Predefined vs data-guided training prescription based on autonomic nervous system variation: A systematic review

Monitoring variations in the functioning of the autonomic nervous system may help personalize training of runners and provide more pronounced physiological adaptations and performance improvements. We systematically reviewed the scientific literature comparing physiological adaptations and/or improvements in performance following training based on responses of the autonomic nervous system (ie, changes in heart rate variability) and predefined training. PubMed, SPORTDiscus, and Web of Science were searched systematically in July 2019. Keywords related to endurance, running, autonomic nervous system, and training. Studies were included if they (a) involved interventions consisting predominantly of running training; (b) lasted at least 3 weeks; (c) reported pre- and post-intervention assessment of running performance and/or physiological parameters; (d) included an experimental group performing training adjusted continuously on the basis of alterations in HRV and a control group; and (e) involved healthy runners. Five studies involving six interventions and 166 participants fulfilled our inclusion criteria. Four HRV-based interventions reduced the amount of moderate- and/or high-intensity training significantly. In five interventions, improvements in performance parameters (3000 m, 5000 m, Loadmax, Tlim) were more pronounced following HRV-based training. Peak oxygen uptake () and submaximal running parameters (eg, LT1, LT2) improved following both HRV-based and predefined training, with no clear difference in the extent of improvement in . Submaximal running parameters tended to improve more following HRV-based training. Research findings to date have been limited and inconsistent. Both HRV-based and predefined training improve running performance and certain submaximal physiological adaptations, with effects of the former training tending to be greater.     

中药生产过程质量控制关键技术研究进展

中医药发展已上升到国家战略层面,在医药行业贯彻实施"中国制造2025"战略的新形势下,中药生产过程质量控制是中药工业需要加快突破的关键领域之一。对中药生产过程质量控制领域在工艺设计、分析检测、过程建模、制造装备等方面的关键共性问题进行解析,综述了中药生产过程质量控制体系中工艺过程理解、生产过程实时分析方法开发、过程控制策略建立3个方面的研究进展;并结合企业研究实践,介绍了质量源于设计(quality by design,Qb D)、过程分析技术(process analytical technology,PAT)、实验设计(design of experiment,DOE)、多变量统计分析等关键技术在上述3个研究方向中的应用进展,分析了实际工业应用的难点问题并对其应用前景进行展望,旨在为中药企业应用和提升生产过程质量控制技术提供参考。     

Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study

IntroductionPredicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs.Materials and methodsWe retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs.ResultsThe WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80).ConclusionOur study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.     

Enhancing Patients' Wayfinding and Visitation Experience Improves Quality of Care

PurposeTo understand the impact of wayfinding challenges on patient hospital visitation experience, organizational costs, and emerging technology trends that may alleviate wayfinding challenges.DesignA review of literature on the relationship of wayfinding to patient's hospital visitation experience.MethodsA literature search identifying issues associated with wayfinding challenges in complex health care facilities was conducted. Case examples are provided to illustrate that the removal of barriers can improve the wayfinding experience. This review also informs technology trends that may effectively facilitate wayfinding in complex health care facilities.FindingsLarge hospital complexes exhibit many characteristics comparable to the physical features of a city environment. These complexities present challenges to patients to navigate to their destinations. An effective wayfinding system is a collection of tools that combines permanent signage, printed information, landmarks, architectural features and design elements, and human interactions. Navigational technology modeling holds the promise to aid patients and individuals with visual and cognitive challenges to find their way to their desired destinations effectively and efficiently, and improves their quality of life.ConclusionsImproved patient travel time correlates to reduce idled nursing time, with cost savings accrued to organization. Research shows that wayfinding mobile applications hold the promise of improving patients' hospital visitation experience.     

Mixed Reality Combined with Three‐Dimensional Printing Technology in Total Hip Arthroplasty: An Updated Review with a Preliminary Case Presentation

Three‐dimensional (3D) printing technology, virtual reality, and augmented reality technology have been used to help surgeons to complete complex total hip arthroplasty, while their respective shortcomings limit their further application. With the development of technology, mixed reality (MR) technology has been applied to improve the success rate of complicated hip arthroplasty because of its unique advantages. We presented a case of a 59‐year‐old man with an intertrochanteric fracture in the left femur, who had received a prior left hip fusion. After admission to our hospital, a left total hip arthroplasty was performed on the patient using a combination of MR technology and 3D printing technology. Before surgery, 3D reconstruction of a certain bony landmark exposed in the surgical area was first performed. Then a veneer part was designed according to the bony landmark and connected to a reference registration landmark outside the body through a connecting rod. After that, the series of parts were made into a holistic reference registration instrument using 3D printing technology, and the patient's data for bone and surrounding tissue, along with digital 3D information of the reference registration instrument, were imported into the head‐mounted display (HMD). During the operation, the disinfected reference registration instrument was installed on the selected bony landmark, and then the automatic real‐time registration was realized by HMD through recognizing the registration landmark on the reference registration instrument, whereby the patient's virtual bone and other anatomical structures were quickly and accurately superimposed on the real body of the patient. To the best of our knowledge, this is the first report to use MR combined with 3D printing technology in total hip arthroplasty.