Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

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Bench to bedside: recent advances in lung cancer pathological research with implications for diagnostic clinical practice

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.     

Prognostic Model and Nomogram for Estimating Survival of Small Breast Cancer: A SEER-based Analysis

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.     

Exploring three levels of interoception in people with functional motor disorders

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.     

The impact of neighborhoods and friendships on interracial anxiety among medical students and residents: A report from the medical student CHANGES study

Objective

To examine the experience of interracial anxiety among health professionals and how it may affect the quality of their interactions with patients from racially marginalized populations. We explored the influence of prior interracial exposure—specifically through childhood neighborhoods, college student bodies, and friend groups—on interracial anxiety among medical students and residents. We also examined whether levels of interracial anxiety change from medical school through residency.

Data Source

Web-based longitudinal survey data from the Medical Student Cognitive Habits and Growth Evaluation Study.

Study Design

We used a retrospective longitudinal design with four observations for each trainee. The study population consisted of non-Black US medical trainees surveyed in their 1st and 4th years of medical school and 2nd and 3rd years of residency. Mixed effects longitudinal models were used to assess predictors of interracial anxiety and assess changes in interracial anxiety scores over time.

Principal Findings

In total, 3155 non-Black medical trainees were followed for 7 years. Seventy-eight percent grew up in predominantly White neighborhoods. Living in predominantly White neighborhoods and having less racially diverse friends were associated with higher levels of interracial anxiety among medical trainees. Trainees' interracial anxiety scores did not substantially change over time; interracial anxiety was highest in the 1st year of medical school, lowest in the 4th year, and increased slightly during residency.

Conclusions

Neighborhood and friend group composition had independent effects on interracial anxiety, indicating that premedical racial socialization may affect medical trainees' preparedness to interact effectively with diverse patient populations. Additionally, the lack of substantial change in interracial anxiety throughout medical training suggests the importance of providing curricular tools and structure (e.g., instituting interracial cooperative learning activities) to foster the development of healthy interracial relationships.     

监测数据统计分析模型在生态学研究中的应用

近年来，环境监测、疾病监测等各种监测网络不断健全，监测系统成为开展生态学研究的重要数据来源。监测数据类型包括了横断面数据、时间序列数据和面板数据，涉及暴露、结局和混杂3个维度。针对该数据的信息属性和结构特点，相关统计学方法逐渐发展完善，出现了一些新的方法、模型。基于数据的时空属性，本文对监测数据在生态学研究中常用模型的原理、适用条件及优劣进行了综述。     

Modelling human pathology of traumatic brain injury in animal models

Traumatic brain injury (TBI) is caused by a head impact with a force exceeding regular exposure from normal body movement which the brain normally can accommodate. People affected include, but are not restricted to, sport athletes in American football, ice hockey, boxing as well as military personnel. Both single and repetitive exposures may affect the brain acutely and can lead to chronic neurodegenerative changes including chronic traumatic encephalopathy associated with the development of dementia. The changes in the brain following TBI include neuroinflammation, white matter lesions, and axonal damage as well as hyperphosphorylation and aggregation of tau protein. Even though the human brain gross anatomy is different from rodents implicating different energy transfer upon impact, especially rotational forces, animal models of TBI are important tools to investigate the changes that occur upon TBI at molecular and cellular levels. Importantly, such models may help to increase the knowledge of how the pathologies develop, including the spreading of tau pathologies, and how to diagnose the severity of the TBI in the clinic. In addition, animal models are helpful in the development of novel biomarkers and can also be used to test potential disease‐modifying compounds in a preclinical setting.     

Extending multilevel spatial models to include spatially varying coefficients

Multilevel models have long been used by health geographers working on questions of space, place, and health. Similarly, health geographers have pursued interests in determining whether or not the effect of an exposure on a health outcome varies spatially. However, relatively little work has sought to use multilevel models to explore spatial variability in the effects of a contextual exposure on a health outcome. Methodologically, extending multilevel models to allow intercepts and slopes to vary spatially is straightforward. The purpose of this paper, therefore, is to show how multilevel spatial models can be extended to include spatially varying covariate effects. We provide an empirical example on the effect of agriculture on malaria risk in children under 5 years of age in the Democratic Republic of Congo.