Malignant biliary obstruction is a challenging condition, requiring a multimodal approach for both diagnosis and treatment. Pancreatic adenocarcinoma and cholangiocarcinoma are the leading causes of ...malignant distal biliary obstruction. Early diagnosis is difficult to establish as biliary obstruction can be the first presentation of the underlying disease, which can already be at an advanced stage. Consequently, the majority of patients (70%) with malignant distal biliary obstruction are unresectable at the time of diagnosis. The association of clinical findings, laboratory tests, imaging, and endoscopic modalities may help in identifying the underlying cause. Novel endoscopic techniques such as cholangioscopy, intraductal ultrasonography, or confocal laser endomicroscopy have been developed with promising results, but are not used in routine clinical practice. As the number of patients with malignant distal biliary obstruction who will undergo curative surgery is limited, endoscopy has a crucial role in palliation, to relieve biliary obstruction. According to the last European guidelines published in the management of biliary obstruction, self-expandable metal stents have a central place in biliary drainage compared to plastic stents. Endoscopic ultrasound has evolved impressively in the last decades. When standard techniques of biliary cannulation by endoscopic retrograde cholangiopancreatography fail, endoscopic ultrasound-guided biliary drainage is a good option compared to percutaneous drainage.
Accelerating growth and global expansion of antimicrobial resistance has deepened the need for discovery of novel antimicrobial agents. Antimicrobial peptides have clear advantages over conventional ...antibiotics which include slower emergence of resistance, broad-spectrum antibiofilm activity, and the ability to favourably modulate the host immune response. Broad bacterial susceptibility to antimicrobial peptides offers an additional tool to expand knowledge about the evolution of antimicrobial resistance. Structural and functional limitations, combined with a stricter regulatory environment, have hampered the clinical translation of antimicrobial peptides as potential therapeutic agents. Existing computational and experimental tools attempt to ease the preclinical and clinical development of antimicrobial peptides as novel therapeutics. This Review identifies the benefits, challenges, and opportunities of using antimicrobial peptides against multidrug-resistant pathogens, highlights advances in the deployment of novel promising antimicrobial peptides, and underlines the needs and priorities in designing focused development strategies taking into account the most advanced tools available.
The physiopathology of sepsis continues to be poorly understood, and despite recent advances in its management, sepsis is still a life-threatening condition with a poor outcome. If new diagnostic ...markers related to sepsis pathogenesis will be identified, new specific therapies might be developed and mortality reduced. Small regulatory non-coding RNAs, microRNAs (miRNAs), were recently linked to various diseases; the aim of our prospective study was to identify miRNAs that can differentiate patients with early-stage sepsis from healthy controls and to determine if miRNA levels correlate with the severity assessed by the Sequential Organ Failure Assessment (SOFA) score.
By using genome-wide miRNA profiling by microarray in peripheral blood leukocytes, we found that miR-150, miR-182, miR-342-5p, and miR-486 expression profiles differentiated sepsis patients from healthy controls. We also proved by quantitative reverse transcription-polymerase chain reaction that miR-150 levels were significantly reduced in plasma samples of sepsis patients and correlated with the level of disease severity measured by the SOFA score, but were independent of the white blood counts (WBC). We found that plasma levels of tumor necrosis factor alpha, interleukin-10, and interleukin-18, all genes with sequence complementarity to miR-150, were negatively correlated with the plasma levels of this miRNA. Furthermore, we identified that the plasma levels ratio for miR-150/interleukin-18 can be used for assessing the severity of the sepsis.
We propose that miR-150 levels in both leukocytes and plasma correlate with the aggressiveness of sepsis and can be used as a marker of early sepsis. Furthermore, we envision miR-150 restoration as a future therapeutic option in sepsis patients.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Nanoparticles exhibit diverse structural and morphological features that are often interconnected, making the correlation of structure/property relationships challenging. In this study a ...multi-structure/single-property relationship of silver nanoparticles is developed for the energy of Fermi level, which can be tuned to improve the transfer of electrons in a variety of applications. By combining different machine learning analytical algorithms, including k-mean, logistic regression, and random forest with electronic structure simulations, we find that the degree of twinning (characterized by the fraction of hexagonal closed packed atoms) and the population of the {111} facet (characterized by a surface coordination number of nine) are strongly correlated to the Fermi energy of silver nanoparticles. A concise three layer artificial neural network together with principal component analysis is built to predict this property, with reduced geometrical, structural, and topological features, making the method ideal for efficient and accurate high-throughput screening of large-scale virtual nanoparticle libraries and the creation of single-structure/single-property, multi-structure/single-property, and single-structure/multi-property relationships in the near future.
The chemical modification of histones at specific DNA regulatory elements is linked to the activation, inactivation and poising of genes. A number of tools exist to predict enhancers from chromatin ...modification maps, but their practical application is limited because they either (i) consider a smaller number of marks than those necessary to define the various enhancer classes or (ii) work with an excessive number of marks, which is experimentally unviable. We have developed a method for chromatin state detection using support vector machines in combination with genetic algorithm optimization, called ChromaGenSVM. ChromaGenSVM selects optimum combinations of specific histone epigenetic marks to predict enhancers. In an independent test, ChromaGenSVM recovered 88% of the experimentally supported enhancers in the pilot ENCODE region of interferon gamma-treated HeLa cells. Furthermore, ChromaGenSVM successfully combined the profiles of only five distinct methylation and acetylation marks from ChIP-seq libraries done in human CD4(+) T cells to predict ∼21,000 experimentally supported enhancers within 1.0 kb regions and with a precision of ∼90%, thereby improving previous predictions on the same dataset by 21%. The combined results indicate that ChromaGenSVM comfortably outperforms previously published methods and that enhancers are best predicted by specific combinations of histone methylation and acetylation marks.
Nutrition in acute pancreatitis Arvanitakis, Marianna; Gkolfakis, Paraskevas; Fernandez Y. Viesca, Michael
Current opinion in clinical nutrition and metabolic care,
09/2021, Letnik:
24, Številka:
5
Journal Article
Recenzirano
Purpose of review
This review aims to discuss recent developments in different topics regarding nutrition and acute pancreatitis (AP), including oral refeeding, nutritional therapy, and implications ...of gut microbiota.
Recent findings
Obesity increases the risk for severe AP and mortality. Considering the worldwide obesity rates, this finding could have major implications in the global outcomes of patients admitted with AP. Recent research confirms that early oral feeding leads to shorter length of stay, fewer complications, and lower costs. In case of intolerance to oral feeding or severe disease, nutritional therapy should be offered within 24–72 h, whereas enteral nutrition (EN) has been shown superior to parenteral nutrition. EN can be administered through gastric or jejunal feeding, depending on digestive tolerance and the presence of ileus. Nevertheless, modalities of EN in patients undergoing endoscopic drainage of pancreatitis-related collections are still undetermined. Weight-loss after discharge occurs frequently and could reflect post-AP pancreatic exocrine failure. Finally, novel research regarding gut microbiota could open new therapeutic opportunities to prevent bacterial translocation and pancreatic necrosis’ infection.
Summary
Despite available evidence many questions regarding nutritional management in patients with AP remain open. Modulation of gut microbiota could play an important role in further therapeutic management.
In this work, we have developed quantitative structure–property relationship (QSPR) models using advanced machine learning algorithms that can rapidly and accurately recognize high-performing metal ...organic framework (MOF) materials for CO2 capture. More specifically, QSPR classifiers have been developed that can, in a fraction of a section, identify candidate MOFs with enhanced CO2 adsorption capacity (>1 mmol/g at 0.15 bar and >4 mmol/g at 1 bar). The models were tested on a large set of 292 050 MOFs that were not part of the training set. The QSPR classifier could recover 945 of the top 1000 MOFs in the test set while flagging only 10% of the whole library for compute intensive screening. Thus, using the machine learning classifiers as part of a high-throughput screening protocol would result in an order of magnitude reduction in compute time and allow intractably large structure libraries and search spaces to be screened.
Pancreatic cancer and cholangiocarcinoma are life threatening oncological conditions with poor prognosis and outcome. Pancreatic cystic lesions are considered precursors of pancreatic cancer as some ...of them have the potential to progress to malignancy. Therefore, accurate identification and classification of these lesions is important to prevent the development of invasive cancer. In the biliary tract, the accurate characterization of biliary strictures is essential for providing appropriate management and avoiding unnecessary surgery. Techniques have been developed to improve the diagnosis, risk stratification, and management of pancreato-biliary lesions. Endoscopic ultrasound (EUS) and associated techniques, such as elastography, contrasted-enhanced EUS, and EUS-guided needle confocal laser endomicroscopy, may improve diagnostic accuracy. In addition, intraductal techniques applied during endoscopic retrograde cholangiopancreatography (ERCP), such as new generation cholangioscopy and in vivo cellular evaluation through probe-based confocal laser endomicroscopy, can increase the diagnostic yield in characterizing indeterminate biliary strictures. Both EUS-guided and intraductal approaches can provide the possibility for tissue sampling with new tools, such as needles, biopsies forceps, and brushes. At the molecular level, novel biomarkers have been explored that provide new insights into diagnosis, risk stratification, and management of these lesions.
Metal–organic frameworks (MOFs) are porous materials with exceptional host–guest properties with huge potential for gas separation. The combinatorial design of MOFs demands the in silico screening of ...the nearly infinite combinations of structural building blocks using efficient computational tools. We report here a novel atomic property weighted radial distribution function (AP-RDF) descriptor tailored for large-scale Quantitative Structure–Property Relationship (QSPR) predictions of gas adsorption of MOFs. A total of ∼58,000 hypothetical MOF structures were used to calibrate correlation models of the methane, N2, and CO2 uptake capacities from grand-canonical Monte Carlo (GCMC) simulations. The principal component analysis (PCA) transform of the AP-RDF descriptors exhibited good discrimination of MOF inorganic SBUs, geometrical properties, and more surprisingly gas uptake capacities. While the simulated uptake capacities correlated poorly to the void fraction, surface area, and pore size, the newly introduced AP-RDF scores yielded outstanding QSPR predictions for an external test set of ∼25,000 MOFs with R 2 values in the range from 0.70 to 0.82. The accuracy of the predictions decreased at low pressures, mainly for MOFs with V2O2 or Zr6O8 inorganic structural building units (SBUs) and organic SBUs with fluorine substituents. The QSPR models can serve as efficient filtering tools to detecting promising high-performing candidates at the early stage of virtual high-throughput screening of novel porous materials. The predictive models of the gas uptake capacities of MOFs are available online via our MOF informatics analysis (MOFIA) tool.
Artificial neural networks (ANNs) have been widely used for medicinal chemistry modeling. In the last two decades, too many reports used MATLAB environment as an adequate platform for programming ...ANNs. Some of these reports comprise a variety of applications intended to quantitatively or qualitatively describe structure-activity relationships. A powerful tool is obtained when there are combined Bayesian-regularized neural networks (BRANNs) and genetic algorithm (GA): Bayesian-regularized genetic neural networks (BRGNNs). BRGNNs can model complicated relationships between explanatory variables and dependent variables. Thus, this methodology is regarded as useful tool for QSAR analysis. In order to demonstrate the use of BRGNNs, we developed a reliable method for predicting the antagonistic activity of 5-amino-3-arylisoxazole derivatives against Human Platelet Thrombin Receptor (PAR-1), using classical 3D-QSAR methodologies: Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). In addition, 3D vectors generated from the molecular structures were correlated with antagonistic activities by multivariate linear regression (MLR) and Bayesian-regularized neural networks (BRGNNs). All models were trained with 34 compounds, after which they were evaluated for predictive ability with additional 6 compounds. CoMFA and CoMSIA were unable to describe this structure-activity relationship, while BRGNN methodology brings the best results according to validation statistics.
PDF
