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As a fundamental element of the transportation system, traffic signs are widely used to guide traffic behaviors. In recent years, drones have emerged as an important tool for monitoring the conditions of traffic signs. However, the existing image processing technique is heavily reliant on image annotations. It is time consuming to build a high-quality dataset with diverse training images and human annotations. In this paper, we introduce the utilization of Vision-language Models (VLMs) in the traffic sign detection task. Without the need for discrete image labels, the rapid deployment is fulfilled by the multi-modal learning and large-scale pretrained networks. First, we compile a keyword dictionary to explain traffic signs. The Chinese national standard is used to suggest the shape and color information. Our program conducts Bootstrapping Language-image Pretraining v2 (BLIPv2) to translate representative images into text descriptions. Second, a Contrastive Language-image Pretraining (CLIP) framework is applied to characterize not only drone images but also text descriptions. Our method utilizes the pretrained encoder network to create visual features and word embeddings. Third, the category of each traffic sign is predicted according to the similarity between drone images and keywords. Cosine distance and softmax function are performed to calculate the class probability distribution. To evaluate the performance, we apply the proposed method in a practical application. The drone images captured from Guyuan, China, are employed to record the conditions of traffic signs. Further experiments include two widely used public datasets. The calculation results indicate that our vision-language model-based method has an acceptable prediction accuracy and low training cost.

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Indole alkaloids are privileged secondary metabolites, and their production may be achieved by the microbial biotransformation of tryptophan analogues. By feeding 1-methyl-L-tryptophan (1-MT) into the culture of endophytic Nigrospora chinensis GGY-3, six novel (1-6) and seven known indole alkaloids (7-13) were generated. Their structures were elucidated by means of NMR spectroscopy, experimental electronic circular dichroism (ECD) spectra, and X-ray crystallography analysis. A Friedel-Crafts reaction was proposed as the key reaction responsible for the formation of the new compounds. Racemates 4 and 6 were separated into isomers by chiral HPLC, with their absolute configurations determined by X-ray and ECD calculation. Compounds 3, 4, and 8 display good herbicidal activity against dicotyledon weed Eclipta prostrata, of which 4 and 8 exhibited 88.50% and 100% inhibition rates on the radicle at 200 µg/mL, respectively, a similar effect compared to the positive control penoxsulam.

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Fine-mode aerosol optical depth (fAOD) is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Currently, the limited data length and high uncertainty of the satellite-based data diminish the applicability of fAOD for climate research. Here, we propose a novel pretrained deep learning framework that can extract information underlying each satellite pixel and use it to create new latent features that can be employed for improving retrieval accuracy in regions without in situ data. With the proposed model, we developed a new global fAOD (at 0.5 µm) data from 2001 to 2020, resulting in a 10% improvement in the overall correlation coefficient (R) during site-based independent validation and a 15% enhancement in non-AERONET site areas validation. Over the past two decades, there has been a noticeable downward trend in global fAOD (-1.39 × 10-3/year). Compared to the general deep-learning model, our method reduces the global trend's previously overestimated magnitude by 7% per year. China has experienced the most significant decline (-5.07 × 10-3/year), which is 3 times greater than the global trend. Conversely, India has shown a significant increase (7.86 × 10-4/year). This study bridges the gap between sparse in situ observations and abundant satellite measurements, thereby improving predictive models for global patterns of fAOD and other climate factors.

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Wildfires generate abundant smoke primarily composed of fine-mode aerosols. However, accurately measuring the fine-mode aerosol optical depth (fAOD) is highly uncertain in most existing satellite-based aerosol products. Deep learning offers promise for inferring fAOD, but little has been done using multiangle satellite data. We developed an innovative angle-dependent deep-learning model (ADLM) that accounts for angular diversity in dual-angle observations. The model captures aerosol properties observed from dual angles in the contiguous United States and explores the potential of Greenhouse gases Observing Satellite-2's (GOSAT-2) measurements to retrieve fAOD at a 460 m spatial resolution. The ADLM demonstrates a strong performance through rigorous validation against ground-based data, revealing small biases. By comparison, the official fAOD product from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Multiangle Imaging Spectroradiometer (MISR) during wildfire events is underestimated by more than 40% over western USA. This leads to significant differences in estimates of aerosol radiative forcing (ARF) from wildfires. The ADLM shows more than 20% stronger ARF than the MODIS, VIIRS, and MISR estimates, highlighting a greater impact of wildfire fAOD on Earth's energy balance.

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Anthropogenic stressors such as urban development, agricultural runoff, and riparian zone degradation impair stream water quality and biodiversity. However, the intricate pathways that connect these stressors at watershed and riparian scales to stream ecosystems-and their interplay with climate and hydrology-remain understudied. In this study, we used Partial Least Squares (PLS) path modeling to examine these pathways and their collective impacts on stream water quality and fish community structures across 233 watersheds in the Great Lakes region. Our study suggests that moderate levels of watershed development enhance overall fish richness, potentially due to increased water temperature and nutrient availability, but reduces both the percentages and richness of cold water and intolerant taxa. Riparian quality exerts indirect effects on water quality with climate and stream order serving as key mediators. Complementing our SEM analysis, we also used Multiple Linear Regression (MLR) models and identified a significant positive relationship between the proportion of clay and agricultural land with TN concentrations. However, TP concentrations are influenced by a more complex set of interactions involving developed areas, soil, and slope. These findings emphasize the necessity of adopting integrated management strategies to preserve the health and integrity of freshwater ecosystems in the Great Lakes region. These strategies should integrate watershed and riparian protection measures while also taking into account the effects of climate change and specific local conditions.

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Radiation-induced lung injury (RILI) is a common and fatal complication of chest radiotherapy. The underlying mechanisms include radiation-induced oxidative stress caused by damage to the deoxyribonucleic acid (DNA) and production of reactive oxygen species (ROS), resulting in apoptosis of lung and endothelial cells and recruitment of inflammatory cells and myofibroblasts expressing NADPH oxidase to the site of injury, which in turn contribute to oxidative stress and cytokine production. Nuclear factor erythroid 2-related factor 2 (Nrf-2) is a vital transcription factor that regulates oxidative stress and inhibits inflammation. Studies have shown that Nrf-2 protects against radiation-induced lung inflammation and fibrosis. This review discusses the protective role of Nrf-2 in RILI and its possible mechanisms.

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Accurate fine-mode and coarse-mode aerosol knowledge is crucial for understanding their impacts on the climate and Earth's ecosystems. However, current satellite-based Fine-Mode Aerosol Optical Depth (FAOD) and Coarse-Mode Aerosol Optical Depth (CAOD) methods have drawbacks including inaccuracies, low spatial coverage, and limited temporal duration. To overcome these issues, we developed new global-scale FAOD and CAOD from 2005 to 2020 using a novel deep learning model capable of the synergistic retrieval of two aerosol sizes. After validation with the aerosol robotic network (AERONET) and sky radiometer network (SKYNET), the new monthly FAOD and CAOD showed significant improvements in accuracy and spatial coverage. From 2005 to 2020, the new data showed that China had the greatest decrease in FAOD and CAOD. In contrast, India and South Latin America had a significant increase in FAOD versus North Africa in CAOD. FAOD in the regions of Argentina, Paraguay, and Uruguay in South America have shown an upward trend. The results reveal that FAOD and CAOD display distinct patterns of change in the same regions, particularly on the west coast of the United States where FAOD is increasing, while CAOD is decreasing. Aside from the year 2020 due to the global COVID-19 pandemic, the analysis showed that although China has seen at least an +85% increase in energy consumption and urban expansion in 2019 compared to 2005 due to the needs of development and construction, the implementation of China's air pollution control policies has led to a significant decrease in FAOD (-46%) and CAOD (-65%) after 2013. This research enriches our comprehension of global fine and coarse aerosol patterns, additional investigations are needed to determine the potential global implications of these changes.

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As surface ozone (O3) gains increasing attention, there is an urgent need for high temporal resolution and accurate O3 monitoring. By taking advantage of the progress in artificial intelligence, deep learning models have been applied to satellite based O3 retrieval. However, the underlying physical mechanisms that influence surface O3 into model construction have rarely been considered. To overcome this issue, we considered the physical mechanisms influencing surface O3 and used them to select relevant variable features for developing a novel deep learning model. We used a wide and deep model architecture to account for linear and non-linear relationships between the variables and surface O3. Using the developed model, we performed hourly inversions of surface O3 retrieval over China from 2017 to 2019 (9:00-17:00, local time). The validation results based on sample-based (site-based) methods yielded an R2 of 0.94 (0.86) and an RMSE of 12.79 (19.13) µg/m3, indicating the accuracy of the models. The average surface O3 concentrations in China in 2017, 2018, and 2019 were 82, 78, and 87 µg/m3, respectively. There was a diurnal pattern in surface O3 in China, with levels rising significantly from 55 µg/m3 at 9:00 a.m. to 96 µg/m3 at 15:00. Between 15:00 and 16:00, the O3 concentration remained stable at 95 µg/m3 and decreased slightly thereafter (16:00-17:00). The results of this study contribute to a deeper understanding of the physical mechanisms of ozone and facilitate further studies on ozone monitoring, thereby enhancing our understanding of the spatiotemporal characteristics of ozone.

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Coarse-mode aerosol optical depths (cAODs) are critical for understanding the impact of coarse particle sizes, especially dust aerosols, on climate. Currently, the limited data length and high uncertainty of satellite products diminish the applicability of cAOD for climate research. Here, we propose a spatiotemporal coaction deep-learning model (SCAM) for the retrieval of global land cAOD (500 nm) from 2001-2021. In contrast to conventional deep-learning models, the SCAM considers the impacts of spatiotemporal feature interactions and can simultaneously describe linear and nonlinear relationships for retrievals. Based on these unique characteristics, the SCAM considerably improved global daily cAOD accuracies and coverages (R = 0.82, root-mean-square error [RMSE] = 0.04). Compared to official products from the multiangle imaging spectroradiometer (MISR), the moderate resolution imaging spectroradiometer (MODIS), and the polarization and directionality of Earth's reflectances (POLDER) instrument, as well as the physical-deep learning (Phy-DL) derived cAOD, the SCAM cAOD improved the monthly R from 0.44 to 0.88 and more accurately captured over the desert regions. Based on the SCAM cAOD, daily dust cases decreased over the Sahara, Thar Desert, Gobi Desert, and Middle East during 2001-2021 (>3 × 10-3/year). The SCAM-retrieved cAOD can contribute considerably to resolving the climate change uncertainty related to coarse-mode aerosols. Our proposed method is highly valuable for reducing uncertainties regarding coarse aerosols and climate interactions.

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Detection of single nucleotide polymorphisms (SNPs) is critical for personalized clinical diagnosis, treatment, and medication. Current clinical detection methods suffer from primer dimerization and require the redesigning of reaction systems for different targets, resulting in a time-consuming and laborious process. Here, we present a robust and versatile method for SNP typing by using tailed primers and universal small molecule probes in combination with a visualized lateral flow assay (LFA). This approach enables not only rapid typing of different targets, but also eliminates the interference of primer dimers and enhances the accuracy and reliability of the results. Our proposed universal assay has been successfully applied to the typing of four SNP loci of clinical samples to verify the accuracy and universality, and the results are consistent with those obtained by Sanger sequencing. Therefore, our study establishes a new universal "typing formula" using nucleic acid tags and small molecule probes that provides a powerful genotyping platform for genetic analysis and molecular diagnostics.

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Ground-level fine particulate matter (PM2.5) and ozone (O3) are air pollutants that can pose severe health risks. Surface PM2.5 and O3 concentrations can be monitored from satellites, but most retrieval methods retrieve PM2.5 or O3 separately and disregard the shared information between the two air pollutants, for example due to common emission sources. Using surface observations across China spanning 2014-2021, we found a strong relationship between PM2.5 and O3 with distinct spatiotemporal characteristics. Thus, in this study, we propose a new deep learning model called the Simultaneous Ozone and PM2.5 inversion deep neural Network (SOPiNet), which allows for daily real-time monitoring and full coverage of PM2.5 and O3 simultaneously at a spatial resolution of 5 km. SOPiNet employs the multi-head attention mechanism to better capture the temporal variations in PM2.5 and O3 based on previous days' conditions. Applying SOPiNet to MODIS data over China in 2022, using 2019-2021 to construct the network, we found that simultaneous retrievals of PM2.5 and O3 improved the performance compared with retrieving them independently: the temporal R2 increased from 0.66 to 0.72 for PM2.5, and from 0.79 to 0.82 for O3. The results suggest that near-real time satellite-based air quality monitoring can be improved by simultaneous retrieval of different but related pollutants. The codes of SOPiNet and its user guide are freely available online at https://github.com/RegiusQuant/ESIDLM.

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A type II polyketide synthase biosynthetic gene cluster (nap) was identified in Streptomyces eurocidicus CGMCC 4.1086 via genome mining. The heterologous expression of the cryptic nap gene cluster in Streptomyces albus J1074 generated dimerized aromatic polyketide naphthocyclinones (1-3), whose structures were determined via extensive analysis using nuclear magnetic resonance and high-resolution electrospray ionization mass spectroscopy. The biological pathway of naphthocyclinone synthesis was revealed via in vivo gene deletion, in vitro biochemical reactions, and comparative genomics. Remarkably, 3 played a crucial role in inhibiting Phytophthora capsici and Phytophthora sojae, with EC50 values of 6.1 and 20.2 µg/mL, respectively. Furthermore, 3 exhibited a potent protective effect against P. capsici and P. sojae in greenhouse tests.

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OBJECTIVE: To analyze the clinical characteristics of hemophagocytic syndrome (HLH) children with different EB virus (EBV) DNA loads, and to explore the relationship between differential indicators and prognosis. METHODS: Clinical data of 73 children with HLH treated in our hospital from January 2015 to April 2022 were collected. According to EBV DNA loads, the children were divided into negative group (≤5×102 copies/ml), low load group (>5×102-<5×105 copies/ml) and high load group (≥5×105copies/ml）. The clinical symptoms and laboratory indexes of the three groups were compared, and the ROC curve was used to determine the best cut-off value of the different indexes. Cox regression model was used to analyze the independent risk factors affecting the prognosis of children, and to analyze the survival of children in each group. RESULTS: The proportion of female children, the swelling rate of liver and spleen lymph nodes and the involvement rate of blood, liver, circulation and central nervous system in the high load group were higher than those in the negative group. The incidence of disseminated intravascular coagulation(DIC) and central nervous system(CNS) involvement in the high load group were higher than those in the low load group. The liver swelling rate and circulatory system involvement rate in the low load group were higher than those in the negative group(P<0.05). PLT counts in the high load group were significantly lower than those in the negative group, and the levels of GGT, TBIL, CK-MB, LDH, TG, SF, and organ involvement were significantly higher than those in the negative group. The levels of CK, LDH, SF and the number of organ involvement in the high load group were significantly higher than those in the low load group. The levels of GGT and TBIL in low load group were significantly higher than those in negative group. In terms of treatment, the proportion of blood purification therapy in the high and low load group was significantly higher than that in the negative group(P<0.01). ROC curve analysis showed that the best cut-off values of PLT, LDH, TG and SF were 49.5, 1139, 3.12 and 1812, respectively. The appellate laboratory indicators were dichotomized according to the cut-off value, and the differential clinical symptoms were included in the Cox regression model. Univariate analysis showed that LDH>1139 U/L, SF>1812 µg/L, dysfunction of central nervous system, number of organ damage, DIC and no blood purification therapy were the risk factors affecting the prognosis of children (P<0.05); Multivariate analysis shows that PLT≤49.5×109/L and dysfunction of central nervous system were risk factors affecting the prognosis of children (P<0.05). Survival analysis showed that there was no significant difference in the survival rate among the three groups. CONCLUSION: The incidence of adverse prognostic factors in children with HLH in the EBV-DNA high load group is higher, and there is no significant difference in the survival rate of the three groups after blood purification therapy. Therefore, early identification and application of blood purification therapy is of great significance for children with HLH in the high load group.

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OBJECTIVE@#To analyze the clinical characteristics of hemophagocytic syndrome (HLH) children with different EB virus (EBV) DNA loads, and to explore the relationship between differential indicators and prognosis.@*METHODS@#Clinical data of 73 children with HLH treated in our hospital from January 2015 to April 2022 were collected. According to EBV DNA loads, the children were divided into negative group (≤5×102 copies/ml), low load group (>5×102-<5×105 copies/ml) and high load group (≥5×105copies/ml）. The clinical symptoms and laboratory indexes of the three groups were compared, and the ROC curve was used to determine the best cut-off value of the different indexes. Cox regression model was used to analyze the independent risk factors affecting the prognosis of children, and to analyze the survival of children in each group.@*RESULTS@#The proportion of female children, the swelling rate of liver and spleen lymph nodes and the involvement rate of blood, liver, circulation and central nervous system in the high load group were higher than those in the negative group. The incidence of disseminated intravascular coagulation(DIC) and central nervous system(CNS) involvement in the high load group were higher than those in the low load group. The liver swelling rate and circulatory system involvement rate in the low load group were higher than those in the negative group(P<0.05). PLT counts in the high load group were significantly lower than those in the negative group, and the levels of GGT, TBIL, CK-MB, LDH, TG, SF, and organ involvement were significantly higher than those in the negative group. The levels of CK, LDH, SF and the number of organ involvement in the high load group were significantly higher than those in the low load group. The levels of GGT and TBIL in low load group were significantly higher than those in negative group. In terms of treatment, the proportion of blood purification therapy in the high and low load group was significantly higher than that in the negative group(P<0.01). ROC curve analysis showed that the best cut-off values of PLT, LDH, TG and SF were 49.5, 1139, 3.12 and 1812, respectively. The appellate laboratory indicators were dichotomized according to the cut-off value, and the differential clinical symptoms were included in the Cox regression model. Univariate analysis showed that LDH>1139 U/L, SF>1812 μg/L, dysfunction of central nervous system, number of organ damage, DIC and no blood purification therapy were the risk factors affecting the prognosis of children (P<0.05); Multivariate analysis shows that PLT≤49.5×109/L and dysfunction of central nervous system were risk factors affecting the prognosis of children (P<0.05). Survival analysis showed that there was no significant difference in the survival rate among the three groups.@*CONCLUSION@#The incidence of adverse prognostic factors in children with HLH in the EBV-DNA high load group is higher, and there is no significant difference in the survival rate of the three groups after blood purification therapy. Therefore, early identification and application of blood purification therapy is of great significance for children with HLH in the high load group.

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Objective:To investigate the prevalence of albuminuria in Chinese residents aged >35 years and its potential association with cardiovascular disease (CVD).Methods:A total of 34 647 Chinese subjects aged ≥35 years were selected by stratified multi-stage random sampling from 2012 to 2015. Data were collected through questionnaires, physical examinations, and laboratory tests. Albuminuria was categorized into 3 types according to urinary albumin-to- creatinine ratio: normal (<30 mg/g), microalbuminuria (MAU, 30-300 mg/g), and macroalbuminuria (≥300 mg/g). Measurement data were expressed as xˉ±s, and t-tests were used for comparisons between indicators. Qualitative data were expressed as rate or constituent ratio, and the χ2 test or Kruskal-Wallis test was used to examine differences. Logistic regression was used for multivariate analyses. SAS 9.4 software was used for statistical analyses, and P<0.05 was considered statistically significant. Results:The prevalence of abnormal albuminuria was 19.1%; the prevalence was 17.2% for MAU and lower in males (13.8%) than females (20.1%, P<0.01). The risk of CVD was higher among subjects with MAU ( OR=1.23, 95% CI 1.12-1.35) and macroalbuminuria ( OR=1.86, 95% CI 1.50-2.32). When MAU was complicated by hypertension and diabetes mellitus, the CVD risk was 1.76 times higher. Conclusions:The prevalence of MAU is high among Chinese subjects aged 35 years and over. Those with MAU have higher CVD risk, especially those with hypertension and diabetes mellitus.

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Precise detection of low-frequency gene mutations surrounded by excess wild-type DNA is important in many aspects of medical fields. Most hybridization-based methods for high-resolution mutant allele analysis are hindered by competition of the complementary strand with single-strand probes for the target strand. Here, we demonstrate that site-specific insertion of endonuclease recognition sites into amplicons allows post-PCR generation of short dsDNA or ssDNA, whereby improves the sensitivity of both melting temperature analysis (MTA) and end-point detection following up. Using a three-staged PCR protocol, enrichment of target gene and incorporation of specific restriction sites in amplicons were ensued with hardly any loss in amplification efficiency and specificity. It enables simultaneous discrimination among a panel of totally 11 EGFR 19 exon deletion mutations via MTA after post-PCR digestion by either FokI only or cooperated with CRISPR-Cas12a, using SYBR green I. By replacement of one double-strand cleavage site with a nickase binding domain post-PCR generation of ssDNA of interest via strand displacement amplification (termed as iSDA) is realized. Our preliminary investigation shows that iSDA permits analysis of single nucleotide variants down to 0.1% allelic-frequency using end-point detection. Given the good compatibility with the majority of mutant-enrich PCR methods, we envision it would advance the current gene profiling technologies to a large extent.

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Hydroxylamine nitrate and hydrazine nitrate are dangerous explosives and toxic chemicals. Catalytic decomposition is an efficient way for disposal of these chemicals. In the current work, a Ru/ZSM-5 catalyst has been fabricated and evaluated for the decomposition of hydroxylamine nitrate and hydrazine nitrate in 1.0 mol L-1 HNO3. The hydroxylamine nitrate and hydrazine nitrate can be thoroughly decomposed under 80 °C. And the Ru/ZSM-5 catalyst can be separated from the reaction mixture and reused at least 130 times with stable catalytic performance. Easy operation, less solid waste generation, and a simple catalytic device make the strategy reported here practical, environmentally friendly, and economically attractive.

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Proton therapy is one of the most effective radiation methods to combat cancer and offers a substantial advantage over conventional photon therapy. Magnetic scanning systems consisting of a magnet and power supply form an integral part of proton therapy and allow the beam position (x, y) to be controlled during spot scanning. Ensuring that the dose is rapidly and precisely distributed within the contour of the tumor requires a high-precision, fast-ramp-speed, and high-stability power supply with accurate control. The present work uses a three-phase bridge rectifier and multiple series-parallel H-bridge converters to propose a prototype power supply for the scanning magnet of a magnetic scanning system. The power supply is controlled using a proportional integral controller using digital signal processing. A prototype of the scanning power supply is developed and tested, and the results demonstrate an output current of ±500 A, a ramp speed of ±40 kA/s, and a short- and long-term stability of less than 20 ppm.

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Objective: To investigate the prevalence, awareness, treatment and control status of dyslipidemia among females aged ≥35 years old across China. Methods: Participants were selected by stratified multistage random sampling method in the "Twelfth Five-Year Plan" National Science and Technology Support Project "Survey on the Prevalence of Important Cardiovascular Diseases and Key Technology Research in China" project. This study is a retrospective, cross-sectional study. A total of 17 418 females aged 35 years and over were included in the current study. The basic information such as age, medical history and menopause was collected by questionnaire. The blood lipid parameters were derived from clinical laboratory examinations. The prevalence of dyslipidemia and the rate of awareness, treatment, and control of dyslipidemia were analyzed in females aged 35 years and over. Results: The age of participants was (56.2±13.0) years old, and the prevalence of dyslipidemia was 33.1% (5 765/17 418). The prevalence rates of high total cholesterol, hypertriglyceridemia, low HDL-C and high LDL-C were 9.7% (1 695/17 418), 11.1% (1 925/17 418), 10.9% (1 889/17 418) and 7.3% (1 262/17 418), respectively. The prevalence of dyslipidemia increased with age and the prevalence of dyslipidemia in women who were not married, Han, menarche age>16 years, obesity, central obesity, alcohol consumption, diabetes, hypertension and family history of cardiovascular disease were higher than those without such characteristics (P<0.05). There were 10 432 (59.9%) menopausal females in this cohort and prevalence of dyslipidemia of these participants was 38.8% (4 048/10 432), which was higher than that of non-postmenopausal females (24.6%, 1 717/6 986) (P<0.05). The awareness rates, treatment rates and control rates of dyslipidemia were 33.9% (1 953/5 765), 15.1% (870/5 765) and 2.5% (143/5 765) respectively among females aged 35 years and over in China. Conclusion: The prevalence of dyslipidemia in Chinese females aged 35 years and over is high, and its awareness, treatment, and control rates need to be optimized.

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The Myh3 (myosin heavy chain 3) gene is a marker gene of muscle cell differentiation and regulates the utilization of energy in muscle cells, but whether it affects the glycolysis process of muscle cells in different states is rarely reported. In this paper, the expression patterns of Myh3 and glycolysis-related genes Pkm (M-type pyruvate kinse), Prkag3 (protein kinase adenosine monophosphate-activated γ3-subunit), and Gsk3β (glycogen synthase kinase-3β) were studied by the qRT-PCR (quantitative-Real-Time-PCR) method using C2C12 cells at different stages of myoblast and adipogenic differentiation as models. It was found that in the process of myoblast differentiation of C2C12 cells, the relative expression trends of Myh3 and glycolysis genes Prkag3 and Pkm were basically the same, and the relative expression levels first increased, reached the peak on the second day of differentiation, and then decreased; glycogen synthase the expression trend of the inhibitory gene Gsk3β was relatively stable. In the process of adipogenic differentiation of C2C12 cells, the relative expression trend of Myh3 and glycolysis genes Prkag3 and Pkm remained basically the same, and the relative expression gradually increased, reaching the highest value on the 8th day of differentiation; glycogen synthase inhibitory gene Gsk3β expression remained stable. In the myogenic differentiation state of C2C12 cells, qRT-PCR and Western blotting were used to detect the effects of interfering Myh3 on the mRNA and protein expressions of glycolysis-related genes Pkm, Prkag3, and Gsk3β. The results showed that after interfering with Myh3, the mRNA expressions of glycolysis genes Pkm and Prkag3 were significantly decreased (P 0.05). The protein levels of Myh3 and Pkm were significantly lower than those in the blank group and NC group. Under the adipogenic differentiation state of C2C12 cells, after interfering with Myh3, the mRNA levels of glycogen synthase inhibitor Gsk3β and glycolysis gene Prkag3 were significantly increased (P<0.01), and the mRNA level of glycolysis gene Pkm was decreased; the protein levels of Myh3 and Pkm in the Myh3 interference group were also lower than those in the blank group and NC group. Based on the above studies, there are significant differences in the levels of glycolysis in C2C12 cells in the myogenic and adipogenic states, and the expression patterns of Myh3 and glycolysis genes are similar. Further results showed that Myh3 suppression could inhibit the glycolysis of C2C12 cells in the myogenic state without affecting the glycogen synthesis. Unlike in the myogenic state, interfering expression of Myh3 in the adipogenic state of C2C12 cells inhibited both glycogen synthesis and glycolysis.