Introduction: Avulsion fracture of the fibular head is a rare entity. Significance of this entity is lies in association with injuries to the ligaments and neurovascular structures attached to it. Presentation of these injuries is quite variable. Management of this injury is still controversial in spite of various available fixation methods. We report various different presentation and options in management of this rarely reported injury. Material and method: A prospective, single centre study of six patients of fracture fibular head was presented with injury to knee joint. All were male with average age 31 years (range 19-44 years). Left knee joint was involved in the four and right was in the two patients. All cases were of sports injury. Final diagnosis was proved by X-ray, 3D CT and magnetic resonance imaging. Three cases among them had complete avulsion of fibular head, 3 cases had undisplaced fracture fibular head; among one case was complicated by fracture shaft tibia. Patients with complete avulsion were treated by open reduction and fixation by using various methods. Complications and outcome were recorded in follow-up for 6 to 24 months (mean 21 months). Results were evaluated using Lysholm knee scores. Results: The average Lysholm knee score was 91.33 points. Excellent result (score 95-100) was seen in 1 case, good results (score 84-94) were seen in 3 cases and fair result (score 65-83) was seen in 1 case. The excellent and good result was 83%. Conclusions: Our study shows that avulsion of fibular head can present from undisplaced fragment to complete avulsion with grade I to III ligamentous injury or/and with peroneal nerve injury. These injuries can manage with various treatment options from conservative to fixation of avulsed fragment by various devices, depending on severity and combined injury.
The two most generally diagnosed Neurodegenerative diseases are the Alzheimer and Parkinson diseases. So this paper presents a fully automated early screening system based on the Capsule network for the classification of these two Neurodegenerative diseases. In this study, we hypothesized that the Neurodegenerative diseases-Caps system based on the Capsule network architecture accurately performs the multiclass i.e. three class classification into either the Alzheimer class or Parkinson class or Healthy control and delivers better results in comparison other deep transfer learning models. The real motivation behind choosing the capsule network architecture is its more resilient nature towards the affine transformations as well as rotational & translational invariance, which commonly persists in the medical image datasets. Apart from this, the capsule networks overcomes the pooling layers related deficiencies from which conventional CNNs are mostly affected and unable to delivers accurate results especially in the tasks related to image classification. The various Computer aided systems based on machine learning for the classification of brain tumors and other types of cancers are already available. Whereas for the classification of Neurodegenerative diseases, the amount of research done is very limited and the number of persons suffering from this type of diseases are increasing especially in developing countries like India, China etc. So there is a need to develop an early screening system for the correct multiclass classification into Alzheimer's, Parkinson's and Normal or Healthy control cases. The Alzheimer disease and Parkinson progression (ADPP) dataset is used in this research study for the training of the proposed Neurodegenerative diseases-Caps system. This ADPP dataset is developed with the aid of both the Parkinson's Progression Markers Initiative (PPMI) and Alzheimer's disease Neuroimaging Initiative (ADNI) databases. There is no such early screening system exist yet, which can perform the accurate classification of these two Neurodegenerative diseases. For the sake of genuine comparison, other popular deep transfer learning models like VGG19, VGG16, ResNet50 and InceptionV3 are implemented and also trained over the same ADPP dataset. The proposed Neurodegenerative diseases-Caps system deliver accuracies of 97.81, 98, 96.81% for the Alzheimer, Parkinson and Healthy control or Normal cases with 70/30 (training/validation split) and performs way better as compare to the other popular Deep transfer learning models.
Introduction: Solitary osteochondroma of phalanx of the hand in an adult are extremely rare and have different presentations depending on the site of origin. Most adult solitary tumour arises either from the distal phalanx or in the carpal bones. Importance of the lesion lies in its various differential diagnoses. We present a rare case report of solitary osteochondroma of the middle phalanx left index finger in an adult. Management and differential diagnosis is discussed. Presentation of Case: 32 year old male presented with osteochondroma arising from dorsal ulnar side of middle phalanx left index finger with limitations of movements at PIP and DIP joint. Patient achieved full finger movements after excision and no recurrence at 18 month follow-up. Conclusion: Solitary osteochondroma of the middle phalanx in an adult is a rare osteocartilaginous lesion. In view of a varied differential diagnosis and a high rate of local recurrence, an early identification and a wide excision is essential.
Utilities around the world are reported to invest a total of around $30 billion over the next few years for installation of more than 300 million smart meters, replacing traditional analog meters [1]. By mid-decade, with full country wide deployment, there will be almost 1.3 billion smart meters in place [1]. Collection of fine-grained energy usage data by these smart meters provides numerous advantages such as energy savings for customers with use of demand optimization, a billing system of higher accuracy with dynamic pricing programs, bidirectional information exchange ability between end-users for better consumer-operator interaction, and so on. However, all these perks associated with fine-grained energy usage data collection threaten the privacy of users. With this technology, customers' personal data such as sleeping cycle, number of occupants, and even type and number of appliances stream into the hands of the utility companies and can be subject to misuse. This research paper addresses privacy violation of consumers' energy usage data collected from smart meters and provides a novel solution for the privacy protection while allowing benefits of energy data analytics. First, we demonstrate the successful application of occupancy detection attacks using a deep neural network method that yields high accuracy results. We then introduce Adversarial Machine Learning Occupancy Detection Avoidance with Blockchain (AMLODA-B) framework as a counter-attack by deploying an algorithm based on the Long Short Term Memory (LSTM) model into the standardized smart metering infrastructure to prevent leakage of consumer's personal information. Our privacy-aware approach protects consumers' privacy without compromising the correctness of billing and preserves operational efficiency without use of authoritative intermediaries.
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