Unilateral high division of the Sciatic Nerve with divided Piriformis

Abstract While doing the routine dissection for the undergraduate students in the department of Anatomy, Nil Ratan Sircar Medical College, Kolkata, few variations were found in the gluteal region of a 70 years old male cadaver, in the year 2013. On the right side the sciatic nerve (SN) pierced the piriformis muscle dividing it into superior and inferior slips and then, after a short distance, divided into tibial and common peroneal nerves in the gluteal region. On the left side, sciatic nerve divided into two terminal branches (common peroneal and tibial nerves) in the lower part of the back of the thigh near the apex of the popliteal fossa as usual. This high division of the sciatic nerve may result in nerve injury during deep intramuscular injections in gluteal region, piriformis syndrome due to compression of the nerve, failed SN block in anesthesia and surgical complications

Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS

AbstractGas chromatography-mass spectrometry (GC-MS) represents an analytical technique with significant practical societal impact. Spectral deconvolution is an essential step for interpreting GC-MS data. No public GC-MS repositories that also enable repository-scale analysis exist, in part because deconvolution requires significant user input. We therefore engineered a scalable machine learning workflow for the Global Natural Product Social Molecular Networking (GNPS) analysis platform to enable the mass spectrometry community to store, process, share, annotate, compare, and perform molecular networking of GC-MS data. The workflow performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization, using a Fast Fourier Transform-based strategy to overcome scalability limitations. We introduce a “balance score” that quantifies the reproducibility of fragmentation patterns across all samples. We demonstrate the utility of the platform with breathomics analysis applied to the early detection of oesophago-gastric cancer, and by creating the first molecular spatial map of the human volatilome.</jats:p

Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data.

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples

Metabolomic Responses of Guard Cells and Mesophyll Cells to Bicarbonate

Anthropogenic CO2 presently at 400 ppm is expected to reach 550 ppm in 2050, an increment expected to affect plant growth and productivity. Paired stomatal guard cells (GCs) are the gate-way for water, CO2, and pathogen, while mesophyll cells (MCs) represent the bulk cell-type of green leaves mainly for photosynthesis. We used the two different cell types, i.e., GCs and MCs from canola (Brassica napus) to profile metabolomic changes upon increased CO2 through supplementation with bicarbonate (HCO3-). Two metabolomics platforms enabled quantification of 268 metabolites in a time-course study to reveal short-term responses. The HCO3- responsive metabolomes of the cell types differed in their responsiveness. The MCs demonstrated increased amino acids, phenylpropanoids, redox metabolites, auxins and cytokinins, all of which were decreased in GCs in response to HCO3-. In addition, the GCs showed differential increases of primary C-metabolites, N-metabolites (e.g., purines and amino acids), and defense-responsive pathways (e.g., alkaloids, phenolics, and flavonoids) as compared to the MCs, indicating differential C/N homeostasis in the cell-types. The metabolomics results provide insights into plant responses and crop productivity under future climatic changes where elevated CO2 conditions are to take center-stage

Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data

Abstract: We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography-mass spectrometry (GC-MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC-MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples. A machine learning workflow enables auto-deconvolution of gas chromatography-mass spectrometry data