Design and properties of ternary alkali-activated binder based on blast furnace slag, recycled powder and waste glass powder.

Ternary alkali-activated binder was prepared by blast furnace slag (GGBS), recycled powder (RP) and waste glass powder (WGP) using simplex centroid design method. By measuring the fluidity, setting time, drying shrinkage and mechanical property of specimen, the complementary effect of GGBS, RP and WGP was discussed. The reaction mechanism and microstructure were explored by X-ray diffraction and scanning electron microscopy. The results reveal that the addition of RP could significantly reduce the fluidity and setting time of paste, while WGP can obviously improve the rheological property and play a retarding role. The workability of paste can be effectively regulated by mixing RP and WGP together. Whether added alone or in combination, RP and WGP can effectively improve the shrinkage performance. In the ternary system, GGBS can be rapidly activated and form a skeleton structure. The fine RP particles can play a good role in filling the structure, and the pozzolanic reaction of WGP gradually occurs, which makes the microstructure more compact. The incorporation of GGBS, RP and WGP can promote the growth of hydration products, improve the density of microstructure, and form a certain complementary effect.

Developing and comparing deep learning and machine learning algorithms for osteoporosis risk prediction.

Introduction: Osteoporosis, characterized by low bone mineral density (BMD), is an increasingly serious public health issue. So far, several traditional regression models and machine learning (ML) algorithms have been proposed for predicting osteoporosis risk. However, these models have shown relatively low accuracy in clinical implementation. Recently proposed deep learning (DL) approaches, such as deep neural network (DNN), which can discover knowledge from complex hidden interactions, offer a new opportunity to improve predictive performance. In this study, we aimed to assess whether DNN can achieve a better performance in osteoporosis risk prediction. Methods: By utilizing hip BMD and extensive demographic and routine clinical data of 8,134 subjects with age more than 40 from the Louisiana Osteoporosis Study (LOS), we developed and constructed a novel DNN framework for predicting osteoporosis risk and compared its performance in osteoporosis risk prediction with four conventional ML models, namely random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and support vector machine (SVM), as well as a traditional regression model termed osteoporosis self-assessment tool (OST). Model performance was assessed by area under 'receiver operating curve' (AUC) and accuracy. Results: By using 16 discriminative variables, we observed that the DNN approach achieved the best predictive performance (AUC = 0.848) in classifying osteoporosis (hip BMD T-score ≤ -1.0) and non-osteoporosis risk (hip BMD T-score > -1.0) subjects, compared to the other approaches. Feature importance analysis showed that the top 10 most important variables identified by the DNN model were weight, age, gender, grip strength, height, beer drinking, diastolic pressure, alcohol drinking, smoke years, and economic level. Furthermore, we performed subsampling analysis to assess the effects of varying number of sample size and variables on the predictive performance of these tested models. Notably, we observed that the DNN model performed equally well (AUC = 0.846) even by utilizing only the top 10 most important variables for osteoporosis risk prediction. Meanwhile, the DNN model can still achieve a high predictive performance (AUC = 0.826) when sample size was reduced to 50% of the original dataset. Conclusion: In conclusion, we developed a novel DNN model which was considered to be an effective algorithm for early diagnosis and intervention of osteoporosis in the aging population.

Celastrol regulates the oligomeric state and chaperone activity of αB-crystallin linked with protein homeostasis in the lens.

Protein misfolding and aggregation are crucial pathogenic factors for cataracts, which are the leading cause of visual impairment worldwide. α-crystallin, as a small molecular chaperone, is involved in preventing protein misfolding and maintaining lens transparency. The chaperone activity of α-crystallin depends on its oligomeric state. Our previous work identified a natural compound, celastrol, which could regulate the oligomeric state of αB-crystallin. In this work, based on the UNcle and SEC analysis, we found that celastrol induced αB-crystallin to form large oligomers. Large oligomer formation enhanced the chaperone activity of αB-crystallin and prevented aggregation of the cataract-causing mutant ßA3-G91del. The interactions between αB-crystallin and celastrol were detected by the FRET (Fluorescence Resonance Energy Transfer) technique, and verified by molecular docking. At least 9 binding patterns were recognized, and some binding sites covered the groove structure of αB-crystallin. Interestingly, αB-R120G, a cataract-causing mutation located at the groove structure, and celastrol can decrease the aggregates of αB-R120G. Overall, our results suggested celastrol not only promoted the formation of large αB-crystallin oligomers, which enhanced its chaperone activity, but also bound to the groove structure of its α-crystallin domain to maintain its structural stability. Celastrol might serve as a chemical and pharmacological chaperone for cataract treatment.

A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture.

Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant factors. We propose a novel staged model that combines advanced imaging and clinical data to improve predictive performance. By using convolutional neural networks (CNNs) to extract features from hip DXA images, along with clinical variables, shape measurements, and texture features, our method provides a comprehensive framework for assessing fracture risk. The study cohort included 547 patients, with 94 experiencing hip fracture. A staged machine learning-based model was developed using two ensemble models: Ensemble 1 (clinical variables only) and Ensemble 2 (clinical variables and DXA imaging features). This staged approach used uncertainty quantification from Ensemble 1 to decide if DXA features are necessary for further prediction. Ensemble 2 exhibited the highest performance, achieving an Area Under the Curve (AUC) of 0.9541, an accuracy of 0.9195, a sensitivity of 0.8078, and a specificity of 0.9427. The staged model also performed well, with an AUC of 0.8486, an accuracy of 0.8611, a sensitivity of 0.5578, and a specificity of 0.9249, outperforming Ensemble 1, which had an AUC of 0.5549, an accuracy of 0.7239, a sensitivity of 0.1956, and a specificity of 0.8343. Furthermore, the staged model suggested that 54.49% of patients did not require DXA scanning. It effectively balanced accuracy and specificity, offering a robust solution when DXA data acquisition is not always feasible. Statistical tests confirmed significant differences between the models, highlighting the advantages of the advanced modeling strategies. Our staged approach offers a cost-effective holistic view of patients' health. It could identify individuals at risk with a high accuracy but reduce the unnecessary DXA scanning. Our approach has great promise to guide interventions to prevent hip fractures with reduced cost and radiation.

omicsMIC: a comprehensive benchmarking platform for robust comparison of imputation methods in mass spectrometry-based omics data.

Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in mass spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometry-based omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. omicsMIC is freely available at https://github.com/WQLin8/omicsMIC.

Benefits and Risks of Antihyperlipidemic Medication in Adults with Different Low-Density Lipoprotein Cholesterol Based on the Number Needed to Treat.

PURPOSE: The objective of this investigation is to examine the benefits and potential risks of these drugs in individuals by varying baseline low-density lipoprotein cholesterol (LDL-C) values, utilizing the concept of the number needed to treat (NNT). METHODS: We extensively searched electronic databases, such as PubMed, EMBASE, Cochrane, and Web of Science, up to 6 August 2023. Baseline LDL-C values were stratified into four categories: < 100, 100-129, 130-159, and ≥ 160 mg/dL. Risk ratios (RRs) and NNT values were computed. RESULTS: This analysis incorporated data from 46 randomized controlled trials (RCTs), encompassing a total of 237,870 participants. The meta-regression analysis demonstrated an incremental diminishing risk of major adverse cardiovascular events (MACE) with increasing baseline LDL-C values. Statins exhibited a significant reduction in MACE [number needed to treat to benefit (NNTB) 31, 95% confidence interval (CI) 25-37], but this effect was observed only in individuals with baseline LDL-C values of 100 mg/dL or higher. Ezetimibe and PCSK9 inhibitors also were effective in reducing MACE (NNTB 18, 95% CI 11-41, and NNTB 18, 95% CI 16-24). Notably, the safety outcomes of statins and ezetimibe did not reach statistical significance, while the incidence of injection-site reactions with PCSK9 inhibitors was statistically significant [number needed to treat to harm (NNTH) 41, 95% CI 80-26]. CONCLUSION: Statins, ezetimibe, and PCSK9 inhibitors demonstrated a substantial capacity to reduce MACE, particularly among individuals whose baseline LDL-C values were relatively higher. The NNT visually demonstrates the gradient between baseline LDL-C and cardiovascular disease (CVD) risk. SYSTEMATIC REVIEW REGISTRATION: Registration: PROSPERO identifier number: CRD42023458630.

Cryptosporidium parvum disrupts intestinal epithelial barrier in neonatal mice through downregulation of cell junction molecules.

BACKGROUND: Cryptosporidium spp. cause watery diarrhea in humans and animals, especially in infants and neonates. They parasitize the apical surface of the epithelial cells in the intestinal lumen. However, the pathogenesis of Cryptosporidium-induced diarrhea is not fully understood yet. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we infected C57BL/6j neonatal mice with C. parvum IIa and IId subtypes, and examined oocyst burden, pathological changes, and intestinal epithelial permeability during the infection. In addition, transcriptomic analyses were used to study the mechanism of diarrhea induced by the C. parvum IId subtype. The neonatal mice were sensitive to both C. parvum IIa and IId infection, but the IId subtype caused a wide oocyst shedding window and maintained the high oocyst burden in the mice compared with the IIa subtype. In addition, the mice infected with C. parvum IId resulted in severe intestinal damage at the peak of infection, leading to increased permeability of the epithelial barrier. The KEGG, GO and GSEA analyses revealed that the downregulation of adherens junction and cell junction molecules at 11 dpi. Meanwhile, E-cadherin, which is associated with adherens junction, was reduced at the protein level in mouse ileum at peak and late infection. CONCLUSIONS/SIGNIFICANCE: C. parvum IId infection causes more severe pathological damage than C. parvum IIa infection in neonatal mice. Furthermore, the impairment of the epithelial barrier during C. parvum IId infection results from the downregulation of intestinal junction proteins.

Effect of plant-soil system on the restoration of community stability after wildfire in the northeast margin of Qinghai-Tibet plateau.

Wildfires, as an environmental filter, are pivotal ecological disturbances that reshape plant communities and soil dynamics, playing a crucial role in regulating biogeographic patterns and ecosystem services. In this study, we aim to explore the effects of wildfires on forest ecosystems, specifically focusing on the plant-soil feedback mechanisms within the northeastern margin of the Qinghai-Tibet Plateau (QTP). Utilizing Partial Least Squares Path Modeling (PLS-PM), we investigated the interrelationships among soil physicochemical properties, enzyme activities, species diversity, and community stability at varying post-fire recovery stages (5, 15, and 23 years). Results indicated that in the early recovery stages, rapid changes in soil properties such as decreased pH (p < 0.001) and increased nutrient availability facilitate the emergence of early successional species with high resource utilization traits. As the ecosystem evolved toward a climax community, the soil and vegetation exhibit increased stability. Furthermore, soil enzyme activities displayed dynamic patterns that corresponded with changes in soil nutrient content, directly influencing the regeneration and diversity of plant communities. Importantly, our study documented a transition in the influence of soil properties on community stability from direct positive effects in initial recovery phases to negative impacts in later stages, while indirect benefits accrue through increased species diversity and enzyme activity. Vegetation composition and structure changed dynamically with recovery time during community succession. Plant nutrient absorption and accumulation affected nutrient dynamics in the soil, influencing plant regeneration, distribution, and diversity. Our results underscore the complex interactions between soil and vegetation that drive the recovery dynamics post-wildfire, highlighting the resilience of forest ecosystems to fire disturbances. This study contributes to the understanding of post-fire recovery processes and offers valuable insights for the management and restoration of fire-affected forest ecosystems.

SpyGlass guided PDT for advanced intraductal papillary mucinous neoplasm of the bile tract: A case report and literature review.

Intraductal papillary mucinous neoplasm of the bile tract is a rare biliary tumor characterized by mucin growth within the bile duct. In the early stages, it often presents without significant obstruction, this often leads to its discovery in the advanced stages. We report a case of a 63-year-old female with an intraductal papillary mucinous neoplasm of the bile duct (IPMN-B). The patient had a history of intrahepatic bile duct stones and biliary ascariasis. She gradually developed symptoms such as jaundice and intermittent fever before admission, and a bile duct biopsy confirmed the diagnosis of IPMN-B. Currently, endoscopic photodynamic therapy (PDT) is considered an effective treatment for bile duct cancer. In this case, we performed two sessions of PDT guided by SpyGlass. The patient experienced complete remission postoperatively, and there has been no evidence of tumor recurrence or metastasis in the three years following the procedure.

Photodynamic therapy inhibits cancer progression and induces ferroptosis and apoptosis by targeting P53/GPX4/SLC7A11 signaling pathways in cholangiocarcinoma.

BACKGROUND: Cholangiocarcinoma (CCA) is a malignant tumor with a poor prognosis. The specific mechanism of photodynamic therapy (PDT) in treating CCA remains unclear. This study aims to investigate the mechanisms of PDT in the treatment of CCA and try to improve the therapeutic effect of PDT by intervening associated signaling pathways. METHODS: The Cell Counting Kit-8 (CCK-8) was used to examine the cytotoxicity of CCA cell lines following PDT. Apoptosis and reactive oxygen species (ROS) levels were measured by flow cytometry. A transmission electron microscope was used to study the changes in cell mitochondria after PDT. The levels of glutathione (GSH), malondialdehyde (MDA), ferrous iron (Fe2+), lactate dehydrogenase (LDH), and lipid peroxide (LPO) were determined. Changes in the expression of apoptosis and ferroptosis-related proteins were determined using quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting. Xenograft tumor models were developed to investigate the effects of PDT on tumor proliferation, apoptosis, and ferroptosis in vivo. RESULTS: PDT inhibited tumor proliferation and induced apoptosis both in vivo and in vitro. This treatment led to swelling and damage of the mitochondria in affected cells. Furthermore, ROS levels rose, accompanied by an increase in the proportion of apoptotic-positive cells. The expressions of Bax and Caspase-3 were upregulated, while the Bcl-2 was downregulated. Meanwhile, PDT triggered ferroptosis, marked by decreased expressions of GPX4 and SLC7A11, and reduced GSH levels. This was accompanied by upregulation of P53 expression and heightened levels of Fe2+, LPO, MDA, and LDH. After inducing the ferroptosis pathway, the therapeutic effect of PDT was enhanced, the tumor tissue was further reduced, and the degree of malignancy was reduced. CONCLUSION: PDT promotes apoptosis and ferroptosis of cholangiocarcinoma cells by activating the P53/SLC7A11/GPX4 signaling pathway and inhibits the growth of cholangiocarcinoma. Inducing ferroptosis can enhance the effectiveness of photodynamic therapy.

Immunoactivation by Cutaneous Blue Light Irradiation Inhibits Remote Tumor Growth and Metastasis.

An improved innate immunity will respond quickly to pathogens and initiate efficient adaptive immune responses. However, up to now, there have been limited clinical ways for effective and rapid consolidation of innate immunity. Here, we report that cutaneous irradiation with blue light of 450 nm rapidly stimulates the innate immunity through cell endogenous reactive oxygen species (ROS) regulation in a noninvasive way. The iron porphyrin-containing proteins, mitochondrial cytochrome c (Cyt-c), and cytochrome p450 (CYP450) can be mobilized by blue light, which boosts electron transport and ROS production in epidermal and dermal tissues. As a messenger of innate immune activation, the increased level of ROS activates the NF-κB signaling pathway and promotes the secretion of immunomodulatory cytokines in skin. Initiated from skin, a regulatory network composed of cytokines and immune cells is established through the circulation system for innate immune activation. The innate immunity activated by whole-body blue light irradiation inhibits tumor growth and metastasis by increasing the infiltration of antitumor neutrophils and tumor-associated macrophages. Our results elucidate the remote immune modulation mechanism of blue light and provide a clinically applicable way for innate immunity activation.

[Multi-index evaluation of different parts of Amomum villosum].

To investigate the quality differences between the seeds and husks of Amomum villosum and explore the rationality of using the seeds without husks, this study determined the content of protocatechuic acid, vanillic acid, epicatechin, quercitrin, volatile oil, water extract, and ethanol extract. The 2,2-diphenyl-1-picrylhydrazyl(DPPH), 2,2-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)(ABTS), and hydroxyl radical scavenging activities were determined to evaluate the antioxidant activities of seeds and husks. The quality differences between the seeds and husks were assessed through orthogonal partial least squares-discriminant analysis(OPLS-DA) and analytic hierarchy process(AHP) combined with the entropy weight method(EWM). Significant differences(P<0.05) were observed in all 10 indicators between the seeds and husks. The levels of epicatechin, quercetin, and volatile oil were higher in the seeds, whereas those of protocatechuic acid, vanillic acid, water extract, and ethanol extract were higher in the husks. The seeds showed stronger scavenging ability against DPPH and ABTS radicals, while the husks showed a stronger scavenging effect on hydroxyl radicals. OPLS-DA significantly discriminated between the seeds and husks. Furthermore, volatile oil, water extract, DPPH radical scavenging rate, quercitrin, ABTS radical scavenging rate, hydroxyl radical scavenging rate, and vanillic acid were selected as the main differential indicators by variable importance in projection(VIP). Comprehensive scores calculated by AHP combined with EWM indicated that the seeds were superior to husks in terms of overall quality. However, there are still some dominant components and a certain antioxidant effect in the husks. Therefore, it is suggested to using Amomi Fructus with a certain amount of husks or utilizing the husks for other purposes.

Osteosarcoma-targeted Cu and Ce based oxide nanoplatform for NIR II fluorescence/magnetic resonance dual-mode imaging and ros cascade amplification along with immunotherapy.

BACKGROUND: As the lethal bone tumor, osteosarcoma often frequently occurs in children and adolescents with locally destructive and high metastasis. Distinctive kinds of nanoplatform with high therapeutical effect and precise diagnosis for osteosarcoma are urgently required. Multimodal optical imaging and programmed treatment, including synergistic photothermal-chemodynamic therapy (PTT-CDT) elicits immunogenetic cell death (ICD) is a promising strategy that possesses high bio-imaging sensitivity for accurate osteosarcoma delineating as well as appreciable therapeutic efficacy with ignorable side-effects. METHODS AND RESULTS: In this study, mesoporous Cu and Ce based oxide nanoplatform with Arg-Gly-Asp (RGD) anchoring is designed and successfully constructed. After loading with indocyanine green, this nanoplatform can be utilized for precisely targeting and efficaciously ablating against osteosarcoma via PTT boosted CDT and the closely following ICD stimulation both in vitro and in vivo. Besides, it provides off-peak fluorescence bio-imaging in the second window of near-infrared region (NIR II, 1000-1700 nm) and Magnetic resonance signal, serves as the dual-mode contrast agents for osteosarcoma tissue discrimination. CONCLUSION: Tumor targeted Cu&Ce based mesoporous nanoplatform permits efficient osteosarcoma suppression and dual-mode bio-imaging that opens new possibility for effectively diagnosing and inhibiting the clinical malignant osteosarcoma.

A novel dual polarization multiplexing RoF system integrating optical fiber and FSO channel with AMI downlink signals.

Using dual polarization multiplexing alternate mark inversion (AMI) downlink signals, a novel radio over fiber (RoF) system integrating optical fiber and FSO channel is designed to adapt to applications in mountainous areas and other complex terrain areas. Optical heterodyne technology and self-mixing homodyne detection method are used to realize high sensitivity detection of the received signals after 25.1 km channel (including 1 km single-mode fiber and 100 m free space link) transmission. Moreover, polarization multiplexing technology is introduced to exponentially increase the transmission capacity of downlink signals. This scheme not only can be compatible with traditional optical fiber transmission systems, but also support the wireless optical access application of millimeter wave signals in RoF systems.

Assessing ammonium pollution and mitigation measures through a modified watershed non-point source model.

Watershed water quality modeling is a valuable tool for managing ammonium (NH4+) pollution. However, simulating NH4+ pollution presents unique challenges due to the inherent instability of NH4+ in natural environment. This study modified the widely-used Soil and Water Assessment Tool (SWAT) model to simulate non-point source (NPS) NH4+ processes, specifically incorporating the simulation of land-to-water NH4+ delivery. The Jiulong River Watershed (JRW) is the study area, a coastal watershed in Southeast China with substantial sewage discharge, livestock farming, and fertilizer application. The results demonstrate that the modified model can effectively simulate the NPS NH4+ processes. It is recommended to use multiple sets of observations to calibrate NH4+ simulation to enhance model reliability. Despite constituting a minor proportion (5.6 %), point source inputs significantly contribute to NH4+ load at watershed outlet (32.4∼51.9 %), while NPS inputs contribute 15.3∼17.3 % of NH4+ loads. NH4+ primarily enters water through surface runoff and lateral flow, with negligible leaching. Average NH4+ land-to-water delivery rate is about 2.35 to 2.90 kg N/ha/a. High delivery rates mainly occur at agricultural areas. Notably, proposed NH4+ mitigation measures, including urban sewage treatment enhancement, livestock manure management improvement, and fertilizer application reduction, demonstrate potential to collectively reduce the NH4+ load at watershed outlet by 1/4 to 1/3 and significantly enhance water quality standard compliance frequency. Insights gained from modeling experience in the JRW offer valuable implications for NH4+ modeling and management in regions with similar climates and significant anthropogenic nitrogen inputs.

Research progress on the association between glycemic variability index derived from CGM and cardiovascular disease complications.

Currently, glycated hemoglobin A1c (HbA1c) has been widely used to assess the glycemic control of patients with diabetes. However, HbA1c has certain limitations in describing both short-term and long-term glycemic control. To more accurately evaluate the glycemic control of diabetes patients, the continuous glucose monitoring (CGM) technology has emerged. CGM technology can provide robust data on short-term glycemic control and introduce new monitoring parameters such as time in range, time above range, and time below range as indicators of glycemic fluctuation. These indicators are used to describe the changes in glycemic control after interventions in clinical research or treatment modifications in diabetes patient care. Recent studies both domestically and internationally have shown that these indicators are not only associated with microvascular complications of diabetes mellitus but also closely related to cardiovascular disease complications and prognosis. Therefore, this article aims to comprehensively review the association between CGM-based glycemic parameters and cardiovascular disease complications by analyzing a large number of domestic and international literature. The purpose is to provide scientific evidence and guidance for the standardized application of these indicators in clinical practice, in order to better evaluate the glycemic control of diabetes patients and prevent the occurrence of cardiovascular disease complications. This research will contribute to improving the quality of life for diabetes patients and provide important references for clinical decision-making.

Chromatin Modifier EP400 Regulates Oocyte Quality and Zygotic Genome Activation in Mice.

Epigenetic modifiers that accumulate in oocytes, play a crucial role in steering the developmental program of cleavage embryos and initiating life. However, the identification of key maternal epigenetic regulators remains elusive. In the findings, the essential role of maternal Ep400, a chaperone for H3.3, in oocyte quality and early embryo development in mice is highlighted. Depletion of Ep400 in oocytes resulted in a decline in oocyte quality and abnormalities in fertilization. Preimplantation embryos lacking maternal Ep400 exhibited reduced major zygotic genome activation (ZGA) and experienced developmental arrest at the 2-to-4-cell stage. The study shows that EP400 forms protein complex with NFYA, occupies promoters of major ZGA genes, modulates H3.3 distribution between euchromatin and heterochromatin, promotes transcription elongation, activates the expression of genes regulating mitochondrial functions, and facilitates the expression of rate-limiting enzymes of the TCA cycle. This intricate process driven by Ep400 ensures the proper execution of the developmental program, emphasizing its critical role in maternal-to-embryonic transition.