Explicit face memory abilities are positively related to the non-intentional encoding of faces: Behavioral and ERP evidence.

Individual differences in face memory abilities have been shown to be related to individual differences in brain activity. The present study investigated brain-behavior relationships for the N250 component in event-related brain potentials, which is taken as a neural sign of face familiarity. We used a task in which a designated, typical target face and several (high- and low-distinctive) nontarget faces had to be distinguished during multiple presentations across a session. Separately, face memory/recognition abilities were measured with easy versus difficult tasks. We replicated an increase of the N250 amplitude to the target face across the session and observed a similar increase for the non-target faces, indicating the build-up of memory representations also for these faces. On the interindividual level, larger across-session N250 amplitude increases to low-distinctive non-target faces were related to faster face recognition as measured in an easy task. These findings indicate that non-intentional encoding of non-target faces into memory is associated with the swift recognition of explicitly learned faces; that is, there is shared variance of incidental and intentional face memory.

Ki67 is a better marker than PRAME in risk stratification of BAP1-positive and BAP1-loss uveal melanomas.

BACKGROUND: Accurate risk stratification of uveal melanoma (UM) patients is important for determining the interval and frequency of surveillance. Loss of BAP1 expression has been shown to be strongly associated with UM-related death and metastasis. METHODS: In this study of 164 enucleated UMs, we assessed the prognostic role of preferentially expressed antigen in melanoma (PRAME) expression and Ki67 proliferation index measured by digital quantitation using QuPath programme in patients with BAP1-positive and BAP1-loss UMs. RESULTS: In univariate analyses with log-rank tests and Kaplan-Meier curves, PRAME further stratified only overall survival (OS) in BAP1-positive and BAP1-loss tumour groups. However, Ki67 further stratified both OS and disease-free survival (DFS) in BAP1-positive and BAP1-loss tumour groups. In multivariate analyses, Ki67 percentage and BAP1 were independent survival predictors for both OS and DFS, whereas PRAME was not a significant covariate. In model comparisons, combined Ki67 and BAP1 performed better than combined PRAME and BAP1 in risk-stratifying patients for both OS and DFS. Ki67 was better than PRAME in risk stratification of BAP1-positive UMs. Low Ki67 index correlated with significantly prolonged DFS in BAP1-loss UMs. CONCLUSION: A panel of Ki67 and BAP1 could be a helpful risk stratification strategy for UM.

An unusual coincidence of giant cervical leiomyoma and incidental ovarian granulosa cell tumor: A case report.

RATIONALE: Leiomyomas are the most common benign tumors of smooth muscle origin in women. They are most frequently found in the submucosal tissue of the uterine corpus; however, they also occur in other areas of the uterus, including the cervix. Their size usually varies between 0.5 to 1.0 cm; however, they can reach great dimensions. A strong correlation between the onset and growth of leiomyomas and estrogen levels was observed. Granulosa cell tumor (GCT) is an infrequent sex cord-stromal ovarian neoplasm. Despite their malignancy, GCTs have a good long-term prognosis. In this study, we present a unique case of coincidence of 2 tumors: leiomyoma of rare location (cervix uteri) and extraordinary size (9, 04 cm diameter) with an adult granulosa cell tumor. PATIENT CONCERNS: A 67-year-old Caucasian woman was transported from an emergency ward to a gynecological surgery department due to a massive vaginal hemorrhage. DIAGNOSES: Preliminary examination showed a presence of an enormous uteri cervix tumor. INTERVENTIONS: Initially, the patient underwent physical and ultrasound examinations. To prevent further bleeding, an urgent surgery (hysterectomy) with bilateral salpingo-oophorectomy was performed. OUTCOME: Postoperative histopathological examination revealed a cervical leiomyoma and the incidental occurrence of an adult GCT in the right ovary. LESSONS: This case shares an interesting coincidence between a rare variant of leiomyoma and GCT. The study suggests that the potential reason for this can be estrogen secreted by the GCT, which causes the enormous size of the patient's cervical leiomyoma and the severe vaginal bleeding. Therefore, we advise it is important in abnormal cases to search for other hidden explanations, as in cases of GCT.

ABCA1 transporter promotes the motility of human melanoma cells by modulating their plasma membrane organization.

BACKGROUND: Melanoma is one of the most aggressive and deadliest skin tumor. Cholesterol content in melanoma cells is elevated, and a portion of it accumulates into lipid rafts. Therefore, the plasma membrane cholesterol and its lateral organization might be directly linked with tumor development. ATP Binding Cassette A1 (ABCA1) transporter modulates physico-chemical properties of the plasma membrane by modifying cholesterol distribution. Several studies linked the activity of the transporter with a different outcome of tumor progression depending on which type. However, no direct link between human melanoma progression and ABCA1 activity has been reported yet. METHODS: An immunohistochemical study on the ABCA1 level in 110 patients-derived melanoma tumors was performed to investigate the potential association of the transporter with melanoma stage of progression and prognosis. Furthermore, proliferation, migration and invasion assays, extracellular-matrix degradation assay, immunochemistry on proteins involved in migration processes and a combination of biophysical microscopy analysis of the plasma membrane organization of Hs294T human melanoma wild type, control (scrambled), ABCA1 Knockout (ABCA1 KO) and ABCA1 chemically inactivated cells were used to study the impact of ABCA1 activity on human melanoma metastasis processes. RESULTS: The immunohistochemical analysis of clinical samples showed that high level of ABCA1 transporter in human melanoma is associated with a poor prognosis. Depletion or inhibition of ABCA1 impacts invasion capacities of aggressive melanoma cells. Loss of ABCA1 activity partially prevented cellular motility by affecting active focal adhesions formation via blocking clustering of phosphorylated focal adhesion kinases and active integrin ß3. Moreover, ABCA1 activity regulated the lateral organization of the plasma membrane in melanoma cells. Disrupting this organization, by increasing the content of cholesterol, also blocked active focal adhesion formation. CONCLUSION: Human melanoma cells reorganize their plasma membrane cholesterol content and organization via ABCA1 activity to promote motility processes and aggressiveness potential. Therefore, ABCA1 may contribute to tumor progression and poor prognosis, suggesting ABCA1 to be a potential metastatic marker in melanoma.

Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients.

Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatment response for glioblastoma. This assessment is, however, complex to perform and associated with high variability due to the high heterogeneity and complexity of the disease. In this work, we tackle this issue and propose a deep learning pipeline for the fully automated end-to-end analysis of glioblastoma patients. Our approach simultaneously identifies tumor sub-regions, including the enhancing tumor, peritumoral edema and surgical cavity in the first step, and then calculates the volumetric and bidimensional measurements that follow the current Response Assessment in Neuro-Oncology (RANO) criteria. Also, we introduce a rigorous manual annotation process which was followed to delineate the tumor sub-regions by the human experts, and to capture their segmentation confidences that are later used while training deep learning models. The results of our extensive experimental study performed over 760 pre-operative and 504 post-operative adult patients with glioma obtained from the public database (acquired at 19 sites in years 2021-2020) and from a clinical treatment trial (47 and 69 sites for pre-/post-operative patients, 2009-2011) and backed up with thorough quantitative, qualitative and statistical analysis revealed that our pipeline performs accurate segmentation of pre- and post-operative MRIs in a fraction of the manual delineation time (up to 20 times faster than humans). Volumetric measurements were in strong agreement with experts with the Intraclass Correlation Coefficient (ICC): 0.959, 0.703, 0.960 for ET, ED, and cavity. Similarly, automated RANO compared favorably with experienced readers (ICC: 0.681 and 0.866) producing consistent and accurate results. Additionally, we showed that RANO measurements are not always sufficient to quantify tumor burden. The high performance of the automated tumor burden measurement highlights the potential of the tool for considerably improving and simplifying radiological evaluation of glioblastoma in clinical trials and clinical practice.

Detecting liver cirrhosis in computed tomography scans using clinically-inspired and radiomic features.

Hepatic cirrhosis is an increasing cause of mortality in developed countries-it is the pathological sequela of chronic liver diseases, and the final liver fibrosis stage. Since cirrhosis evolves from the asymptomatic phase, it is of paramount importance to detect it as quickly as possible, because entering the symptomatic phase commonly leads to hospitalization and can be fatal. Understanding the state of the liver based on the abdominal computed tomography (CT) scans is tedious, user-dependent and lacks reproducibility. We tackle these issues and propose an end-to-end and reproducible approach for detecting cirrhosis from CT. It benefits from the introduced clinically-inspired features that reflect the patient's characteristics which are often investigated by experienced radiologists during the screening process. Such features are coupled with the radiomic ones extracted from the liver, and from the suggested region of interest which captures the liver's boundary. The rigorous experiments, performed over two heterogeneous clinical datasets (two cohorts of 241 and 32 patients) revealed that extracting radiomic features from the liver's rectified contour is pivotal to enhance the classification abilities of the supervised learners. Also, capturing clinically-inspired image features significantly improved the performance of such models, and the proposed features were consistently selected as the important ones. Finally, we showed that selecting the most discriminative features leads to the Pareto-optimal models with enhanced feature-level interpretability, as the number of features was dramatically reduced (280×) from thousands to tens.

Convolutional ProteinUnetLM competitive with long short-term memory-based protein secondary structure predictors.

The protein secondary structure (SS) prediction plays an important role in the characterization of general protein structure and function. In recent years, a new generation of algorithms for SS prediction based on embeddings from protein language models (pLMs) is emerging. These algorithms reach state-of-the-art accuracy without the need for time-consuming multiple sequence alignment (MSA) calculations. Long short-term memory (LSTM)-based SPOT-1D-LM and NetSurfP-3.0 are the latest examples of such predictors. We present the ProteinUnetLM model using a convolutional Attention U-Net architecture that provides prediction quality and inference times at least as good as the best LSTM-based models for 8-class SS prediction (SS8). Additionally, we address the issue of the heavily imbalanced nature of the SS8 problem by extending the loss function with the Matthews correlation coefficient, and by proper assessment using previously introduced adjusted geometric mean (AGM) metric. ProteinUnetLM achieved better AGM and sequence overlap score than LSTM-based predictors, especially for the rare structures 310-helix (G), beta-bridge (B), and high curvature loop (S). It is also competitive on challenging datasets without homologs, free-modeling targets, and chameleon sequences. Moreover, ProteinUnetLM outperformed its previous MSA-based version ProteinUnet2, and provided better AGM than AlphaFold2 for 1/3 of proteins from the CASP14 dataset, proving its potential for making a significant step forward in the domain. To facilitate the usage of our solution by protein scientists, we provide an easy-to-use web interface under https://biolib.com/SUT/ProteinUnetLM/.

ABCA1 transporter promotes the motility of human melanoma cells by modulating their plasma membrane organization

BACKGROUND: Melanoma is one of the most aggressive and deadliest skin tumor. Cholesterol content in melanoma cells is elevated, and a portion of it accumulates into lipid rafts. Therefore, the plasma membrane cholesterol and its lateral organization might be directly linked with tumor development. ATP Binding Cassette A1 (ABCA1) transporter modulates physico-chemical properties of the plasma membrane by modifying cholesterol distribution. Several studies linked the activity of the transporter with a different outcome of tumor progression depending on which type. However, no direct link between human melanoma progression and ABCA1 activity has been reported yet. METHODS: An immunohistochemical study on the ABCA1 level in 110 patients-derived melanoma tumors was performed to investigate the potential association of the transporter with melanoma stage of progression and prognosis. Furthermore, proliferation, migration and invasion assays, extracellular-matrix degradation assay, immunochemistry on proteins involved in migration processes and a combination of biophysical microscopy analysis of the plasma membrane organization of Hs294T human melanoma wild type, control (scrambled), ABCA1 Knockout ( ABCA1 KO) and ABCA1 chemically inactivated cells were used to study the impact of ABCA1 activity on human melanoma metastasis processes. RESULTS: The immunohistochemical analysis of clinical samples showed that high level of ABCA1 transporter in human melanoma is associated with a poor prognosis. Depletion or inhibition ofABCA1 impacts invasion capacities of aggressive melanoma cells. Loss of ABCA1 activity partially prevented cellular motility by affecting active focal adhesions formation via blocking clustering of phosphorylated focal adhesion kinases and active integrin ß3. Moreover, ABCA1 activity regulated the lateral organization of the plasma membrane in melanoma cells. Disrupting this organization, by increasing the content of cholesterol, also blocked active focal adhesion formation. CONCLUSION: Human melanoma cells reorganize their plasma membrane cholesterol content and organization via ABCA1 activity to promote motility processes and aggressiveness potential. Therefore, ABCA1 may contribute to tumor progression and poor prognosis, suggesting ABCA1 to be a potential metastatic marker in melanoma.

Machine learning models demonstrate that clinicopathologic variables are comparable to gene expression prognostic signature in predicting survival in uveal melanoma.

PURPOSE: Since molecular assays are not accessible to all uveal melanoma patients, we aim to identify cost-effective prognostic tool in risk stratification using machine learning models based on routine histologic and clinical variables. EXPERIMENTAL DESIGN: We identified important prognostic parameters in a discovery cohort of 164 enucleated primary uveal melanomas from 164 patients without prior therapies. We then validated the prognostic prediction of top important parameters identified in the discovery cohort using 80 uveal melanomas from the Tumor Cancer Genome Atlas database with available gene expression prognostic signature (GEPS). The performance of three different survival analysis models (Cox proportional hazards (CPH), random survival forest (RSF), and survival gradient boosting (SGB)) was compared against GEPS using receiver operating curves (ROC). RESULTS: In all three selection methods, BAP1 status, nucleoli size, age, mitotic rate per 1 mm2, and ciliary body infiltration were identified as significant overall survival (OS) predictors; and BAP1 status, nucleoli size, largest basal tumor diameter, tumor-infiltrating lymphocyte density, and tumor-associated macrophage density were identified as significant progression-free survival (PFS) predictors. ROC plots for the median survival time point showed that significant parameters in SGB studied model can predict OS better than GEPS. For PFS, SGB model performed similarly to GEPS. The time-dependent area under the curve (AUC) showed SGB model performing better than GEPS in predicting OS and metastatic risk. CONCLUSIONS: Our study shows that routine histologic and clinical variables are adequate for patient risk stratification in comparison with not readily accessible GEPS.

Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs.

An important problem in many fields dealing with noisy time series, such as psychophysiological single trial data during learning or monitoring treatment effects over time, is detecting a change in the model underlying a time series. Here, we present a new method for detecting a single changepoint in a linear time series regression model, termed residuals permutation-based method (RESPERM). The optimal changepoint in RESPERM maximizes Cohen's effect size with the parameters estimated by the permutation of residuals in a linear model. RESPERM was compared with the SEGMENTED method, a well-established and recommended method for detecting changepoints, using extensive simulated data sets, varying the amount and distribution characteristics of noise and the location of the change point. In time series with medium to large amounts of noise, the variance of the detected changepoint was consistently smaller for RESPERM than SEGMENTED. Finally, both methods were applied to a sample dataset of single trial amplitudes of the N250 ERP component during face learning. In conclusion, RESPERM appears to be well suited for changepoint detection especially in noisy data, making it the method of choice in neuroscience, medicine and many other fields.

Lightweight ProteinUnet2 network for protein secondary structure prediction: a step towards proper evaluation.

BACKGROUND: The prediction of protein secondary structures is a crucial and significant step for ab initio tertiary structure prediction which delivers the information about proteins activity and functions. As the experimental methods are expensive and sometimes impossible, many SS predictors, mainly based on different machine learning methods have been proposed for many years. Currently, most of the top methods use evolutionary-based input features produced by PSSM and HHblits software, although quite recently the embeddings-the new description of protein sequences generated by language models (LM) have appeared that could be leveraged as input features. Apart from input features calculation, the top models usually need extensive computational resources for training and prediction and are barely possible to run on a regular PC. SS prediction as the imbalanced classification problem should not be judged by the commonly used Q3/Q8 metrics. Moreover, as the benchmark datasets are not random samples, the classical statistical null hypothesis testing based on the Neyman-Pearson approach is not appropriate. RESULTS: We present a lightweight deep network ProteinUnet2 for SS prediction which is based on U-Net convolutional architecture and evolutionary-based input features (from PSSM and HHblits) as well as SPOT-Contact features. Through an extensive evaluation study, we report the performance of ProteinUnet2 in comparison with top SS prediction methods based on evolutionary information (SAINT and SPOT-1D). We also propose a new statistical methodology for prediction performance assessment based on the significance from Fisher-Pitman permutation tests accompanied by practical significance measured by Cohen's effect size. CONCLUSIONS: Our results suggest that ProteinUnet2 architecture has much shorter training and inference times while maintaining results similar to SAINT and SPOT-1D predictors. Taking into account the relatively long times of calculating evolutionary-based features (from PSSM in particular), it would be worth conducting the predictive ability tests on embeddings as input features in the future. We strongly believe that our proposed here statistical methodology for the evaluation of SS prediction results will be adopted and used (and even expanded) by the research community.

Segmenting pediatric optic pathway gliomas from MRI using deep learning.

Optic pathway gliomas are low-grade neoplastic lesions that account for approximately 3-5% of brain tumors in children. Assessing tumor burden from magnetic resonance imaging (MRI) plays a central role in its efficient management, yet it is a challenging and human-dependent task due to the difficult and error-prone process of manual segmentation of such lesions, as they can easily manifest different location and appearance characteristics. In this paper, we tackle this issue and propose a fully-automatic and reproducible deep learning algorithm built upon the recent advances in the field which is capable of detecting and segmenting optical pathway gliomas from MRI. The proposed training strategies help us elaborate well-generalizing deep models even in the case of limited ground-truth MRIs presenting example optic pathway gliomas. The rigorous experimental study, performed over two clinical datasets of 22 and 51 multi-modal MRIs acquired for 22 and 51 patients with optical pathway gliomas, and a public dataset of 494 pre-surgery low-/high-grade glioma patients (corresponding to 494 multi-modal MRIs), and involving quantitative, qualitative and statistical analysis revealed that the suggested technique can not only effectively delineate optic pathway gliomas, but can also be applied for detecting other brain tumors. The experiments indicate high agreement between automatically calculated and ground-truth volumetric measurements of the tumors and very fast operation of the proposed approach, both of which can increase the clinical utility of the suggested software tool. Finally, our deep architectures have been made open-sourced to ensure full reproducibility of the method over other MRI data.

The N250 event-related potential as an index of face familiarity: a replication study.

The neural correlates of face individuation-the acquisition of memory representations for novel faces-have been studied only in coarse detail and disregarding individual differences between learners. In their seminal study, Tanaka et al. (Tanaka et al. 2006 J. Cogn. Neurosci. 18, 1488-1497. (doi:10.1162/jocn.2006.18.9.1488)) required the identification of a particular novel face across 70 trials and found that the N250 component in the EEG event-related potentials became more negative from the first to the second half of the experiment, where it reached a similar amplitude as a well-known face. We were unable to directly replicate this finding in our study when we used the original split of trials. However, when we applied a different split of trials we observed very similar changes in N250 amplitude. We conclude that the N250 component is indeed sensitive to the build-up of a robust representation of a face in memory; the time course of this process appears to vary as a function of variables that may be determined in future research.

Role of PFKFB3 and PFKFB4 in Cancer: Genetic Basis, Impact on Disease Development/Progression, and Potential as Therapeutic Targets.

Glycolysis is a crucial metabolic process in rapidly proliferating cells such as cancer cells. Phosphofructokinase-1 (PFK-1) is a key rate-limiting enzyme of glycolysis. Its efficiency is allosterically regulated by numerous substances occurring in the cytoplasm. However, the most potent regulator of PFK-1 is fructose-2,6-bisphosphate (F-2,6-BP), the level of which is strongly associated with 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase activity (PFK-2/FBPase-2, PFKFB). PFK-2/FBPase-2 is a bifunctional enzyme responsible for F-2,6-BP synthesis and degradation. Four isozymes of PFKFB (PFKFB1, PFKFB2, PFKFB3, and PFKFB4) have been identified. Alterations in the levels of all PFK-2/FBPase-2 isozymes have been reported in different diseases. However, most recent studies have focused on an increased expression of PFKFB3 and PFKFB4 in cancer tissues and their role in carcinogenesis. In this review, we summarize our current knowledge on all PFKFB genes and protein structures, and emphasize important differences between the isoenzymes, which likely affect their kinase/phosphatase activities. The main focus is on the latest reports in this field of cancer research, and in particular the impact of PFKFB3 and PFKFB4 on tumor progression, metastasis, angiogenesis, and autophagy. We also present the most recent achievements in the development of new drugs targeting these isozymes. Finally, we discuss potential combination therapies using PFKFB3 inhibitors, which may represent important future cancer treatment options.

ProteinUnet-An efficient alternative to SPIDER3-single for sequence-based prediction of protein secondary structures.

Predicting protein function and structure from sequence remains an unsolved problem in bioinformatics. The best performing methods rely heavily on evolutionary information from multiple sequence alignments, which means their accuracy deteriorates for sequences with a few homologs, and given the increasing sequence database sizes requires long computation times. Here, a single-sequence-based prediction method is presented, called ProteinUnet, leveraging an U-Net convolutional network architecture. It is compared to SPIDER3-Single model, based on long short-term memory-bidirectional recurrent neural networks architecture. Both methods achieve similar results for prediction of secondary structures (both three- and eight-state), half-sphere exposure, and contact number, but ProteinUnet has two times fewer parameters, 17 times shorter inference time, and can be trained 11 times faster. Moreover, ProteinUnet tends to be better for short sequences and residues with a low number of local contacts. Additionally, the method of loss weighting is presented as an effective way of increasing accuracy for rare secondary structures.

Effects of curcumin based PDT on the viability and the organization of actin in melanotic (A375) and amelanotic melanoma (C32) - in vitro studies.

Curcumin is a turmeric, antioxidative compound, well-known of its anti-cancer properties. Nowadays more and more effort is made in the field of enhancing the efficiency of the anticancer therapies. Combining the photoactive properties of curcumin with the superficial localization of melanoma and photodynamic therapy (PDT) seems to be a promising treatment method. The research focused on the evaluation of the curcumin effectiveness as an anticancer therapeutic agent in the in vitro treatment of melanotic (A375) and amelanotic (C32) melanoma cell lines. Keratinocytes (HaCat) and fibroblasts (HGF) were used to assess the impact of the therapy on the skin tissue. The aim of the study was to investigate the cell death after exposure to light irradiation after preincubation with curcumin. Additionaly the authors analized the interactions between curcumin and the actin cytoskeleton. The cytotoxic effect initiated by curcumin and increased by irradiation confirm the usefulness of the flavonoid in the PDT approach. Depending on curcumin concentration and incubation time, melanoma cells survival rate ranged from: 93.68 % (C32 cell line, 10 µM, 24 h) and 83.47 % (A375 cell line, 10 µM, 24 h) to 8.98 % (C32 cell line, 50 µM, 48 h) and 12.42 % (A375 cell line, 50 µM, 48 h). Moreover, photodynamic therapy with curcumin increased the number of apoptotic and necrotic cells in comparison to incubation with curcumin without irradiation. The study demonstrated that PDT induced caspase-3 overexpression and DNA cleavage in the studied cell lines. The cells revealed decreased proliferation after the therapy due to the actin cytoskeleton rearrangement. Although effective, the therapy remains not selective towards melanoma cells.

3PO as a Selective Inhibitor of 6-Phosphofructo-2-Kinase/Fructose-2,6-Biphosphatase 3 in A375 Human Melanoma Cells.

BACKGROUND/AIM: The occurrence of BRAFV600E mutation causes an up-regulation of the B-raf kinase activity leading to the stabilization of hypoxia-inducible factor 1-alpha (HIF-1α) - the promoter of the 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3) enzyme. The aim of the study was to examine the effect of the (2E)-3-(3-Pyridinyl)-1-(4-pyridinyl)-2-propen-1-one (3PO), as an inhibitor of PFKFB3, on human melanoma cells (A375) with endogenous BRAFV600E mutation. MATERIALS AND METHODS: A375 cells were exposed to different concentrations of 3PO and the following tests were performed: docking, cytotoxicity assay, immunocytochemistry staining glucose uptake, clonogenic assay, holotomography imaging, and flow cytometry. RESULTS: Our studies revealed that 3PO presents a dose-dependent and time-independent cytotoxic effect and promotes apoptosis of A375 cells. Furthermore, the obtained data indicate that 3PO induces cell cycle arrest in G1/0 and glucose uptake reduction. CONCLUSION: Taking all together, our research demonstrated a here should be proapoptotic and antiproliferative effect of 3PO on A375 human melanoma cells.

Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in diagnosis and grading of brain tumors. Although manual DCE biomarker extraction algorithms boost the diagnostic yield of DCE-MRI by providing quantitative information on tumor prognosis and prediction, they are time-consuming and prone to human errors. In this paper, we propose a fully-automated, end-to-end system for DCE-MRI analysis of brain tumors. Our deep learning-powered technique does not require any user interaction, it yields reproducible results, and it is rigorously validated against benchmark and clinical data. Also, we introduce a cubic model of the vascular input function used for pharmacokinetic modeling which significantly decreases the fitting error when compared with the state of the art, alongside a real-time algorithm for determination of the vascular input region. An extensive experimental study, backed up with statistical tests, showed that our system delivers state-of-the-art results while requiring less than 3 min to process an entire input DCE-MRI study using a single GPU.

In Search of Panacea-Review of Recent Studies Concerning Nature-Derived Anticancer Agents.

Cancers are one of the leading causes of deaths affecting millions of people around the world, therefore they are currently a major public health problem. The treatment of cancer is based on surgical resection, radiotherapy, chemotherapy or immunotherapy, much of which is often insufficient and cause serious, burdensome and undesirable side effects. For many years, assorted secondary metabolites derived from plants have been used as antitumor agents. Recently, researchers have discovered a large number of new natural substances which can effectively interfere with cancer cells' metabolism. The most famous groups of these compounds are topoisomerase and mitotic inhibitors. The aim of the latest research is to characterize natural compounds found in many common foods, especially by means of their abilities to regulate cell cycle, growth and differentiation, as well as epigenetic modulation. In this paper, we focus on a review of recent discoveries regarding nature-derived anticancer agents.