Synthesis of 1-arylamido-2-oxo-4-methylpyrido[b]phenothiazines

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Conditional over-expression of PITX1 causes skeletal muscle dystrophy in mice

Paired-like homeodomain transcription factor 1 (PITX1) was specifically up-regulated in patients with facioscapulohumeral muscular dystrophy (FSHD) by comparing the genome-wide mRNA expression profiles of 12 neuromuscular disorders. In addition, it is the only known direct transcriptional target of the double homeobox protein 4 (DUX4) of which aberrant expression has been shown to be the cause of FSHD. To test the hypothesis that up-regulation of PITX1 contributes to the skeletal muscle atrophy seen in patients with FSHD, we generated a tet-repressible muscle-specific Pitx1 transgenic mouse model in which expression of PITX1 in skeletal muscle can be controlled by oral administration of doxycycline. After PITX1 was over-expressed in the skeletal muscle for 5 weeks, the mice exhibited significant loss of body weight and muscle mass, decreased muscle strength, and reduction of muscle fiber diameters. Among the muscles examined, the tibialis anterior, gastrocnemius, quadricep, bicep, tricep and deltoid showed significant reduction of muscle mass, while the soleus, masseter and diaphragm muscles were not affected. The most prominent pathological change was the development of atrophic muscle fibers with mild necrosis and inflammatory infiltration. The affected myofibers stained heavily with NADH-TR with the strongest staining in angular-shaped atrophic fibers. Some of the atrophic fibers were also positive for embryonic myosin heavy chain using immunohistochemistry. Immunoblotting showed that the p53 was up-regulated in the muscles over-expressing PITX1. The results suggest that the up-regulation of PITX1 followed by activation of p53-dependent pathways may play a major role in the muscle atrophy developed in the mouse model

Atomic spectrometry update: Review of advances in the analysis of metals, chemicals and materials

There has been a large increase in the number of papers published that are relevant to this review over this review period. The growth in popularity of LIBS is rapid, with applications being published for most sample types. This is undoubtedly because of its capability to analyse in situ on a production line (hence saving time and money) and its minimally destructive nature meaning that both forensic and cultural heritage samples may be analysed. It also has a standoff analysis capability meaning that hazardous materials, e.g. explosives or nuclear materials, may be analysed from a safe distance. The use of mathematical algorithms in conjunction with LIBS to enable improved accuracy has proved a popular area of research. This is especially true for ferrous and non-ferrous samples. Similarly, chemometric techniques have been used with LIBS to aid in the sorting of polymers and other materials. An increase in the number of papers in the subject area of alternative fuels was noted. This was at the expense of papers describing methods for the analysis of crude oils. For nanomaterials, previous years have seen a huge number of single particle and field flow fractionation characterisations. Although several such papers are still being published, the focus seems to be switching to applications of the nanoparticles and the mechanistic aspects of how they retain or bind with other analytes. This is the latest review covering the topic of advances in the analysis of metals, chemicals and materials. It follows on from last year's review1-6 and is part of the Atomic Spectrometry Updates series

A novel deep neural network framework for biomedical named entity recognition

With the dramatic improvements in the field of bio-informatics, extracting information from text and analyzing the association between the entities has received more attention in the past few years. Entity Recognition (ER) meant to extract and recognize the entities from any text. Biomedical Named Entity Recognition (BNER) gets more and more attention from the researchers since it is a fundamental task in biomedical information extraction. Various methods has been proposed to perform the task of BioNER. Different kind of approaches are dictionary based, rule based approaches, traditional machine learning approaches that combines supervised and unsupervised methods and neural network based approach. The state-of-art systems previously adopted various supervised machine learning methods Hidden Markov Models (HMMs), Maximum Entropy Markov Models (MEMMs), Support vector machines(SVM),Structural Support Vector Machines(SSVMS),Conditional Random Fields(CRF) to derive semantic and syntatic features from annotated datasets. However, CRF is one of the most successful method used for NER and it has obtained finest result because of the robustness and ability for sequence lebelling task. Recently, studies have demonstrated the application of deep learning based approaches for biomedical named entity recognition (BioNER) and shown promising results

Novel mutations in a second primary gastric cancer in a patient treated for primary colon cancer

Abstract A 60-year-old man presented with complaints of abdominal pain and melena. Patient had a history of colon cancer 16 years back and had undergone right hemi colectomy for microsatellite instability (MSI) negative, mismatch repair (MMR) stable, T2N0 disease with no mutations on next-generation sequencing (NGS). Investigations revealed a second primary in stomach (intestinal type of adenocarcinoma) with no recurrent lesions in colon or distant metastasis. He was started on CapOx with Bevacizumab and developed gastric outlet obstruction. Total gastrectomy with D2 lymphadenectomy and Roux-en-Y oesophageao-jejunal pouch anastomosis was done. The histopathology showed intestinal type of adenocarcinoma with pT3N2 disease. NGS showed 3 novel mutations in KMT2A, LTK, and MST1R gene. The pathway enrichment analysis and Gene Ontology were carried out, followed by the construction of protein–protein interaction network to discover associations among the genes. The results suggested that these mutations have not been reported in gastric cancer earlier and despite not having a direct pathway of carcinogenesis they probably act through modulation of host of miRNA’s. Further studies are needed to investigate the role of KMT2A, LTK, and MST1R gene in gastric carcinogenesis

Not Available

Not AvailableThis paper presents the results of a study involving the estimation of genetic resource diversity in a set of 37 accessions belonging to Luffa acutangula, L. aegyptiaca, L. hermaphrodita, L. graveolens and L. echinata, collected from diverse phytogeographical regions of India. The results will be useful to support the identification, classification, conservation, genetic enhancement and effective utilization of the Luffa germplasm in genetic resource programmes.Not Availabl

Adsorption of C2 gases over CeO2-based catalysts: synergism of cationic sites and anionic vacancies

The synthesis of novel and efficient catalysts for acetylene hydrogenation exhibiting high selectivity towards ethylene is important due to the presence of selective acetylene hydrogenation reaction in petrochemical processing. Since adsorption of C2 gases constitutes the primary step in catalytic hydrogenation and governs the selectivity of the catalysts, we have explored the C2-adsorption potential of reducible CeO2-based systems. The adsorption of C2-gases over CeO2-based materials was assessed using experimental in situ spectroscopic techniques and in silico theoretical studies based on density functional theory. The effect of Pd2+ substitution on adsorption was studied. The addition of Pd2+-ions was found to enhance the adsorption of the gases. Theoretical calculations provided insights into the modes of adsorption, adsorption energetics and reactant–catalyst interactions. The role of the presence of cationic substitution and anionic vacancies in strengthening the adsorption of gases was established. Pd-substituted reduced CeO2 showed activity for the adsorption of all C2 gases. On the basis of the aforementioned experimental and theoretical observations, the catalysts were tested for acetylene hydrogenation, and Pd-substituted CeO2 was found to be a good catalyst for the reaction with complete acetylene conversion observed below 100 °C.by Manjusha C. Padole, Bhanu Pratap Gangwar, Aman Pandey, Aditi Singhal, Sudhanshu Sharma and Parag A. Deshpand

Diagnosis of Intracranial Tumors via the Selective CNN Data Modeling Technique

A brain tumor occurs in humans when a normal cell turns into an aberrant cell inside the brain. Primarily, there are two types of brain tumors in Homo sapiens: benign tumors and malignant tumors. In brain tumor diagnosis, magnetic resonance imaging (MRI) plays a vital role that requires high precision and accuracy for diagnosis, otherwise, a minor error can result in severe consequences. In this study, we implemented various configured convolutional neural network (CNN) paradigms on brain tumor MRI scans that depict whether a person is a brain tumor patient or not. This paper emphasizes objective function values (OFV) achieved by various CNN paradigms with the least validation cross-entropy loss (LVCEL), maximum validation accuracy (MVA), and training time (TT) in seconds, which can be used as a feasible tool for clinicians and the medical community to recognize tumor patients precisely. Experimentation and evaluation were based on a total of 2189 brain MRI scans, and the best architecture shows the highest accuracy of 0.8275, maximum objective function value of 1.84, and an area under the ROC (AUC-ROC) curve of 0.737 to accurately recognize and classify whether or not a person has a brain tumor