A review on conventional and modern breeding approaches for developing climate resilient crop varieties: NA

The escalating threat of climate change is a major challenge to global food security. One of the ways to mitigate its impact is by developing crops that can withstand environmental stresses such as drought, heat, and salinity. Plant breeders have been employing conventional and modern approaches to achieve climate-resilient crops. Climate-resilient crops refer to both crop and crop varieties that exhibit improved tolerance towards biotic and abiotic stresses. These crops possess the capacity to maintain or even increase their yields when exposed to various stress conditions, such as drought, flood, heat, chilling, freezing and salinity. Conventional breeding entails selecting and crossing plants with desirable traits, while modern breeding deploys molecular techniques to identify and transfer specific genes associated with stress tolerance. However, the effectiveness of both methods is contingent on the crop species and the targeted stress. Advancements in gene editing, such as CRISPER-cas9  and genomics-assisted breeding, offer new opportunities to hasten the development of climate-resilient crops. These new technologies include Marker Assisted Selection, Genome-Wide Association Studies, Mutation breeding, Transcriptomics, Genomics, and more. The review concludes that these cutting-edge techniques have the potential to enhance the speed and precision of developing crops that can endure the challenges posed by climate change

Nanotechnology-Based Celastrol Formulations and Their Therapeutic Applications

Celastrol (also called tripterine) is a quinone methide triterpene isolated from the root extract of Tripterygium wilfordii (thunder god vine in traditional Chinese medicine). Over the past two decades, celastrol has gained wide attention as a potent anti-inflammatory, anti-autoimmune, anti-cancer, anti-oxidant, and neuroprotective agent. However, its clinical translation is very challenging due to its lower aqueous solubility, poor oral bioavailability, and high organ toxicity. To deal with these issues, various formulation strategies have been investigated to augment the overall celastrol efficacy in vivo by attempting to increase the bioavailability and/or reduce the toxicity. Among these, nanotechnology-based celastrol formulations are most widely explored by pharmaceutical scientists worldwide. Based on the survey of literature over the past 15 years, this mini-review is aimed at summarizing a multitude of celastrol nanoformulations that have been developed and tested for various therapeutic applications. In addition, the review highlights the unmet need in the clinical translation of celastrol nanoformulations and the path forward

An investigation of regression as an avenue to find precision-runtime trade-off for object segmentation

The ability to finely segment different instances of various objects in an environment forms a critical tool in the perception tool-box of any autonomous agent. Traditionally instance segmentation is treated as a multi-label pixel-wise classification problem. This formulation has resulted in networks that are capable of producing high-quality instance masks but are extremely slow for real-world usage, especially on platforms with limited computational capabilities. This thesis investigates an alternate regression-based formulation of instance segmentation to achieve a good trade-off between mask precision and run-time. Particularly the instance masks are parameterized and a CNN is trained to regress to these parameters, analogous to bounding box regression performed by an object detection network. In this investigation, the instance segmentation masks in the Cityscape dataset are approximated using irregular octagons and an existing object detector network (i.e., SqueezeDet) is modified to regresses to the parameters of these octagonal approximations. The resulting network is referred to as SqueezeDetOcta. At the image boundaries, object instances are only partially visible. Due to the convolutional nature of most object detection networks, special handling of the boundary adhering object instances is warranted. However, the current object detection techniques seem to be unaffected by this and handle all the object instances alike. To this end, this work proposes selectively learning only partial, untainted parameters of the bounding box approximation of the boundary adhering object instances. Anchor-based object detection networks like SqueezeDet and YOLOv2 have a discrepancy between the ground-truth encoding/decoding scheme and the coordinate space used for clustering, to generate the prior anchor shapes. To resolve this disagreement, this work proposes clustering in a space defined by two coordinate axes representing the natural log transformations of the width and height of the ground-truth bounding boxes. When both SqueezeDet and SqueezeDetOcta were trained from scratch, SqueezeDetOcta lagged behind the SqueezeDet network by a massive ≈ 6.19 mAP. Further analysis revealed that the sparsity of the annotated data was the reason for this lackluster performance of the SqueezeDetOcta network. To mitigate this issue transfer-learning was used to fine-tune the SqueezeDetOcta network starting from the trained weights of the SqueezeDet network. When all the layers of the SqueezeDetOcta were fine-tuned, it outperformed the SqueezeDet network paired with logarithmically extracted anchors by ≈ 0.77 mAP. In addition to this, the forward pass latencies of both SqueezeDet and SqueezeDetOcta are close to ≈ 19ms. Boundary adhesion considerations, during training, resulted in an improvement of ≈ 2.62 mAP of the baseline SqueezeDet network. A SqueezeDet network paired with logarithmically extracted anchors improved the performance of the baseline SqueezeDet network by ≈ 1.85 mAP. In summary, this work demonstrates that if given sufficient fine instance annotated data, an existing object detection network can be modified to predict much finer approximations (i.e., irregular octagons) of the instance annotations, whilst having the same forward pass latency as that of the bounding box predicting network. The results justify the merits of logarithmically extracted anchors to boost the performance of any anchor-based object detection network. The results also showed that the special handling of image boundary adhering object instances produces more performant object detectors

Preference Of Analgesic Drug For Pain Control Following Extraction Of Teeth - A Retrospective Study

 The aim of this retroperspective study was to assess the preference of analgesic drugs administered for pain control following dental extraction of teeth. The study was conducted in a University based setting.The study group of this research were patients who reported to the clinics with moderate to severe toothache/ dental pain who required tooth extraction.Case sheets were reviewed between June 2019 to March 2020.Data were analyzed using SPSS software (IBM SPSS Statistics, Version 24.0, Armonk, NY: IBM Corp). Chi square test was applied to find the association between the parameters and the level of significance.A total of 7888 patients were involved in this study. 45.7% were female patients and 54.3% were male patients. About 70.3% of the population falls in the age category 21-60 years.Around 45.8% of the population were prescribed with Paracetamol. The commonly prescribed combination analgesic of choice was Paracetamol and Aceclofenac for 26.6% of the patients. We conclude that middle aged patients in the age group 21- 60 years were prescribed Paracetamol as the first analgesic of choice for postoperative dental pain control

Variation in the active compounds among natural populations of Swertia cordata

Swertia cordata (Wall. ex G. Don) C.B. Clarke is an important medicinal plant of the family Gentianaceae and is found distributed throughout temperate regions of the Himalaya. The species is used in various ethno-medicinal systems and as an adulterant of Swertia chirayita. Plants collected during the flowering stage from four different populations were air dried and crushed to make extract. The extract was analyzed using HPLC for the presence of bioactive molecules. Quantitative variations exist in the bioactive compounds among different populations. Variations among studied populations are due to long term adaptation in particular ecological niche. As S. chirayita has been banned for collection due to rarity in natural populations, S. cordata may be used as an alternate source. Presence of amarogentin, amaroswerin, and mangiferin increases the medicinal importance along with further research on chemistry, pharmacology, domestication, and crop improvement aspects of S. cordata

Variation in the active compounds among natural populations of <em>Swertia cordata</em>

224-229Swertia cordata (Wall. ex G. Don) C.B. Clarke is an important medicinal plant of the family Gentianaceae and is found distributed throughout temperate regions of the Himalaya. The species is used in various ethno-medicinal systems and as an adulterant of Swertia chirayita. Plants collected during the flowering stage from four different populations were air dried and crushed to make extract. The extract was analyzed using HPLC for the presence of bioactive molecules. Quantitative variations exist in the bioactive compounds among different populations. Variations among studied populations are due to long term adaptation in particular ecological niche. As S. chirayita has been banned for collection due to rarity in natural populations, S. cordata may be used as an alternate source. Presence of amarogentin, amaroswerin, and mangiferin increases the medicinal importance along with further research on chemistry, pharmacology, domestication, and crop improvement aspects of S. cordata

A versatile method for enumeration and characterization of circulating tumor cells from patients with breast cancer

Aim: To establish a standardized protocol for the isolation and enumeration of circulating tumor cells (CTCs) from peripheral blood of patients with metastatic breast cancer.Methods: The protocol used tumor cells spiked in a lymphoid cell line with detection by flow cytometry and quantitative reverse transcription polymerase chain reaction (QRT-PCR). Cells of the human mammary cancer subtypes were spiked into Jurkat cells, which served as the lymphocyte designate in numbers from 10 to 500 per 105 Jurkat cells. This mixed population was probed for CD45, EpCAM, and pancytokeratin acquired from flow cytometry and characterized by microscopy. QRT-PCR was done for CK-19, MUC-1, EpCAM, and GAPDH. Validation was attained with blood samples from 22 patients with metastatic breast cancer and 20 healthy individuals.Results: Flow cytometry could detect 1 breast cancer cell per 100,000 Jurkat cells, with similar detection levels in the breast cancer subtypes. Samples from patients with breast cancer showed a range of CTCs from 1-85 per 10 mL of blood. Quantitation of expression for EpCAM, CK-19, Muc-1, and Her2neu confirmed the presence of CTCs in 76% of samples.Conclusion: Density gradient and immunomagnetic enrichment accomplished isolation of CTCs and quantitation was achieved using flow cytometry. Combined QRT-PCR and imaging further validated these findings, rendering a robust methodology