Objective To look into the experience and feels of the family caregivers of brain tumor patients undergoing surgery. Methods The phenomenological methodology was used in the study. Eighteen family caregivers nursing brain tumor patients were selected as our target. Semi-structured interviews were performed to investigate their feelings and experience during the preoperative period. Result Three topics from our study were concluded: inappropriate emotional reaction, inexperience in the disease management and insufficiency in social support. Conclusion Medical staff and institutions should provide emotional, nursing technical support and professional knowledge for the caregivers so that they can improve their adaption ability and relieve the stress.

Objective: To study the level of squamous cell antigen (SCC?Ag) and cyclin E in cervical intraepithelial neoplasia ( CIN) and cervical cancer, and to explore the clinical significance. Methods: From Nov 2012 to Nov 2015, 308 patients with CIN or cervical cancer were chosen as research objects, and 170 patients with uterine leiomyoma or uterine gland muscle disease for hyster?ectomy and after pathological examination of the normalcases were chosen as control group. The SCC?Ag level, positive expression rate in blood, and positive expression of Cyclin E in cervical tissue for patients with different cervical lesions were compared. Results: The SCC?Aglevel , positive expression rate in blood, and positive expression of Cyclin E in cervical tissue for patients with cervical canc?er, CIN were higher that those in the control group ( P<0?05) . The SCC?Aglevel, positive expression rate in blood, and positive ex?pression of Cyclin E in different CIN grades and different stages of cervical cancer patient had significant difference ( P<0?05) . Con?clusion: Blood SCC?Ag level and positive expression, Cyclin E positive expression in the cervical tissue have a close relationship with occurrence and development of CIN and cervical cancer .

Objective: To evaluate the efficacy and side effects of tocilizumab for the treatment of systemic juvenile idiopathic arthritis. Method: In this prospective self case-control study, the children diagnosed with refractory systemic juvenile idiopathic arthritis admitted to Department of Rheumatism and Immunology of Children's Hospital Affiliated to Capital Institute of Pediatrics from December 2013 to June 2016 were enrolled and information before and after treatment of tocilizumab was analyzed. The tocilizumab was introvenously guttae in a dose of 8-12 mg/kg every 2 weeks. Complete blood count, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) were tested before and after the application of tocilizumab. Detailed clinical manifestations were recorded. All results were analyzed by χ² test and t test. Result: Forty patients with a median age of (6.6±3.7) years were enrolled, including 15 males and 25 females. All of the patients presented with fever and 38 patients got normal temperature 24-48 hours after treatment with tocilizumab. Symptoms disappeared in 13 and improved in 4 patients after treatment among the 17 patients who presented with arthritis. Within the 10 patients who manifested with rashes, 9 patients' rashes disappeared without relapse accompanied by the normalization of temperature after the treatment of tocilizumab. One patient got normal temperature but intermittently emerged rashes after symptoms of arthritis improved. In the 40 patients, 38 well tolerated tocilizumab while 2 showed rashes and chill which disappeared shortly after antianaphylaxis treatment. No severe treatment-related infection was found in any patients. According to the study, the white blood cell counts(×10⁹/L), CRP(mg/L) and ESR(mm/1h) tested 2 weeks after the treatment with tocilizumab were significantly lower than that before treatment(12.1±1.2 vs. 16.5±1.8, 47±8 vs. 67±9, 21±5 vs. 57±6, t=2.75, 3.98, 5.22, P=0.009, 0, 0, respectively). No significant changes were found in concentration of IL-6 and TNF-α (65(207) vs. 45(137) ng/L, and 14(6) vs. 17(19)ng/L, Z=-1.247 and-1.285, P=0.212 and 0.199 respectively). Conclusion Tocilizumab is a treatment with good efficacy and safety for refractory systemic juvenile idiopathic arthritis. Adverse effects would be found in some patients.

Objective·To develop a machine learning approach for early identification of metabolic syndromes associated with inflammatory metabolic state changes in breast cancer patients after neoadjuvant therapy,using common laboratory and transthoracic echocardiography indices.Methods·Female patients with primary invasive breast cancer diagnosed at the Department of Breast Surgery,Renji Hospital,Shanghai Jiao Tong University School of Medicine,between September 2020 and September 2022,were included.General patient information,laboratory test results,and transthoracic echocardiography data were collected.After feature extraction,five machine learning algorithms,including random forest(RF),gradient boosting(GB),support vector machine(SVM),K-nearest neighbor(KNN),and decision tree(DT),were applied to construct a prediction model for the changes of the patients' metabolic state after neoadjuvant therapy,and the prediction performances of the five models were compared.Results·A total of 232 cases with valid clinical data were included,comprising 135 cases before neoadjuvant therapy and 97 cases after completing 4 cycles of neoadjuvant therapy.Feature extraction identified five key features:white blood cell count,hemoglobin,high-density lipoprotein(HDL),interleukin-2 receptor,and interleukin-8.In the multi-feature analysis,the area under the receiver operating characferistic curve(AUC)was higher in the combination of white blood cell count,hemoglobin and HDL compared to the combination of interleukin-2 receptor and interleukin-8(RF:0.928 vs 0.772,GB:0.900 vs 0.792,SVM:0.941 vs 0.764,KNN:0.907 vs 0.762,DT:0.799 vs 0.714).The RF,SVM,and GB models showed higher AUC(0.928,0.941,0.900)and accuracy(0.914,0.897,0.776).The SVM model exhibited superior accuracy in the training data compared to the RF and GB models(P=0.394,0.122 and 0.097,respectively).Conclusion·The SVM model can be used to establish a prediction model for identifying breast cancer patients at high risk of developing inflammatory metabolic state-related metabolic syndrome after neoadjuvant therapy by incorporating five common clinical indicators,namely,white blood cell count,hemoglobin,high-density lipoprotein,interleukin-2 receptor,and interleukin-8.SVM modeling may be useful for clinicians to establish individualized screening protocols based on a patient's inflammatory metabolic state.