Objective To systematically evaluate the effects of total intravenous anesthesia and inhalational anesthesia on postoperative recovery in patients undergoing transsphenoidal pituitary tumor resection.Methods A comprehensive search was conducted in international biomedical databases including Ovid Medline,Embase,CINAHL(EBSCO),Cochrane Library,and Web of Science,from inception to July 4,2023.Additionally,ClinicalTrials.gov and the World Health Organization International Clinical Trials Registry Platform were searched for ongoing and completed trials.The randomized controlled trials(RCT)comparing total intravenous anesthesia and inhalational anesthesia in patients undergoing transsphenoidal surgery for pituitary tumors were included.The methodological quality of the included studies was evaluated by the Cochrane Collaboration tool.Relevant data were extracted and synthesized for analysis.Results A total of 327 records were identified,of which eight RCTs met the inclusion criteria.Four studies showed that the patients receiving desflurane or sevoflurane anesthesia experienced faster emergence from anesthesia than those receiving propofol.Two studies indicated that patients in the propofol group had lower levels of emergence agitation and a lower incidence of early postoperative nausea and vomiting.The results on postoperative cognitive function were inconsistent across studies.No differences were found between the groups in terms of postoperative complications or overall recovery quality during hospitalization.Conclusions Inhalational anesthesia appears to provide an advantage in promoting faster emergence following transsphenoidal pituitary surgery,whereas total intravenous anesthesia may contribute to smoother and more stable recovery.Further high-quality studies are needed to clarify the effects of different anesthetic techniques on both short- and long-term postoperative recovery.
Humans ; Anesthesia, Intravenous ; Pituitary Neoplasms/surgery* ; Anesthesia, Inhalation ; Randomized Controlled Trials as Topic ; Anesthesia Recovery Period ; Pituitary Gland/surgery* ; Postoperative Period

AIM To study the chemical constituents from the water fraction of rhizoma of Smilax trinervula Miq.and their biological activities.METHODS Polyamide,silica gel,Sephadex LH-20,ODS and semi-preparative HPLC were used for isolation and purification,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The antitumor activities were determined by MTT mothod,and the inhibitory activities on α-glucosidase were determined by PNPG method.RESULTS Eleven compounds were isolated and identified as tyrosine(1),uridine(2),2-(2',3',4'-trihydroxybutyl)-6-(2",3",4"-trihydroxybutyl)-pyrazine(3),2-(1',2',3',4'-tetrahydroxybutyl)-6-(2",3",4"-trihydroxybutyl)-pyrazine(4),2-(1',2',3',4'-tetrahydroxybutyl)-5-(2",3",4"-trihydroxybutyl)-pyrazine(5),uracil(6),2-(1',2',3',4'-tetrahydroxybutyl)-5-(1",2",3",4"-tetrahydroxybutyl)-pyrazine(7),dioscin(8),shikimic acid(9),pyrazine(10),3,4-dihydroxyphenyethyl alcohol 8-O-β-D-glycopyranoside(11).The IC50 values of compounds 8 to human breast cancer cell MCF-7 was(2.36±0.26)μg/mL,and the IC50 values of compounds 3-5 and 7 to α-glucosidase were(1.54±0.15)-(10.53±0.38)μg/mL.CONCLUSION Compounds 1-7,10 are isolated from Smilax genus for the first time,and compound 9,11 are first isolated from this plant.Compound 8 has anti-tumor activity,and compounds 3-5,7 have α-glucosidase inhibitory activities.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Traditional scientific research management suffers from problems such as scattered archives,lack of effective dynamic process supervision for projects,weak data analysis capabilities,and low sharing,making it difficult to meet the unified management of multiple hospital areas in medical groups and the independent management of each branch.The group has devel-oped an intelligent scientific research management system based on artificial big data by constructing the framework and designing the content of various demand modules for scientific research management.This system achieves full coverage management of sci-entific research activities,designing interrelated information data networks for scientific research project process management,a-chievement management,academic(specialized)construction management,postdoctoral management,etc.,realizing informa-tionization and refined management of multiple campuses within the group.Analyze the technological values of each hospital,de-partment,and personnel to provide powerful reference for decision-makers.The information-based scientific research manage-ment system is an important guarantee for hospital groups to intelligently manage and efficiently carry out scientific research work.

AIM To study the chemical constituents from the water fraction of rhizoma of Smilax trinervula Miq.and their biological activities.METHODS Polyamide,silica gel,Sephadex LH-20,ODS and semi-preparative HPLC were used for isolation and purification,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The antitumor activities were determined by MTT mothod,and the inhibitory activities on α-glucosidase were determined by PNPG method.RESULTS Eleven compounds were isolated and identified as tyrosine(1),uridine(2),2-(2',3',4'-trihydroxybutyl)-6-(2",3",4"-trihydroxybutyl)-pyrazine(3),2-(1',2',3',4'-tetrahydroxybutyl)-6-(2",3",4"-trihydroxybutyl)-pyrazine(4),2-(1',2',3',4'-tetrahydroxybutyl)-5-(2",3",4"-trihydroxybutyl)-pyrazine(5),uracil(6),2-(1',2',3',4'-tetrahydroxybutyl)-5-(1",2",3",4"-tetrahydroxybutyl)-pyrazine(7),dioscin(8),shikimic acid(9),pyrazine(10),3,4-dihydroxyphenyethyl alcohol 8-O-β-D-glycopyranoside(11).The IC50 values of compounds 8 to human breast cancer cell MCF-7 was(2.36±0.26)μg/mL,and the IC50 values of compounds 3-5 and 7 to α-glucosidase were(1.54±0.15)-(10.53±0.38)μg/mL.CONCLUSION Compounds 1-7,10 are isolated from Smilax genus for the first time,and compound 9,11 are first isolated from this plant.Compound 8 has anti-tumor activity,and compounds 3-5,7 have α-glucosidase inhibitory activities.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Traditional scientific research management suffers from problems such as scattered archives,lack of effective dynamic process supervision for projects,weak data analysis capabilities,and low sharing,making it difficult to meet the unified management of multiple hospital areas in medical groups and the independent management of each branch.The group has devel-oped an intelligent scientific research management system based on artificial big data by constructing the framework and designing the content of various demand modules for scientific research management.This system achieves full coverage management of sci-entific research activities,designing interrelated information data networks for scientific research project process management,a-chievement management,academic(specialized)construction management,postdoctoral management,etc.,realizing informa-tionization and refined management of multiple campuses within the group.Analyze the technological values of each hospital,de-partment,and personnel to provide powerful reference for decision-makers.The information-based scientific research manage-ment system is an important guarantee for hospital groups to intelligently manage and efficiently carry out scientific research work.

Objective To understand the respiratory protection competency of staff in hospitals.Methods Staff from six hospitals of different levels and characteristics in Beijing were selected,including doctors,nurses,medical technicians,and servicers,to conduct knowledge assessment on respiratory protection competency.According to exposure risks of respiratory infectious diseases,based on actual cases and daily work scenarios,content of respira-tory protection competency assessment was designed from three aspects:identification of respiratory infectious di-seases,transmission routes and corresponding protection requirements,as well as correct selection and use of masks.The assessment included 6,6,and 8 knowledge points respectively,with 20 knowledge points in total,all of which were choice questions.For multiple-choice questions,full marks,partial marks,and no mark were given respective-ly if all options were correct,partial options were correct and without incorrect options,and partial options were correct but with incorrect options.Difficulty and discrimination analyses on question of each knowledge point was conducted based on classical test theory.Results The respiratory protection competency knowledge assessment for 326 staff members at different risk levels in 6 hospitals showed that concerning the 20 knowledge points,more than 60％participants got full marks for 6 points,while the proportion of full marks for other questions was relatively low.Less than 10％participants got full marks for the following 5 knowledge points:types of airborne diseases,types of droplet-borne diseases,conventional measures for the prevention and control of healthcare-associated infec-tion with respiratory infectious diseases,indications for wearing respirators,and indications for wearing medical protective masks.Among the 20 knowledge questions,5,1,and 14 questions were relatively easy,medium,and difficult,respectively;6,1,4,and 9 questions were with discrimination levels of ≥0.4,0.30-0.39,0.20-0.29,and ≤0.19,respectively.Conclusion There is still much room for hospital staff to improve their respiratory protection competency,especially in the recognition of diseases with different transmission routes and the indications for wearing different types of masks.

Objective To investigate the effects of intrauterine perfusion with granulocyte colony-stimulating factor(G-CSF)on the endometrial thickness,volume,and blood flow parameters of patients with thin endometrium and their clinical outcomes.Methods We designed a prospective non-randomized synchronous controlled trial and recruited patients with thin endometrium who underwent frozen-thawed embryo transfer(FET)at Mianyang Central Hospital between September 1,2021 and September 1,2023.They were divided into two groups,an experimental group of patients who received the experimental treatment of intrauterine perfusion with G-CSF and a control group of patients who did not receive the experimental treatment.The general data and the clinical outcomes of the two groups were analyzed and compared.The endometrial thickness,volume and blood flow parameters of patients in the experimental group before and after intrauterine perfusion with G-CSF were analyzed.Results The clinical data of 83 patients were included in the study.The experimental group included 51 cases,while the control group included 31 cases.There were no significant differences in the baseline data between the two groups.The clinical pregnancy rate of the experimental group(56.86% )was higher than that of the control group(50.00% )and the rate of spontaneous abortion in the experimental group(27.59% )was lower than that in the control group(37.50% ),but the differences were not statistically significant(P>0.05).In the experimental group,the postperfusion endometrial thickness([0.67±0.1]cm)was greater than the preperfusion endometrial thickness([0.59±0.09]cm),the postperfusion([1.84±0.81]cm3)was greater than the preperfusion endometrial volume([1.54±0.69]cm3),and the postperfusion vascularization flow index(VFI)(1.97±2.82)was greater than the preperfusion VFI(0.99±1.04),with all the differences being statistically significant(P<0.05).Conclusion Intrauterine perfusion with G-CSF can enhance the endometrial thickness,volume,and some blood flow parameters in patients with thin endometrium.

The purpose of this study is to investigate whether chrysin reduces cerebral ischemia-reperfusion injury(CIRI) by inhi-biting ferroptosis in rats. Male SD rats were randomly divided into a sham group, a model group, high-, medium-, and low-dose chrysin groups(200, 100, and 50 mg·kg~(-1)), and a positive drug group(Ginaton, 21.6 mg·kg~(-1)). The CIRI model was induced in rats by transient middle cerebral artery occlusion(tMCAO). The indexes were evaluated and the samples were taken 24 h after the operation. The neurological deficit score was used to detect neurological function. The 2,3,5-triphenyl tetrazolium chloride(TTC) staining was used to detect the cerebral infarction area. Hematoxylin-eosin(HE) staining and Nissl staining were used to observe the morphological structure of brain tissues. Prussian blue staining was used to observe the iron accumulation in the brain. Total iron, lipid pero-xide, and malondialdehyde in serum and brain tissues were detected by biochemical reagents. Real-time quantitative polymerase chain reaction(RT-qPCR), immunohistochemistry, and Western blot were used to detect mRNA and protein expression of solute carrier fa-mily 7 member 11(SLC7A11), transferrin receptor 1(TFR1), glutathione peroxidase 4(GPX4), acyl-CoA synthetase long chain family member 4(ACSL4), and prostaglandin-endoperoxide synthase 2(PTGS2) in brain tissues. Compared with the model group, the groups with drug intervention showed restored neurological function, decreased cerebral infarction rate, and alleviated pathological changes. The low-dose chrysin group was selected as the optimal dosing group. Compared with the model group, the chrysin groups showed reduced content of total iron, lipid peroxide, and malondialdehyde in brain tissues and serum, increased mRNA and protein expression levels of SLC7A11 and GPX4, and decreased mRNA and protein expression levels of TFR1, PTGS2, and ACSL4. Chrysin may regulate iron metabolism via regulating the related targets of ferroptosis and inhibit neuronal ferroptosis induced by CIRI.
Rats ; Male ; Animals ; Rats, Sprague-Dawley ; Ferroptosis ; Signal Transduction ; Brain Ischemia/metabolism* ; Cyclooxygenase 2/metabolism* ; RNA, Messenger ; Cerebral Infarction ; Reperfusion Injury/metabolism* ; Malondialdehyde ; Infarction, Middle Cerebral Artery