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The UiO-66-NH2 is initially modified with melamine via a post-synthetic approach. CuO NPs are then anchored via the available functional groups on the surface of the modified MOF.

In structural biology, collision cross sections (CCS) from ion mobility mass spectrometry (IM-MS) measurements are routinely compared to computationally or experimentally derived protein structures. Here, we investigate whether CCS data can inform about the shape of a protein in the absence of specific reference structures. Analysis of the proteins in the CCS database shows that protein complexes with low apparent densities are structurally more diverse than those with a high apparent density. Using the CCS, molecular weight, and oligomeric states to mine the Protein Data Bank (PDB) for potentially similar protein structures, we find that we can distinguish oblate-and prolate-shaped protein complexes. We then apply the strategy to an integral membrane protein by comparing the shapes of a prokaryotic and an eukaryotic sodium/proton antiporter homologue. We conclude that mining the PDB with IM-MS data is a time-effective way to derive low-resolution structural models. File list (2) download file view on ChemRxiv main_chemrxiv.docx (2.05 MiB) download file view on ChemRxiv Supplementary Tables 1-4.pdf (50.74 KiB)

With nearly 10 million deaths, cancer is the leading cause of mortality worldwide. Along with major key parameters that control cancer treatment management, such as diagnosis, resistance to the classical and new chemotherapeutic reagents continues to be a significant problem. Intrinsic or acquired chemoresistance leads to cancer recurrence in many cases that eventually causes failure in the successful treatment and death of cancer patients. Various determinants, including tumor heterogeneity and tumor microenvironment, could cause chemoresistance through a diverse range of mechanisms. In this review, we summarize the key determinants and the underlying mechanisms by which chemoresistance appears. We then describe which strategies have been implemented and studied to combat such a lethal phenomenon in the management of cancer treatment, with emphasis on the need to improve the early diagnosis of cancer complemented by combination therapy.

Artificial neural networks (ANNs) have been used in a wide variety of real-world applications and it emerges as a promising field across various branches of medicine. This review aims to identify the role of ANNs in spinal diseases. Literature were searched from electronic databases of Scopus and Medline from 1993 to 2020 with English publications reported on the application of ANNs in spinal diseases. The search strategy was set as the combinations of the following keywords: “artificial neural networks,” “spine,” “back pain,” “prognosis,” “grading,” “classification,” “prediction,” “segmentation,” “biomechanics,” “deep learning,” and “imaging.” The main findings of the included studies were summarized, with an emphasis on the recent advances in spinal diseases and its application in the diagnostic and prognostic procedures. According to the search strategy, a set of 3,653 articles were retrieved from Medline and Scopus databases. After careful evaluation of the abstracts, the full texts of 89 eligible papers were further examined, of which 79 articles satisfied the inclusion criteria of this review. Our review indicates several applications of ANNs in the management of spinal diseases including (1) diagnosis and assessment of spinal disease progression in the patients with low back pain, perioperative complications, and readmission rate following spine surgery; (2) enhancement of the clinically relevant information extracted from radiographic images to predict Pfirrmann grades, Modic changes, and spinal stenosis grades on magnetic resonance images automatically; (3) prediction of outcomes in lumbar spinal stenosis, lumbar disc herniation and patient-reported outcomes in lumbar fusion surgery, and preoperative planning and intraoperative assistance; and (4) its application in the biomechanical assessment of spinal diseases. The evidence suggests that ANNs can be successfully used for optimizing the diagnosis, prognosis and outcome prediction in spinal diseases. Therefore, incorporation of ANNs into spine clinical practice may improve clinical decision making.

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The overexpression of epithelial cell adhesion molecule (EpCAM), a proto-oncogene, affects progression, treatment, and diagnosis of many adenocarcinomas. C-myc has been shown to be a downstream target of EpCAM and is also one of the most important proto-oncogenes routinely overexpressed in breast cancer. However, cooverexpression of EpCAM and c-myc genes has not been investigated in breast cancer tissues, particularly in Iranian population. The aim of this study was to assess the expression of EpCAM and c-myc genes in malignant breast cancer tissues using reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) followed by analyses of the association between the outcomes. In this study, 122 fresh tissues, including 104 malignant and 18 benign samples, were disrupted by mortar and pestle, and then the RNA was isolated from the samples and converted to cDNA. The relative expression levels of EpCAM and c-myc genes were measured by 2 method using RT-qPCR. EpCAM protein level was also assessed in 66 cases using Western blot technique. Using RT-qPCR method, our results showed that EpCAM was overexpressed in 48% of malignant and 11.1% of benign samples. Evaluating EpCAM protein overexpression in a portion of samples depicted the fully concordance rate between Western blot and RT-qPCR techniques. C-myc expression was first evaluated by RT-qPCR method, showing the overexpression rate of 39% and 28% in malignant and benign samples, respectively. These data were also quite concordant with the clinically available immunohistochemistry reports of the same samples studied in this study. Importantly, overexpression of EpCAM and c-myc was significantly associated and showed an agreement of 57.3%. This study demonstrated the cooverexpression of EpCAM and c-myc in breast tumours collected from breast cancer patients of the Iranian population. EpCAM and c-myc positive cases were significantly associated with reduced and enhanced risk of ER/PR positivity respectively. However, both were associated with grade III of breast cancer.