineering with Lightweight Graph Denoising Neural Networks.

Protein engineering faces challenges in finding optimal mutants from a massive pool of candidate mutants. In this study, we introduce a deep-learning-based data-efficient fitness prediction tool to steer protein engineering. Our methodology establishes a lightweight graph neural network scheme for protein structures, which efficiently analyzes the microenvironment of amino acids in wild-type proteins and reconstructs the distribution of the amino acid sequences that are more likely to pass natural selection. This distribution serves as a general guidance for scoring proteins toward arbitrary properties on any order of mutations. Our proposed solution undergoes extensive wet-lab experimental validation spanning diverse physicochemical properties of various proteins, including fluorescence intensity, antigen-antibody affinity, thermostability, and DNA cleavage activity. More than 40% of ProtLGN-designed single-site mutants outperform their wild-type counterparts across all studied proteins and targeted properties. More importantly, our model can bypass the negative epistatic effect to combine single mutation sites and form deep mutants with up to seven mutation sites in a single round, whose physicochemical properties are significantly improved. This observation provides compelling evidence of the structure-based model's potential to guide deep mutations in protein engineering. Overall, our approach emerges as a versatile tool for protein engineering, benefiting both the computational and bioengineering communities.

Simple, Efficient, and Scalable Structure-Aware Adapter Boosts Protein Language Models.

Fine-tuning pretrained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised learning approaches. As a widely applied powerful technique in natural language processing, employing parameter-efficient fine-tuning techniques could potentially enhance the performance of PLMs. However, the direct transfer to life science tasks is nontrivial due to the different training strategies and data forms. To address this gap, we introduce SES-Adapter, a simple, efficient, and scalable adapter method for enhancing the representation learning of PLMs. SES-Adapter incorporates PLM embeddings with structural sequence embeddings to create structure-aware representations. We show that the proposed method is compatible with different PLM architectures and across diverse tasks. Extensive evaluations are conducted on 2 types of folding structures with notable quality differences, 9 state-of-the-art baselines, and 9 benchmark data sets across distinct downstream tasks. Results show that compared to vanilla PLMs, SES-Adapter improves downstream task performance by a maximum of 11% and an average of 3%, with significantly accelerated convergence speed by a maximum of 1034% and an average of 362%, the training efficiency is also improved by approximately 2 times. Moreover, positive optimization is observed even with low-quality predicted structures. The source code for SES-Adapter is available at https://github.com/tyang816/SES-Adapter.

Retracted: Long Non-Coding RNA Plasmacytoma Variant Translocation 1 (PVT1) Enhances Proliferation, Migration, and Epithelial-Mesenchymal Transition (EMT) of Pituitary Adenoma Cells by Activating ß-Catenin, c-Myc, and Cyclin D1 Expression.

The Editors of Medical Science Monitor wish to inform you that the above manuscript has been retracted from publication due to concerns with the credibility and originality of the study, the manuscript content, and the Figure images. Reference: Yihua Zhang, Yang Tan, Hao Wang, Minhui Xu, Lunshan Xu. Long Non-Coding RNA Plasmacytoma Variant Translocation 1 (PVT1) Enhances Proliferation, Migration, and Epithelial-Mesenchymal Transition (EMT) of Pituitary Adenoma Cells by Activating ß-Catenin, c-Myc, and Cyclin D1 Expression. Med Sci Monit, 2019; 25: 7652-7659. DOI: 10.12659/MSM.917110.

Development and validation of an artificial intelligence model for predicting de novo distant bone metastasis in breast cancer: a dual-center study.

OBJECTIVE: Breast cancer has become the most prevalent malignant tumor in women, and the occurrence of distant metastasis signifies a poor prognosis. Utilizing predictive models to forecast distant metastasis in breast cancer presents a novel approach. This study aims to utilize readily available clinical data and advanced machine learning algorithms to establish an accurate clinical prediction model. The overall objective is to provide effective decision support for clinicians. METHODS: Data from 239 patients from two centers were analyzed, focusing on clinical blood biomarkers (tumor markers, liver and kidney function, lipid profile, cardiovascular markers). Spearman correlation and the least absolute shrinkage and selection operator regression were employed for feature dimension reduction. A predictive model was built using LightGBM and validated in training, testing, and external validation cohorts. Feature importance correlation analysis was conducted on the clinical model and the comprehensive model, followed by univariate and multivariate regression analysis of these features. RESULTS: Through internal and external validation, we constructed a LightGBM model to predict de novo bone metastasis in newly diagnosed breast cancer patients. The area under the receiver operating characteristic curve values of this model in the training, internal validation test, and external validation test1 cohorts were 0.945, 0.892, and 0.908, respectively. Our validation results indicate that the model exhibits high sensitivity, specificity, and accuracy, making it the most accurate model for predicting bone metastasis in breast cancer patients. Carcinoembryonic Antigen, creatine kinase, albumin-globulin ratio, Apolipoprotein B, and Cancer Antigen 153 (CA153) play crucial roles in the model's predictions. Lipoprotein a, CA153, gamma-glutamyl transferase, α-Hydroxybutyrate dehydrogenase, alkaline phosphatase, and creatine kinase are positively correlated with breast cancer bone metastasis, while white blood cell ratio and total cholesterol are negatively correlated. CONCLUSION: This study successfully utilized clinical blood biomarkers to construct an artificial intelligence model for predicting distant metastasis in breast cancer, demonstrating high accuracy. This suggests potential clinical utility in predicting and identifying distant metastasis in breast cancer. These findings underscore the potential prospect of developing economically efficient and readily accessible predictive tools in clinical oncology.

Small molecule targeting CELF1 RNA-binding activity to control HSC activation and liver fibrosis.

CUGBP Elav-like family member 1 (CELF1), an RNA-binding protein (RBP), plays important roles in the pathogenesis of diseases such as myotonic dystrophy, liver fibrosis and cancers. However, targeting CELF1 is still a challenge, as RBPs are considered largely undruggable. Here, we discovered that compound 27 disrupted CELF1-RNA binding via structure-based virtual screening and biochemical assays. Compound 27 binds directly to CELF1 and competes with RNA for binding to CELF1. Compound 27 promotes IFN-γ secretion and suppresses TGF-ß1-induced hepatic stellate cell (HSC) activation by inhibiting CELF1-mediated IFN-γ mRNA decay. In vivo, compound 27 attenuates CCl4-induced murine liver fibrosis. Furthermore, the structure-activity relationship analysis was performed and compound 841, a derivative of compound 27, was identified as a selective CELF1 inhibitor. In conclusion, targeting CELF1 RNA-binding activity with small molecules was achieved, which provides a novel strategy for treating liver fibrosis and other CELF1-mediated diseases.

Residents' willingness to engage in carbon generalized system of preferences - A personality insight study based on the extended theory of planned behavior.

In the context of China's dual carbon target, reducing personal carbon emissions has been identified as a crucial strategy to achieve the target. The 2022 Digital China Development Report emphasizes the significance of implementing the Carbon Generalized System of Preferences (CGSP) in driving individual carbon reduction efforts in China. However, the psychological factors influencing public participation in CGSP are still unknown. Based on the Extended Theory of Planned Behavior (TPB), this study explored the psychological factors of different personality trait groups' participation in the CGSP and categorized 712 respondents into Compatible, Positive, Responsible, and Susceptible based on the MINI-IPIP scale and the K-means method. The results show that the strength of willingness to engage (WTE) in CGSP was ranked as: Compatible > Positive > Responsible > Susceptible and the WTE of compatible groups is more influenced by attitude, while Perceived Behavioral Control (PBC) plays a more crucial role in other groups. Personal Norms (PN) and Policy Awareness (PA) were significant for all specific personality groups except the Susceptible group. Surprisingly subjective norms had little to do with WTE. We believe that policymakers should consider the impact of PBC on WTE when formulating policies and raise the expectation of residents in terms of the value they can obtain from participating in CGSP. Additionally, promotional activities related to PN and PA in connection with CGSP should be conducted. These efforts may help individuals better engage in CGSP.

Salvianolic Acid B Alleviates Liver Injury by Regulating Lactate-Mediated Histone Lactylation in Macrophages.

Salvianolic acid B (Sal B) is the primary water-soluble bioactive constituent derived from the roots of Salvia miltiorrhiza Bunge. This research was designed to reveal the potential mechanism of Sal B anti-liver injury from the perspective of macrophages. In our lipopolysaccharide-induced M1 macrophage model, Sal B showed a clear dose-dependent gradient of inhibition of the macrophage trend of the M1 type. Moreover, Sal B downregulated the expression of lactate dehydrogenase A (LDHA), while the overexpression of LDHA impaired Sal B's effect of inhibiting the trend of macrophage M1 polarization. Additionally, this study revealed that Sal B exhibited inhibitory effects on the lactylation process of histone H3 lysine 18 (H3K18la). In a ChIP-qPCR analysis, Sal B was observed to drive a reduction in H3K18la levels in the promoter region of the LDHA, NLRP3, and IL-1ß genes. Furthermore, our in vivo experiments showed that Sal B has a good effect on alleviating CCl4-induced liver injury. An examination of liver tissues and the Kupffer cells isolated from those tissues proved that Sal B affects the M1 polarization of macrophages and the level of histone lactylation. Together, our data reveal that Sal B has a potential mechanism of inhibiting the histone lactylation of macrophages by downregulating the level of LDHA in the treatment of liver injury.

Mechanism of Mulberry Leaves and Black Sesame in Alleviating Slow Transit Constipation Revealed by Multi-Omics Analysis.

Traditional Chinese medicine (TCM) possesses the potential of providing good curative effects with no side effects for the effective management of slow transit constipation (STC), an intestinal disease characterized by colonic dyskinesia. Mulberry leaves (Morus alba L.) and black sesame (Sesamum indicum L.), referred to as SH, are processed and conditioned as per standardized protocols. SH has applications as food and medicine. Accordingly, we investigated the therapeutic potential of SH in alleviating STC. The analysis of SH composition identified a total of 504 compounds. The intervention with SH significantly improved intestinal motility, reduced the time for the first black stool, increased antioxidant activity, and enhanced water content, thereby effectively alleviating colon damage caused by STC. Transcriptome analysis revealed the SH in the treatment of STC related to SOD1, MUC2, and AQP1. The analysis of 16S rRNA gene sequences indicated notable differences in the abundance of 10 bacteria between the SH and model. Metabolomic analysis further revealed that SH supplementation increased the levels of nine metabolites associated with STC. Integrative analysis revealed that SH modulated amino acid metabolism, balanced intestinal flora, and targeted key genes (i.e., SOD1, MUC2, AQP1) to exert its effects. SH also inhibited the AQP1 expression and promoted SOD1 and MUC2 expression.

Rational Design of Meso-Diaminopimelate Dehydrogenase with Enhanced Reductive Amination Activity for Efficient Production of d-p-Hydroxyphenylglycine.

d-p-hydroxyphenylglycine (d-HPG) is an important intermediate in the pharmaceutical industry. In this study, a tri-enzyme cascade for the production of d-HPG from l-HPG was designed. However, the amination activity of Prevotella timonensis meso-diaminopimelate dehydrogenase (PtDAPDH) toward 4-hydroxyphenylglyoxylate (HPGA) was identified as the rate-limiting step. To overcome this issue, the crystal structure of PtDAPDH was solved, and a "binding pocket and conformation remodeling" strategy was developed to improve the catalytic activity toward HPGA. The best variant obtained, PtDAPDHM4, exhibited a catalytic efficiency (kcat/Km) that was 26.75-fold higher than that of the wild type. This improvement was due to the enlarged substrate-binding pocket and enhanced hydrogen bond networks around the active center; meanwhile, the increased number of interdomain residue interactions drove the conformation distribution toward the closed state. Under optimal transformation conditions, PtDAPDHM4 produced 19.8 g/L d-HPG from 40 g/L racemate DL-HPG in a 3 L fermenter within 10 h, with 49.5% conversion and >99% enantiomeric excess. Our study provides an efficient three-enzyme cascade pathway for the industrial production of d-HPG from racemate DL-HPG. IMPORTANCE d-p-hydroxyphenylglycine (d-HPG) is an important intermediate in the synthesis of antimicrobial compounds. d-HPG is mainly produced via chemical and enzymatic approaches, and enzymatic asymmetric amination employing diaminopimelate dehydrogenase (DAPDH) is considered an attractive method. However, the low catalytic activity of DAPDH toward bulky 2-keto acids limits its applications. In this study, we identified a DAPDH from Prevotella timonensis and created a mutant, PtDAPDHM4, which exhibited a catalytic efficiency (kcat/Km) toward 4-hydroxyphenylglyoxylate that was 26.75-fold higher than that of the wild type. The novel strategy developed in this study has practical value for the production of d-HPG from inexpensive racemate DL-HPG.

27% slope efficiency in a WGM microcavity enabled by an Yb:YAG crystalline film.

The choice of a laser gain medium is crucial in achieving efficient and high-power outputs of optically stimulated WGM microcavity lasers. This work employs an Yb:YAG crystalline film as the gain medium for the microdisk laser. The Yb:YAG crystalline film is exfoliated from a bulk of a Yb:YAG crystal by the ion-implantation-enhanced etching method. The crystalline film is shaped into a microdisk through focused ion beam milling. This Yb:YAG microdisk laser achieves a single-mode laser output (with a side-mode-suppression ratio of 27.8 dB) under a 946 nm laser pumping. The maximum slope efficiency reaches 27% with a maximum output power of 1.1 mW.

Treatment outcomes after radiofrequency ablation in patients with non-B non-C hepatocellular carcinoma within Milan criteria: comparison with HBV-related hepatocellular carcinoma.

PURPOSE: This study aims to evaluate the treatment outcomes of radiofrequency ablation (RFA) for patients with non-B non-C hepatocellular carcinoma (HCC) (NBNC-HCC) within Milan criteria, as well as to compare them with those of patients with hepatitis B virus (HBV)-related HCC (HBV-HCC). METHODS: From January 2007 to February 2020, 303 patients with primary HCC who underwent RFA were retrospectively reviewed, including 259 patients with HBV-HCC (HBV-HCC group) and 44 patients with NBNC-HCC (NBNC-HCC group). The clinical characteristics and treatment survivals were evaluated and compared. Moreover, the propensity score matching was used to reduce selection bias. RESULTS: A significantly lower proportion of cirrhosis was observed in the NBNC-HCC group (p = .048). Before propensity score matching, local tumor progression, disease-free survival, and overall survival after RFA showed no significant differences between the two groups (all p > .05). After matching, the overall survival rates in the NBNC-HCC group were significantly better than those in the HBV-HCC group (p = .042). Moreover, for patients with NBNC-HCC, tumor size (hazard ratio = 8.749, 95% confidence interval, 1.599-47.849; p = .012) was the only independent predictor of local tumor progression. CONCLUSIONS: Patients with NBNC-HCC within the Milan criteria after RFA had better long-term survival than patients with HBV-HCC, although larger, prospective and multicenter trials are required to validate these results.

The effects of natural products and bioactive ingredients of traditional Chinese medicine on apoptosis of ovarian granulosa cells.

Abnormal ovarian function is the main manifestation of female reproductive toxicity. Granulosa cells (GCs) play an important role in determining the fate of follicles and are the main effector cells of the female reproductive system. Excessive apoptosis of GCs leads to pathological folliculogenesis and further reproductive damage. However, drugs available for treatment of female reproductive toxicity are limited. Recent studies have confirmed that various natural products and bioactive ingredients of traditional Chinese medicine (TCM) can inhibit apoptosis of GCs and protect ovarian function. In this review, the mechanisms underlying the proapoptotic and antiapoptotic effects of natural products and bioactive ingredients of TCM on the proliferation, function, and apoptosis of GCs are summarized based on the findings of reports published over the past 10 years as reference for the treatment of female reproductive toxicity.