Critical Prognostic Factors in Cerebral Venous Sinus Thrombosis: An Observational Study.

Background: Cerebral venous sinus thrombosis (CVST) presents with a wide variety of neurological symptoms in various combinations and has a high mortality rate of up to 50%. Recent advances in neuroimaging and therapeutic interventions have brought it down to 10%-20%. The study aims to identify critical prognostic factors associated with poor outcomes in patients with CVST. Materials and Methods: All cases of CVST aged >18 years from July 2015 to July 2020 who were not terminally ill and bedridden before the illness were evaluated at the entry point for various risk factors and after 30 days for outcome assessment with the modified Rankin scale (mRS). The outcome was dichotomized, applying mRS <3 as a good outcome, and analyzed with the Chi-square test or the Fischer's exact test in a bivariate analysis to identify associated variables. Results: A total of 149 subjects were studied. Glasgow Comma Scale (GCS) <9 (P<0.001), focal neurological deficits (P = 0.05), the presence of a mass effect (P<0.001), and the need for decompressive hemicraniectomy (P<0.001) were associated with poor outcomes. Age, gender, diagnostic delay, seizures at onset, papilledema, parenchymal lesions, deep sinus involvement, and multiple sinus thrombosis were not associated with a poor outcome. Conclusion: In our study, early diagnosis and treatment of CVST is associated with an overall favorable outcome even in the presence of traditional poor prognostic factors such as age, seizures at onset, deep sinus involvement, and multiple sinus involvement in the face of conventional risk factors. A large country-wide prospective study might help in elucidating the poor prognostic factors.

V-NeuroStack: Open-source 3D time stack software for identifying patterns in neuronal data.

Understanding functional correlations between the activities of neuron populations is vital for the analysis of neuronal networks. Analyzing large-scale neuroimaging data obtained from hundreds of neurons simultaneously poses significant visualization challenges. We developed V-NeuroStack, a novel network visualization tool to visualize data obtained using calcium imaging of spontaneous activity of neurons in a mouse brain slice as well as in vivo using two-photon imaging. V-NeuroStack creates 3D time stacks by stacking 2D time frames for a time-series dataset. It provides a web interface to explore and analyze data using both 3D and 2D visualization techniques. Previous attempts to analyze such data have been limited by the tools available to visualize large numbers of correlated activity traces. V-NeuroStack's 3D view is used to explore patterns in dynamic large-scale correlations between neurons over time. The 2D view is used to examine any timestep of interest in greater detail. Furthermore, a dual-line graph provides the ability to explore the raw and first-derivative values of activity from an individual or a functional cluster of neurons. V-NeuroStack can scale to datasets with at least a few thousand temporal snapshots. It can potentially support future advancements in in vitro and in vivo data capturing techniques to bring forth novel hypotheses by allowing unambiguous visualization of massive patterns in neuronal activity data.

Relevance of Geographical and Biochemical Factors in Causation of Cerebral Venous Sinus Thrombosis: An Observational Analytical Study.

Objectives: There is a higher prevalence of cerebral venous sinus thrombosis (CVST) in more recent times, owing to increased awareness, clinical diagnostic skills, and advancements in neuroimaging modalities. This study aimed to identify and characterize the geographical, clinical, and etiological profiles of patients with CVST that may be relevant to planning appropriate diagnostic and therapeutic strategies to improve functional recovery. Methods and Results: A retrospective observational study was carried out at a tertiary care hospital between March 2014 and October 2018. The demographics and clinical profile of the hospitalized patients were extracted from the Medical Record Division. Choropleth maps were created to present the geographic distribution of the patients with CVST admitted to our hospital. A total of 145 patients with CVST were included in the study. Etiological factors revealed striking abnormalities in red blood cells counts and serum homocysteine. Analyzing the geographical distribution of the patients with CVST showed most of the patients hailed from Central Karnataka Plateau 106 (73%). Polycythemia was most commonly seen in patients residing in the Central Karnataka Plateau 21 (62%). Conclusion: It is inferred that large scale community-based studies to identify a genetic abnormality like a mutant erythropoietin gene should be undertaken to plan effective diagnostic, therapeutic, and preventive measures.

Discovering Gene Regulatory Elements Using Coverage-Based Heuristics.

Data mining algorithms and sequencing methods (such as RNA-seq and ChIP-seq) are being combined to discover genomic regulatory motifs that relate to a variety of phenotypes. However, motif discovery algorithms often produce very long lists of putative transcription factor binding sites, hindering the discovery of phenotype-related regulatory elements by making it difficult to select a manageable set of candidate motifs for experimental validation. To address this issue, the authors introduce the motif selection problem and provide coverage-based search heuristics for its solution. Analysis of 203 ChIP-seq experiments from the ENCyclopedia of DNA Elements project shows that our algorithms produce motifs that have high sensitivity and specificity and reveals new insights about the regulatory code of the human genome. The greedy algorithm performs the best, selecting a median of two motifs per ChIP-seq transcription factor group while achieving a median sensitivity of 77 percent.

Corrigendum: Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution.

This corrects the article DOI: 10.1038/nature22364.

Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution.

The early detection of relapse following primary surgery for non-small-cell lung cancer and the characterization of emerging subclones, which seed metastatic sites, might offer new therapeutic approaches for limiting tumour recurrence. The ability to track the evolutionary dynamics of early-stage lung cancer non-invasively in circulating tumour DNA (ctDNA) has not yet been demonstrated. Here we use a tumour-specific phylogenetic approach to profile the ctDNA of the first 100 TRACERx (Tracking Non-Small-Cell Lung Cancer Evolution Through Therapy (Rx)) study participants, including one patient who was also recruited to the PEACE (Posthumous Evaluation of Advanced Cancer Environment) post-mortem study. We identify independent predictors of ctDNA release and analyse the tumour-volume detection limit. Through blinded profiling of postoperative plasma, we observe evidence of adjuvant chemotherapy resistance and identify patients who are very likely to experience recurrence of their lung cancer. Finally, we show that phylogenetic ctDNA profiling tracks the subclonal nature of lung cancer relapse and metastasis, providing a new approach for ctDNA-driven therapeutic studies.

Enhanced biodegradation of low and high-density polyethylene by novel bacterial consortia formulated from plastic-contaminated cow dung under thermophilic conditions.

The current study aimed to devise eco-friendly, safe, and cost-effective strategies for enhanced degradation of low- and high-density polyethylene (LDPE and HDPE) using newly formulated thermophilic microbial consortia from cow dung and to assess the biodegradation end products. The plastic-degrading bacteria from cow dung samples gathered from highly plastic-acclimated environments were enriched by standard protocols. The degradation ability was comprehended by zone of clearance method, and the percentage of degradation was monitored by weight reduction process. The best isolates were characterized by standard microbiological and molecular biology protocols. The best isolates were employed to form several combinations of microbial consortia, and the degradation end products were analyzed. The stability of 16S ribosomal DNA (rDNA) was predicted by bioinformatics approach. This study identified 75 ± 2, 55 ± 2, 60 ± 3, and 43 ± 3% degradation for LDPE strips, pellets, HDPE strips, and pellets, respectively, for a period of 120 days (p < 0.05) at 55 °C by the formulated consortia of IS1-IS4, and the degradation efficiency was found to be better in comparison with other formulations. The end product analysis by Fourier transform infrared, scanning electron microscopy, energy-dispersive spectroscopy, and nuclear magnetic resonance showed major structural changes and formation of bacterial biofilm on plastic surfaces. These novel isolates were designated as Bacillus vallismortis bt-dsce01, Psuedomonas protegens bt-dsce02, Stenotrophomonas sp. bt-dsce03, and Paenibacillus sp.bt-dsce04 by 16S rDNA sequencing and suggested good gene stability with minimum Gibb's free energy. Therefore, this study imparts substantial information regarding the utilization of these thermophilic microbial consortia from cow dung for rapid polyethylene removal.