A deep transfer learning-based protocol accelerates full quantum mechanics calculation of protein.

Effective full quantum mechanics (FQM) calculation of protein remains a grand challenge and of great interest in computational biology with substantial applications in drug discovery, protein dynamic simulation and protein folding. However, the huge computational complexity of the existing QM methods impends their applications in large systems. Here, we design a transfer-learning-based deep learning (TDL) protocol for effective FQM calculations (TDL-FQM) on proteins. By incorporating a transfer-learning algorithm into deep neural network (DNN), the TDL-FQM protocol is capable of performing calculations at any given accuracy using models trained from small datasets with high-precision and knowledge learned from large amount of low-level calculations. The high-level double-hybrid DFT functional and high-level quality of basis set is used in this work as a case study to evaluate the performance of TDL-FQM, where the selected 15 proteins are predicted to have a mean absolute error of 0.01 kcal/mol/atom for potential energy and an average root mean square error of 1.47 kcal/mol/$ {\rm A^{^{ \!\!\!o}}} $ for atomic forces. The proposed TDL-FQM approach accelerates the FQM calculation more than thirty thousand times faster in average and presents more significant benefits in efficiency as the size of protein increases. The ability to learn knowledge from one task to solve related problems demonstrates that the proposed TDL-FQM overcomes the limitation of standard DNN and has a strong power to predict proteins with high precision, which solves the challenge of high precision prediction in large chemical and biological systems.

FAM83D promotes the progression of 4NQO-induced esophageal carcinoma via inhibiting FBWX7.

The present study aimed to explore the expression and regulatory role of FAM83D in the different developmental stages of esophageal squamous cell carcinoma (ESCC) to determine the effect of FAM83D on the proliferation, migration, and invasion of ESCC cells and to elucidate its underlying molecular mechanism. Immunohistochemistry (IHC) analysis revealed that the expression of FAM83D was obviously elevated in ESCC tissues compared to adjacent normal tissues. Furthermore, the FAM83D levels was positively correlated with tumor size, TNM stage, T stage, and N stage, while it was negatively correlated with FBXW7 expression, Karnofsky Performance Status (KPS) score, and survival rate. Subsequently, ESCC cell lines with low FAM83D expression were constructed using RNA interference technology to investigate the impact of FAM83D on the biological behavior of ESCC cells. Silencing of FAM83D inhibited the proliferation and migration of ESCC cells but promoted apoptosis. Furthermore, a reduction in FAM83D expression may also induce cell cycle arrest at the G0/G1 phase and regulate the expression of proteins related to epithelial-mesenchymal transition (EMT), the cell cycle, and apoptosis. Further research indicated that silencing FAM83D led to the upregulation of FBXW7 expression. These results suggested that FAM83D may exert its effects on ESCC by downregulating FBXW7. Additionally, using a 4NQO solution in the drinking water to establish an ESCC mouse model, IHC analysis revealed that FAM83D expression levels were positively correlated with the pathological grade of esophageal lesions in the mice and negatively correlated with the expression levels of FBXW7 and E-cadherin. The above results demonstrated that FAM83D may facilitate the progression of ESCC by negatively regulating FBXW7 expression and that FAM83D could represent a promising therapeutic target for ESCC.

Glycolysis in Peritubular Endothelial Cells and Microvascular Rarefaction in CKD.

BACKGROUND: Peritubular endothelial cell dropout leading to microvascular rarefaction is a common manifestation of chronic kidney disease (CKD). The role of metabolism reprogramming in peritubular endothelial cell loss in CKD is undetermined. METHODS: Single-cell sequencing and metabolic analysis were used to characterize metabolic profile of peritubular endothelial cells from CKD patients and from CKD mouse models. In vivo and in vitro models demonstrated metabolic reprogramming in peritubular endothelial cells in conditions of CKD and its contribution to microvascular rarefaction. RESULTS: Here, we identified glycolysis as a top dysregulated metabolic pathway in peritubular endothelial cells from CKD patients. Specifically, CKD peritubular endothelial cells were hypoglycolytic while displaying an anti-angiogenic response with decreased proliferation and increased apoptosis. The hypoglycolytic phenotype of peritubular endothelial cells was recapitulated in CKD mouse models and in peritubular endothelial cells stimulated by hydrogen peroxide (H2O2). Mechanically, oxidative stress, through activating a redox sensor kruppel-like transcription factor 9, downregulated the glycolytic activator 6-phosphofructo-2-kinase/fructose-2, 6-bisphosphatase (PFKFB3) expression, thereby reprogramming peritubular endothelial cells towards a hypoglycolytic phenotype. PFKFB3 overexpression in peritubular endothelial cells restored H2O2-induced reduction in glycolysis and cellular ATP levels, and enhanced the G1/S cell cycle transition, enabling peritubular endothelial cells to improve proliferation and reduce apoptosis. Consistently, restoration of peritubular endothelial cell glycolysis in CKD mice, via overexpressing endothelial Pfkfb3, reversed the anti-angiogenic response in peritubular endothelial cells and protected the kidney from microvascular rarefaction and fibrosis. In contrast, suppression of glycolysis by endothelial Pfkfb3 deletion exacerbated microvascular rarefaction and fibrosis in CKD mice. CONCLUSIONS: Our study revealed a disrupted regulation of glycolysis in peritubular endothelial cells as an initiator of microvascular rarefaction in CKD. Restoration of peritubular endothelial cell glycolysis in CKD kidney improved microvascular rarefaction and ameliorated fibrotic lesions.

Murepavadin promotes the killing efficacies of aminoglycoside antibiotics against Pseudomonas aeruginosa by enhancing membrane potential.

Murepavadin is a peptidomimetic that specifically targets the lipopolysaccharide transport protein LptD of Pseudomonas aeruginosa. Here, we found that murepavadin enhances the bactericidal efficacies of tobramycin and amikacin. We further demonstrated that murepavadin enhances bacterial respiration activity and subsequent membrane potential, which promotes intracellular uptake of aminoglycoside antibiotics. In addition, the murepavadin-amikacin combination displayed a synergistic bactericidal effect in a murine pneumonia model.

Versatile Bovine Serum Albumin as Ingenious Electron Operator-Enhanced Photoelectrochemical Biosensing for Ultrasensitive Detection of miRNA.

Bovine serum albumin (BSA) has been widely used in biosensors as a blocking agent. Herein, conformist BSA was first exploited as an ingenious operator to enhance the photocurrent response of (2Z,2'Z)-2,2'-(1,4-phenylene)bis(3-(4-(bis(4-methoxyphenyl)amino)phenyl)acrylonitrile) (TPDCN)-based photoelectrochemical (PEC) platform via manipulating the electron transfer process of the detection system. Concretely, the presence of target molecules triggered catalytic hairpin assembly reaction and subsequently powered terminal deoxynucleotidyl transferase-mediated signal amplification to produce the AgNP@BSA-DNA dendrimer nanostructure. After being treated with HNO3, a large amount of BSA could be released from the dendrimer nanostructure. When they were transferred to the TPDCN-based PEC platform, the photocurrent response of the biosensor was largely enhanced because BSA can manipulate the electrons of TPDCN via a well-matched energy level to form a new electron transfer track. Meanwhile, tryptophan (Trp) in BSA could be oxidized to quinone Trp-O under photoirradiation, which can facilitate the oxidation of ascorbate and generate more H+ to promote the migration of photogenerated electrons. As a result, the proposed PEC biosensor exhibits excellent analytical performance for detection of miRNA-21 (as a model target) over a wide linear range of 0.01 to 10,000 pM with detection limit as low as 4.7 fM. Overall, this strategy provides a new perspective on constructing efficient PEC biosensors, which expands the potential applications in bioanalysis and clinical diagnosis.

CEEM: a Chemically Explainable Deep Learning Platform for Identifying Compounds with Low Effective Mass.

The semiconductor industry occupies a crucial position in the fields of integrated circuits, energy, and communication systems. Effective mass (mE ), which is closely related to electron transition, thermal excitation, and carrier mobility, is a key performance indicator of semiconductor. However, the highly neglected mE is onerous to measure experimentally, which seriously hinders the evaluation of semiconductor properties and the understanding of the carrier migration mechanisms. Here, a chemically explainable effective mass predictive platform (CEEM) is constructed by deep learning, to identify n-type and p-type semiconductors with low mE . Based on the graph network, a versatile explainable network is innovatively designed that enables CEEM to efficiently predict the mE of any structure, with the area under the curve of 0.904 for n-type semiconductors and 0.896 for p-type semiconductors, and derive the most relevant chemical factors. Using CEEM, the currently largest mE database is built that contains 126 335 entries and screens out 466 semiconductors with low mE for transparent conductive materials, photovoltaic materials, and water-splitting materials. Moreover, a user-friendly and interactive CEEM web is provided that supports query, prediction, and explanation of mE . CEEM's high efficiency, accuracy, flexibility, and explainability open up new avenues for the discovery and design of high-performance semiconductors.

Unlocking Potential of Pyrochlore in Energy Systems via Soft Voting Ensemble Learning.

In traditional machine learning (ML)-based material design, the defects of low prediction accuracy, overfitting and low generalization ability are mainly caused by the training of a single ML model. Here, a Soft Voting Ensemble Learning (SVEL) approach is proposed to solve the above issues by integrating multiple ML models in the same scene, thus pursuing more stable and reliable prediction. As a case study, SVEL is applied to develop the broad chemical space of novel pyrochlore electrocatalysts with the molecular formula of A2B2O7, to explore promising pyrochlore oxides and accelerate predictions of unknown pyrochlore in the periodic table. The model successfully established the structure-property relationship of pyrochlore, and selected six cost-effective pyrochlore from the periodic table with a high prediction accuracy of 91.7%, all of which showed good electrocatalytic performance. SVEL not only effectively avoids the high costs of experimentation and lengthy computations, but also addresses biases arising from data scarcity in single models. Furthermore, it has significantly reduced the research cycle of pyrochlore by ≈ 22 years, offering broad prospects for accelerating the development of materials genomics. SVEL method is intended to integrate multiple AI models to provide broader model training clues for the AI material design community.

Angstrom-Scale 2D Channels Designed For Osmotic Energy Harvesting.

Confronting the impending exhaustion of traditional energy, it is urgent to devise and deploy sustainable clean energy alternatives. Osmotic energy contained in the salinity gradient of the sea-river interface is an innovative, abundant, clean, and renewable osmotic energy that has garnered considerable attention in recent years. Inspired by the impressively intelligent ion channels in nature, the developed angstrom-scale 2D channels with simple fabrication process, outstanding design flexibility, and substantial charge density exhibit excellent energy conversion performance, opening up a new era for osmotic energy harvesting. However, this attractive research field remains fraught with numerous challenges, particularly due to the complexities associated with the regulation at angstrom scale. In this review, the latest advancements in the design of angstrom-scale 2D channels are primarily outlined for harvesting osmotic energy. Drawing upon the analytical framework of osmotic power generation mechanisms and the insights gleaned from the biomimetic intelligent devices, the design strategies are highlighted for high-performance angstrom channels in terms of structure, functionalization, and application, with a particular emphasis on ion selectivity and ion transport resistance. Finally, current challenges and future prospects are discussed to anticipate the emergence of more anomalous properties and disruptive technologies that can promote large-scale power generation.

A Room-Temperature Self-Healing Liquid Metal-Infilled Microcapsule Driven by Coaxial Flow Focusing for High-Performance Lithium-Ion Battery Anode.

Liquid metals have attracted a lot of attention as self-healing materials in many fields. However, their applications in secondary batteries are challenged by electrode failure and side reactions due to the drastic volume changes during the "liquid-solid-liquid" transition. Herein, a simple encapsulated, mass-producible method is developed to prepare room-temperature liquid metal-infilled microcapsules (LMMs) with highly conductive carbon shells as anodes for lithium-ion batteries. Due to the reasonably designed voids in the microcapsule, the liquid metal particles (LMPs) can expand freely without damaging the electrode structure. The LMMs-based anodes exhibit superior capacity of rete-performance and ultra-long cycling stability remaining 413 mAh g-1 after 5000 cycles at 5.0 A g-1. Ex situ X-ray powder diffraction (XRD) patterns and electrochemical impedance spectroscopy (EIS) reveal that the LMMs anode displays a stable alloying/de-alloying mechanism. DFT calculations validate the electronic structure and stability of the room-temperature LMMs system. These findings will bring some new opportunities to develop high-performance battery systems.

Anode-Free Aqueous Aluminum Ion Batteries.

Aqueous aluminum ion batteries (AAIBs) possess the advantages of high safety, cost-effectiveness, eco-friendliness and high theoretical capacity. However, the Al2O3 film on the Al anode surface, a natural physical barrier to the plating of hydrated aluminum ions, is a key factor in the decomposition of the aqueous electrolyte and the severe hydrogen precipitation reaction. To circumvent the obnoxious Al anode, a proof-of-concept of an anode-free AAIB is first proposed, in which Al2TiO5, as a cathode pre-aluminum additive (Al source), can replenish Al loss by over cycling. The Al-Cu alloy layer, formed by plating Al on the Cu foil surface during the charge process, possesses a reversible electrochemical property and is paired with a polyaniline (cathode) to stimulate the battery to exhibit high initial discharge capacity (175 mAh g-1), high power density (≈410 Wh L-1) and ultra-long cycle life (4000 cycles) with the capacity retention of ≈60% after 1000 cycles. This work will act as a primer to ignite the enormous prospective researches on the anode-free aqueous Al ion batteries.

Exploring the co-operativity of secretory structures for defense and pollination in flowering plants.

MAIN CONCLUSION: In flowers multiple secretory systems cooperate to deliver specialized metabolites to support specific roles in defence and pollination. The collective roles of cell types, enzymes, and transporters are discussed. The interplay between reproductive strategies and defense mechanisms in flowering plants has long been recognized, with trade-offs between investment in defense and reproduction predicted. Glandular trichomes and secretory cavities or ducts, which are epidermal and internal structures, play a pivotal role in the secretion, accumulation, and transport of specialized secondary metabolites, and contribute significantly to defense and pollination. Recent investigations have revealed an intricate connection between these two structures, whereby specialized volatile and non-volatile metabolites are exchanged, collectively shaping their respective ecological functions. However, a comprehensive understanding of this profound integration remains largely elusive. In this review, we explore the secretory systems and associated secondary metabolism primarily in Asteraceous species to propose potential shared mechanisms facilitating the directional translocation of these metabolites to diverse destinations. We summarize recent advances in our understanding of the cooperativity between epidermal and internal secretory structures in the biosynthesis, secretion, accumulation, and emission of terpenes, providing specific well-documented examples from pyrethrum (Tanacetum cinerariifolium). Pyrethrum is renowned for its natural pyrethrin insecticides, which accumulate in the flower head, and more recently, for emitting an aphid alarm pheromone. These examples highlight the diverse specializations of secondary metabolism in pyrethrum and raise intriguing questions regarding the regulation of production and translocation of these compounds within and between its various epidermal and internal secretory systems, spanning multiple tissues, to serve distinct ecological purposes. By discussing the cooperative nature of secretory structures in flowering plants, this review sheds light on the intricate mechanisms underlying the ecological roles of terpenes in defense and pollination.

Clustered tree regression to learn protein energy change with mutated amino acid.

Accurate and effective prediction of mutation-induced protein energy change remains a great challenge and of great interest in computational biology. However, high resource consumption and insufficient structural information of proteins severely limit the experimental techniques and structure-based prediction methods. Here, we design a structure-independent protocol to accurately and effectively predict the mutation-induced protein folding free energy change with only sequence, physicochemical and evolutionary features. The proposed clustered tree regression protocol is capable of effectively exploiting the inherent data patterns by integrating unsupervised feature clustering by K-means and supervised tree regression using XGBoost, and thus enabling fast and accurate protein predictions with different mutations, with an average Pearson correlation coefficient of 0.83 and an average root-mean-square error of 0.94kcal/mol. The proposed sequence-based method not only eliminates the dependence on protein structures, but also has potential applications in protein predictions with rare structural information.

An inductive transfer learning force field (ITLFF) protocol builds protein force fields in seconds.

Accurate simulation of protein folding is a unique challenge in understanding the physical process of protein folding, with important implications for protein design and drug discovery. Molecular dynamics simulation strongly requires advanced force fields with high accuracy to achieve correct folding. However, the current force fields are inaccurate, inapplicable and inefficient. We propose a machine learning protocol, the inductive transfer learning force field (ITLFF), to construct protein force fields in seconds with any level of accuracy from a small dataset. This process is achieved by incorporating an inductive transfer learning algorithm into deep neural networks, which learn knowledge of any high-level calculations from a large dataset of low-level method. Here, we use a double-hybrid density functional theory (DFT) as a case functional, but ITLFF is suitable for any high-precision functional. The performance of the selected 18 proteins indicates that compared with the fragment-based double-hybrid DFT algorithm, the force field constructed by ITLFF achieves considerable accuracy with a mean absolute error of 0.0039 kcal/mol/atom for energy and a root mean square error of 2.57 $\mathrm{kcal}/\mathrm{mol}/{\AA}$ for force, and it is more than 30 000 times faster and obtains more significant efficiency benefits as the system increases. The outstanding performance of ITLFF provides promising prospects for accurate and efficient protein dynamic simulations and makes an important step toward protein folding simulation. Due to the ability of ITLFF to utilize the knowledge acquired in one task to solve related problems, it is also applicable for various problems in biology, chemistry and material science.

Nocturnal burst emissions of germacrene D from the open disk florets of pyrethrum flowers induce moths to oviposit on a nonhost and improve pollination success.

Recent studies show that nocturnal pollinators may be more important to ecosystem function and food production than is currently appreciated. Here, we describe an agricultural field study of pyrethrum (Tanacetum cinerariifolium) flower pollination. Pyrethrum is genetically self-incompatible and thus is reliant on pollinators for seed set. Our pollinator exclusion experiment showed that nocturnal insects, particularly moths, significantly contribute to seed set and quality. We discovered that the most abundant floral volatile, the sesquiterpene (-)-germacrene D (GD), is key in attracting the noctuid moths Peridroma saucia and Helicoverpa armigera. Germacrene D synthase (GDS) gene expression regulates the specific GD production and accumulation in flowers, which, in contrast to related species, lose the habit of closing at night. We did observe that female moths also oviposited on pyrethrum leaves and flower peduncles, but found that only a small fraction of those eggs hatched. Larvae were severely stunted in development, most likely due to the presence of pyrethrin defense compounds. This example of exploitative mutualism, which blocks the reproductive success of the moth pollinator and depends on nocturnal interactions, is placed into an ecological context to explain why it may have developed.

Genomic basis of the distinct biosynthesis of ß-glucogallin, a biochemical marker for hydrolyzable tannin production, in three oak species.

Hydrolyzable tannins (HTs), predominant polyphenols in oaks, are widely used in grape wine aging, feed additives, and human healthcare. However, the limited availability of a high-quality reference genome of oaks greatly hampered the recognition of the mechanism of HT biosynthesis. Here, high-quality reference genomes of three Asian oak species (Quercus variabilis, Quercus aliena, and Quercus dentata) that have different HT contents were generated. Multi-omics studies were carried out to identify key genes regulating HT biosynthesis. In vitro enzyme activity assay was also conducted. Dual-luciferase and yeast one-hybrid assays were used to reveal the transcriptional regulation. Our results revealed that ß-glucogallin was a biochemical marker for HT production in the cupules of the three Asian oaks. UGT84A13 was confirmed as the key enzyme for ß-glucogallin biosynthesis. The differential expression of UGT84A13, rather than enzyme activity, was the main reason for different ß-glucogallin and HT accumulation. Notably, sequence variations in UGT84A13 promoters led to different trans-activating activities of WRKY32/59, explaining the different expression patterns of UGT84A13 among the three species. Our findings provide three high-quality new reference genomes for oak trees and give new insights into different transcriptional regulation for understanding ß-glucogallin and HT biosynthesis in closely related oak species.

Advanced gastrointestinal stromal tumor: reliable classification of imatinib plasma trough concentration via machine learning.

AIM: Patients with advanced gastrointestinal stromal tumors (GISTs) exhibiting an imatinib plasma trough concentration (IM Cmin) under 1100 ng/ml may show a reduced drug response rate, leading to the suggestion of monitoring for IM Cmin. Consequently, the objective of this research was to create a customized IM Cmin classification model for patients with advanced GISTs from China. METHODS: Initial data and laboratory indicators from patients with advanced GISTs were gathered, and the above information was segmented into a training set, validation set, and testing set in a 6:2:2 ratio. Key variables associated with IM Cmin were identified to construct the classification model using the least absolute shrinkage and selection operator (LASSO) regression and forward stepwise binary logistic regression. Within the training and validation sets, nine ML classification models were constructed via the resampling method and underwent comparison through the Brier scores, the areas under the receiver-operating characteristic curve (AUROC), the decision curve, and the precision-recall (AUPR) curve to determine the most suitable model for this dataset. Two methods of internal validation were used to assess the most suitable model's classification performance: tenfold cross-validation and random split-sample validation (test set), and the value of the test set AUROC was used to evaluate the model's classification performance. RESULTS: Six key variables (gender, daily IM dose, metastatic site, red blood cell count, platelet count, and percentage of neutrophils) were ultimately selected to construct the classification model. In the validation set, it is found by comparison that the Extreme Gradient Boosting (XGBoost) model has the largest AUROC, the lowest Brier score, the largest area under the decision curve, and the largest AUPR value. Furthermore, as evaluated via internal verification, it also performed well in the test set (AUROC = 0.725). CONCLUSION: For patients with advanced GISTs who receive IM, initial data and laboratory indicators could be used to accurately estimate whether the IM Cmin is below 1100 ng/ml. The XGBoost model may stand a chance to assist clinicians in directing the administration of IM.

Mitochondria-Targeted Natural Antioxidant Nanosystem for Diabetic Vascular Calcification Therapy.

The development of nanotherapy targeting mitochondria to alleviate oxidative stress is a critical therapeutic strategy for vascular calcification (VC) in diabetes. In this study, we engineered mitochondria-targeted nanodrugs (T4O@TPP/PEG-PLGA) utilizing terpinen-4-ol (T4O) as a natural antioxidant and mitochondrial protector, PEG-PLGA as the nanocarrier, and triphenylphosphine (TPP) as the mitochondrial targeting ligand. In vitro assessments demonstrated enhanced cellular uptake of T4O@TPP/PEG-PLGA, with effective mitochondrial targeting. This nanodrug successfully reduced oxidative stress induced by high glucose levels in vascular smooth muscle cells. In vivo studies showed prolonged retention of the nanomaterials in the thoracic aorta for up to 24 h. Importantly, experiments in diabetic VC models underscored the potent antioxidant properties of T4O@TPP/PEG-PLGA, as evidenced by its ability to mitigate VC and restore mitochondrial morphology. These results suggest that these nanodrugs could be a promising strategy for managing diabetic VC.

Rutin attenuates ensartinib-induced hepatotoxicity by non-transcriptional regulation of TXNIP.

Ensartinib, an approved ALK inhibitor, is used as a first-line therapy for advanced ALK-positive non-small cell lung cancer in China. However, the hepatotoxicity of ensartinib seriously limits its clinical application and the regulatory mechanism is still elusive. Here, through transcriptome analysis we found that transcriptional activation of TXNIP was the main cause of ensartinib-induced liver dysfunction. A high TXNIP level and abnormal TXNIP translocation severely impaired hepatic function via mitochondrial dysfunction and hepatocyte apoptosis, and TXNIP deficiency attenuated hepatocyte apoptosis under ensartinib treatment. The increase in TXNIP induced by ensartinib is related to AKT inhibition and is mediated by MondoA. Through screening potential TXNIP inhibitors, we found that the natural polyphenolic flavonoid rutin, unlike most reported TXNIP inhibitors can inhibit TXNIP by binding to TXNIP and partially promoting its proteasomal degradation. Further studies showed rutin can attenuate the hepatotoxicity of ensartinib without antagonizing its antitumor effects. Accordingly, we suggest that TXNIP is the key cause of ensartinib-induced hepatotoxicity and rutin is a potential clinically safe and feasible therapeutic strategy for TXNIP intervention.

Short-term reproductive outcomes analysis and prediction of the modified uterine stent treatment for mild to moderate intrauterine adhesions: experience at a single institution.

BACKGROUND: To evaluate the efficacy of modified uterine stent in the treatment of mild-to-moderate intrauterine adhesions and explore the relative indicators affecting prognosis prediction. METHODS: A total of 115 patients with mild-to-moderate intrauterine adhesions received a modified uterine stent placement after hysteroscopy adhesiolysis. The second-look hysteroscopy operated after 3 months surgery, and the third-look hysteroscopy operated after 6 months surgery if necessary. The stent was removed when the cavity shape was repaired, then the reproductive outcomes were followed up one year. RESULTS: Menstrual blood volume, endometrial thickness and volume had increased significantly after 3 months surgery. The rates of cavity repaired were 86.96% (100/115) after 3 months surgery and 100% (115/115) after 6 months surgery cumulatively. Endometrial thickness after 3-months surgery was positively associated with uterine cavity shape repaired (P<0.01). The receive operating characteristic (ROC) curve showed the rate of uterine cavity shape repaired predicted by the model was 0.92, based on the endometrial thickness after 3-months surgery. The rate of pregnancy was 86.09% (99/115) in one year, while the rate of miscarriage accounted for 26.26% (26/99). The median time interval between stent removal and subsequent conception was 3 months. It showed adhesion recurrence was the risk factor for subsequent pregnancy (P<0.01). CONCLUSIONS: A modified uterine stent placement under hysteroscopy was an effective approach for mild-to-moderate intrauterine adhesions, which is easy to operate and worthy for clinical promotion. Endometrial thickness measured by ultrasonography probably has predictive value in adhesion recurrence and subsequent pregnancy. TRIAL REGISTRATION: ChiCTR2100051524. Date of registration (retrospectively registered): 26/09/2021.

Effect of alfalfa supplementary change dietary non-fibrous carbohydrate (NFC) to neutral detergent fiber (NDF) ratio on rumen fermentation and microbial function in Gansu alpine fine wool sheep (Ovis aries).

Bodyweight loss and rumen microbial dysfunction of grazing sheep was a challenge for the sheep production industry during cold season, which were considered to correlated with under-roughage-feeding. Alfalfa is a good roughage supplementary for ruminants, which can improve grazing sheep bodyweight-loss and rumen microbial dysfunction during grass-withering period. This study evaluated the effects of alfalfa hay supplementary change dietary non-fibrous carbohydrate/neutral detergent fiber (NFC/NDF) ratios on rumen fermentation and microbial function of Gansu alpine fine wool sheep during extreme cold season. 120 ewes (3-4 yrs) with an average body weight of 28.71 ± 1.22 kg were allocated randomly into three treatments, and fed NFC/NDF of 1.92 (H group), 1.11 (M group), and 0.68 (L group), respectively. This study was conducted for 107 d, including 7 d of adaption to the diets. The rumen fermentation parameters and microbial characteristics were measured after the end of feeding trials. The results showed that the concentrations of sheep body weight, nitrogen components (Total-N, Soluble protein-N and Ammonia-N), blood biochemical indices (LDH, BUN and CHO) and ruminal volatile fatty acids (TVFA and propionate) significantly increased with an increase in the proportion of NFC/NDF ratios (p < .05), and the acetate and acetate/propionat ratio presented a contrary decreasing trend (p < .05). A total of 1018 OTUs were obtained with 97% consistency. Ruminococcus, Ruminococcaceae and Prevotella were observed as the predominant phyla in ruminal fluid microbiota. Higher NFC/NDF ratios with Alfalfa supplementary increased the richness and diversity of ruminal fluid microbiota, and decreased ruminal fluid microbiota beta-diversity. Using clusters of orthologous groups (COG), the ruminal fluid microbiota of alfalfa supplementary feeding showed low immune pathway and high carbohydrate metabolism pathway. In summary, the study suggested that there was an increasing tendency in dietary NFC/NDF ratio of 1.92 in body weight, ruminal fermentation, microbial community composition and fermentation characteristics through developing alfalfa supplementary system.