Selection of lansoprazole from an FDA-approved drug library to inhibit the Alzheimer's disease seed-dependent formation of tau aggregates.

The efficacy of current treatments is still insufficient for Alzheimer's disease (AD), the most common cause of Dementia. Out of the two pathological hallmarks of AD amyloid-ß plaques and neurofibrillary tangles, comprising of tau protein, tau pathology strongly correlates with the symptoms of AD. Previously, screening for inhibitors of tau aggregation that target recombinant tau aggregates have been attempted. Since a recent cryo-EM analysis revealed distinct differences in the folding patterns of heparin-induced recombinant tau filaments and AD tau filaments, this study focused on AD seed-dependent tau aggregation in drug repositioning for AD. We screened 763 compounds from an FDA-approved drug library using an AD seed-induced tau aggregation in SH-SY5Y cell-based assay. In the first screening, 180 compounds were selected, 72 of which were excluded based on the results of lactate dehydrogenase assay. In the third screening with evaluations of soluble and insoluble tau, 38 compounds were selected. In the fourth screening with 3 different AD seeds, 4 compounds, lansoprazole, calcipotriene, desogestrel, and pentamidine isethionate, were selected. After AD seed-induced real-time quaking-induced conversion, lansoprazole was selected as the most suitable drug for repositioning. The intranasal administration of lansoprazole for 4 months to AD seed-injected mice improved locomotor activity and reduced both the amount of insoluble tau and the extent of phosphorylated tau-positive areas. Alanine replacement of the predicted binding site to an AD filament indicated the involvement of Q351, H362, and K369 in lansoprazole and C-shaped tau filaments. These results suggest the potential of lansoprazole as a candidate for drug repositioning to an inhibitor of tau aggregate formation in AD.

Decoding the Intricacies of Statin-Associated Muscle Symptoms.

PURPOSE OF REVIEW: Hyperlipidemia is the major cardiovascular morbidity and mortality risk factor. Statins are the first-line treatment for hyperlipidemia. Statin-associated muscle symptoms (SAMS) are the main reason for the discontinuation of statins among patients. The purpose of this review is to guide clinicians to recognize the difference between self-limited and autoimmune statin myopathy in addition to the factors that potentiate them. Finally, treatment strategies will be discussed. This review mostly focuses on new data in the past 3 years. RECENT FINDINGS: Recent findings suggest that SAMS is a complex and multifactorial condition that involves mitochondrial dysfunction, oxidative stress, and immune-mediated mechanisms. Effective management of SAMS requires a thorough evaluation of the patient's symptoms, risk factors, and medication history, as well as consideration of alternative treatment options. While statins are effective in reducing the risk of cardiovascular events, their use is associated with a range of adverse effects, including SAMS.

Liver-boosting potential: chicory compound-mediated silver nanoparticles for hepatoprotection-biochemical and histopathological insights.

Background: Liver disease is a serious health concern in today's world, posing a challenge to both healthcare providers and pharmaceutical companies. Most synthetic drugs and chemicals cause liver damage accounting for approximately 10% of acute hepatitis and 50% of acute liver failure. Purpose: The present study aimed to evaluate the hepato-protective activity of an extract of chicory formulation assisted by silver nanoparticles against carbon tetra chloride (CCl4)-induced hepatic damage in rat's liver. Methods: Rats of the Wistar strain (Rattus norvegicus) were used to test the in vivo hepato-protective efficacy at various doses. Rats were randomly divided into nine groups, each containing six rats. The groups were as follows: first group (control), second group (CCl4), third group, silymarin (20 mg/kg of body weight), fourth group (CCl4+chicory) (1.75 mg/kg of b. wt), fifth group (CCl4 + chicory at the dose of 2.35 mg/kg), sixth group (CCl4 + chicory of 3.25 mg/kg), seventh group (CCl4 +AgNPs 1.75 mg/kg of b. wt.), eighth group (CCl4 + AgNPs 2.35 mg/kg of body weight), and ninth group (CCl4 + AgNPs 3.25 mg/kg of b. wt.). Blood samples were taken 24 h after the last administration (i.e., 30th day). The blood samples were analyzed for different serum enzymes such as ALP (alkaline phosphatase), ALT (alanine transaminase), bilirubin (Blr), triglyceride, and cholesterol. Histology liver sections were performed. Results: Treatment with AgNPs and chicory extract showed significant hepato-protective activity in a dose-dependent manner. In three doses, the chicory extract at a rate of 3.25 mg/kg of body weight significantly reduced elevated levels of biochemical markers in comparison to CCl4-intoxicated rats. Histology of the liver sections from CCl4-treated rats revealed inflammation of hepatocytes, necrosis, cytoplasmic degeneration, vacuolization, and a deformed central vein. The chicory formulation extract exhibited a remarkable recovery percentage in the liver architecture that was higher than the drug (i.e., silymarin). While treatment with AgNPs also repaired the degenerative changes and restored the normal form of the liver, chicory formulation extract possessed more hepato-protective potential as compared to AgNPs by regulating biochemical and histo-pathological parameters. Conclusion: This study can be used as confirmation of the hepato-protective potential of chicory compounds for possible use in the development programs of drugs to treat liver diseases.

Municipal solid waste management: Identification and analysis of technology selection criteria using Fuzzy Delphi and Fuzzy DEMATEL technique.

Municipal solid waste management (MSWM) poses a considerable challenge to developing countries like Bangladesh because of the rising waste generation rates and lack of effective management practices such as illegal open dumping and informal waste collection. One of the crucial factors in the successful management of MSW is to select the appropriate technology which is a complex multi-criteria and laborious process. Despite the global emphasis on the importance of MSWM in the literature, there is a lack of studies conducted in developing countries that effectively identify and analyze the critical performance criteria for appropriate technology selection. This research aims to address this shortcoming by identifying, and prioritizing the selection criteria and finally investigating the inter-relationship between them and the degree to which they affect or are affected by one another. First, a thorough literature review and expert consultation were employed to determine a set of 21 key criteria using the Fuzzy Delphi method (FDM). Later, taking into account the imprecise and subjective nature of the DEMATEL method on human judgements, the Fuzzy DEMATEL technique was employed to investigate the cause-effect relationships among the identified criteria. The findings of the study demonstrated that 14 criteria were categorized as causal elements that have the most significant influence on the MSWM technology selection process and 7 criteria were categorized as effect. The selection of MSWM technology demands greater consideration of the top three ranked criteria, namely T4- Access to Technology (AT), T8- Feasibility (F), and the Ec6-Infrastructure requirements (IR). By identifying the pertinent criteria, structures and interrelationships, the outcome of the study can facilitate a better understanding of causal relationships among the criteria that require specific consideration from the decision-makers and allow them to select appropriate MSW management technology.

Factors affecting the consumption of iodized salt by pregnant women in Karachi.

Background and Objective: Globally iodine deficiency disorder (IDD) is a major preventable cause of cognitive impairment in new born. In developing countries, every year 38 million newborn develop cognitive impairment as a result of iodine deficiency. Iodine consumption by pregnant women is affected by many factors. Hence, we conducted this study to identify factors associated with IDD. To know the effects of different factors on use of iodized salt by pregnant women visiting antenatal outpatient department (OPD) at a public sector tertiary care hospital of Karachi. Methods: Pregnant women (n=360) visiting antenatal OPD at public sector tertiary care hospital of Karachi were interviewed using a structured questionnaire. Systematic random sampling method was employed. Data was collected from March 2017 to January 2018. Chi-square test was applied to identify factors associated with IDD. Results: Thirty one (31% ) of pregnant women were consuming iodized salt in their homes. The percentage of participants who heard about iodized salt for the first time was 24%. Twelve percent (12%) reported that price of iodized salt is more than that of normal salt. Ninety eight (98%) of pregnant women replied that they were not informed about the importance of iodine or iodine requirement during pregnancy by their doctor or health care provider. A statistically significant association was observed between educational status (p=0.001) and household income (p<0.001) with the use of iodized salt. Conclusion: Low education, low income of study participants are identified as factors related to limited consumption and incorrect practices related to the use of iodized salt. In order to address iodine deficiency, there is a need to not only sensitize the expectant mothers about the adverse outcome of maternal iodine deficiency for their unborn child but also to introduce an awareness program at the antenatal clinics by the Health Professional for the antenatal care seeking women. There is also an immense need of support from Government side as well to make strategies and policy changes at the national level to ensure the availability, accessibility and affordability of iodized salt.

Parasitism preference of Chalcid hymenopteran Dirhinus giffardii (Silvestri) confirms higher parasitism against housefly (Musca domestica) (Diptera: Muscidae) pupae.

The housefly, Musca domestica (Diptera: Muscidae), is capable of transmitting many pathogens that cause severe diseases in humans and animals. Mostly the management tactics rely on synthetic chemicals, but these chemicals creates lethal effects on biological ecosystem. For natural and safe options, bio-control strategy is one of the choice. The present study was a part of such effort to use this strategy and validate the biological performance of the potential pupal parasitoid Dirhinus giffardii (Silvestri) against house fly and provide alternative and safe control of filthy flies. This is the first report on parasitism potential and preference of D. giffardii against house fly. The D. giffardii, early reported as an effective pupal parasitoid of tephritid flies, here in the case presented, showed overall 70% reduction in the house fly population by parasitizing pupae. The parasitism efficiency and longevity of hymenopteran parasitoid was remarkably noted two-fold higher and one-fold more female production on house fly pupae as compared to primary hosts (Tephritids). Furthermore, sex ratio of the resultant progeny was also confirmed the dominancy of female by 74% as compared to males. Based on the novel findings we therefore conclude that D. giffardii is the best bio-control agent for controlling house flies.

Knowledge, attitude and practices regarding implants among women of childbearing age.

A cross-sectional survey was conducted from February 2016 to September 2016 to assess the knowledge, attitude and practices regarding contraceptive implants among women of childbearing age visiting the Reproductive Health Services Centre, Civil Hospital, Karachi. A total of 396 adult, non-pregnant, married women of childbearing age, between 18 and 49 years, were interviewed using a structured questionnaire. Only 153 (38.6%) of the respondents had any knowledge about implants, out of whom 122 (79.7%) had acquired the information from family planning clinics. Almost two thirds of the respondents, 267(67.4%) were in favour of using implants as a contraceptive method. Moreover, 244 (61.6%) respondents were of the opinion that if given a choice, they will use implanon, though out of the 316 (79.8%) respondents who had ever used contraceptives, only 3 (0.9%) used implants. Despite a favourable attitude, limited knowledge and poor practices of the respondents were the highlights of the study findings.

Physical mapping of chromosome 7J and a purple coleoptile gene from Thinopyrum intermedium in the common wheat background.

MAIN CONCLUSION: A physical map of Thinopyrum intermedium chromosome 7J was constructed using translocation mapping, and a new seedling purple coleoptile gene was mapped to the bin of FL 0.35-0.63 of 7JS. Thinopyrum intermedium (2n = 6x = 42, JJJsJsStSt), a wild relative of common wheat, harbors numerous beneficial genes for wheat improvement. Previous studies showed that wheat-Th. intermedium partial amphiploid TAF46 and its derived addition line L1 had a purple coleoptile, which was derived from Th. intermedium chromosome 7J. To identify and physically map the purple coleoptile gene, 12 wheat-Th. intermedium 7J translocation lines were analyzed by sequential multicolor fluorescence in situ hybridization (mc-FISH), PCR-based landmark unique gene (PLUG) and intron targeting (IT) markers. A physical map of the 7J chromosome was constructed, consisting of eight chromosomal bins with 89 markers. Seedling evaluation of the coleoptile colors of all tested materials indicated that the purple coleoptile gene was located to the bin with a fraction length (FL) of 0.35-0.63 on chromosome 7JS. Furthermore, based on the syntenic relationships between Th. intermedium and wheat chromosomes, we developed a new chromosome 7J-specific EST-PCR marker from the chromosomal region corresponding to the purple coleoptile gene through the Triticeae multi-omics database. The approach of designing chromosome-specific markers has facilitated fine mapping of the Thinopyrum-specific purple coleoptile gene, and these translocation lines will be valuable for studying the function of the purple coleoptile gene in anthocyanin biosynthesis.

Kolb's Learning Style Inventory 4.0 and its association with traditional and problem based learning teaching methodologies in medical students.

OBJECTIVES: To assess learning styles and the association of various teaching methodologies of medical students. METHODS: A cross-sectional study was carried out amongst 523 medical students of Baqai Medical College, Baqai Medical University, Karachi, from July 2019 to October 2019. All students from first to final year, who attended the undergraduate MBBS program were included. The study instrument was a questionnaire containing students' demographic details, David Kolb's Learning Style Inventory 4.0 and traditional and PBL teaching methodologies were asked. The association of various learning styles and preferred teaching methodologies with year of study was also assessed by using Pearson's chi-square test. RESULTS: Out of 523 students, 518 returned the completed questionnaire. A majority of the students had either imagining or experiencing learning style. No change in learning style was observed between years of study. A significant association between the teaching methodologies and year of study was found in the imagining (p=0.033) and experiencing (p=0.044) learning style groups. CONCLUSION: Students from different years of study at medical school did not have significantly different learning styles though the student's preferences to teaching methodologies seem to change over time in the respective learning style groups. Longitudinal studies are necessary to identify the factors influencing such change and explore the association between learning styles over time on teaching methodologies in medical education.

Revisiting the physical mutagenesis for sugarcane improvement: a stomatal prospective.

This study was conducted to investigate the influence of gamma rays on stomatal parameters and the interaction of these traits with agronomy of the sugarcane crop. Three genotypes of sugarcane (NIA-0819, NIA-98, and BL4) were exposed to four doses of gamma radiation (10, 20, 30, and 40 Gy) and then subjected to field trials. Stomatal length, width, and density were observed using scanning electron microscopy. Moreover, agronomic and sugar-related characteristics of the crop were determined at maturity. The stomatal parameters augmented at two lower doses of gamma radiation (10 and 20 Gy) and declined under higher doses (30 and 40 Gy). The maximum stomatal length was observed in NIA-0819 at 10 Gy (63.71 µm), whereas NIA-98 and BL4 demonstrated highest stomatal length under 20 Gy (54.11 and 57.40 µm, respectively), indicating a role of genetic factors in varietal response. Similar trend was noticed for stomatal width and density as well. The lowermost treatment (10 Gy) of NIA-0819 produced maximum stomatal density (115.31 stomata mm-2 on the abaxial surface). Adaxial stomatal density was significantly lower than the abaxial one. Sugar quality attributes revealed a different tendency. Sucrose contents of BL4 increased gradually from 12.33% at control to 14.54% at 40 Gy. Yield and yield-contributing traits of genotypes indicated a strong positive correlation with the stomatal parameters. The present study suggested that gamma radiations cause variations in stomatal characteristics of sugarcane. These changes further influence the photosynthetic activity and instigate a direct impact on the agronomic performance of the crop.

Knowledge, attitude and practice regarding management of health care waste among private dental practitioners.

A knowledge, attitude and practice cross-sectional survey was conducted among dental practitioners doing private practice in district central, Karachi after checking eligibility and taking their written informed consent. The practitioners were interviewed by the principal investigator with the help of a pre-tested structured questionnaire developed specifically for this study. The study results revealed that 175(88.8%) of the participants were aware that waste management guidelines are applicable to them, 184(93.4%) were aware that healthcare waste is a source of infection whereas 145(73.6%) were aware about the colour coding of healthcare waste bags/containers. Moreover, 165(84.1%) thought that health care waste disposal protocol lessens the chance of injuries and infections whereas 182(92.0%) were in favour of the need to continue medical education about health care waste management. It is recommended that healthcare waste should be segregated and disposed of in a safe manner to protect the people at risk and the environment.

Detecting prostate cancer using deep learning convolution neural network with transfer learning approach.

Prostate Cancer in men has become one of the most diagnosed cancer and also one of the leading causes of death in United States of America. Radiologists cannot detect prostate cancer properly because of complexity in masses. In recent past, many prostate cancer detection techniques were developed but these could not diagnose cancer efficiently. In this research work, robust deep learning convolutional neural network (CNN) is employed, using transfer learning approach. Results are compared with various machine learning strategies (Decision Tree, SVM different kernels, Bayes). Cancer MRI database are used to train GoogleNet model and to train Machine Learning classifiers, various features such as Morphological, Entropy based, Texture, SIFT (Scale Invariant Feature Transform), and Elliptic Fourier Descriptors are extracted. For the purpose of performance evaluation, various performance measures such as specificity, sensitivity, Positive predictive value, negative predictive value, false positive rate and receive operating curve are calculated. The maximum performance was found with CNN model (GoogleNet), using Transfer learning approach. We have obtained reasonably good results with various Machine Learning Classifiers such as Decision Tree, Support Vector Machine RBF kernel and Bayes, however outstanding results were obtained by using deep learning technique.

Antibiotic prescription patterns for treating dental infections in children among general and pediatric dentists in teaching institutions of Karachi, Pakistan.

BACKGROUND: Antibiotics are regularly prescribed by dental professionals in their practice, for the purpose of dental treatment as well as for the prevention of infection. The inappropriate use of antibiotics is a significant factor in the rise of antibiotic resistance. There is an immediate need for the advancement of prescribing guidelines and instructive polices to encourage the rational and appropriate utilization of medications especially antibiotics in dentistry. OBJECTIVE: The aim of this study was to identify the frequency of antibiotic prescription for treating dental infections in children among dentists in teaching institutions of Karachi, Pakistan and whether they are adhering to the prescribed international guidelines. METHODS: A cross-sectional study was conducted in three private and two public colleges of Karachi. After taking written informed consent and checking the inclusion criteria, a total of 380 participants were interviewed using a pre-designed validated questionnaire which included demographic profile and clinical case scenarios. Data were entered and analyzed on SPSS version 20. Inferential analysis was performed using chi-square test. The significance level was set at 0.05. RESULTS: Of the 380 subjects, a majority (71.3%) treated 15 or less children per month (n = 271) while 28.7% of dentists (n = 109) treated more than 15 children per month. Overall adherence to American Academy of Pediatric Dentistry guidelines was low from 26.1% to 44.2%. The difference between adherence of dentists with low and high volume of pediatric patients was significantly different for case scenarios 1, 3, 4 and 5 (p<0.001 for all) where dentists who treated 15 or less children per month were more likely to be adherent to standard antibiotic prescription guidelines than those who treated more than 15 children per month. CONCLUSIONS: This study shows that majority of dentists, particularly dentists with high volume of pediatric patients lacked adherence to professional guidelines for prescribing antibiotics for treating dental infection in children. There seem to be a lack of harmony between the recommended professional guidelines and the antibiotic prescribing pattern of dentists. Regular updates and continuing medical education for the health professionals regarding comprehensible and specific professional guidelines may lead to improved adherence of antibiotics prescription amongst dentists.

Simple contents and good readability: Improving health literacy for LEP populations.

Accessing and receiving quality healthcare in an unfamiliar health system is a significant challenge for many new immigrants and refugees. This study aims to provide a three-phase model to develop a web-based health information website that helps populations with limited English proficiency (LEP) increase health literacy and improve healthcare service access. METHOD: First, we conducted a needs assessment from community leaders and service providers. Second, we developed contents from credible sources and tested each item using multiple readability tests. Last, we revised each item to lower the readability and retest its readability. RESULTS: The average reading level for the original 99 topics was assessed at 10.84 (SD= 3.26). After revisions, we were able to lower the readability to 8.56 (SD= 2.96), which was around two grade levels lower, on average. CONCLUSION: the main purpose for building an English based health information website was to assist the population with LEP. By using simple English with lower readability, it will ease the translation process. This study demonstrates a process to develop suitable contents for populations in need. In the future, incorporating visual aid and other multimedia will be beneficial in user engagement and knowledge retention.

Knowledge and practice regarding dog bite management among general practitioners of District Malir, Karachi.

OBJECTIVE: To assess knowledge and practice regarding dog bite management among general practitioners in a suburban setting. METHODS: This cross-sectional knowledge and practice study was conducted from March 2017 to October 2017 in district Malir, Karachi, and comprised general practitioners conveniently recruited from 32 randomly selected union councils of the district. The participants were interviewed with the help of a self-generated structured questionnaire. Data were analysed in SS PS 21 . RESULTS: Of the 92 practitioners, 67(72.8%) were males, 43(46.7%) had>10 years' experience, and 63(68.5%)were privately employed. The overall mean age of the sample was 43.77±11.5 years. Mean knowledge scores varied significantly across categories of experience only (p=0.020), withthe lessexperienced practitioners having significantly higher mean knowledge compared to the seniors. CONCLUSIONS: The experience of the general practitioners significantly affected their knowledge with recent graduates found to have higher mean k nowledge scores than older graduates.

Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques.

The adaptability of heart to external and internal stimuli is reflected by the heart rate variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes. Based on the nonlinear, nonstationary, and highly complex dynamics of the controlling mechanism of the cardiovascular system, linear HRV measures have limited capability to accurately analyze the underlying dynamics. In this study, we propose an automated system to analyze HRV signals by extracting multimodal features to capture temporal, spectral, and complex dynamics. Robust machine learning techniques, such as support vector machine (SVM) with its kernel (linear, Gaussian, radial base function, and polynomial), decision tree (DT), k-nearest neighbor (KNN), and ensemble classifiers, were employed to evaluate the detection performance. Performance was evaluated in terms of specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). The highest performance was obtained using SVM linear kernel (TA = 93.1%, AUC = 0.97, 95% CI [lower bound = 0.04, upper bound = 0.89]), followed by ensemble subspace discriminant (TA = 91.4%, AUC = 0.96, 95% CI [lower bound 0.07, upper bound = 0.81]) and SVM medium Gaussian kernel (TA = 90.5%, AUC = 0.95, 95% CI [lower bound = 0.07, upper bound = 0.86]). The results reveal that the proposed approach can provide an effective and computationally efficient tool for automatic detection of congestive heart failure patients.

Electrolyte imbalance in critically ill paediatric patients.

OBJECTIVE: To determine the frequency and outcome of electrolyte imbalance in seriously ill children admitted in Paediatric Intensive Care Unit (PICU) of a public sector hospital in Karachi. METHODS: All children between the ages of one month to 12 years admitted in PICU from May 2017 to October 2017 were included. Blood samples were drawn to determine the baseline sodium, potassium, calcium, magnesium and phosphorous and followed 24 hourly or earlier, if needed (Those who had imbalance at admission or on subsequent repeat labs as per protocol). RESULTS: A total of 101 children were included in the study. Electrolyte imbalance was seen in 84%. A single electrolyte imbalance was noted in 30.58%. Hypocalcemia was the most frequent abnormality noted in 57.6%. Among the total expiries during the study period 91% had electrolyte imbalance. Mortality within 48 hours and duration of stay was also increased in these patients. CONCLUSION: Electrolyte imbalance is an important prognostic indicator in critically ill patients.

Regression analysis for detecting epileptic seizure with different feature extracting strategies.

Due to the excitability of neurons in the brain, a neurological disorder is produced known as epilepsy. The brain activity of patients suffering from epilepsy is monitored through electroencephalography (EEG). The multivariate nature of features from time domain, frequency domain, complexity and wavelet entropy based, and the statistical features were extracted from healthy and epileptic subjects using the Bonn University database and seizure and non-seizure intervals using the CHB MIT database. The robust machine learning regression methods based on regression, support vector regression (SVR), regression tree (RT), ensemble regression, Gaussian process regression (GPR) were employed for detecting and predicting epileptic seizures. Performance was measured in terms of root mean square error (RMSE), squared error, mean square error (MSE) and mean absolute error (MAE). Moreover, detailed optimization was performed using a RT to predict the selected features from each feature category. A deeper analysis was conducted on features and tree regression methods where optimal RMSE and MSE results were obtained. The best optimal performance was obtained using the ensemble boosted regression tree (BRT) and exponential GPR with an RMSE of 0.47, an MSE (0.22), an R Square (RS) (0.25) and an MAE (0.30) using the Bonn University database and support vector machine (SVM) fine Gaussian with RMSE (0.63634), RS (0.03), MSE (0.40493) and MAE (0.31744); squared exponential GPR and rational quadratic GPR with an RMSE of 0.63841, an RS (0.03), an MSE (0.40757) and an MAE (0.3472) was obtained using the CHB MIT database. A further deeper analysis for the prediction of selected features was performed on an RT to compute the optimal feasible point, observed and estimated function values, function evaluation time, objective function evaluation time and overall elapsed time.

Detecting Brain Tumor using Machines Learning Techniques Based on Different Features Extracting Strategies.

BACKGROUND: Brain tumor is the leading cause of death worldwide. It is obvious that the chances of survival can be increased if the tumor is identified and properly classified at an initial stage. MRI (Magnetic Resonance Imaging) is one source of brain tumors detection tool and is extensively used in the diagnosis of brain to detect blood clots. In the past, many researchers developed Computer-Aided Diagnosis (CAD) systems that help the radiologist to detect the abnormalities in an efficient manner. OBJECTIVE: The aim of this research is to improve the brain tumor detection performance by proposing a multimodal feature extracting strategy and employing machine learning techniques. METHODS: In this study, we extracted multimodal features such as texture, morphological, entropybased, Scale Invariant Feature Transform (SIFT), and Elliptic Fourier Descriptors (EFDs) from brain tumor imaging database. The tumor was detected using robust machine learning techniques such as Support Vector Machine (SVM) with kernels: polynomial, Radial Base Function (RBF), Gaussian; Decision Tree (DT), and Naïve Bayes. Most commonly used Jack-knife 10-fold Cross- Validation (CV) was used for testing and validation of dataset. RESULTS: The performance was evaluated in terms of specificity, sensitivity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), False Positive Rate (FPR), Total Accuracy (TA), Area under the receiver operating Curve (AUC), and P-value. The highest performance of 100% in terms of Specificity, Sensitivity, PPV, NPV, TA, AUC using Naïve Bayes classifiers based on entropy, morphological, SIFT and texture features followed by Decision Tree classifier with texture features (TA=97.81%, AUC=1.0) and SVM polynomial kernel with texture features (TA=94.63%). The highest significant p-value was obtained using SVM polynomial with texture features (P-value 2.65e-104) followed by SVM RB with texture features (P-value 1.96e-98). CONCLUSION: The results reveal that Naïve Bayes followed by Decision Tree gives highest detection accuracy based on entropy, morphological, SIFT and texture features.

Past, present and future of hepatitis E virus infection: Zoonotic perspectives.

The origin of hepatitis E virus (HEV) is not fully understood, but it is considered an emerging zoonotic pathogen. To date, HEV has been isolated from many animal species. The family Hepeviridae consists of two genera. The genus Orthohepevirus includes four distinct species (A, B, C, and D), each with distinct genotypes. Within the Orthohepevirus A species, HEV-1 and HEV-2 host ranges are restricted to humans, whereas genotypes 3 and 4 primarily infect a wide range of diverse animal species, in addition to being zoonotic to humans. Swine and wild boar species were previously thought to be the primary natural HEV reservoir, but recently rabbits have also been identified as major carriers. Moreover, increasing the number of HEV infections within the food supply chain underscore the important role of farming and food processing practices in limiting virus transmission. Notably, a Chinese commercial vaccine has the potential to protect humans and possibly animal reservoirs from HEV infection. This review summarizes the status of HEV infection worldwide in different animal species and outlines various modes of zoonotic transmission, with reference to cross-species transmission and recent vaccine developments.