OBJECTIVETo study the effect of the volatile oil from nutmeg on liver microsomal cytochrome P450 in mice.

METHODMice were administered the volatile oil from nutmeg at 0.4, 0.8 and 1.2 mg x g(-1), respectively, twice a day for 10 days. And then, the contents of liver microsomal cytochrome P450 (CYP), cytochrome b5 (Cytb5), MDA and GST in serum were examined by UV chromatography method.

RESULTThe contents of liver CYP, Cytb5 and GST in serum were increased significantly (P < 0.01) and the contents of MDA was reduced significantly (P < 0.01).

CONCLUSIONThe volatile oil from nutmeg showed induction effect on the hepatic microsomal CYP in mice.

Animals ; Cytochrome P-450 Enzyme System ; metabolism ; Cytochromes b5 ; metabolism ; Drugs, Chinese Herbal ; pharmacology ; Female ; Gene Expression Regulation, Enzymologic ; Male ; Mice ; Microsomes, Liver ; drug effects ; enzymology ; Myristica fragrans ; chemistry ; Oils, Volatile ; pharmacology ; Plant Oils ; pharmacology

Calcium-binding protein is an indispensable protein which performs extensive and important functions in the growth of Schistosoma japonicum. Based on our primary study on tegument surface proteins of S. japonicun, a cDNA encoding a 66 kDa calcium-binding protein of S. japonicum (Chinese strain) was cloned, sequence analysis revealed that it was identical with that of SjIrV1 of Philippines strains S. japonicum. The expression of SjIrV1 were detected by Real-time PCR, using cDNA templates isolated from 7, 14, 21, 28, 35 and 42 days worms and the results revealed that the gene was expressed in all investigated stages, and the mRNA level of SjIrV1 is much higher in 42 d female worms than that in 42 d male worms. The cDNA containing the open reading frame of IrV1 was subcloned into a pET28a (+) vector and transformed into competent Escherichia coli BL21 for expression. The recombinant protein was purified using a Ni-NTA purification system, and confirmed by high performance liquid chromatography (RP-HPLC) and tandem mass spectrometry (MS/MS). Western blotting analysis showed that recombinant SjIrV1 (rSjIrV1) could be recognized by the S. japonicum infected mouse serum and the mouse serum specific to rSjIrV1, respectively. Immunofluorescence observation exhibited that SjIrV1 was mainly distributed on the tegument of the 35-day adult worms. ELISA test revealed that IgG, IgG1 and IgG2a antibodies are significantly increased in the serum of rSjIrV1 vaccinated mice. The study suggested that rSjIrV1 might play an important role in the development of S. japonicum.
Animals ; Antibodies, Helminth ; blood ; Calcium-Binding Proteins ; genetics ; metabolism ; Cloning, Molecular ; Escherichia coli ; metabolism ; Female ; Genetic Vectors ; Helminth Proteins ; genetics ; metabolism ; Male ; Mice ; Recombinant Proteins ; genetics ; metabolism ; Schistosoma japonicum ; genetics ; metabolism

Objective:To evaluate the incidence of malnutrition in hospitalized patients with liver cirrhosis using the Global Leadership Initiative on Malnutrition (GLIM) criteria, and to identify the risk factors of malnutrition.Methods:A total of 305 inpatients with liver cirrhosis were included from the Department of Gastroenterology of the People's Hospital of Xiangxi Tujia and Miao Autonomous Prefecture between January 1, 2023 to September 30, 2023. The patient characteristics, clinical indicators, complications, unplanned readmission within 90 days and death outcomes were collected. Malnutrition was diagnosed according to the GLIM criteria, the differences in various indicators were compared between the malnutrition group and the well-nourished group, and the influencing factors of malnutrition were analyzed. The relationship between malnutrition and short-term prognosis was also discussed.Results:Among the enrolled patients, 188 (61.6%) were diagnosed with malnutrition per GLIM criteria, of whom 18 were with Child-Turcotte-Pugh (CTP) Grade A liver function, 108 Grade B, and 62 Grade C. Compared with the well-nourished group, the malnutrition group showed prolonged length of hospital stay ( P<0.001), lower body mass index P=0.003), lower levels of red blood cell count, hemoglobin, serum albumin, serum albumin/globulin ratio, and serum calcium ( P<0.001), higher level of aspartate aminotransferase ( P=0.025), and increased levels of C-reactive protein, total bilirubin, international normalized ratio, plasma prothrombin time and model for end-stage liver disease score ( P<0.001). The proportion of patients with ascites, esophageal gastric varices, upper gastrointestinal bleeding, hepatic encephalopathy and unplanned readmission within 90 days were significantly increased ( P<0.001) in malnutrition group, and the mortality within 90 days was also higher ( P=0.042). Logistic regression analysis identified lower body mass index level ( OR=0.910, P=0.038), ascites ( OR=0.065, P<0.001) and upper gastrointestinal bleeding ( OR=0.184, P=0.001) as the influencing factors of malnutrition in liver cirrhosis patients. Conclusions:The prevalent malnutrition in patients with liver cirrhosis may affect the short-term prognosis. Lower body mass index, ascites and upper gastrointestinal bleeding are influencing factors for malnutrition in liver cirrhosis patients. Therefore, patients with liver cirrhosis should be screened for malnutrition to allow timely nutritional intervention measures.

Background Diagnosis of pneumoconiosis by radiologist reading chest X-ray images is affected by many factors and is prone to misdiagnosis/missed diagnosis. With the rapid development of artificial intelligence in the field of medical imaging, whether artificial intelligence can be used to read images of pneumoconiosis deserves consideration. Objective Three deep learning models for identifying presence of pneumoconiosis were constructed based on deep convolutional neural network. An optimal model was selected by comparing diagnostic efficiency of the three models. Methods Digital radiography (DR) chest images were collected between June 2017 and December 2020 from 7 hospitals and standard radiograph quality control protocol was also followed. The DR chest images with positive results were classified into the positive group, while those without pneumoconiosis were classified into the negative group. The collected chest radiographs were labeled by experts who had passed the assessment of reading radiographs,and the experts were constantly assessed for consistency in the labeling process based on an expectation-maximization algorithm. The labeled data were cleaned, archived, and preprocessed, and then were grouped into a training set and a verification set. Three deep convolutional neural network models TMNet, ResNet-50, and ResNeXt-50 were constructed and trained by ten-fold cross-validation method to obtain an optimal model. Five hundred cases of DR chest radiographs that were not included in the training set and the validation set were collected, and identified by five senior experts as the gold standard, named the test set. The accuracy rate, sensitivity, specificity, area under curve (AUC), and other indexes of the three models were derived after testing, and the efficiency of the three models was evaluated and compared. Results A total of 24867 DR chest radiographs of the training set and the validation set were collected in this study, including 6978 images in the positive group and 17889 images in the negative group. There were 312 cases of pulmonary abnormalities such as pneumothorax and pulmonary tuberculosis. A total of nine experts labeled the chest radiographs, the labeling consistency rate of pneumoconiosis (non-staging) was above 88%, and the labeling consistency rate of pneumoconiosis staging ranged from 84.68% to 93.66%. The diagnostic accuracy, sensitivity, specificity, and AUC of TMNet were 95.20%, 99.66%, 88.61%, and 0.987, respectively. The indicators of ResNeXt were 87.00%, 89.93%, 82.67%, and 0.911, respectively. Those of ResNet were 84.00%, 85.91%, 81.19%, and 0.912, respectively. All these indexes of TMNet were higher than those of ResNeXt-50 and ResNet-50 models. The AUC differences between TMNet and the other two models were both statistically significant (P<0.001). Conclusion All the three convolutional neural network models can effectively diagnose the presence of pneumoconiosis, among which TMNet provides the best efficiency.

Based on the correlation between Qi and blood in traditional Chinese medicine， the collateral disease theory puts forward that the Qi-collateral go hand in hand with the vessel-collateral of the brain， and to be as close as lips to teeth in structure and function， which is an important basis for the function of brain governing mind. And this theory proposes that deficiency/stagnancy of collateral-Qi， stagnation of collaterals and loss of consciousness are the main pathogenesis of Alzheimer's disease（AD）， which is different from the research strategy of modern medicine focusing on neurons. It is suggested that it is necessary to treat AD from two aspects， including neuronal protection（elimination of pathological products such as β-amyloid and phosphorylated tau protein） and cerebral microvascular protection（protection of cerebral microvascular structure and function， promotion of therapeutic angiogenesis and increase of cerebral blood flow. Tongxinluo capsules is a representative drug for dredging collaterals developed under the guidance of the therapeutic principle of collaterals need circulation， it can protect microvessels and play a neuroprotective role mediated by vascular protection. Clinical studies have confirmed that Tongxinluo capsules can effectively treat AD， vascular dementia and cognitive impairment related diseases， which can provide new ideas and effective treatment ways to prevent and treat AD from neurovascular protection in a comprehensive manner.