Diabetes mellitus (DB) is the most challenging and fastest-growing global public health challenge. An estimated 10.5% of the global adult population has been suffering from diabetes, and almost half of them are undiagnosed. The growing at-risk population further exacerbated the scarce health resources where the adults worldwide with impaired glucose tolerance (IGT) and impaired fasting glycaemia (IFG) were estimated at around 10.6% and 6.2%, respectively. All the current diabetes screening methods are invasive and opportunistic and must be conducted in a hospital or a laboratory by trained professionals. At-risk subjects might remain undetected for years and miss the precious time window for early intervention in preventing or delaying the onset of diabetes and its complications. This study was conducted at  KK Women’s and Children’s Hospital of Singapore, and five hundred participants were recruited (mean age 38.73 $\pm$ 10.61 years; mean BMI 24.4 $\pm$ 5.1 kg/m\textsuperscript{2}). The blood glucose levels, for most participants, were measured before and after 75g of sugary drink using both the conventional glucometer (Accu-Chek Performa) and the wrist-worn wearable. The results obtained from the glucometer were used as the ground truth measurements. We propose leveraging photoplethysmography (PPG) sensors and machine learning techniques to incorporate this into an affordable wrist-worn wearable device to detect elevated blood glucose levels ($\geqslant 7.8 mmol/L $) non-invasively. Multiple machine learning models were trained and assessed with 10-fold cross-validation using subject demographic data and critical features extracted from the PPG measurements as predictors. Support vector machine (SVM) with a  radial basis function kernel has the best detection performance with an average accuracy of 84.7%, a sensitivity of 81.05%, a specificity of 88.3%,  a precision of 87.51%, a geometric mean of 84.54% and F-score of 84.03%. Hence, PPG measurements can be utilized to identify subjects with elevated blood glucose measurements and assist in the screening of subjects for diabetes risk.

Freezing of Gait is the most disabling gait disturbance in Parkinson’s disease. For the past decade, there has been a growing interest in applying machine learning and deep learning models to wearable sensor data to detect Freezing of Gait episodes. In our study, we recruited sixty-seven Parkinson’s disease patients who have been suffering from Freezing of Gait, and conducted two clinical assessments while the patients wore two wireless Inertial Measurement Units on their ankles. We converted the recorded time-series sensor data into continuous wavelet transform scalograms and trained a Convolutional Neural Network to detect the freezing episodes. The proposed model achieved a generalisation accuracy of 89.2% and a geometric mean of 88.8%.

Background and Purpose Asthma is characterized by airway inflammation, mucus hypersecretion and airway hyperresponsiveness (AHR). The activation of cholinergic anti‐inflammatory pathway (CAP) through nicotinic agents has been shown to control experimental asthma. Yet, the effects of vagus nerve stimulation (VNS)-induced CAP on allergic inflammation remain unknown. Experimental Approach BALB/c mice were sensitized and challenged with house dust mite (HDM) extract, and treated with active VNS (5Hz, 0.5 ms, 0.1 mA). Bronchoalveolar lavage (BAL) fluid was assessed for total and differential cell counts and cytokine levels. Lungs were examined by histopathology and electron microscopy. AHR in response to methacholine was also measured. Key Results In the HDM mouse asthma model, active but not sham VNS reduced BAL fluid total and differential cell counts, blocked mucus hypersecretion and suppressed choline acetyltransferase (ChAT) expression in bronchial epithelial cells. Besides, active VNS also abated HDM-induced elevation of type 2 cytokines IL-4 and IL-5. Furthermore, goblet cell hyperplasia and collagen deposition were diminished in VNS-treated mice. Mechanistically, VNS was found to block the phosphorylation of transcription factor STAT6 and the level of IRF4 in total lung lysates. Finally, VNS abrogated methacholine-induced AHR in asthma mice. Therapeutic effects of VNS were abolished by prior administration with α-bungarotoxin, a specific inhibitor of α7 nicotinic receptors (α7nAChR). Conclusion Our data revealed the protective effects of VNS on various clinical features in allergic airway inflammation model. VNS, a clinically approved therapy for depression and epilepsy, appears to be a promising new strategy for controlling allergic asthma through α7nAChR.