Acute brucella melitensis M16 infection model in mice treated with tumor necrosis factor-alpha inhibitors

Introduction: There is limited data in the literature about brucellosis related to an intracellular pathogen and anti-tumor necrosis factor alpha (anti-TNFα) medication. The aim of this study was to evaluate acute Brucella infections in mice receiving anti-TNFα drug treatment. Methodology: Anti-TNFα drugs were injected in mice on the first and fifth days of the study, after which the mice were infected with B. melitensis M16 strain. Mice were sacrificed on the fourteenth day after infection. Bacterial loads in the liver and spleen were defined, and histopathological changes were evaluated. Results: Neither the liver nor the spleen showed an increased bacterial load in all anti-TNFα drug groups when compared to a non-treated, infected group. The most significant histopathological findings were neutrophil infiltrations in the red pulp of the spleen and apoptotic cells with hepatocellular pleomorphism in the liver. There was no significant difference among the groups in terms of previously reported histopathological findings, such as extramedullary hematopoiesis and granuloma formation. Conclusions: There were no differences in hepatic and splenic bacterial load and granuloma formation, which indicate worsening of the acute Brucella infection in mice; in other words, anti-TNFα treatment did not exacerbate the acute Brucella spp. infection in mice. © 2015 Kutlu et al

First Order Phase Transition in the 3-dimensional Blume-Capel Model on a Cellular Automaton

The first order phase transition of the three-dimensional Blume Capel are investigated using cooling algorithm which improved from Creutz Cellular Automaton for the $D/J=2.9$ parameter value in the first order phase transition region. The analysis of the data using the finite-size effect and the histogram technique indicate that the magnetic susceptibility maxima and the specific heat maxima increase with the system volume ($L^{d}$) at $$.Comment: 13 pages, 4 figure

Exploiting Pretrained Biochemical Language Models for Targeted Drug Design

Motivation: The development of novel compounds targeting proteins of interest is one of the most important tasks in the pharmaceutical industry. Deep generative models have been applied to targeted molecular design and have shown promising results. Recently, target-specific molecule generation has been viewed as a translation between the protein language and the chemical language. However, such a model is limited by the availability of interacting protein-ligand pairs. On the other hand, large amounts of unlabeled protein sequences and chemical compounds are available and have been used to train language models that learn useful representations. In this study, we propose exploiting pretrained biochemical language models to initialize (i.e. warm start) targeted molecule generation models. We investigate two warm start strategies: (i) a one-stage strategy where the initialized model is trained on targeted molecule generation (ii) a two-stage strategy containing a pre-finetuning on molecular generation followed by target specific training. We also compare two decoding strategies to generate compounds: beam search and sampling. Results: The results show that the warm-started models perform better than a baseline model trained from scratch. The two proposed warm-start strategies achieve similar results to each other with respect to widely used metrics from benchmarks. However, docking evaluation of the generated compounds for a number of novel proteins suggests that the one-stage strategy generalizes better than the two-stage strategy. Additionally, we observe that beam search outperforms sampling in both docking evaluation and benchmark metrics for assessing compound quality. Availability and implementation: The source code is available at https://github.com/boun-tabi/biochemical-lms-for-drug-design and the materials are archived in Zenodo at https://doi.org/10.5281/zenodo.6832145Comment: 12 pages, to appear in Bioinformatic

Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties

Machine learning models have found numerous successful applications in computational drug discovery. A large body of these models represents molecules as sequences since molecular sequences are easily available, simple, and informative. The sequence-based models often segment molecular sequences into pieces called chemical words (analogous to the words that make up sentences in human languages) and then apply advanced natural language processing techniques for tasks such as $\textit{de novo}$ drug design, property prediction, and binding affinity prediction. However, the chemical characteristics and significance of these building blocks, chemical words, remain unexplored. This study aims to investigate the chemical vocabularies generated by popular subword tokenization algorithms, namely Byte Pair Encoding (BPE), WordPiece, and Unigram, and identify key chemical words associated with protein-ligand binding. To this end, we build a language-inspired pipeline that treats high affinity ligands of protein targets as documents and selects key chemical words making up those ligands based on tf-idf weighting. Further, we conduct case studies on a number of protein families to analyze the impact of key chemical words on binding. Through our analysis, we find that these key chemical words are specific to protein targets and correspond to known pharmacophores and functional groups. Our findings will help shed light on the chemistry captured by the chemical words, and by machine learning models for drug discovery at large.Comment: 16 pages, 11 figures, new computational analysis and extended case studie

Comparison of serum and bronchoalveolar lavage fluid sialic acid levels between malignant and benign lung diseases

BACKGROUND: It is known that tissue and serum sialic acid levels may be altered by malignant transformation. In this study, sialic acid levels were determined in bronchoalveolar lavage fluid (BAL) and serum in two groups of patients with lung cancer and non-malignant diseases of the lung. METHODS: Colorimetric methods were used for determination sialic acid in serum and in BAL samples. Flexible bronchoscopy was used to obtain the latter. RESULTS: Sialic acid levels in bronchoalveolar lavage fluid and serum did not show any statistically significant difference between subjects with malignant and the non-malignant lung diseases (p > 0.05). Sialic acid levels were also unrelated to the stage and localization of the tumor (p > 0.05). CONCLUSIONS: Sialic acid levels do not appear to be a good marker for discriminating malignant from non-malignant diseases of the lung

T1DBase: update 2011, organization and presentation of large-scale data sets for type 1 diabetes research

T1DBase (http://www.t1dbase.org) is web platform, which supports the type 1 diabetes (T1D) community. It integrates genetic, genomic and expression data relevant to T1D research across mouse, rat and human and presents this to the user as a set of web pages and tools. This update describes the incorporation of new data sets, tools and curation efforts as well as a new website design to simplify site use. New data sets include curated summary data from four genome-wide association studies relevant to T1D, HaemAtlas—a data set and tool to query gene expression levels in haematopoietic cells and a manually curated table of human T1D susceptibility loci, incorporating genetic overlap with other related diseases. These developments will continue to support T1D research and allow easy access to large and complex T1D relevant data sets

Postresectional lung injury in thoracic surgery pre and intraoperative risk factors: a retrospective clinical study of a hundred forty-three cases

<p>Abstract</p> <p>Introduction</p> <p>Acute respiratory dysfunction syndrome (ARDS), defined as acute hypoxemia accompanied by radiographic pulmonary infiltrates without a clearly identifiable cause, is a major cause of morbidity and mortality after pulmonary resection. The aim of the study was to determine the pre and intraoperative factors associated with ARDS after pulmonary resection retrospectively.</p> <p>Methods</p> <p>Patients undergoing elective pulmonary resection at Adnan Menderes University Medical Faculty Thoracic Surgery Department from January 2005 to February 2010 were included in this retrospective study. The authors collected data on demographics, relevant co-morbidities, the American Society of Anesthesiologists (ASA) Physical Status classification score, pulmonary function tests, type of operation, duration of surgery and intraoperative fluid administration (fluid therapy and blood products). The primary outcome measure was postoperative ARDS, defined as the need for continuation of mechanical ventilation for greater than 48-hours postoperatively or the need for reinstitution of mechanical ventilation after extubation. Statistical analysis was performed with Fisher exact test for categorical variables and logistic regression analysis for continuous variables.</p> <p>Results</p> <p>Of one hundred forty-three pulmonary resection patients, 11 (7.5%) developed postoperative ARDS. Alcohol abuse (p = 0.01, OR = 39.6), ASA score (p = 0.001, OR: 1257.3), resection type (p = 0.032, OR = 28.6) and fresh frozen plasma (FFP)(p = 0.027, OR = 1.4) were the factors found to be statistically significant.</p> <p>Conclusion</p> <p>In the light of the current study, lung injury after lung resection has a high mortality. Preoperative and postoperative risk factor were significant predictors of postoperative lung injury.</p

Caspase-3 dependent nitrergic neuronal apoptosis following cavernous nerve injury is mediated via RhoA and ROCK activation in major pelvic ganglion

Axonal injury due to prostatectomy leads to Wallerian degeneration of the cavernous nerve (CN) and erectile dysfunction (ED). Return of potency is dependent on axonal regeneration and reinnervation of the penis. Following CN injury (CNI), RhoA and Rho-associated protein kinase (ROCK) increase in penile endothelial and smooth muscle cells. Previous studies indicate that nerve regeneration is hampered by activation of RhoA/ROCK pathway. We evaluated the role of RhoA/ROCK pathway in CN regulation following CNI using a validated rat model. CNI upregulated gene and protein expression of RhoA/ROCK and caspase-3 mediated apoptosis in the major pelvic ganglion (MPG). ROCK inhibitor (ROCK-I) prevented upregulation of RhoA/ROCK pathway as well as activation of caspase-3 in the MPG. Following CNI, there was decrease in the dimer to monomer ratio of neuronal nitric oxide synthase (nNOS) protein and lowered NOS activity in the MPG, which were prevented by ROCK-I. CNI lowered intracavernous pressure and impaired non-adrenergic non-cholinergic-mediated relaxation in the penis, consistent with ED. ROCK-I maintained the intracavernous pressure and non-adrenergic non-cholinergic-mediated relaxation in the penis following CNI. These results suggest that activation of RhoA/ROCK pathway mediates caspase-3 dependent apoptosis of nitrergic neurons in the MPG following CNI and that ROCK-I can prevent post-prostatectomy ED

Clusters of Conserved Beta Cell Marker Genes for Assessment of Beta Cell Phenotype

The aim of this study was to establish a gene expression blueprint of pancreatic beta cells conserved from rodents to humans and to evaluate its applicability to assess shifts in the beta cell differentiated state. Genome-wide mRNA expression profiles of isolated beta cells were compared to those of a large panel of other tissue and cell types, and transcripts with beta cell-abundant and -selective expression were identified. Iteration of this analysis in mouse, rat and human tissues generated a panel of conserved beta cell biomarkers. This panel was then used to compare isolated versus laser capture microdissected beta cells, monitor adaptations of the beta cell phenotype to fasting, and retrieve possible conserved transcriptional regulators.Journal ArticleSCOPUS: ar.jinfo:eu-repo/semantics/publishe