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Studies on the adoption of voice recognition in health care have mostly focused on turnaround time and error rate, with less attention paid to the impact on the efficiency of the providers.<br />To study the impact of voice recognition on the efficiency of grossing biopsy specimens.<br />Timestamps corresponding to barcode scanning for biopsy specimen bottles and cassettes were retrieved from the pathology information system database. The time elapsed between scanning a specimen bottle and the corresponding first cassette was the length of time spent on the gross processing of that specimen and is designated as the specimen time. For the first specimen of a case, the specimen time additionally included the time spent on dictating the clinical information. Therefore, the specimen times were divided into the following 2 categories first-specimen time and subsequent-specimen time. The impact of voice recognition on specimen times was studied using both univariate and multivariate analyses.<br />Specimen complexity, prosector variability, length of clinical information text, and the number of biopsies the prosector grossed that day were the major determinants of specimen times. Adopting voice recognition had a negligible impact on specimen times.<br />Adopting voice recognition in the gross room removes the need to hire transcriptionists without negatively impacting the efficiency of the prosectors, resulting in an overall cost saving. Using computer scripting to automatically enter clinical information (received through the electronic order interface) into report templates may potentially increase the grossing efficiency in the future.<br />Adopting voice recognition in the gross room removes the need to hire transcriptionists without negatively impacting the efficiency of the prosectors, resulting in an overall cost saving. Using computer scripting to automatically enter clinical information (received through the electronic order interface) into report templates may potentially increase the grossing efficiency in the future.<br />Human Leukocyte Antigen (HLA) is a polymorphic protein of the immune system with a central role in organ transplantation. Organ recipients can be sensitized against HLA from previous exposure, which increases the likelihood of antidonor immune responses and subsequently organ rejection. HLA matching represents an attractive option to improve graft function, reduce sensitization of recipients in first transplantations, and improve organ allocation.<br />To examine the feasibility of the reintroduction of HLA matching into the criteria in the Johannesburg program, we retrospectively assessed HLA types in our donor population.<br />HLA types of 782 deceased and related living donors from 2015 until 2019 were recorded and analyzed to identify the most common HLA types and haplotypes. A virtual crossmatch was also done to examine the anti-HLA antibodies in the recipient population compared with the common HLA types identified in this study.<br />Of the commonest HLA types identified, at least 1 was present in 732 (93.6%)mendation that the pretransplantation workup should not reinclude HLA matching.<br />Procedures for structural modeling of protein-protein complexes (protein docking) produce a number of models which need to be further analyzed and scored. Scoring can be based on independently determined constraints on the structure of the complex, such as knowledge of amino acids essential for the protein interaction. Previously, we showed that text mining of residues in freely available PubMed abstracts of papers on studies of protein-protein interactions may generate such constraints. However, absence of post-processing of the spotted residues reduced usability of the constraints, as a significant number of the residues were not relevant for the binding of the specific proteins.<br />We explored filtering of the irrelevant residues by two machine learning approaches, Deep Recursive Neural Network (DRNN) and Support Vector Machine (SVM) models with different training/testing schemes. The results showed that the DRNN model is superior to the SVM model when training is performed on the PMC-OA full-text articles and applied to classification (interface or non-interface) of the residues spotted in the PubMed abstracts. When both training and testing is performed on full-text articles or on abstracts, the performance of these models is similar. Thus, in such cases, there is no need to utilize computationally demanding DRNN approach, which is computationally expensive especially at the training stage. [https://www.selleckchem.com/products/Telaprevir(VX-950).html Telaprevir HCV Protease inhibitor] The reason is that SVM success is often determined by the similarity in data/text patterns in the training and the testing sets, whereas the sentence structures in the abstracts are, in general, different from those in the full text articles.<br />The code and the datasets generated in this study are available at https//gitlab.ku.edu/vakser-lab-public/text-mining/-/tree/2020-09-04.<br />Supplementary data are available at Bioinformatics online.<br />Supplementary data are available at Bioinformatics online.Bisphenol A (BPA), a ubiquitous endocrine-disrupting chemical, interferes with reproduction and is also considered an obesogen. The neuropeptide Y (NPY) neurons of the hypothalamus control both food intake and reproduction and have emerged as potential targets of BPA. These functionally diverse subpopulations of NPY neurons are differentially regulated by peripheral signals, such as estrogen and leptin. Whether BPA also differentially alters Npy expression in subpopulations of NPY neurons, contributing to BPA-induced endocrine dysfunction is unclear. We investigated the response of 6 immortalized hypothalamic NPY-expressing cell lines to BPA treatment. BPA upregulated Npy mRNA expression in 4 cell lines (mHypoA-59, mHypoE-41, mHypoA-2/12, mHypoE-42), and downregulated Npy in 2 lines (mHypoE-46, mHypoE-44). This differential expression of Npy occurred concurrently with differential expression of estrogen receptor mRNA levels. Inhibition of G-protein coupled estrogen receptor GPR30 or estrogen receptor β prevented the BPA-mediated decrease in Npy, whereas inhibition of energy sensor 5' adenosine monophosphate-activated protein kinase (AMPK) with compound C prevented BPA-induced increase in Npy. BPA also altered neuroinflammatory and oxidative stress markers in both mHypoA-59 and mHypoE-46 cell lines despite the differential regulation of Npy. Remarkably, treatment with BPA in an antioxidant-rich media, Neurobasal A (NBA), or with reactive oxygen species scavenger tauroursodeoxycholic acid mitigated the BPA-induced increase and decrease in Npy. Furthermore, 2 antioxidant species from NBA-N-acetylcysteine and vitamin B6-diminished the induction of Npy in the mHypoA-59 cells, demonstrating these supplements can counteract BPA-induced dysregulation in certain subpopulations. Overall, these results illustrate the differential regulation of Npy by BPA in neuronal subpopulations, and point to oxidative stress as a pathway that can be targeted to block BPA-induced Npy dysregulation in hypothalamic neurons.