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Cervical cancer is a critical imperilment to a female's health due to its malignancy and fatality rate. The disease can be thoroughly cured by locating and treating the infected tissues in the preliminary phase. The traditional practice for screening cervical cancer is the examination of cervix tissues using the Papanicolaou (Pap) test. Manual inspection of pap smears involves false-negative outcomes due to human error even in the presence of the infected sample. Automated computer vision diagnosis revamps this obstacle and plays a substantial role in screening abnormal tissues affected due to cervical cancer. Here, in this paper, we propose a hybrid deep feature concatenated network (HDFCN) following two-step data augmentation to detect cervical cancer for binary and multiclass classification on the Pap smear images. This network carries out the classification of malignant samples for whole slide images (WSI) of the openly accessible SIPaKMeD database by utilizing the concatenation of features extracted from the fine-tuning of the deep learning (DL) models, namely, VGG-16, ResNet-152, and DenseNet-169, pretrained on the ImageNet dataset. The performance outcomes of the proposed model are compared with the individual performances of the aforementioned DL networks using transfer learning (TL). Our proposed model achieved an accuracy of 97.45% and 99.29% for 5-class and 2-class classifications, respectively. Additionally, the experiment is performed to classify liquid-based cytology (LBC) WSI data containing pap smear images. [ABSTRACT FROM AUTHOR]

The aim of this paper was to explore the mechanism of bladder meridian massage (BMM) on anxiety in rats with chronic stress. Chronic stress induced rats to establish rat anxiety model. The sugar water preference (SPF), tail suspension time (TST), and forced swimming time (FST) of rats were measured. The levels of superoxide dismutase (SOD), malondialdehyde (MDA), and inflammatory cytokines in serum and hippocampus of rats were detected. Brain neurotransmitters (dopamine (DA), 5- hydroxytryptamine (5-HT), and norepinephrine (NE)) were detected by enzyme-linked immunosorbent assay (ELISA) kits. Immunohistochemistry and western blotting were used to detect autophagy protein expression in hippocampus of rats. BMM significantly increased SPF, decreased TST and FST, increased SOD level in serum and hippocampus, and decreased MDA level and cytokine level. BMM reversed the changes of neurotransmitters. At the same time, BMM significantly decreased autophagy protein expression in hippocampus of rats. The above results show that BMM significantly relieve anxiety induced by chronic stress in rats. [ABSTRACT FROM AUTHOR]

Disease diagnosis faces challenges such as misdiagnosis, lack of diagnosis, and slow diagnosis. There are several machine learning techniques that have been applied to address these challenges, where a set of symptoms is applied to a classification model that predicts the presence or absence of a disease. To improve on the performance of these techniques, this paper presents a technique which involves feature selection using principal component analysis (PCA), a hybrid kernel-based support vector machine (HKSVM) classification model and hyperparameter optimization using genetic algorithm (GA). The HKSVM in this paper introduces a new way of combining three kernels: Radial basis function (RBF), linear, and polynomial. Combining local (RBF) and global (linear and polynomial) kernels has the effect of improved model performance. This is because the local kernels are better able to distinguish points closer to each other while the global kernels are more suited to distinguish points that are far away from each other. The PCA-GA-HKSVM is used on 7 different medical datasets, with two datasets being multiclass datasets and 5 datasets being binary. Performance evaluation metrics used were accuracy, precision, and recall. It was observed that the PCA-GA-HKSVM offered better performance than the single kernel support vector machines (SVMs). [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is an inflammatory disease damaging the myelin sheath in the central and peripheral nervous system in the brain and spinal cord. Optic Neuritis (ON) is one of the most prevalent ocular demonstrations of MS. The current diagnosis protocol for MS is MRI, but newer modalities like Optical Coherence Tomography (OCT) are already of interest in early detection and progression analysis. OCT reveals the symptoms of MS in the Central Nervous System (CNS) through cross-sectional images from neural retinal layers. Previous works on OCT were mostly focused on the thickness of retinal layers; however, texture features seem also to have information in this regard. In this research, we introduce a new pipeline that constructs layer-stacked (LS) images containing data from each specific layer. A variety of texture features are then extracted from LS images to differentiate between healthy controls and ON/None-ON MS cases. Furthermore, the definition of texture extraction methods is tailored for this application. After performing a vast survey on available texture analysis methods, a treasury of powerful features is collected in this paper. As a primary work, this paper shows the ability of such features in the diagnosis of HC and MS (ON and None-ON) cases. Our findings show that the texture features are powerful to diagnose MS cases. Furthermore, adding information of conventional thickness values to texture features improves considerably the discrimination between most of the target groups including HC vs. MS, HC vs. MS-None-ON, and HC vs. MS-ON. [ABSTRACT FROM AUTHOR]

Purpose. A non-invasive way of assessing post-transplant renal graft function has been needed. This study aimed to assess the micro-structural and micro-functional status of graft kidneys by using intravoxel incoherent motion- (IVIM-) diffusion-weighted imaging (DWI) to investigate delayed graft function (DGF) immediately after transplantation. Method. A prospective study was conducted on 37 patients, 14 with early graft function (EGF) and 23 with DGF (9 with complication, 14 without) who underwent IVIM-DWI, most often within 1-7 days after kidney transplantation. A total of 37 cases were collected and all the participants have been well-informed and signed their consents. In addition, the study conducted in this paper was approved by the Ethics Committee of Clinical Research, Taichung Veterans General Hospital (IRB number: CE14065). Using biexponential analysis of slow diffusion coefficient ( D slow ), fast diffusion coefficient ( D fast ), and perfusion fraction was performed. The apparent diffusion coefficient (ADC) was calculated by use of a monoexponential model. All parameters were measured from three different regions-of-interest (ROI), covering the entire renal parenchyma, cortex, and medulla. Results. D slow , perfusion fraction, and ADC were significantly higher in patients with EGF than DGF (all p values values <0.001). Especially, ADC measured from ROI covering the entire kidney parenchyma had the best cut-off value (1.93μm2/msec) with the highest area under the receiver operating characteristic curve (AUC 0.943) in differentiating EGF from DGF. For analysis of pair-wise differences, only the perfusion fraction values, measured from the ROI covering the renal cortex, were significantly higher in 14 DGF patients with no complications than in the 9 DGF patients with complications, with the best cut-off value of 12.3% and the AUC of 0.844. Conclusion. Noninvasive IVIM-DWI reliably differentiates DGF from EGF after kidney transplantation, and it may aid in identifying posttransplant complications and indications for renal biopsy. [ABSTRACT FROM AUTHOR]

At present, percutaneous nephrolithotomy has become an option for hospitals increasingly to treat calculus. However, due to the certain risk of percutaneous nephrolithotomy, it may lead to postoperative complications. Analyzing the preoperative factors of septic shock after percutaneous nephrolithotomy can provide guidance for reducing the incidence of septic shock after PCNL. This article establishes a logistic regression model based on preoperative factors, uses statistical methods, and uses the professional statistical software SPSS to create a database and analyze the data. The method of classification data analysis was used to determine various influencing factors including patient factors, stone factor, and preoperative factors and combined B-ultrasound guidance and percutaneous nephrolithotomy to reduce the risk of surgery. The experimental results found that the percutaneous nephroscope laser lithotripsy guided by the smart Internet of Things can effectively perform lithotripsy; in this paper, the preoperative data of different complications are obtained, and the risk of surgery is closely related to the operation time. The operation time exceeds 90 minutes and causes the risk of severe bleeding after PCNL. It is twice the operation time of less than 90 minutes and 5 times the operation time of less than 60 minutes. [ABSTRACT FROM AUTHOR]

Biomedical named entity recognition (BioNER) from clinical texts is a fundamental task for clinical data analysis due to the availability of large volume of electronic medical record data, which are mostly in free text format, in real-world clinical settings. Clinical text data incorporates significant phenotypic medical entities (e.g., symptoms, diseases, and laboratory indexes), which could be used for profiling the clinical characteristics of patients in specific disease conditions (e.g., Coronavirus Disease 2019 (COVID-19)). However, general BioNER approaches mostly rely on coarse-grained annotations of phenotypic entities in benchmark text dataset. Owing to the numerous negation expressions of phenotypic entities (e.g., "no fever," "no cough," and "no hypertension") in clinical texts, this could not feed the subsequent data analysis process with well-prepared structured clinical data. In this paper, we developed Human-machine Cooperative Phenotypic Spectrum Annotation System (http://www.tcmai.org/login, HCPSAS) and constructed a fine-grained Chinese clinical corpus. Thereafter, we proposed a phenotypic named entity recognizer: Phenonizer, which utilized BERT to capture character-level global contextual representation, extracted local contextual features combined with bidirectional long short-term memory, and finally obtained the optimal label sequences through conditional random field. The results on COVID-19 dataset show that Phenonizer outperforms those methods based on Word2Vec with an F1-score of 0.896. By comparing character embeddings from different data, it is found that character embeddings trained by clinical corpora can improve F -score by 0.0103. In addition, we evaluated Phenonizer on two kinds of granular datasets and proved that fine-grained dataset can boost methods' F1-score slightly by about 0.005. Furthermore, the fine-grained dataset enables methods to distinguish between negated symptoms and presented symptoms. Finally, we tested the generalization performance of Phenonizer, achieving a superior F1-score of 0.8389. In summary, together with fine-grained annotated benchmark dataset, Phenonizer proposes a feasible approach to effectively extract symptom information from Chinese clinical texts with acceptable performance. [ABSTRACT FROM AUTHOR]

Determining an optimal decision model is an important but difficult combinatorial task in imbalanced microarray-based cancer classification. Though the multiclass support vector machine (MCSVM) has already made an important contribution in this field, its performance solely depends on three aspects: the penalty factor C, the type of kernel, and its parameters. To improve the performance of this classifier in microarray-based cancer analysis, this paper proposes PSO-PCA-LGP-MCSVM model that is based on particle swarm optimization (PSO), principal component analysis (PCA), and multiclass support vector machine (MCSVM). The MCSVM is based on a hybrid kernel, i.e., linear-Gaussian-polynomial (LGP) that combines the advantages of three standard kernels (linear, Gaussian, and polynomial) in a novel manner, where the linear kernel is linearly combined with the Gaussian kernel embedding the polynomial kernel. Further, this paper proves and makes sure that the LGP kernel confirms the features of a valid kernel. In order to reveal the effectiveness of our model, several experiments were conducted and the obtained results compared between our model and other three single kernel-based models, namely, PSO-PCA-L-MCSVM (utilizing a linear kernel), PSO-PCA-G-MCSVM (utilizing a Gaussian kernel), and PSO-PCA-P-MCSVM (utilizing a polynomial kernel). In comparison, two dual and two multiclass imbalanced standard microarray datasets were used. Experimental results in terms of three extended assessment metrics (F-score, G-mean, and Accuracy) reveal the superior global feature extraction, prediction, and learning abilities of this model against three single kernel-based models. [ABSTRACT FROM AUTHOR]

Introduction. Studies on the pharyngeal airway space (PAS) changes using three-dimensional computed tomography (CT) have shed more light on patients with Class III than Class II malocclusion. This paper focuses on analyzing the long-term changes in the PAS and evaluating the postoperative association between these PAS and skeletal changes in patients with skeletal Class II malocclusion who have undergone orthognathic surgery. Methods. The records of 21 patients with skeletal Class II malocclusion who had undergone orthognathic surgery were included. The anatomical modifications in both jaws, changes in volume, sectional area (SA), minimum sectional area (MSA), and anterior-posterior (AP) and transverse (TV) width in the airway at one month before surgery (T 0), and one month (T 1) and one year (T 2) after surgery were analyzed using CT images. The association between the skeletal and airway changes was evaluated between T 0 , T 1 , and T 2. Results. After surgery, the ANS, A point, and PNS demonstrated significant posterior and superior movement. The B point and the pogonion exhibited substantial anterior and superior movement. The total and inferior oropharyngeal volumes (vol 3, vol 4) notably increased, while the nasopharyngeal volume (vol 1) decreased. The anterior-posterior movement at the ANS and PNS after surgery was significantly associated with the total volume, vol 2, vol 3, SA 1, MSA, and TV width 1, while substantial association with the total volume was found at the pogonion. Conclusion. Thus, an ideal treatment plan can be formulated for patients with skeletal Class II malocclusion by considering the postoperative PAS changes. [ABSTRACT FROM AUTHOR]

This study is aimed at modeling biodigestion systems as a function of the most influencing parameters to generate two robust algorithms on the basis of the machine learning algorithms, including adaptive network-based fuzzy inference system (ANFIS) and least square support vector machine (LSSVM). The models are assessed utilizing multiple statistical analyses for the actual values and model outcomes. Results from the suggested models indicate their great capability of predicting biogas production from vegetable food, fruits, and wastes for a variety of ranges of input parameters. The values that are calculated for the mean relative error (MRE %) and mean squared error (MSE) were 29.318 and 0.0039 for ANFIS, and 2.951 and 0.0001 for LSSVM which shows that the latter model has a better ability to predict the target data. Finally, in order to have additional certainty, two analyses of outlier identification and sensitivity were performed on the input parameter data that proved the proposed model in this paper has higher reliability in assessing output values compared with the previous model. [ABSTRACT FROM AUTHOR]

In this paper, the Trolox equivalent antioxidant capacity (TEAC) is estimated through a robust machine-learning algorithm known as the Particle Swarm Optimization-based Extreme Learning Machine (PSO-ELM) model. For this purpose, a large dataset from previously published reports was gathered. Various analyses were performed to evaluate the proposed model. The results of the statistical analysis showed that this model can predict the actual values with high accuracy, so that the calculated R 2 and RMSE values were equal to 0.973 and 3.56, respectively. Sensitivity analysis was also performed on the effective input parameters. The leverage technique was also performed to check the accuracy of real data, and the results showed that the majority of data are reliable. This simple yet accurate model can be very powerful in predicting the Trolox equivalent antioxidant capacity values and can be a good alternative to laboratory data. [ABSTRACT FROM AUTHOR]

The clinical research faces numerous challenges, from patient enrollment to data privacy concerns and regulatory requirements to spiraling costs. Blockchain technology has the potential to overcome these challenges, thus making clinical trials transparent and enhancing public trust in a fair and open process with all stakeholders because of its distinct features such as data immutability and transparency. This paper proposes a permissioned blockchain platform to ensure clinical data transparency and provides secure clinical trial-related solutions. We explore the core functionalities of blockchain applied to clinical trials and illustrate its general principle concretely. These clinical trial operations are automated using the smart contract, which ensures traceability, prevents a posteriori reconstruction, and securely automates the clinical trial. A web-based user interface is also implemented to visualize the data from the blockchain and ease the interaction with the blockchain network. A proof of concept is implemented on Hyperledger Fabric in the case study of clinical management for multiple clinical trials to demonstrate the designed approach's feasibility. Lastly, the experiment results demonstrate the efficiency and usability of the proposed platform. [ABSTRACT FROM AUTHOR]

Purpose. The potential of UV-mediated photofunctionalization to enhance the resin-based luting agent bonding performance to aged materials was investigated. Methods. Sixty samples of each material were prepared. Yttria-stabilized zirconia (YZr) and Pd-Au alloy (Pd-Au) plates were fabricated and sandblasted. Lithium disilicate glass-ceramic (LDS) was CAD-CAM prepared and ground with #800 SiC paper. Half of the specimens were immersed in machine oil for 24 h to simulate the carbon adsorption. Then, all of the specimens (noncarbon- and carbon-adsorbed) were submitted to UV-mediated photofunctionalization with a 15 W UV-LED (265 nm, 300 mA, 7692 μW/cm2) for 0 (control groups), 5, and 15 min and subjected to contact angle (Ɵ) measurement and bonded using a resin cement (Panavia™ V5, Kuraray Noritake, Japan). The tensile bond strength (TBS) test was performed after 24 h. The Ɵ (°) and TBS (MPa) data were statistically analyzed using two-way ANOVA and Bonferroni correction tests (α = 0.05). Results. In the carbon-adsorbed groups, UV-mediated photofunctionalization for 5 min significantly decreased Ɵ of all materials and increased TBS of YZr, and UV for 15 min significantly increased the TBS of LDS and Pd-Au. In noncarbon-adsorbed groups, UV-photofunctionalization did not significantly change the Ɵ or TBS except YZr specimens UV-photofunctionalized for 15 min. Conclusion. UV-mediated photofunctionalization might have removed the adsorbed hydrocarbon molecules from the materials' surfaces and enhanced bond strengths of Panavia™ V5 to YZr, LDS, and Pd-Au. Additionally, UV-mediated photofunctionalization improved the overall TBS of YZr. Further investigation on the optimum conditions of UV photofunctionalization on indirect restorative materials should be conducted. [ABSTRACT FROM AUTHOR]

Cancer is a complex disease residing in various tissues of human body, accompanied with many abnormalities and mutations in genomes, transcriptome, and epigenome. Early detection plays a crucial role in extending survival time of all major cancer types. Recent advances in microarray and sequencing techniques have given more support to identifying effective biomarkers for early detection of cancer. MicroRNAs (miRNAs) are more and more frequently used as candidates for biomarkers in cancer related studies due to their regulation of target gene expression. In this paper, the comparative analysis is used to discover miRNA expression patterns in cancer versus normal samples on early stage of eight prevalent cancer types. Our work focuses on the specific miRNAs biomarkers identification and function analysis. Several identified miRNA biomarkers in this paper are matched well with those reported in existing researches, and most of them could serve as potential candidate indicators for clinical early diagnosis applications. [ABSTRACT FROM AUTHOR]

Nosocomial infections, in lay term known as hospital acquired infections, are caused mainly by airborne pathogens found in healthcare facilities and their surroundings. The aim of this study was to quantify and identify bacteria and fungi in a hospital, which is an understudied area of air quality in Sri Lanka. Air samples were collected in agar medium and petri plates containing sterile filter papers. The number of culturable and total airborne microorganisms was estimated by manual counting and fluorescent microscopy, respectively. The morphologically distant bacteria and fungi were identified by DNA sequencing. The statistical analysis revealed significant variances between studied sites (p < 0.05) where Outpatients Department and Respiratory Unit showed higher levels of airborne microbial load. Culturable microbial count was higher at noon (hospital visiting hours) compared to other sampling periods (after hospital visiting hours) within the hospital. Total count of airborne microbes was found to be the highest during the afternoon. The most sensitive zones such as Operating Theatre and Intensive Care Unit showed considerably higher counts of airborne microbes. Identification by molecular means revealed the presence of human pathogens in the hospital air including Bacillus sp, Micrococcus sp, Pseudomonas sp, Staphylococcu ssp, Exiguobacterium sp, Enterobacter sp, Escherichia sp, Sphingomonas sp, Massilia sp, Kocuria sp, Fusarium sp, and Aspergillus sp. In conclusion, the results from this study indicate that the hospital air was generally contaminated. Therefore, the implementation of proactive policies and strategies are needed to monitor hospital air quality in sensitive zones as well as other areas of the hospitals. [ABSTRACT FROM AUTHOR]

Background. One of the fatal disorders causing death is malignant melanoma, the deadliest form of skin cancer. The aim of the modern dermatology is the early detection of skin cancer, which usually results in reducing the mortality rate and less extensive treatment. This paper presents a study on classification of melanoma in the early stage of development using SVMs as a useful technique for data classification. Method. In this paper an automatic algorithm for the classification of melanomas in their early stage, with a diameter under 5 mm, has been presented. The system contains the following steps: image enhancement, lesion segmentation, feature calculation and selection, and classification stage using SVMs. Results. The algorithm has been tested on 200 images including 70 melanomas and 130 benign lesions. The SVM classifier achieved sensitivity of 90% and specificity of 96%. The results indicate that the proposed approach captured most of the malignant cases and could provide reliable information for effective skin mole examination. Conclusions. Micro-melanomas due to the small size and low advancement of development create enormous difficulties during the diagnosis even for experts. The use of advanced equipment and sophisticated computer systems can help in the early diagnosis of skin lesions. [ABSTRACT FROM AUTHOR]

This paper proposes a method using multidomain features and support vector machine (SVM) for classifying normal and abnormal heart sound recordings. The database was provided by the PhysioNet/CinC Challenge 2016. A total of 515 features are extracted from nine feature domains, i.e., time interval, frequency spectrum of states, state amplitude, energy, frequency spectrum of records, cepstrum, cyclostationarity, high-order statistics, and entropy. Correlation analysis is conducted to quantify the feature discrimination abilities, and the results show that “frequency spectrum of state”, “energy”, and “entropy” are top domains to contribute effective features. A SVM with radial basis kernel function was trained for signal quality estimation and classification. The SVM classifier is independently trained and tested by many groups of top features. It shows the average of sensitivity, specificity, and overall score are high up to 0.88, 0.87, and 0.88, respectively, when top 400 features are used. This score is competitive to the best previous scores. The classifier has very good performance with even small number of top features for training and it has stable output regardless of randomly selected features for training. These simulations demonstrate that the proposed features and SVM classifier are jointly powerful for classifying heart sound recordings. [ABSTRACT FROM AUTHOR]

Data quality is an important issue in cancer registration. This paper provides a comprehensive overview of the four main data quality indicators (comparability, validity, timeliness, and completeness) for the Cancer Registry Zurich and Zug (Switzerland). We extracted all malignant cancer cases (excluding non-melanoma skin cancer) diagnosed between 1980 and 2014 in the canton of Zurich. Methods included the proportion of morphologically verified cases (MV%), the proportion of DCN and DCO cases (2009–2014), cases with primary site uncertain (PSU%), the stability of incidence rates over time, age-specific incidence rates for childhood cancer, and mortality:incidence (MI) ratios. The DCO rate decreased from 6.4% in 1997 to 0.8% in 2014 and was <5% since 2000. MV% was 95.5% in 2014. PSU% was <3% over the whole period. The incidence rate of all tumours increased over time with site-specific fluctuations. The overall M:I ratio decreased from 0.58 in 1980 to 0.37 in 2014. Overall, data quality of the Cancer Registry Zurich and Zug was acceptable according to the methods presented in this review. Most indicators improved over time with low DCO rates, high MV%, low PSU%, relatively low M:I ratios and age-specific incidence of childhood cancer within reference ranges. [ABSTRACT FROM AUTHOR]

This study evaluated the effect of air abrasion before and after sintering with different particle type, shape, and size on the surface morphology, monoclinic phase transformation, and bond strength between resin cement and zirconia surface using primer containing silane and MDP. Airborne particle abrasion (APA) was performed on zirconia before and after sintering with different particle shape and size (50 μm Al2O3 and 25 μm silica powder). 120 square shaped presintered zirconia samples (Amann Girrbach) were prepared (3 mm height × 10 mm width × 10 mm length) and polished with grit papers #800, 1000, 1200, 1500, and 2000. Samples were divided into 6 groups according to surface treatment—group A: (control) no surface treatment; group B: APA 50 μm Al2O3 before sintering (BS); group C: APA 50 μm Al2O3 after sintering (AS); group D: APA25 μm silica powder (BS); group E: APA25 μm silica powder (AS) at a pressure of 3.5 bar; and group F: APA 25 μm silica powder (AS) at a pressure of 4 bar. Samples were analyzed using XRD, AFM, and SEM. The samples were submitted to shear bond strength (SBS) test. A dual cure resin cement (RelyX Ultimate) and primer (Scotchbond Universal) were used. Data were analyzed with ANOVA and Tukey test (α≥0.05). APA in group B significantly increased the surface roughness when compared to all other groups. A significant monoclinic phase transformation (t-m) value was observed in groups C and F and a reverse transformation occurred in presintered groups. The SBS value of group A was 11.58±1.43 and the highest significant shear bond strength value was for groups B (15.86±1.92) and C (17.59±2.21 MPa) with no significant difference between them. Conclusions. The use of APA 50 μm Al2O3 before sintering and the application of primer containing MDP seem to be valuable methods for durable bonding with zirconia. The use of APA 50 μm Al2O3 after sintering induced the highest (t-m) phase transformation. [ABSTRACT FROM AUTHOR]

This paper introduces a method for feature extraction and emotion recognition based on empirical mode decomposition (EMD). By using EMD, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) automatically. Multidimensional information of IMF is utilized as features, the first difference of time series, the first difference of phase, and the normalized energy. The performance of the proposed method is verified on a publicly available emotional database. The results show that the three features are effective for emotion recognition. The role of each IMF is inquired and we find that high frequency component IMF1 has significant effect on different emotional states detection. The informative electrodes based on EMD strategy are analyzed. In addition, the classification accuracy of the proposed method is compared with several classical techniques, including fractal dimension (FD), sample entropy, differential entropy, and discrete wavelet transform (DWT). Experiment results on DEAP datasets demonstrate that our method can improve emotion recognition performance. [ABSTRACT FROM AUTHOR]

Identification of differentially expressed (DE) genes with two or more conditions is an important task for discovery of few biomarker genes. Significance Analysis of Microarrays (SAM) is a popular statistical approach for identification of DE genes for both small- and large-sample cases. However, it is sensitive to outlying gene expressions and produces low power in presence of outliers. Therefore, in this paper, an attempt is made to robustify the SAM approach using the minimum β-divergence estimators instead of the maximum likelihood estimators of the parameters. We demonstrated the performance of the proposed method in a comparison of some other popular statistical methods such as ANOVA, SAM, LIMMA, KW, EBarrays, GaGa, and BRIDGE using both simulated and real gene expression datasets. We observe that all methods show good and almost equal performance in absence of outliers for the large-sample cases, while in the small-sample cases only three methods (SAM, LIMMA, and proposed) show almost equal and better performance than others with two or more conditions. However, in the presence of outliers, on an average, only the proposed method performs better than others for both small- and large-sample cases with each condition. [ABSTRACT FROM AUTHOR]

Essential oil of Eucalyptus globulus presents several pharmacological properties. However, their therapeutic efficacy may be affected by limitations due to several conditions, rendering it difficult to obtain stable and effective pharmaceutical formulations. The use of nanotechnology is an alternative to improve their characteristics aiming to ensure their stability and effectiveness. Furthermore, studies about the possible toxic effects of nanostructures are necessary to evaluate safety when the formulation comes into contact with human cells. Hence, in this paper, we evaluate for the first time the stability and in vitro cytogenotoxicity of nanoemulsions containing Eucalyptus globulus in peripheral blood mononuclear cells. As a result, the stability study found that the best condition for storage up to 90 days was refrigeration (4°C); it was the condition that best preserved the nanometric features. The content of the major compounds of oil was maintained after nanoencapsulation and preserved over time. In tests to evaluate the safety of this formulation, we can conclude that, at a low concentration (approximately 0.1%), Eucalyptus globulus nanoemulsion did not cause toxicity in peripheral blood mononuclear cells and also showed a protective effect in cells against possible damage when compared to oil in free form. [ABSTRACT FROM AUTHOR]

The aim of the paper is to identify the breast malignant and benign lesions using the features of apparent diffusion coefficient (ADC), perfusion fraction f, pseudodiffusion coefficient D⁎, and true diffusion coefficient D from intravoxel incoherent motion (IVIM). There are 69 malignant cases (including 9 early malignant cases) and 35 benign breast cases who underwent diffusion-weighted MRI at 3.0 T with 8 b-values (0~1000 s/mm2). ADC and IVIM parameters were determined in lesions. The early malignant cases are used as advanced malignant and benign tumors, respectively, so as to assess the effectiveness on the result. A predictive model was constructed using Support Vector Machine Binary Classification (SVMBC, also known Support Vector Machine Discriminant Analysis (SVMDA)) and Partial Least Squares Discriminant Analysis (PLSDA) and compared the difference between them both. The D value and ADC provide accurate identification of malignant lesions with b=300, if early malignant tumor was considered as advanced malignant (cancer). The classification accuracy is 93.5% for cross-validation using SVMBC with ADC and tissue diffusivity only. The sensitivity and specificity are 100% and 87.0%, respectively, r2cv=0.8163, and root mean square error of cross-validation (RMSECV) is 0.043. ADC and IVIM provide quantitative measurement of tissue diffusivity for cellularity and are helpful with the method of SVMBC, getting comprehensive and complementary information for differentiation between benign and malignant breast lesions. [ABSTRACT FROM AUTHOR]

Metabolomics is the sophisticated and high-throughput technology based on the entire set of metabolites which is known as the connector between genotypes and phenotypes. For any phenotypic changes, potential metabolite (biomarker) identification is very important because it provides diagnostic as well as prognostic markers and can help to develop new biomolecular therapy. Biomarker identification from metabolomics data analysis is hampered by the use of high-throughput technology that provides high dimensional data matrix which contains missing values as well as outliers. However, missing value imputation and outliers handling techniques play important role in identifying biomarker correctly. Although several missing value imputation techniques are available, outliers deteriorate the accuracy of imputation as well as the accuracy of biomarker identification. Therefore, in this paper we have proposed a new biomarker identification technique combining the groupwise robust singular value decomposition, t-test, and fold-change approach that can identify biomarkers more correctly from metabolomics dataset. We have also compared the performance of the proposed technique with those of other traditional techniques for biomarker identification using both simulated and real data analysis in absence and presence of outliers. Using our proposed method in hepatocellular carcinoma (HCC) dataset, we have also identified the four upregulated and two downregulated metabolites as potential metabolomic biomarkers for HCC disease. [ABSTRACT FROM AUTHOR]

With the explosion of healthcare information, there has been a tremendous amount of heterogeneous textual medical knowledge (TMK), which plays an essential role in healthcare information systems. Existing works for integrating and utilizing the TMK mainly focus on straightforward connections establishment and pay less attention to make computers interpret and retrieve knowledge correctly and quickly. In this paper, we explore a novel model to organize and integrate the TMK into conceptual graphs. We then employ a framework to automatically retrieve knowledge in knowledge graphs with a high precision. In order to perform reasonable inference on knowledge graphs, we propose a contextual inference pruning algorithm to achieve efficient chain inference. Our algorithm achieves a better inference result with precision and recall of 92% and 96%, respectively, which can avoid most of the meaningless inferences. In addition, we implement two prototypes and provide services, and the results show our approach is practical and effective. [ABSTRACT FROM AUTHOR]

Nicotinamide N-methyltransferase (NNMT) catalyzes the methylation of nicotinamide. Our previous works indicate that NNMT is involved in the body mass index and energy metabolism, and recently the association between a SNP (rs694539) of NNMT and a variety of cardiovascular diseases was reported. At present, more than 200 NNMT single nucleotide polymorphisms (SNPs) have been identified in the databases of the human genome projects; however, the association between rs694539 variation and hyperlipidemia has not been reported yet, and whether there are any SNPs in NNMT significantly associated with hyperlipidemia is still unclear. In this paper, we selected 19 SNPs in NNMT as the tagSNPs using Haploview software (Haploview 4.2) first and then performed a case-control study to observe the association between these tagSNPs and hyperlipidemia and finally applied physiological approaches to explore the possible mechanisms through which the NNMT polymorphism induces hyperlipidemia. The results show that a SNP (rs1941404) in NNMT is significantly associated with hyperlipidemia, and the influence of rs1941404 variation on the resting energy expenditure may be the possible mechanism for rs1941404 variation to induce hyperlipidemia. [ABSTRACT FROM AUTHOR]

This paper investigates the protective effect of interleukin-1 receptor antagonist (IL-1Ra) released from hyaluronic acid chitosan (HA-CS) microspheres in a controlled manner on IL-1β-induced inflammation and apoptosis in chondrocytes. The IL-1Ra release kinetics was characterized by an initial burst release, which was reduced to a linear release over eight days. Chondrocytes were stimulated with 10 ng/ml IL-1β and subsequently incubated with HA-CS-IL-1Ra microspheres. The cell viability was decreased by IL-1β, which was attenuated by HA-CS-IL-1Ra microspheres as indicated by an MTT assay. ELISA showed that HA-CS-IL-1Ra microspheres inhibited IL-1β-induced inflammation by attenuating increases in NO2- and prostaglandin E2 levels as well as increase in glycosaminoglycan release. A terminal deoxyribonucleotide transferase deoxyuridine triphosphate nick-end labeling assay revealed that the IL-1β-induced chondrocyte apoptosis was decreased by HA-CS-IL-1Ra microspheres. Moreover, HA-CS-IL-1Ra microspheres blocked IL-1β-induced chondrocyte apoptosis by increasing B-cell lymphoma 2 (Bcl-2) and decreasing Bcl-2-associated X protein and caspase-3 expressions at mRNA and protein levels, as indicated by reverse-transcription quantitative polymerase chain reaction and western blot analysis, respectively. The results of the present study indicated that HA-CS-IL-1Ra microspheres as a controlled release system of IL-1Ra possess potential anti-inflammatory and antiapoptotic properties in rat chondrocytes due to their ability to regulate inflammatory factors and apoptosis associated genes. [ABSTRACT FROM AUTHOR]

Sublethal concentrations (sub-MICs) of certain disinfectants are no longer effective in removing biofilms from abiotic surfaces and can even promote the formation of biofilms. Bacterial cells can probably adapt to these low concentrations of disinfectants and defend themselves by way of biofilm formation. In this paper, we report on three Staphylococcus aureus biofilm formers (strong B+++, moderate B++, and weak B+) that were cultivated with sub-MICs of commonly used disinfectants, ethanol or chloramine T, and quantified using Syto9 green fluorogenic nucleic acid stain. We demonstrate that 1.25–2.5% ethanol and 2500 μg/mL chloramine T significantly enhanced S. aureus biofilm formation. To visualize differences in biofilm compactness between S. aureus biofilms in control medium, 1.25% ethanol, or 2500 μg/mL chloramine T, scanning electron microscopy was used. To describe changes in abundance of surface-exposed proteins in ethanol- or chloramine T-treated biofilms, surface proteins were prepared using a novel trypsin shaving approach and quantified after dimethyl labeling by LC-LTQ/Orbitrap MS. Our data show that some proteins with adhesive functions and others with cell maintenance functions and virulence factor EsxA were significantly upregulated by both treatments. In contrast, immunoglobulin-binding protein A was significantly downregulated for both disinfectants. Significant differences were observed in the effect of the two disinfectants on the expression of surface proteins including some adhesins, foldase protein PrsA, and two virulence factors. [ABSTRACT FROM AUTHOR]

Objective. The objective of this paper is to compare the impact of supervised walking and resistance training upon the walking distance in PAD patients. Materials and Methods. The examination involved 50 PAD patients at the 2nd stage of the disease according to Fontaine’s scale. The participants were randomly allocated to two groups: one exercising on the treadmill (n=24) and one performing resistance exercises of lower limbs (n=26). Results. The 12-week program of supervised rehabilitation led to a significant increase in the intermittent claudication distance measured both on the treadmill and during the 6-minute walking test. The group training on the treadmill showed a statistically significant increase of the initial claudication distance (ICD) and the absolute claudication distance (ACD) measured on the treadmill, as well as of ICD and the total walking distance (TWD) measured during the 6-minute walking test. Within the group performing resistance exercises, a statistically significant improvement was observed in the case of parameters measured on the treadmill: ICD and ACD. Conclusions. The supervised rehabilitation program, in the form of both walking and resistance exercises, contributes to the increase in the intermittent claudication distance. The results obtained in both groups were similar. [ABSTRACT FROM AUTHOR]

Objective. To confirm that physical activity program improves the symptoms of dementia and the most effective physical activity was selected to help establish exercise programs. Methods. Three databases, PubMed, Science Direct, and Willey online, were used to collect articles. The databases were published between January 2005 and December 2015. Keywords such as “dementia,” and “physical activity” were used in searching for papers. As a result, nine studies were selected in the second screening of the meta-analyses. Results. The improvement in the dementia symptom of physical capacity was 1.05 (high effect size, 95% CI: 0.03 to 0.73), ability of activity of daily living was 0.73 (slightly high effect size, 95% CI: 0.23 to 1.23), cognitive function was 0.46 (medium effect size, 95% CI: 0.26 to 0.66), and psychological state was 0.39 (lower than the medium effect size, 95% CI: 0.01 to 0.77). Conclusion. The physical activity for patients with dementia had an effect on the improvement of physical capacity and combined exercise was the most effective physical activity. [ABSTRACT FROM AUTHOR]

Gene expression data composed of thousands of genes play an important role in classification platforms and disease diagnosis. Hence, it is vital to select a small subset of salient features over a large number of gene expression data. Lately, many researchers devote themselves to feature selection using diverse computational intelligence methods. However, in the progress of selecting informative genes, many computational methods face difficulties in selecting small subsets for cancer classification due to the huge number of genes (high dimension) compared to the small number of samples, noisy genes, and irrelevant genes. In this paper, we propose a new hybrid algorithm HICATS incorporating imperialist competition algorithm (ICA) which performs global search and tabu search (TS) that conducts fine-tuned search. In order to verify the performance of the proposed algorithm HICATS, we have tested it on 10 well-known benchmark gene expression classification datasets with dimensions varying from 2308 to 12600. The performance of our proposed method proved to be superior to other related works including the conventional version of binary optimization algorithm in terms of classification accuracy and the number of selected genes. [ABSTRACT FROM AUTHOR]

Objectives. The aim was to investigate the effects of three different types of resistance training implementation. Design. Randomized controlled trial. Methods. Inactive, overweight women (n=143), mean BMI 31.3±5.2 kg/m2, mean age 39.9±10.5 years, were randomized to one of the following groups: A (BodyPump group training), B (individual follow-up by a personal trainer), C (nonsupervised exercise), or D (controls). The intervention included 12 weeks of 45–60 minutes’ full-body resistance training three sessions per week. The outcomes in this paper are all secondary outcome measures: exercise motivation, self-perceived health, and quality of life. Results. Adherence averaged 26.1±10.3 of 36 prescribed sessions. After the intervention period, all three training groups (A–C) had better scores on exercise motivation (A=43.9±19.8, B=47.6±15.4, C=48.4±17.8) compared to the control group (D) (26.5±18.2) (p<0.001). Groups B and C scored better on self-perceived health (B=1.9±0.8, C=2.3±0.8), compared to group D (3.0±0.6) (p<0.001). For quality of life measurement, there was no statistically significant difference between either intervention groups or the control. Conclusions. Resistance training contributed to higher scores in important variables related to exercise motivation and self-perceived health. Low adherence showed that it was difficult to motivate previously inactive, overweight women to participate in regular strength training. [ABSTRACT FROM AUTHOR]

Genome assembly in its two decades of history has produced significant research, in terms of both biotechnology and computational biology. This contribution delineates sequencing platforms and their characteristics, examines key steps involved in filtering and processing raw data, explains assembly frameworks, and discusses quality statistics for the assessment of the assembled sequence. Furthermore, the paper explores recent Ubuntu-based software environments oriented towards genome assembly as well as some avenues for future research. [ABSTRACT FROM AUTHOR]

A new fungus Cladosporium oxysporum GQ-3 producing extracellular xylanase was isolated from decaying agricultural waste and identified based on the morphology and comparison of internal transcribed spacer (ITS) rDNA gene sequence. C. oxysporum produced maximum xylanase activity of 55.92 U/mL with wheat bran as a substrate and NH4Cl as a nitrogen source. Mg2+ improved C. oxysporum xylanase production. Partially purified xylanase exhibited maximum activity at 50°C and pH 8.0, respectively, and showed the stable activity after 2-h treatment in pH 7.0–8.5 or below 55°C. Mg2+ enhanced the xylanase activity by 2% while Cu2+ had the highest inhibition ratio of 57.9%. Furthermore, C. oxysporum xylanase was resistant to most of tested neutral and alkaline proteases. Our findings indicated that Cladosporium oxysporum GQ-3 was a novel xylanase producer, which could be used in the textile processes or paper/feed industries. [ABSTRACT FROM AUTHOR]

The analysis and interpretation of high-resolution computed tomography (HRCT) images of the chest in the presence of interstitial lung disease (ILD) is a time-consuming task which requires experience. In this paper, a computer-aided diagnosis (CAD) scheme is proposed to assist radiologists in the differentiation of lung patterns associated with ILD and healthy lung parenchyma. Regions of interest were described by a set of texture attributes extracted using differential lacunarity (DLac) and classical methods of statistical texture analysis. The proposed strategy to compute DLac allowed a multiscale texture analysis, while maintaining sensitivity to small details. Support Vector Machines were employed to distinguish between lung patterns. Training and model selection were performed over a stratified 10-fold cross-validation (CV). Dimensional reduction was made based on stepwise regression (F-test, p value < 0.01) during CV. An accuracy of 95.8 ± 2.2% in the differentiation of normal lung pattern from ILD patterns and an overall accuracy of 94.5 ± 2.1% in a multiclass scenario revealed the potential of the proposed CAD in clinical practice. Experimental results showed that the performance of the CAD was improved by combining multiscale DLac with classical statistical texture analysis. [ABSTRACT FROM AUTHOR]

Purpose. The aim of the study is to demonstrate that the ocular dynamics of the anterior chamber of the eye can be estimated quantitatively by means of optical coherence tomography (OCT). Methods. A commercial high speed, high resolution optical coherence tomographer was used. The sequences of tomographic images of the iridocorneal angle of three subjects were captured and each image from the sequence was processed in MATLAB environment in order to detect and identify the contours of the cornea and iris.Thedata on pulsatile displacements of the cornea and iris and the changes of the depth of the gap between themwere retrieved from the sequences. Finally, the spectral analysis of the changes of these parameters was performed. Results. The results of the temporal and spectral analysis manifest the ocular microfluctuation that might be associated with breathing (manifested by 0.25Hz peak in the power spectra), heart rate (1-1.5Hz peak), and ocular hemodynamics (3.75-4.5Hz peak). Conclusions. This paper shows that the optical coherence tomography can be used as a tool for noninvasive estimation of the ocular dynamics of the anterior segment of the eye, but its usability in diagnostics of the ocular hemodynamics needs further investigations. [ABSTRACT FROM AUTHOR]

Postmarketing drug surveillance is a crucial aspect of the clinical research activities in pharmacovigilance and pharmacoepidemiology. Successful utilization of available Electronic Health Record (EHR) data can complement and strengthen postmarketing safety studies. In terms of the secondary use of EHRs, access and analysis of patient data across different domains are a critical factor; we address this data interoperability problem between EHR systems and clinical research systems in this paper. We demonstrate that this problem can be solved in an upper level with the use of common data elements in a standardized fashion so that clinical researchers can work with different EHR systems independently of the underlying information model. Postmarketing Safety Study Tool lets the clinical researchers extract data from different EHR systems by designing data collection set schemas through common data elements. The tool interacts with a semantic metadata registry through IHE data element exchange profile. Postmarketing Safety Study Tool and its supporting components have been implemented and deployed on the central data warehouse of the Lombardy region, Italy, which contains anonymized records of about 16 million patients with over 10-year longitudinal data on average. Clinical researchers in Roche validate the tool with real life use cases. [ABSTRACT FROM AUTHOR]

The Electronic Health Records for Clinical Research (EHR4CR) project aims to develop services and technology for the leverage reuse of Electronic Health Records with the purpose of improving the efficiency of clinical research processes. A pilot program was implemented to generate evidence of the value of using the EHR4CR platform. The user acceptance of the platform is a key success factor in driving the adoption of the EHR4CR platform; thus, it was decided to evaluate the user satisfaction. In this paper, we present the results of a user satisfaction evaluation for the EHR4CR multisite patient count cohort system. This study examined the ability of testers (n=22 and n=16 from 5 countries) to perform three main tasks (around 20 minutes per task), after a 30-minute period of self-training. The System Usability Scale score obtained was 55.83 (SD: 15.37), indicating a moderate user satisfaction. The responses to an additional satisfaction questionnaire were positive about the design of the interface and the required procedure to design a query. Nevertheless, the most complex of the three tasks proposed in this test was rated as difficult, indicating a need to improve the system regarding complicated queries. [ABSTRACT FROM AUTHOR]

The process for using statistical inference to establish personalized treatment strategies requires specific techniques for data-analysis that optimize the combination of competing therapies with candidate genetic features and characteristics of the patient and disease. A wide variety of methods have been developed. However, heretofore the usefulness of these recent advances has not been fully recognized by the oncology community, and the scope of their applications has not been summarized. In this paper, we provide an overview of statistical methods for establishing optimal treatment rules for personalized medicine and discuss specific examples in various medical contexts with oncology as an emphasis. We also point the reader to statistical software for implementation of the methods when available. [ABSTRACT FROM AUTHOR]

In genome-wide association study (GWAS), robust genetic association tests such as maximum of three CATTs (MAX3), each corresponding to recessive, additive, and dominant genetic models, the minimum p value of Pearson’s Chi-square test with 2 degrees of freedom, and CATT based on additive genetic model (MIN2), genetic model selection (GMS), and genetic model exclusion (GME) methods have been shown to provide better power performance under wide range of underlying genetic models. In this paper, we demonstrate how these robust tests can be applied to the replication study of GWAS and how the overall statistical significance can be evaluated using the combined test formed by p values of the discovery and replication studies. [ABSTRACT FROM AUTHOR]

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined. [ABSTRACT FROM AUTHOR]

Proteins of the same functional family (for example, kinases) may have significantly different lengths. It is an open question whether such variation in length is random or it appears as a response to some unknown evolutionary driving factors. The main purpose of this paper is to demonstrate existence of factors affecting prokaryotic gene lengths. We believe that the ranking of genomes according to lengths of their genes, followed by the calculation of coefficients of association between genome rank and genome property, is a reasonable approach in revealing such evolutionary driving factors. As we demonstrated earlier, our chosen approach, Bubble-sort, combines stability, accuracy, and computational efficiency as compared to other ranking methods. Application of Bubble Sort to the set of 1390 prokaryotic genomes confirmed that genes of Archaeal species are generally shorter than Bacterial ones. We observed that gene lengths are affected by various factors: within each domain, different phyla have preferences for short or long genes; thermophiles tend to have shorter genes than the soil-dwellers; halophiles tend to have longer genes. We also found that species with overrepresentation of cytosines and guanines in the third position of the codon (GC3 content) tend to have longer genes than species with low GC3 content. [ABSTRACT FROM AUTHOR]

While pulsed-field gel electrophoresis (PFGE) is recognized as the gold standard method for clonality analysis, MALDI-TOF MS has recently been spotlighted as an alternative tool for species identification. Herein, we compared the dendrograms of multi-drug-resistant (MDR) Acinetobacter baumannii isolates by using MALDI-TOF MS with those by using PFGE. We used direct colony and protein extraction methods for MALDI-TOF MS dendrograms. The isolates with identical PFGE patterns were grouped into different branches in MALDI-TOF MS dendrograms. Among the isolates that were classified as very close isolates in MALDI-TOF MS dendrogram, PFGE band patterns visually showed complete differences. We numeralized similarity among isolates by measuring distance levels. The Spearman rank correlation coefficient values were 0.449 and 0.297 between MALDI-TOF MS dendrogram using direct colony and protein extraction method versus PFGE, respectively. This study is the first paper focusing solely on the dendrogram function of MALDI-TOF MS compared with PFGE. Although MALDI-TOF MS is a promising tool to identify species in a rapid manner, our results showed that MALDI-TOF MS dendrograms could not substitute PFGE for MDR Acinetobacter baumannii clonality analysis. [ABSTRACT FROM AUTHOR]

Inflammatory disorders affect many people worldwide, and medicinal plants are used to ameliorate these health problems. This paper reports the antiedematogenic and analgesic evaluation of Copaifera langsdorffii Desf. leaves hydroethanolic extract (Cop) and two of its isolated compounds: quercetin-3-O-α-L-rhamnopyranosyl (quercitrin) and kaempferol-3-O-α-L-rhamnopyranosyl (afzelin). For that, the following experimental protocols were undertaken locomotor performance, writhing induced by acetic acid, antinociceptivity induced by formalin, hot plate latency, paw oedema induced by carrageenan and dextran, and cell migration induced by lipopolysaccharide (LPS), as well as the measurement of nitric oxide (NO), tumor necrosis factor alpha (TNF-α), interleukin 6 (IL-6), and interleukin 10 (IL-10) in macrophages. Neither the extract nor the isolated compounds displayed analgesic activity. The obtained results showed that C. langsdorffii extract possesses antiedematogenic properties acting on peripheral sites, whereas quercitrin and afzelin are not involved. Moreover, these properties are not associated with cell migration inhibition, TNF-α, IL-6, or IL-10 regulation. [ABSTRACT FROM AUTHOR]

Chitosan is a biopolymer derived from chitin which is naturally occurring in the exoskeleton of crustaceans. This paper reports dissolution and regeneration of chitosan by directly dissolving in an ionic liquid solvent, 1-butyl-3-methylimidazolium chloride (BMIMCl). This will provide an ideal platform to solubilise these kinds of polymers to achieve the dissolution. The current study dissolved chitosan from crab shell utilising BMIMCl as a solvent and characterised the resultant regenerated polymer. The regenerated chitosan showed increased hydrogen bonding when characterised by Fourier transform infrared (FTIR) spectral analysis. In addition, the study also compared the characteristics of regenerated and generic chitosan. The regenerated chitosan was also evaluated for antimicrobial properties and showed to possess antibacterial features similar to the commercial grade. This method can be utilised in future for blending of polymers with chitosan in a dissolved phase. [ABSTRACT FROM AUTHOR]

Support vector machine (SVM) is a powerful technique for classification. However, SVM is not suitable for classification of large datasets or text corpora, because the training complexity of SVMs is highly dependent on the input size. Recent developments in the literature on the SVM and other kernel methods emphasize the need to consider multiple kernels or parameterizations of kernels because they provide greater flexibility. This paper shows a multikernel SVM to manage highly dimensional data, providing an automatic parameterization with low computational cost and improving results against SVMs parameterized under a brute-force search. The model consists in spreading the dataset into cohesive term slices (clusters) to construct a defined structure (multikernel). The new approach is tested on different text corpora. Experimental results show that the new classifier has good accuracy compared with the classic SVM, while the training is significantly faster than several other SVM classifiers. [ABSTRACT FROM AUTHOR]

In order to understand disease pathogenesis, improve medical diagnosis, or discover effective drug targets, it is important to identify significant genes deeply involved in human disease. For this purpose, many earlier approaches attempted to prioritize candidate genes using gene expression profiles or SNP genotype data, but they often suffer from producing many false-positive results. To address this issue, in this paper, we propose a meta-analysis strategy for gene prioritization that employs three different genetic resources—gene expression data, single nucleotide polymorphism (SNP) genotype data, and expression quantitative trait loci (eQTL) data—in an integrative manner. For integration, we utilized an improved technique for the order of preference by similarity to ideal solution (TOPSIS) to combine scores from distinct resources. This method was evaluated on two publicly available datasets regarding prostate cancer and lung cancer to identify disease-related genes. Consequently, our proposed strategy for gene prioritization showed its superiority to conventional methods in discovering significant disease-related genes with several types of genetic resources, while making good use of potential complementarities among available resources. [ABSTRACT FROM AUTHOR]

Recently, we have found that a number of microRNAs (miRNAs) and proteins are involved in the response to acupuncture therapy in hypertensive rats. Our bioinformatics study suggests an association between these miRNAs and proteins, which include miR-339 and sirtuin 2 (Sirt2). In this paper, we aimed to investigate whether Sirt2 was a direct target of miR-339 in neurons. In human SH-SY5Y cells, the luciferase assay implied that Sirt2 was likely a target of miRNA-339. Overexpression of miR-339 downregulated Sirt2 expression, while knockdown of miR-339 upregulated Sirt2 expression in human SH-SY5Y cells and rat PC12 cells. In addition, overexpression of miR-399 increased the acetylation of nuclear factor-κB (NF-κB) and forkhead box protein O1 (FOXO1) in SH-SY5Y cells, which are known targets of Sirt2. Our findings demonstrate that miR-339 regulates Sirt2 in human and rat neurons. Since Sirt2 plays a critical role in multiple important cellular functions, our data imply that acupuncture may act through epigenetic changes and subsequent action on their targets, such as miRNA-339/Sirt2/NF-κB/FOXO1 axis. Some physiological level changes of neurons after altering the miR-339 levels are needed to validate the suggested therapeutic role of miR-339/Sirt2/NF-κB/FOXO1 axis in response to acupuncture therapy in the future work. [ABSTRACT FROM AUTHOR]

Prostate cancer is the second leading cause of cancer-related death. The androgen deprivation therapy is the standard treatment for advanced stages. Unfortunately, virtually all tumors become resistant to androgen withdrawal. The progression to castration-resistance is not fully understood, although a recent paper has suggested translationally controlled tumor protein to be implicated in the process. The present study was designed to investigate the role of this protein in prostate cancer, focusing on the correlation between its expression level with tumor differentiation and response to treatment. We retrieved 292 prostatic cancer specimens; of these 153 had been treated only by radical prostatectomy and 139 had undergone radical prostatectomy after neoadjuvant treatment with combined androgen blockade therapy. Non-neoplastic controls were represented by 102 prostatic peripheral zone specimens. In untreated patients, the expression of the protein, evaluated by RT-qPCR and immunohistochemistry, was significantly higher in tumor specimens than in non-neoplastic control, increasing as Gleason pattern and score progressed. In treated prostates, the staining was correlated with the response to treatment. An association between protein expression and the main clinicopathological factors involved in prostate cancer aggressiveness was identified. These findings suggest that the protein may be a promising prognostic factor and a target for therapy. [ABSTRACT FROM AUTHOR]

Background and Objectives. Adverse conditions in the prenatal environment and in the first years of life are independently associated with increased risk for cardiovascular disease. This paper aims to study the relation between birthweight, growth in the first year of life, and subclinical atherosclerosis in adults. Methods. 88 adults aged between 20 and 31 were submitted to sociodemographic qualities, anthropometric data, blood pressure measurements, metabolic profile, and evaluation of subclinical atherosclerosis. Results. Birthweight <2,500 grams (g) was negatively correlated with (a) increased waist-to-hip ratio (WHR), according to regression coefficient (RC) equal to −0.323, 95% CI [−0.571, −0.075] P<0.05; (b) diastolic blood pressure (RC = −4.744, 95% CI [−9.017, −0.470] P<0.05); (c) low HDL-cholesterol (RC = −0.272, 95% CI [−0.516, −0.029] P<0.05); (d) frequency of intima-media thickness (IMT) of left carotid >75th percentile (RC = −0.242, 95% CI [−0.476, −0.008] P<0.05). Birthweight >3,500 g was associated with (a) BMI >25.0 kg/m2, (RC = 0.317, 95% CI [0.782, 0.557] P<0.05); (b) increased waist circumference (RC = 0.284, 95% CI [0.054, 0.513] P<0.05); (c) elevated WHR (RC = 0.280, 95% CI [0.054, 0.505] P<0.05); (d) minimum subcutaneous adipose tissue (SAT) (RC = 4.354, 95% CI [0.821, 7.888] P<0.05); (e) maximum SAT (RC = 7.095, 95% CI [0.608, 13.583] P<0.05); (f) right lobe of the liver side (RC = 6.896, 95% CI [1.946, 11.847] P<0.001); (g) frequency’s right lobe of the liver >75th percentile (RC = 0.361, 95% CI [0.169, 0.552] P<0.001). Weight gain in the first year of life was inversely correlated with (a) mean IMT of left carotid (RC = −0.046, 95% CI [−0.086, −0.006] P<0.05; (b) frequency IMT of left carotid >75th percentile (RC = −0.253, 95% CI [−0.487, −0.018] P<0.05); (c) mean IMT (RC = −0.038, 95% CI [0.073, −0.002] P<0.05); (d) the frequency of the mean IMT >75th percentile (RC = −0.241, 95% CI [−0.442, −0.041] P<0.05). Conclusions. Adults birthweight <2,500 g and >3,500 g and with insufficient weight gain in the first year of life have showed different metabolic phenotypes, but all of them were related to subclinical atherosclerosis. [ABSTRACT FROM AUTHOR]