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。     

Correcting forensic DNA errors

DNA mixture interpretation can produce opposing conclusions by qualified forensic analysts, even within the same laboratory. The long-delayed publication of the National Institutes of Standards and Technology (NIST) study of 109 North American crime laboratories in this journal demonstrates this most clearly. This latest study supports earlier work that shows common methods such as the Combined Probability of Inclusion (CPI) have wrongly included innocent people as contributors to DNA mixtures. The 2016 President's Council of Advisors on Science and Technology report concluded, “In summary, the interpretation of complex DNA mixtures with the CPI statistic has been an inadequately specified—and thus inappropriately subjective—method. As such, the method is clearly not foundationally valid” [7]. The adoption of probabilistic genotyping by many laboratories will certainly prevent some of these errors from occurring in the future, but the same laboratories that produced past errors can also now review old cases with their new software—without additional bench work. It is critical that laboratories adopt procedures and policies to do this.     

Identification of key genes and pathways in discoid lupus skin via bioinformatics analysis

Discoid lupus erythematosus (DLE) is the most common skin manifestation of lupus; however, the molecular mechanisms underlying DLE remain unknown. Therefore, we aimed to identify key differentially expressed genes (DEGs) in discoid lupus skin and investigate their potential pathways.To identify candidate genes involved in the occurrence and development of the disease, we downloaded the microarray datasets {"type":"entrez-geo","attrs":{"text":"GSE52471","term_id":"52471"}}GSE52471 and {"type":"entrez-geo","attrs":{"text":"GSE72535","term_id":"72535"}}GSE72535 from the Gene Expression Database (GEO). DEGs between discoid lupus skin and normal controls were selected using the GEO2R tool and Venn diagram software (http://bioinformatics.psb.ugent.be/webtools/Venn/). The Database for Annotation, Visualization, and Integrated Discovery (DAVID), Enrichr, and Cytoscape ClueGo were used to analyze the Kyoto Encyclopedia of Gene and Genome pathways and gene ontology. Protein-protein interactions (PPIs) of these DEGs were further assessed using the Search Tool for the Retrieval Interacting Genes version 10.0.Seventy three DEGs were co-expressed in both datasets. DEGs were predominantly upregulated in receptor signaling pathways of the immune response. In the PPI network, 69 upregulated genes were selected. Furthermore, 4 genes (CXCL10, ISG15, IFIH1, and IRF7) were found to be significantly upregulated in the RIG-I-like receptor signaling pathway, from analysis of Enrichr and Cytoscape ClueGo.The results of this study may provide new insights into the potential molecular mechanisms of DLE. However, further experimentation is required to confirm these findings.