Basing on the machine learning model to analyse the coronary calcification score and the coronary flow reserve score to evaluate the degree of coronary artery stenosis.

AIM: To obtain the coronary artery calcium score (CACS) for each branch in coronary artery computed tomography angiography (CCTA) examination combined with the flow fraction reserve (FFR) of each branch in the coronary artery detected by CT and apply a machine learning model (ML) to analyse and predict the severity of coronary artery stenosis. METHODS: All patients who underwent coronary computed tomography angiography (CCTA) from January 2019 to April 2022 in the HOSPITAL (T.C.M) AFFILIATED TO SOUTHWEST MEDICAL UNIVERSITY) were retrospectively screened, and their sex, age, characteristics of lipid-containing lesions, coronary calcium score (CACS) and CT-FFR values were collected. Five machine learning models, random forest (RF), k-nearest neighbour algorithm (KNN), kernel logistic regression, support vector machine (SVM) and radial basis function neural network (RBFNN), were used as predictive models to evaluate the severity of coronary stenosis. RESULTS: Among the five machine learning models, the SVM model achieved the best prediction performance, and the prediction accuracy of mild stenosis was up to 90%. Second, age and male sex were important influencing factors of increasing CACS and decreasing CT-FFR. Moreover, the critical CACS value of myocardial ischemia >200.70 was calculated. CONCLUSION: Through computer machine learning model analysis, we prove the importance of CACS and FFR in predicting coronary stenosis, especially the prominent vector machine model, which promotes the application of artificial intelligence computer learning methods in the field of medical analysis.

Identification of DNA-binding proteins via Multi-view LSSVM with independence criterion.

DNA-binding proteins actively participate in life activities such as DNA replication, recombination, gene expression and regulation and play a prominent role in these processes. As DNA-binding proteins continue to be discovered and increase, it is imperative to design an efficient and accurate identification tool. Considering the time-consuming and expensive traditional experimental technology and the insufficient number of samples in the biological computing method based on structural information, we proposed a machine learning algorithm based on sequence information to identify DNA binding proteins, named multi-view Least Squares Support Vector Machine via Hilbert-Schmidt Independence Criterion (multi-view LSSVM via HSIC). This method took 6 feature sets as multi-view input and trains a single view through the LSSVM algorithm. Then, we integrated HSIC into LSSVM as a regular term to reduce the dependence between views and explored the complementary information of multiple views. Subsequently, we trained and coordinated the submodels and finally combined the submodels in the form of weights to obtain the final prediction model. On training set PDB1075, the prediction results of our model were better than those of most existing methods. Independent tests are conducted on the datasets PDB186 and PDB2272. The accuracy of the prediction results was 85.5% and 79.36%, respectively. This result exceeded the current state-of-the-art methods, which showed that the multi-view LSSVM via HSIC can be used as an efficient predictor.

Comparative study of the efficacy of dexmedetomidine and fentanyl on anxiety and pain of parturients with different COMTva1158met genotypes.

OBJECTIVE: This study investigates the effects of COMTval158met gene polymorphism on maternal anxiety and pain during delivery and on the analgesic and anxiety efficacy of dexmedetomidine during delivery. METHODS: Sixty-one pregnant women, who were hospitalized in our hospital from January to November of 2016 were recruited and randomly divided into two groups, F and D groups. The pregnant women in the F group were given labor analgesia with ropivacaine combined with fentanyl. The pregnant women in the D group were given labor analgesia with ropivacaine combined with dexmedetomidine. Before and after labor analgesia, the genotype of COMT in the blood from two groups was detected, and the situation of labor anxiety and analgesia was analyzed. Then, the relationship between labor anxiety, analgesia, and COMT polymorphism was analyzed. RESULTS: In the 61 pregnant women, there were 30 women of wild homozygotes (GG) of COMT, 22 women of mutant heterozygotes (GA), and nine women of mutant homozygotes (AA), the mutation rate of allele A was 23.77%. The anxiety status score, anxiety trait score, and pain score in the AA genotype were significantly higher than those in the GG and GA genotype (p < 0.05). There was a significant difference in the efficacy of GG and AA genotypes between groups D and F for treating labor anxiety (p < 0.05), the efficacy of group D was better than that of group F in treating delivery anxiety, there was no significant difference in anxiety scores between the two groups in GA genotypes (p > 0.05); there was no significant difference in pain between group D and F in GG, GA, and AA genotypes (p > 0.05). There was no significant difference in pain and anxiety scores between the three genotypes in group D (p > 0.05), there was significant difference in pain scores among the three genotypes in group F (p < 0.05), but there was no significant difference in anxiety (p > 0.05). CONCLUSIONS: The mutation of the COMTval158met gene leads to increased anxiety and pain during childbirth. The effect of dexmedetomidine on the anxiety of GG and AA genotypes is better than that of fentanyl, and the mutation of the COMTval158met gene has no impact on dexmedetomidine effect.

miRNA-124-3p targeting of LPIN1 attenuates inflammation and apoptosis in aged male rats cardiopulmonary bypass model of perioperative neurocognitive disorders.

Perioperative neurocognitive disorder (PND) is recently recommended to define the cognitive decrease during the perioperative period. However, the disease's underlying mechanisms remain unclear. MicroRNAs (miRNAs) are noncoding RNAs that play a vital role in regulating neuroregeneration and neuronal apoptosis. In this study, miR-124-3p was significantly reduced in the PND rat model after a cardiopulmonary bypass (CPB) procedure. MicroRNA-124 (miR-124)-3p-overexpressed lentivirus was constructed and injected via the intracerebroventricular method before CPB. Morris Water Maze test (WMW) and the Open-Field test (OFT) were used to measure behavior changes, data shows decline of cognitive function of rats after CPB. PND rats expressed higher Aß and p-Tau Protein by using immunohistochemistry (IHC) analyses and Enzyme-Linked Immune Sorbent Assay (ELISA). Moreover, the results of IHC, ELISA, Western Blot analysis (WB) and Terminal-deoxynucleotidyl Transferase Mediated Nick End Labeling Assay (TUNEL) showed CPB procedure induced inflammation and apoptosis in rats with PND. The data also revealed the protective function of miR-124-3p overexpression against PND in relieving inflammation, cell apoptosis, and alleviating repaired cognitive function. Moreover, miR-124-3p was predicted by directly targeting LPIN1. This study gives a novel viewpoint that miR-124-3p could improve the state of PND via modulating LPIN1, therefore providing a new strategy for preventing and treating PND in a preclinical application.

Hernsubanine E, a new hasubanan alkaloid from Stephania hernandifolia.

A new hasubanan alkaloid, hernsubanine E (1), as well as two known compounds p-hydroxybenzaldehyde (2) and (-)-syringaresinol (3) have been isolated from the whole plants of Stephania hernandifolia by various column chromatographic methods. Their structures were identified by physicochemical properties and spectral analyses. Compounds 2 and 3 were isolated from the genus of Stephania for the first time.

[Hasubanan type alkaloids in Stephania hernandifolia].

OBJECTIVE: To study the hasubanan type alkaloids in Stephania hernandifolia. METHOD: The dried herbs of S. hernandifolia. were extracted with 95% ethanol. After removal of the solvent, the residue was first partitioned between acid water and petroleum ether, then the aqueous layer was basified and extracted with chloroform to obtain crude alkaloids. Column chromatograghic methods with on silica gel, Rp-18, MCI CHP 20P, Sephadex LH-20 were applied for the isolation and purification of the crude alkaloid fraction. The structures were elucidated by their physicochemical properties and spectral data. RESULT: Nine hasubanan type alkaloids were obtained and identified as aknadinine(1), longanone(2), stephasunoline (3), N-methylstephuline(4), epistephamiersine(5), prostephabyssine(6), aknadilactam(7), dihydroepistephamiersine(8), hasubanonine(9). CONCLUSION: Compounds 2-8 were isolated from this plant for the first time.

[Alkaloids in stems and leaves of Stephania cepharantha].

OBJECTIVE: To study the alkaloids in the stems and leaves of Stephania cepharantha. METHOD: The dried stems and leaves of S. cepharantha were percolated with 95% ethanol and the solvent was removed by rotary evaporation to give a concentrate, and the concentrate was extracted by petroleum ether and chloroform. Column chromatograghy on MCI CHP 20P, silica gel, Rp-18, Sephadex LH-20 and polyamide were applied for the isolation and purification of the chloroform fraction. The structures were elucidated by their physicochemical properties and spectral data. RESULT: Eleven alkaloids were obtained and identified as lysicamine (1), tetrahadropalmatine (2), palmatine (3), isocorydione (4), corydalmine (5), corypalmine (6), sinoracutine (7), sinoacutine (8), cepharamine (9), isocorydine (10) and corydine (11). CONCLUSION: Compounds 2-7 were isolated from S. cepharantha for the first time, and compound 7 was isolated from the genus Stephania for the first time, compound 4 was isolated from the Menispermaceae family for the first time.

[Study on the alkaloids in the stems and leaves of Stephania cepharantha (II)].

OBJECTIVE: To study the alkaloids in the stems and leaves of Stephania cepharantha Hayata. METHODS: The dried stems and leaves of Stephania cepharantha Hayata were percolated with 95% ethanol and the solvent was removed by rotary evaporation to give a concentrate, and the concentrate was extracted by petroleum ether and chloroform. Column chromatograghy on MCI CHP 20P, silica gel, Rp-18, Sephadex LH-20 and polyamide were applied for the isolation and purification of the chloroform fraction. The structures were elucidated by their physicochemical properties and spectral data. RESULTS: Five alkaloids were obtained and identified as, Stephasunoline (I) Aknadinine (II), Discretamine (III), Acutumine (IV), Sinomenine (V). CONCLUSION: Compounds I, III, IV are isolated from this plant for the first time, and compound IV is isolated from the genus for the first time.

[Study on the alkaloids in Stephania hernandifolia].

OBJECTIVE: To study the alkaloids in Stephania hernandifolia. METHODS: The dried herbs of S. hernandifolia were extracted with 95% ethanol. After removal of the solvent, the residue was first partitioned between acid water and petroleum ether, then the aqueous layer was basified and extracted with chloroform to obtain crude alkaloids. Column chromatography on silica gel, Rp-18, MCI CHP 20P, Sephadex LH-20 were applied for the isolation and purification of the crude alkaloids fraction. The structures were elucidated by their physicochemical properties and spectral data. RESULTS: Six alkaloids were obtained and identified as Cepharamine (I), l-tetrahadropalmatine (II), Plmatine (III), Stephamiersine (IV), Telitoxine (V), Daurioxoisoporphine D (VI). CONCLUSIONS: Compounds I - VI are isolated from this plant for the first time, and compounds V, VI are isolated from the plants of Stephania for the first time.