OBJECTIVETo investigate the effects of carbon disulfide (CS(2)) inhalation on the lipid levels of ApoE knockout gene mice and C57BL/6J mice.

METHODSFifty-one male ApoE gene knockout mice were randomly divided into four groups: CS(2)-exposed normal diet group, CS(2)-unexposed normal diet group, CS(2)-exposed high-fat diet group, and CS(2)-unexposed high-fat diet group. Fifty male C57BL/6J mice were divided into four groups in the same way. The exposed groups received 1000 mg/m3 CS(2) by static inhalation (5h/d, 5d/w) for four weeks. The weight of each mouse was determined and recorded once a week. On the 14th day of exposure, six mice in each group were randomly selected to measure serum total cholesterol (TC) levels. On the 28th day of exposure, the serum levels of TC and low-density lipoprotein (LDL) in the remaining mice were measured.

RESULTSThe mean weight gain of exposed groups was less than that of the unexposed groups. On the 14th and 28th days of experiment, the TC levels of the CS2-exposed high-fat diet group were significantly higher than those of the CS(2)-unexposed high-fat diet group among ApoE knockout gene mice (P < 0.01 for both). On the 14th day of experiment, the TC levels of the CS(2)-unexposed high-fat diet group were significantly higher than those of the CS(2)-unexposed normal-diet group among C57BL/6J mice group (P < 0.05). On the 28th day of experiment, the LDL levels of the CS(2)-exposed high-fat diet group were significantly higher than those of the CS(2)-unexposed high-fat diet group among ApoE knockout gene mice (P = 0.003).

CONCLUSIONCS(2) exposure, high-fat diet, and ApoE gene knockout can elevate blood lipids in mice, thus increasing the risk of atherosclerosis.

Administration, Inhalation ; Animals ; Apolipoproteins E ; genetics ; Atherosclerosis ; Body Weight ; Carbon Disulfide ; toxicity ; Diet, High-Fat ; adverse effects ; Gene Knockout Techniques ; Lipid Metabolism ; drug effects ; Lipids ; blood ; Lipoproteins, LDL ; Male ; Mice ; Mice, Inbred C57BL ; Mice, Knockout

OBJECTIVETo study the influences of carbon disulfide (CS2) exposure on fatty acid metabolism in apolipoprotein E (ApoE) knockout mice and C57BL/6J mice.

METHODSTwenty-four male ApoE knockout mice were randomly and equally divided into four groups: a CS2-exposed normal diet group, a CS2-unexposed normal diet group, a CS2-exposed high-fat diet group, and a CS2-unexposed high-fat diet group. Twenty-four C57BL/6J male mice were divided into four groups in the same way. The CS2-exposed groups were exposed to CS2 (1 g/m(3)) by static inhalation for 5 hours a day, 5 days a week. After two weeks, the whole blood of mice was collected. Methyl ester derivatization of fatty acids was performed using an acid-catalyzed method. Fatty acid contents before and after exposure were compared by gas chromatography-mass spectroscopy.

RESULTSThere were significant differences in fatty acid contents of mice between the four groups. For the C57BL/6J mice, the arachidic acid contents in the CS2-exposed high-fat diet group were significantly lower than those in the CS2-unexposed high-fat diet group (P = 0.045 0). For the ApoE knockout mice, the arachidonic acid contents in the CS2-exposed normal diet group were significantly lower than those in the CS2-unexposed control diet group (P = 0.045 2). For the ApoE knockout mice, the γ-linolenic acid contents in the CS2-exposed high-fat diet group were significantly higher than those in the unexposed high-fat diet group (P = 0.044 7).

CONCLUSIONExposure to CS2 can induce fatty acid metabolism disorder in mice, indicating that CS2 may increase the risk of atherosclerosis and other cardiovascular diseases.

Administration, Inhalation ; Animals ; Apolipoproteins E ; genetics ; Atherosclerosis ; Carbon Disulfide ; toxicity ; Diet, High-Fat ; Fatty Acids ; chemistry ; Lipid Metabolism ; drug effects ; Male ; Mice ; Mice, Inbred C57BL ; Mice, Knockout

Objective:To investigate the application value of joint friction sounds in diagnosing meniscus injury of the knee based on machine learning models.Methods:A case-control study was conducted to analyze the clinical data of 17 patients with meniscus injury of the knee (meniscus injury group) admitted to Sir Run Run Shaw Hospital Affiliated to Nanjing Medical University from August 2020 to October 2022, as well as 75 recruited healthy subjects without knee joint diseases (healthy group). The knee joint friction sounds of the subjects were collected in a relatively quiet environment (peak value below 40 dB). The sounds collected in a flexion-extension-flexion mode of exercise were split and divided randomly with a ratio of 4∶1 into the training set (125 segments from the meniscal injury group and 187 segments from the healthy group) and the test set (33 segments from the meniscal injury group and 47 segments from the healthy group). The sounds obtained in a sit-stand-sit mode of exercise were split and divided randomly with a ratio of 4∶1 into the training set (81 segments from the meniscal injury group and 164 segments from the healthy group) and the test set (20 segments from the meniscal injury group and 40 segments from the healthy group). Four machine learning models were built, including support vector machine with linear kernels, radial basis function support vector machine, random forest, and extremely randomized trees. The learning training of the model was performed on the training set, and its model performance was verified with the test set. The time required in a single collection of joint friction sound from the subjects and the interpretation of data analysis was recorded. Knee function of the subjects were scored according to the Lysholm Score before and at 1 day after the test. The accuracy rates of diagnosis of meniscus injury with friction sounds under the two modes of exercise were compared based on the test results to yield an optimal one. The effectiveness of the four models was compared to find the best machine learning model fitting the data frame of this study according to the test results such as accuracy, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC) obtained with the optimal mode of exercise. The diagnostic accuracy, misdiagnosis rate and missed diagnosis rate of joint friction sound for meniscal injury under the optimal machine learning model with the optimal mode of exercise were observed.Results:The time required in a single collection of joint friction sound ranged from 5 to 10 minutes [(7.1±1.3)minutes], when the time required for interpretation of data analysis was approximately 1 minute. The Lysholm Score before and after the test was (75.6±4.0)points and (77.7±3.7)points respectively in the meniscal injury group ( P>0.05), and (99.6±0.9)points and (99.5±1.0)points respectively in the healthy group ( P>0.05). The diagnosing accuracy rates for flexion-extension-flexion of exercise and sit-stand-sit modes of exercise were 0.775 and 0.817 under the support vector machine model with linear kernels; 0.813 and 0.900 under the radial basis function support vector machine model; 0.800 and 0.867 under the random forest model; 0.800 and 0.900 under the extremely randomized tree model. The accuracy rates for sit-stand-sit mode of exercise were all higher than those for flexion-extension-flexion mode of exercise. In the sit-stand-sit mode of exercise, the extremely randomized tree model had an accuracy rate of 0.900, sensitivity of 0.900, specificity of 0.950, F1 score of 0.900, and AUC of 0.942, which were higher than those under the remaining 3 models, showing better machine learning efficacy. Under the extremely randomized tree model in the sit-stand-sit mode of exercise, 22 (18 true positive and 4 false positive) were diagnosed as meniscal injury and 38 (36 true negative and 2 false negative) as healthy out of 60 segments in the test set (20 from the meniscal injury group and 40 from the healthy group). The diagnostic accuracy of joint friction sounds in diagnosing meniscus injury of the knee was 0.900, with the misdiagnosis rate of 0.100 and the missed diagnosis rate of 0.100. Conclusion:Diagnosis of meniscus injury of the knee with joint friction sounds can shorten time and enhance safety during the examination process. The diagnostic model using machine learning-based artificial intelligence is faster and more stable, which can be used as a diagnostic marker for such injury.