从“肾毒”论治慢性肾脏病＊

慢性肾脏病是一种以肾脏功能的损伤为主要表现，并可累及全身多脏器的慢性进展性疾病。中医辨证治疗本病在延缓病情、改善预后等方面具有显著的优势。通过中医理论研究及临床经验总结，笔者提出从“肾毒”论治慢性肾脏病，认为其不仅是本病的致病因素，更是重要的病理产物，是本病发生发展、迁延难愈的关键。在治疗上提出以“益肾元、解肾毒”为大法，并根据不同兼夹辨证施治，采用药对，长治缓图。以“肾毒”立论拟定的经验方苏茵解毒方，在临床治疗和实验研究上均显示出明显的效果，并已制备成院内制剂临床使用，在疗效及经济效益方面均具有显著的优势。     

Applications of geospatial analyses in health research among homeless people: A systematic scoping review of available evidence

BackgroundUnequal housing access resulted in more than 150 million homeless people worldwide, with millions more expected to be added every year due to the ongoing climate-related crises. Homeless population has a counterproductive effect on the social, psychological integration efforts by the community and exposure to other severe health-related issues. Geographic Information Systems (GIS) have long been applied in urban planning and policy, housing and homelessness, and health-related research.MethodsWe used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to systematically review 24 articles collected from multiple databases (n = 10) that focused on health-related issues among homeless people and used geospatial analysis techniques in their research.ResultsOur findings indicated a geographic clustering of case study locations– 26 out of the 31 case study sites are from the USA and Canada. Studies used spatial analysis techniques to identify hotspots, clusters and patterns of patient location and population distribution. Studies also reported relationships among the location of homeless shelters and substance use, discarded needles, different infectious and non-infectious disease clusters.ConclusionMost studies were restricted in analyzing and visualizing the patterns and disease clusters; however, geospatial analyses techniques are useful and offer diverse techniques for a more sophisticated understanding of the spatial characteristics of the health issues among homeless people. Better integration of GIS in health research among the homeless would help formulate sensible policies to counter health inequities among this vulnerable population group.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

A review of approaches to 18F radiolabelling affinity peptides and proteins

Affinity peptide and protein‐ (APP) based radiotracers are an increasingly popular class of radiotracer in positron emission tomography (PET), which was once dominated by the use of small molecule radiotracers. Radiolabelled monoclonal antibodies (mAbs) are important examples of APPs, yet a preference for smaller APPs, which exhibit fast pharmacokinetics and permit rapid PET aided diagnosis, has become apparent. ¹⁸F exhibits favourable physical characteristics for APP radiolabelling and has been described as an ideal PET radionuclide. Notwithstanding, ¹⁸F radiolabelling of APP is challenging, and this is echoed in the literature where a number of diverse approaches have been adopted. This review seeks to assess and compare the approaches taken to ¹⁸F APP radiolabelling with the intention of highlighting trends within this expanding field. Generic themes have emerged in the literature, namely the use of mild radiolabelling conditions, a preference of site‐specific methodologies with an impetus for short, automated procedures which produce high‐yielding [¹⁸F]APPs.     

Medication Adherence App for Food Pantry Clients With Diabetes: A Feasibility Study

Low-income food pantry clients are unable to adhere to the diet and physical activity recommendations of the American Diabetes Association. The aim of the study is to test the feasibility of using a mobile phone app to improve diabetes medication adherence. Clients with uncontrolled type 2 diabetes were enrolled in a mobile phone app featuring 70 days of text message reminders and incentives. The app and the 4-item Morisky Medication Adherence Scale evaluated medication adherence. Clinically significant medication adherence of 93% was achieved with use of the app. Phone app use is feasible among urban low-income clients to improve medication adherence.     

Clinical Outcomes at 1 Year Following Transcatheter Left Atrial Appendage Occlusion in the United States

ObjectivesThe aim of this study was to report 1-year clinical outcomes following commercial transcatheter left atrial appendage occlusion (LAAO) in the United States.BackgroundThe National Cardiovascular Data Registry LAAO Registry was initiated to meet a condition of Medicare coverage and allow the assessment of clinical outcomes. The 1-year rates of thromboembolic events after transcatheter LAAO in such a large cohort of “real-world” patients have not been previously reported.MethodsPatients entered into the National Cardiovascular Data Registry LAAO Registry for a Watchman procedure between January 1, 2016, and December 31, 2018, were included. The primary endpoint was ischemic stroke. Key secondary endpoints included the rate of ischemic stroke or systemic embolism, mortality, and major bleeding. Major bleeding was defined as any bleeding requiring hospitalization, and/or causing a decrease in hemoglobin level > 2g/dL, and/or requiring blood transfusion that was not hemorrhagic stroke. The Kaplan-Meier method was used for 1-year estimates of cumulative event rates.ResultsThe study population consisted of 36,681 patients. The mean age was 76.0 ± 8.1 years, the mean CHA₂DS₂-VASc score was 4.8 ± 1.5, and the mean HAS-BLED score was 3.0 ± 1.1. Prior stroke was present in 25.5%, clinically relevant bleeding in 69.5%, and intracranial bleeding in 11.9%. Median follow-up was 374 days (IQR: 212-425 days). The Kaplan-Meier–estimated 1-year rate of ischemic stroke was 1.53% (95% CI: 1.39%-1.69%), the rate of ischemic stroke or systemic embolism was 2.19% (95% CI: 2.01%-2.38%), and the rate of mortality was 8.52% (95% CI: 8.19%-8.87%). The 1-year estimated rate of major bleeding was 6.93% (95% CI: 6.65%-7.21%). Most bleeding events occurred between discharge and 45 days following the procedure.ConclusionsThis study characterizes important outcomes in a national cohort of patients undergoing transcatheter LAAO in the United States. Clinicians and patients can integrate these data in shared decision making when considering this therapy.     

轴向载荷分担比指导外固定器动力化的临床应用

目的:探讨轴向载荷分担比用于胫腓骨骨干骨折术后指导外固定器轴向动力化促进骨折愈合的有效性。方法:选取外固定器治疗的胫腓骨骨干骨折患者100例,随机分为观察组50例,对照组50例。观察组在轴向载荷分担比指导下行外固定器轴向动力化治疗,对照组未行动力化,随访比较两组的治疗效果。结果:所有患者均获随访,随访时间4～12个月,平均6.5个月,治疗期间所有患者均未出现外固定针断裂、松动及再骨折等并发症,观察组1例骨搬移患者轴向载荷分担比5%,X线片显示骨折断端有连续性骨痂通过,拆除外固定器连接杆后发生移位,恢复原数值行轴向加压再动力化,现已愈合。观察组除外1例骨搬移患者,其余49例患者外固定器固定时间为[(24.4±4.7)周],骨折临床愈合时间为[(22.4±4.7)周],与对照组50例患者外固定器固定时间[(29.3±5.6)周],骨折临床愈合时间[(27.3±5.6)周]比较,显著减少(P0.05)。结论:外固定器轴向载荷分担比指导胫腓骨骨干骨折外固定术后轴向动力化可以加速骨折愈合,但不适合骨搬移截骨端已硬化患者。