INTRODUCTION: Trastuzumab deruxtecan (T-DXd) has revolutionised treatment for metastatic breast cancer (MBC). While effective, its high cost and toxicities, such as fatigue and nausea, pose challenges. METHOD: Medical records from the Joint Breast Cancer Registry in Singapore were used to study MBC patients treated with T-DXd (February 2021-June 2024). This study was conducted to address whether reducing dose intensity and density may have an adverse effect on treatment outcomes. RESULTS: Eighty-seven MBC patients were treated with T-DXd, with a median age of 59 years. At the time of data cutoff, 32.1% of patients were still receiving T-DXd. Over half (54%) of the patients received treatment with an initial relative dose intensity (RDI) of <;85%. Overall median real-world progression-free survival (rwPFS) was 8.1 months. rwPFS was similar between RDI groups (<85%: 8.7 months, <85%: 8.1 months, P=0.62). However, human epidermal growth receptor 2 (HER2)-positive patients showed significantly better rwPFS outcomes compared to HER2-low patients (8.8 versus 2.5 months, P<0.001). Only 16% with central nervous system (CNS) involvement had CNS progressive disease on treatment. No significant progression-free survival (PFS) differences were found between patients with or without CNS disease, regardless of RDI groups. Five patients (5.7%) developed interstitial lung disease (ILD), with 3 (3.4%) having grade 3 events. Two required high-dose steroids and none were rechallenged after ILD. There were no fatalities. CONCLUSION Our study demonstrated that reduced dose intensity and density had no significant impact on rwPFS or treatment-related toxicities. Furthermore, only 5.7% of patients developed ILD. T-Dxd provided good control of CNS disease, with 82% of patients achieving CNS disease control.
Humans ; Female ; Breast Neoplasms/mortality* ; Middle Aged ; Trastuzumab/adverse effects* ; Aged ; Adult ; Singapore/epidemiology* ; Antineoplastic Agents, Immunological/adverse effects* ; Camptothecin/adverse effects* ; Immunoconjugates/adverse effects* ; Retrospective Studies ; Progression-Free Survival ; Receptor, ErbB-2/metabolism* ; Neoplasm Metastasis ; Dose-Response Relationship, Drug ; Treatment Outcome ; Registries

Objective:To evaluate the efficacy and safety of blinatumomab in adult patients with relapsed/refractory(R/R)or measurable residual disease(MRD)positive B-cell acute lymphoblastic leukemia(B-ALL)in the real world.Methods:The clinical data of 30 B-ALL patients received at least 1 course of blinatumomab therapy in the Chinese PLA General Hospital from January 1st,2021 to December 31st,2023 were retrospectively analyzed,including pre-treatment baseline clinical feature,post-treatment complete response(CR),CR with partial hematologic recovery(CRh),CR with incomplete hematologic recovery(CRi),complete MRD response rate,MRD response rate(MRD＜10-4),overall survival(OS),and disease-free survival(DFS),as well as drug-related adverse reactions.Results:Among 5 patients who were not assessed 4 were MRD negative and 1 did not receive bone marrow biopsy.In the R/R B-ALL group(13 cases),11 patients achieved CR/CRh/CRi and 10 patients achieved complete MRD response.In MRD+group(12 cases),9 patients achieved overall MRD response and 7 patients achieved complete MRD response.The median follow-up time was 8.4(95％CI:6.3-10.4)months.The median OS was 15.5(95％CI:0.7-30.3)months in the R/R group,while not reached in the MRD+group.The median DFS of the two groups were not reached.Drug-related adverse reactions occurred in 22 patients,and pyrexia was the most common(13 cases).Grade ≥3 adverse reactions occurred in 15 patients,and neutropenia was the most common(9 cases).Cytokine release syndrome occurred in 6 patients,including 5 cases with grade 1 and 1 case with grade 3.No patients interrupted therapy or died due to drug-related adverse reactions.Conclusion:Blinatumomab is effective in the treatment of R/R or continuous MRD+B-ALL with acceptable adverse reactions.

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.

To elucidate the mechanism by which steaming affects the quality of Curcuma kwangsiensis root tubers, methods such as LSCM, RVA, dual-wavelength spectrophotometry, LF-NMR, and LC-MS were employed to qualitatively and quantitatively detect changes in starch gelatinization characteristics, water distribution, and material composition of C. kwangsiensis root tubers under different steaming durations. Based on multivariate statistical analysis, the correlation between differences in gelatinization parameters, water distribution, and terpenoid material composition was investigated. The results indicate that steaming affects both starch gelatinization and water distribution in C. kwangsiensis. During the steaming process, transformations occur between amylose and amylopectin, as well as between semi-bound water and free water. After 60 min of steaming, starch gelatinization and water distribution reached an equilibrium state. The content of amylopectin, the amylose-to-amylopectin ratio, and parameters such as gelatinization temperature, viscosity, breakdown value, and setback value were significantly correlated(P≤0.05). Additionally, the amylose-to-amylopectin ratio was significantly correlated with total free water and total water content(P≤0.05). Steaming induced differences in the material composition of C. kwangsiensis root tubers. Clustering of primary metabolites in the OPLS-DA model was distinct, while secondary metabolites were classified into 9 clusters using the K-means clustering algorithm. Differential terpenoid metabolites such as(-)-α-curcumene were significantly correlated with zerumbone, retinal, and all-trans-retinoic acid(P<0.05). Curcumenol was significantly correlated with isoalantolactone and ursolic acid(P<0.05), while all-trans-retinoic acid was significantly correlated with both zerumbone and retinal(P<0.05). Alpha-tocotrienol exhibited a significant correlation with retinal and all-trans-retinoic acid(P<0.05). Amylose was extremely significantly correlated with(-)-α-curcumene, curcumenol, zerumbone, retinal, all-trans-retinoic acid, and α-tocotrienol(P<0.05). Amylopectin was significantly correlated with zerumbone(P<0.05) and extremely significantly correlated with(-)-α-curcumene, curcumenol, zerumbone, retinal, all-trans-retinoic acid, and 9-cis-retinoic acid(P<0.01). The results provide scientific evidence for elucidating the mechanism of quality formation of steamed C. kwangsiensis root tubers as a medicinal material.
Curcuma/chemistry* ; Starch/chemistry* ; Multivariate Analysis ; Water/chemistry* ; Terpenes/analysis* ; Plant Roots/chemistry* ; Plant Tubers/chemistry* ; Drugs, Chinese Herbal/chemistry*

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 evaluate the efficacy and safety of blinatumomab in adult patients with relapsed/refractory(R/R)or measurable residual disease(MRD)positive B-cell acute lymphoblastic leukemia(B-ALL)in the real world.Methods:The clinical data of 30 B-ALL patients received at least 1 course of blinatumomab therapy in the Chinese PLA General Hospital from January 1st,2021 to December 31st,2023 were retrospectively analyzed,including pre-treatment baseline clinical feature,post-treatment complete response(CR),CR with partial hematologic recovery(CRh),CR with incomplete hematologic recovery(CRi),complete MRD response rate,MRD response rate(MRD＜10-4),overall survival(OS),and disease-free survival(DFS),as well as drug-related adverse reactions.Results:Among 5 patients who were not assessed 4 were MRD negative and 1 did not receive bone marrow biopsy.In the R/R B-ALL group(13 cases),11 patients achieved CR/CRh/CRi and 10 patients achieved complete MRD response.In MRD+group(12 cases),9 patients achieved overall MRD response and 7 patients achieved complete MRD response.The median follow-up time was 8.4(95％CI:6.3-10.4)months.The median OS was 15.5(95％CI:0.7-30.3)months in the R/R group,while not reached in the MRD+group.The median DFS of the two groups were not reached.Drug-related adverse reactions occurred in 22 patients,and pyrexia was the most common(13 cases).Grade ≥3 adverse reactions occurred in 15 patients,and neutropenia was the most common(9 cases).Cytokine release syndrome occurred in 6 patients,including 5 cases with grade 1 and 1 case with grade 3.No patients interrupted therapy or died due to drug-related adverse reactions.Conclusion:Blinatumomab is effective in the treatment of R/R or continuous MRD+B-ALL with acceptable adverse reactions.

Through a compound induction method, combined with neurobehavioral, macroscopic characterization and objective pathological evaluation indicators, a murine depression model of liver depression transforming into fire syndrome was constructed and confirmed. The model was constructed using a combination of sleep deprivation, light exposure, and alternate-day food deprivation. Evaluation was conducted at three levels: face validity, constructs validity, and predictive validity. The establishment of the liver depression transforming into fire syndrome depression model was further validated through the counterproof of traditional Chinese medicine formulas. In terms of face validity, compared to the control group, mice in the model group exhibited typical depressive symptoms in neurobehavioral assessments; the general observation of the model group mice reveals disheveled and lackluster fur, along with delayed and easily agitated responses. Additionally, there is a substantial increase in water consumption. In the sleep phase detection of mouse, the model group showed a significant increase in the proportion of time spent in the wake phase during sleep, accompanied by a significant decrease in the proportions of time spent in both non-rapid eye movement (NREM) and rapid eye movement (REM) sleep phases. There are significant differences in physiological indicators such as average blood flow velocity, blood flow rate, tongue, urine, and claw color (r values) in the internal carotid artery. Structural validity demonstrated that levels of 5-hydroxytryptamine (5-HT), dopamine (DA), and γ-aminobutyric acid (GABA) in the hippocampus of model mice decreased significantly, while acetylcholine (ACh), tryptophan (Try), and glutamic acid (Glu) levels increased significantly. Treatment with Danzhi Xiaoyao San led to varying degrees of restoration in the aforementioned neurotransmitters. In terms of predictive validity, the antidepressant paroxetine effectively ameliorated depressive behaviors in the model mice, and the classic formula Danzhi Xiaoyao San demonstrated varying degrees of improvement in depression-related indicators. In conclusion, this study has established a novel animal model of liver-stagnation with the fire depression, thereby expanding the repertoire of traditional Chinese medicine depression models. This development contributes to the diversification of melancholic syndrome models in Chinese medicine, offering a broader spectrum of options for scientific exploration, efficacy evaluation, and drug screening in the future prevention and treatment of depression with traditional Chinese medicine. Animal experiments were conducted with the approval and supervision of the Animal Ethics Committee of Jiangxi University of Traditional Chinese Medicine (ethics number: 20230313040).

Glutamate-ammonia ligase (GLUL, also known as glutamine synthetase) is a crucial enzyme that catalyzes ammonium and glutamate into glutamine in the ATP-dependent condensation. Although GLUL plays a critical role in multiple cancers, the expression and function of GLUL in gastric cancer remain unclear. In the present study, we have found that the expression level of GLUL was significantly lower in gastric cancer tissues compared with adjacent normal tissues, and correlated with N stage and TNM stage, and low GLUL expression predicted poor survival for gastric cancer patients. Knockdown of GLUL promoted the growth, migration, invasion and metastasis of gastric cancer cells in vitro and in vivo, and vice versa, which was independent of its enzyme activity. Mechanistically, GLUL competed with β-Catenin to bind to N-Cadherin, increased the stability of N-Cadherin and decreased the stability of β-Catenin by alerting their ubiquitination. Furthermore, there were lower N-Cadherin and higher β-Catenin expression levels in gastric cancer tissues compared with adjacent normal tissues. GLUL protein expression was correlated with that of N-Cadherin, and could be the independent prognostic factor in gastric cancer. Our findings reveal that GLUL stabilizes N-Cadherin by antagonizing β-Catenin to inhibit the progress of gastric cancer.

AIM To study the quassinoids and lignans from Brucea javanica (L.) Merr.and their anti-inflammatory activities.METHODS The extract was isolated and purified by silica gel,MCI,Sephadex LH-20 and HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.RESULTS Seven quassinoids were isolated and identified as bruceanic acid E (1),15-O-(3-hydroxy-3-methy1butanoyl)-brucealide (2),bruceine L (3),aglycone of yadanzioside D (4),javanicolide A (5),javanicolide C (6),bruceanic acid A (7).Four lignans were isolated and identified as cleomiscosin A (8),ceplignan (9),threo-guaiacylglycerol 8'-(4-hydroxymethyl-2-methoxyphenyl) ether (10),erythro-guaiacylglycerol 8'-(4-hydroxy-methyl-2-methoxyphenyl) ether (11).Compounds 3,8 could inhibit NO release in RAW264.7 cells.CONCLUSION Compounds 9-11 are isolated from Brucea genus for the first time.Compounds 3 and 8 have anti-inflammatory activities.

Objective:To investigate the clinical characteristics and prognosis of single center adult chronic myeloid leukemia in chronic phase(CML-CP).Methods:Clinical data of 41 adult CML-CP patients in Department of Hematology,Shanghai Fengxian District Central Hospital from January 2015 to May 2021 were retrospectively analyzed.The clinical characteristics and prognosis of patients between＜60 years group and ≥ 60 years group were compared.Results:The 41 patients included 27(65.9％)males and 14(34.1％)females.The median age of the patients was 56(19-84)years,with 22 cases(53.7％)＜60 years and 19 cases(46.3％)≥60 years.Univariate analysis indicated that the proportions of patients with comorbidities,intermediate/high-risk Sokal score,myelofibrosis,and lactate dehydrogenase ≥1 000 U/L were significantly increased in ≥60 years group compared with＜60 years group at initial diagnosis(all P＜0.05).There were no statistical differences in the distribution of sex,ELST score,white blood cell count,platelet count,peripheral blood basophil percentage,peripheral blood eosinophil percentage and bone marrow primitive cell percentage between the two groups(P＞0.05).The proportion of patients taking reduced-dose imatinib in≥60 years group significantly increased(P＜0.001).Patients＜60 years had a higher proportion of molecular biological remission after treatment of tyrosine kinase inhibitors(TKIs)than patients ≥ 60 years(P＜0.001).The incidence of non-hematologic adverse reactions to TKI therapy significantly increased in patients ≥ 60 years(P＜0.001).Multivariate analysis showed that no adverse factors affecting the efficacy and prognosis of TKI.Conclusion:Compared with adult CML-CP patients＜60 years,patients ≥ 60 years gain fewer benefits from TKI treatment and increased adverse reactions.