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PurposePropofol, a widely utilized anesthetic, is employed to alleviate pain and anxiety in outpatient oocyte retrieval procedures. However, its potential impact and safety profile in the context of in vitro fertilization and embryo transfer (IVF-ET) remain unclear.MethodsThis retrospective study enrolled 1187 patients undergoing IVF-ET, and divided into two groups depending on whether they received propofol (propofol group, n=140) or not (control group, n=1047) for anesthesia during oocyte retrieval.ResultsThe baseline characteristics were comparable between the groups. Compared with control group, the number of oocytes retrieved in propofol group was more (p=0.012), while both the estradiol (E2) level on the trigger day and the pre-ovulatory follicle count were higher in propofol group ((p20) to eliminate the influence of inconsistency in the estimation of the pre-ovulatory follicle count between the two groups. Analysis revealed that the use of propofol during oocyte retrieval was particularly advantageous in the subgroup with a pre-ovulatory follicle count of 11–20, yielding a higher oocyte retrieval rate (p

ABSTRACT Senecavirus A (SVA) is an emerging viral pathogen that threatens the global swine industry significantly. The major clinical symptoms of SVA-infected animals are vesicular lesions, but various diseases can cause the same symptoms, which makes it difficult to distinguish SVA from other vesicular diseases clinically. The absence of an effective and safe vaccine necessitates the development of a simple, specific, and sensitive serological detection method for SVA antibodies. The VP2 protein of SVA, characterized by high immunogenicity and sequence conservatism, is an essential target for serological diagnosis. In this study, a double-antigen sandwich enzyme-linked immunosorbent assay [ELISA (DAgS-ELISA)] based on VP2 protein expressed by Escherichia coli was established for SVA antibody detection. With a cut-off value of 0.237, this assay demonstrated outstanding performance, showing high sensitivity and sharp specificity, which is manifested in the absence of cross-reaction with classical swine fever virus (CSFV), African swine fever virus (ASFV), pseudorabies virus (PRV), and porcine reproductive and respiratory syndrome virus (PRRSV), and foot-and-mouth disease virus (FMDV) serotype A and O. Additionally, the repeatability of the method is remarkable, as shown by the coefficients variation (CV) of both the intra- and inter-assay below 10%. By detecting 166 clinical sera, it was found that the kappa value of the DAgS-ELISA was 0.78 compared with that of the virus neutralization test (VNT), indicating a high level of consistency. In general, this method has high sensitivity, sharp specificity, remarkable repeatability, sound consistency, and low cost, making it a reliable and effective tool for detecting SVA antibodies.IMPORTANCESVA has rapidly become prevalent in many countries, and its outbreaks have threatened the global swine industry significantly. The major clinical symptoms of SVA-infected animals are vesicular lesions that are similar to other vesicular diseases, making it difficult to distinguish SVA. Currently, no commercial vaccines are available for SVA; therefore, effective diagnosis of SVA infection is vital for its prevention and control. In this study, VP2 protein of SVA was expressed by E. coli, and a double-antigen sandwich enzyme-linked immunosorbent assay [ELISA (DAgS-ELISA)] for SVA antibodies detection was successfully established based on the VP2 protein. The DAgS-ELISA has a high sensitivity, sharp specificity, remarkable repeatability, sound consistency, and low cost for detecting SVA antibodies. Therefore, the DAgS-ELISA established in this study may be a reliable and effective tool for detecting SVA antibodies and may be used to strengthen the monitoring and prevention of SVA epidemic in the long run.

To accurately predict the detonation and safety performances of high-energy explosives, it is necessary to investigate their reaction mechanisms on different scales, which, however, presents a challenge due to the complex reaction kinetics of the explosives and a lack of experimental methods presently. This work introduces the time-resolved pump-probe experiments capabilities aiming at high-energy explosives based on large-scale laser facilities and presents the recent progress in research on the dynamic process of the explosives, obtaining the following understandings: (1) First, the micron-sized single-crystal 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) can be compressed to an overdriven detonation state at a laser facility, followed by the shock Hugoniot measurements of TATB; (2) Second, high resolution transient X-ray radiography makes it possible to achieve the dynamic imaging of the internal deformation, damage, and reaction dynamics of high-energy explosive under dynamic loading; (3) Third, the phase transformation and chemical reaction products of the shock-compressed explosives can be investigated using dynamic X-ray diffraction or scattering spectra; (4) Finally, the structural changes, molecular reactions, molecular bond cleavage, and intermediate product components of explosives under ultrafast pumping can be explored using ultrafast laser spectroscopy. Large-scale laser facilities can provide various flexible pump-probe methods, including laser shock loading, transient X-ray imaging, dynamic X-ray diffraction, and ultrafast spectroscopy, allowing a series of experiments to be carried out to evaluate different levels of ignitions from low-pressure to overdriven detonations. Furthermore, the facilities also enable in situ, real-time investigations of the internal deformation, phase transition, and ultrafast dynamics of explosives under dynamic loading at high spatial and temporal resolutions. The study of the reaction kinetics and mechanisms of high-energy explosives on microscopic-mesoscopic scales provides an efficient means to unravel the mystery of explosive reactions.

The neutron induced nuclear reaction cross section of fission products is related with the neutron flux and the reactor burnup, which plays an important role in accurately designing nuclear engineering. To predict (n, 2n) reaction cross sections especially those without experimental data, the relevant features were analyzed and the experimental data set were established on the basis of sorting out the experimental data recorded in EXFOR library. This work includes 5 294 (n, 2n) cross section measured results, among which a lot of experimental data concentrate around 14 MeV incident energy. Moreover, there may be divergence between measurements due to system error and negligence error. Faced with these real and defective data, researchers discovered laws behind them and established the compound nucleus reaction models. However this means a heavy workload. It would be surprising if machine learning could reach a quantitative level close to the evaluation results. The 8 features include the proton number Z, the mass number A, the single nucleon separation energy of both proton and neutron, the Casten factor, the level density, the pairing correction, and the incident energy. The back propagation artificial neural network (ANN) and decision tree (DT) models were built to learn the experimental data set, respectively, adopting PyTorch and XGBOOST toolboxes. Draw lessons from the variational auto-encoder network, the 2 sub-networks with the same internal structure, which contains 128 neurons in 2 hidden layers, were designed to learn the mean and variance respectively. The boosting model integrates 16 decision trees. The training set includes 4 000 uniform and randomly selected data, while the remaining data constitute the test set. The results show that both ANN and XGBOOST models describe the experimental cross section data well, moreover model gives a smooth and continuous curve, indicating a certain predictive ability. For the case of lack of experimental data, the predictions are also basically consistent with the evaluation nuclear reaction data libraries. Compared with the XGBOOST model, the ANN model has somewhat better generalization ability in the range of neutron incident energy above 20 MeV. In the test set, the ANN predictions with a mean absolute percentage error (MAPE) deviation less than 10% from the experimental data account for more than 85%. Therefore we successfully established machine learning models to analysis and predicate (n, 2n) reaction cross section. On the other hand, through traditional nuclear reaction models, one can intuitively understand the relationship among physical quantities and build a picture of the reaction mechanism. However, machine learning models are often regarded as difficult to understand black boxes, which is questionable for physicists. In future it is planned to further apply machine learning for the nuclear data research to verify the rationality of the method.

Neutron-induced fission nuclear reaction data are critical for understanding nuclear physics, engineering, and technology. The accuracy of fission cross section of key fuel nuclides and sub-actinide nuclei is increasingly important with the development of new nuclear energy system concepts. Machine learning is a powerful tool for data analysis and modeling, and can be used to extract features from large amounts of data without requiring encoding for a specific task. Neural networks, a subset of machine learning, are particularly effective for mapping input to output for tasks with a sufficient number of target-valued data. In a recent study, researchers applied machine learning methods to analyze fission nuclear reaction cross section data. The team collected fission cross section data for the uranium isotope chain from the experimental nuclear reaction data (EXFOR) library and five sets of frequently used evaluation data from the evaluated nuclear data file (ENDF) library. The neutron-induced fission cross section data of 233-239U in the energy range from 2 to 20 MeV were selected as the training data set. To obtain fission cross sections for a given neutron number, proton number, and corresponding incident neutron energy, the feedforward neural network (FNN) was trained using fission cross section data from experimental measurement of the uranium isotope chain to achieve maximum agreement with experimental and evaluation data. The results show that the fission cross section data generated by the machine learning method can reproduce the step structure of the fission cross section well and is very close to the results of experimental and evaluation data. This provides a research basis for large-scale analysis of fission cross section data of easy-to-fission nuclei. The application of machine learning to nuclear physics and engineering is still in its early stages, and there are several challenges that need to be addressed. One major challenge is the lack of high-quality data sets, which are essential for training and testing machine learning algorithm. In addition, the complexity of nuclear reactions and the need for accurate modeling of the underlying physics present additional challenges. Despite these challenges, machine learning has the potential to revolutionize both the research and practice of nuclear physics and engineering. The ability to analyze and model large amounts of data can help researchers better understand the underlying physics of nuclear reactions and improve the accuracy of predictions. Machine learning algorithm can also be used to optimize the design and operation of nuclear reactors, leading to improved safety and efficiency. In conclusion, machine learning method is a promising approach for analyzing and modeling nuclear physics and engineering data. The application of neural networks to fission cross section data provides a new perspective on the understanding and prediction of nuclear reactions. Continued research and development of machine learning algorithm can contribute to the safe and efficient use of nuclear energy for generations to come.

ObjectiveTo explore the role of hippocampal microglia activation and related proinflammatory factors in depression-like behavior induced by chronic stress in rats with ischemic stroke.MethodsA total of seventy-five healthy male Sprague-Dawley rats were used to establish a right middle cerebral artery occlusion (MCAO) model with 70 min ischemia. One week after MCAO, rats were assigned to stroke and "stroke + stress" groups according to the SPSS random number generator. And according to time course, the former was divided into 1-week stroke, 2-week stroke and 4-week stroke groups, while the latter was divided into "2-week stroke + 1-week stress" (referred to as "1-week stress") and "4-week stroke + 3-week stress" (as "3-week stress") groups, with 15 rats in each group. "Chronic unpredictable mild stress plus isolation" was used in the stress group. The body weight, open field test (OFT), novelty-suppressed feeding test (NSFT) and sugar preference index (SPI) of rats were evaluated at each time point. Residual brain volume ratio was detected by TTC staining. The expression of Iba-1, IL-1β and IL-18 in the affected hippocampus was detected by Western blot. The fluorescencs intensity of Iba-1 in the CA1 region of the affected hippocampus was detected by immunofluorescence staining.ResultsTwo weeks after MCAO, compared with the 2-week stroke group, the body weight and counts of rearing and grid crossing of OFT decreased in the 1-week stress group (all PPP>0.05). Western blot showed that, compared with the 4-week stroke group or 1-week stress group, the expression of Iba-1, IL-1β and IL-18 in the affected hippocampus was significantly higher in the 3-week stress group (all PPPP

Multidrug resistance (MDR) hinders treatment efficacy in cancer therapy. One typical mechanism contributing to MDR is the overexpression of permeability-glycoprotein (P-gp) encoded by ATP-binding cassette subfamily B member 1 (ABCB1). Basic helix-loop-helix family member e40 (BHLHE40) is a well-known transcription factor that has pleiotropic effects including the regulation of cancer-related processes. However, whether BHLHE40 regulates MDR is still unknown. Chromatin immunoprecipitation-seq study revealed BHLHE40 occupancy in the promoter of ABCB1 gene. Adriamycin (ADM)-resistant human chronic myeloid leukemia cells (K562/A) and human breast cancer cells (MCF-7/A) were established. BHLHE40 expression was downregulated in the ADM-resistant cell lines. Overexpression of BHLHE40 resensitized resistant cells to ADM, promoted cell apoptosis in vitro and suppressed tumor growth in vivo, whereas BHLHE40 knockdown induced resistance to ADM in parental cells. Moreover, we found that BHLHE40 regulated drug resistance by directly binding to the ABCB1 promoter (-1605 to -1597) and inactivating its transcription. In consistence, the expression of BHLHE40 was negatively correlated with ABCB1 in various cancer cells, while positively with cancer cell chemosensitivity and better prognosis of patients with breast cancer. The study reveals the role of BHLHE40 as a transcriptional suppressor on the expression of ABCB1, major ABC transporter in chemoresistance. The findings extend the function of BHLHE40 in tumor progression and provides a novel mechanism for the reversal of multidrug resistance.

Neutron induced nuclear reaction on light nuclei is an important subject in nuclear data research. The reaction of the n+d three nucleons system is also an important platform for examining the nucleonnucleon interaction theories. The progress on calculations of neutron induced nuclear reactions on deuterons with the FaddeevAGS equations was introduced in this paper. The equations were solved with the wave packet continuum discretization method in momentum space. The calculated results include the angular distributions of elastic scattering, doubledifferential cross sections of the emitting neutrons and protons from breakup reactions, excitation functions of the total elastic scattering cross sections, and the excitation function of the total breakup reaction cross sections. The elastic scattering cross sections were calculated with the separable Paris nucleonnucleon interaction for incident energy below 20 MeV and with the Nijmegen potential for incident energy above 20 MeV. A comparison between results with the Paris potential, which consists of both s and pwave nucleonnucleon (NN) interactions, and those with the swave Yamaguchi potential for the n+d elastic scattering suggest that NN interactions that are beyond the swave are necessary to describe the experimental data, especially at higher incident energy. Due to practical reasons, calculations of the breakup cross sections, namely, the double differential cross sections of the emitting neutrons and protons are limited with a separable swave Yamaguchitype nucleonnucleon interaction, which limited the calculation of the double differential cross sections to below 20 MeV. Fair agreements are found between the results in this paper and the experimental data and with the evaluated data in CENDL32, ENDF/BⅧ.0, JENDL5, and JEFF33.

Nuclear data are fundamental data for nuclear energy, nuclear engineering, nuclear technology applications and nuclear science research, and have always received national attention. In particular, the importance of neutroninduced fission nuclear reaction data is unquestionable and the theoretical evaluation of nuclear data is required for a long period of time. The reaction mechanism of neutron induced fission reaction is very complicated and needs to be described by several different nuclear reaction models, such as optical potential model and compound nuclei model and so on. Nowadays, the most famous international nuclear reaction program systems are TALYS and EMPIRE. In China, the program FUNF is a fission nuclear reaction program system with our own independent intellectual property rights, which was developed by Prof. Zhang Jingshang of the China Nuclear Data Center. Each of these program systems need more than 40 parameters to be adjusted. Therefore, it is necessary to optimize a large number of parameters at the same time in order to correctly describe the nuclear fission reaction data. In this work, the FUNF program system was used to calculate the fission reaction data of neutron induced 238U. The fast neutron reaction data of fissile materials with incident energy from about 1 keV up to 20 MeV were calculated by FUNF code. It consists of the spherical optical model, the HauserFeshbach model, and the angular momentum dependent exciton model to describe the emission from compound nucleus to the discrete levels of the residual nuclei in preequilibrium process, meanwhile equilibrium reaction process was described by the HauserFeshbach model with width fluctuation correction. Based on the FUNF nuclear fission reaction model, the parameters of the FUNF program system was adjusted by the advanced optimization method MINUIT. MINUIT is a numerical minimization software library in the Fortron by CERN staff Fred James in the 1970 s. Then, combined with the parallel calculation method of MPI (message passing interface), preliminary calculations for the neutron induced fission reaction of 238U were performed. The results show that the FUNF parameters obtained by the MINUIT optimization method can well describe the fission reaction data of 238U. Next, the procedure will continue to be optimized to further improve the efficiency of the tuning parameters and the accuracy of the parameters. And the application to the relevant fission nuclear reactions will be developed to improve the accuracy of our fission nuclear reaction data.

Background and purpose: Cerebrovascular disease (CVD) poses a substantial risk for depression. Elevated levels of alkaline phosphatase (ALP) serve not only as an independent predictive factor for acute cerebrovascular events and unfavorable prognoses but also as a significant predictor of depression in premenopausal women. Nevertheless, the association between elevated ALP levels and vascular depression (VDe) in patients presenting with white matter hyperintensities (WMHs) remains unclear. Method: In a cross-sectional survey, 265 individuals diagnosed with CVD were incorporated. Baseline demographic information, fasting blood parameters, and MRI data were systematically gathered for analysis. All patients were divided into a severe WMHs (sWMHs) group and a mild WMHs (mWMHs) group based on their Fazekas score. Univariate analysis of potential variables among different subgroups of patients with scores of Hamilton Rating Scale for Depression (HAMD) was performed. Subsequently, the diagnostic effectiveness of multivariables for positive VDe within two WMHs groups was assessed using binary logistic regression. The diagnostic capability of the multivariate approach for VDe was further scrutinized through ordinal logistic regression. Results: (1) Hypersensitivity C-reactive protein (hs-CRP, p = 0.031), high-density lipoprotein cholesterol (HDL-C, p = 0.038), apolipoprotein A1 (APOA1, p = 0.009), and ALP (p = 0.011) exhibited distinct expression in patients with mWMHs across varying HAMD scores. In contrast, erythrocyte counts (p = 0.024), hemoglobin (Hb, p = 0.011), hs-CRP (p = 0.002), and ALP (p = 0.021) displayed differential expression in patients with sWMHs across different HAMD scores. (2) ALP and hs-CRP combined with APOA1 or Hb can improve the diagnostic efficiency of positive VDe in sWMHs [AUC = 0.849, 95% CI (0.753, 0.946), p < 0.001] or mWMHs [AUC = 0.718, 95% CI (0.603, 0.834), p = 0.002] patients, respectively. (3) Alkaline phosphatase (ALP) [OR = 1.016, 95% CI (1.003, 1.028), p = 0.016] is correlated with VDe in patients with sWMHs, a relationship that persisted even following adjustments for age and sex. Conclusion: The amalgamation of multiple markers enhances the diagnostic efficacy of VDe through WMHs classification. Serum ALP is associated with VDe in sWMHs patients. [ABSTRACT FROM AUTHOR]

The neutron induced nuclear reaction cross sections of fission products are related with the neutron fiux and the reactor burnup, which are important for the accurate of nuclear engineering design. To predict the (n,2n) reaction cross section, especially those lack of experimental measurements, we analyzed the relevant features and establish the experimental data set on the basis of sorting out the experimental data recorded in EXFOR library. The back propagation artificial neural network (ANN) and decision tree (DT) models are built to learn the experimental data set, respectively, adopting PyTorch and XGBOOST toolboxes. we report that machine learning models are applied to analysis and predicate (n,2n) reaction cross section.

Due to the difficulty in dealing with non-stationary and nonlinear vibration signals using the single decomposition method, it is difficult to extract weak fault features from complex noise; therefore, this paper proposes a fault feature extraction method for rolling bearings based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods. CEEMDAN was used to decompose the signal, and the signal was then screened and reconstructed according to the component envelope kurtosis. Based on the kurtosis of the maximum envelope spectrum as the fitness function, the sparrow search algorithm (SSA) was used to perform adaptive parameter optimization for VMD, which decomposed the reconstructed signal into several IMF components. According to the kurtosis value of the envelope spectrum, the optimal component was selected for an envelope demodulation analysis to realize fault feature extraction for rolling bearings. Finally, by using open data sets and experimental data, the accuracy of envelope kurtosis and envelope spectrum kurtosis as a component selection index was verified, and the superiority of the proposed feature extraction method for rolling bearings was confirmed by comparing it with other methods.

Hepatocellular carcinoma (HCC) is an aggressive human cancer with increasing incidence worldwide. Multiple efforts have been made to explore pharmaceutical therapies to treat HCC, such as targeted tyrosine kinase inhibitors, immune based therapies and combination of chemotherapy. However, limitations exist in current strategies including chemoresistance for instance. Tumor initiation and progression is driven by reprogramming of metabolism, in particular during HCC development. Recently, metabolic associated fatty liver disease (MAFLD), a reappraisal of new nomenclature for non-alcoholic fatty liver disease (NAFLD), indicates growing appreciation of metabolism in the pathogenesis of liver disease, including HCC, thereby suggesting new strategies by targeting abnormal metabolism for HCC treatment. In this review, we introduce directions by highlighting the metabolic targets in glucose, fatty acid, amino acid and glutamine metabolism, which are suitable for HCC pharmaceutical intervention. We also summarize and discuss current pharmaceutical agents and studies targeting deregulated metabolism during HCC treatment. Furthermore, opportunities and challenges in the discovery and development of HCC therapy targeting metabolism are discussed.

Metastasis, is a development of secondary tumor growths at a distance from the primary site, and closely related to poor prognosis and mortality. However, there is still no effective treatment for metastatic cancer. Therefore, there is an urgent need to find an effective therapy for cancer metastasis. Plenty of evidence indicates that miR-9 can function as a promoter or suppressor in cancer metastasis and coordinate multistep of metastatic process. In this review, we summarize the different roles of miR-9 with the corresponding molecular mechanisms in metastasis of twelve common cancers and the multiple mechanisms underlying miR-9-mediated regulation of metastasis, benefiting the further research of miR-9 and metastasis, and hoping to bridge it with clinical applications.

Due to their symmetrized dot pattern, rolling bearings are more susceptible to noise than time–frequency characteristics. Therefore, this article proposes a symmetrized dot pattern extraction method based on the Frobenius and nuclear hybrid norm penalized robust principal component analysis (FNHN-RPCA) as well as decomposition and reconstruction. This method focuses on denoising the vibration signal before calculating the symmetric dot pattern. Firstly, the FNHN-RPCA is used to remove the non-correlation between variables to realize the separation of feature information and interference noise. After, the residual interference noise, irrelevant information, and fault features in the separated signal are clearly located in different frequency bands. Then, the ensemble empirical mode decomposition is applied to decompose this information into different intrinsic mode function components, and the improved DPR/KLdiv criterion is used to select components containing fault features for reconstruction. In addition, the symmetrized dot pattern is used to visualize the reconstructed signal. Finally, method validation and comparative analysis are conducted on the CWRU datasets and experimental bench data, respectively. The results show that the improved criteria can accurately complete the screening task, and the proposed method can effectively reduce the impact of strong noise interference on SDPs.

Objective This study analyzed the ultrasound residents' Scientific Research Competencies to provide basis for training program for capacity building of scientific research. Methods This study conducted a self-reported questionnaire by the ultrasound resident trainees in Peking Union Medical College Hospital, in order to define their Scientific Research Competencies based on different grades or education backgrounds. Results The result showed that there were differences between the resident trainees from different grades or with different education backgrounds in terms of Research Competencies. The ranking of average scores of the resident trainees from different grades were,from high to low, the third year, the first year and second year (256±48, 231±28, 214±48). And the ranking of average scores of the trainees with different education backgrounds were, from high to low, clinical post-doctoral fellows, graduate students, and standardized training residents (294±18, 236 ±33, 214±42). The results of detailed research capacity, all resident trainees were lack of the capacity of research design and fund application, paper writing, and learning research knowledge by reading. Conclusions It is suggested that hospitals need to provide hierarchical guidance to residents in order to improve their Scientific Research Competencies to meet the requirement of modern medical science.

Objective To explore the hierarchical and advanced training mode applied in the standardized residency training to improve the quality of the program in ultrasound specialty. Methods Stratified training was conducted for ultrasound residents with different educational backgrounds, advanced training programs were developed according to grade and training stage design, and a hierarchical and advanced training system for residents training in ultrasound specialty was established, which was applied to the 2016 class of residents. The quality of ultrasonic reports of 2013 class residents before the reform and 2016 class residents after the reform was compared to evaluate the effect of the teaching reform. Results After the implementation of the hierarchical and advanced training program, the pathological coincidence rate reported by the 2016 class residents was 97.2%, while that reported by the 2013 class residents before the reform was 92.8%. The pathological coincidence rate of ultrasonic reports in the grade after the teaching reform was significantly higher than that before (P＜0.05). Conclusions The hierarchical and advanced training mode improves the training quality of ultrasound residents and can be applied in the standardized training of residents in ultrasound specialty.

Abstract Background Gastric cancer stem cells (CSCs) are the main causes of metastasis and drug resistance. We previously indicated that miR-375 can inhibit Helicobacter pylori-induced gastric carcinogenesis; here, we aim to explore the effects and mechanisms of miR-375 on gastric cancer (GC) cell stemness. Methods Lentivirus infection was used to construct GC cells with ectopic expression of miR-375. In vitro and in vivo experiments, including analysis of tumor spheroid formation, CD44+ sub-population with stemness, stemness marker expression, and tumor-initiating ability, were performed to evaluate the effects of miR-375 on the stemness of GC cells. Furthermore, microarray and bioinformatics analysis were performed to search the potential targets of miR-375 in GC cells. Luciferase reporter, RNA immunoprecipitation, and RNA-FISH assays were carried out to verify the targeting of miR-375. Subsequently, combined with tissue microarray analysis, erastin-resistant GC cells, transmission electron microscopy, a series of agonists and oxidative stress markers, the underlying mechanisms contributing to miR-375-mediated effects were explored. Results MiR-375 reduced the stemness of GC cells in vitro and in vivo. Mechanistically, SLC7A11 was identified as a direct target of miR-375 and miR-375 attenuated the stemness of GC cells mainly through triggering SLC7A11-dependent ferroptosis. Conclusion MiR-375 can trigger the ferroptosis through targeting SLC7A11, which is essential for miR-375-mediated inhibition on GC cell stemness. These results suggest that the miR-375/SLC7A11 regulatory axis could serve as a potential target to provoke the ferroptosis and thus attenuate the stemness of GC cells.

Snowstorms frequently hit large parts of the Northern Hemisphere, and their causative factors have been drawing increasing attention in recent years. As the first in-depth study on the mechanisms of a snowstorm associated with a low-level cold front (LLCF) and low-level westerly jet (LLWJ) in the western mountainous region of the Junggar Basin, Xinjiang, based on both observations and numerical simulation, the major findings of this work are as follows: At the early stage, instabilities were mainly dominated by inertial instability (II) occurring near the core region of the LLWJ, while the lower level was mainly controlled by the baroclinic component of moist potential vorticity (MPV2), which was mainly contributed by the vertical shear of the horizontal wind, which is also located near the LLWJ. At the later stage, II was released significantly, whereas the MPV2 still supported snowfall clouds. Further analysis based on the decomposition of the frontogenetical forcing required for the release of the instabilities indicated that the slantwise term was the major contributor, whereas convergence and deformation also played significant roles at low levels above the windward slope. The slantwise term resulted from the combined effects of baroclinicity due to the LLCF and the inhomogeneity of the momentum due to the LLWJ.

Understanding the lattice evolution of hexanitrostilbene (HNS) is crucial for ensuring its safety and reliability under shock loading. However, the lack of in situ, real-time diagnostics has limited the availability of lattice parameters for shock-loaded explosives. In this study, we utilized dynamic X-ray diffraction technology to obtain the diffraction spectrum of laser shock-loaded HNS and to determine its temporal evolution. Additionally, by improving the laser energy, we initiated HNS and obtained the diffraction spectrum of detonation products during the detonation process. The experimental results showed the presence of a diamond structure in the detonation product, suggesting the existence of either diamond or diamond-like carbon. Our research not only elucidates the crystal structure of shock-loaded HNS and its detonation products but also provides an avenue for laboratory-scale investigations into dynamically loaded explosives, which furnishing an opportunity to unveil the underlying mechanism governing explosive dynamic response behavior. [ABSTRACT FROM AUTHOR]