BMP-2 Dependent Increase of Soft Tissue Density in Arthrofibrotic TKA

Arthrofibrosis after total knee arthroplasty (TKA) is difficult to treat, as its aetiology remains unclear. In a previous study, we established a connection between the BMP-2 concentration in the synovial fluid and arthrofibrosis after TKA. The hypothesis of the present study was, therefore, that the limited range of motion in arthrofibrosis is caused by BMP-2 induced heterotopic ossifications, the quantity of which is dependent on the BMP-2 concentration in the synovial fluid

TFE3 activation in a TSC1-altered malignant PEComa: challenging the dichotomy of the underlying pathogenic mechanisms

Perivascular epithelioid cell tumors (PEComas) form a family of rare mesenchymal neoplasms that typically display myomelanocytic differentiation. Upregulation of mTOR signaling due the inactivation of TSC1/2 (Tuberous Sclerosis 1 and 2) is believed to be a key oncogenic driver in this disease. Recently, a subgroup of PEComas harboring TFE3 (Transcription Factor E3) rearrangements and presenting with a distinctive morphology has been identified. TSC1/2 and TFE3 aberrations are deemed to be mutually exclusive in PEComa, with two different pathogenic mechanisms assumed to lead to tumorigenesis. Here, we challenge this dichotomy by presenting a case of a clinically aggressive TCS1-mutated PEComa displaying a TFE3-altered phenotype. FISH analysis was suggestive of a TFE3 inversion; however, RNA and whole genome sequencing was ultimately unable to identify a fusion involving the gene. However, a copy number increase of the chromosomal region encompassing TFE3 was detected and transcriptome analysis confirmed upregulation of TFE3, which was also seen at the protein level. Therefore, we believe that the TSC1/2-mTOR pathway and TFE3 overexpression can simultaneously contribute to tumorigenesis in PEComa. Our comprehensive genetic analyses add to the understanding of the complex pathogenic mechanisms underlying PEComa and harbor insights for clinical treatment options

An Epstein-Barr Virus Anti-Apoptotic Protein Constitutively Expressed in Transformed Cells and Implicated in Burkitt Lymphomagenesis: The Wp/BHRF1 Link

Two factors contribute to Burkitt lymphoma (BL) pathogenesis, a chromosomal translocation leading to c-myc oncogene deregulation and infection with Epstein-Barr virus (EBV). Although the virus has B cell growth–transforming ability, this may not relate to its role in BL since many of the transforming proteins are not expressed in the tumor. Mounting evidence supports an alternative role, whereby EBV counteracts the high apoptotic sensitivity inherent to the c-myc–driven growth program. In that regard, a subset of BLs carry virus mutants in a novel form of latent infection that provides unusually strong resistance to apoptosis. Uniquely, these virus mutants use Wp (a viral promoter normally activated early in B cell transformation) and express a broader-than-usual range of latent antigens. Here, using an inducible system to express the candidate antigens, we show that this marked apoptosis resistance is mediated not by one of the extended range of EBNAs seen in Wp-restricted latency but by Wp-driven expression of the viral bcl2 homologue, BHRF1, a protein usually associated with the virus lytic cycle. Interestingly, this Wp/BHRF1 connection is not confined to Wp-restricted BLs but appears integral to normal B cell transformation by EBV. We find that the BHRF1 gene expression recently reported in newly infected B cells is temporally linked to Wp activation and the presence of W/BHRF1-spliced transcripts. Furthermore, just as Wp activity is never completely eclipsed in in vitro–transformed lines, low-level BHRF1 transcripts remain detectable in these cells long-term. Most importantly, recognition by BHRF1-specific T cells confirms that such lines continue to express the protein independently of any lytic cycle entry. This work therefore provides the first evidence that BHRF1, the EBV bcl2 homologue, is constitutively expressed as a latent protein in growth-transformed cells in vitro and, in the context of Wp-restricted BL, may contribute to virus-associated lymphomagenesis in vivo

Prevalence of vitamin D deficiency among Turkish, Moroccan, Indian and sub-Sahara African populations in Europe and their countries of origin: an overview

Public Health and primary carePrevention and community carePrevention, Population and Disease management (PrePoD

Magnetic Field Penetration Depth in the Heavy-Electron Superconductor U Be

We report the observation of a T2 temperature dependence of the magnetic field penetration depth in UBe13 at low temperatures. We show that this behavior is consistent with an anisotropic gap function for an axial P-wave state. Our results further show that the Landau parameter Fls appears to be small. © 1986 The American Physical Society

Intelligent mining of large-scale bio-data: bioinformatics applications

Today, there is a collection of a tremendous amount of bio-data because of the computerized applications worldwide. Therefore, scholars have been encouraged to develop effective methods to extract the hidden knowledge in these data. Consequently, a challenging and valuable area for research in artificial intelligence has been created. Bioinformatics creates heuristic approaches and complex algorithms using artificial intelligence and information technology in order to solve biological problems. Intelligent implication of the data can accelerate biological knowledge discovery. Data mining, as biology intelligence, attempts to find reliable, new, useful and meaningful patterns in huge amounts of data. Hence, there is a high potential to raise the interaction between artificial intelligence and bio-data mining. The present paper argues how artificial intelligence can assist bio-data analysis and gives an up-to-date review of different applications of bio-data mining. It also highlights some future perspectives of data mining in bioinformatics that can inspire further developments of data mining instruments. Important and new techniques are critically discussed for intelligent knowledge discovery of different types of row datasets with applicable examples in human, plant and animal sciences. Finally, a broad perception of this hot topic in data science is given