Biceps Pulley Lesions: Diagnostic Accuracy of Nonarthrographic Shoulder MRI and the Value of Various Diagnostic Signs.

BACKGROUND: There is limited data in the literature regarding the role of nonarthrographic MRI for detecting biceps pulley (BP) lesions. PURPOSE: To assess the accuracy of nonarthrographic MRI for detecting BP lesions, and to evaluate the diagnostic value of various MRI signs (superior glenohumeral ligament discontinuity/nonvisibility, long head of biceps (LHB) displacement sign or subluxation/dislocation, LHB tendinopathy, and supraspinatus and subscapularis tendon lesions) in detecting such lesions. STUDY TYPE: Retrospective. POPULATION: 84 patients (32 in BP-lesion group and 52 in BP-intact group-as confirmed by arthroscopy). FIELD STRENGTH/SEQUENCE: 1.5-T, T1-weighted turbo spin echo (TSE), T2-weighted TSE, and proton density-weighted TSE spectral attenuated inversion recovery (SPAIR) sequences. ASSESSMENT: Three radiologists independently reviewed all MRI data for the presence of BP lesions and various MRI signs. The MRI signs and final MRI diagnoses were tested for accuracy regarding detecting BP lesions using arthroscopy results as the reference standard. Furthermore, the inter-reader agreement (IRA) between radiologists was determined. STATISTICAL TESTS: Student's t-tests, Chi-squared, and Fisher's exact tests, and 4-fold table test were used. The IRA was calculated using Kappa statistics. A P-value <0.05 was considered statistically significant. RESULTS: The sensitivity, specificity, and accuracy of nonarthrographic MRI for detecting BP lesions were 65.6%-78.1%, 90.4%-92.3%, and 81%-86.9%, respectively. The highest accuracy was noticed for the LHB displacement sign (84.5%-86.9%), and the highest sensitivity was registered for the LHB tendinopathy sign (87.5%). Furthermore, the highest specificity was observed for the LHB displacement sign and LHB subluxation/dislocation sign (98.1%-100%). The IRA regarding final MRI diagnosis and MRI signs of BP lesions was good to very good (κ = 0.76-0.98). DATA CONCLUSION: Nonarthrographic shoulder MRI may show good diagnostic accuracy for detecting BP lesions. The LHB displacement sign could serve as the most accurate and specific sign for diagnosis of BP lesions. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy.

OBJECTIVES: Non-contrast computed tomography of the brain (NCCTB) is commonly used to detect intracranial pathology but is subject to interpretation errors. Machine learning can augment clinical decision-making and improve NCCTB scan interpretation. This retrospective detection accuracy study assessed the performance of radiologists assisted by a deep learning model and compared the standalone performance of the model with that of unassisted radiologists. METHODS: A deep learning model was trained on 212,484 NCCTB scans drawn from a private radiology group in Australia. Scans from inpatient, outpatient, and emergency settings were included. Scan inclusion criteria were age ≥ 18 years and series slice thickness ≤ 1.5 mm. Thirty-two radiologists reviewed 2848 scans with and without the assistance of the deep learning system and rated their confidence in the presence of each finding using a 7-point scale. Differences in AUC and Matthews correlation coefficient (MCC) were calculated using a ground-truth gold standard. RESULTS: The model demonstrated an average area under the receiver operating characteristic curve (AUC) of 0.93 across 144 NCCTB findings and significantly improved radiologist interpretation performance. Assisted and unassisted radiologists demonstrated an average AUC of 0.79 and 0.73 across 22 grouped parent findings and 0.72 and 0.68 across 189 child findings, respectively. When assisted by the model, radiologist AUC was significantly improved for 91 findings (158 findings were non-inferior), and reading time was significantly reduced. CONCLUSIONS: The assistance of a comprehensive deep learning model significantly improved radiologist detection accuracy across a wide range of clinical findings and demonstrated the potential to improve NCCTB interpretation. CLINICAL RELEVANCE STATEMENT: This study evaluated a comprehensive CT brain deep learning model, which performed strongly, improved the performance of radiologists, and reduced interpretation time. The model may reduce errors, improve efficiency, facilitate triage, and better enable the delivery of timely patient care. KEY POINTS: • This study demonstrated that the use of a comprehensive deep learning system assisted radiologists in the detection of a wide range of abnormalities on non-contrast brain computed tomography scans. • The deep learning model demonstrated an average area under the receiver operating characteristic curve of 0.93 across 144 findings and significantly improved radiologist interpretation performance. • The assistance of the comprehensive deep learning model significantly reduced the time required for radiologists to interpret computed tomography scans of the brain.

Sulconazole-Loaded Solid Lipid Nanoparticles for Enhanced Antifungal Activity: In Vitro and In Vivo Approach.

Solid lipid nanoparticles (SLNs) have the advantages of a cell-specific delivery and sustained release of hydrophobic drugs that can be exploited against infectious diseases. The topical delivery of hydrophobic drugs needs pharmaceutical strategies to enhance drug permeation, which is a challenge faced by conventional formulations containing a drug suspended in gel, creams or ointments. We report the fabrication and optimization of SLNs with sulconazole (SCZ) as a model hydrophobic drug and then a formulation of an SLN-based topical gel against fungal infections. The SLNs were optimized through excipients of glyceryl monostearate and Phospholipon® 90 H as lipids and tween 20 as a surfactant for its size, drug entrapment and sustained release and resistance against aggregation. The SCZ-SLNs were physically characterized for their particle size (89.81 ± 2.64), polydispersity index (0.311 ± 0.07), zeta potential (-26.98 ± 1.19) and encapsulation efficiency (86.52 ± 0.53). The SCZ-SLNs showed sustained release of 85.29% drug at the 12 h timepoint. The TEM results demonstrated spherical morphology, while DSC, XRD and FTIR showed the compatibility of the drug inside SLNs. SCZ-SLNs were incorporated into a gel using carbopol and were further optimized for their rheological behavior, pH, homogeneity and spreadability on the skin. The antifungal activity against Candida albicans and Trichophyton rubrum was increased in comparison to a SCZ carbopol-based gel. In vivo antifungal activity in rabbits presented faster healing of skin fungal infections. The histopathological examination of the treated skin from rabbits presented restoration of the dermal architecture. In summary, the approach of formulating SLNs into a topical gel presented an advantageous drug delivery system against mycosis.

Potential Association of Doxycycline With the Onset of Primary Sclerosing Cholangitis: A Case Series.

BACKGROUND: Primary sclerosing cholangitis (PSC) is linked to inflammatory bowel diseases (IBD). Evidence suggests an association between the gut microbiome and PSC. However, the putative relationship between exposure to antibiotics and onset of PSC has never been reported. We observed 3 cases in which patients without antecedent liver or bowel issues developed symptoms leading to diagnosis of IBD and subsequently PSC after being exposed to doxycycline. We aimed to identify, through the PSC Partners national patient registry, additional cases of PSC in which there is a temporal relationship between exposure to doxycycline and onset of PSC or PSC-IBD. AREAS OF UNCERTAINTY: The etiopathogenesis of PSC remains an enigma. DATA SOURCES: We collected data from patients with PSC and PSC-IBD in which there seemed to be a temporal relationship between exposure to doxycycline and PSC. Time from doxycycline exposure to: (1) onset of PSC or PSC-IBD symptoms and (2) diagnosis of PSC were documented for each patient. Descriptive statistical analyses were performed. RESULTS: We identified 6 additional patients with PSC or PSC-IBD in whom there was a temporal relationship between exposure to doxycycline and onset of PSC or PSC-IBD. The median age of these 9 patients was 20 years, 6 were female, and 7 had ulcerative colitis. The median time from doxycycline exposure to onset of first symptoms was 3 months, and median time from doxycycline exposure to diagnosis of PSC was 15 months. THERAPEUTIC HYPOTHESIS: We describe 9 cases of PSC and PSC-IBD in which there seem to be a temporal relationship between exposure to doxycycline and onset of PSC.

Mitochondrial Dysfunction Plays Central Role in Nonalcoholic Fatty Liver Disease.

Nonalcoholic fatty liver disease (NAFLD) is a global pandemic that affects one-quarter of the world's population. NAFLD includes a spectrum of progressive liver disease from steatosis to nonalcoholic steatohepatitis (NASH), fibrosis, and cirrhosis and can be complicated by hepatocellular carcinoma. It is strongly associated with metabolic syndromes, obesity, and type 2 diabetes, and it has been shown that metabolic dysregulation is central to its pathogenesis. Recently, it has been suggested that metabolic- (dysfunction) associated fatty liver disease (MAFLD) is a more appropriate term to describe the disease than NAFLD, which puts increased emphasis on the important role of metabolic dysfunction in its pathogenesis. There is strong evidence that mitochondrial dysfunction plays a significant role in the development and progression of NAFLD. Impaired mitochondrial fatty acid oxidation and, more recently, a reduction in mitochondrial quality, have been suggested to play a major role in NAFLD development and progression. In this review, we provide an overview of our current understanding of NAFLD and highlight how mitochondrial dysfunction contributes to its pathogenesis in both animal models and human subjects. Further we discuss evidence that the modification of mitochondrial function modulates NAFLD and that targeting mitochondria is a promising new avenue for drug development to treat NAFLD/NASH.

AI-driven deep convolutional neural networks for chest X-ray pathology identification.

BACKGROUND: Chest X-ray images are widely used to detect many different lung diseases. However, reading chest X-ray images to accurately detect and classify different lung diseases by doctors is often difficult with large inter-reader variability. Thus, there is a huge demand for developing computer-aided automated schemes of chest X-ray images to help doctors more accurately and efficiently detect lung diseases depicting on chest X-ray images. OBJECTIVE: To develop convolution neural network (CNN) based deep learning models and compare their feasibility and performance to classify 14 chest diseases or pathology patterns based on chest X-rays. METHOD: Several CNN models pre-trained using ImageNet dataset are modified as transfer learning models and applied to classify between 14 different chest pathology and normal chest patterns depicting on chest X-ray images. In this process, a deep convolution generative adversarial network (DC-GAN) is also trained to mitigate the effects of small or imbalanced dataset and generate synthetic images to balance the dataset of different diseases. The classification models are trained and tested using a large dataset involving 91,324 frontal-view chest X-ray images. RESULTS: In this study, eight models are trained and compared. Among them, ResNet-152 model achieves an accuracy of 67% and 62% with and without data augmentation, respectively. Inception-V3, NasNetLarge, Xcaption, ResNet-50 and InceptionResNetV2 achieve accuracy of 68%, 62%, 66%, 66% and 54% respectively. Additionally, Resnet-152 with data augmentation achieves an accuracy of 83% but only for six classes. CONCLUSION: This study solves the problem of having fewer data by using GAN-based techniques to add synthetic images and demonstrates the feasibility of applying transfer learning CNN method to help classify 14 types of chest diseases depicting on chest X-ray images.

Automated detection of diabetic retinopathy using custom convolutional neural network.

Diabetic retinopathy is an eye deficiency that affects retina as a result of the patient having diabetes mellitus caused by high sugar levels, which may eventually lead to macular edema. The objective of this study is to design and compare several deep learning models that detect severity of diabetic retinopathy, determine risk of leading to macular edema, and segment different types of disease patterns using retina images. Indian Diabetic Retinopathy Image Dataset (IDRiD) dataset was used for disease grading and segmentation. Since images of the dataset have different brightness and contrast, we employed three techniques for generating processed images from the original images, which include brightness, color and, contrast (BCC) enhancing, color jitters (CJ), and contrast limited adaptive histogram equalization (CLAHE). After image preporcessing, we used pre-trained ResNet50, VGG16, and VGG19 models on these different preprocessed images both for determining the severity of the retinopathy and also the chances of macular edema. UNet was also applied to segment different types of diseases. To train and test these models, image dataset was divided into training, testing, and validation data at 70%, 20%, and 10% ratios, respectively. During model training, data augmentation method was also applied to increase the number of training images. Study results show that for detecting the severity of retinopathy and macular edema, ResNet50 showed the best accuracy using BCC and original images with an accuracy of 60.2% and 82.5%, respectively, on validation dataset. In segmenting different types of diseases, UNet yielded the highest testing accuracy of 65.22% and 91.09% for microaneurysms and hard exudates using BCC images, 84.83% for optic disc using CJ images, 59.35% and 89.69% for hemorrhages and soft exudates using CLAHE images, respectively. Thus, image preprocessing can play an important role to improve efficacy and performance of deep learning models.

The PSC scientific community resource: an asset for multi-omics interrogation of primary sclerosing cholangitis.

BACKGROUND: Primary sclerosing cholangitis (PSC) is a rare, chronic cholestatic liver disease that often progresses to end-stage liver disease and/or the development of hepatobiliary neoplasia. Lack of prognostic tools and treatment options for PSC is driven in part by our poor understanding of its pathogenesis, which is thought to be complex, the interaction of genetic variants, environmental influences and biological response throughout the course of disease. The PSC Scientific Community Resource (PSC-SCR) seeks to overcome previous shortcomings by facilitating novel research in PSC with the ultimate goals of individualizing patient care and improving patient outcomes. METHODS: PSC patients who receive their health care at Mayo Clinic or a collaborating site are identified by chart review and invited in person or by mail to participate. Non-Mayo patients are offered enrollment if they provide sufficient access to their medical records to evaluate inclusion/exclusion criteria. Controls without liver disease are identified with assistance of the Mayo Clinic Biobank. Participant consent is obtained at the beginning of the recruitment process by mail-in, electronic or face-to-face protocols. Clinical data is extracted from the medical record by qualified physicians and entered in a custom designed database. Participants fill out a custom-designed, comprehensive questionnaire, which collects scientifically relevant demographic and clinical information. Biospecimens are collected using mail-in kits thar are returned via overnight carrier service and processed by the biospecimen accessioning and processing facility at Mayo Clinic, which coordinates sample transfers and provides required sample preparation services. The resource is currently being utilized to perform omics-scale projects investigating the exposome, metabolome, methylome, immunome and microbiome in PSC. Datasets and residual biospecimens will be shared with researchers proposing scientifically sound PSC-focused research with approval of the appropriate review boards. DISCUSSION: Patient-based studies leveraging the latest technologies for targeted and wide-scale interrogation of multiple omics layers offer promise to accelerate PSC research through discovery of unappreciated aspects of disease pathogenesis. However, the rarity of PSC severely limits such studies. Here we describe our effort to overcome this limitation, the PSC-SCR, a repository of patient biospecimens coupled with clinical and omics data for use by the broader PSC research community.

Fast and Accurate Detection of COVID-19 Along With 14 Other Chest Pathologies Using a Multi-Level Classification: Algorithm Development and Validation Study.

BACKGROUND: COVID-19 has spread very rapidly, and it is important to build a system that can detect it in order to help an overwhelmed health care system. Many research studies on chest diseases rely on the strengths of deep learning techniques. Although some of these studies used state-of-the-art techniques and were able to deliver promising results, these techniques are not very useful if they can detect only one type of disease without detecting the others. OBJECTIVE: The main objective of this study was to achieve a fast and more accurate diagnosis of COVID-19. This study proposes a diagnostic technique that classifies COVID-19 x-ray images from normal x-ray images and those specific to 14 other chest diseases. METHODS: In this paper, we propose a novel, multilevel pipeline, based on deep learning models, to detect COVID-19 along with other chest diseases based on x-ray images. This pipeline reduces the burden of a single network to classify a large number of classes. The deep learning models used in this study were pretrained on the ImageNet dataset, and transfer learning was used for fast training. The lungs and heart were segmented from the whole x-ray images and passed onto the first classifier that checks whether the x-ray is normal, COVID-19 affected, or characteristic of another chest disease. If it is neither a COVID-19 x-ray image nor a normal one, then the second classifier comes into action and classifies the image as one of the other 14 diseases. RESULTS: We show how our model uses state-of-the-art deep neural networks to achieve classification accuracy for COVID-19 along with 14 other chest diseases and normal cases based on x-ray images, which is competitive with currently used state-of-the-art models. Due to the lack of data in some classes such as COVID-19, we applied 10-fold cross-validation through the ResNet50 model. Our classification technique thus achieved an average training accuracy of 96.04% and test accuracy of 92.52% for the first level of classification (ie, 3 classes). For the second level of classification (ie, 14 classes), our technique achieved a maximum training accuracy of 88.52% and test accuracy of 66.634% by using ResNet50. We also found that when all the 16 classes were classified at once, the overall accuracy for COVID-19 detection decreased, which in the case of ResNet50 was 88.92% for training data and 71.905% for test data. CONCLUSIONS: Our proposed pipeline can detect COVID-19 with a higher accuracy along with detecting 14 other chest diseases based on x-ray images. This is achieved by dividing the classification task into multiple steps rather than classifying them collectively.

Current Understandings on Magnesium Deficiency and Future Outlooks for Sustainable Agriculture.

The productivity of agricultural produce is fairly dependent on the availability of nutrients and efficient use. Magnesium (Mg2+) is an essential macronutrient of living cells and is the second most prevalent free divalent cation in plants. Mg2+ plays a role in several physiological processes that support plant growth and development. However, it has been largely forgotten in fertilization management strategies to increase crop production, which leads to severe reductions in plant growth and yield. In this review, we discuss how the Mg2+ shortage induces several responses in plants at different levels: morphological, physiological, biochemical and molecular. Additionally, the Mg2+ uptake and transport mechanisms in different cellular organelles and the role of Mg2+ transporters in regulating Mg2+ homeostasis are also discussed. Overall, in this review, we critically summarize the available information about the responses of Mg deficiency on plant growth and development, which would facilitate plant scientists to create Mg2+-deficiency-resilient crops through agronomic and genetic biofortification.

Clinical outcome of Montelukast Sodium in Children with Adenoid Hypertrophy.

BACKGROUND & OBJECTIVES: Generally, the blockage of upper respiratory tract in children is seen with the hypertrophy of adenoids and tonsils. Normally for patients with adenoid hypertrophy (AH), Adenoidectomy with or without Tonsillectomy is carried out, however it has its own complications like haemorrhage and recurrence of adenoid tissue. Consequently, therapeutic approach has increased extraordinary consideration rather than surgical procedure. The inflammatory process proposed for AH has prompted the utilization of anti-inflammatory drugs to treat this issue. The objective of this study was to assess the impacts of Montelukast sodium in children with enlarged adenoids. METHODS: A randomized controlled trail was performed from April 2018 to March 2019 in the Otorhinolaryngology clinic of Dr. Akbar Niazi Teaching Hospital, Islamabad. In this randomized, placebo treatment-controlled trial, 60 children aged 4-12 years meeting inclusion criteria were isolated into two groups. The study group was treated with Montelukast sodium 5mg consistently for three months while the control group got placebo treatment for a similar timeframe. A questionnaire was filled by parents/ guardians of every child before and after the intervention to evaluate the severity of sleep discomfort, snoring and mouth breathing. RESULTS: Following 3 months of treatment, significant reduction in size of the adenoids was seen in 76% of study group compared with just 3% of control group getting placebo treatment. CONCLUSION: Montelukast sodium seems to be effective in the reduction of the size of adenoids and improvement in clinical manifestations. It can be viewed as a viable option in contrast to surgical treatment in children with hypertrophy of adenoids.

Open-label prospective therapeutic clinical trials: oral vancomycin in children and adults with primary sclerosing cholangitis.

BACKGROUND: Oral vancomycin (OV) in primary sclerosing cholangitis (PSC) has been evaluated as a potential therapeutic agent. We report the long-term biochemical course and outcomes of patients with PSC treated with OV. METHODS: Patients were enrolled in 2 open-label clinical trials (ClinicalTrials.gov Identifier: NCT01802073 and NCT01322386) and offered OV at 50 mg/kg/day in 3 divided doses if weight <30kg, and 500 mg 3 times/day if weight ≥30kg. Patients with biliary strictures requiring stenting or awaiting liver transplant were excluded. Liver biochemistry, MRCP and histology were documented at baseline and while on OV. The primary outcome was a decrease in elevated gamma glutamyl transferase (GGT), alkaline phosphatase (ALP), and/or alanine aminotransferase (ALT) from baseline. RESULTS: 30 subjects were enrolled, and 29 additional subjects who learned of the clinical trial requested OV (total n = 59; median age was 13.5 years [range, 1.5-44 years]; 64.4% were male; and 94.9% had inflammatory bowel disease [IBD]). The median treatment duration was 2.7 years (range, 0.2-14 years). Ninety-six percent (57/59), 81.3% (48/59), and 94.9% (56/59) experienced reduction of GGT, ALP, and ALT, respectively. Furthermore, 39% (23/59), 22% (13/59), and 55.9% (33/59) experienced normalization of GGT, ALP, and ALT, respectively, within the first 6 months of OV treatment. One patient underwent liver transplantation 8 years after beginning OV treatment, and one developed biliary strictures requiring endoscopic intervention. OV was well-tolerated by patients, and no patient developed treatment-related adverse events. CONCLUSION: In PSC, OV was well-tolerated and was associated with improvement in liver chemistry. A randomized placebo-controlled clinical trial is warranted.

Surveillance for hepatobiliary cancers in patients with primary sclerosing cholangitis.

Primary sclerosing cholangitis (PSC) is a risk factor for cholangiocarcinoma (CCA) and gallbladder carcinoma (GBCa). Surveillance for GBCa is recommended, but the clinical utility of surveillance for other hepatobiliary cancers (HBCa) in PSC, namely CCA and hepatocellular carcinoma (HCC), remains unclear. We aimed to determine whether surveillance is associated with better survival after diagnosis of HBCa in patients with PSC. Medical records of PSC patients seen at the Mayo Clinic Rochester from 1995 to 2015 were reviewed. Patients were included if they had ≥1 year of follow-up and developed HBCa. Patients were categorized according to their surveillance status (abdominal imaging, carbohydrate antigen 19-9, and alpha-fetoprotein). The primary endpoints were HBCa recurrence, HBCa-related death, and all-cause mortality. Overall survival was assessed by the Kaplan-Meier survival method; HBCa-related survival was assessed using competing risk regression. Tests of significance were two-tailed, and a P value <0.05 was considered statistically significant. From 1995 to 2015, a total of 79 of 830 PSC patients were diagnosed with HBCa. Cumulative follow-up was 712 and 283 person-years pre- and post-HBCa diagnosis, respectively. Seventy-eight percent of patients (54/79) developed CCA, 21% (17/79) HCC, 6% (5/79) GBCa, 3% (2/79) both CCA and HCC, and 1% (1/79) both HCC and GBCa. Fifty-one percent (40/79) were under HBCa surveillance, and 49% (39/79) were not. Patients in the surveillance group had significantly higher 5-year overall survival (68% versus 20%, respectively; P < 0.001) and significantly lower 5-year probability of experiencing an HBCa-related adverse event (32% versus 75%, respectively; P < 0.001) compared with the no-surveillance group. CONCLUSION: This study demonstrates that HBCa surveillance significantly improves outcomes, including survival, in patients with PSC. (Hepatology 2018;67:2338-2351).

Prevalence and correlates of autism spectrum disorder in Qatar: a national study.

BACKGROUND: Few epidemiological data on autism spectrum disorders (ASD) exist for Arabic countries. We conducted the first survey of ASD in Qatar, a population with high consanguinity level. METHODS: This cross-sectional survey was conducted from 2015 to 2018 in Qatar school-age children (N = 176,960) from national and immigrant families. Children diagnosed with ASD were identified through medical centers and special needs schools. Records were abstracted and supplemented by parental interviews. Additionally, children attending 93 schools were screened; ASD case status was confirmed in random samples of screen-positive and screen-negative children. Prevalence was estimated after taking into account different sampling fractions and participation rates at each survey phase. RESULTS: One thousand three hundred and ninety-three children already diagnosed with ASD were identified. Among 9,074 school survey participants, 760 screen-negative children and 163 screen-positive children were evaluated; 17 were confirmed to have ASD including five children newly diagnosed. Prevalence was 1.14% (95% CI: 0.89-1.46) among 6- to 11-year-olds. ASD was reported in full siblings/extended relatives in 5.9% (95% CI: 0.042-0.080)/11.8% (95% CI: 0.095-0.146) families. First-degree consanguinity in Qatari cases (45%) was comparable to known population levels. Among 844 ASD cases (mean age: 7.2 years; 81% male), most children experienced language delay (words: 75.1%; phrase speech: 91.4%), and 19.4% reported developmental regression. At the time of the survey, persisting deficits in expressive language (19.4%) and peer interactions (14.0%) were reported in conjunction with behavioral problems (ADHD: 30.2%; anxiety: 11.0%). In multivariate logistic regression, ASD severity was associated with parental consanguinity, gestational diabetes, delay in walking, and developmental regression. CONCLUSIONS: ASD prevalence in Qatar is consistent with recent international studies. The methods employed in this study should help designing comparable surveys in the region. We estimated that 187,000 youths under age 20 have ASD in Gulf countries. This figure should assist in planning health and educational services for a young, fast-growing population.

Prediction of antioxidant proteins by incorporating statistical moments based features into Chou's PseAAC.

Antioxidant proteins are considered crucial in the areas of research on life sciences and pharmacology. They prevent damage to cells and DNA which are caused by free radicals. The role of antioxidants in the ageing process makes them more significant in their accurate identification. Disease preventions through antioxidant protein have also been the area of study in recent past. The existing process to identify and test every single antioxidant protein in order to obtain its properties is inefficient and expensive. Due to this nature, many pharmaceutical agents have reflected antioxidant proteins as attractive targets. Approaches based on computational methodologies have appeared to be as a highly desirable resource in the annotation and determination process of antioxidant proteins. In this study, we have developed a method that is built on computation intelligence and statistical moments based features for prediction. Our proposed system has achieved better accuracy than state-of-art systems in the prediction of antioxidant proteins from non-antioxidant proteins using 10-fold-cross-validation tests. These outcomes suggest that the use of statistical moments with a multilayer neural network could bear more effective and efficient results.

Predicting membrane proteins and their types by extracting various sequence features into Chou's general PseAAC.

For many biological functions membrane proteins (MPs) are considered crucial. Due to this nature of MPs, many pharmaceutical agents have reflected them as attractive targets. It bears indispensable importance that MPs are predicted with accurate measures using effective and efficient computational models (CMs). Annotation of MPs using in vitro analytical techniques is time-consuming and expensive; and in some cases, it can prove to be intractable. Due to this scenario, automated prediction and annotation of MPs through CM based techniques have appeared to be useful. Based on the use of computational intelligence and statistical moments based feature set, an MP prediction framework is proposed. Furthermore, the previously used dataset has been enhanced by incorporating new MPs from the latest release of UniProtKB. Rigorous experimentation proves that the use of statistical moments with a multilayer neural network, trained using back-propagation based prediction techniques allows more thorough results.

Negative Predictive Value of Contrast-Enhanced Ultrasound in Differentiating Avascular Solid-Appearing From Vascularized Masses: A Retrospective Consecutive Study.

OBJECTIVES: To determine the negative predictive value (NPV) of contrast-enhanced ultrasound (CEUS) to establish the lack of vascularity in a mass. METHODS: This work was an Institutional Review Board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study. Acquisition of consent was waived. We included all CEUS examinations performed for tissue characterization between 2004 and 2014 that reported showing no vascularity in a mass. Contrast-enhanced ultrasound findings were considered true-negative when there was stability on imaging for at least 1 year or no evidence of a solid mass, if biopsied, and false-negative if there was lesion growth on imaging within 12 months or an indication of a solid mass on the pathologic examination, if biopsied. One author reviewed all of the reports and follow-up examinations. We conducted a consensus review of all false-negative findings mixed with an equal number of true-negative findings by 2 reviewers, who were blinded to the final results. RESULTS: The study population consisted of 97 CEUS examinations in 97 patients, including 48 women and 49 men (mean age ± SD, 65 ± 14 years). Examinations were performed for lesion characterization in the liver (n = 23), pancreas (n = 17), kidney (n = 54), 1 gallbladder, 1 adnexa, and 1 peritoneal lesion. The overall false-negative rate on the official prospective review was 2% (2 of 97). Two false-negative findings were correctly identified on the consensus review. The NPV of CEUS was 97.9% (95 of 97; 95% confidence interval, 93%- 99%) on the official review. CONCLUSIONS: Contrast-enhanced ultrasound has a very high NPV to exclude the presence of flow in a mass, and it can be used to exclude the presence of a solid mass.

A Treatise to Computational Approaches Towards Prediction of Membrane Protein and Its Subtypes.

Membrane proteins are vital mediating molecules responsible for the interaction of a cell with its surroundings. These proteins are involved in different functionalities such as ferrying of molecules and nutrients across membrane, recognizing foreign bodies, receiving outside signals and translating them into the cell. Membrane proteins play significant role in drug interaction as nearly 50% of the drug targets are membrane proteins. Due to the momentous role of membrane protein in cell activity, computational models able to predict membrane protein with accurate measures bears indispensable importance. The conventional experimental methods used for annotating membrane proteins are time-consuming and costly and in some cases impossible. Computationally intelligent techniques have emerged to be as a useful resource in the automation of prediction and hence the annotation process. In this study, various techniques have been reviewed that are based on different computational intelligence models used for prediction process. These techniques were formulated by different researchers and were further evaluated to provide a comparative analysis. Analysis shows that the usage of support vector machine-based prediction techniques bears more assiduous results.

Multiple cutaneous and hepatic hemangiomas in infants.

OBJECTIVES: The objectives of the study were to determine the rate of hepatic hemangiomas in infants with cutaneous infantile hemangiomas that were screened by abdominal ultrasound; identify morphological subtypes and number of cutaneous infantile hemangiomas that are likely to suggest the presence of hepatic hemangiomas; and identify clinical history, physical findings, or laboratory abnormalities that may predict hepatic involvement. METHODS: A retrospective study was conducted between 2000 and 2007 on 37 infants with cutaneous hemangiomas who underwent abdominal ultrasound for hepatic hemangiomas. Infants were classified into subgroups based upon the morphology of their cutaneous hemangioma(s). Data collected included clinical history, physical examination findings, sonographic findings, laboratory results, treatment(s) rendered, and clinical outcome. RESULTS: Eight of 37 infants (22%) had hepatic hemangiomas. Infants with miliary (30-100 pinpoint/small cutaneous hemangiomas), six or more small cutaneous hemangiomas, and one large with one or more small cutaneous hemangiomas were more likely to have concurrent hepatic hemangiomas. No other clinical findings were associated with hepatic involvement. CONCLUSIONS: Similar to other studies, our study found clinically asymptomatic hepatic hemangiomas in 22% of infants with multiple cutaneous infantile hemangiomas. No clinical findings studied were predictive of hepatic involvement.