Factors affecting the incorporation of extracorporeally irradiated autograft for the treatment of bone tumours-a retrospective analysis from a tertiary referral centre.

PURPOSE: Re-implantation of the tumor bearing autograft following extracorporeal radiation therapy (ECRT) has been established as an oncologically safe biological reconstruction technique following resection of bone sarcomas. However, factors affecting the ECRT graft-host bone incorporation have not been fully investigated. An insight into the factors that influence graft incorporation can circumvent the complications and increase graft survival. METHODS: A total of 96 osteotomies in 48 patients with intercalary resections of primary extremity bone sarcomas (mean age 15.8 years, mean follow-up 42.1 months) were analyzed retrospectively for factors ECRT autograft-host bone union. RESULTS: On univariate analysis, age < 20 years, metaphyseal osteotomy site, V-shaped diaphyseal osteotomy, and use of additional plate at diaphyseal osteotomy had a significantly faster time to union, while gender, tumour type, bone involved, resection length, chemotherapy, type of fixation, and use of intra-medullary fibula did not influence union time. In multivariate analysis, V-shaped diaphyseal osteotomy and use of additional plate at diaphyseal ostetomy were the independent factors with favourable time to union. None of the analyzed factors was found to have a significant effect on the union rate. The major complications were non-union in 11.4% patients, graft failure in 2.1%, infection in 12.5%, and soft tissue local recurrences in 14.5% patients. CONCLUSION: Modified diaphyseal osteotomy and augmentation of the stability of the reconstruction using additional small plates enhance the incorporation of ECRT autograft.

The Role of Conservative Management in the Avascular Necrosis of the Femoral Head: A Review of Systematic Reviews.

Introduction/Background: Multiple medical and surgical treatments have been described in the early stages of Avascular Necrosis (AVN) of the femoral head which delay the disease progression. Similarly, multiple studies, trials, reviews, and systematic reviews exist for the various treatments described and their outcomes but with no consensus over which is superior. So in this study, we reviewed the systematic reviews of all the conservative therapies for AVN of the femoral head systematically to identify a single or a combination of non-surgical treatment choices in the initial stages of the disease. Methodology: A thorough literature search has been carried out in January 2022 through the use of Pubmed, EMBASE, and Cochrane electronic databases using PRISMA guidelines. The Mesh words and Keywords used were "femoral head AVN", "Conservative management", and "Systematic Reviews". The inclusion criteria used during the screening were, any systematic reviews which included patients with AVN either idiopathic or secondary, who are managed with conservative therapies like bisphosphonates, Hyper Baric Oxygen Therapy (HBOT), Shock wave therapies like Extracorporeal Shock Wave Therapy (ESWT), or electrical therapy like Pulsed Electro Magnetic Field (PEMF). The quality of the included systematic reviews was assessed using AMSTAR-2 criteria. Results: The initial search yielded 364 studies which on screening based on our inclusion criteria finally resulted in seven systematic reviews to be included in the present study. There were two systematic reviews for Hyper Baric Oxygen Therapy (HBOT), two for Extracorporeal Shock Wave Therapy (ESWT), one for electrical stimulation modalities like Pulsed Electro Magnetic Field (PEMF), and two for bisphosphonates. The follow-up of the patients in the included systematic reviews varied from 6 weeks to 10 years. The total number of patients varied from 77 to over 1000 across the systematic reviews. Almost all of the studies included a control group that either received the intended treatment with adjuncts or did not receive any treatment at all. Because of the heterogeneous nature of included articles in the systematic reviews, meta-analysis was performed in only three of the included systematic reviews. Conclusion: Of all the modalities of treatment described, bisphosphonates are easily available and cost-effective and do not require any hospital resources/machinery for delivering the treatment. So they can be used as an initial line of treatment for patients with early stages of AVN (Ficat and Arlet 1-3) and based on the hospital availability of resources could be supplemented with any of the biophysical modalities (ESWT/PEMF/HBOT) for maximum efficacy to delay the disease progression. Level of Clinical Evidence: Systematic review. Supplementary Information: The online version contains supplementary material available at 10.1007/s43465-023-00818-5.

Design and development of smart Internet of Things-based solid waste management system using computer vision.

Municipal solid waste (MSW) management currently requires critical attention in ensuring the best principles of socio-economic attributes such as environmental protection, economic sustainability, and mitigation of human health problems. Numerous surveys on the waste management system reveal that approximately 90% of the MSW systems are improperly disposing the wastages in open dumps and landfills. Classifying the wastages into biodegradable and non-biodegradable helps converting them into usable energy and disposing properly. The advancements of effective computational approaches like artificial intelligence and image processing provide wide range of solutions for the present problem identified in MSW management. The computational approaches can be programmed to classify wastes that help to convert them into usable energy. Existing methods of waste classification in MSW remain unresolved due to poor accuracy and higher error rate. This paper presents an experimented effective computer vision-based MSW management solution with the help of the Internet of Things (IoT), and machine learning (ML) techniques namely regression, classification, clustering, and correlation rules for the perception of solid waste images. A ground-up built convolutional neural network (CNN) and CNN by the inception of ResNet V2 models trained through transfer learning for image classification. ResNet V2 supports training large datasets in deep neural networks to achieve improved accuracy and reduced error rate in identity mapping. In addition, batch normalization and mixed hybrid pooling techniques are incorporated in CNN to improve stability and yield state of art performance. The proposed model identifies the type of waste and classifies them as biodegradable or non-biodegradable to collect in respective waste bins precisely. Furthermore, observation of performance metrics, accuracy, and loss ensures the effective functions of the proposed model compared to other existing models. The proposed ResNet-based CNN performs waste classification with 19.08% higher accuracy and 34.97% lower loss than the performance metrics of other existing models.