Publications by authors named "G N Manjunatha Reddy"

1,781 Publications

  • Page 1 of 1

Chronic Effects of Maternal Low-Protein and Low-Quality Protein Diets on Body Composition, Glucose-Homeostasis and Metabolic Factors, Followed by Reversible Changes upon Rehabilitation in Adult Rat Offspring.

Nutrients 2021 Nov 18;13(11). Epub 2021 Nov 18.

Biochemistry Division, ICMR-National Institute of Nutrition, Hyderabad 500007, India.

Several studies suggest that the maternal protein content and source can affect the offspring's health. However, the chronic impact of maternal quality and quantity protein restriction, and reversible changes upon rehabilitation, if any, in the offspring, remains elusive. This study examined the effects of maternal low-quality protein (LQP) and low-protein (LP) intake from preconception to post-weaning, followed by rehabilitation from weaning, on body composition, glucose-homeostasis, and metabolic factors in rat offspring. Wistar rats were exposed to normal protein (NP; 20% casein), LQP (20% wheat gluten) or LP (8% casein) isocaloric diets for 7 weeks before pregnancy until lactation. After weaning, the offspring were exposed to five diets: NP, LQP, LQPR (LQP rehabilitated with NP), LP, and LPR (LP rehabilitated with NP) for 16 weeks. Body composition, glucose-homeostasis, lipids, and plasma hormones were investigated. The LQP and LP offspring had lower bodyweight, fat and lean mass, insulin and HOMA-IR than the NP. The LQP offspring had higher cholesterol, T3 and T4, and lower triacylglycerides and glucose, while these were unaltered in LP compared to NP. The majority of the above outcomes were reversed upon rehabilitation. These results suggest that the chronic exposure of rats to maternal LQP and LP diets induced differential adverse effects by influencing body composition and metabolism, which were reversed upon rehabilitation.
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http://dx.doi.org/10.3390/nu13114129DOI Listing
November 2021

Knee Implant Identification by Fine-Tuning Deep Learning Models.

Indian J Orthop 2021 Oct 28;55(5):1295-1305. Epub 2021 Sep 28.

Department of Orthopaedic Surgery, Stanford University, Redwood City, CA USA.

Background: Identification of implant model from primary knee arthroplasty in pre-op planning of revision surgery is a challenging task with added delay. The direct impact of this inability to identify the implants in time leads to the increase in complexity in surgery. Deep learning in the medical field for diagnosis has shown promising results in getting better with every iteration. This study aims to find an optimal solution for the problem of identification of make and model of knee arthroplasty prosthesis using automated deep learning models.

Methods: Deep learning algorithms were used to classify knee arthroplasty implant models. The training, validation and test comprised of 1078 radiographs with a total of 6 knee arthroplasty implant models with anterior-posterior (AP) and lateral views. The performance of the model was calculated using accuracy, sensitivity, and area under the receiver-operating characteristic curve (AUC), which were compared against multiple models trained for comparative in-depth analysis with saliency maps for visualization.

Results: After training for a total of 30 epochs on all 6 models, the model performing the best obtained an accuracy of 96.38%, the sensitivity of 97.2% and AUC of 0.985 on an external testing dataset consisting of 162 radiographs. The best performing model correctly and uniquely identified the implants which could be visualized using saliency maps.

Conclusion: Deep learning models can be used to differentiate between 6 knee arthroplasty implant models. Saliency maps give us a better understanding of which regions the model is focusing on while predicting the results.
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http://dx.doi.org/10.1007/s43465-021-00529-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8586384PMC
October 2021

Carpal bone fracture not to be missed.

Emerg Med J 2021 Dec;38(12):e8

Trauma & Orthopaedics, Warrington and Halton Hospitals NHS Foundation Trust, Warrington, UK.

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http://dx.doi.org/10.1136/emermed-2019-209005DOI Listing
December 2021

Resolving Atomic-Scale Interactions in Non-Fullerene Acceptor Organic Solar Cells with Solid-State NMR Spectroscopy, Crystallographic Modelling, and Molecular Dynamics Simulations.

Adv Mater 2021 Nov 24:e2105943. Epub 2021 Nov 24.

University of Lille, CNRS, Centrale Lille Institut, Univ. Artois, UMR 8181, Unité de Catalyse et Chimie du Solide, Lille, F-59000, France.

Fused-ring core non-fullerene acceptors (NFAs), designated "Y-series", have enabled high-performance organic solar cells (OSCs) achieving over 18% power conversion efficiency (PCE). Since the introduction of these NFAs, much effort has been expended to understand the reasons for their exceptional performance. While several studies have identified key optoelectronic properties that govern high PCEs, little is known about the molecular level origins of large variations in performance, spanning from 5 to 18% PCE, e.g., in the case of PM6:Y6 OSCs. Here, we introduce a combined solid-state NMR, crystallography, and molecular modelling approach to elucidate the atomic-scale interactions in Y6 crystals, thin films, and PM6:Y6 bulk heterojunction (BHJ) blends. We show the Y6 morphologies in BHJ blends are not governed by the morphology in neat films or single crystals. Notably, PM6:Y6 blends processed from different solvents self-assemble into different structures and morphologies, whereby the relative orientations of the sidechains and end groups of the Y6 molecules to their fused-ring cores play a crucial role in determining the resulting morphology and overall performance of the solar cells. The molecular-level understanding of BHJs enabled by this approach will guide the engineering of next-generation NFAs for stable and efficient OSCs. This article is protected by copyright. All rights reserved.
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http://dx.doi.org/10.1002/adma.202105943DOI Listing
November 2021

Monitoring of Mutual Interference Behavior of Trichogramma brassicae (Hymenoptera: Trichogrammatidae) over 45 Generations of Rearing on Angoumois Grain Moth.

Neotrop Entomol 2021 Nov 9. Epub 2021 Nov 9.

USDA-ARS-Southern Insect Pest Management Research Unit, MS, Stoneville, USA.

Trichogramma brassicae (Bezdenko) is one of the most common species of natural enemies used in augmentative biological control programs in many countries. Understanding of the foraging behavior of a parasitoid can help us to improve its performance under field conditions. This study is the first assessment of trends in mutual interference behavior of T. brassicae under long-term mass rearing (over 45 generations) on a common factitious host, Sitotroga cerealella (Olivier). Our results revealed that the total parasitism rate of T. brassicae reared on S. cerealella eggs was significantly affected by parasitoid densities and number of generations under continuous rearing. Also, parasitoid density and number of generations in rearing had significant effects on the per capita parasitism rate. Meanwhile, per capita searching efficiencies were different in sequential generations and at different densities. The number of hosts parasitized per parasitoid decreased on day 1 of the experiment with increasing parasitoid density, showing the effect of mutual interference. The linear regression between the natural logarithm of per capita searching efficiency and the natural logarithm of parasitoid density showed an inverse relationship. While the m (interference coefficient) values increased, the Q (quest constant) values had a decreasing trend over 45 generations. The highest (- 0.167) and lowest (- 0.242) values of m were observed in G45 and G5, respectively. Accordingly, G5 and G45 had the highest (0.053) and lowest (0.023) Q values, respectively. Thus, it seems the negative effects of mutual interference decreased over generations.
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http://dx.doi.org/10.1007/s13744-021-00919-6DOI Listing
November 2021
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