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22 months follow-up of deep marginal acquisition with thermacut bur in management of deep subgingival interproximal carious lesions: a case report.
BMC Oral Health
January 2025
Professor of Conservative Dentistry, Faculty of Dentistry, Cairo University, Giza, Egypt.
Background: Minimally invasive dentistry is now becoming the forefront of restorative dentistry, involving less traumatic treatment protocols, conservation of tooth structure and surrounding tissues, enhancing the long-term survivability of treated teeth, and improving the overall quality of life for patients.
Objective: The current case report was conducted to evaluate acquiring deep subgingival interproximal carious lesions by the mean of thermacut bur gingivectomy, in terms of patient satisfaction through pain evaluation, Bleeding on Probing, Pocket Depth, Crestal Bone Level evaluation, and restoration evaluation using modified USPHS criteria.
Material And Methods: A patient with a deep proximal cavity in the posterior tooth was thoroughly examined and underwent Thermacut Bur Gingivectomy (TBG) after caries removal followed by direct resin composite restoration of the prepared cavity.
Effect of feedback-integrated reflection, on deep learning of undergraduate medical students in a clinical setting.
BMC Med Educ
January 2025
Riphah international university, Rawalpindi, Pakistan.
Background: Reflection fosters self-regulated learning by enabling learners to critically evaluate their performance, identify gaps, and make plans to improve. Feedback, in turn, provides external insights that complement reflection, helping learners recognize their strengths and weaknesses, adjust their learning strategies, and enhance clinical reasoning and decision-making skills. However, reflection alone may not produce the desirable effects unless coupled with feedback.
View Article and Find Full Text PDFDistinct detection and discrimination sensitivities in visual processing of real versus unreal optic flow.
Psychon Bull Rev
January 2025
NYU-ECNU Institute of Brain and Cognitive Science, New York University Shanghai, Shanghai, China.
We examined the intricate mechanisms underlying visual processing of complex motion stimuli by measuring the detection sensitivity to contraction and expansion patterns and the discrimination sensitivity to the location of the center of motion (CoM) in various real and unreal optic flow stimuli. We conducted two experiments (N = 20 each) and compared responses to both "real" optic flow stimuli containing information about self-movement in a three-dimensional scene and "unreal" optic flow stimuli lacking such information. We found that detection sensitivity to contraction surpassed that to expansion patterns for unreal optic flow stimuli, whereas this trend was reversed for real optic flow stimuli.
View Article and Find Full Text PDFIoT-based prediction model for aquaponic fish pond water quality using multiscale feature fusion with convolutional autoencoder and GRU networks.
Sci Rep
January 2025
Department of Computer Science, Kebri Dehar University, 250, Kebri Dehar, Ethiopia.
The Internet of Things (IoT)-based smart solutions have been developed to predict water quality and they are becoming an increasingly important means of providing efficient solutions through communication technologies. IoT systems are used for enabling connection between various devices based on the ability to gather and collect information. Furthermore, IoT systems are designed to address the environment and the automation industry.
View Article and Find Full Text PDFDeep neural networks have an inbuilt Occam's razor.
Nat Commun
January 2025
Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford, UK.
The remarkable performance of overparameterized deep neural networks (DNNs) must arise from an interplay between network architecture, training algorithms, and structure in the data. To disentangle these three components for supervised learning, we apply a Bayesian picture based on the functions expressed by a DNN. The prior over functions is determined by the network architecture, which we vary by exploiting a transition between ordered and chaotic regimes.
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