Meningiomas represent one of the most frequently reported non-glial, primary brain and central nervous system (CNS) tumors. Meningiomas often display a spectrum of anomalous locations and morphological attributes, deterring their timely diagnosis. Majority of them are sporadic in nature and thus the present-day screening strategies, including radiological investigations, often result in misdiagnosis due to their aberrant and equivocal radiological facets. Therefore, it is pertinent to explore less invasive and patient-friendly biofluids such as serum for their screening and diagnostics. The utility of serum Raman spectroscopy in diagnosis and therapeutic monitoring of cancers has been reported in the literature. In the present study, for the first time, to the best of our knowledge, we have explored Raman spectroscopy to classify the sera of meningioma and control subjects. For this exploration, 35 samples each of meningioma and control subjects were accrued and the spectra revealed variance in the levels of DNA, proteins, lipids, amino acids and β-carotene, i.e., a relatively higher protein, DNA and lipid content in meningioma. Subsequent Principal Component Analysis (PCA) and Principal Component-Linear Discriminant Analysis (PC-LDA) followed by Leave-One-Out Cross-Validation (LOOCV) and limited independent test data, in a patient-wise approach, yielded a classification efficiency of 92% and 80% for healthy and meningioma, respectively. Additionally, in the analogous analysis between healthy and different grades of meningioma, similar results were obtained. These results indicate the potential of Raman spectroscopy in differentiating meningioma. As present methods suffer from known limitations, with the prospective validation on a larger cohort, serum Raman spectroscopy could be an adjuvant/alternative approach in the clinical management of meningioma.
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.
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 individuals. 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 individuals 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 individuals 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.
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