Nondestructive Measurement Technique for Substandard Amoxicillin Based on Thermal Approach.

In this study, we introduce a new nondestructive measurement technique based on a thermal approach for the determination of substandard amoxicillin. The quality control of amoxicillin is critical for patient safety, and one of the essential parameters for its evaluation is the content of the active ingredient. Traditional methods for assessing amoxicillin content are defined by their time-consuming nature, reliance on skilled personnel, and frequent necessity for specific reagents. The proposed device aims to provide a rapid and low-cost alternative that can accurately measure the amoxicillin content without damaging the sample. The method validation results indicate coefficient of determination (R2) exceeding 0.99, with percent recoveries falling within the range of 98.70-103.40%. The calculated values for limit of detection and limit of quantitation were determined to be 28.11 and 85.17 mg/L, respectively. Our experiments employed amoxicillin samples with predetermined concentrations, all of which were below the standard quality. It was observed that the proposed analytical device effectively quantifies the amoxicillin content in aqueous solutions. Each measurement took no more than 10 min, underscoring the efficiency of the analysis process. The experiments were validated through independent testing at the Government Pharmaceutical Organization in Thailand and the department of engineering science in Oxford, which provides strong evidence for the effectiveness and robustness of the technique. Overall, this study demonstrates the feasibility of using a thermal approach for the nondestructive measurement of substandard amoxicillin.

Association of Generation and Group Size With the Usage of a Mobile Health App in Thailand: Secondary Analysis of the ThaiSook Cohort Study.

BACKGROUND: In Thailand, The National Science and Technology Development Agency developed ThaiSook, a behavior-tracking app, to promote healthy lifestyles. The Faculty of Medicine, Prince of Songkla University (MED PSU)×ThaiSook Healthier Challenge encouraged employees to use the app over a 28-day period (from July 11 to August 7, 2022). Until recently, no previous studies have examined the association of generations and group sizes with mobile health (mHealth) app use. Understanding these relationships can inform the design of effective mHealth interventions and facilitate targeted interventions. OBJECTIVE: This study aimed to (1) compare the overall app usage and logging function across different generations and group sizes and (2) describe the demographic characteristics of the participants of the MED PSU×ThaiSook Healthier Challenge. METHODS: We conducted a secondary data analysis of the data from the ThaiSook prospective cohort study. Data were collected through the app and comprised demographic characteristics (ie, age, sex, weight, height, and group size) and behaviors (ie, water consumption, fruit and vegetable consumption, sleep hours, and exercise). The outcomes consisted of users who used the app for at least 80% of the participation period (≥23 days). Bivariate tests (Pearson chi-square test for categorical variables and Mann-Whitney U and Kruskal-Wallis tests for continuous variables) were conducted over sex, generations, initial BMI, and group size. Finally, multiple logistic regression models were used to examine the relationship between the independent variables used by the ThaiSook app and consistent users who had used the app for at least 80% of the participation period. RESULTS: Of the 827 participants, most were female (734/827, 88.8%), belonged to a medium-sized group of 6-10 members (479/827, 57.9%), and belonged to generation Y (377/761, 49.5%). Multivariate analysis revealed that the overall app usage was 2.09 times higher in women than in men (adjusted odds ratio [AOR] 2.09, 95% CI 1.27-3.44). The older generations used all logging functions more frequently than did generation Y (baby boomers AOR 2.54, 95% CI 1.31-4.92; generation X AOR 1.96, 95% CI 1.42-2.72). The use of all logging functions was higher among participants belonging to larger groups than among those belonging to smaller groups (large groups AOR 2.85, 95% CI 1.58-5.16; medium groups AOR 2.06, 95% CI 1.47-2.88). Water logging was most used (806/827, 97.5% participants), followed by food, sleep, and workout logging. CONCLUSIONS: The MED PSU×ThaiSook Healthier Challenge participants were mostly females from generation Y and medium-sized groups. Water logging was most frequently used, followed by fruit and vegetable logging. The results indicate that generation and group size were significantly associated with consistent and daily usage (P<.05). Older generations and larger groups engaged with the app more consistently than younger generations and smaller groups and individuals.

Workout Logging Through an mHealth App for Weight Reduction Among Different Generations: Secondary Analysis of the MED PSU×ThaiSook Healthier Challenge.

BACKGROUND: Being overweight or obese presents a major risk factor for noncommunicable diseases (NCDs) such as cardiovascular disease, diabetes, and musculoskeletal disorders. These problems are preventable and solvable via weight reduction and increased physical activity and exercise. The number of adults who are overweight or affected by obesity has tripled in the last 4 decades. Using mobile health (mHealth) apps can help users with health issues, including reducing their weight by restricting their daily calorie intake, which can be recorded along with other parameters, such as physical activity and exercise. These features could further enhance health and prevent NCDs. ThaiSook, a Thai mHealth app developed by the National Science and Technology Development Agency, aims to promote healthy lifestyles and reduce the risk behaviors of NCDs. OBJECTIVE: This study aimed to determine whether ThaiSook users were successful in 1-month weight reduction and identify which demographic factors or logging functions were associated with significant weight reduction. METHODS: A secondary data analysis was performed using data collected from the "MED PSU×ThaiSook Healthier Challenge," a month-long challenge to encourage a healthy lifestyle. We enrolled 376 participants to evaluate the study outcomes. The variables, comprising demographic characteristics (ie, sex, generation, group size, and BMI), were classified into 4 groups: normal (18.5-22.9 kg/m2), overweight (23-24.9 kg/m2), obese I (25-29.9 kg/m2), and obese II (BMI ≥30 kg/m2). Logging functions (ie, water, fruit and vegetables, sleep, workout, step, and run) were classified into 2 groups: consistent (≥80%) and inconsistent (<80%) users. Weight reduction was categorized into 3 groups: no weight reduction, slight weight reduction (0%-3%), and significant weight reduction (>3%). RESULTS: Of 376 participants, most were female (n=346, 92%), had normal BMI (n=178, 47.3%), belonged to Generation Y (n=147, 46.7%), and had a medium group size (6-10 members; n=250, 66.5%). The results showed that 56 (14.9%) participants had 1-month significant weight loss, and the median weight reduction of the group was -3.85% (IQR -3.40% to -4.50%). Most participants (264/376, 70.2%) experienced weight loss, with an overall median weight loss of -1.08% (IQR -2.40% to 0.00%). The factors associated with significant weight reduction were consistently logging workouts (adjusted odds ratio [AOR] 1.69, 95% CI 1.07-2.68), being Generation Z (AOR 3.06, 95% CI 1.01-9.33), and being overweight or being obese compared to those with normal BMI (AOR 2.66, 95% CI 1.41-5.07; AOR 1.76, 95% CI 1.08-2.87, respectively). CONCLUSIONS: More than half of the "MED PSU×ThaiSook Healthier Challenge" participants achieved a slight weight reduction, and 14.9% (56/376) of users lost significant weight. Factors including workout logging, being Generation Z, being overweight, and being obese were associated with significant weight reduction.

Classification of Parkinson's disease from smartphone recording data using time-frequency analysis and convolutional neural network.

BACKGROUND: Parkinson's disease (PD) is a long-term neurodegenerative disease of the central nervous system. The current diagnosis is dependent on clinical observation and the abilities and experience of a trained specialist. One of the symptoms that affects most patients is voice impairment. OBJECTIVE: Voice samples are non-invasive data that can be collected remotely for diagnosis and disease progression monitoring. In this study, we analyzed voice recording data from a smartphone as a possible medical self-diagnosis tool by using only one-second voice recording. The data from one of the largest mobile PD studies, the mPower study, was used. METHODS: A total of 29,798 ten-second voice recordings on smartphone from 4,051 participants were used for the analysis. The voice recordings were from sustained phonation by participants saying /aa/ for ten seconds into an iPhone microphone. A dataset comprising 385,143 short one-second audio samples was generated from the original ten-second voice recordings. The samples were converted to a spectrogram using a short-time Fourier transform. CNN models were then applied to classify the samples. RESULTS: Classification accuracies of the proposed method with LeNet-5, ResNet-50, and VGGNet-16 are 97.7 ± 0.1%, 98.6 ± 0.2%, and 99.3 ± 0.1%, respectively. CONCLUSIONS: We achieve a respectable classification performance using a generalized approach on a dataset with a large number of samples. The result emphasizes that an analysis based on one-second clip recorded on a smartphone could be a promising non-invasive and remotely available PD biomarker.

Residual effects of combined vibratory and plantar stimulation while seated influences plantar pressure and spatiotemporal gait measures in individuals with Parkinson's disease exhibiting freezing of gait.

Introduction: Combined plantar pressure and vibratory stimulation has been shown to decrease freezing of gait (FOG) episodes and improve spatiotemporal gait parameters compared to single stimulation in Parkinson's disease (PD) patients with FOG. However, the effect of combined plantar stimulations on plantar pressure analysis has never been explored. Methods: Forty PD patients with frequent FOG were allocated to either FOG shoes embedded with a 100 Hz vibratory stimulation at the Achilles tendons and a soft thickened silicone pad at the hallux and sole, or sham shoes with a non-working vibratory motor and a flat non-pressure silicone pad (20 patients per arm) while seated for 96 s. The objective gait and plantar pressure analysis were measured immediately after the stimulation. Outcomes included the normalized percentage of changes in percent FOG (%FOG) and plantar pressure in the heel-strike and push-off phase that were compared between pre- and post-stimulations. Results: The FOG shoes group showed significantly decreased %FOG (81.5 ± 28.9% vs. 6.8 ± 22.1%, p < 0.001), plantar pressure in the heel-strike (47.8 ± 43.7% vs. 4.3 ± 9.8%, p < 0.001), plantar pressure in the push-off (57.7 ± 59.6% vs. 6.2 ± 11.6%, p < 0.001), force time integral (FTI) (40.9 ± 32.5% vs. 6.6 ± 17.3%, p < 0.001), and decreased heel contact time (19.3 ± 12.3% vs. 22.7 ± 32.5%, p < 0.001) when compared to the sham group. There was a strong negative correlation between %FOG and peak plantar pressure (r = -0.440, p = 0.005), plantar pressure in the heel-strike (r = -0.847, p < 0.001). Conclusion: Our study demonstrated that the FOG shoe could decrease FOG episodes by improving the heel-strike pressure, toe push-off and normalized heel-to-toe plantar pressure, suggesting that modification inputs from the peripheral sensory systems might significant improvement in FOG in PD.

Vibratory and plantar pressure stimulation: Steps to improve freezing of gait in Parkinson's disease.

INTRODUCTION: Freezing of gait (FOG) is a devastating symptom that develops in patients with advanced Parkinson's disease (PD) and is often unresponsive to pharmacological treatment. Recent research suggests that FOG may result from dysfunctional plantar peripheral sensory systems. The impact of combined plantar pressure and vibratory stimulation over vibratory or pressure alone on FOG remains unexplored. METHODS: PD patients with FOG were randomised into four groups and treated with combined vibratory and pressure stimulation, vibratory stimulation alone, pressure stimulation alone, or controls (no stimulation). Vibratory stimulation targeted both Achilles' tendons. Simultaneous bilateral pressure stimulation was applied to the first hallux, first metatarsal bone, and the sole. The primary outcome included normalized percent changes in percent FOG measured both pre- and immediately post-stimulation. Other outcomes including clinical rating scale, response to questionnaires, number and duration of freezing episodes, and spatiotemporal gait parameters at pre- and freezing episodes were also explored. RESULTS: Sixty PD patients participated in the study. Patients who were treated with combined vibratory and pressure stimulation responded with significant decreases in normalized percent changes of percent FOG (62.75 ± 25.54%, p < 0.001) compared with those treated with vibration alone (11.38 ± 8.29%, p < 0.001), pressure alone (15.15 ± 16.18%, p < 0.001), or controls (8.59 ± 16.85%, p < 0.001). CONCLUSION: Our study demonstrated the benefit of combined vibratory and pressure stimulation on FOG suggesting that this strategy might be developed as a novel treatment modality for PD patients with FOG.

Non-destructive measurement technique for water content in organic solvents based on a thermal approach.

The water content of organic solvents is one of the crucial properties that affect the quality of the products and the efficiency of the manufacturing processes. The established water determination methods such as Karl Fischer titration and gas chromatography require skilled operators, specific reagents, and prolonged measurement time. Thus, they are not suitable for both on-line and in-line applications. In this study, we aim to develop a real-time and low-cost device with reliable accuracy. The proposed device based on a newly developed thermal approach could non-destructively detect the water content in multiple organic solvents at low concentrations with high accuracy and without the use of any specific reagent. Experiments were performed for the determination of water content in organic solvents such as methanol, ethanol, and isopropanol. The results show that the proposed device is feasible for the water content determination in methanol, ethanol, and isopropanol at 0-1% w/w. A Bland-Altman plot to illustrate the differences in measurements between the proposed device and coulometric Karl Fischer titration shows that most of the measurements lie within the limits of agreement where 95% of the differences between the two methods are expected to fall in the range of -0.13% and 0.09%.

Parkinson's disease severity clustering based on tapping activity on mobile device.

In this study, we investigated the relationship between finger tapping tasks on the smartphone and the MDS-UPDRS I-II and PDQ-8 using the mPower dataset. mPower is a mobile application-based study for monitoring key indicators of PD progression and diagnosis. Currently, it is one of the largest, open access, mobile Parkinson's Disease studies. Data from seven modules with a total of 8,320 participants who provided the data of at least one task were released to the public researcher. The modules comprise demographics, MDS-UPDRS I-II, PDQ-8, memory, tapping, voice, and walking. Finger-tapping is one of the tasks that easy to perform and has been analyzed for the quantitative measurement of PD. Therefore, participants who performed both the tapping activity and MDS-UPDRS I-II rating scale were selected for our analysis. Note that the MDS-UPDRS mPower Survey only contains parts of the original scale and has not been clinimetrically tested for validity and reliability. We obtained a total of 1851 samples that contained the tapping activity and MDS-UPDRS I-II for the analysis. Nine features were selected to represent tapping activity. K-mean was applied as an unsupervised clustering algorithm in our study. For determining the number of clusters, the elbow method, Sihouette score, and Davies-Bouldin index, were employed as supporting evaluation metrics. Based on these metrics and expert opinion, we decide that three clusters were appropriate for our study. The statistical analysis found that the tapping features could separate participants into three severity groups. Each group has different characteristics and could represent different PD severity based on the MDS-UPDRS I-II and PDQ-8 scores. Currently, the severity assessment of a movement disorder is based on clinical observation. Therefore, it is highly dependant on the skills and experiences of the trained movement disorder specialist who performs the procedure. We believe that any additional methods that could potentially assist with quantitative assessment of disease severity, without the need for a clinical visit would be beneficial to both the healthcare professionals and patients.

Classification of 41 Hand and Wrist Movements via Surface Electromyogram Using Deep Neural Network.

Surface electromyography (sEMG) is a non-invasive and straightforward way to allow the user to actively control the prosthesis. However, results reported by previous studies on using sEMG for hand and wrist movement classification vary by a large margin, due to several factors including but not limited to the number of classes and the acquisition protocol. The objective of this paper is to investigate the deep neural network approach on the classification of 41 hand and wrist movements based on the sEMG signal. The proposed models were trained and evaluated using the publicly available database from the Ninapro project, one of the largest public sEMG databases for advanced hand myoelectric prosthetics. Two datasets, DB5 with a low-cost 16 channels and 200 Hz sampling rate setup and DB7 with 12 channels and 2 kHz sampling rate setup, were used for this study. Our approach achieved an overall accuracy of 93.87 ± 1.49 and 91.69 ± 4.68% with a balanced accuracy of 84.00 ± 3.40 and 84.66 ± 4.78% for DB5 and DB7, respectively. We also observed a performance gain when considering only a subset of the movements, namely the six main hand movements based on six prehensile patterns from the Southampton Hand Assessment Procedure (SHAP), a clinically validated hand functional assessment protocol. Classification on only the SHAP movements in DB5 attained an overall accuracy of 98.82 ± 0.58% with a balanced accuracy of 94.48 ± 2.55%. With the same set of movements, our model also achieved an overall accuracy of 99.00% with a balanced accuracy of 91.27% on data from one of the amputee participants in DB7. These results suggest that with more data on the amputee subjects, our proposal could be a promising approach for controlling versatile prosthetic hands with a wide range of predefined hand and wrist movements.

Support vector machine classification of Parkinson's disease and essential tremor subjects based on temporal fluctuation.

Tremor is a common symptom shared in both Parkinson's disease (PD) and Essential tremor (ET) subjects. The differential diagnosis of PD and ET tremor is important since the realization of treatment depends on specific medication. A novel feature is developed based on a hypothesis that tremor of PD subject has a larger fluctuation during resting than action task. Tremor signal is collected using a triaxial gyroscope sensor attached to subject's finger during kinetic and resting task. The angular velocity signal is analyzed by transforming a one-dimensional to two-dimensional signal using a relation of signal and its delay versions. Tremor fluctuation is defined as the area of 95% confidence ellipse covering the two-dimensional signal. The tremor fluctuation during kinetic and resting task is used as classification features. The support vector machine is used as a classifier and tested with 10-fold cross-validation. This novel feature provides a perfect PD/ET classification with 100% accuracy, sensitivity and specificity.

A customized simulation system with computer integrated auto-evaluation function for upper endoscopy training.

Competence in performing endoscopy requires a considerable amount of hands-on practice for a physician to master. In an effort to reduce the involvement of patients in the training process, simulation has become an important part of endoscopic training. This paper presents a low-cost training and evaluation system for upper endoscopy that serves as an additional or alternative tool, particularly for entry-level gastroenterology fellows. The protocol of making a customized mechanical training model that provides realistic visual appearances of the upper GI tract was created. The materials and methods used in the fabrication are outlined step-by-step in this report. A software application associated with the mechanical model was also developed to provide help for trainee during practice, evaluate the performance automatically upon completion of the training tasks, and record the performance and results in a database for review or further analysis by the trainee or instructor. Our software provides trainee with an interactive training experience. A survey obtained from sixteen gastroenterologists shows a promising feasibility of using the developed system. The developed low-cost upper endoscopy simulator can provide both training and evaluation functions with substantially low investment.