Mahasarakham University

This research introduces an omega-shaped resonator coupled split-ring resonator (OSR-SRR) microwave sensor designed for non-invasive glucose monitoring in solutions and fingertip samples.Enhanced with machine learning, the sensor detects glucose levels for non-diabetic and diabetic individuals.Fabricated via PCB processes, the sensor′s performance was characterized by reflection coefficient (S₁₁) and resonance frequency (F_r).Simulations analyzed the effects of skin and blood layer properties on the sensor′s response.Linear regression assessed the impact of fingertip properties, while nonlinear regression predicted glucose levels from measurements.Exptl. validation, using a vector network analyzer, included in-vitro and in-vivo methods with microfluidic channels, cylindrical tubes, and fingertip measurements for glucose concentrations between 0-200 mg/dL.The sensor achieved sensitivities of 282.20 x 10^-3 dB/(mg/dL) for S₁₁ and 0.56 MHz/(mg/dL) for F_r.Glucose level classification was performed using supervised learning with a multilayer perceptron (MLP) and unsupervised K-means clustering.Compact, cost-effective, and highly sensitive, the proposed sensor reliably measures glucose in both solutions and fingertip samples.

Mycoplasma spp. are hemotropic parasites that attach to the surface of red blood cells, causing hemolytic anemia in infected cats. These pathogens can result in significant clinical consequences, including death if left untreated. Despite their importance, molecular studies on Mycoplasma infections in Thailand remain limited. This study aimed to determine the prevalence of Mycoplasma spp. infections in cats, identify specific species involved, and assess hematologic changes in infected cats. Additionally, phylogenetic relationships of Mycoplasma species were analyzed.

A total of 149 blood samples were collected from cats visiting veterinary hospitals in four provinces of Thailand including Samut Prakan, Udon Thani, Chaiyaphum, and Maha Sarakham for routine check-ups, vaccinations, or illness, without specific signs of infection. PCR targeting the 16S rRNA gene was used to detect Mycoplasma spp., followed by DNA sequencing and phylogenetic analysis for species identification.

The overall prevalence of Mycoplasma spp. infection was 17.45% (95% CI: 11.73-24.51), comprising Candidatus Mycoplasma haemominutum (CMhm) (14.09%), Mycoplasma haemofelis (Mhf) (2.01%), and Candidatus Mycoplasma turicensis (CMt) (1.34%). Sequencing of 26 positive PCR products identified CMhm in 21 samples, Mhf in 3 samples, and CMt in 2 samples, revealing seven distinct haplotypes. All Mycoplasma species identified in this study showed 99-100% nucleotide similarity with those available in the GenBank database. Hematologic analysis revealed no significant changes in most parameters, except for a statistically significant decrease in platelet count (P < 0.01).

This study provides information on the molecular epidemiology and genetic identification of Mycoplasma spp., contributing to a better understanding of hemoplasma infections in Thai cats, which will aid in the control of these microorganisms.

This article introduces the design of a substrate integrated waveguide (SIW) resonator-based microwave sensor, integrated with a multilayer perceptron (MLP) neural network, for the accurate classification and quantification of water contaminants in fuel oil.The system utilizes key features derived from the microwave sensor output, including the magnitude of the transmission coefficient (S₂₁) and the resonance frequency (F_r), to train the MLP network.In the development of the sensor, the prototype was able to effectively monitor water contamination in fuel oil, spanning concentrations from 0 % to 100 %, by filling a cylindrical tube positioned at the sensor′s center.Operating within the frequency range of 1.50-1.55 GHz, the sensor demonstrated robust performance.The study further explores the correlation between water contamination levels and sensor response using both linear and nonlinear regression analyses, complemented by the application of an MLP neural network for contaminant classification.The proposed sensor achieves non-contact measurement of water contaminants with sensitivities of 0.12 dB/% and 0.88 MHz/%.Given its high sensitivity, real-time monitoring capabilities, user-friendly design, cost-effectiveness, and non-invasive approach, the sensor holds significant promise for applications in the petroleum industry.