ORCID Profile
0000-0002-0678-1165
Current Organisation
Baker IDI Heart and Diabetes Institute
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Publisher: Springer Science and Business Media LLC
Date: 09-2023
Publisher: Elsevier BV
Date: 04-2022
DOI: 10.1016/J.ARR.2022.101575
Abstract: Depression is linked to Alzheimer's disease (AD) but it is unclear whether depression is also associated with cognitive decline in the preclinical phase and mild cognitive impairment (MCI). Previous meta-analyses have only investigated AD as an outcome without accounting for in iduals showing cognitive decline that does not meet the diagnostic criteria for AD. Other potentially modifiable risk factors such as bone loss have also been less explored and there remains uncertainty around their temporal relationship with cognitive decline. To conduct a systematic review and meta-analysis investigating depression and bone loss as risk factors for subsequent cognitive decline. A comprehensive search strategy was developed and applied using four databases MEDLINE Complete, Embase, PsycINFO and CINAHL Complete. The pooled summary effects were estimated as odds ratios with 95% confidence intervals using a random-effects model. The study protocol was registered with PROSPERO (ID: CRD42020159369). A total of 75 longitudinal cohort studies were identified for meta-analysis, of which 70 examined the impact of depression on cognitive decline and five examined the impact of bone loss. Prior exposure to depression was found to be associated with cognitive score reduction (OR 1.33 95% CI 1.17, 1.51), MCI incidence (OR 1.52 95% CI 1.28, 1.79) and AD incidence (OR 1.79 95% CI 1.46, 2.2). Bone loss was also associated with the incidence of AD (OR=1.81 95% CI 1.28, 2.55). Overall, the results support the hypothesis that depression is associated with subsequent cognitive decline. Bone loss was also found to be associated with AD incidence however, due to the small number of studies, the results should be viewed with caution.
Publisher: American Diabetes Association
Date: 20-06-2023
DOI: 10.2337/DB23-1318-P
Publisher: Springer Science and Business Media LLC
Date: 18-04-2019
DOI: 10.1038/S41598-019-42576-1
Abstract: The environmental considerations attributing to the escalation of carbon dioxide emissions have raised alarmingly. Consequently, the concept of sequestration and biological conversion of CO 2 by photosynthetic microorganisms is gaining enormous recognition. In this study, in an attempt to discern the synergistic CO 2 tolerance mechanisms, metabolic responses to increasing CO 2 concentrations were determined for Synechococcus elongatus PCC 11801, a fast-growing, novel freshwater strain, using quantitative proteomics. The protein expression data revealed that the organism responded to elevated CO 2 by not only regulating the cellular transporters involved in carbon-nitrogen uptake and assimilation but also by inducing photosynthesis, carbon fixation and glycolysis. Several components of photosynthetic machinery like photosystem reaction centers, phycobilisomes, cytochromes, etc. showed a marked up-regulation with a concomitant downshift in proteins involved in photoprotection and redox maintenance. Additionally, enzymes belonging to the TCA cycle and oxidative pentose phosphate pathway exhibited a decline in their expression, further highlighting that the demand for reduced cofactors was fulfilled primarily through photosynthesis. The present study brings the first-ever comprehensive assessment of intricate molecular changes in this novel strain while shifting from carbon-limited to carbon-sufficient conditions and may pave the path for future host and pathway engineering for production of sustainable fuels through efficient CO 2 capture.
Publisher: Springer New York
Date: 2019
DOI: 10.1007/978-1-4939-9706-0_6
Abstract: Proteomics is an indispensable tool for disease biomarker discovery. It is widely used for the analysis of biological fluids such as cerebrospinal fluid (CSF), blood, and saliva, which further aids in our understanding of disease incidence and progression. CSF is often the biospecimen of choice in case of intracranial tumors, as rapid changes in the tumor microenvironment can be easily assessed due to its close proximity to the brain. On the contrary studies comprising of serum or plasma s les do not truly reflect the underlying molecular alterations due to the presence of protective blood-brain barrier. We have described in here the detailed workflows for two advanced proteomics techniques, namely, 2D-DIGE (two-dimensional difference in-gel electrophoresis) and iTRAQ (isobaric tag for relative and absolute quantitation), for CSF analysis. Both of these techniques are very sensitive and widely used for quantitative proteomics analysis.
Publisher: Mary Ann Liebert Inc
Date: 04-2016
Publisher: Wiley
Date: 12-2020
DOI: 10.1002/ALZ.047349
Publisher: Springer Science and Business Media LLC
Date: 24-10-2022
DOI: 10.1038/S41598-022-20587-9
Abstract: Although several genetic polymorphisms have been linked with the risk of Alzheimer’s disease, less is known about their impact on cognitive performance among cognitively healthy in iduals. Our aim was to investigate the association of the genetic variant, rs744373 in the bridging integrator 1 gene ( BIN1 ), the strongest genetic risk factor for Alzheimer’s disease after the APOE ε4 allele, with different cognitive domains among non-demented older men. Cognitive function was measured using the CogState Brief Battery, which assessed cognitive performance across four domains: psychomotor function, visual attention, recognition memory and working memory. Linear regression analysis revealed that in iduals with the BIN1 risk allele performed poorly on the recognition memory task as compared to those without the risk allele. However, this was in contrast with the in iduals who harboured the APOE ε4 risk allele as they displayed better performance on the recognition task in comparison to those without the ε4 risk allele. To the best of our knowledge, this is the first study that demonstrates genetic variation in BIN1 to be a better predictor of recognition memory than APOE , which remains the biggest genetic risk factor for Alzheimer’s disease.
Publisher: Royal Society of Chemistry (RSC)
Date: 2018
DOI: 10.1039/C8AN00224J
Abstract: Serum Raman spectroscopy is explored to classify meningiomas, which are the most frequently reported non-malignant brain and CNS tumors and are often misdiagnosed. We find the technique can be prospectively extended to the screening of high-risk populations and for therapeutic monitoring.
No related grants have been discovered for Kanika Mehta.