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.     

Is low or high body mass index in patients operated for oral squamous cell carcinoma associated with the perioperative complication rate?

The aim of this study was to analyse the effect of body mass index (BMI), both low and high values, on the perioperative complication rate in patients with oral squamous cell carcinoma (OSCC). The medical records of 259 patients operated between 2014 and 2017 for OSCC were reviewed. Univariate and multivariate analyses were performed. Sixty of the 259 patients developed 87 complications. Low or high BMI was not associated with the perioperative complication rate. A longer operating time and increased blood loss were associated with a higher perioperative complication rate and higher Clavien–Dindo grade. Low BMI, American Society of Anesthesiologists score 2 and 3, a longer operating time, and increased blood loss were associated with a longer hospital stay. Low BMI was associated with a longer hospital stay. Neither low nor high BMI was associated with the perioperative complication rate. A longer operating time and increased blood loss were associated with a higher perioperative complication rate and higher Clavien–Dindo grade.     

Buried Bumper Syndrome: A common complication of levodopa intestinal infusion for Parkinson disease

BackgroundPercutaneous endoscopic gastrostomy (PEG) is required for Levodopa/Carbidopa Intestinal Gel (LCIG) delivery in patients with advanced Parkinson's disease (PD) as well as for enteral feeding in a variety of neurological disorders. Buried Bumper Syndrome (BBS) is a serious complication of PEG. The frequency of BBS in patients receiving LCIG treatment has never been reported.ObjectivesTo compare the frequency of BBS in patients on LCIG treatment or on enteral feeding over the past 12 years and identify possible risk factors.MethodsWe reviewed prospectively recorded data from 2009 to 2020 on two case-series: LCIG-treated PD patients and non-PD patients on enteral nutrition. We identified all BBS incidences. Patients’ characteristics, clinical manifestations, BBS management, possible risk factors and outcomes were analyzed.ResultsDuring the 12 years, 35 PD patients underwent PEG insertion for LCIG infusion, and 123 non-PD patients for nutritional support. There were eight cases of BBS in six PD patients (17.1%). Six of them were effectively managed without treatment discontinuation. Of the enteral feeding patients, only one developed BBS (0.8%) (p < 0.001). We identified inappropriate PEG site aftercare, weight gain, early onset PD, longer survival, treatment duration, dementia and PEG system design as potential risk factors for BBS development.ConclusionsBBS occurs more frequently in LCIG patients than in patients receiving enteral feeding. If detected early, it can be successfully managed, and serious sequalae or treatment discontinuation can be avoided. Regular endoscopic follow-up visits of LCIG-treated patients and increased awareness in patients and clinicians are recommended.     

A guide to oral vaccination: Highlighting electrospraying as a promising manufacturing technique toward a successful oral vaccine development

Most vaccines approved by regulatory bodies are administered via intramuscular or subcutaneous injections and have shortcomings, such as the risk of needle-associated blood infections, pain and swelling at the injection site. Orally administered vaccines are of interest, as they elicit both systemic and mucosal immunities, in which mucosal immunity would neutralize the mucosa invading pathogen before the onset of an infection. Hence, oral vaccination can eliminate the injection associated adverse effects and enhance the person's compliance. Conventional approaches to manufacturing oral vaccines, such as coacervation, spray drying, and membrane emulsification, tend to alter the structural proteins in vaccines that result from high temperature, organic and toxic solvents during production. Electrohydrodynamic processes, specifically electrospraying, could solve these challenges, as it also modulates antigen release and has a high loading efficiency. This review will highlight the mucosal immunity and biological basis of the gastrointestinal immune system, different oral vaccine delivery approaches, and the application of electrospraying in vaccines development.     

Treatment sequence network meta-analysis in Crohn’s disease: a methodological case study

Objective: Several biologic therapies are available for the treatment of mild-to-moderate Crohn’s disease (CD). This network meta-analysis (NMA) aimed to assess the comparative efficacy of ustekinumab, adalimumab, vedolizumab and infliximab in the maintenance of clinical response and remission after 1?year of treatment.

Methods: A systematic literature search was performed to identify relevant randomized controlled trials (RCTs). Key outcomes of interest were clinical response (CD activity index [CDAI] reduction of 100 points; CDAI-100) and remission (CDAI score under 150 points; CDAI < 150). A treatment sequence Bayesian NMA was conducted to account for the re-randomization of patients based on different clinical definitions, the lack of similarity of the common comparator for each trial and the full treatment pathway from the induction phase onwards.

Results: Thirteen RCTs were identified. Ustekinumab 90?mg q8w was associated with statistically significant improvement in clinical response relative to placebo and vedolizumab 300?mg. For clinical remission, ustekinumab 90?mg q8w was associated with statistically significant improvement relative to placebo and vedolizumab 300?mg q8w. Findings from sub-population analyses had similar results but were not statistically significant.

Conclusions: The NMA suggest that ustekinumab is associated with the highest likelihood of reaching response or remission at 1?year compared with placebo, adalimumab and vedolizumab. Results should be interpreted with caution because this is a novel methodology; however, the treatment sequence analysis may be the most methodologically sound analysis to derive estimates of comparative efficacy in CD in the absence of head-to-head evidence.