Pediatric Renovascular Hypertension: A Pediatric Interventional Radiologist's Perspective.

Renovascular hypertension (RVH) contributes close to one-fourth of the secondary etiologies of hypertension in children and a delay in diagnosis can result in adverse clinical outcomes. RVH in children is clinically silent with elevations in blood pressure measurements sometimes as its sole manifestation. Only a high index of suspicion by the clinician can prompt its detection. Despite the availability of other imaging modalities like ultrasound, computed tomography, and magnetic resonance imaging, digital subtraction angiography is still considered the gold standard to make a diagnosis of RVH. Angioplasty is considered the treatment of choice in appropriately selected patients. In this article, we shall focus on the various imaging findings, and management of RVH in children, which requires a multidisciplinary approach with a special focus on the role of interventional radiology.

Deep Learning Models for Classification of Deciduous and Permanent Teeth From Digital Panoramic Images.

INTRODUCTION: Dental radiographs are essential in the diagnostic process in dentistry. They serve various purposes, including determining age, analyzing patterns of tooth eruption/shedding, and treatment planning and prognosis. The emergence of digital radiography technology has piqued interest in using artificial intelligence systems to assist and guide dental professionals. These cutting-edge technologies assist in streamlining decision-making processes by enabling entity classification and localization tasks. With the integration of artificial Intelligence algorithms tailored for pediatric dentistry applications and utilizing automated tools, there is an optimistic outlook on improving diagnostic capabilities while reducing stress and fatigue among clinicians. METHODOLOGY: The dataset comprised 620 images (mixed dentition: 314, permanent dentition: 306). Panoramic radiographs taken were within the age range of 4-16 years. The classification of deciduous and permanent teeth involved training CNN-based models using different architectures such as Resnet, AlexNet, and EfficientNet, among others. A ratio of 70:15:15 was utilized for training, validation, and testing, respectively. RESULT AND CONCLUSION: The findings indicated that among the models proposed, EfficientNetB0 and EfficientNetB3 exhibited superior performance. Both EfficientNetB0 and EfficientNetB3 achieved an accuracy rate, precision, recall, and F1 scores of 98% in classifying teeth as either deciduous or permanent. This implies that these models were highly accurate in identifying patterns/features within the dataset used for evaluation.

Ultrasound evaluation of peripheral nerves of the lower limb in diabetic peripheral neuropathy.

PURPOSE: Comparative evaluation of the cross-sectional area (CSA) of the sural and tibial nerves using ultrasound in diabetic peripheral neuropathy (DPN). METHOD: This study involved 210 participants divided into 3 groups: type 2 diabetics with DPN, type 2 diabetics without DPN, and controls. Ultrasound evaluation of the sural and tibial nerves was done using a 5-17 MHz linear transducer. The CSA of both the nerves was recorded in both lower limbs. Nerve conduction studies (NCS) were also done in all diabetic patients. RESULTS: There were 70 participants in each group. All the three groups were gender-matched and no significant difference was observed between CSA values of the sural and tibial nerves between males and females and also in right and left lower limbs within a group. The sural nerve CSA was significantly higher in the DPN group than in diabetic patients without DPN and controls. The sensitivity of the sural nerve CSA was higher than the tibial nerve with a cut-off value of 4.41 mm2 for CSA of the sural nerve. CONCLUSION: The sural nerve CSA at the superior border of lateral malleolus can be used for screening DPN. With well-established cut-off values may result in early initiation of the therapy even in NCS negative cases, thus reducing the morbidity associated with the disease. Ultrasound could be used as a first diagnostic tool for detection of DPN which can be followed by NCS in cases where ultrasound is non-diagnostic.

ZiF-Predict: a web tool for predicting DNA-binding specificity in C2H2 zinc finger proteins.

Engineering zinc finger protein motifs for specific DNA targets in genomes is critical in the field of genome engineering. We have developed a computational method for predicting recognition helices for C2H2 zinc fingers that bind to specific target DNA sites. This prediction is based on artificial neural network using an exhaustive dataset of zinc finger proteins and their target DNA triplets. Users can select the option for two or three zinc fingers to be predicted either in a modular or synergistic fashion for the input DNA sequence. This method would be valuable for researchers interested in designing specific zinc finger transcription factors and zinc finger nucleases for several biological and biomedical applications. The web tool ZiF-Predict is available online at http://web.iitd.ac.in/~sundar/zifpredict/.