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The aim of this study is to extract cortical and trabecular features of the mandible and to develop a novel combinational model of mandibular cortical thickness, trabecular bone area and age in order to predict low bone mineral density or osteoporosis from a dental panoramic radiograph. The study involved 64 south Indian women (age = 52.5 ± 12.7 years) categorised into two groups (normal and low bone mineral density) based on total femur bone mineral density. The dental panoramic radiographs were obtained by a digital scanner, and measurement of total bone mineral density at the right femur was performed by a dual-energy X-ray absorptiometry scanner. The mandibular cortical thickness and panoramic mandibular index were measured bilaterally, and the mean values were considered. The region of interest of 128 × 128 pixels around the mental foramen region was manually cropped and subjected to pre-processing, normalisation and average threshold-based segmentation to determine trabecular bone area. Multiple linear regression analyses of cortical and trabecular measures along with age were performed to develop a combinational model to classify subjects as normal and low bone mineral density. The proposed approach demonstrated strong correlation (r = 0.76; p < 0.01) against the total bone mineral density and resulted in accuracy, sensitivity and positive predictive values of 0.84, 0.92 and 0.85, respectively; the receiver operating characteristic outcomes disclosed that the area under the curve was 0.89.Our results suggest that the proposed combinational model could be useful to diagnose subjects with low bone mineral density.

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Objective: The objective of this study is to design an automated ocular drug delivery system controlled by an open-source syringe pump, which in turn is to be controlled using Arduino UNO.Methods: The Arduino UNO is the microcontroller used to control the entire setup. The drug delivery is executed using the open-source syringe pump. A 12V NEMA 17 stepper motor is used to execute the pump movements. To facilitate the ease of use, an Android application developed using Android Studio has been developed, where inputs are given in the form of the required drug dose. The application plays the role of converting the requested drug dose into the number of steps required to run the stepper motor. The stepper motor works based on simple lead-screw mechanism.Results: The designed syringe pump has been tested using water. It was determined that for 42 steps a drug volume of 30 μl is dispensed by the designed device. Typically, a drop volume ranges from 33.8 μl to 63.4 μl and the device that has been discussed in this article can deliver a minimum volume of 30 μl.Conclusion: Using this device, excessive use of drugs can be minimalized, thus reducing the capital investment put behind ocular drugs.

Objective: Diabetes is a metabolic disorder occurring either due to the inadequate secretion of insulin or ineffective utilization of insulin by the body. The study was aimed to identify the variations of the complete blood count (CBC) parameters among the diabetic and normal individuals and to derive an empirical formula to estimate hemoglobin A 1 c (HbA 1 c) of an individual using CBC parameters.Methods: A total of 83 subjects (mean age: 52.8±9.0 years) involved in the study, among which 39 (mean age: 49.1±8.8 years) were normal and 44 (mean age: 56±7.8 years) were diabetic. The blood was drawn from the participants and was subjected to CBC analysis using automated hematology analyzer. The stepwise linear regression model was used to determine the empirical formula to estimate HbA 1 c using the CBC parameters. The Student's t-test was performed to identify the group differences.Results: A negative correlation was observed for Hb (r=−0.35**, p<0.001) and packed cell volume (PCV) (r=−0.23**, p<0.05) against HbA 1 c. The CBC parameters Hb, erythrocyte sedimentation rate, PCV, red blood cells count, mean corpuscular volume, and mean corpuscular Hb exhibited a statistically significant difference at the level (p<0.05) between the normal and diabetic groups. The empirically derived formula yielded sensitivity, specificity, positive predictive value, negative predictive value, and accuracy measures of 91%, 49%, 67%, 83%, and 71%, respectively, in diagnosing diabetes based on the estimated HbA 1 c. Conclusion:The empirical formula derived to estimate HbA 1 c could be useful in the prediction of diabetes with an appreciable accuracy.