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《Diagnostic Histopathology》2022,28(11):493-500
After decades of relative stagnation lung cancer is emerging as a disease type where rapid progress is being made in diagnosis and therapy, as well as in our understanding of disease biology. Much of this progress is of immediate impact to diagnosticians, and more is likely to affect diagnostic practice in the near future. In this review we seek to briefly summarize several key areas of active research of immediate or probable imminent value to trainee and consultant pulmonary pathologists alike. We cover some major changes in tumour classification, grading, and patient stratification, as well as considering the state of the art in machine-assisted interpretation of lung cancer histology, and the use of genetically modified lung cancer models. 相似文献
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《Clinical breast cancer》2021,21(5):e497-e505
BackgroundDifferent clinicopathologic characteristics could contribute to inconsistent prognoses of small breast neoplasms (T1a/T1b). This study was done to conduct a retrospective analysis and establish a clinical prediction model to predict individual survival outcomes of patients with small carcinomas of the breast.Materials and MethodsBased on the Surveillance, Epidemiology, and End Results (SEER) database, eligible patients with small breast carcinomas were analyzed. Univariate analysis and multivariate analysis were performed to clarify the indicators of overall survival. Pooling risk factors enabled nomograms to be constructed and further predicted 3-year, 5-year, and 10-year survival of patients with small breast cancer. The model was internally validated for discrimination and calibration.ResultsA total of 17,543 patients with small breast neoplasms diagnosed between 2013 and 2016 were enrolled. Histologic grade, lymph node stage, estrogen receptor or progesterone receptor status, and molecular subtypes of breast cancer were regarded as the risk factors of prognosis in a Cox proportional hazards model (P < .05). A nomogram was constructed to give predictive accuracy toward individual survival rate of patients with small breast neoplasms.ConclusionsThis prognostic model provided a robust and effective method to predict the prognosis of patients with small breast cancer. 相似文献
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IntroductionA three-level model of interoception has recently been defined. We aim to study the interoceptive processing in individuals with functional motor disorder (FMD).MethodsTwenty-two patients with FMD were compared to 23 healthy controls. They underwent a protocol measuring different levels of interoception including: accuracy (a heart-beat tracking task), awareness (participant's confidence level) and sensibility (the Body Awareness Questionnaire-BAQ). Depression, anxiety and alexithymia were assessed by means of validated clinical scales.ResultsThe FMD group showed a lower cardiac interoceptive accuracy and sensibility than healthy controls but they did not differ in terms of awareness (p = 0.03 and 0.005 respectively). They were aware of their poor performance in the accuracy task. Cardiac interoceptive accuracy positively correlated with the BAQ sub-scales “Predict Body Reaction” (r = 0.49, p = 0.001) and “Sleep-Wake Cycle” (r = 0.52, p < 0.001). A mediation analysis showed a significant indirect effect of group on cardiac interoceptive accuracy through BAQ “Predict Body Reaction” (b = −2.95, 95% BCa CI[-7.2;-0.2]). The direct effect of group on “Predict Body Reaction” was still significant (b = − 6.95, p = 0.02, 95% CI[-13.18;-0.73]).ConclusionsPeople with FMD have impaired cardiac interoceptive accuracy and sensibility but no difference in metacognitive interoception compared to healthy controls. 相似文献
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目的比较基因组拷贝数变异测序(CNV-seq)技术和染色体核型分析和在产前胎儿遗传学诊断中的应用价值。方法收集来我院有产前诊断指征进行羊水穿刺的259例孕妇,取材后,送检染色体核型分析和CNV-seq,比较两种方法在产前诊断中的优缺点。结果259例标本中,共诊断异常染色体核型及微缺失微重复23例,总阳性诊断率8.88%(23/259),CNV-seq结果显示,共有22例染色体拷贝数异常(12例三体+9例微缺失微重复+1例三倍体),检出率为8.49%;染色体核型分析结果显示为:17例染色体异常(12例三体+3例结构异常+1例嵌合型+1例三倍体),检出率为6.56%。此外还检出染色体多态7例。结论CNV-seq与染色体核型分析对于染色体非整倍体的检测效力一致,CNV-seq能检测染色体微缺失微重复,染色体核型分析则能诊断出三体具体核型,在怀疑性染色体异常时,建议行两者联合检测。 相似文献
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Marie V. Plaisime PhD MPH Marie Jipguep-Akhtar PhD Joseph J. Locascio PhD Harolyn M. E. Belcher MD MHS Rachel R. Hardeman PhD MPH Katherine Picho-Kiroga PhD Sylvia P. Perry PhD Sean M. Phelan PhD MPH Michelle van Ryn PhD LMFT MPH John F. Dovidio PhD 《Health services research》2023,58(Z2):229-237