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Sensitive electrochemical detection of glycated hemoglobin (HbA1c) using cobalt metal-organic framework/two-dimensional molybdenum diselenide nanocomposite-based immunosensors amplified by polyoxometalate/DNA aptamer
(2025-04-01)
Anuthum, Siriporn
;
Papan, Phakorn
;
Pasena, Arnat
;
Yimklan, Saranphong
;
Aramrat, Chanchanok
Clinical diagnosis and long-term diabetes management are advanced by monitoring glycated hemoglobin A1c (HbA1c) levels. New sensitive sandwich-like immunosensors for the diagnosis of early diabetes toward detecting HbA1c and hemoglobin (Hb) are demonstrated for the first time. DNA aptamers are used for signal amplification in the sensors for the detection of HbA1c and Hb. The immunosensors are constructed by coating with a cobalt-based metal-organic framework (Co-MOF)/two-dimensional molybdenum diselenide (2D MoSe<inf>2</inf>) composite onto a working electrode of an ItalSens screen-printed electrode (SPE) inserted into a Sensit/Smart Potentiostat affixed to a smartphone. After the immobilization of the antibodies, the detection is obtained by incubating the resultant SPEs in target solutions and then detecting the response of Keggin-type polyoxometalate (POM) bound on the DNA aptamer chains. In the selected potential window, the POM (silicotungstic acid, H<inf>4</inf>[α−SiW<inf>12</inf>O<inf>40</inf>]) used in this study exhibits the electron-transfer processes I and II ([α-SiW<inf>12</inf>O<inf>40</inf>]4−/5− and [α-SiW<inf>12</inf>O<inf>40</inf>]5−/6−, respectively) in the acidic buffer electrolyte. Our proposed device demonstrates exceptional performance in the recovery test of %HbA1c in healthy human plasma samples. The sensitivity, selectivity, and stability of this immunosensor are exceedingly outstanding, which makes it one of the potential analytical devices for diagnosing early diabetes by a %HbA1c assay.
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Non-invasive techniques with vital signs for glucose monitoring
(2025-01-01)
Mahittikorn, Pawarit
;
Tangkiatphaibun, Parawee
;
Thitathan, Thitisart
;
Udomtanasub, Pholchanok
;
Piyawattanametha, Wibool
This study is focusing on integrating finger sleeves for machine-learning with Near-infrared (NIR) spectroscopy and additional sensors techniques. It applied light emitting diodes (LEDs) at 660 nm, 880 nm, 940 nm wavelengths and photodetectors and a galvanic skin response (GSR) and a temperature sensor to read the signal from patients’ fingers. These sensors are attached to the finger sleeves to make it easy to wear for this continuous glucose monitoring. After the data was collected from the NIR spectroscopy and multiple sensors it has used in the machine learning models to predict the blood sugar level. For the machine learnings that was selected in this study are Linear regression, and Random forest model. Which the R2 result was 0.07 and -0.27 respectively.
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Prevalence of tuberculosis (TB), including multi-drug-resistant and extensively-drug-resistant TB, and association with occupation in adults at Sirindhorn Hospital, Bangkok
(2022-03-01)
Mphande-Nyasulu, Fingani Annie
;
Puengpipattrakul, Paisal
;
Praipruksaphan, Mathuwadee
;
Keeree, Arada
;
Ruanngean, Karakade
Background: Tuberculosis (TB) affects both child and adult populations worldwide. Objectives: This retrospective study was conducted to survey the prevalence of TB and its association with patient occupation in an adult population diagnosed with TB at Sirindhorn Hospital in 2018. Methods: Data were extracted from the medical records of 186 patients with TB, and prevalence and odds ratios were calculated. Results: Pulmonary (83.3%) and extrapulmonary TB (17.7%) were observed among the cases. Overall, 70.4% of cases were male and 29.6% were female. Mono-drug-resistant TB, multi-drug-resistant TB and extensively-drug-resistant TB were observed in 2.72%, 4.1% and 0.68% of cases, respectively. Although not statistically significant, individuals with comorbidities had a 2.16-fold [95% confidence interval (CI) 0.33–13.98] higher risk of TB compared with those without comorbidities. Unemployed patients with TB were 4-fold (95% CI 0.82–19.42) more likely to have hypertension than employed patients or traders. The risk of TB among patients with human immunodeficiency virus (HIV) infection was 2.22-fold (95% CI 0.93–5.31) higher among females compared with males, and relapsed patients had a 0.92-fold (95% CI 0.19–4.47) lower risk of HIV infection as a comorbidity compared with new TB cases. Conclusion: Patient occupation could play a role in the prevalence of TB among communities. The highest prevalence of TB was observed among unemployed subjects, and unemployed patients with TB were more likely to have hypertension as a comorbidity. Mapping the zones/areas of residence for patients with TB could assist in identifying TB hot spots, and could improve understanding of the drivers of the high TB burden and associated socio-economic factors. More studies are required to further understand the drivers that are leading to the high TB burden and the risks posed by occupations.
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A Personalized Food Recommendation Chatbot System for Diabetes Patients
(2020-01-01)
Thongyoo, Phupat
;
Anantapanya, Phuttipong
;
Jamsri, Pornsuree
;
Chotipant, Supannada
Diabetes is a disorder of the body that is unable to produce enough insulin. Diabetes causes the body to improperly burn sugar, which affects the blood sugar level leaving a sugar residue. Diabetes is related to genes, body weight, lack of exercise and aging. When patients with diabetes neglect good nutrition this can cause many health problems. This research, therefore, develops a chatbot named “Waan-Noy” to recommend a diet suitable for individuals with diabetes and build a cooperative health society. Our chatbot recommends personalized eating. It is suitable for use by diabetes patients as indicated by their evaluations. Through use of nutrition therapy controls, Waan-Noy recommends specific foods. The user’s evaluation is divided into 3 areas: content, design, and implementation to determine user degree of satisfaction with Waan-Noy.
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Comparison of Bayesian Networks for Diabetes Prediction
(2019-01-01)
Leerojanaprapa, Kanogkan
;
Sirikasemsuk, Kittiwat
A Bayesian network (BN) can be used to predict the prevalence of diabetes from the cause–effect relationship among risk factors. By applying a BN model, we can capture the interdependencies between direct and indirect risks hierarchically. In this study, we propose to investigate and compare the predictive performances of BN models with non-hierarchical (BNNH), and non-hierarchical and reduced variables (BNNHR) structures, hierarchical structure by expert judgment (BNHE), and hierarchical learning structure (BNHL) with type-2 diabetes. ROC curves, AUC, percentage error, and F1 score were applied to compare performances of those classification techniques. The results of the model comparison from both datasets (training and testing) obtained from the Thai National Health Examination Survey IV ensured that BNHE can predict the prevalence of diabetes most effectively with the highest AUC values of 0.7670 and 0.7760 from the training and the testing dataset, respectively.

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