Determinants of receptor tyrosine phosphatase homophilic adhesion: Structural comparison of PTPRK and PTPRM extracellular domains.

Type IIB receptor protein tyrosine phosphatases are cell surface transmembrane proteins that engage in cell adhesion via their extracellular domains (ECDs) and cell signaling via their cytoplasmic phosphatase domains. The ECDs of type IIB receptor protein tyrosine phosphatases form stable, homophilic, and trans interactions between adjacent cell membranes. Previous work has demonstrated how one family member, PTPRM, forms head-to-tail homodimers. However, as the interface was composed of residues conserved across the family, the determinants of homophilic specificity remain unknown. Here, we have solved the X-ray crystal structure of the membrane-distal N-terminal domains of PTPRK that form a head-to-tail dimer consistent with intermembrane adhesion. Comparison with the PTPRM structure demonstrates interdomain conformational differences that may define homophilic specificity. Using small-angle X-ray scattering, we determined the solution structures of the full-length ECDs of PTPRM and PTPRK, identifying that both are rigid extended molecules that differ in their overall long-range conformation. Furthermore, we identified one residue, W351, within the interaction interface that differs between PTPRM and PTPRK and showed that mutation to glycine, the equivalent residue in PTPRM, abolishes PTPRK dimer formation in vitro. This comparison of two members of the receptor tyrosine phosphatase family suggests that homophilic specificity is driven by a combination of shape complementarity and specific but limited sequence differences.

Structural, Thermodynamic and Enzymatic Characterization of N,N-Diacetylchitobiose Deacetylase from Pyrococcus chitonophagus.

Chitin is a major source of energy and macroelements for many organisms. An important step in its degradation is the deacetylation of chitin or its fragments. Deacetylase from the extremophile Pyrococcus chitonophagus has been analyzed by X-ray crystallography, small-angle X-ray scattering, differential scanning calorimetry, isothermal titration calorimetry and NMR to determine its structure, thermodynamics and enzymatic properties. It is a hexameric, zinc-containing metalloenzyme that retains its structural integrity up to temperatures slightly exceeding 100 °C. It removes the acetyl group specifically from the non-reducing end of the sugar substrate. Its main substrate is N,N-diacetylchitobiose but it also active, at a reduced level, toward N-acetyl-d-glucosamine or a trimer of N-acetyl-d-glucosamine units. Crystallographic analysis includes the structure of the enzyme with its main substrate approaching the active site in a monodentate manner, replacing the single water molecule that is bound at the Zn2+ cation when the ligand is absent. The Zn2+ cation remains tetrahedrally coordinated, with three of its ligands provided by the protein's conserved His-Asp-His triad. The crystal structures are consistent with the reaction mechanism proceeding via an anhydride intermediate. Hydrolysis as the first step cannot be ruled out in a hydrated environment but no defined 'hydrolytic water' site can be identified in the analyzed structures.

Investigation of the effectiveness of CFRP strengthening of concrete made with recycled waste PET fine plastic aggregate.

In recent decades, several studies have considered the use of plastic waste as a partial substitute for aggregate in green concrete. Such concrete has been limited to non-structural applications due to its low strength. This raises whether such concrete can be enhanced for use in some structural applications. This paper reports an attempt to develop a structural-grade concrete containing plastic waste aggregate with high proportions of substitution and confined with carbon fiber reinforced polymer (CFRP) fabrics. Experimental research was conducted involving the casting and testing 54 plain and confined concrete cylinders. A concrete mixture was designed in which the fine aggregate was partially replaced by polyethylene terephthalate (PET) waste plastic at ratios of 0%, 25%, and 50%, and with different w/c ratios of 0.40, 0.45, and 0.55. The results show that confinement has a substantial positive effect on the compressive behavior of PET concrete. The enhancement efficiency increases by 8-190%, with higher enhancement levels for higher substitution ratios. Adding one layer of CFRP fabric raises the ultimate strength of samples that have lost compressive strength to a level close to that of unconfined samples not containing PET. This confinement is accompanied by an increase in the slope of the stress-strain curve and greater axial and lateral strain values at failure. For the specimens confined by CFRP fabric, PET aggregate can be used as a partial substitute for sand at a replacement ratio of up to 50% by volume for structural applications. This paper also considers the ability of existing models to predict the strength of confined-PET concrete circular cross-sections by comparing model predictions with experimental results. The strength of confined PET concrete elements can't be accurately predicted by any of the models that are already out there. It's important to come up with a new model for these elements.

Proposing several model techniques including ANN and M5P-tree to predict the compressive strength of geopolymer concretes incorporated with nano-silica.

Geopolymers are innovative cementitious materials that can completely replace traditional Portland cement composites and have a lower carbon footprint than Portland cement. Recent efforts have been made to incorporate various nanomaterials, most notably nano-silica (nS), into geopolymer concrete (GPC) to improve the composite's properties and performance. Compression strength (CS) is one of the essential properties of all types of concrete composites, including geopolymer concrete. As a result, creating a credible model for forecasting concrete CS is critical for saving time, energy, and money, as well as providing guidance for scheduling the construction process and removing formworks. This paper presents a large amount of mixed design data correlated to mechanical strength using empirical correlations and neural networks. Several models, including artificial neural network, M5P-tree, linear regression, nonlinear regression, and multi-logistic regression models, were utilized to create models for forecasting the CS of GPC incorporated with nS. In this case, about 207 tested CS values were collected from literature studies and then analyzed to promote the models. For the first time, eleven effective variables were employed as input model parameters during the modeling process, including the alkaline solution to binder ratio, binder content, fine and coarse aggregate content, NaOH and Na2SiO3 content, Na2SiO3/NaOH ratio, molarity, nS content, curing temperatures, and ages. The developed models were assessed using different statistical tools such as root mean squared error, mean absolute error, scatter index, objective function value, and coefficient of determination. Based on these statistical assessment tools, results revealed that the ANN model estimated the CS of GPC incorporated with nS more accurately than the other models. On the other hand, the alkaline solution to binder ratio, molarity, NaOH content, curing temperature, and ages were those parameters that have significant influences on the CS of GPC incorporated with nS.

Statistical Methods for Modeling the Compressive Strength of Geopolymer Mortar.

In recent years, geopolymer has been developed as an alternative to Portland cement (PC) because of the significant carbon dioxide emissions produced by the cement manufacturing industry. A wide range of source binder materials has been used to prepare geopolymers; however, fly ash (FA) is the most used binder material for creating geopolymer concrete due to its low cost, wide availability, and increased potential for geopolymer preparation. In this paper, 247 experimental datasets were obtained from the literature to develop multiscale models to predict fly-ash-based geopolymer mortar compressive strength (CS). In the modeling process, thirteen different input model parameters were considered to estimate the CS of fly-ash-based geopolymer mortar. The collected data contained various mix proportions and different curing ages (1 to 28 days), as well as different curing temperatures. The CS of all types of cementitious composites, including geopolymer mortars, is one of the most important properties; thus, developing a credible model for forecasting CS has become a priority. Therefore, in this study, three different models, namely, linear regression (LR), multinominal logistic regression (MLR), and nonlinear regression (NLR) were developed to predict the CS of geopolymer mortar. The proposed models were then evaluated using different statistical assessments, including the coefficient of determination (R2), root mean squared error (RMSE), scatter index (SI), objective function value (OBJ), and mean absolute error (MAE). It was found that the NLR model performed better than the LR and MLR models. For the NLR model, R2, RMSE, SI, and OBJ were 0.933, 4.294 MPa, 0.138, 4.209, respectively. The SI value of NLR was 44 and 41% lower than the LR and MLR models' SI values, respectively. From the sensitivity analysis result, the most effective parameters for predicting CS of geopolymer mortar were the SiO2 percentage of the FA and the alkaline liquid-to-binder ratio of the mixture.

A review on utilizing machine learning technology in the fields of electronic emergency triage and patient priority systems in telemedicine: Coherent taxonomy, motivations, open research challenges and recommendations for intelligent future work.

BACKGROUND: With the remarkable increasing in the numbers of patients, the triaging and prioritizing patients into multi-emergency level is required to accommodate all the patients, save more lives, and manage the medical resources effectively. Triaging and prioritizing patients becomes particularly challenging especially for the patients who are far from hospital and use telemedicine system. To this end, the researchers exploiting the useful tool of machine learning to address this challenge. Hence, carrying out an intensive investigation and in-depth study in the field of using machine learning in E-triage and patient priority are essential and required. OBJECTIVES: This research aims to (1) provide a literature review and an in-depth study on the roles of machine learning in the fields of electronic emergency triage (E-triage) and prioritize patients for fast healthcare services in telemedicine applications. (2) highlight the effectiveness of machine learning methods in terms of algorithms, medical input data, output results, and machine learning goals in remote healthcare telemedicine systems. (3) present the relationship between machine learning goals and the electronic triage processes specifically on the: triage levels, medical features for input, outcome results as outputs, and the relevant diseases. (4), the outcomes of our analyses are subjected to organize and propose a cross-over taxonomy between machine learning algorithms and telemedicine structure. (5) present lists of motivations, open research challenges and recommendations for future intelligent work for both academic and industrial sectors in telemedicine and remote healthcare applications. METHODS: An intensive research is carried out by reviewing all articles related to the field of E-triage and remote priority systems that utilise machine learning algorithms and sensors. We have searched all related keywords to investigate the databases of Science Direct, IEEE Xplore, Web of Science, PubMed, and Medline for the articles, which have been published from January 2012 up to date. RESULTS: A new crossover matching between machine learning methods and telemedicine taxonomy is proposed. The crossover-taxonomy is developed in this study to identify the relationship between machine learning algorithm and the equivalent telemedicine categories whereas the machine learning algorithm has been utilized. The impact of utilizing machine learning is composed in proposing the telemedicine architecture based on synchronous (real-time/ online) and asynchronous (store-and-forward / offline) structure. In addition to that, list of machine learning algorithms, list of the performance metrics, list of inputs data and outputs results are presented. Moreover, open research challenges, the benefits of utilizing machine learning and the recommendations for new research opportunities that need to be addressed for the synergistic integration of multidisciplinary works are organized and presented accordingly. DISCUSSION: The state-of-the-art studies on the E-triage and priority systems that utilise machine learning algorithms in telemedicine architecture are discussed. This approach allows the researchers to understand the modernisation of healthcare systems and the efficient use of artificial intelligence and machine learning. In particular, the growing worldwide population and various chronic diseases such as heart chronic diseases, blood pressure and diabetes, require smart health monitoring systems in E-triage and priority systems, in which machine learning algorithms could be greatly beneficial. CONCLUSIONS: Although research directions on E-triage and priority systems that use machine learning algorithms in telemedicine vary, they are equally essential and should be considered. Hence, we provide a comprehensive review to emphasise the advantages of the existing research in multidisciplinary works of artificial intelligence, machine learning and healthcare services.

Biliary stent migration: why, how, and what?

BACKGROUND AND PURPOSE: The frequency, risk factors as well as the sites of biliary stent migration are variable in the literature. This retrospective study investigated the frequency of biliary stent migration, why biliary stents migrated, how the migrated stents affected the patients, and what are the different techniques retrieved the migrated stents. PATIENTS AND METHODS: Out of 876 stented patients, 74 patients (8.4%) had their stents migrated. Patients with and without migrated stents were compared regarding endoscopy and stent-related parameters. The sequels of stent migrations were reported. Furthermore, the methods used for stent retrieval were reviewed. RESULTS: Proximal and distal stent migration occurred at a rate of 3 and 5.5%, respectively. The independent predictors for stent migration were moderate to marked common bile duct (CBD) dilation, complete sphincterotomy, the use of balloon dilation, and stent insertion for more than 1 month. Cholangitis and stent obstruction was the most commonly reported adverse event (n = 18, 24.3%). Distal stent migration associated with two cases of bleeding due to duodenal wall injury, and two cases of duodenal perforation. All the retained migrated stents in the current study were retrieved by endoscopy using extraction balloon, Dormia basket, snares, and foreign body forceps. CONCLUSION: Biliary stent migration occurs at a rate of 8.4%. Stents do migrate because of dilated CBD, wide sphincterotomy, and biliary balloon dilation. Furthermore, wide, straight stents inserted for more than 1 month easily migrate. The migrated stents migrated intraluminal in the CBD, duodenum or the colon. All the retained migrated stents were retrieved endoscopically.

Evaluation of some trace elements in sera of asthma patients: a case control study.

INTRODUCTION: Asthma is a chronic inflammatory disease of the airways characterized by recurrent respiratory symptoms of dyspnea, wheezing, chest tightness, and cough. AIM: This study aims to investigate the concentrations of Cu, Zn, Mg, Mn, Fe, Cr, Ni, and Al in the serum of asthmatic patients. MATERIALS AND METHODS: An atomic absorption technique was used to determine the levels of trace elements. The study included sixty asthmatic patients and ninety healthy individuals as control group their ages ranging from 20 to 45 years. RESULTS: We found a significant increase of the levels of copper, iron, and aluminum by 20%, 54%, and 47% (p<0.01), respectively, in the asthmatic patients as compared with the controls. On the other hand, in comparison with the controls, the levels of zinc, magnesium, manganese, and nickel were found to be significantly decreased (p<0.01) by 24%, 16%, 53%, and 81%, respectively, in the asthmatic patient group. Moreover, chromium level showed non-significant differences (p>0.05) between patients and control group. CONCLUSIONS: The increased level of Cu and Fe may reflect their potential role in the pathogenesis of asthma. Furthermore, the serum iron levels tend to be increased as a result of the inflammatory process that occurs in asthma. Low levels of Zn, Mg, and Mn may contribute to allergic diseases due to their role in the synthesis of certain antioxidants or to their effect on the immune system.

Detection of Metallo-ß-Lactamase Producing Pseudomonas aeruginosa Isolated from Public and Private Hospitals in Baghdad, Iraq.

Metallo-ß-lactamase (MBL) producing Pseudomonas aeruginosa has been reported to be an important nosocomial infection. Its intrinsic and acquired resistance to various antimicrobial agents and its ability to develop multidrug resistance imposes a serious therapeutic problem. Different clinical samples were collected from public and private hospitals in Baghdad city, Iraq. Bacterial identification was done using conventional cultural, biochemical tests, and VITEk 2 system. Minimum inhibitory concentration (MIC) testing was performed using VITEK 2 automated system. Each P. aeruginosa isolates showed resistance to Carbapenems (Imipenem and Meropenem) were subjected to Imipenem-EDTA combined disc synergy test (CDST) to investigate the production of MBL (confirmative test). The presence of bla-genes encoded IMP, VIM, and SPM-1 was detected by conventional PCR technique. A total of 75 P. aeruginosa isolates were isolated, 16 (21.3%) were able to grow on MacConkey agar supplemented with Meropenem 4mg/L (MMAC). The MIC of different antibiotics showed that 6 (37.5 %) isolates were Carbapenem resistant, MIC ≥16 µg/ml while 4 (25%) isolates appear to be MBL producer using CDST test. PCR assay revealed that 3 (50%), 1 (16.6%) of the carbapenem resistant isolates harbored blaIMP, blaSPM-1 genes, respectively. blaVIM gene was not detected in this study. The prevalence of multi-drug resistant P. aeruginosa isolates especially Carbapenem resistant bacteria was increased in Baghdad province. The blaIMP was the predominant among the MBLs genes in P. aeruginosa in this study.