OBJECTIVE: To summarize the relevant evidence of testicular cancer patients' fertility management which provides a basis for fertility guidance for young testicular cancer patients. METHODS: The evidence, guidelines, expert consensus, evidence summary, systematic review and Meta-analysis on fertility-related clinical decision-making were searched from computer decision support systems, relevant guideline websites, evidence-based databases, original research databases, and professional association websites at home and abroad. The search period was set dating from the establishment of the database to July 2024. Two researchers with evidence-based nursing research background independently completed the quality evaluation, evidence extraction and summary of the literature. RESULTS: A total of 21 articles were selected, including 4 clinical decisions, 8 guidelines, 4 expert consensuses, 3 systematic reviews and 2 Meta-analyses. Thirty-three pieces of best evidence from six aspects were summarized, including fertility assessment, fertility counseling, fertility preservation timing, fertility preservation regimen, anti-tumor therapy and fertility, and contraceptive timing. CONCLUSION The best evidence summarized in this study provides a basis for clinical medical staff to carry out fertility management in testicular cancer patients. In clinical application, medical staff need to fully consider the patient's wishes in combination with the clinical situation, and promote the maximum benefit of the patient, so as to further improve the life quality of the patients.
Humans ; Male ; Evidence-Based Medicine ; Fertility ; Fertility Preservation ; Testicular Neoplasms/therapy* ; Systematic Reviews as Topic

Objective To construct 5 machine-learning models and compare their performance in predicting the associations between pre-pregnancy socio-psycho-behavioral exposures of both spouses and preconception outcomes.Methods Based on Chongqing Preconception Reproductive Health and Birth Outcome Cohort of volunteers recruited from Chongqing Health Center for Women and Children during January 2019 and March 2022,5 447 couples were recruited and surveyed through interviewer-interview for the demographic and social-psychological-behavioral data of both spouses(221 variables).According to the inclusion and exclusion criteria,4 097 couples were finally included,and randomly assigned into a training set(n=2 867 spouses)and a validation set(n=1 230 spouses)at a ratio of 7∶3.Feature analysis and collinear screening were applied to select the potential exposure factors.In consideration of difficulty to carry out semen parameters analysis in primary healthcare institutions,feature Set 1 including sperm parameters and feature Set 2 excluding semen parameters were constructed by including or excluding sperm quality simultaneously in the training set and the validation set.Five algorithms,that is,Logistic Regression,Naive Bayes,Random Forest,Gradient Boosting Machine,and Support Vector Machine,were used to construct preconception outcome prediction models,and the parameters of each model were optimized using random search combined with grid search.The predictive performance of each model was compared using precision,recall,F1 score,area under the receiver operating characteristic curve(AUC),and calibration curve.The optimal model was then selected by comparing the changes in the predictive ability of the questionnaire data for fertility outcomes with or without semen parameters.Results There were 24 variables screened out in feature Set 1,and 16 variables in feature Set 2.In feature Set 1,the gradient boosting machine performed better,with a relatively higher AUC value(0.651)and better F1 score(0.61).The logistic regression model performed stably(AUC value=0.647)and was suitable as the reference model.The random forest(AUC value=0.641),Naive Bayes(AUC value=0.641),and support vector machine(AUC value=0.634)performed second-best.By utilizing the gradient boosting machine,comparable results were found between the predictions from feature sets with or without semen parameters,as in feature Set 1,the AUC value of its validation set was 0.651(95%CI:0.629～0.681),the prediction accuracy was 0.63,the recall rate was 0.65,and the average precision value F1 was 0.61;and in feature Set 2,the AUC value of its validation set was 0.649(95%CI:0.624～0.663),and both the calibration curves were close to the ideal curve.The prediction results indicated that in feature Set 1,the features highly negatively correlated with preconception outcomes were female age,male age,and no pregnancy within 1 year without contraception,while the features highly positively correlated with preconception outcomes were female pregnancy history,total sperm vitality,and use of contraceptive measures before enrollment.Conclusion Among the 5 machine-learning algorithms performed in this cohort data,the gradient boosting machine shows slightly better performance.There are 24 factors being associated with preconception outcomes in both spouses,and the performance of the simplified model excluding semen parameters is not significantly declined.It is feasible to use machine-learning methods to predict human preconception outcomes through social-psychological-behavioral questionnaires.

Objective: To analyze the spatiotemporal distribution of life expectancy (LE) and health-adjusted life expectancy (HALE) in Guangzhou from 2010 to 2019, and quantize the comprehensive impact of different causes and sequelae on health. Methods: The LE, HALE, and cause-excluded health adjusted life expectancy (CEHALE) were estimated using cause-of-death surveillance datasets from Guangzhou Municipal Center for Disease Control and Prevention from 2010 to 2019 and open data from the Global Burden of Disease Study. Joinpoint log-linear regression model was used to analyze the temporal trend and described spatial distribution. Results: In 2019, the LE in residents in Guangzhou was 82.9 years (80.1 years in men and 85.9 years in women), and the HALE was 75.6 years (74.0 years in men and 77.3 years in women). Compared with the urban fringe, the central urban area had higher LE and HALE, and the differences between LE and HALE were small. The LE and HALE in Guangzhou showed an increasing trend from 2010 to 2019. The LE increased by 2.8 years (AAPC=0.4, 95%CI: 0.3-0.4), with the increase of 2.8 years in men and 2.9 years in women. The HALE increased by 2.4 years (AAPC=0.3, 95%CI: 0.3-0.4), with the increase of 2.5 years in men and 2.2 years in women. The median healthy life lost due to communicable, maternal, neonatal, and nutritional diseases was 6.2 years (AAPC=-4.2, 95%CI: -5.3--3.1), while the median healthy life lost due to non-communicable diseases was 14.7 years (AAPC=1.6, 95%CI: 0.9-2.3), the median healthy life expectancy reduced by injury was 6.3 years (AAPC=-3.5, 95%CI: -4.5--2.6). Musculoskeletal disorders, skin and subcutaneous diseases, cardiovascular diseases, nutritional deficiencies, diabetes and kidney diseases were the top five diseases causing healthy life expectancy loss. Conclusion: The LE and HALE in residents in Guangzhou increased steadily from 2010 to 2019, but the quality of life in the urban fringe was lower than that of the central urban area. Non-communicable diseases were the leading causes of healthy life expectancy loss. Health policies and prevention measures should be developed according to area specific characteristics, and social medical resources should be rationally allocated to key diseases to reduce their disease burden.
Cost of Illness ; Female ; Health Status ; Humans ; Infant, Newborn ; Life Expectancy ; Male ; Noncommunicable Diseases ; Quality of Life

OBJECTIVE: Improvement in the quality of life is reflected in the narrowing of the gap between health-adjusted life expectancy (HALE) and life expectancy (LE). The effect of megacity expansion on narrowing the gap is rarely reported. This study aimed to disclose this potential relationship. METHODS: Annual life tables were constructed from identified death records and population counts from multiple administrative sources in Guangzhou, China, from 2010 to 2020. Joinpoint regression was used to evaluate the temporal trend. Generalized principal component analysis and multilevel models were applied to examine the county-level association between the gap and social determinants. RESULTS: Although LE and HALE in megacities are increasing steadily, their gap is widening. Socio-economic and health services are guaranteed to narrow this gap. Increasing personal wealth, a growing number of newborns and healthy immigrants, high urbanization, and healthy aging have helped in narrowing this gap. CONCLUSION In megacities, parallel LE and HALE growth should be highly considered to narrow their gap. Multiple social determinants need to be integrated as a whole to formulate public health plans.
Cities ; Health Status ; Humans ; Infant, Newborn ; Life Expectancy ; Quality of Life ; Social Determinants of Health

Objective: To observe the blood perfusion volume variation pattern in the body surface microcirculation at the Yuan-Primary and the Xi-Cleft points during the menstrual cycle in female college students with moderate constitution under normal physiological state of the uterus; to explore the specific laws of the body surface microcirculation at the Yuan-Primary and Xi-Cleft points in response to the uterine qi and blood changes under normal physiological conditions, and to provide the experimental basis for the specificity of acupoints reflecting the uterine function. Methods: Forty-three healthy and moderate constitution female college students with regular menstrual cycles, without dysmenorrhea and not yet giving birth were recruited. Bilateral Yuan-Primary points [Taichong (LR 3), Taibai (SP 3) and Taixi (KI 3)] and Xi-Cleft points [Zhongdu (LR 6), Diji (SP 8) and Shuiquan (KI 5)], belonging to the three yin meridians of foot and adjacent to the spinal cord segment of the uterus, were selected as the detection acupoints; the crossing point of the three yin meridians of foot [Sanyinjiao (SP 6)], the uterus-related meridian acupoint [Xuehai (SP 10)], the uterus-non-related meridian acupoint [Xuanzhong (GB 39)], and the non-meridian non-acupoint point were selected as the control points. The laser speckle blood flow imaging technique was used to monitor the blood perfusion volume in skin microcirculation at the above points at the menstrual, follicular, ovulatory, and luteal phases of the subjects. Results: The blood perfusion volume in the body surface microcirculation at the right Zhongdu (LR 6) at the ovulatory phase was higher than that at the menstrual, follicular and luteal phases (all P<0.05); there was no significant difference in the microcirculation blood perfusion volume at the other points among different phases (all P>0.05). Conclusion: The blood perfusion volume in the body surface microcirculation at Zhongdu (LR 6), the Xi-Cleft point of the Liver Meridian, shows a specific response to qi and blood changes in the uterus of women with moderate constitution.