对基地班学生实施导师制的体会

本总结了2届基地班学生参加生理学科导师组活动的情况,提出几点经验与体会。根据基地班学生的特点和实施导师的制的初衷,运用问题先导法,培养学生的创新意识,加强师生交流,使之成为合格的基础医学人才。     

高校师资队伍建设的构想

师资是一所高校最重要的资源 ,是高校实现人才培养、科学研究和社会服务三大功能的主体力量 ,也是一所高校在人才培养、科学创造与学术成就以及社会影响力等方面获得竞争优势的决定性因素。在新的历史时期 ,高等院校师资队伍建设的总体目标 ,是建设一支素质良好、结构优化、富有活力、具有创新能力的师资队伍。其具体措施是完善选人机制 ,优化教师资源配置 ;采取得力措施 ,优化教师队伍成才环境 ;建立激励机制 ,加强学科带头人和骨干教师队伍建设 ;强化竞争机制 ,实施和完善教师聘任制 ;强化教师培训 ,全面提高教师素质 ;加大投入 ,确保教师队伍建设落到实处     

Machine Learning Predicts the Fall Risk of Total Hip Arthroplasty Patients Based on Wearable Sensor Instrumented Performance Tests

BackgroundThe prevalence of falls affects the wellbeing of aging adults and places an economic burden on the healthcare system. Integration of wearable sensors into existing fall risk assessment tools enables objective data collection that describes the functional ability of patients. In this study, supervised machine learning was applied to sensor-derived metrics to predict the fall risk of patients following total hip arthroplasty.MethodsAt preoperative, 2-week, and 6-week postoperative appointments, patients (n = 72) were instrumented with sensors while they performed the timed-up-and-go walking test. Preoperative and 2-week postoperative data were used to form the feature sets and 6-week total times were used as labels. Support vector machine and linear discriminant analysis classifier models were developed and tested on various combinations of feature sets and feature reduction schemes. Using a 10-fold leave-some-subjects-out testing scheme, the accuracy, sensitivity, specificity, and area under the receiver-operator curve (AUC) were evaluated for all models.ResultsA high performance model (accuracy = 0.87, sensitivity = 0.97, specificity = 0.46, AUC = 0.82) was obtained with a support vector machine classifier using sensor-derived metrics from only the preoperative appointment. An overall improved performance (accuracy = 0.90, sensitivity = 0.93, specificity = 0.59, AUC = 0.88) was achieved with a linear discriminant analysis classifier when 2-week postoperative data were added to the preoperative data.ConclusionThe high accuracy of the fall risk prediction models is valuable for patients, clinicians, and the healthcare system. High-risk patients can implement preventative measures and low-risk patients can be directed to enhanced recovery care programs.     

Enforcing Imprecise Constraints on Generative Adversarial Networks for Emulating Physical Systems

Generative adversarial networks (GANs) were initially proposed to generate images by learning from a large number of samples. Recently, GANs have been used to emulate complex physical systems such as turbulent flows. However, a critical question must be answered before GANs can be considered trusted emulators for physical systems: do GANs-generated samples conform to the various physical constraints? These include both deterministic constraints (e.g., conservation laws) and statistical constraints (e.g., energy spectrum of turbulent flows). The latter have been studied in a companion paper (Wu et al., Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems. Journal of Computational Physics. 406, 109209, 2020). In the present work, we enforce deterministic yet imprecise constraints on GANs by incorporating them into the loss function of the generator. We evaluate the performance of physics-constrained GANs on two representative tasks with geometrical constraints (generating points on circles) and differential constraints (generating divergence-free flow velocity fields), respectively. In both cases, the constrained GANs produced samples that conform to the underlying constraints rather accurately, even though the constraints are only enforced up to a specified interval. More importantly, the imposed constraints significantly accelerate the convergence and improve the robustness in the training, indicating that they serve as a physics-based regularization. These improvements are noteworthy, as the convergence and robustness are two well-known obstacles in the training of GANs.     

Employing computational linguistics techniques to identify limited patient health literacy: Findings from the ECLIPPSE study

ObjectiveTo develop novel, scalable, and valid literacy profiles for identifying limited health literacy patients by harnessing natural language processing.Data SourceWith respect to the linguistic content, we analyzed 283 216 secure messages sent by 6941 diabetes patients to physicians within an integrated system''s electronic portal. Sociodemographic, clinical, and utilization data were obtained via questionnaire and electronic health records.Study DesignRetrospective study used natural language processing and machine learning to generate five unique “Literacy Profiles” by employing various sets of linguistic indices: Flesch‐Kincaid (LP_FK); basic indices of writing complexity, including lexical diversity (LP_LD) and writing quality (LP_WQ); and advanced indices related to syntactic complexity, lexical sophistication, and diversity, modeled from self‐reported (LP_SR), and expert‐rated (LP_Exp) health literacy. We first determined the performance of each literacy profile relative to self‐reported and expert‐rated health literacy to discriminate between high and low health literacy and then assessed Literacy Profiles’ relationships with known correlates of health literacy, such as patient sociodemographics and a range of health‐related outcomes, including ratings of physician communication, medication adherence, diabetes control, comorbidities, and utilization.Principal FindingsLP_SR and LP_Exp performed best in discriminating between high and low self‐reported (C‐statistics: 0.86 and 0.58, respectively) and expert‐rated health literacy (C‐statistics: 0.71 and 0.87, respectively) and were significantly associated with educational attainment, race/ethnicity, Consumer Assessment of Provider and Systems (CAHPS) scores, adherence, glycemia, comorbidities, and emergency department visits.ConclusionsSince health literacy is a potentially remediable explanatory factor in health care disparities, the development of automated health literacy indicators represents a significant accomplishment with broad clinical and population health applications. Health systems could apply literacy profiles to efficiently determine whether quality of care and outcomes vary by patient health literacy; identify at‐risk populations for targeting tailored health communications and self‐management support interventions; and inform clinicians to promote improvements in individual‐level care.     

Mindfulness-based interventions for eating disorders: The potential to mobilize multiple associative-learning change mechanisms

Mindfulness is a two-component skill that includes mindful awareness (attentional monitoring of present moment experience) and mindful acceptance (adopting an attitude of acceptance toward this experience). Although mindfulness-based interventions (MBIs) are efficacious for many conditions, there is a lack of research on MBIs for eating disorders (EDs). We propose that MBIs may be promising for EDs given their potential to mobilize not one, but multiple associative-learning change mechanisms in EDs–defined as adaptive processes of change involving one of two forms of associative-learning: Pavlovian and operant learning. We hypothesize how MBIs–via increasing either mindful awareness or mindful acceptance–may mobilize up to eight associative-learning change mechanisms, two involving Pavlovian learning, and six involving operant learning. We also elaborate on similarities and differences between MBIs and CBT approaches for EDs, as well as opportunities for synergy. Finally, we present recommendations for future research related to the development and evaluation of novel MBI interventions for EDs and the testing of mechanisms and patient-treatment matching hypotheses.     

Practice improves even the simplest movements

Summary Three subjects practiced accurate, fast elbow flexions of 54° to a 3° wide target. Movements of 36°, 54° and 72° were then tested. Comparison over the three distances showed that the normally monotonic relationship between movement distance and movement time is alterable by specific training. Subjects learn to go faster over the practiced distance by refining their neural commands to the muscles. The benefits of practice only partially transfer to other distances. We conclude that many of the relationships seen among movement variables in simple tasks are plastic in nature and affected by prior experience.