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This paper provides two different methods to diagnose osteoporosis in women; the first method is the fractal analysis evaluated by CBCT at two bone locations (the mandible and the second cervical vertebrae) to see if there is any correlation between the two. At the same time, the second method is deep convolutional neural networks (DCNNs). One hundred eighty-eight patients' mandibular CBCT images were used, and DCNN models based on the ResNet-101 framework were employed. Dual X-ray absorptiometry of the hip and lumbar spine revealed that 139 of the 188 postmenopausal women tested had osteoporosis, whereas 49 had average bone mineral density. The second cervical vertebra and the mandible were selected as locations of interest for FD analysis on the CBCT images. Measurement accuracy, both within and between observers' agreements, and correlations between two data sets were all calculated. To evaluate osteoporosis, we used a segmented, three-phase approach. Stage 1 was devoted to the identification of mandibular bone slices. In Stage 2, the coordinates for the mandible's cross-sectional views were established, and Stage 3 calculated the thickness of the mandible bone, emphasizing osteoporotic variations. The average FD values within the interest area of the mandible were significantly lower in people with osteoporosis than in those with average bone mineral density. At the same time, the two groups had no significant difference in FD values at the second cervical vertebra. For the mandibular site, areas beneath the curve were 0.644 (P = 0.008), while the area under the curve for the vertebral site was 0.531 (P = 0.720). DCNN training in the first stage yielded an astounding 98.85% training accuracy, the second stage decreased L1 loss to a meager 1.02 pixels, and the bone thickness computation method used in the last stage had a mean squared error of 0. 8377. We concluded that FD was underutilized even though it distinguished between women with normal BMD and those with osteoporosis in the mandibular area. Additionally, even with small mandibular CBCT datasets, the results show the value of a modular transfer learning approach for osteoporosis detection. [ABSTRACT FROM AUTHOR]

Traumatic finger digit amputation may affect patient's psychology also along with a serious injury to the affected hand. Recently, advanced micro-surgical re-implantation can help save a damaged human finger that is badly injured and/or amputated. Considering its associated financial burden, a vast majority of such patients cannot afford it. For such patients, a hand/finger prosthesis may act as a blessing, boosting social performance. In the present case report, an innovative type of prosthesis finger replacement method is discussed. [ABSTRACT FROM AUTHOR]

30 dentures selected for the study were disinfected using effervescent denture tablets. Then the dentures were trimmed 1.5mmfrom fitting surface and relined with NBR soft lining material. Sample was scraped from denture fitting surface after one month and two months duration and the changes in colony forming units per mm after each month's time were measured and marked. After one month's duration, 24 samples were reported sterile and 6 showed colonies of Candida albicans & Staphylococcus aureus. After 2 months duration, 28 out of 30 samples became positive with C. albicans & S. aureus colonies and aerobic spore bearing units. NBR soft liner material was sterile till one month of clinical usage so it can be prescribed as a safe soft lining material till one month of clinical usage in a controlled environment of oral hygiene [ABSTRACT FROM AUTHOR]