Journal of Applied Science and Emerging Technology

Development of Smear-Sputum Slides with Acid Fast Bacilli Positive Using Polyvinylidene Fluoride Nanofiber Filters

Kanokwan Kittiniyom — 2025-08-14

Acid-fast staining of sputum is a simple and widely used method for diagnosis of pulmonary tuberculosis worldwide.
This method is high specificity but requires the expertise of the operator in analysis. Therefore, quality assurance of the testing method is essential, with sputum smear slides used as one of the quality control materials. This study aimed to develop a method for the preparing sputum smear slides using cultured acid fast bacilli (AFB) which were dispersed by filtration through a polyvinylidene fluoride (PVDF) nanofiber membrane coated with polyvinyl alcohol (PVA). Four types of the homemade nanofiber filters as 2Hz 8M, 2Hz 4M, 3Hz 8M and 3Hz 4M with a porosity of up to 99.98 percent and filter thicknesses of 158, 222, 48 and 81 micrometers, respectively were compared for filtration efficiency. When used to filter AFB, the 2Hz 4M PVDF membrane produced sputum smear slides with a high number of single AFB (averaging 4.0 ± 2.8 cells per oil power field) and small clusters of 2-5 AFB per cluster (averaging 4.67 ± 0.2 clusters per oil power field). The proportion of single AFB by the 2Hz 4M was higher than the number of AFB clumping in clusters compared to the use of other types of filters. In the preparation process of sputum smear slides, the concentration of AFB could be adjusted to give the result of AFB 3+, AFB2+, and AFB 1+ according to the World Health Organization's reporting criteria by diluting AFB at a 1:10 ratio for each reporting level. This prepared slides closely resembled the actual patient sputum, showing leukocytes and welled-dispersed single AFB under microscopic examination. This study is the first to apply nanofiber membrane filtration for dispersing AFB in sputum smear preparation. This method is simple, practical, and suitable for use in quality assurance of acid-fast staining in clinical laboratories as well as in training healthcare personnel.

The Impact of Extract from Astragalus membranaceus on Blood Coagulation in Individuals with Diabetes Mellitus

Duangmanee Sanmun — 2025-08-14

Diabetes-related vascular problems might result from persistent hyperglycemia, and individuals with diabetes have an increased risk of blood clot formation compared to healthy controls. Anticoagulant drugs are medications used to dissolve blood clots; various agents have been employed to improve platelet function and minimize thrombosis in these patients. Medicinal plants can be used as alternatives with potentially fewer side effects. Interestingly, Astragalus membranaceus (also known as Huangqi) extract was obtained for in vitro investigation. This study aimed to assess the anticoagulant impact of Huangqi on human plasma. The research focused on prothrombin time (PT) and activated partial thromboplastin time (aPTT) among diabetic patients. In vitro anticoagulant effects were evaluated using plasma treated with the extract, while DMSO served as a negative control. The results showed that the baseline PT in untreated plasma from the healthy group was 11.79 seconds, compared to 11.69 seconds in the diabetic group. A significant difference (P<0.05) was observed between healthy donors and diabetic patients; the mean aPTT in diabetic patients was 26.03 seconds, while in controls, it was 28.52 seconds. The present study highlighted the potent antithrombin effect of Huangqi extract at a dosage of 50 mg/ml. The PT and aPTT assays were significantly prolonged by Huangqi (P<0.001), demonstrating effective anticoagulant activity. This suggests that Huangqi may benefit patients who are at high risk of thrombosis.

Modeling Global Ice Volume Changes: A Nonlinear Autoregressive Neural Network Approach

Porawat Visutsak — 2025-08-14

A single time series prediction problem is solved with a neural network. The nonlinear autoregressive (NAR) type of network is used. The network is trained in an open loop and then transformed to closed loop for multistep prediction. The prediction is made 20 time steps into the future. The delay is removed from the network to get the prediction one time step earlier. The shallow neural network is trained on the global ice volume dataset, which contains 219 measurements of global ice volume over 440,000 years. The network is able to predict future ice volume based on past values with a high degree of accuracy. Three different backpropagation training algorithms were used to train the network: Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The Levenberg-Marquardt algorithm achieved the lowest MSE (0.02257 at epoch 13) and the highest R² (0.99254). The Bayesian Regularization algorithm achieved an MSE of 0.027209 at epoch 4 and an R² of 0.99192. The Scaled Conjugate Gradient algorithm achieved an MSE of 0.01878 at epoch 3 and an R² of 0.99018. This work contributes to the field of climate studies by providing a tool for predicting future ice volume. This information can be used to better understand Earth’s glacial cycles and to develop strategies for mitigating the effects of climate change.

Development of Ideal Gas Kit using Pressure and Temperature Sensors via Arduino

Preedaporn Suwandee — 2025-08-14

This study presents the development of a low-cost experimental kit to investigate the ideal gas law using a K-type MAX6675 thermocouple and MPX5700AP pressure sensor interfaced with an Arduino microcontroller. The kit enables real-time measurements and LCD display of pressure and temperature data, making it suitable for classroom demonstration and inquiry-based learning. Experiments based on Boyle’s law and the combined gas law were conducted, and the data showed a high correlation with theoretical predictions. The calculated error in the gas constant was only 0.08%, and the estimated system volume was 2.03 cc, and also the number of moles of gas was -32.30 × 10moles. Compared with existing commercial kits, the proposed design is more cost-effective, portable, and educationally versatile. The findings support the pedagogical value of using microcontroller-based sensors in physics education.

The Application of Using Local Plant for Determination of Formalin Contaminated in Food

Warangkhana Khaodee — 2025-08-14

This research aimed to apply extracted from local plants to determine formalin residue in food. The precipitation would be appeared from this reaction. Four plants in Phyllanthaceae family were studied, 2 pants were consisted of adenosine (Phyllanthus acidus and Sauropus androgynus) and another 2 were Antidesma bunius and Phyllanthus emblica. It was found that only the extract from Sauropus androgynus was precipitated with formalin. The Sauropus androgynus extract was made to be powder by freeze drying process. Using 0.5 gram of extract powder in 5 milliliters of formalin solution showed 5% w/v formalin concentration as the detection limit This extract powder was then applied to determine formalin in squid and white shrimp. For method validation, the results were compared with those determined by commercial formalin test kit. Formalin detection of this developed method showed the similar results with all commercial products. This research showed the useful of determination of formalin in food by using plant extracted to be simple and rapid test kit. Moreover, this research is also adding value to the local plant.