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Institution

University of Calcutta

EducationKolkata, India
About: University of Calcutta is a education organization based out in Kolkata, India. It is known for research contribution in the topics: Population & Schiff base. The organization has 8900 authors who have published 19712 publications receiving 259067 citations. The organization is also known as: Calcutta University & CU.


Papers
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Journal ArticleDOI
Daniel J. Klionsky1, Kotb Abdelmohsen2, Akihisa Abe3, Joynal Abedin4  +2519 moreInstitutions (695)
TL;DR: In this paper, the authors present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macro-autophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes.
Abstract: In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure flux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation, it is imperative to target by gene knockout or RNA interference more than one autophagy-related protein. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways implying that not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular assays, we hope to encourage technical innovation in the field.

5,187 citations

Journal Article
TL;DR: The overall findings indicate that rats grow rapidly during their childhood and become sexually mature at about the sixth week, but attain social maturity 5-6 months later, which must be taken into consideration while analyzing the results or selecting the dose of any research in rats when age is a crucial factor.
Abstract: By late 18th or early 19th century, albino rats became the most commonly used experimental animals in numerous biomedical researches, as they have been recognized as the preeminent model mammalian system. But, the precise correlation between age of laboratory rats and human is still a subject of debate. A number of studies have tried to detect these correlations in various ways, But, have not successfully provided any proper association. Thus, the current review attempts to compare rat and human age at different phases of their life. The overall findings indicate that rats grow rapidly during their childhood and become sexually mature at about the sixth week, but attain social maturity 5-6 months later. In adulthood, every day of the animal is approximately equivalent to 34.8 human days (i.e., one rat month is comparable to three human years). Numerous researchers performed experimental investigations in albino rats and estimated, in general, while considering their entire life span, that a human month resembles every-day life of a laboratory rat. These differences signify the variations in their anatomy, physiology and developmental processes, which must be taken into consideration while analyzing the results or selecting the dose of any research in rats when age is a crucial factor.

1,408 citations

Posted ContentDOI
Spyridon Bakas1, Mauricio Reyes, Andras Jakab2, Stefan Bauer3  +435 moreInstitutions (111)
TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

1,165 citations

Journal ArticleDOI
TL;DR: In this article, the authors present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes.
Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

1,129 citations

Journal ArticleDOI
TL;DR: The potential of a common semiconductor, ZnO, has been explored as an effective catalyst for the photodegradation of two model dyes: Methylene Blue and Eosin Y and substantial reduction of COD was achieved.
Abstract: Semiconductor photocatalysis often leads to partial or complete mineralization of organic pollutants. Upon irradiation with UV/visible light, semiconductors catalyze redox reactions in presence of air/O2 and water. Here, the potential of a common semiconductor, ZnO, has been explored as an effective catalyst for the photodegradation of two model dyes: Methylene Blue and Eosin Y. A 16 W lamp was the source of UV-radiation in a batch reactor. The effects of process parameters like, catalyst loading, initial dye concentration, airflow rate, UV-radiation intensity, and pH on the extent of photo degradation have been investigated. Substantial reduction of COD, besides removal of colour, was also achieved. A rate equation for the degradation based on Langmuir-Hinshelwood model has been proposed.

1,049 citations


Authors

Showing all 9026 results

NameH-indexPapersCitations
Sukalyan Chattopadhyay10675637548
Arun Majumdar10245952464
Sajal K. Das85112429785
Debashish Bhattacharya7731818541
Hari M. Srivastava76112642635
Sankar Mitra7326017830
Maurizio D'Incalci7258120379
Sankar K. Pal7044623727
Sondipon Adhikari6245713707
Lalji Singh6029713821
Kalipada Pahan5922310638
Tapas K. Hazra571079034
Sushil K. Mahata552639542
Suman Chakraborty5366411769
Samir Kumar Pal5235610901
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202382
2022214
20211,352
20201,357
20191,152
20181,133