Dynamics of macrozoobenthos in the Southern Bulgarian Black Sea coastal and open-sea areas

The paper presents results of analysis of 96 macrozoobenthic samples, collected on a seasonal basis in Bourgas Bay and in open-sea areas offshore Cape Emine (Bulgarian Black Sea) in 1996 and 1998. In total 96 taxa were established, distributed in four groups: Polychaeta, Mollusca, Crustacea and “Diversa”. The average density of populations was 1756 ind.m-2 with a predominating abundance of Polychaeta species. The average biomass estimated was 183.02 g.m-2, formed mainly by representatives of Mollusca. The latter species were measured together with the shells, which appraised their individual weights. Seven of the species found had a coefficient of constancy more than 50%. These were the most adapted species to the environmental conditions of the investigated areas. The quantitative and qualitative assessments in this study demonstrate an increasing tendency in the parameters obtained (density, biomass, species diversity) in comparison with previous investigations in the early 1990-s, when intensive anthropogenic influence was widely perceived to misbalance the Black Sea ecosystem.The method of Warwick (1986) applied to characterize the water quality of the studied areas allowed us to define them as rather clean or moderately polluted aquatories

Foliar epidermis morphology in Quercus (subgenus Quercus, section Quercus) in Iran

The foliar morphology of trichomes, epicuticular waxes and stomata in Quercus cedrorum, Q. infectoria subsp. boissieri, Q. komarovii, Q. longipes, Q. macranthera, Q. petraea subsp. iberica and Q. robur subsp. pedunculiflora were studied by scanning electron microscopy. The trichomes are mainly present on abaxial leaf surface in most species, but rarely they appear on adaxial surface. Five trichome types are identified as simple uniseriate, bulbous, solitary, fasciculate and stellate. The stomata of all studied species are of the anomocytic type, raised on the epidermis. The stomata rim may or may not be covered with epicuticular. The epicuticular waxes are mostly of the crystalloid type but smooth layer wax is observed in Q. robur subsp. pedunculiflora. Statistical analysis revealed foliar micromorphological features as been diagnostic characters in Quercus

Validation of potential RNA biomarkers for prostate cancer diagnosis and monitoring in plasma and urinary extracellular vesicles

Introduction: Prostate cancer (PCa), one of the most prevalent malignancies affecting men worldwide, presents significant challenges in terms of early detection, risk stratification, and active surveillance. In recent years, liquid biopsies have emerged as a promising non-invasive approach to complement or even replace traditional tissue biopsies. Extracellular vesicles (EVs), nanosized membranous structures released by various cells into body fluids, have gained substantial attention as a source of cancer biomarkers due to their ability to encapsulate and transport a wide range of biological molecules, including RNA. In this study, we aimed to validate 15 potential RNA biomarkers, identified in a previous EV RNA sequencing study, using droplet digital PCR. Methods: The candidate biomarkers were tested in plasma and urinary EVs collected before and after radical prostatectomy from 30 PCa patients and their diagnostic potential was evaluated in a test cohort consisting of 20 benign prostate hyperplasia (BPH) and 20 PCa patients’ plasma and urinary EVs. Next, the results were validated in an independent cohort of plasma EVs from 31 PCa and 31 BPH patients. Results: We found that the levels of NKX3-1 (p = 0.0008) in plasma EVs, and tRF-Phe-GAA-3b (p &lt; 0.0001) tRF-Lys-CTT-5c (p &lt; 0.0327), piR-28004 (p = 0.0081) and miR-375-3p (p &lt; 0.0001) in urinary EVs significantly decreased after radical prostatectomy suggesting that the main tissue source of these RNAs is prostate and/or PCa. Two mRNA biomarkers—GLO1 and NKX3-1 showed promising diagnostic potential in distinguishing between PCa and BPH with AUC of 0.68 and 0.82, respectively, in the test cohort and AUC of 0.73 and 0.65, respectively, in the validation cohort, when tested in plasma EVs. Combining these markers in a biomarker model yielded AUC of 0.85 and 0.71 in the test and validation cohorts, respectively. Although the PSA levels in the blood could not distinguish PCa from BPH in our cohort, adding PSA to the mRNA biomarker model increased AUC from 0.71 to 0.76. Conclusion: This study identified two novel EV-enclosed RNA biomarkers–NKX3-1 and GLO1–for the detection of PCa, and highlights the complementary nature of GLO1, NKX3-1 and PSA as combined biomarkers in liquid biopsies of PCa.</p

An inventory of the Aspergillus niger secretome by combining in silico predictions with shotgun proteomics data

<p>Abstract</p> <p>Background</p> <p>The ecological niche occupied by a fungal species, its pathogenicity and its usefulness as a microbial cell factory to a large degree depends on its secretome. Protein secretion usually requires the presence of a N-terminal signal peptide (SP) and by scanning for this feature using available highly accurate SP-prediction tools, the fraction of potentially secreted proteins can be directly predicted. However, prediction of a SP does not guarantee that the protein is actually secreted and current <it>in silico </it>prediction methods suffer from gene-model errors introduced during genome annotation.</p> <p>Results</p> <p>A majority rule based classifier that also evaluates signal peptide predictions from the best homologs of three neighbouring <it>Aspergillus </it>species was developed to create an improved list of potential signal peptide containing proteins encoded by the <it>Aspergillus niger </it>genome. As a complement to these <it>in silico </it>predictions, the secretome associated with growth and upon carbon source depletion was determined using a shotgun proteomics approach. Overall, some 200 proteins with a predicted signal peptide were identified to be secreted proteins. Concordant changes in the secretome state were observed as a response to changes in growth/culture conditions. Additionally, two proteins secreted via a non-classical route operating in <it>A. niger </it>were identified.</p> <p>Conclusions</p> <p>We were able to improve the <it>in silico </it>inventory of <it>A. niger </it>secretory proteins by combining different gene-model predictions from neighbouring Aspergilli and thereby avoiding prediction conflicts associated with inaccurate gene-models. The expected accuracy of signal peptide prediction for proteins that lack homologous sequences in the proteomes of related species is 85%. An experimental validation of the predicted proteome confirmed <it>in silico </it>predictions.</p

Neuroactive steroids in depression and anxiety disorders: Clinical studies

Certain neuroactive steroids modulate ligand-gated ion channels via non-genomic mechanisms. Especially 3 alpha-reduced pregnane steroids are potent positive allosteric modulators of the gamma-aminobutyric acid type A (GABA(A)) receptor. During major depression, there is a disequilibrium of 3 alpha-reduced neuroactive steroids, which is corrected by clinically effective pharmacological treatment. To investigate whether these alterations are a general principle of successful antidepressant treatment, we studied the impact of nonpharmacological treatment options on neuroactive steroid concentrations during major depression. Neither partial sleep deprivation, transcranial magnetic stimulation, nor electroconvulsive therapy affected neuroactive steroid levels irrespectively of the response to these treatments. These studies suggest that the changes in neuroactive steroid concentrations observed after antidepressant pharmacotherapy more likely reflect distinct pharmacological properties of antidepressants rather than the clinical response. In patients with panic disorder, changes in neuroactive steroid composition have been observed opposite to those seen in depression. However, during experimentally induced panic induction either with cholecystokinine-tetrapeptide or sodium lactate, there was a pronounced decline in the concentrations of 3 alpha-reduced neuroactive steroids in patients with panic disorder, which might result in a decreased GABAergic tone. In contrast, no changes in neuroactive steroid concentrations could be observed in healthy controls with the exception of 3 alpha,5 alpha-tetrahydrodeoxycorticosterone. The modulation of GABA(A) receptors by neuroactive steroids might contribute to the pathophysiology of depression and anxiety disorders and might offer new targets for the development of novel anxiolytic compounds. Copyright (c) 2006 S. Karger AG, Basel

GAN-based multiple adjacent brain MRI slice reconstruction for unsupervised alzheimer’s disease diagnosis

Unsupervised learning can discover various unseen diseases, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's Disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with disease stages. Therefore, we propose a two-step method using Generative Adversarial Network-based multiple adjacent brain MRI slice reconstruction to detect AD at various stages: (Reconstruction) Wasserstein loss with Gradient Penalty + L1 loss---trained on 3 healthy slices to reconstruct the next 3 ones---reconstructs unseen healthy/AD cases; (Diagnosis) Average/Maximum loss (e.g., L2 loss) per scan discriminates them, comparing the reconstructed/ground truth images. The results show that we can reliably detect AD at a very early stage with Area Under the Curve (AUC) 0.780 while also detecting AD at a late stage much more accurately with AUC 0.917; since our method is fully unsupervised, it should also discover and alert any anomalies including rare disease.Comment: 10 pages, 4 figures, Accepted to Lecture Notes in Bioinformatics (LNBI) as a volume in the Springer serie

Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning

The crucial components of a conventional image registration method are the choice of the right feature representations and similarity measures. These two components, although elaborately designed, are somewhat handcrafted using human knowledge. To this end, these two components are tackled in an end-to-end manner via reinforcement learning in this work. Specifically, an artificial agent, which is composed of a combined policy and value network, is trained to adjust the moving image toward the right direction. We train this network using an asynchronous reinforcement learning algorithm, where a customized reward function is also leveraged to encourage robust image registration. This trained network is further incorporated with a lookahead inference to improve the registration capability. The advantage of this algorithm is fully demonstrated by our superior performance on clinical MR and CT image pairs to other state-of-the-art medical image registration methods

Slx8 removes Pli1-dependent protein-SUMO conjugates including SUMOylated Topoisomerase I to promote genome stability

Peer reviewedPublisher PD

Improved recovery of urinary small extracellular vesicles by differential ultracentrifugation

Extracellular vesicles (EVs) are lipid-membrane enclosed structures that are associated with several diseases, including those of genitourinary tract. Urine contains EVs derived from urinary tract cells. Owing to its non-invasive collection, urine represents a promising source of biomarkers for genitourinary disorders, including cancer. The most used method for urinary EVs separation is differential ultracentrifugation (UC), but current protocols lead to a significant loss of EVs hampering its efficiency. Moreover, UC protocols are labor-intensive, further limiting clinical application. Herein, we sought to optimize an UC protocol, reducing the time spent and improving small EVs (SEVs) yield. By testing different ultracentrifugation times at 200,000g to pellet SEVs, we found that 48 min and 60 min enabled increased SEVs recovery compared to 25 min. A step for pelleting large EVs (LEVs) was also evaluated and compared with filtering of the urine supernatant. We found that urine supernatant filtering resulted in a 1.7-fold increase on SEVs recovery, whereas washing steps resulted in a 0.5 fold-decrease on SEVs yield. Globally, the optimized UC protocol was shown to be more time efficient, recovering higher numbers of SEVs than Exoquick-TC (EXO). Furthermore, the optimized UC protocol preserved RNA quality and quantity, while reducing SEVs separation time.</p