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.

OBJECTIVE To systematically evaluate the efficacy and safety of tislelizumab in the treatment of advanced non- small cell lung cancer （NSCLC）. METHODS Computerized searches were conducted in PubMed， Embase， the Cochrane Library， CNKI， Wanfang and other Chinese and English databases to collect randomized controlled trials （RCTs） on tislelizumab for advanced NSCLC. The search period was from the establishment of the databases to December 2024. After strictly screening the literature， extracting data and conducting quality evaluations in accordance with the inclusion and exclusion criteria， a meta-analysis was performed using RevMan 5.3 and Stata 16.0 software. RESULTS A total of 18 RCTs involving 2 337 patients were included， with 1 283 in the experimental group and 1 054 in the control group. The meta-analysis results showed that the objective response rate [RR＝1.61， 95%CI （1.48， 1.75）， P＜0.000 01]， disease control rate [RR＝1.21， 95%CI （1.13， 1.29）， P＜0.000 01]， progression free survival [HR＝0.55， 95%CI （0.45， 0.66）， P＜0.000 01]， and overall survival [HR＝0.78， 95%CI（0.62， 0.97）， P＝0.03] were significantly better in the experimental group than in the control group. There was no statistically significant difference in the incidence of adverse reactions between the two groups [RR＝1.00， 95%CI （0.73， 1.37）， P＝1.00]； among the common adverse reactions， only the incidence of liver function impairment was significantly higher in the experimental group than in the control group [RR＝1.30， 95%CI （1.10， 1.54）， P＜0.01]. CONCLUSIONS Tislelizumab in combination with chemotherapy or targeted drugs significantly improves the efficacy in patients with advanced NSCLC without increasing the risk of adverse reactions overall. However， liver function should be closely monitored during treatment.

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.

Acute pancreatitis(AP)is one of the most common clinically critical diseases,and pancreatic acinar cell death is a central event in the pathological course of AP and a key factor in determining the degree of local or systemic inflammatory damage as well as overall pancreatitis.Programmed cell death is an active and orderly way of death that is regulated by program and widely exists in the development of organisms.A variety of programmed death modes are widely involved in the damage of AP acinar cells,mainly including autophagy,pyrodeath and iron death,etc.This study systematically reviews the mechanism of programmed death in AP acinar cells.The aim is to provide a new understanding of the specific pathogenesis of AP,and to provide new ideas and strategies for the treatment of the disease.

Objective To investigate the effect of injection volume on clinical efficacy after minimally invasive puncture and drainage in the treatment of moderate basal ganglia cerebral hemorrhage underlying controlling the dosage of drugs in the elderly.Methods After successfully inserting the puncture needle into the center point in the largest layer long axis,the liquid hematoma was aspirated,and 40,000 units urokinase was injected into the hematoma cavity twice a day(2 mL in the small volume Group(SG),and 4 mL in the large volume group(LG),respectively).The needle were then adjusted and removed according to the hematoma clearance.The clearance rate of hematoma,intracranial pressure,edema volume,mortality,incidence of complications,neurological deficit score(NDS)and activities of daily living(ADL)were compared and analyzed.Results There were no significant differences in the first hematoma clearance rate,postoperative ICP and mortality between the two groups(P>0.05).Comparing to the SG,there was a relative higher postoperative hematoma clearance rate and lower volume of perihematoma edema in the LG(P<0.05).Meanwhile,the needle retention time,urokinase usage and cumulative complications at 28 days after operation were lower than in the SG(P<0.05).For two parameters representing long-term outcome,the NDS and ADL scores were much better in LG than in SG(P<0.05).Conclusion Large volume injection can effectively drain the hematoma,shorten the needle retention time,reduce the usage of urokinase,decrease the incidence of postoperative complications and improve the prognosis of patients,thereby providing effective and safe method in treating moderate basal ganglia cerebral hemorrhage in the elderly.

Objective:To investigate the clinical efficacy and safety of ixazomib-containing regimens in the treatment of patients with multiple myeloma(MM).Methods:A retrospective analysis was performed on the clinical efficacy and adverse reactions of 32 MM patients treated with a combined regimen containing ixazomib in the Hematology Department of the First People's Hospital of Lianyungang from January 2020 to February 2022.Among the 32 patients,15 patients were relapsed and refractory multiple myeloma(R/RMM)(R/RMM group),17 patients who responded to bortezomib induction therapy but converted to ixazomib-containing regimen due to adverse events(AE)or other reasons(conversion treatment group).The treatment included IPD regimen(ixazomib+pomalidomide+dexamethasone),IRD regimen(ixazomib+lenalidomide+dexamethasone),ICD regimen(ixazomib+cyclophosphamide+dexamethasone),ID regimen(ixazomib+dexamethasone).Results:Of 15 R/RMM patients,overall response rate(ORR)was 53.3％(8/15),among them,1 achieved complete response(CR),2 achieved very good partial response(VGPR)and 5 achieved partial response(PR).The ORR of the IPD,IRD,ICD and ID regimen group were 100％(3/3),42.9％(3/7),33.3％(1/3),50％(1/2),respectively,there was no statistically significant difference in ORR between four groups(x2=3.375,P=0.452).The ORR of patients was 50％after first-line therapy,42.9％after second line therapy,60％after third line therapy or more,with no statistically significant difference among them(x2=2.164,P=0.730).In conversion treatment group,ORR was 88.2％(15/17),among them,6 patients achieved CR,5 patients achieved VGPR and 4 patients achieved PR.There was no statistically significant difference in ORR between the IPD(100％,3/3),IRD(100％,6/6),ICD(100％,3/3)and ID(60％,3/5)regimen groups(x2=3.737,P=0.184).The median progression-free survival(PFS)time of R/RMM patients was 9 months(95％CI:6.6-11.4 months),the median overall survival(OS)time was 18 months(95％CI:11.8-24.4 months).The median PFS time of conversion treatment group was 15 months(95％CI:7.3-22.7 months),the median OS time not reached.A total of 10 patients suffered grade 3-4 adverse event(AE).The common hematological toxicities were leukocytopenia,anemia,thrombocytopenia.The common non-hematological toxicities were gastrointestinal symptoms(diarrhea,nausea and vomit),peripheral neuropathy,fatigue and infections.Grade 1-2 peripheral neurotoxicity occurred in 7 patients.Conclusion:The ixazomib-based chemotherapy regimens are safe and effective in R/RMM therapy,particularly for conversion patients who are effective for bortezomib therapy.The AE was manageable and safe.

Acute pancreatitis(AP)is one of the most common clinically critical diseases,and pancreatic acinar cell death is a central event in the pathological course of AP and a key factor in determining the degree of local or systemic inflammatory damage as well as overall pancreatitis.Programmed cell death is an active and orderly way of death that is regulated by program and widely exists in the development of organisms.A variety of programmed death modes are widely involved in the damage of AP acinar cells,mainly including autophagy,pyrodeath and iron death,etc.This study systematically reviews the mechanism of programmed death in AP acinar cells.The aim is to provide a new understanding of the specific pathogenesis of AP,and to provide new ideas and strategies for the treatment of the disease.

OBJECTIVE: To investigate the efficacy, prognosis and safety of decitabine combined with modified EIAG regimen in the treatment of patients with relapsed/refractory acute myeloid leukemia (AML) and high-risk myelodysplastic syndrome (MDS). METHODS: The clinical data of 44 patients with relapsed/refractory AML and high-risk MDS admitted to our hospital from January 2017 to December 2020 were analyzed retrospectively. The patients were equally divided into D-EIAG group (decitabine combined with EIAG regimen) and D-CAG group (decitabine combined with CAG regimen) according to clinical treatment regimen. The complete response (CR), CR with incomplete hematologic recover (CRi), morphologic leukemia-free state (MLFS), partial response (PR), overall response rate (ORR), modified composite complete response (mCRc), overall survival (OS) time, 1-year OS rate, myelosuppression and adverse reactions between the two groups were compared. RESULTS: In D-EIAG group, 16 patients (72.7%) achieved mCRc (CR+CRi+MLFS), 3 patients (13.6%) achieved PR, and ORR (mCRc+PR) was 86.4%. In D-CAG group, 9 patients (40.9%) achieved mCRc, 6 patients (27.3%) achieved PR, and ORR was 68.2%. Difference was observed in mCRc rate between the two groups (P=0.035), but not in ORR (P>0.05). The median OS time of D-EIAG group and D-CAG group was 20 (2-38) months and 16 (3-32) months, and 1-year OS rate was 72.7% and 59.1%, respectively. There was no significant difference in 1-year OS rate between the two groups (P>0.05). After induction chemotherapy, the median time for absolute neutrophil count recovery to 0.5×10⁹/L in D-EIAG group and D-CAG group was 14 (10-27) d and 12 (10-26) d, for platelet count recovery to 20×10⁹/L was 15 (11-28) d and 14 (11-24)d, the median red blood cell suspension transfusion volume was 8 (6-12) U and 6 (6-12) U, and the median apheresis platelet transfusion volume was 4 (2-8) U and 3 (2-6) U, respectively. There were no statistically significant differences in comparison of the above indicators between the two groups (P>0.05). The hematological adverse reactions of patients were mainly myelosuppression. Grade III-IV hematological adverse events occurred in both groups (100%), with no increase in the incidence of non-hematological toxicities such as gastrointestinal reactions or liver function damage. CONCLUSION Decitabine combined with EIAG regimen in the treatment of relapsed/refractory AML and high-risk MDS can improve remission rate, provide an opportunity for subsequent therapies, and have no increase in adverse reactions compared with D-CAG regimen.
Humans ; Decitabine/therapeutic use* ; Treatment Outcome ; Retrospective Studies ; Cytarabine ; Myelodysplastic Syndromes/drug therapy* ; Leukemia, Myeloid, Acute/drug therapy* ; Bone Marrow Diseases/drug therapy* ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use*

The modernization and development of traditional Chinese medicine has led to higher standards for the quality of traditional Chinese medicine products. The extraction process is a crucial component of traditional Chinese medicine production, and it directly impacts the final quality of the product. However, the currently relied upon methods for quality assurance of the extraction process, such as simple wet chemical analysis, have several limitations, including time consumption and labor intensity, and do not offer precise control of the extraction process. As a result, there is significant value in incorporating near-infrared spectroscopy (NIRS) in the production process of traditional Chinese medicine to improve the quality control of the final products. In this study, we focused on the extraction process of Xiao'er Xiaoji Zhike oral liquid (XXZOL), using near-infrared spectra collected by both a Fourier transform near-infrared spectrometer and a portable near-infrared spectrometer. We used the concentration of synephrine, a quality control index component specified by the pharmacopoeia, to achieve rapid and accurate detection in the extraction process. Moreover, we developed a model transfer method to facilitate the transfer of models between the two types of near-infrared spectrometers (analytical grade and portable), thus resolving the low resolution, poor performance, and insufficient prediction accuracy issues of portable instruments. Our findings enable the rapid screening and quality analysis of XXZOL onsite, which is significant for quality monitoring during the traditional Chinese medicine production process.

Ultra high performance liquid chromatography tandem linear ion trap orbitrap mass spectrometry (UHPLC-LTQ-orbitrap-MS) was applied to analyze and identify flavonoids and phenylethanoid glycosides in the Tibetan herb Lagotis brevituba Maxim. A method of data-dependent scan coupling with dynamic exclusion was developed for analyzing flavonoids and phenylethanoid glycosides under positive and negative ion mode of electrospray ionization (ESI). The compounds of Lagotis brevituba Maxim. were systematically identified through exact molecular mass, fragmentation patterns, retention time and reported references. A total of 167 compounds were detected, of which 84 were flavonoids and 83 were phenylethanoid glycosides, which greatly enriched the number and types of flavonoids and phenylethanol glycosides in Lagotis genus medicinal plants. Baohuoside Ⅰ, 4 disaccharide O-glycoside flavonoids (composed of deoxyhexose and glucuronic acid), 9 C-glycoside flavonoids, 15 tetrasaccharide phenylethanoid glycosides and 5 phenylethanoid glycosides with substituents on the β-position of the phenylethyl group were identified in Lagotis genus medicinal plants for the first time. This study provides scientific support for elucidating the material basis and improving the quality control of Lagotis brevituba Maxim.