Objective To optimize primary cultures techniques of isolating neonatal rat cardiomyocytes and to com?pare three different methods of extractingβ3-adrenergic receptor(β3-AR)membrane protein from cultured neonatal rat car?diomyocytes. Methods TypeⅡcollagen and differential velocity adhesion were used to collect primary cardiomyocytes. To?tal protein method, ultracentrifugation method, extract kit method were used to isolate cardiomyocytesβ3-AR membrane pro?teins. The BCA method was applied for protein quantification. Relative content ofβ3-AR membrane protein and GADPH in the sample were examined by western blot. Results Optimizing culture and isolation skills can produce a great quantity of cardiomyocytes in high concentration.The kit method acquired a higher level of protein concentration(8.26±0.29)g/L than to?tal protein method(5.12±0.47)g/L does than ultracentrifugation method(3.20±0.37)g/L does all of which were with signifi?cant difference(P < 0.05). The concentration of β3-AR membrane protein was higher if obtained by kit method(0.22 ± 0.05)than ultracentrifugation method(0.09 ± 0.03)than total protein method (0.01 ± 0.01) with significant difference(P <0.05). Conclusion optimizing methodology can obtain abundant myocardial cells in high concentraion. The kit method of isolating primary culturedβ3-AR membrane proteins result in improved concentration and specificity of membrane protein.

To establish a method for the determination of formaldehyde and glyoxal simultaneously in varenicline tartrate active pharmaceutical ingredient (API) and its intermediate, formaldehyde and glyoxal were derivatized by 2, 4-dinitrophenylhydrazine (2,4-DNPH) to improve the HPLC retention and UV detection sensitivity. Separation was performed on a C8 (150 mm × 4.6 mm, 5 μm) column by linear gradient elution using acetonitrile and water as the mobile phase; the detective wavelength was set at 380 nm.Formaldehyde and glyoxal were quantitatively determined by an external reference method.Linear calibration was established for both formaldehyde and glyoxal in the range from 0.094 to 1.88 μg/mL.The detection and the quantification limits were 0.047 μg/mL (19 μg/g) and 0.094 μg/mL (38 μg/g), respectively.The recoveries were (95.0±1.1)% and (99.4 ± 2.6)% for formaldehyde and glyoxal, respectively.This method has been fully validated to be applicable to quantitative analysis of trace amount of formaldehyde and glyoxal in varenicline tartrate API and its intermediate.Test results demonstrated that the contents of both formaldehyde and glyoxal met the permitted daily exposure (PDE) limits for the finished products of varenicline tartrate API as well as its intermediate, though the glyoxal contents in the crude intermediates were likely to exceed the limit.The established method is valuable for the manufacturing process and quality control of varenicline tartrate.

Objective To study the injury risk and fatigue status of firefighters with different training postures under load-bearing conditions to reduce the occurrence of physical injuries and occupational diseases.Methods First,a questionnaire was administered to investigate the training injury conditions of firefighters in a fire-rescue brigade.Considering the exercise fatigue factor,which accounts for the highest proportion of injury causes,lower back analysis,static strength analysis,fatigue analysis,comfort analysis,and other human factor analysis tools in Jack software were used to analyze four common firefighter water-shooting training postures.Training postures while climbing a five-storey building with loads and a hooked ladder were also simulated.Results Injury caused by exercise fatigue accounted for 69.8%of injuries and was the most important injury-causing factor.The risk of knee and ankle joint injuries increased in all four water-shooting postures.The comfort levels of the four water-shooting postures from high to low were shoulder,standing,kneeling,and lying postures.For the entire dynamic training process,while climbing the five-storey building with loads and climbing the hooked ladder,firefighters did not have an increased risk of lower back injury but had an increased risk of ankle and knee joint injuries.Conclusions Some training postures are uncomfortable for firefighters,and they experience body discomfort during firefighting training with loads,thereby increasing injury risk.These results provide scientific references for the prevention and reduction of firefighter training injuries,and the formulation of reasonable training plans and targeted protective measures.

Objective To predict the micro-level energy release rate in the rat femoral cortical bone and investigate the variation in the micro-level energy release rate with age.Methods Based on previous experimental data and numerical simulation of fracture modes for cortical bone,load-displacement curves and fracture modes measured by simulation and experiment were compared,and the micro-level energy release rates of rat femoral cortical bone at different months were predicted by back-calculation.Results It was predicted that the micro-level energy release rate of rat femoral cortical bone at 1-,3-,5-,7-,9-,11-,and 15-month age was 0.08-0.12,0.12-0.14,0.15-0.19,0.25-0.28,0.23-0.25,0.19-0.22,and 0.13-0.16 N/mm,respectively.Conclusions The decrease in the microlevel energy release rate with increasing age led to a decreasing failure load,indicating that the microlevel energy release rate is one of the main factors determining fracture occurrence;however,no significant decrease was observed at the time of fracture,indicating that the microlevel energy release rate was not linearly proportional to the fracture time.These results can help explain the mechanism of cortical bone fractures at the clinical level.

Objective:To explore the benefits of transradial diagnostic cerebral angiography in elderly patients and its correlation with morphometric parameters of the aortic arch.Methods:Clinical data and aortic arch CTA imaging parameters of patients who underwent cerebral angiography at the Department of Neurosurgery, Beijing Hospital, between May 2022 and April 2023 were retrospectively analyzed.The study aimed to compare the time taken for angiography via radial artery access in elderly patients versus younger patients, as well as via femoral artery access, and to evaluate the associated aortic arch morphology parameters.Results:A total of 101 patients' data were analyzed, with 67 males(66.3%)and an average age of 63.4±12.0 years.Among them, 69 patients(68.3%)were aged 60 and above.The arterial approach for 44 patients(43.6%)was radial, while 57 cases(56.4%)used the femoral artery approach.In the elderly group, 14 cases(20.6%), 31 cases(45.6%), and 23 cases(33.8%)had type Ⅲ aortic arch, respectively.For younger patients, 17 cases(53.1%), 12 cases(37.5%), and 3 cases(9.4%)fell into these categories.The distribution difference was statistically significant( χ2=12.765, P=0.002).Elderly patients had a larger aortic arch width angle compared to younger patients(106°±12°and 100°±12°, t=2.334, P=0.022).The time for whole-brain angiography via radial artery was shorter for elderly patients than via femoral artery(39.8±29.5 minutes and 52.2±28.4 minutes, respectively, t=1.845, P=0.070).In young patients, there was no significant time difference between the two approaches(42.3±30.4 minutes for radial artery and 34.6±11.2 minutes for femoral artery, t=1.026, P=0.313).In the type Ⅱ aortic arch group, the average times for transradial and transfemoral approaches were 38.1±21.7 minutes and 46.7±32.2 minutes, respectively( t=1.020, P=0.314).The average times for the type Ⅲ aortic arch group were 41.9±37.3 minutes and 48.9±20.7 minutes, respectively.Correlation analysis revealed a significant negative correlation between the duration of radial artery access and the distance from the origin of the innominate artery to the left subclavian artery(Pearson correlation coefficien( r=-0.372, P=0.014). Conclusions:In elderly patients, particularly those with type Ⅱ or Ⅲ aortic arch or a wide aortic arch, diagnostic cerebral angiography using transradial access is preferable to femoral access.The distance between the innominate artery and the left subclavian artery origin could impact the duration of the procedure.

Using machine learning algorithms to construct an early prediction model of brucellosis to improve the diagnosis efficiency of Brucellosis. This study was a case-control study. 2 381 brucellosis patients from Beijing Ditan Hospital affiliated to Capital Medical University were retrospectively collected as case group, and healthy people from Beijing Chaoyang Hospital affiliated to Capital Medical University were collected as control group from May 9, 2011 to November 29, 2021. The relevant clinical information and full blood count results of 13 257 data were collected and five algorithms of machine learning were used to construct an early predication model of brucellosis by using machine learning: random forest, Naive Bayes, decision tree, logistic regression and support vector machine;14 074 data (2 143 cases incase group and 11 931 cases in control group) were used to establish the early predication model of brucellosis, and 1 564 (238 cases in case group and 1 326 cases in control group) data were used to test the predication efficiency of the brucellosis model. The results showed that the support vector machine algorithm has the best predication performance by comparing the five machine learning models. The area under receiver curve (AUC) of receiver operating characteristic (ROC) was 0.991, and the accuracy, precision, specificity and Recall were 95.6%, 95.5%, 95.4% and 95.9%, respectively. Based on the SHAP plot, platelet distribution width (PDW) and basophil relative value (BASO%) results were low, and men with high coefficient of variation (R-CV), erythrocyte hemoglobin concentration (MCHC), and platelet volume (MPV) were predicted to be at high risk of brucellosis. Platelet distribution width (PDW) contributed the most to the prediction model, followed by red blood cell distribution width coefficient of variation (R-CV). In conclusion, the establishment of a high-precision early predication method of brucellosis based on machine learning may be of great significance for the early detection and treatment of brucellosis patients.
Male ; Humans ; Retrospective Studies ; Case-Control Studies ; Bayes Theorem ; Algorithms ; Machine Learning

Using machine learning algorithms to construct an early prediction model of brucellosis to improve the diagnosis efficiency of Brucellosis. This study was a case-control study. 2 381 brucellosis patients from Beijing Ditan Hospital affiliated to Capital Medical University were retrospectively collected as case group, and healthy people from Beijing Chaoyang Hospital affiliated to Capital Medical University were collected as control group from May 9, 2011 to November 29, 2021. The relevant clinical information and full blood count results of 13 257 data were collected and five algorithms of machine learning were used to construct an early predication model of brucellosis by using machine learning: random forest, Naive Bayes, decision tree, logistic regression and support vector machine;14 074 data (2 143 cases incase group and 11 931 cases in control group) were used to establish the early predication model of brucellosis, and 1 564 (238 cases in case group and 1 326 cases in control group) data were used to test the predication efficiency of the brucellosis model. The results showed that the support vector machine algorithm has the best predication performance by comparing the five machine learning models. The area under receiver curve (AUC) of receiver operating characteristic (ROC) was 0.991, and the accuracy, precision, specificity and Recall were 95.6%, 95.5%, 95.4% and 95.9%, respectively. Based on the SHAP plot, platelet distribution width (PDW) and basophil relative value (BASO%) results were low, and men with high coefficient of variation (R-CV), erythrocyte hemoglobin concentration (MCHC), and platelet volume (MPV) were predicted to be at high risk of brucellosis. Platelet distribution width (PDW) contributed the most to the prediction model, followed by red blood cell distribution width coefficient of variation (R-CV). In conclusion, the establishment of a high-precision early predication method of brucellosis based on machine learning may be of great significance for the early detection and treatment of brucellosis patients.
Male ; Humans ; Retrospective Studies ; Case-Control Studies ; Bayes Theorem ; Algorithms ; Machine Learning

【Objective】 To develop a prognosis model based on CT images using radiomics method for patients with osteonecrosis of the femoral head (ONFH) and to investigate the additional prediction value of the imaging features of the contralateral normal femoral head regions for the prognosis prediction. 【Methods】 A total of 51 patients were included in this retrospective study. All the patients had preoperative CT images. For each patient, two regions of interest (ROIs) were involved, including the osteonecrosis region and the contralateral normal femoral head region. A total of 968 radiomics features were extracted for each patient. We made both the univariate and multivariable analyses. Three models were developed based on the features of osteonecrosis region, contralateral normal femoral head region, and both regions. The 10 times of repeated random experiments were used for model construction and validation. The average performance of the 10 times of experiments was reported as the results. 【Results】 For the features of osteonecrosis region, 37 features showed significant predictive value, with the mean AUC value of 0.708 2±0.029 9. The AUC of the constructed prediction model was 0.911 0±0.029 4 and 0.688 6±0.089 3 for the training set and validation set, respectively. For the features of contralateral normal femoral head region, 14 features showed significant predictive value, with the mean AUC value of 0.703 6±0.006 9. The AUC value of the constructed model for the training set and validation set was 0.867 2±0.039 5 and 0.669 0±0.072 6, respectively. For the models developed based on combined features, the AUC value was higher than that of the models developed based on osteonecrosis region features (training set: 0.935 8±0.016 6 vs. validation set: 0.737 9±0.090 8). 【Conclusion】 We developed a novel CT images-based radiomics method to predict postoperative prognosis in patients with ONFH. Furthermore, the features of contralateral normal femoral head region has additional prediction value. Combining the imaging features of osteonecrosis region and contralateral normal femoral head region can obtain more accurate prediction of prognosis in patients with ONFH.

The visual impairment and blindness caused by myopia have become a global burden, and the World Health Organization has included the prevention and control of myopia in the global program for preventing blindness. In China, the development of myopia is showing a trend with higher incidence, younger age, and higher refractive errors. Moving forward the port of prevention and control myopia has become an important strategy to address the current predicament. Premyopia refers to the stage in children where the refractive power is ≤+0.75 D and >-0.50 D, and there are multiple risk factors during this stage that can potentially lead to myopia. Currently, the incidence of premyopia and its transformation into myopia is high, and the key prevention and control measures include building a predictive model for the transformation of premyopia into myopia, emphasizing the reduction of exposure to risk factors, using low-concentration atropine eye drops, red light therapy, and optical defocus intervention. This article provides a comprehensive review of the current situation regarding the incidence of premyopia and its transformation into myopia, as well as the research progress on existing prevention and control measures, with the aim of providing relevant references for the prevention and control of myopia during the premyopia stage.