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Exclusive Hormonal Therapy in Breast Cancer in Elderly Women:clinical response and Survival over 10 Years

Journal of Pharmacy and Pharmacology 2021年第9期9卷 296-302页

作者： Bintcha Walter Schmitt Andy Dangbemey Patrice Tadrist Brahim Minsili Hervé Adje Yves Bagnan Tonato Unitéde gynécologie et Obstétrique/FSS-universitéd’Abomey Calavi CotonouBP 188Bénin Facultédes sciences médicales et paramédicales-Aix-Marseille université 13284 Marseille Cedex 07France Centre hospitalier Intercommunal Aix-Pertuis 13616 Aix en Provence Cedex 01France Département de santépublique/FSS-universitéd’Abomey Calavi CotonouBP 188Bénin

Breast cancer is the first cancer of women in the world and in *** very elderly patients,the treatment options are often very *** hormone therapy has shown similar or even better results than *** is why we decided to evaluate the clinical response following exclusive hormonal therapy and the 5,then 10 years survival in these very elderly *** was a retrospective,longitudinal cohort-type study with descriptive and analytical *** study population consisted of 59 patients,with an average age of 85 ***(15,25%)of our patients had a complete clinical response after two years of treatment,16(27%)a partial clinical response,23(39%)lesion stabilization and 5(9%)cancer *** presence of metastasis at diagnosis increased the risk of cancer progression by *** 5-year survival was 72.5%,and breast cancer mortality 5.88%.The 10-year survival was 27.5%and breast cancer mortality 15%.In the age group 85 and over increased the risk of death by 3.25 in the first 10 years of *** clinical response after 2 years was marked by a low rate of cancer *** over 5 and 10 years was mostly related to patient comorbidities.

关键词： Exclusive hormonal therapy neoadjuvant hormonal therapy elderly women breast cancer clinical response cancer survival cancer mortality cancer France

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Early prediction of clinical response to etanercept treatment in juvenile idiopathic arthritis using machine learning

Early prediction of clinical response to etanercept treatmen...

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2021第26届广东省药师周大会

作者： Xiaolan Mo Xiujuan Chen Chifong Ieong Song Zhang Huiyi Li Jiali Li Guohao Lin Guangchao Sun Fan He Yanling He Ying Xie Ping Zeng Yilu Chen Huiying Liang Huasong Zeng Department of Pharmacy Guangzhou Women and Children's Medical Center Guangzhou Medical University Institute of clinical Pharmacology School of Pharmaceutical Sciences Sun Yat-sen University Institute of Pediatrics Guangzhou Women and Children's Medical Center Guangzhou Medical University Pediatric Allergy Immunology & Rheumatology Department Guangzhou Women and Children's Medical Center Guangzhou Medical University Department of Pharmacy Guangzhou institute of dermatology Department of Pharmacy Sun Yat-sen Memorial Hospital Sun Yat-sen University

Background and Aims: At present, there is a lack of simple and reliable model for early prediction of the efficacy of etanercept in the treatment of juvenile idiopathic arthritis(JIA). This study aimed to generate and validate prediction models of etanercept efficacy in patients with JIA before administration using machine learning algorithms based on electronic medical record(EMR).Materials and Methods: EMR data of 87 JIA patients treated with etanercept between January2011 and December 2018 were collected retrospectively. The response of etanercept was evaluated by using DAS44/ESR-3 simplified standard. The stepwise forward and backward method based on information gain was applied to select features. Five machine learning algorithms, including Extreme Gradient Boosting(XGBoost), Random Forest(RF), Gradient Boosting Decision Tree(GBDT), Extremely Random Trees(ET) and Logistic Regression(LR)were used for model generation and validation with fifty-fold stratified cross-validation. EMR data of additional 14 patients were collected for external validation of the ***: Tender joint count(TJC), Time interval, Lymphocyte percentage(LYM), and Weight were screened out and included in the final model. The model generated by the XGBoost algorithm based on the above 4 features had the best predictive performance: sensitivity 75%,specificity 66.67%, accuracy 72.22%, AUC 79.17%, ***: A pre-administration model with good prediction performance for etanercept response in JIA was developed using advanced machine learning algorithms. Clinicians and pharmacists can use this simple and accurate model to predict etanercept response of JIA early and avoid treatment failure or adverse effects.

关键词： etanercept juvenile idiopathic arthritis machine learning prediction models clinical response

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Effi cacy of tricyclic antidepressants in irritable bowel syndrome:A meta-analysis

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World Journal of Gastroenterology 2009年第13期15卷 1548-1553页

作者： Roja Rahimi Shekoufeh Nikfar Ali Rezaie Mohammad Abdollahi Pharmaceutical Sciences Research Center Tehran University of Medical Sciences Food & Drug Affairs and Food & Drug Laboratory Research CenterMinistry of Health & Medical Education Faculty of Medicine University of Alberta Faculty of Pharmacy and Pharmaceutical Sciences Research CenterTehran University of Medical Sciences

We aimed to evaluate the efficacy of tricyclic antidepressants(TCAs) as a therapeutic option for irritable bowel syndrome(IBS) through meta-analysis of randomized controlled *** the years 1966 until September 2008,PubMed,Scopus,Web of Science,and Cochrane Central Register of Controlled Trials were searched for double-blind,placebo-controlled trials investigating the effi cacy of TCAs in the management of *** randomized,placebo-controlled clinical trials met our criteria and were included in the *** used in the treatment arm of these trials included amitriptyline,imipramine,desipramine,doxepin and *** pooled relative risk for clinical improvement with TCA therapy was 1.93(95% CI:1.44 to 2.6,Pclinically and statistically signifi cant control of IBS symptoms.

关键词： Systematic review Meta-analysis Tricyclic antidepressants Irritable bowel syndrome Efficacy clinical response Abdominal pain

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Added value of systemic inflammation markers for monitoring response to neoadjuvant chemotherapy in breast cancer patients

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World Journal of clinical Cases 2022年第11期10卷 3389-3400页

作者： Zi-Rui Ke Wei Chen Man-Xiu Li Shun Wu Li-Ting Jin Tie-Jun Wang Department of Breast Surgery Hubei Cancer HospitalTongji Medical CollegeHuazhong University of Science and Technology and Hubei Provincial Clinical Research Center for Breast CancerWuhan 430079Hubei ProvinceChina

BACKGROUND Complete response after neoadjuvant chemotherapy(r NACT) elevates the surgical outcomes of patients with breast cancer, however, non-r NACT have a higher risk of death and *** To establish novel machine learning(ML)-based predictive models for predicting probability of r NACT in breast cancer patients who intends to receive *** A retrospective analysis of 487 breast cancer patients who underwent mastectomy or breast-conserving surgery and axillary lymph node dissection following neoadjuvant chemotherapy at the Hubei Cancer Hospital between January 1, 2013, and October 1, 2021. The study cohort was divided into internal training and testing datasets in a 70:30 ratio for further analysis. A total of twenty-four variables were included to develop predictive models for r NACT by multiple MLbased algorithms. A feature selection approach was used to identify optimal predictive factors. These models were evaluated by the receiver operating characteristic(ROC) curve for predictive *** Analysis identified several significant differences between the r NACT and nonr NACT groups, including total cholesterol, low-density lipoprotein, neutrophilto-lymphocyte ratio, body mass index, platelet count, albumin-to-globulin ratio, platelet-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio. The areas under the curve of the six models ranged from 0.81 to 0.96. Some ML-based models performed better than models using conventional statistical methods in both ROC curves. The support vector machine(SVM) model with twelve variables introduced was identified as the best predictive *** By incorporating retreatment serum lipids and serum inflammation markers, it is feasible to develop ML-based models for the preoperative prediction of r NACT and therefore facilitate the choice of treatment, particularly the SVM, which can improve the prediction of r NACT in patients with breast cancer.

关键词： Breast cancer Neoadjuvant chemotherapy clinical response Machine learning Prediction

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