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Androgen receptor (AR) is a ligand-responsive transcription factor that drives terminal differentiation of the prostatic luminal epithelia. By contrast, in tumors originating from these cells, AR chromatin occupancy is extensively reprogrammed to activate malignant phenotypes, the molecular mechanisms of which remain unknown. Here, we show that tumor-specific AR enhancers are critically reliant on H3K36 dimethyltransferase activity of NSD2. NSD2 expression is abnormally induced in prostate cancer, where its inactivation impairs AR transactivation potential by disrupting over 65% of its cistrome. NSD2-dependent AR sites distinctively harbor the chimeric FOXA1:AR half-motif, which exclusively comprise tumor-specific AR enhancer circuitries defined from patient specimens. NSD2 inactivation also engenders increased dependency on the NSD1 paralog, and a dual NSD1/2 PROTAC degrader is preferentially cytotoxic in AR-dependent prostate cancer models. Altogether, we characterize NSD2 as an essential AR neo-enhanceosome subunit that enables its oncogenic activity, and position NSD1/2 as viable co-targets in advanced prostate cancer.

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The androgen receptor (AR) is a ligand-responsive transcription factor that binds at enhancers to drive terminal differentiation of the prostatic luminal epithelia. By contrast, in tumors originating from these cells, AR chromatin occupancy is extensively reprogrammed to drive hyper-proliferative, metastatic, or therapy-resistant phenotypes, the molecular mechanisms of which remain poorly understood. Here, we show that the tumor-specific enhancer circuitry of AR is critically reliant on the activity of Nuclear Receptor Binding SET Domain Protein 2 (NSD2), a histone 3 lysine 36 di-methyltransferase. NSD2 expression is abnormally gained in prostate cancer cells and its functional inhibition impairs AR trans-activation potential through partial off-loading from over 40,000 genomic sites, which is greater than 65% of the AR tumor cistrome. The NSD2-dependent AR sites distinctly harbor a chimeric AR-half motif juxtaposed to a FOXA1 element. Similar chimeric motifs of AR are absent at the NSD2-independent AR enhancers and instead contain the canonical palindromic motifs. Meta-analyses of AR cistromes from patient tumors uncovered chimeric AR motifs to exclusively participate in tumor-specific enhancer circuitries, with a minimal role in the physiological activity of AR. Accordingly, NSD2 inactivation attenuated hallmark cancer phenotypes that were fully reinstated upon exogenous NSD2 re-expression. Inactivation of NSD2 also engendered increased dependency on its paralog NSD1, which independently maintained AR and MYC hyper-transcriptional programs in cancer cells. Concordantly, a dual NSD1/2 PROTAC degrader, called LLC0150, was preferentially cytotoxic in AR-dependent prostate cancer as well as NSD2-altered hematologic malignancies. Altogether, we identify NSD2 as a novel subunit of the AR neo-enhanceosome that wires prostate cancer gene expression programs, positioning NSD1/2 as viable paralog co-targets in advanced prostate cancer.

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Transforming growth factor-ß (TGF-ß) is a multifunctional cytokine that regulates the expression of ECM-associated genes during early injury. Tissue fibrosis development is driven by synergistic cues between the evolving biochemical and mechanical milieu. Few studies have addressed the role of substrate stiffness on TGF-ß activity and extracellular matrix (ECM)-associated genes. We used a commercial formulation of polydimethylsiloxane (PDMS) to fabricate substrates of 40 kPa, 300 kPa, and 1.5 MPa stiffness, and cultured the HMF3S fibroblasts on substrates. We quantified TGF-ß protein secreted by HMF3S cells on different substrates using a TGF-ß responsive promoter reporter assay. We also tested for variations in gene expression levels on the substrates using RT-PCR and Western blotting and determined the MMP-2 and MMP-9 activities with gelatin zymography. The results showed that TGF-ß protein activation was significantly compromised at lower stiffnesses. The expression of integrin α5 decreased on lower stiffness substrates and correlated with inefficient TGF-ß protein activation. Collagen I, collagen III, and MMP-2 expression levels were lower on softer substrates; there was little MMP-9 activity on all substrates. Cell and nuclear morphologies were more rounded on compliant substrates, correlating with increased tubulin expression. Proliferations were higher on stiffer substrates, whereas cells on softer substrates showed cell cycle arrest. These results demonstrated critical feedback mechanisms between substrate stiffness and ECM regulation by fibroblasts, relevant in fibrosis.

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Loss of the tumor suppressive activity of the protein phosphatase 2A (PP2A) is associated with cancer, but the underlying molecular mechanisms are unclear. PP2A holoenzyme comprises a heterodimeric core, a scaffolding A subunit and a catalytic C subunit, and one of over 20 distinct substrate-directing regulatory B subunits. Methylation of the C subunit regulates PP2A heterotrimerization, affecting B subunit binding and substrate specificity. Here, we report that the leucine carboxy methyltransferase (LCMT1), which methylates the L309 residue of the C subunit, acts as a suppressor of androgen receptor (AR) addicted prostate cancer (PCa). Decreased methyl-PP2A-C levels in prostate tumors is associated with biochemical recurrence and metastasis. Silencing LCMT1 increases AR activity and promotes castration-resistant prostate cancer growth. LCMT1-dependent methyl-sensitive AB56αCme heterotrimers target AR and its critical coactivator MED1 for dephosphorylation, resulting in the eviction of the AR-MED1 complex from chromatin and loss of target gene expression. Mechanistically, LCMT1 is regulated by S6K1-mediated phosphorylation-induced degradation requiring the ß-TRCP, leading to acquired resistance to anti-androgens. Finally, feedforward stabilization of LCMT1 by small molecule activator of phosphatase (SMAP) results in attenuation of AR-signaling and tumor growth inhibition in anti-androgen refractory PCa. These findings highlight methyl-PP2A-C as a prognostic marker and that the loss of LCMT1 is a major determinant in AR-addicted PCa, suggesting therapeutic potential for AR degraders or PP2A modulators in prostate cancer treatment.

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Identifying patients with Coronavirus disease-2019 (COVID-19) who may have a severe illness is essential for timely intervention and decreasing the fatality rate. In the present study, we evaluated the performance of Monocyte Distribution Width (MDW) as a prognostic marker for identifying disease severity in COVID-19 patients. We included 145 patients with PCR-confirmed COVID-19 infection in the study. The performance of MDW was evaluated by calculating the area under the receiver operating characteristic curve (AUC), specificity, sensitivity, negative predictive value, and positive predictive value. Further analysis was conducted for the disease outcome, comparing COVID-19 patients discharged (n = 135) to deceased COVID-19 patients (n = 10). As a marker of disease severity, MDW demonstrated an AUC of 0.702 (95% CI 0.620-0.775) in ROC analysis. If MDW is considered a marker of patient outcome, AUC was 0.916 (95% CI 0.862-0.953), comparing deceased COVID-19 patients vs. those who survived. At a cut-off of > 25.4 on admission, MDW correlates well with poor disease outcomes in COVID-19 patients. MDW can be considered a helpful parameter in predicting the severity of COVID-19 disease and patient outcomes. Its role and incorporation in the standard diagnostic algorithm and management of COVID-19 patients need further validation. Supplementary Information: The online version contains supplementary material available at 10.1007/s12288-023-01665-y.

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PURPOSE: Glioblastoma (GBM) is a highly invasive tumor. Despite advances in treatment modalities, tumor recurrence is common, seen mainly in the peritumoral brain zone (PBZ). We aimed to molecularly characterize PBZ, to understand the pathobiology of tumor recurrence. METHODS/PATIENTS: We selected eight differentially regulated genes from our previous transcriptome profiling study on tumor core and PBZ. Expression of selected genes were validated in GBM (tumor core and PBZ, n = 37) and control (n = 22) samples by real time quantitative polymerase chain reaction (qPCR). Serine protease inhibitor clade A, member 3 (SERPINA3) was selected for further functional characterization in vitro by gene knockdown approach in glioma cells. Its protein expression by immunohistochemistry (IHC) was correlated with other clinically relevant GBM markers, patient prognosis and tumor recurrence. RESULTS: The mRNA expression of selected genes from the microarray data validated in tumor core and PBZ and was similar to publicly available databases. SERPINA3 knock down in vitro showed decreased tumor cell proliferation, invasion, migration, transition to mesenchymal phenotype, stemness and radioresistance. SERPINA3 protein expression was higher in PBZ compared to tumor core and also was higher in older patients, IDH wild type and recurrent tumors. Finally, its expression showed positive correlation with poor patient prognosis. CONCLUSIONS: SERPINA3 expression contributes to aggressive GBM phenotype by regulating pro-tumorigenic actions in vitro and is associated with adverse clinical outcome.

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Ensemble classifiers using clustering have significantly improved classification and prediction accuracies of many systems. These types of ensemble approaches create multiple clusters to train the base classifiers. However, the problem with this is that each class might have many clusters and each cluster might have different number of samples, so an ensemble decision based on large number of clusters and different number of samples per class within a cluster produces biased and inaccurate results. Therefore, in this article, we propose a novel methodology to create an appropriate number of strong data clusters for each class and then balance them. Furthermore, an ensemble framework is proposed with base classifiers trained on strong and balanced data clusters. The proposed approach is implemented and evaluated on 24 benchmark data sets from the University of California Irvine (UCI) machine learning repository. An analysis of results using the proposed approach and the existing state-of-the-art ensemble classifier approaches is conducted and presented. A significance test is conducted to further validate the efficacy of the results and a detailed analysis is presented.

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INTRODUCTION: Mitochondrial DNA (mtDNA) content in several solid tumors was found to be lower than in their normal counterparts. However, there is paucity of literature on the clinical significance of mtDNA content in glioblastoma and its effect on treatment response. Hence, we studied the prognostic significance of mtDNA content in glioblastoma tumor tissue and the effect of mtDNA depletion in glioblastoma cells on response to treatment. MATERIALS AND METHODS: 130 newly diagnosed glioblastomas, 32 paired newly diagnosed and recurrent glioblastomas and 35 non-neoplastic brain tissues were utilized for the study. mtDNA content in the patient tumor tissue was assessed and compared with known biomarkers and patient survival. mtDNA was chemically depleted in malignant glioma cell lines, U87, LN229. The biology and treatment response of parent and depleted cells were compared. RESULTS: Lower range of mtDNA copy number in glioblastoma was associated with poor overall survival (p = 0.01), progression free survival (p = 0.04) and also with wild type IDH (p = 0.02). In recurrent glioblastoma, mtDNA copy number was higher than newly diagnosed glioblastoma in the patients who received RT (p = 0.01). mtDNA depleted U87 and LN229 cells showed higher survival fraction post radiation exposure when compared to parent lines. The IC50 of TMZ was also higher for mtDNA depleted U87 and LN229 cells. The depleted cells formed more neurospheres than their parent counterparts, thus showing increased stemness of mtDNA depleted cells. CONCLUSION: Low mtDNA copy number in glioblastoma is associated with poor patient survival and treatment resistance in cell lines possibly by impacting stemness of the glioblastoma cells.

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PURPOSE: IGFBP2 is one of the highly expressed genes in glioblastoma (GBM). It has both IGF dependent and independent activities. IGF independent actions are mediated by the activation of integrin signalling through its RGD motif present at C-terminal domain. One of the actions of IGFBP2 is to regulate ß-catenin by the inactivation of GSK3ß, which preferentially accumulates in the cytoplasm. The mechanism of nuclear ß-catenin regulation by IGFBP2 and role of cytoplasmic ß-catenin is not clear. We aimed to understand the mechanism in GBM cell lines. METHODS: The gene expression studies were performed by RT-PCR, western blot analysis; the knockdown of genes was performed by shRNA transfection; RNAIP and luciferase reporter assays were utilized to study the cytoplasmic regulation of genes by ß-catenin; neurosphere assays were performed to study the stemness of cells. RESULTS: IGFBP2 overexpression or treatment in GBM cells regulates ß-catenin, TRIM33 (E3 ubiquitin ligase) and Oct4 genes. TRIM33 was induced by IGFBP2. ß-catenin was found to accumulate predominantly in the cytoplasm and nuclear ß-catenin was depleted by IGFBP2 induced TRIM33. IGFBP2 regulated cytoplasmic ß-catenin binds to 3' UTR of Oct4 RNA. IGFBP2 was also able to induce stemness of glioma cells. CONCLUSIONS: IGFBP2 induces TRIM33 which regulates the nuclear ß-catenin protein. In addition, IGFBP2 stabilizes the cytoplasmic ß-catenin which is involved in the regulation of Oct4 transcript and consequently induction of stemness of glioma cells.

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Mixed-ligand oxidovanadium(IV) ß-diketonates having NNN-donor dipicolylamine-conjugated to boron-dipyrromethene (BODIPY in L1) and diiodo-BODIPY (in L2) moieties, namely, [VO(L1)(acac)]Cl (1), [VO(L2)(acac)]Cl (2), and [VO(L1)(dbm)]Cl (3), where acac and dbm are monoanionic O,O-donor acetylacetone and 1,3-diphenyl-1,3-propanedione, were prepared, characterized, and tested for their photoinduced anticancer activity in visible light. Complexes 1 and 2 were structurally characterized as their PF6 - salts (1a and 2a) by X-ray crystallography. They showed VIVN3O3 six-coordinate geometry with dipicolylamine base as the facial ligand. The non-iodinated BODIPY complexes displayed absorption maxima at ∼501 nm, while it is ∼535 nm for the di-iodinated 2 in 10% DMSO-PBS buffer medium (pH = 7.2). Complexes 1 and 3 being green emissive (λem, ∼512 nm; λex, 470 nm; ΦF, ∼0.10) in 10% aqueous DMSO were used for cellular imaging studies. Complex 3 localized primarily in the mitochondria of the cervical HeLa cells with a co-localization coefficient value of 0.7. The non-emissive diiodo-BODIPY complex 2 showed generation of singlet oxygen (ΦΔ ≈ 0.47) on light activation. Annexin-V assay showed singlet oxygen-mediated cellular apoptosis, making this complex a targeted PDT agent.

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We introduce a self-assembling polypeptide-based nanotube system having the ability to specifically target cancer cells. The nanotubes target the cancer cell surface through integrin engagement with the help of multiple RGD units present along their surface. While the nanotubes are non-toxic towards cells in general, they can be loaded with suitable drugs to be released in a sustained manner in cancer cells. In addition, the nanotubes can be utilized for cellular imaging using any covalently tagged fluorescent dye. They are stable over a wide range of temperature due to intermolecular disulphide bonds formed during the self-assembly process. At the same time, presence of disulphide bonds provides a redox molecular switch for their degradation. Taken together this system provides a unique avenue for multimodal formulation in cancer therapy.

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In field (on tree) fruit sizing has value in assessing crop health and for yield estimation. As the mobile phone is a sensor and communication rich device carried by almost all farm staff, an Android application ("FruitSize") was developed for measurement of fruit size in field using the phone camera, with a typical assessment rate of 240 fruit per hour achieved. The application was based on imaging of fruit against a backboard with a scale using a mobile phone, with operational limits set on camera to object plane angle and camera to object distance. Image processing and object segmentation techniques available in the OpenCV library were used to segment the fruit from background in images to obtain fruit sizes. Phone camera parameters were accessed to allow calculation of fruit size, with camera to fruit perimeter distance obtained from fruit allometric relationships between fruit thickness and width. Phone geolocation data was also accessed, allowing for mapping fruits of data. Under controlled lighting, RMSEs of 3.4, 3.8, 2.4, and 2.0 mm were achieved in estimation of avocado, mandarin, navel orange, and apple fruit diameter, respectively. For mango fruit, RMSEs of 5.3 and 3.7 mm were achieved on length and width, benchmarked to manual caliper measurements, under controlled lighting, and RMSEs of 5.5 and 4.6 mm were obtained in-field under ambient lighting.

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In-field mango fruit sizing is useful for estimation of fruit maturation and size distribution, informing the decision to harvest, harvest resourcing (e.g., tray insert sizes), and marketing. In-field machine vision imaging has been used for fruit count, but assessment of fruit size from images also requires estimation of camera-to-fruit distance. Low cost examples of three technologies for assessment of camera to fruit distance were assessed: a RGB-D (depth) camera, a stereo vision camera and a Time of Flight (ToF) laser rangefinder. The RGB-D camera was recommended on cost and performance, although it functioned poorly in direct sunlight. The RGB-D camera was calibrated, and depth information matched to the RGB image. To detect fruit, a cascade detection with histogram of oriented gradients (HOG) feature was used, then Otsu's method, followed by color thresholding was applied in the CIE L*a*b* color space to remove background objects (leaves, branches etc.). A one-dimensional (1D) filter was developed to remove the fruit pedicles, and an ellipse fitting method employed to identify well-separated fruit. Finally, fruit lineal dimensions were calculated using the RGB-D depth information, fruit image size and the thin lens formula. A Root Mean Square Error (RMSE) = 4.9 and 4.3 mm was achieved for estimated fruit length and width, respectively, relative to manual measurement, for which repeated human measures were characterized by a standard deviation of 1.2 mm. In conclusion, the RGB-D method for rapid in-field mango fruit size estimation is practical in terms of cost and ease of use, but cannot be used in direct intense sunshine. We believe this work represents the first practical implementation of machine vision fruit sizing in field, with practicality gauged in terms of cost and simplicity of operation.

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This paper introduces a novel concept for creating an ensemble of classifiers. The concept is based on generating an ensemble of classifiers through clustering of data at multiple layers. The ensemble classifier model generates a set of alternative clustering of a dataset at different layers by randomly initializing the clustering parameters and trains a set of base classifiers on the patterns at different clusters in different layers. A test pattern is classified by first finding the appropriate cluster at each layer and then using the corresponding base classifier. The decisions obtained at different layers are fused into a final verdict using majority voting. As the base classifiers are trained on overlapping patterns at different layers, the proposed approach achieves diversity among the individual classifiers. Identification of difficult-to-classify patterns through clustering as well as achievement of diversity through layering leads to better classification results as evidenced from the experimental results.

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OBJECTIVE: The main objective of this paper is to present a novel learning algorithm for the classification of mass abnormalities in digitized mammograms. METHODS AND MATERIAL: The proposed approach consists of new network architecture and a new learning algorithm. The original idea is based on the introduction of an additional neuron in the hidden layer for each output class. The additional neurons for benign and malignant classes help in improving memorization ability without destroying the generalization ability of the network. The training is conducted by combining minimal distance-based similarity/random weights and direct calculation of output weights. RESULTS: The proposed approach can memorize training patterns with 100% retrieval accuracy as well as achieve high generalization accuracy for patterns which it has never seen before. The grey-level and breast imaging reporting and data system-based features from digitized mammograms are extracted and used to train the network with the proposed architecture and learning algorithm. The best results achieved by using the proposed approach are 100% on training set and 94% on test set. CONCLUSION: The proposed approach produced very promising results. It has outperformed existing classification approaches in terms of classification accuracy, generalization and memorization abilities, number of iterations, and guaranteed training on a benchmark database.

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In this paper, we present a novel approach of implementing a combination methodology to find appropriate neural network architecture and weights using an evolutionary least square based algorithm (GALS).1 This paper focuses on aspects such as the heuristics of updating weights using an evolutionary least square based algorithm, finding the number of hidden neurons for a two layer feed forward neural network, the stopping criterion for the algorithm and finally some comparisons of the results with other existing methods for searching optimal or near optimal solution in the multidimensional complex search space comprising the architecture and the weight variables. We explain how the weight updating algorithm using evolutionary least square based approach can be combined with the growing architecture model to find the optimum number of hidden neurons. We also discuss the issues of finding a probabilistic solution space as a starting point for the least square method and address the problems involving fitness breaking. We apply the proposed approach to XOR problem, 10 bit odd parity problem and many real-world benchmark data sets such as handwriting data set from CEDAR, breast cancer and heart disease data sets from UCI ML repository. The comparative results based on classification accuracy and the time complexity are discussed.

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