Currently, there is increased interest in finding appropriate food-grade green extraction systems capable of extracting these bioactive compounds from dietary mushrooms for applications in various food, pharmacological, or nutraceutical formulations. Herein, we evaluated a modified Swiss water process (SWP) method using alkaline and acidic pH at low and high temperature under pressurized conditions as a suitable green food grade solvent to obtained extracts enriched with myco-nutrients (dietary phenolics, total antioxidants (TAA), vitamins, and minerals) from Chaga. Ultra-high performance liquid chromatography coupled to high resolution accurate mass tandem mass spectrometry (UHPLC-HRAMS-MS/MS) was used to assess the phenolic compounds and vitamin levels in the extracts, while inductively coupled plasma mass spectrometry (ICP-MS) was used to determine the mineral contents. Over 20 phenolic compounds were quantitatively evaluated in the extracts and the highest total phenolic content (TPC) and total antioxidant activity (TAA) was observed at pH 11.5 at 100 °C. The most abundant phenolic compounds present in Chaga extracts included phenolic acids such as protocatechuic acid 4-glucoside (0.7–1.08 µg/mL), syringic acid (0.62–1.18 µg/mL), and myricetin (0.68–1.3 µg/mL). Vitamins are being reported for the first time in Chaga. Not only, a strong correlation was found for TPC with TAA (r-0.8, <0.0001), but also, with individual phenolics (i.e., Salicylic acid), lipophilic antioxidant activity (LAA), and total antioxidant minerals (TAM). pH 2.5 at 100 °C treatment shows superior effects in extracting the B vitamins whereas pH 2.5 at 60 and 100 °C treatments were outstanding for extraction of total fat-soluble vitamins. Vitamin E content was the highest for the fat-soluble vitamins in the Chaga extract under acidic pH (2.5) and high temp. (100 °C) and ranges between 50 to 175 µg/100 g Chaga. Antioxidant minerals ranged from 85.94 µg/g (pH7 at 100 °C) to 113.86 µg/g DW (pH2.5 at 100 °C). High temperature 100 °C and a pH of 2.5 or 9.5. The treatment of pH 11.5 at 100 °C was the most useful for recovering phenolics and antioxidants from Chaga including several phenolic compounds reported for the first time in Chaga. SWP is being proposed herein for the first time as a novel, green food-grade solvent system for the extraction of myco-nutrients from Chaga and have potential applications as a suitable approach to extract nutrients from other matrices. Chaga extracts enriched with bioactive myconutrients and antioxidants may be suitable for further use or applications in the food and nutraceutical industries.

Background Using embedded sensors, instrumented walkways provide clinicians with important information regarding gait disturbances. However, because raw data are summarized into standard gait variables, there may be some salient features and patterns that are ignored. Multiple sclerosis (MS) is an inflammatory neurodegenerative disease which predominantly impacts young to middle-aged adults. People with MS may experience varying degrees of gait impairments, making it a reasonable model to test contemporary machine leaning algorithms. In this study, we employ machine learning techniques applied to raw walkway data to discern MS patients from healthy controls. We achieve this goal by constructing a range of new features which supplement standard parameters to improve machine learning model performance. Results Eleven variables from the standard gait feature set achieved the highest accuracy of 81%, precision of 95%, recall of 81%, and F1-score of 87%, using support vector machine (SVM). The inclusion of the novel features (toe direction, hull area, base of support area, foot length, foot width and foot area) increased classification accuracy by 7%, recall by 9%, and F1-score by 6%. Conclusions The use of an instrumented walkway can generate rich data that is generally unseen by clinicians and researchers. Machine learning applied to standard gait variables can discern MS patients from healthy controls with excellent accuracy. Noteworthy, classifications are made stronger by including novel gait features (toe direction, hull area, base of support area, foot length and foot area).

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Machine learning can discern meaningful information from large datasets. Applying machine learning techniques to raw sensor data from instrumented walkways could automatically detect subtle changes in walking and balance. Multiple sclerosis (MS) is a neurological disorder in which patients report varying degrees of walking and balance disruption. This study aimed to determine whether machine learning applied to walkway sensor data could classify severity of self-reported symptoms in MS patients. Ambulatory people with MS (n = 107) were asked to rate the severity of their walking and balance difficulties, from 1-No problems to 5-Extreme problems, using the MS-Impact Scale-29. Those who scored less than 3 (moderately) were assigned to the “mild” group (n = 35), and those scoring higher were in the “moderate” group (n = 72). Three machine learning algorithms were applied to classify the “mild” group from the “moderate” group. The classification achieved 78% accuracy, a precision of 85%, a recall of 90%, and an F1 score of 87% for distinguishing those people reporting mild from moderate walking and balance difficulty. This study demonstrates that machine learning models can reliably be applied to instrumented walkway data and distinguish severity of self-reported impairment in people with MS.

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