Evaluation of anticonvulsant activity of amlodipine in albino rats

Background: The objective of the study was to evaluate the anticonvulsant activity of amlodipine in albino rats.Methods: Anticonvulsant activity of amlodipine was done in three graded doses (1 mg/kg, 2 mg/kg, 4 mg/kg), and combination group with low dose of amlodipine (1 mg/kg) and standard drug (phenytoin) in maximal electroshock seizures (MES) experimental animal model.Results: Amlodipine in dose of 2, 4 mg/kg showed dose dependent significant anticonvulsant effect and combination of low dose amlodipine and low dose of standard drug also showed significant anticonvulsant effect in MES model.Conclusions: Amlodipine is having anticonvulsant activity and also potentiated the anticonvulsant effect of phenytoin in MES model.

Anterior Segmental Distraction Osteogenesis in the Hypoplastic Cleft Maxilla : Report of five cases

Orthognathic surgery and distraction osteogenesis play a prime role in the correction of maxillary hypoplasia in patients with cleft lip and palate (CLP). Advancement of the anterior maxilla alone without interfering with the velopharyngeal sphincter may be advantageous in cleft patients, who more commonly have speech deficits and dental crowding. We present a case series of anterior maxillary segmental distraction for maxillary hypoplasia in 5 CLP patients with a one-year follow-up. A custom-made tooth-borne distraction device with a hyrax screw positioned anteroposteriorly was used. The evaluation comprised of hard and soft tissue analysis and speech assessment. A stable occlusion with positive overjet and correction of dental-crowding without extraction was achieved at one year post-distraction. Facial profile and lip support improved. There was no deterioration in speech

Optimizing Inference Distribution for Efficient Kidney Tumor Segmentation Using a UNet-PWP Deep-Learning Model with XAI on CT Scan Images

Kidney tumors represent a significant medical challenge, characterized by their often-asymptomatic nature and the need for early detection to facilitate timely and effective intervention. Although neural networks have shown great promise in disease prediction, their computational demands have limited their practicality in clinical settings. This study introduces a novel methodology, the UNet-PWP architecture, tailored explicitly for kidney tumor segmentation, designed to optimize resource utilization and overcome computational complexity constraints. A key novelty in our approach is the application of adaptive partitioning, which deconstructs the intricate UNet architecture into smaller submodels. This partitioning strategy reduces computational requirements and enhances the model’s efficiency in processing kidney tumor images. Additionally, we augment the UNet’s depth by incorporating pre-trained weights, therefore significantly boosting its capacity to handle intricate and detailed segmentation tasks. Furthermore, we employ weight-pruning techniques to eliminate redundant zero-weighted parameters, further streamlining the UNet-PWP model without compromising its performance. To rigorously assess the effectiveness of our proposed UNet-PWP model, we conducted a comparative evaluation alongside the DeepLab V3+ model, both trained on the “KiTs 19, 21, and 23” kidney tumor dataset. Our results are optimistic, with the UNet-PWP model achieving an exceptional accuracy rate of 97.01% on both the training and test datasets, surpassing the DeepLab V3+ model in performance. Furthermore, to ensure our model’s results are easily understandable and explainable. We included a fusion of the attention and Grad-CAM XAI methods. This approach provides valuable insights into the decision-making process of our model and the regions of interest that affect its predictions. In the medical field, this interpretability aspect is crucial for healthcare professionals to trust and comprehend the model’s reasoning

Stability Constants of Ternary Chelates of Ni(II), Co(II), Zn(II), Cd(II) & Mn(II) with Histidine as a Primary Ligand & 2-Phenylacetohydroxamic Acid as a Secondary Ligand

379-38

Photo stabilization of Polymethylmeth-acrylate by Ni(II) Hydroxamates

516-51

Thermodynamics of Complex Formation: Part I-Complexes of Cu(II), Ni(II), Co(II), Mn(II), Zn(II) & Cd(II) with N-2' –Diphenylacetohydroxamic Acid

327-32

Binary Chelates of N, 2'-Diphenylacetohydroxamic Acid with Cu(II), Ni(II), Co(II), Mo(II), Zo(II), Cd(II) & Dioxouranium(VI) & Ternary & Quaternary Chelates of Dioxouranium(VI) with Hydroxamic Acids

1136-113

Hardware-efficient scheme for trailer robot parking by truck robot in an indoor environment with rendezvous

Autonomous grounded vehicle-based social assistance/service robot parking in an indoor environment is an exciting challenge in urban cities. There are few efficient methods for parking multi-robot/agent teams in an unknown indoor environment. The primary objective of autonomous multi-robot/agent teams is to establish synchronization between them and to stay in behavioral control when static and when in motion. In this regard, the proposed hardware-efficient algorithm addresses the parking of a trailer (follower) robot in indoor environments by a truck (leader) robot with a rendezvous approach. In the process of parking, initial rendezvous behavioral control between the truck and trailer robots is established. Next, the parking space in the environment is estimated by the truck robot, and the trailer robot parks under the supervision of the truck robot. The proposed behavioral control mechanisms were executed between heterogenous-type computational-based robots. Optimized sensors were used for traversing and the execution of the parking methods. The truck robot leads, and the trailer robot mimics the actions in the execution of path planning and parking. The truck robot was integrated with FPGA (Xilinx Zynq XC7Z020-CLG484-1), and the trailer was integrated with Arduino UNO computing devices; this heterogenous modeling is adequate in the execution of trailer parking by a truck. The hardware schemes were developed using Verilog HDL for the FPGA (truck)-based robot and Python for the Arduino (trailer)-based robot.Published versionThis work was supported by a Science and Engineering Research Board (SERB) grant funded by the Indian government (No. ECR/2016/001848)