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Linear assumption on the level of stiffness in a tissue shows a significant correlation with disease. Photoacoustic imaging techniques that are non-contact by design have been developed in this study to detect differences in phantom (soft tissue mimicking materials) stiffness. This study aims to detect differences in phantom stiffness based on the results of image reconstruction at the resonance frequency. Four phantom agars with differing concentrations were made to achieve different stiffnesses. The position of each phantom agar's highest photoacoustic signal amplitude is identified by a frequency modulation sweep. The characterization results show an increase in resonance frequency along with an increase in phantom stiffness. The image difference can be detected because the intensity of the photoacoustic image in samples that have a resonance frequency with laser modulation is comparatively higher than in other samples. [ABSTRACT FROM AUTHOR]

Introduction: Indonesia ranked seventh in the prevalence of diabetes mellitus in the world. Improved glycemic control has been proven to prevent the diabetes and its complications. Adherence to treatment, weight regulation with benchmarks for Body Mass Index (BMI) indicators, activity or exercise habits, diet, and smoking history play an important role in glycemic control. Objective: The aim of this study was to identify factors affecting glycemic control in outpatient with diabetes mellitus. Methods: A cross sectional study among 32 diabetes mellitus patients. This study was conducted from April 2019 to September 2019 at Rumah Sakit Nasional Diponegoro (RSND), Semarang. The glycemic control status were assesed by HbA1c. Factors such medication adherence, diet adherence, smoking status, and physical activity were assesed with a structured questionnaire. Anthropometric measurement done to get the BMI status. Data were analyzed with bivariat analysis with chi square test (p<0.05). Result: The factors that were significantly have relationship with glycemic control status are diet adherence (p=0.046), and medication adherence (p=0.040). Physical activity, smoking status and BMI doesn't have significant relationship with glycemic control status. Conclusion: Factors affecting glycemic control of diabetes mellitus patients in this study were diet adherence and medication adherence. History of smoking, BMI, and physical activity were not factors that influenced the control status in diabetes mellitus patients in this study. [ABSTRACT FROM AUTHOR]

• Noninvasive photoacoustic imaging (PAI) • Low-cost diode laser. • PA signal as gingival inflammation indicator. Periodontal disease and dental caries are the most common health issues encountered in dental practice. A new technique for detecting periodontitis is photoacoustic imaging (PAI) with a low-cost diode laser modality. This study investigates the capability of a PAI system in detecting gingival inflammation by using an animal model. The modeling of gingival inflammation in this study used Sprague-Dawley rats, which were divided into the following four groups: healthy, incipient inflammation (7 days of inflammation induction), moderate inflammation (14 days of inflammation induction), and advanced inflammation (21 days of inflammation induction). A custom-built PAI, which comprised a near-infrared diode laser, a condenser microphone, and a mechanical system in the form of a computer numerical control (CNC), was used for imaging samples. The imaging source was a continuous-wave diode laser, whose intensity was modulated in this study by adjusting the duty cycle of the laser exposure. The laser modulation frequency and duty cycle were 13 kHz and 50 %, respectively, to image gingiva in a PAI system. The photoacoustic (PA) intensities of gingiva in the advanced inflammation, moderate inflammation, incipient inflammation, and healthy groups were 104.37, 97.57, 91.27, and 64.37 dB, respectively. Analysis of PA images showed that the inflammation groups produced PA with higher intensity than the healthy group. The study shows that a simple PAI system can be used to distinguish between healthy and inflamed gingiva. [ABSTRACT FROM AUTHOR]