乳腺癌复发因素预测模型构建

构建乳腺癌复发因素预测模型,分析最优化算法以及乳腺癌复发特征参数并对相关问题进行探讨,包括随机对照试验应用情况、再复发重要因素以及预测模型算法确定、C5.0算法比较研究等.     

气象因素对呼吸道疾病的影响及预报研究

目的　研究气象条件对呼吸道疾病发病影响,并建立最佳预报模型。方法　收集湖州市2006年呼吸道疾病发病资料以及同期气象资料,进行Spearman相关分析;采用决策树自动交互检测方法（AID）建立预报模型,并对模型进行评价。结果　相关分析显示,最低气温、水汽压、相对湿度、最小湿度、降水量、日照时数与呼吸道疾病存在相关性,按周建立气象因素与呼吸道疾病预报模型和三个预警等级,预报模型拟合结果IR/Isup2/sup=0.3857。结论　呼吸道疾病发病与气象因素密切相关,按周建立预警、预报模型效果较好,可行性较佳。     

Non-Invasive Fetal K Status Prediction: 7 Years of Experience

IntroductionIn the Kell blood group system, the K and k antigens are the clinically most important ones. Maternal anti-K IgG antibodies can lead to the demise of a K-positive fetus in early pregnancy. Intervention can save the fetus. Prenatal K status prediction of the fetus in early pregnancy is desirable and gives a good basis for pregnancy risk management. We present the results from 7 years of clinical experience in predicting fetal K status as well as some theoretical considerations relevant for design of the assay and evaluation of results.MethodsBlood was collected from 43 women, all immunized against K, at a mean gestational age of 18 weeks (range 10–38). A total of 56 consecutive samples were tested. The KEL *01.01 /KEL *02 single nucleotide variant that determines K status was amplified from maternal plasma DNA by PCR without allele specificity. The PCR product was sequenced by NGS technology, and the number of sequenced KEL *01.01 and KEL *02 reads were counted. Prediction of the fetal K status was based on this count and was compared with the serologically determined K status of the newborns.ResultsAll fetal K predictions were in accordance with postnatal serology where available (n = 34), using our current data analysis.ConclusionWe have developed an NGS-based method for the non-invasive prediction of fetal K status. This approach requires special considerations in terms of primer design, stringent preanalytical sample handling, and careful analytical procedures. We analyzed samples starting at GA 10 weeks and demonstrated the correct prediction of fetal K status. This assay enables timely clinical intervention in pregnancies at risk of hemolytic disease of the fetus and newborn caused by maternal anti-K IgG antibodies.     

胆总管结石患者ERCP术后早期胆道感染的动态预测工具开发及验证

目的 开发胆总管结石患者经内镜逆行性胰胆管造影术(ERCP)后发生早期胆道感染(PEEBI)的预测工具,以期辅助临床进行ERCP术前决策和术后早期个性化干预。 方法 采用观察性双向队列研究方法,选取某医院接受ERCP的胆总管结石住院患者,分别采用有向无环图(DAGs)和最小绝对收缩与选择算子法(LASSO) 进行基于logistic回归的PEEBI预测,比较预测模型,并进行内外部验证。 结果 2020年1月1日—2023年9月30日,共纳入接受ERCP的胆总管结石患者2 121例,其中77例(3.6%)发生了PEEBI,且主要集中在术后前2天(66.2%)。影响PEEBI的主要因素为非医源的患者相关因素,即糖尿病(OR=2.43, 95%CI: 1.14~4.85)、胆管恶性肿瘤(OR=3.95, 95%CI: 1.74~8.31)和十二指肠乳头憩室(OR=4.39, 95%CI: 1.86~9.52)。DAGs模型较LASSO模型的综合判别能力高3.0% (P=0.007),且DAGs模型外部验证中的区分性能(D=0.133,P=0.894)和校准性能(χ²=5.499,P=0.703)均表现良好。 结论 本研究构建的DAGs模型具有良好的预测性能,临床可在该工具的协助下采取针对性的围手术期早期预防措施,以减少PEEBI的发生。     

婴儿期社会性反应评估对18月龄孤独症行为预测作用队列研究

目的 识别孤独症在婴幼儿早期的行为发育特点,为早期干预赢得时机。方法 采用前瞻性队列研究的方法建立队列,记录各阶段研究对象的生长发育状况,分阶段填写适当的《婴儿社会性反应问卷》并计算得分,在儿童18个月龄时,用《CHAT-23问卷》进行孤独症行为的筛查,计算3、6、9、12个月龄儿童社会性反应得分,并根据百分位数P₂₅和P₇₅将其划分为低分组、中分组及高分组,采用χ²方法分析18个月龄儿童的孤独症行为在各月龄不同分组中的分布情况,利用二元logistic回归模型分析12月龄内婴儿的社会性反应与18月龄孤独症行为的关联效应。结果 本研究共纳入3月龄（3个月±7 d）的婴儿1 207人,截止到18月龄,追踪到队列儿童共1 085人,筛查出孤独症行为儿童99人,检出率9.1%; 3、6、9、12月龄的低分组孤独症行为检出率依次为16.1%、13.3%、13.7%和14.5%;单因素分析结果显示,各月龄社会性反应得分中低分组的孤独症行为检出率均高于高分组,且各月龄差异均有统计学意义（P<0.05）;二元logistic回归分析显示,低分组是发生孤独症谱系障碍（ASDs）的危险因素,3、6、9和12月龄的RR值（95%CI）分别为2.94（1.50~5.75）、3.28（1.32~8.09）、2.38（1.16~4.88）和4.90（2.08~11.49）。结论 虽然孤独症行为的机制目前尚不明确,但是在儿童12月龄内某些特定行为对ASDs具有预测作用,可为早期干预提供依据。     

山西某煤矿接尘工人煤工尘肺发病预测

目的 预测山西某煤矿研究队列内接尘工人未来预期寿命内煤工尘肺的发病人数,确定发病危险人群。方法 将该煤矿1970-2011年开始接尘的4771人作为研究对象,采用寿命表方法,分析不同开始接尘年代队列、不同工种队列的累计发病率及年平均发病率,预测健康接尘工人未来预期寿命内煤工尘肺发病人数。结果 预测未来预期寿命内约有497人发病,其中1970~年代开始接尘工人约32人发病,1980~年代开始接尘工人约465人发病,1980~年代开始接尘工人发病率最高(11.17%);各工种发病人数由高到低分别为掘进工约245人,采煤工178人,混合工64人,辅助工10人,掘进工发病率最高(75.85%);发病人员主要集中在目前40~50岁人群,约324人发病;未来30年内约有416人发病,占总发病人数的83.70% 。结论 掘进工及1980年以后开始接尘的工人为尘肺高发人群,属于重点保护对象。     

Development of a Prediction Model for Positive Surgical Margin in Robot-Assisted Laparoscopic Radical Prostatectomy

A positive surgical margin (PSM) is reported to have some connection to the occurrence of biochemical recurrence and tumor metastasis in prostate cancer after the operation. There are no clinically usable models and the study is to predict the probability of PSM after robot-assisted laparoscopic radical prostatectomy (RALP) based on preoperative examinations. It is a retrospective cohort from a single center. The Lasso method was applied for variable screening; logistic regression was employed to establish the final model; the strengthened bootstrap method was adopted for model internal verification; the nomogram and web calculator were used to visualize the model. All the statistical analyses were based on the R-4.1.2. The main outcome was a pathologically confirmed PSM. There were 151 PSMs in the 903 patients, for an overall positive rate of 151/903 = 16.7%; 0.727 was the adjusted C statistic, and the Brier value was 0.126. Hence, we have developed and validated a predictive model for PSM after RALP for prostate cancer that can be used in clinical practice. In the meantime, we observed that the International Society of Urological Pathology (ISUP) score, Prostate Imaging Reporting and Data System (PI-RADS) score, and Prostate-Specific Antigen (PSA) were the independent risk factors for PSM.     

When action prediction grows old: An fMRI study

Predicting the unfolding of others'' actions (action prediction) is crucial for successfully navigating the social world and interacting efficiently. Age‐related changes in this domain have remained largely unexplored, especially for predictions regarding simple gestures and independent of contextual information or motor expertise. Here, we evaluated whether healthy aging impacts the neurophysiological processes recruited to anticipate, from the observation of implied‐motion postures, the correct conclusion of simple grasping and pointing actions. A color‐discrimination task served as a control condition to assess the specificity of the age‐related effects. Older adults showed reduced efficiency in performance that was yet not specific to the action prediction task. Nevertheless, fMRI results revealed task‐specific age‐related differences: while both groups showed stronger recruitment of the lateral occipito‐temporal cortex bilaterally during the action prediction than the control task, the younger participants additionally showed a higher bilateral engagement of parietal regions. Importantly, in both groups, the recruitment of visuo‐motor processes in the right posterior parietal cortex was a predictor of good performance. These results support the hypothesis of decreased involvement of sensorimotor processes in cognitive tasks when processing action‐ and body‐related stimuli in healthy aging. These results have implications for social interaction, which requires the fast reading of others'' gestures.     

A Bayesian network predicting survival of cervical cancer patients—Based on surveillance,epidemiology, and end results

This study aimed to build a comprehensive model for predicting the overall survival (OS) of cervical cancer patients who received standard treatments and to build a series of new stages based on the International Federation of Gynecologists and Obstetricians (FIGO) stages for better such predictions. We collected the cervical cancer patients diagnosed since the year 2000 from the Surveillance, Epidemiology, and End Results (SEER) database. Cervical cancer patients who received radiotherapy or surgery were included. Log‐rank tests and Cox regression were used to identify potential factors of OS. Bayesian networks (BNs) were built to predict 3‐ and 5‐year survival. We also grouped the patients into new stages by clustering their 5‐year survival probabilities based on FIGO stage, age, and tumor differentiation. Cox regression suggested black ethnicity, adenocarcinoma, and single status as risks for poorer prognosis, in addition to age and stage. A total of 43,749 and 39,333 cases were finally eligible for the 3‐ and 5‐year BNs, respectively, with 11 variables included. Cluster analysis and Kaplan‐Meier curves indicated that it was best to divide the patients into nine modified stages. The BNs had excellent performance, with area under the curve and maximum accuracy of 0.855 and 0.804 for 3‐year survival, and 0.851 and 0.787 for 5‐year survival, respectively. Thus, BNs are excellent candidates for predicting cervical cancer survival. It is necessary to consider age and tumor differentiation when estimating the prognosis of cervical cancer using FIGO stages.