Performance of Bituminous Binder Modified with Recycled Plastic Pellets

Finding beneficial uses for waste plastics has been an environmental challenge for municipalities. A limited number of studies have investigated the performance of asphalt mixtures containing plastic waste in cold regions that experience freeze-thaw cycling. The objective of this study is to evaluate the impact of adding two types of recycled plastic pellets on the high- and low-temperature performance of bituminous binders. Nylon-based (NP) and polyester-based (PP) recycled plastic pellets were used in this study. A PG 58-28 bituminous binder was modified by different dosages of NP and PP plastic pellets. The impact of adding Elvaloy copolymer and polyphosphoric acid on the modified binders was also investigated. Results showed that using recycled plastic pellets as a modifier for bituminous binders improved their elastic response and rutting resistance without affecting their low-temperature performance. The PP modifier showed better elastic behavior and rutting resistance than the NP modifier.

A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework

Autism Spectrum Disorder (ASD) is characterized as a neurodevelopmental disorder with a heterogeneous nature, influenced by genetics and exhibiting diverse clinical presentations. In this study, we dissect Autism Spectrum Disorder (ASD) into its behavioral components, mirroring the diagnostic process used in clinical settings. Morphological features are extracted from magnetic resonance imaging (MRI) scans, found in the publicly available dataset ABIDE II, identifying the most discriminative features that differentiate ASD within various behavioral domains. Then, each subject is categorized as having severe, moderate, or mild ASD, or typical neurodevelopment (TD), based on the behavioral domains of the Social Responsiveness Scale (SRS). Through this study, multiple artificial intelligence (AI) models are utilized for feature selection and classifying each ASD severity and behavioural group. A multivariate feature selection algorithm, investigating four different classifiers with linear and non-linear hypotheses, is applied iteratively while shuffling the training-validation subjects to find the set of cortical regions with statistically significant association with ASD. A set of six classifiers are optimized and trained on the selected set of features using 5-fold cross-validation for the purpose of severity classification for each behavioural group. Our AI-based model achieved an average accuracy of 96%, computed as the mean accuracy across the top-performing AI models for feature selection and severity classification across the different behavioral groups. The proposed AI model has the ability to accurately differentiate between the functionalities of specific brain regions, such as the left and right caudal middle frontal regions. We propose an AI-based model that dissects ASD into behavioral components. For each behavioral component, the AI-based model is capable of identifying the brain regions which are associated with ASD as well as utilizing those regions for diagnosis. The proposed system can increase the speed and accuracy of the diagnostic process and result in improved outcomes for individuals with ASD, highlighting the potential of AI in this area.

Independent of Primary Sclerosing Cholangitis and Cirrhosis, Early Adulthood Obesity Is Associated with Cholangiocarcinoma

BACKGROUND

It is estimated that 6% to 20% of all cholangiocarcinoma (CCA) diagnoses are explained by primary sclerosing cholangitis (PSC), but the underlying risk factors in the absence of PSC are unclear. We examined associations of different risk factors with intrahepatic cholangiocarcinoma (ICC) and extrahepatic cholangiocarcinoma (ECC) in the United States.

METHODS

We conducted a case-control study of 121 patients with ECC and 308 patients with ICC treated at MD Anderson Cancer Center between May 2014 and March 2020, compared with 1,061 healthy controls. Multivariable logistic regression analysis was applied to estimate the adjusted OR (AOR) and 95% confidence interval (CI) for each risk factor.

RESULTS

Being Asian, diabetes mellitus, family history of cancer, and gallbladder stones were associated with higher odds of developing ICC and ECC. Each 1-unit increase in body mass index in early adulthood (ages 20-40 years) was associated with a decrease in age at diagnosis of CCA (6.7 months, P < 0.001; 6.1 months for ICC, P = 0.001; 8.2 months for ECC, P = 0.007). A family history of cancer was significantly associated with the risk of ICC and ECC development; the AORs (95% CI) were 1.11 (1.06-1.48) and 1.32 (1.01-2.00) for ICC and ECC, respectively.

CONCLUSIONS

In this study, early adulthood onset of obesity was significantly associated with CCA and may predict early diagnosis at younger age than normal weight individuals.

IMPACT

The study highlights the association between obesity and CCA, independent of PSC. There is a need to consider the mechanistic pathways of obesity in the absence of fatty liver and cirrhosis.

Ectopic PLAG1 induces muscular dystrophy in the mouse

Even though various genetic mutations have been identified in muscular dystrophies (MD), there is still a need to understand the biology of MD in the absence of known mutations. Here we reported a new mouse model of MD driven by ectopic expression of PLAG1. This gene encodes a developmentally regulated transcription factor known to be expressed in developing skeletal muscle, and implicated as an oncogene in certain cancers including rhabdomyosarcoma (RMS), an aggressive soft tissue sarcoma composed of myoblast-like cells. By breeding loxP-STOP-loxP-PLAG1 (LSL-PLAG1) mice into the MCK-Cre line, we achieved ectopic PLAG1 expression in cardiac and skeletal muscle. The Cre/PLAG1 mice died before 6 weeks of age with evidence of cardiomyopathy significantly limiting left ventricle fractional shortening. Histology of skeletal muscle revealed dystrophic features, including myofiber necrosis, fiber size variation, frequent centralized nuclei, fatty infiltration, and fibrosis, all of which mimic human MD pathology. QRT-PCR and Western blot revealed modestly decreased Dmd mRNA and dystrophin protein in the dystrophic muscle, and immunofluorescence staining showed decreased dystrophin along the cell membrane. Repression of Dmd by ectopic PLAG1 was confirmed in dystrophic skeletal muscle and various cell culture models. In vitro studies showed that excess IGF2 expression, a transcriptional target of PLAG1, phenocopied PLAG1-mediated down-regulation of dystrophin. In summary, we developed a new mouse model of a lethal MD due to ectopic expression of PLAG1 in heart and skeletal muscle. Our data support the potential contribution of excess IGF2 in this model. Further studying these mice may provide new insights into the pathogenesis of MD and perhaps lead to new treatment strategies.

Computed tomography-guided stereotactic surgery in the management of brain lesions: A single-center experience

Background

The present study presents our experience with computed tomography (CT)-guided stereotactic surgery in managing deep-seated brain lesions and provides a background in the expanding fields of morphological stereotactic neurosurgery.

Methods

We conducted this retrospective cohort study on 80 patients managed at the Department of Neurosurgery, Zagazig University Hospitals, Zagazig, Egypt, between January 2019 to January 2021. We targeted patients with morphological stereotactic surgeries performed as the primary management modality of their treatment.

Results

A total of 80 patients, with a mean age of 44.3 years, were included in the study. The stereotactic targets were supratentorial in 71 patients (88.75%), infratentorial in seven patients (8.75%), and both supraand infratentorial in two patients (2.5%). The lesions showed enhancements with IV contrast in 55 patients (68.75%). Stereotactic procedures were performed under local anesthesia in 64 patients and general anesthesia in 16 patients. Of the 80 stereotactic procedures, 52 were biopsies (65%). We observed a significant improvement in the postoperative Karnofsky performance score compared to the postoperative score (63.4 ± 19.8 vs. 56.7 ± 15.4, P = 0.001). The level of agreement between clinical, radiological, and final pathological diagnosis was assessed; it was complete in 47.5% of the patients. The postprocedural CT scan demonstrated intracranial hemorrhage in five patients (6.25%); four (5%) were silent with no neurological complications.

Conclusion

This study provided evidence that the stereotactic procedure is easy to perform, accurate in targeting the lesion, and spares patients from undergoing major surgical procedures. Stereotactic applications of spontaneous intracerebral hemorrhage, deep-seated abscesses, encysted tumors, or medically refractory benign intracranial hypertension can improve the outcome even in medically high-risk patients.

Understanding the Role of Connectivity Dynamics of Resting-State Functional MRI in the Diagnosis of Autism Spectrum Disorder: A Comprehensive Study

In addition to the standard observational assessment for autism spectrum disorder (ASD), recent advancements in neuroimaging and machine learning (ML) suggest a rapid and objective alternative using brain imaging. This work presents a pipelined framework, using functional magnetic resonance imaging (fMRI) that allows not only an accurate ASD diagnosis but also the identification of the brain regions contributing to the diagnosis decision. The proposed framework includes several processing stages: preprocessing, brain parcellation, feature representation, feature selection, and ML classification. For feature representation, the proposed framework uses both a conventional feature representation and a novel dynamic connectivity representation to assist in the accurate classification of an autistic individual. Based on a large publicly available dataset, this extensive research highlights different decisions along the proposed pipeline and their impact on diagnostic accuracy. A large publicly available dataset of 884 subjects from the Autism Brain Imaging Data Exchange I (ABIDE-I) initiative is used to validate our proposed framework, achieving a global balanced accuracy of 98.8% with five-fold cross-validation and proving the potential of the proposed feature representation. As a result of this comprehensive study, we achieve state-of-the-art accuracy, confirming the benefits of the proposed feature representation and feature engineering in extracting useful information as well as the potential benefits of utilizing ML and neuroimaging in the diagnosis and understanding of autism.

Isolation, Identification, and Genetic Characterization of Antibiotic Resistance of Salmonella Species Isolated from Chicken Farms

Salmonella is a major cause of foodborne outbreaks. It causes gastroenteritis in humans and animals. This micro-organism causes severe illness in chickens and has a major impact on chicken productivity and the poultry industry. This study aimed to address the prevalence of Salmonella infection in broiler chicken farms in Kafrelsheikh, Gharbia, and Menofeya provinces in Egypt during 2020-2022. This work also aimed to evaluate the genetic characterization and antibiotic resistance of the isolated Salmonella strains. Clinical signs and mortalities were observed and recorded. In total, 832 samples were collected from 52 broiler flocks, including 26 from both one-week-old and 6-week-old chicken farms from different organs (liver, intestinal content, spleen, and gallbladder). The prevalence of Salmonella infections was reported in the study region to be 36.54%. Of the 26 one-week-old farms surveyed, 11 (42.31%) and 8/26 (30.77%) of the six-week-old broiler chicken farms had Salmonella infections. Recovered isolates were serotyped as 9 (47.37%) S. enteritidis O 1,9,12, ad monophasic H: g, m: -, 6 (31.58.%) S. shangani 2, (10.53%) S. gueuletapee 1, (5.26%) S. II (salamae), and 1 (5.26%) untypable. The results showed that Salmonella infection was predominant in one-week-old chicks compared to infection in six-week-old and uninfected flocks. All Salmonella isolates were resistant to ampicillin and erythromycin, while all isolates were sensitive to ciprofloxacin, chloramphenicol, and levofloxacin. The isolates also contained 10.53% (2/19) streptomycin, 10.53% (2/21) gentamicin, 15.79% (3/19) doxycycline, and 26.32% (5/19) lincomycin and colistin. The phenotypically resistant Salmonella samples against ampicillin, erythromycin, and macrolide harbored bla _TEM , bla _SHV , ermB, ereA, mphA, and ermB, respectively. This baseline data on Salmonella spp. prevalence, serotyping, and antibiotic profiles are combined to define the antimicrobial resistance to this endemic disease. Elucidation of the mechanisms underlying this drug resistance should be of general importance in understanding both the treatment and prevention of Salmonella infection in this part of Egypt.

Special Issue "Computer Aided Diagnosis Sensors"

Sensors used to diagnose, monitor or treat diseases in the medical domain are known as medical sensors [...].

EP-524 Implementing Simulation Based Learning (SBL) to Reduce the Gap in Surgical Training, A Theoretical Evaluation Approach

Background

The current surgical training is severely affected by COVID-19 pandemic with redeployment and reduced number of elective procedure across NHS hospitals, this has affected both core and higher surgical trainees, rendering the traditional apprenticeship model obsolete. It became evident that the future of Surgical training and innovation will require a combination of simulation and operative exposure to overcome the obstacle of reduced exposure in surgical education and operative training.

Discussion

In our theoretical analysis, we will discuss the efficacy, safety and impact of relying on SBL to fill the gaps in surgical training.

Clinical exposure alone will not be sufficient to train procedure based speciality trainees to their highest proficiency. SBL is one design that is supported by learning theories such as Transformational Learning and Experiential Learning Theory.

In a high fidelity simulation, such as laparoscopic simulation courses, all concepts of facilitated learning are fulfilled which strongly supports our hypothesis.

On balance, given the complexity of skills learnt, it remains difficult to measure the efficacy of transferring the learnt capabilities into practice and standardise this among learners. SBL also leaves non-technical skills un-assessed in depth.

Conclusion

The disruption of training due to COVID-19 affected our procedure based learning, this leaves us with a dilemma to catch-up with these unmet training needs. SBL could be one of the adjuncts that fill in the gaps on the short and medium term. Implementing SBL in surgical training curriculum, should be evaluated for efficacy and cost effectiveness.

From HIV to COVID-19, Molecular mechanisms of pathogens' trade-off and persistence in the community, potential targets for new drug development

BACKGROUND

On the staggering emergence of the Omicron variant, numerous questions arose about the evolution of virulence and transmissibility in microbes.

MAIN BODY OF THE ABSTRACT

The trade-off hypothesis has long speculated the exchange of virulence for the sake of superior transmissibility in a wide array of pathogens. While this certainly applies to the case of the Omicron variant, along with influenza virus, various reports have been allocated for an array of pathogens such as human immunodeficiency virus (HIV), malaria, hepatitis B virus (HBV) and tuberculosis (TB). The latter abide to another form of trade-off, the invasion-persistence trade-off. In this study, we aim to explore the molecular mechanisms and mutations of different obligate intracellular pathogens that attenuated their more morbid characters, virulence in acute infections and invasion in chronic infections.

SHORT CONCLUSION

Recognizing the mutations that attenuate the most morbid characters of pathogens such as virulence or persistence can help in tailoring new therapies for such pathogens. Targeting macrophage tropism of HIV by carbohydrate-binding agents, or targeting the TMPRSS2 receptors to prevent pulmonary infiltrates of COVID-19 is an example of how important is to recognize such genetic mechanisms.

Phase II Study of Ramucirumab in Advanced Biliary Tract Cancer Previously Treated By Gemcitabine-Based Chemotherapy

PURPOSE

VEGF receptor-2 (VEGFR-2)-mediated angiogenesis contributes to pathogenesis of biliary tract cancers (BTC). We investigated ramucirumab, a mAb targeting VEGFR-2 for treatment of advanced, chemorefractory BTC.

PATIENTS AND METHODS

This is a phase II, single-arm trial for advanced, unresectable, pre-treated patients with BTC with ECOG 0/1, adequate liver, renal, and marrow functions. Ramucirumab was administered at 8 mg/kg, 2 weekly with restaging performed 8 weekly. Primary endpoint was progression-free survival (PFS). Secondary endpoints were overall response rate (ORR), disease control rate (DCR), overall survival (OS), and toxicity. Exploratory endpoints included correlation of tumor mutational status with PFS and OS.

RESULTS

61 patients were enrolled: the median age was 58.5 years; 59 with stage IV disease; 62%, intrahepatic cholangiocarcinoma; 22%, gallbladder cancer; and 16%, extrahepatic cholangiocarcinoma. All received prior chemotherapy: 52% had 1 prior, and rest ≥2 prior lines. Median treatment duration was 10.1 weeks (range, 2.1-86). Median PFS was 3.2 months [95% confidence interval (CI), 2.1-4.8]; median OS, 9.5 months (95% CI, 5.8-13.6). One (1.7%) patient achieved partial response; 26 (43.3%), stable disease; and 25 (41.7%), disease progression; DCR, 45%. Median 6-month PFS and OS rates were 32% (95% CI, 0.22-0.46) and 58% (95% CI, 0.47-0.72). The majority of toxicities were grade 1 or 2; grade 3 proteinuria (1, 2%), hypertension (13, 22%), and pulmonary embolism (1, 2%), and grade 4 gastrointestinal bleeding (1, 2%) occurred.

CONCLUSIONS

Ramucirumab was well tolerated and resulted in PFS similar to that achieved with other chemotherapy regimens used historically for chemorefractory BTC.

The immunogenomic landscape of resected intrahepatic cholangiocarcinoma

BACKGROUND AND AIMS

Cholangiocarcinoma (CCA) is a deadly and highly therapy-refractory cancer of the bile ducts, with early results from immune checkpoint blockade trials showing limited responses. Whereas recent molecular assessments have made bulk characterizations of immune profiles and their genomic correlates, spatial assessments may reveal actionable insights.

APPROACH AND RESULTS

Here, we have integrated immune checkpoint-directed immunohistochemistry with next-generation sequencing of resected intrahepatic CCA samples from 96 patients. We found that both T-cell and immune checkpoint markers are enriched at the tumor margins compared to the tumor center. Using two approaches, we identify high programmed cell death protein 1 or lymphocyte-activation gene 3 and low CD3/CD4/inducible T-cell costimulator specifically in the tumor center as associated with poor survival. Moreover, loss-of-function BRCA1-associated protein-1 mutations are associated with and cause elevated expression of the immunosuppressive checkpoint marker, B7 homolog 4.

CONCLUSIONS

This study provides a foundation on which to rationally improve and tailor immunotherapy approaches for this difficult-to-treat disease.

The Role of Structure MRI in Diagnosing Autism

This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to be ASD or typically developed (TD) based on a trained machine-learning (ML) model. This approach opens the hope for developing a new CAD system for early personalized diagnosis of ASD. We propose a framework to extract the cerebral cortex from structural MRI as well as identifying the altered areas in the cerebral cortex. This framework consists of the following five main steps: (i) extraction of cerebral cortex from structural MRI; (ii) cortical parcellation to a standard atlas; (iii) identifying ASD associated cortical markers; (iv) adjusting feature values according to sex and age; (v) building tailored neuro-atlases to identify ASD; and (vi) artificial neural networks (NN) are trained to classify ASD. The system is tested on the Autism Brain Imaging Data Exchange (ABIDE I) sites achieving an average balanced accuracy score of 97±2%. This paper demonstrates the ability to develop an objective CAD system using structure MRI and tailored neuro-atlases describing specific developmental patterns of the brain in autism.

Whole genome sequencing in a Knobloch syndrome family confirms the molecular diagnosis

BACKGROUND

To establish the molecular diagnosis in two brothers presenting with the ocular features of Knobloch Syndrome using whole genome sequencing (WGS).

METHODS

Clinical examination and ophthalmological phenotyping were completed under general anaesthesia. DNA samples were tested on a targeted retinal dystrophy next-generation sequencing panel. Subsequently, WGS was performed to identify additional variants.

RESULTS

Clinical examination confirmed the diagnosis of Knobloch Syndrome. Targeted sequencing identified a novel heterozygous frameshift pathogenic variant in COL18A1, c.2864dupC; p.(Gly956ArgfsX20), inherited from their mother. A second paternally inherited heterozygous missense variant was identified in both brothers, c.5014 G > A; p.(Asp1672Asn), which was initially considered to have too high frequency to be pathogenic (MAF 8.8%). This led to an in-depth analysis of the COL18A1 locus using WGS data, which confirmed that Asp1672Asn is a likely pathogenic hypomorphic allele.

CONCLUSION

To date, all confirmed genetic diagnoses of Knobloch syndrome are attributable to variants in COL18A1. The family described here has a heterozygous novel loss of function variant. Detailed analysis of WGS data combined with family segregation studies concluded that although Asp1672Asn has a high population frequency, it is the most likely second pathogenic variant in our family. This supports the hypothesis that this is a hypomorphic allele, which, in combination with a loss of function pathogenic variant, leads to Knobloch syndrome.To our knowledge, this is the first time that WGS has been used to confirm a molecular diagnosis of Knobloch syndrome in this way and has provided further insight into the molecular mechanisms in this rare disorder.

EP.TH.528The Accuracy of the Multi-slice detector CT Scan (MDCT) in staging borderline resectable periampullary carcinoma

Objectives

Assess MDCT accuracy in staging cancers periampullary cancers.

Introduction

Periampullary malignancies are highly aggressive with poor outcomes. Surgery is the only curative option. It is crucial to define the patients who can advantage from a Whipple’s resection and who can avoid.

Methodology and Results

RCT investigated randomly 28 patients over 15 months. The patients were sub-divided into 2 groups. Group A, we relied mainly on the MDCT for preoperative staging, while in Group B staging laparoscopy was added before the abdominal exploration. Sensitivity of the MDCT and its accuracy were 100% in defining the signs of irresectability. For borderline staging, the accuracy of the scan was 62.5% and 71%, in groups A and B. The Overall accuracy of MDCT was 75%. It decreased to 68.1% for borderline lesions. The addition of staging laparoscopy to the diagnostic work up, increased the accuracy to 92.5%. The camera test was able to see occult findings which were missed in the images. liver Mets and malignant peritoneal fluid were localized in 18% and 9% respectively. 3 cases in group A and one in group B underwent unnecessary laparotomy. Thus, the false negative incidences were 21% and 7% in group A and B, with overall incidence 14.2%.

Conclusion

MDCT is highly sensitive and specific with high stage periampullary cancers. These parameters drop in border tumors with reduced accuracy in detecting the degree of vascular abutment and distant-occult findings. Addition of other adjuncts to decrease the rate of un-indicated laparotomy is advised.

90 Penetrating Abdominal Trauma: Comperative Study Between Operative and Conservative Management

Aim

to Assess the feasibility and safety of selective non-operative management in penetrating abdominal injuries and to identify a protocol for selection of patient’s candidates for non-operative management.

Method

In this comparative study 40 abdominal stab victims (admitted to Emergency Department of Alexandria Main University Hospital) were selected during 6 months period where 20 patients were suitable for non-operative management according to strict selection criteria whereas the other 20 patients were operated according to clinical and/or radiological indications or on basis of department protocol, the results were compared in view of final outcome.

Results

In our study, 15 patients were assigned for operative management according to selected clinical and/or radiological indications only 3 of them (20%) had non-therapeutic laparotomies, On the other hand, five patients were explored on basis of department protocol in violation of our indications for exploration; four of them (80%) were non-therapeutic. So, the rate of non-therapeutic laparotomies was significantly higher when done mandatory without selected clinical and radiological indications.

Conclusions

Assessment of vital signs together with abdominal examination are the most important and dependable tools in decision making in penetrating abdominal trauma patients. Patients with shock on admission (but responding to resuscitation), proved low grade solid organ injury (by CT), and proved intraperitoneal collection (by US or CT) could be managed conservatively regarding that they remain vitally and clinically stable. If failure of conservation occurs, it is usually during the 1st 24 hours after admission.

A Personalized Computer-Aided Diagnosis System for Mild Cognitive Impairment (MCI) Using Structural MRI (sMRI)

Alzheimer's disease (AD) is a neurodegenerative disorder that targets the central nervous system (CNS). Statistics show that more than five million people in America face this disease. Several factors hinder diagnosis at an early stage, in particular, the divergence of 10-15 years between the onset of the underlying neuropathological changes and patients becoming symptomatic. This study surveyed patients with mild cognitive impairment (MCI), who were at risk of conversion to AD, with a local/regional-based computer-aided diagnosis system. The described system allowed for visualization of the disorder's effect on cerebral cortical regions individually. The CAD system consists of four steps: (1) preprocess the scans and extract the cortex, (2) reconstruct the cortex and extract shape-based features, (3) fuse the extracted features, and (4) perform two levels of diagnosis: cortical region-based followed by global. The experimental results showed an encouraging performance of the proposed system when compared with related work, with a maximum accuracy of 86.30%, specificity 88.33%, and sensitivity 84.88%. Behavioral and cognitive correlations identified brain regions involved in language, executive function/cognition, and memory in MCI subjects, which regions are also involved in the neuropathology of AD.

Synthesis, Characterization, In Vitro Anticancer Potentiality, and Antimicrobial Activities of Novel Peptide-Glycyrrhetinic-Acid-Based Derivatives

Glycyrrhetinic acid (GA) is one of many interesting pentacyclic triterpenoids showing significant anticancer activity by triggering apoptosis in tumor cell lines. This study deals with the design and synthesis of new glycyrrhetinic acid (GA)-amino acid peptides and peptide ester derivatives. The structures of the new derivatives were established through various spectral and microanalytical data. The novel compounds were screened for their in vitro cytotoxic activity. The evaluation results showed that the new peptides produced promising cytotoxic activity against the human breast MCF-7 cancer cell line while comparing to doxorubicin. On the other hand, only compounds 3, 5, and 7 produced potent activity against human colon HCT-116 cancer cell line. The human liver cancer (HepG-2) cell line represented a higher sensitivity to peptide 7 (IC50; 3.30 μg/mL), while it appeared insensitive to the rest of the tested peptides. Furthermore, compounds 1, 3, and 5 exhibited a promising safety profile against human normal skin fibroblasts cell line BJ-1. In order to investigate the mode of action, compound 5 was selected as a representative example to study its in vitro effect against the apoptotic parameters and Bax/BCL-2/p53/caspase-7/caspase-3/tubulin, and DNA fragmentation to investigate beta (TUBb). Additionally, all the new analogues were subjected to antimicrobial assay against a panel of Gram-positive and Gram-negative bacteria and the yeast candida Albicans. All the tested GA analogues 1-8 exhibited more antibacterial effect against Micrococcus Luteus than gentamicin, but they exhibited moderate antimicrobial activity against the tested bacterial and yeast strains. Molecular docking studies were also simulated for compound 5 to give better rationalization and put insight to the features of its structure.

Non-extractive spectrophotometric determination of memantine HCl, clomipramine HCl and fluvoxamine maleate in pure form and in pharmaceutical products by ion-pair complex formation with rose bengal

OBJECTIVES

The objective of this work is to develop a simple, sensitive and non-extractive spectrophotometric method for the determination of memantine HCl (MEM), clomipramine HCl (CLM) and fluvoxamine maleate (FLV).

MATERIAL AND METHODS

The proposed method was based on the formation of colored ion-pair complexes between the basic nitrogen of the target drugs and rose bengal (RB) dye in a weak acidic medium.

RESULTS

The formed complexes were measured at 576nm for MEM, CLM and at 575nm for FLV. The reaction conditions were optimized to obtain the maximum color intensity. Beer's law was obeyed in the range of 2-20, 1-16 and 6-30μg/mL for MEM, CLM and FLV, respectively. The limit of detection (LOD) was 0.476, 0.185, 0.806 and the limit of quantitation (LOQ) was 1.443, 0.559 and 2.443 for MEM, CLM and FLV, respectively. The composition ratio of the ion-pair complexes was found to be 1:1 as determined by Job's method.

CONCLUSION

The proposed method was applied successfully for the analysis of the cited drugs in pure and dosage forms. Results of the proposed method were statistically compared with the reported methods by applying student's t- and F-tests and no significant differences were observed.

Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images

The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support.

Comparative Modeling of Frequency Mixing Measurements of Magnetic Nanoparticles Using Micromagnetic Simulations and Langevin Theory

Dual frequency magnetic excitation of magnetic nanoparticles (MNP) enables enhanced biosensing applications. This was studied from an experimental and theoretical perspective: nonlinear sum-frequency components of MNP exposed to dual-frequency magnetic excitation were measured as a function of static magnetic offset field. The Langevin model in thermodynamic equilibrium was fitted to the experimental data to derive parameters of the lognormal core size distribution. These parameters were subsequently used as inputs for micromagnetic Monte-Carlo (MC)-simulations. From the hysteresis loops obtained from MC-simulations, sum-frequency components were numerically demodulated and compared with both experiment and Langevin model predictions. From the latter, we derived that approximately 90% of the frequency mixing magnetic response signal is generated by the largest 10% of MNP. We therefore suggest that small particles do not contribute to the frequency mixing signal, which is supported by MC-simulation results. Both theoretical approaches describe the experimental signal shapes well, but with notable differences between experiment and micromagnetic simulations. These deviations could result from Brownian relaxations which are, albeit experimentally inhibited, included in MC-simulation, or (yet unconsidered) cluster-effects of MNP, or inaccurately derived input for MC-simulations, because the largest particles dominate the experimental signal but concurrently do not fulfill the precondition of thermodynamic equilibrium required by Langevin theory.

153 Staging Laparoscopy in Bilio-Pancreatic Malignancies Compared To Radiological Staging: A Comparative Study

Introduction

The study was to compare the accuracy of laparoscopy in staging and selecting patients diagnosed with malignant obstructive jaundice, to the traditional investigation.

Method

a prospective study conducted in the period between September 2017 and December 2018.30 patients, having malignant jaundice, were divided into two groups for cancer staging to assess their resectability and operability. Staging of 15 patients in group A was limited to conventional diagnostic methods, while 15 patients were in group B,where Laparoscopy was added.

Results

Results showed that the accuracy of routine investigations in staging was 73%, while that of laparoscopy was 93%.The number of cases under staged by imagings were 8 cases(these were 3 in Group A and 5 in group B) thus they were diagnosed as operable. On the other hand, just 1 case was misdiagnosed by the laparoscopy. Regarding the morbidity and mortality, there were variable complications among those who had unrequired laparotomies including one mortality case.on the other side,the incidence of complications were markedly decreased in group B,with no mortality incidence.

Conclusions

Diagnostic laparoscopy has a crucial role in staging people with malignant jaundice and may decrease the rate of unnecessary laparotomy in people found to have resectable disease by conventional imagings.

A robust DWT-CNN-based CAD system for early diagnosis of autism using task-based fMRI

PURPOSE

Task-based fMRI (TfMRI) is a diagnostic imaging modality for observing the effects of a disease or other condition on the functional activity of the brain. Autism spectrum disorder (ASD) is a pervasive developmental disorder associated with impairments in social and linguistic abilities. Machine learning algorithms have been widely utilized for brain imaging aiming for objective ASD diagnostics. Recently, deep learning methods have been gaining more attention for fMRI classification. The goal of this paper is to develop a convolutional neural network (CNN)-based framework to help in global diagnosis of ASD using TfMRI data that are collected from a response to speech experiment.

METHODS

To achieve this goal, the proposed framework adopts a novel imaging marker integrating both spatial and temporal information that are related to the functional activity of the brain. The developed pipeline consists of three main components. In the first step, the collected TfMRI data are preprocessed and parcellated using the Harvard-Oxford probabilistic atlas included with the fMRIB Software Library (FSL). Second, a group analysis using FSL is performed between ASD and typically developing (TD) children to identify significantly activated brain areas in response to the speech task. In order to reduce brain spatial dimensionality, a K-means clustering technique is performed on such significant brain areas. Informative blood oxygen level-dependent (BOLD) signals are extracted from each cluster. A compression step for each extracted BOLD signal using discrete wavelet transform (DWT) has been proposed. The adopted wavelets are similar to the expected hemodynamic response which enables DWT to compress the BOLD signal while highlighting its activation information. Finally, a deep learning 2D CNN network is used to classify the patients as ASD or TD based on extracted features from the previous step.

RESULTS

Preliminary results on 100 TfMRI dataset (50 ASD, 50 TD) obtain 80% correct global classification using tenfold cross validation (with sensitivity = 84%, specificity = 76%).

CONCLUSION

The experimental results show the high accuracy of the proposed framework and hold promise for the presented framework as a helpful adjunct to currently used ASD diagnostic tools.

Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey

Prostate cancer is one of the most identified cancers and second most prevalent among cancer-related deaths of men worldwide. Early diagnosis and treatment are substantial to stop or handle the increase and spread of cancer cells in the body. Histopathological image diagnosis is a gold standard for detecting prostate cancer as it has different visual characteristics but interpreting those type of images needs a high level of expertise and takes too much time. One of the ways to accelerate such an analysis is by employing artificial intelligence (AI) through the use of computer-aided diagnosis (CAD) systems. The recent developments in artificial intelligence along with its sub-fields of conventional machine learning and deep learning provide new insights to clinicians and researchers, and an abundance of research is presented specifically for histopathology images tailored for prostate cancer. However, there is a lack of comprehensive surveys that focus on prostate cancer using histopathology images. In this paper, we provide a very comprehensive review of most, if not all, studies that handled the prostate cancer diagnosis using histopathological images. The survey begins with an overview of histopathological image preparation and its challenges. We also briefly review the computing techniques that are commonly applied in image processing, segmentation, feature selection, and classification that can help in detecting prostate malignancies in histopathological images.

A Deep-Learning Framework for the Detection of Oil Spills from SAR Data

Oil leaks onto water surfaces from big tankers, ships, and pipeline cracks cause considerable damage and harm to the marine environment. Synthetic Aperture Radar (SAR) images provide an approximate representation for target scenes, including sea and land surfaces, ships, oil spills, and look-alikes. Detection and segmentation of oil spills from SAR images are crucial to aid in leak cleanups and protecting the environment. This paper introduces a two-stage deep-learning framework for the identification of oil spill occurrences based on a highly unbalanced dataset. The first stage classifies patches based on the percentage of oil spill pixels using a novel 23-layer Convolutional Neural Network. In contrast, the second stage performs semantic segmentation using a five-stage U-Net structure. The generalized Dice loss is minimized to account for the reduced oil spill representation in the patches. The results of this study are very promising and provide a comparable improved precision and Dice score compared to related work.

An efficient multi-factor authentication scheme based CNNs for securing ATMs over cognitive-IoT

Nowadays, the identity verification of banks' clients at Automatic Teller Machines (ATMs) is a very critical task. Clients' money, data, and crucial information need to be highly protected. The classical ATM verification method using a combination of credit card and password has a lot of drawbacks like Burglary, robbery, expiration, and even sudden loss. Recently, iris-based security plays a vital role in the success of the Cognitive Internet of Things (C-IoT)-based security framework. The iris biometric eliminates many security issues, especially in smart IoT-based applications, principally ATMs. However, integrating an efficient iris recognition system in critical IoT environments like ATMs may involve many complex scenarios. To address these issues, this article proposes a novel efficient full authentication system for ATMs based on a bank's mobile application and a visible light environments-based iris recognition. It uses the deep Convolutional Neural Network (CNN) as a feature extractor, and a fully connected neural network (FCNN)-with Softmax layer-as a classifier. Chaotic encryption is also used to increase the security of iris template transmission over the internet. The study and evaluation of the effects of several kinds of noisy iris images, due to noise interference related to sensing IoT devices, bad acquisition of iris images by ATMs, and any other system attacks. Experimental results show highly competitive and satisfying results regards to accuracy of recognition rate and training time. The model has a low degradation of recognition accuracy rates in the case of using noisy iris images. Moreover, the proposed methodology has a relatively low training time, which is a useful parameter in a lot of critical IoT based applications, especially ATMs in banking systems.

Identifying brain areas correlated with ADOS raw scores by studying altered dynamic functional connectivity patterns

Altered functional connectivity patterns play an important role in explaining autism spectrum disorder related impairments. In order to examine such connectivity, resting state functional MRI is the most commonly used technique. To date, the majority of works in this area examine a whole time series of brain activation as a discrete stationary process. This study proposes a more detailed analysis of how functional connectivity fluctuates over time and how it is used to quantify instances demonstrating overconnectivity or underconnectivity. Non-parametric surrogates test identifies the areas where underconnectivity or overconnectivity correlate with the Autism Diagnosis Observation Schedule. In addition, this study shows how the areas identified affect the subjects behaviors. Our ultimate goal is a personalized autism diagnosis and treatment CAD system, where each subject impairments are distinctly mapped so they can be addressed with targeted treatments.

A Case Report Of A Spindle Cell Sarcoma With Ntrk-3 Rearrangement And A Novel Gene Fusion Partner (Ntrk3-Eif2s2)

Casestudy

A group of spindle cell tumors with characteristic morphologic features, co-expression of CD34 and S100 and recurrent gene rearrangements in RAF-1, BRAF, and NTRK has recently been described. These tumors were found to be previously labeled as malignant peripheral nerve sheath tumor, infantile fibrosarcoma or unclassified spindle cell sarcoma. We describe a case of a 25-year-old female who presented with a right thigh mass. She underwent an ultrasound-guided biopsy showing a spindle cell tumor with co-expression of CD34 and S100 and subsequently underwent resection of the mass. Gross examination showed a 7.5 cm multi-lobulated, tan-pink, hemorrhagic and fleshy mass. Histologically, the tumor was relatively well-demarcated and consisted of spindle cells with moderate to high cellularity in a patternless architecture. The spindle cells showed moderate to marked pleomorphism, pale amphophilic cytoplasm, ovoid-to-elongated nuclei with vesicular chromatin, and occasional prominent nucleoli. Areas of prominent perivascular and stromal hyalinization were seen. Mitotic activity was brisk with up to 33 mitoses per 10 high power fields. Necrosis representing approximately 5% of the mass was identified. On immunohistochemistry, the tumor cells showed strong and diffuse positivity for CD34 and S100 and were negative for SOX10, broad-spectrum cytokeratin, EMA, SMA, Desmin, STAT6, MUC4, TLE1, and H3K27me3 (retained nuclear expression). EIF2S2-NTRK3 fusion gene was detected using next generation sequencing analysis.

Conclusion

A few cases of NTRK3 spindle cell sarcomas, other than classic infantile fibrosarcoma, have been previously reported in the literature with fusion genes involving ETV6, EML4, and STRN, among others. A gene fusion involving NTRK3 and EIF2S2 has not been previously reported. NTRK3-fused sarcomas typically show high-grade morphology and aggressive clinical behavior. Identification of NTRK-fused sarcomas is clinically important, as these advanced tumors are potentially amenable to NTRK inhibition. In our case, patient received adjuvant post-operative radiation therapy and returned with lung metastasis 5 months after surgery.

A DEEP LEARNING-BASED CAD SYSTEM FOR RENAL ALLOGRAFT ASSESSMENT: DIFFUSION, BOLD, AND CLINICAL BIOMARKERS

Recently, studies for non-invasive renal transplant evaluation have been explored to control allograft rejection. In this paper, a computer-aided diagnostic system has been developed to accommodate with an early-stage renal transplant status assessment, called RT-CAD. Our model of this system integrated multiple sources for a more accurate diagnosis: two image-based sources and two clinical-based sources. The image-based sources included apparent diffusion coefficients (ADCs) and the amount of deoxygenated hemoglobin (R2*). More specifically, these ADCs were extracted from 47 diffusion weighted magnetic resonance imaging (DW-MRI) scans at 11 different b-values (b0, b50, b100, …, b1000 s/mm²), while the R2* values were extracted from 30 blood oxygen level-dependent MRI (BOLD-MRI) scans at 5 different echo times (2ms, 7ms, 12ms, 17ms, and 22ms). The clinical sources included serum creatinine (SCr) and creatinine clearance (CrCl). First, the kidney was segmented through the RT-CAD system using a geometric deformable model called a level-set method. Second, both ADCs and R2* were estimated for common patients (N = 30) and then were integrated with the corresponding SCr and CrCl. Last, these integrated biomarkers were considered the discriminatory features to be used as trainers and testers for future deep learning-based classifiers such as stacked auto-encoders (SAEs). We used a k-fold cross-validation criteria to evaluate the RT-CAD system diagnostic performance, which achieved the following scores: 93.3%, 90.0%, and 95.0% in terms of accuracy, sensitivity, and specificity in differentiating between acute renal rejection (AR) and non-rejection (NR). The reliability and completeness of the RT-CAD system was further accepted by the area under the curve score of 0.92. The conclusions ensured that the presented RT-CAD system has a high reliability to diagnose the status of the renal transplant in a non-invasive way.

Automated Diagnosis of Diabetic Retinopathy Using Clinical Biomarkers, Optical Coherence Tomography, and Optical Coherence Tomography Angiography

PURPOSE

To determine if combining clinical, demographic, and imaging data improves automated diagnosis of nonproliferative diabetic retinopathy (NPDR).

DESIGN

Cross-sectional imaging and machine learning study.

METHODS

This was a retrospective study performed at a single academic medical center in the United States. Inclusion criteria were age >18 years and a diagnosis of diabetes mellitus (DM). Exclusion criteria were non-DR retinal disease and inability to image the macula. Optical coherence tomography (OCT) and OCT angiography (OCTA) were performed, and data on age, sex, hypertension, hyperlipidemia, and hemoglobin A1c were collected. Machine learning techniques were then applied. Multiple pathophysiologically important features were automatically extracted from each layer on OCT and each OCTA plexus and combined with clinical data in a random forest classifier to develop the system, whose results were compared to the clinical grading of NPDR, the gold standard.

RESULTS

A total of 111 patients with DM II were included in the study, 36 with DM without DR, 53 with mild NPDR, and 22 with moderate NPDR. When OCT images alone were analyzed by the system, accuracy of diagnosis was 76%, sensitivity 85%, specificity 87%, and area under the curve (AUC) was 0.78. When OCT and OCTA data together were analyzed, accuracy was 92%, sensitivity 95%, specificity 98%, and AUC 0.92. When all data modalities were combined, the system achieved an accuracy of 96%, sensitivity 100%, specificity 94%, and AUC 0.96.

CONCLUSIONS

Combining common clinical data points with OCT and OCTA data enhances the power of computer-aided diagnosis of NPDR.

A Comprehensive Framework for Differentiating Autism Spectrum Disorder From Neurotypicals by Fusing Structural MRI and Resting State Functional MRI

Autism spectrum disorder is a neurodevelopmental disorder characterized by impaired social abilities and communication difficulties. The golden standard for autism diagnosis in research rely on behavioral features, for example, the autism diagnosis observation schedule, the Autism Diagnostic Interview-Revised. In this study we introduce a computer-aided diagnosis system that uses features from structural MRI (sMRI) and resting state functional MRI (fMRI) to help predict an autism diagnosis by clinicians. The proposed system is capable of parcellating brain regions to show which areas are most likely affected by autism related abnormalities and thus help in targeting potential therapeutic interventions. When tested on 18 data sets (n = 1060) from the ABIDE consortium, our system was able to achieve high accuracy (sMRI 0.75-1.00; fMRI 0.79-1.00), sensitivity (sMRI 0.73-1.00; fMRI 0.78-1.00), and specificity (sMRI 0.78-1.00; fMRI 0.79-1.00). The proposed system could be considered an important step toward helping physicians interpret results of neuroimaging studies and personalize treatment options. To the best of our knowledge, this work is the first to combine features from structural and functional MRI, use them for personalized diagnosis and achieve high accuracies on a relatively large population.

A convolutional neural network for the screening and staging of diabetic retinopathy

Diabetic retinopathy (DR) is a serious retinal disease and is considered as a leading cause of blindness in the world. Ophthalmologists use optical coherence tomography (OCT) and fundus photography for the purpose of assessing the retinal thickness, and structure, in addition to detecting edema, hemorrhage, and scars. Deep learning models are mainly used to analyze OCT or fundus images, extract unique features for each stage of DR and therefore classify images and stage the disease. Throughout this paper, a deep Convolutional Neural Network (CNN) with 18 convolutional layers and 3 fully connected layers is proposed to analyze fundus images and automatically distinguish between controls (i.e. no DR), moderate DR (i.e. a combination of mild and moderate Non Proliferative DR (NPDR)) and severe DR (i.e. a group of severe NPDR, and Proliferative DR (PDR)) with a validation accuracy of 88%-89%, a sensitivity of 87%-89%, a specificity of 94%-95%, and a Quadratic Weighted Kappa Score of 0.91–0.92 when both 5-fold, and 10-fold cross validation methods were used respectively. A prior pre-processing stage was deployed where image resizing and a class-specific data augmentation were used. The proposed approach is considerably accurate in objectively diagnosing and grading diabetic retinopathy, which obviates the need for a retina specialist and expands access to retinal care. This technology enables both early diagnosis and objective tracking of disease progression which may help optimize medical therapy to minimize vision loss.

The developed Arabic version of the Hearing Handicap Inventory for the Elderly

Background

Hearing impairment ranks third on the list of chronic health conditions of the elderly, after arthritis and hypertension. As average lifespans increase, it is likely that the proportion of people with hearing loss will also increase. The purpose of the study was to develop, standardize, and apply an Arabic version of the Hearing Handicap Inventory for the Elderly (HHIE).

Results

The mean age of the 100 subjects included in the pretest was 64.92 ± 5.937 with age ranged from 60 to 84 years. The average score for each item (simple, clear, and relevant) for each separate question obtained more than 80% which is considered valid. One hundred percent of the participants reported that the entire inventory appeared simple, clear, and relevant, we further implied the jury opinion; the total score average of our jury for the entire inventory was calculated to determine the face validity of the questionnaire and found to be 89.81%. Responses of all participants for each question were collected and showed questions 8, 21, 6, 7, and 14 obtained the highest response results for both yes and sometimes. The HHIE showed high reliability (p value < 0.001) for all questions. The demographic data of the forty participants showed no statistically significant difference between the complaining group of hearing loss and the non-complaining group as regards age and gender. There was a highly statistically significant difference between the complaining group and the non-complaining group regarding the HHIE. The sensitivity of the HHIE was 79% for severe auditory handicapping and only 24% for mild-to-moderate auditory handicapping.

Conclusion

The developed Arabic version of the HHIE has high reliability, validity, simplicity, and clarity which found consistent with the original English questionnaire and it performed well in the detection of hearing loss in elderly Egyptians. It can be applied in a large population and for use in surveys.

A multimodal computer-aided diagnostic system for precise identification of renal allograft rejection: Preliminary results

PURPOSE

Early assessment of renal allograft function post-transplantation is crucial to minimize and control allograft rejection. Biopsy - the gold standard - is used only as a last resort due to its invasiveness, high cost, adverse events (e.g., bleeding, infection, etc.), and the time for reporting. To overcome these limitations, a renal computer-assisted diagnostic (Renal-CAD) system was developed to assess kidney transplant function.

METHODS

The developed Renal-CAD system integrates data collected from two image-based sources and two clinical-based sources to assess renal transplant function. The imaging sources were the apparent diffusion coefficients (ADCs) extracted from 47 diffusion-weighted magnetic resonance imaging (DW-MRI) scans at 11 different b-values (b0, b50, b100, ..., b1000 s/mm 2 ), and the transverse relaxation rate (R2*) extracted from 30 blood oxygen level-dependent MRI (BOLD-MRI) scans at 5 different echo times (TEs = 2, 7, 12, 17, and 22 ms). Serum creatinine (SCr) and creatinine clearance (CrCl) were the clinical sources for kidney function evaluation. The Renal-CAD system initially performed kidney segmentation using the level-set method, followed by estimation of the ADCs from DW-MRIs and the R2* from BOLD-MRIs. ADCs and R2* estimates from 30 subjects that have both types of scans were integrated with their associated SCr and CrCl. The integrated biomarkers were then used as our discriminatory features to train and test a deep learning-based classifier, namely stacked autoencoders (SAEs) to differentiate non-rejection (NR) from acute rejection (AR) renal transplants.

RESULTS

Using a leave-one-subject-out cross-validation approach along with SAEs, the Renal-CAD system demonstrated 93.3% accuracy, 90.0% sensitivity, and 95.0% specificity in differentiating AR from NR. Robustness of the Renal-CAD system was also confirmed by the area under the curve value of 0.92. Using a stratified tenfold cross-validation approach, the Renal-CAD system demonstrated its reproducibility and robustness by a diagnostic accuracy of 86.7%, sensitivity of 80.0%, specificity of 90.0%, and AUC of 0.88.

CONCLUSION

The obtained results demonstrate the feasibility and efficacy of accurate, noninvasive identification of AR at an early stage using the Renal-CAD system.

Micellar high performance liquid chromatographic method for separation and validation of two anti-hepatitis C- virus drugs in pure form, human plasma and human urine

OBJECTIVES

In the present study, an eco- friendly micellar liquid chromatographic technique was validated for separation and quantification of two drugs; namely ribavirin (RIV), and sofosbuvir (SBV) in pure form, pharmaceuticals containing them, human plasma and human urine. These drugs are administered co-administered for treatment of Hepatitis C virus (HCV) that causes hepatitis C in humans.

MATERIAL AND METHODS

These drugs were separated using Nucleosil 100-5 phenyl column. Sodium dodecyl sulphate (SDS) solution (0.05M, pH 7.0) containing triethylamine (0.3%) and n-butanol (10%) was used as a mobile phase with 1.2 mLmin^-1 flow rate and 215nm detection wavelength. Nine minutes were required for resolving the two drugs from the matrix.

RESULTS

The method showed good linearity for RIV and SBV with correlation coefficients (r²) more than 0.9996 within the concentration ranges of (20-400) and (40-400) ngmL^-1 in pure form, (30-300) and (50-300) ngmL^-1 in human plasma and (20-400) and (40-400) ngmL^-1 in human urine, respectively.

CONCLUSION

The recommended method was applied for examination of RIV and SBV in pure and pharmaceuticals. The obtained results were statistically matched with reported methods with no significant differences. Also, the recommended method was effectively applied for estimation of both drugs in spiked human urine and plasma without purification or extraction steps and real samples of plasma and urine of humans having therapy of RIV and SBV, as well as, performing tablets dissolution-rate tests with satisfactory results.

EARLY ASSESSMENT OF RENAL TRANSPLANTS USING BOLD-MRI: PROMISING RESULTS

Non-invasive evaluation of renal transplant function is essential to minimize and manage renal rejection. A computer-assisted diagnostic (CAD) system was developed to evaluate kidney function post-transplantation. The developed CAD system utilizes the amount of blood-oxygenation extracted from 3D (2D + time) blood oxygen level-dependent magnetic resonance imaging (BOLD-MRI) to estimate renal function. BOLD-MRI scans were acquired at five different echo-times (2, 7, 12, 17, and 22) ms from 15 transplant patients. The developed CAD system first segments kidneys using the level-sets method followed by estimation of the amount of deoxyhemoglobin, also known as apparent relaxation rate (R2*). These R2* estimates were used as discriminatory features (global features (mean R2*) and local features (pixel-wise R2*)) to train and test state-of-the-art machine learning classifiers to differentiate between non-rejection (NR) and acute renal rejection. Using a leave-one-out cross-validation approach along with an artificial neural network (ANN) classifier, the CAD system demonstrated 93.3% accuracy, 100% sensitivity, and 90% specificity in distinguishing AR from non-rejection . These preliminary results demonstrate the efficacy of the CAD system to detect renal allograft status non-invasively.

A Novel Framework for Early Detection of Hypertension using Magnetic Resonance Angiography

Hypertension is a leading mortality cause of 410,000 patients in USA. Cerebrovascular structural changes that occur as a result of chronically elevated cerebral perfusion pressure are hypothesized to precede the onset of systemic hypertension. A novel framework is presented in this manuscript to detect and quantify cerebrovascular changes (i.e. blood vessel diameters and tortuosity changes) using magnetic resonance angiography (MRA) data. The proposed framework consists of: 1) A novel adaptive segmentation algorithm to delineate large as well as small blood vessels locally using 3-D spatial information and appearance features of the cerebrovascular system; 2) Estimating the cumulative distribution function (CDF) of the 3-D distance map of the cerebrovascular system to quantify alterations in cerebral blood vessels' diameters; 3) Calculation of mean and Gaussian curvatures to quantify cerebrovascular tortuosity; and 4) Statistical and correlation analyses to identify the relationship between mean arterial pressure (MAP) and cerebral blood vessels' diameters and tortuosity alterations. The proposed framework was validated using MAP and MRA data collected from 15 patients over a 700-days period. The novel adaptive segmentation algorithm recorded a 92.23% Dice similarity coefficient (DSC), a 94.82% sensitivity, a 99.00% specificity, and a 10.00% absolute vessels volume difference (AVVD) in delineating cerebral blood vessels from surrounding tissues compared to the ground truth. Experiments demonstrated that MAP is inversely related to cerebral blood vessel diameters (p-value < 0.05) globally (over the whole brain) and locally (at circle of Willis and below). A statistically significant direct correlation (p-value < 0.05) was found between MAP and tortuosity (medians of Gaussian and mean curvatures, and average of mean curvature) globally and locally (at circle of Willis and below). Quantification of the cerebrovascular diameter and tortuosity changes may enable clinicians to predict elevated blood pressure before its onset and optimize medical treatment plans of pre-hypertension and hypertension.

Quantifying Argonaute 2 (Ago2) expression to stratify breast cancer

BACKGROUND

Argonaute-2 (Ago2) is an essential component of microRNA biogenesis implicated in tumourigenesis. However Ago2 expression and localisation in breast cancer remains undetermined. The aim was to define Ago2 expression (mRNA and protein) and localisation in breast cancer, and investigate associations with clinicopathological details.

METHODS

Ago2 protein was stained in breast cancer cell lines and tissue microarrays (TMAs), with intensity and localization assessed. Staining intensity was correlated with clinicopathological details. Using independent databases, Ago2 mRNA expression and gene alterations in breast cancer were investigated.

RESULTS

In the breast cancer TMAs, 4 distinct staining intensities were observed (Negative, Weak, Moderate, Strong), with 64.2% of samples stained weak or negatively for Ago2 protein. An association was found between strong Ago2 staining and, the Her2 positive or basal subtypes, and between Ago2 intensity and receptor status (Estrogen or Progesterone). In tumours Ago2 mRNA expression correlated with reduced relapse free survival. Conversely, Ago2 mRNA was expressed significantly lower in SK-BR-3 (HER2 positive) and BT-20 (Basal/Triple negative) cell lines. Interestingly, high levels of Ago2 gene amplification (10-27%) were observed in breast cancer across multiple patient datasets. Importantly, knowledge of Ago2 expression improves predictions of breast cancer subtype by 20%, ER status by 15.7% and PR status by 17.5%.

CONCLUSIONS

Quantification of Ago2 improves the stratification of breast cancer and suggests a differential role for Ago2 in breast cancer subtypes, based on levels and cellular localisation. Further investigation of the mechanisms affecting Ago2 dysregulation will reveal insights into the molecular differences underpinning breast cancer subtypes.

Automated framework for accurate segmentation of leaf images for plant health assessment

Leaf segmentation is significantly important in assisting ecologists to automatically detect symptoms of disease and other stressors affecting trees. This paper employs state-of-the-art techniques in image processing to introduce an accurate framework for segmenting leaves and diseased leaf spots from images. The proposed framework integrates an appearance model that visually represents the current input image with the color prior information generated from RGB color images that were formerly saved in our database. Our framework consists of four main steps: (1) Enhancing the accuracy of the segmentation at minimum time by making use of contrast changes to automatically identify the region of interest (ROI) of the entire leaf, where the pixel-wise intensity relations are described by an electric field energy model. (2) Modeling the visual appearance of the input image using a linear combination of discrete Gaussians (LCDG) to predict the marginal probability distributions of the grayscale ROI main three classes. (3) Calculating the pixel-wise probabilities of these three classes for the color ROI based on the color prior information of database images that are segmented manually, where the current and prior pixel-wise probabilities are used to find the initial labels. (4) Refining the labels with the generalized Gauss-Markov random field model (GGMRF), which maintains the continuity. The proposed segmentation approach was applied to the leaves of mangrove trees in Abu Dhabi in the United Arab Emirates. Experimental validation showed high accuracy, with a Dice similarity coefficient 90% for distinguishing leaf spot from healthy leaf area.