Impact of automobile exhaust on biochemical and genomorphic characteristics of Mimusops elengi L. growing along roadsides of Lahore city, Pakistan

Automobile exhaust releases different types of pollutants that are at great risk to the air quality of the environment and incidental distress to the nature of roadside plants. Mimusops elengi L. is an evergreen medicinal tree cultivated along the roadside of Lahore City. This research aimed to investigate physiological, morphological and genomorphic characteristics of M. elengi under the influence of air pollution from vehicles. Healthy and mature leaves were collected from trees on Canal Bank and Mall roads of Lahore as the experimental sites and control sites were 20 km away from the experimental site. Different physiochemical, morphological, air pollution tolerance index (APTI) and molecular analysis for the detection of DNA damage were performed through comet assay. The results demonstrated the mean accumulated Cd, Pb, Cu and Ni heavy metal contents on the leaves were higher than the control plants (1.27, 3.22, 1.32 and 1.46 μg mg-1). APTI of trees was 9.04. Trees in these roads significantly (p < 0.01) had a lower leaf area, petiole length and leaf dry matter content in comparison to control site. Increased comet tail showed that DNA damage was higher for roadside trees than trees in the control area. For tolerance of air pollution, it necessary to check the APTI value for the M. elengi at the polluted road side of Lahore city. For long-term screening, the source and type of pollutants and consistent monitoring of various responses given by the trees should be known.

Scalable synthesis of high-quality, reduced graphene oxide with a large C/O ratio and its dispersion in a chemically modified polyimide matrix for electromagnetic interference shielding applications

High-purity reduced graphene oxide (RGO or rGO) with appreciable conductivity is a desired conductive filler for lightweight polymer composites used in coatings, electronics, catalysts, electromagnetic interference (EMI) shielding, and energy storage devices. However, the intrinsic conductivity and the uniform dispersion of RGO in relatively polar matrices are challenging, leading to poor overall conductivity and performance of the composite material. The reported study improved the RGO intrinsic conductivity by increasing its C/O ratio while also simultaneously enhancing its compatibility with the polyimide (PI) matrix through ester linkages for better dispersion. A two-step reduction method drastically increased the number of structural defects and carbon content in the resulting RGO, corresponding to a maximum ID/IG and C/O of 1.54 and ∼87, respectively. Moreover, the 2D nanosheets with limited hydroxyl (-OH) groups effectively interacted with anhydride-terminated polyamic acid (AT-PAA) through chemical linkages to make high-performance RGO/PI nanocomposites. Consequently, the polymer matrix composites possessed the highest direct current conductivity of 15.27 ± 0.61 S cm-1 for 20 wt% of the prepared RGO. Additionally, the composite material was highly stiff (3.945 GPa) yet flexible (easily bent through 180°), lightweight (∼0.34 g cm-3), and capable of forming thin films (162 ± 15 μm). Unlike most polymer matrix composites, it showcased one of its class's highest thermal stabilities (a weight loss of only 5% at 638 °C). Ultimately, the composite performed as an effective electromagnetic interference (EMI) shielding material in the X-Band (8 to 12 GHz), demonstrating outstanding shielding effectiveness (SE), shielding effectiveness per unit thickness (SEt), specific shielding effectiveness (SSE), and absolute shielding effectiveness (SSEt) of 46 dB, 2778 dB cm-2, 138 dB cm3 g-1, and 8358 dB cm2 g-1, respectively. As a consequence of this research, the high-purity RGO and its high-performance PI matrix nanocomposites are anticipated to find practical applications in conductive coatings and flexible substrates demanding high-temperature stability.

Survey regarding the use of endodontic posts among dentists of twin cities, Rawalpindi and Islamabad, Pakistan

The purpose of the study was to assess the knowledge, attitude, and practices of dentists of twin cities regarding the use of endodontic posts in root canal treated tooth. A questionnaire was created and distributed among dentists of Rawalpindi and Islamabad via social media platforms regarding the use of posts. The results revealed that majority (60%) of the participants used endodontic posts for teeth with adequate ferrule, and believed that the function of endodontic posts is to retain the core material (50.5%). Glass fibre posts were preferred for anterior teeth (87%), whereas metal posts were favoured in posterior teeth (63%). It was concluded that the main function of the endodontic post is to retain the core material. The commonest indication is when there is at least 2mm of ferrule present and the optimal post length is 2/3rd of the root canal.

Study of synergistic effects induced by novel base composites on heavy metals removal and pathogen inactivation

The green-collar strategies for nanomaterial synthesis with novel structural competencies have received significant attention in nanotechnology owing to their potential benefits. The utilization of silica nanoparticles for wastewater treatment through heavy metal ions remediation is the focal point of the present study. With this intent, silica was extracted from bagasse ash by the sol-gel method and modified using chitosan. Chemical and physical characteristics of silica(S), silica/Chitosan (SCs), were reckoned through X-ray Powder Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), and Scanning Electron Microscopy (SEM) and the efficiency of synthesized biomaterials for removing heavy metal ions. Cadmium and Lead from wastewater was evaluated by conducting closed batch experiments. Isotherm and kinetics models were applied to understand the adsorption mechanism. Results of heavy metal ions removal showed that the S possesses the highest removal efficiency of 88% for cadmium. Equilibrium was established within 56 min following a Langmuir isotherm model and pseudo-second-order reaction. The synthesized biomaterials were also tested against the fungal (Aspergillus Niger) and bacterial strains (Escherichia coli and Staphylococcus aureus) to determine their antimicrobial properties Maximum inhibition of 26 mm was shown by SCs for E.coli. Synthesized samples were not so effective for A.niger. The high adsorption potential of silica nanoparticles reveals their potential to treat wastewater containing inorganic pollutants like calcium and lead released from the sugar industry firsthand, thereby building a circular economy by controlling the pollution from source to sink. The synthesized silica nanoparticles and silica/chitosan biomaterials demonstrated high adsorption potential for heavy metal ions, making them promising candidates for integration into Algal Membrane Bioreactors to enhance wastewater treatment efficiency and remove toxic pollutants. Their multifunctional properties, including antimicrobial activity, also offer potential for improving microbial control within AMBRs, ensuring a more effective and sustainable wastewater treatment process.

Rapid Method for Quantification of Iron (Fe+3) from Ferrazone (NaFe-EDTA) in Fortified Wheat Flour

Conventional methods for quantifying the added iron in wheat flour are time-consuming and costly. A rapid method (Time/Sample: 95 min) was developed by modifying the conventional standard method (Time/Sample: 560 min) and validated. Linearity and linear regression of the rapid method presented excellent correlation coefficient (R²) values (0.9976 to 0.9991), which were close to 1, while the limits of agreement (LOA) were in the range of -0.01 to 0.06 mg/kg. The limits of detection (LOD)/specificity and limits of quantitation (LOQ)/sensitivity values were found to be 0.03 and 0.09 mg/kg, respectively. The rapid method was subjected to validation, wherein the precision of intra-assay, inter-assay, and inter-person was determined to be within the range of 1.35-7.25%. These results indicate a high level of accuracy and precision of the method. The percent relative standard deviation (RSD) for recoveries at varying spiking levels, that is, 5, 10, and 15 mg/kg, was determined at 1.33 lying far below the upper limit of acceptability (RSD < 20). Overall, the developed rapid method can be sustainably alternate for conventional methods owing to its ability to produce accurate, precise, robust, and reproducible results.

Knowledge, attitude, and perceptions about cancer genetic testing in clinical practice in Karachi, Pakistan

Healthcare professionals (HCP) play an important role in the practical application of genetic screening tests but often feel inadequately prepared for cancer genetic testing (CGT) in clinical care. As the complexity of gene-related malignancies increases, it demands HCPs' preparedness to cater to patients' needs. Therefore, the aim of our study is to assess the knowledge, attitude, and practices of HCPs in Pakistan regarding the application of cancer genetics. Our cross-sectional survey was conducted from April 2022 to June 2022 amongst HCPs at a private and a governmental institution in Karachi, Pakistan. Non-probability random convenience sampling was used to select the population; however. non-clinical HCPs, as well as Interns, were excluded from our study. A total of 210 HCPs, 56.7% (119) bearing an experience of over 5 years of clinical experience, were included in this study. Most respondents from both hospitals deemed their knowledge inadequate, with only 2% (2) and 1.8% (2) being extremely knowledgeable, respectively. 68.6% (144) HCPs displayed a positive attitude towards CGT, with 55.2% (116) participants perceiving CGT in a positive light. As compared to the private sector, significantly more HCPs in the public sector dedicated ≥ 5 h/week for CME (P = 0.006), and were better prepared to counsel patients (P = 0.021) and interpret results concerning CGT (P = 0.020). Additionally, screening tests for specific cancer types were popularly considered a worthwhile avenue of investment to improve the current state of CGT in our healthcare system [47.6% (N = 100)]. Demonstrating a lack of knowledge among Pakistani doctors, our results call upon the need for additional training concerning CGT in both the public and private sectors alike. Understanding specific gaps in knowledge may further help enhance post-graduate training programs and eventually lead to effective incorporation of CGT into our healthcare setting.

Deep features optimization based on a transfer learning, genetic algorithm, and extreme learning machine for robust content-based image retrieval

The recent era has witnessed exponential growth in the production of multimedia data which initiates exploration and expansion of certain domains that will have an overwhelming impact on human society in near future. One of the domains explored in this article is content-based image retrieval (CBIR), in which images are mostly encoded using hand-crafted approaches that employ different descriptors and their fusions. Although utilization of these approaches has yielded outstanding results, their performance in terms of a semantic gap, computational cost, and appropriate fusion based on problem domain is still debatable. In this article, a novel CBIR method is proposed which is based on the transfer learning-based visual geometry group (VGG-19) method, genetic algorithm (GA), and extreme learning machine (ELM) classifier. In the proposed method, instead of using hand-crafted features extraction approaches, features are extracted automatically using a transfer learning-based VGG-19 model to consider both local and global information of an image for robust image retrieval. As deep features are of high dimension, the proposed method reduces the computational expense by passing the extracted features through GA which returns a reduced set of optimal features. For image classification, an extreme learning machine classifier is incorporated which is much simpler in terms of parameter tuning and learning time as compared to other traditional classifiers. The performance of the proposed method is evaluated on five datasets which highlight the better performance in terms of evaluation metrics as compared with the state-of-the-art image retrieval methods. Its statistical analysis through a nonparametric Wilcoxon matched-pairs signed-rank test also exhibits significant performance.

Colchicine anti-inflammatory therapy for non-intensive care unit hospitalized COVID-19 patients: results from a pilot open-label, randomized controlled clinical trial

Systemic inflammation is a hallmark of severe coronavirus disease-19 (COVID-19). Anti-inflammatory therapy is considered crucial to modulate the hyperinflammatory response (cytokine storm) in hospitalized COVID-19 patients. There is currently no specific, conclusively proven, cost-efficient, and worldwide available anti-inflammatory therapy available to treat COVID-19 patients with cytokine storm. The present study aimed to investigate the treatment benefit of oral colchicine for hospitalized COVID-19 patients with suspected cytokine storm. Colchicine is an approved drug and possesses multiple anti-inflammatory mechanisms. This was a pilot, open-label randomized controlled clinical trial comparing standard of care (SOC) plus oral colchicine (colchicine arm) vs. SOC alone (control arm) in non-ICU hospitalized COVID-19 patients with suspected cytokine storm. Colchicine treatment was initiated within first 48 hours of admission delivered at 1.5 mg loading dose, followed by 0.5 mg b.i.d. for next 6 days and 0.5 mg q.d. for the second week. A total of 96 patients were randomly allocated to the colchicine (n=48) and control groups (n=48). Both colchicine and control group patients experienced similar clinical outcomes by day 14 of hospitalization. Treatment outcome by day 14 in colchicine vs control arm: recovered and discharged alive: 36 (75.0%) vs. 37 (77.1%), remain admitted after 14-days: 4 (8.3%) vs. 5 (10.4%), ICU transferred: 4 (8.3%) vs. 3 (6.3%), and mortality: 4 (8.3%) vs. 3 (6.3%). The speed of improvement of COVID-19 acute symptoms including shortness of breath, fever, cough, the need of supplementary oxygen, and oxygen saturation level, was almost identical in the two groups. Length of hospitalization was on average 1.5 day shorter in the colchicine group. There was no evidence for a difference between the two groups in the follow-up serum levels of inflammatory biomarkers including C-reactive protein (CRP), D-dimer, lactate dehydrogenase (LDH), ferritin, interleukin-6 (IL-6), high-sensitivity troponin T (hs-TnT) and N-terminal pro b-type natriuretic peptide (NT pro-BNP). According to the results of our study, oral colchicine does not appear to show clinical benefits in non-ICU hospitalized COVID-19 patients with suspected cytokine storm. It is possible that the anti-inflammatory pathways of colchicine are not crucially involved in the pathogenesis of COVID-19.

Role of CSR in achieving customers’ satisfaction and retention during COVID-19 and post-pandemic period: Empirical evidence from emerging nations

BACKGROUND: COVID-19 is an ongoing virus disease also recognized as a coronavirus pandemic that propelled the world to rethink organizational strategies during this unprecedented challenge. Although research on CSR has broadly been done over the past decades; nonetheless, how CSR can contribute a leading role in engaging the stakeholders such as customers during this pandemic period and post-pandemic is an important research gap that ought to be uncovered. OBJECTIVES: This study explores the impact of CSR on external stakeholders like customers and how organizations can dramatically sustain the relationships during the COVID-19 period. First, this study investigates the relationships between CSR and customer satisfaction (CS). Second, this study explores the relationships between CSR and customer retention (CR). Finally, the moderating impact of gender and education were examined among the proposed relationships. METHODS: Using the survey of 500 respondents, this study prospected the linkages among CSR, CS, and CR from China using a convenience sampling approach. The questionnaires were disseminated to 700 Chinese online shoppers between Jan 2020 and March 2020 and explored using SEM model. RESULTS: It found that customers are more attached and satisfied with those organizations that are socially responsible and value their stakeholders, especially during uncertain situations like COVID-19 since presently revealed a positive relationship between CSR and CS. Second, it is found that there is a positive influence of CSR on CR as well. Finally, the study affirmed the positive nexus of gender and education as the moderators among CSR, CR, and CS. CONCLUSION: CSR is always on the front line blending social and environmental goals into business operations, especially during uncertain times and challenges. Undeniably, the COVID-19 pandemic is not only a global health emergency but is also leading to a major global challenge that drives organizations to revisit policies to sustain the relationships with their stakeholders. This study concluded the positive nexus of CSR and affirmed the positive role in sustaining relationships with customers during distinct uncertainties like COVID-19.

Copy-move image forged information detection and localisation in digital images using deep convolutional network

Image tempering is one of the significant issues in the modern era. The use of powerful tools for image editing with advanced technology and its widespread on social media raised questions on data integrity. Currently, the protection of images is uncertain and a severe concern, mainly when it transfers over the Internet. Thus, it is essential to detect an anomaly in images through artificial intelligence techniques. The simple way of image forgery is called copy-move, where a part of an image is replicated in the same image to hide unwanted content of the image. However, image processing through handcrafted features usually looks for pattern concerns with duplicate content, limiting their employment for huge data classification. On the other side, deep learning approaches achieve promising results, but their performance depends on training data with fine-tuning of hyperparameters. Thus, we proposed a custom convolutional neural network (CNN) architecture with a pre-trained model ResNet101 through a transfer learning approach. For this purpose, both models are trained on five different datasets. In both cases, the impact of the model is evaluated through accuracy, precision, recall, F-score and achieved the highest 98.4% accuracy using the Coverage dataset.

Skin cancer detection from dermoscopic images using deep learning and fuzzy k-means clustering

Melanoma skin cancer is the most life-threatening and fatal disease among the family of skin cancer diseases. Modern technological developments and research methodologies made it possible to detect and identify this kind of skin cancer more effectively; however, the automated localization and segmentation of skin lesion at earlier stages is still a challenging task due to the low contrast between melanoma moles and skin portion and a higher level of color similarity between melanoma-affected and -nonaffected areas. In this paper, we present a fully automated method for segmenting the skin melanoma at its earliest stage by employing a deep-learning-based approach, namely faster region-based convolutional neural networks (RCNN) along with fuzzy k-means clustering (FKM). Several clinical images are utilized to test the presented method so that it may help the dermatologist in diagnosing this life-threatening disease at its earliest stage. The presented method first preprocesses the dataset images to remove the noise and illumination problems and enhance the visual information before applying the faster-RCNN to obtain the feature vector of fixed length. After that, FKM has been employed to segment the melanoma-affected portion of skin with variable size and boundaries. The performance of the presented method is evaluated on the three standard datasets, namely ISBI-2016, ISIC-2017, and PH2, and the results show that the presented method outperforms the state-of-the-art approaches. The presented method attains an average accuracy of 95.40, 93.1, and 95.6% on the ISIC-2016, ISIC-2017, and PH2 datasets, respectively, which is showing its robustness to skin lesion recognition and segmentation.

Prevalence and determinants of stunting among preschool and school-going children in the flood-affected areas of Pakistan

Stunting is a significant public health problem in low- and middle-income countries. This study assessed the prevalence of stunting and associated risk factors of stunting among preschool and school-going children in flood-affected areas of Pakistan. A cross-sectional study was conducted by visiting 656 households through multi-stage sampling. Respondent's anthropometric measurements, socio-demographic information and sanitation facilities were explored. A logistic regression model was used to determine determinants of stunting, controlling for all possible confounders. The overall prevalence of stunting in children was 40.5%, among children 36.1% boys and 46.3% of girls were stunted. The prevalence of stunting in under-five children was 50.7%. Female children (OR=1.35, 95% CI:0.94-2.0), children aged 13-24 months (OR=6.5, 95% CI: 3.0-13.9), mothers aged 15-24 years (OR=4.4, 95% CI: 2.6-7.2), joint family (OR=2.1, 95% CI: 1.4-3.0) did not have access to improved drinking water (OR=3.3, 95% CI: 1.9-5.9), and the toilet facility (OR=2.8, 95% CI, 1.9-4.3), while the children from district Nowshera (OR=1.7, 95% CI: 0.9-3.2) were significantly (P<0.05) associated in univariate analysis. The regression model revealed that child age, maternal age, family type, quality of water, and toilet facility, were the significant (P<0.05) factors contributing to child stunting in the flood-hit areas. Identification of key factors might be helpful for policymakers in designing comprehensive community-based programs for the reduction of stunting in flood-affected areas. In disasters such as flood, the detrimental consequences of the stunting problem could be even more on children. Evidence-based education and care must be provided to the families in the flood-affected regions to reduce the stunting problem. The determinants of stunting should be targeted by making comprehensive policies regarding proper nutrition, livelihood, clean water, and sanitation facilities in flood-hit regions.

Single and multiple regions duplication detections in digital images with applications in image forensic

‘With the help of powerful image editing software, various image modifications are possible which are known as image forgeries. Copy-move is the easiest way of image manipulation, wherein an area of the image is copied and replicated in the same image. The major reason for performing this forgery is to conceal undesirable contents of the image. Thus, means are required to unveil the presence of duplicated areas in an image. In this article, an effective and efficient approach for copy-move forgery detection (CMFD) is proposed, which is based on stationary wavelet transform (SWT), speeded-up robust features (SURF), and a novel scaled density-based spatial clustering of applications with noise (sDBSCAN) clustering. The SWT allows the SURF descriptor to extract only energy-rich features from the input image. The SURF features can detect the tampered regions even under post-processing attacks like contrast adjustment, scaling, and affine transformation on the images. On the extracted features, a novel scaled density-based spatial clustering of applications with noise (sDBSCAN) clustering algorithm is applied to detect forged regions with high accuracy as it can easily identify the clusters of arbitrary shapes and sizes and can filter the outliers. For performance evaluation, three publicly available datasets namely MICC-F220, MICC-F2000, and image manipulation dataset (IMD) are employed. The qualitative and quantitative analysis demonstrates that the proposed approach outperforms state-of-the-art CMFD approaches in the presence of different post-processing attacks.

Localization and classification of human facial emotions using local intensity order pattern and shape-based texture features

Facial emotion recognition system (FERS) recognize the person’s emotions based on various image processing stages including feature extraction as one of the major processing steps. In this study, we presented a hybrid approach for recognizing facial expressions by performing the feature level fusion of a local and a global feature descriptor that is classified by a support vector machine (SVM) classifier. Histogram of oriented gradients (HoG) is selected for the extraction of global facial features and local intensity order pattern (LIOP) to extract the local features. As HoG is a shape-based descriptor, with the help of edge information, it can extract the deformations caused in facial muscles due to changing emotions. On the contrary, LIOP works based on the information of pixels intensity order and is invariant to change in image viewpoint, illumination conditions, JPEG compression, and image blurring as well. Thus both the descriptors proved useful to recognize the emotions effectively in the images captured in both constrained and realistic scenarios. The performance of the proposed model is evaluated based on the lab-constrained datasets including CK+, TFEID, JAFFE as well as on realistic datasets including SFEW, RaF, and FER-2013 dataset. The optimal recognition accuracy of 99.8%, 98.2%, 93.5%, 78.1%, 63.0%, 56.0% achieved respectively for CK+, JAFFE, TFEID, RaF, FER-2013 and SFEW datasets respectively.

Brain tumor segmentation using K-means clustering and deep learning with synthetic data augmentation for classification

Image processing plays a major role in neurologists' clinical diagnosis in the medical field. Several types of imagery are used for diagnostics, tumor segmentation, and classification. Magnetic resonance imaging (MRI) is favored among all modalities due to its noninvasive nature and better representation of internal tumor information. Indeed, early diagnosis may increase the chances of being lifesaving. However, the manual dissection and classification of brain tumors based on MRI is vulnerable to error, time-consuming, and formidable task. Consequently, this article presents a deep learning approach to classify brain tumors using an MRI data analysis to assist practitioners. The recommended method comprises three main phases: preprocessing, brain tumor segmentation using k-means clustering, and finally, classify tumors into their respective categories (benign/malignant) using MRI data through a finetuned VGG19 (i.e., 19 layered Visual Geometric Group) model. Moreover, for better classification accuracy, the synthetic data augmentation concept i s introduced to increase available data size for classifier training. The proposed approach was evaluated on BraTS 2015 benchmarks data sets through rigorous experiments. The results endorse the effectiveness of the proposed strategy and it achieved better accuracy compared to the previously reported state of the art techniques.

ESREEM: Efficient Short Reads Error Estimation Computational Model for Next-generation Genome Sequencing

Aims:

To assess the error profile in NGS data, generated from high throughput sequencing machines.

Background:

Short-read sequencing data from Next Generation Sequencing (NGS) are currently being generated by a number of research projects. Depicting the errors produced by NGS platforms and expressing accurate genetic variation from reads are two inter-dependent phases. It has high significance in various analyses, such as genome sequence assembly, SNPs calling, evolutionary studies, and haplotype inference. The systematic and random errors show incidence profile for each of the sequencing platforms i.e. Illumina sequencing, Pacific Biosciences, 454 pyrosequencing, Complete Genomics DNA nanoball sequencing, Ion Torrent sequencing, and Oxford Nanopore sequencing. Advances in NGS deliver galactic data with the addition of errors. Some ratio of these errors may emulate genuine true biological signals i.e., mutation, and may subsequently negate the results. Various independent applications have been proposed to correct the sequencing errors. Systematic analysis of these algorithms shows that state-of-the-art models are missing.

Objective:

In this paper, an effcient error estimation computational model called ESREEM is proposed to assess the error rates in NGS data.

Methods:

The proposed model prospects the analysis that there exists a true linear regression association between the number of reads containing errors and the number of reads sequenced. The model is based on a probabilistic error model integrated with the Hidden Markov Model (HMM).

Result:

The proposed model is evaluated on several benchmark datasets and the results obtained are compared with state-of-the-art algorithms.

Conclusions:

Experimental results analyses show that the proposed model efficiently estimates errors and runs in less time as compared to others.

Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture

Brain tumor is one of the most dreadful natures of cancer and caused a huge number of deaths among kids and adults from the past few years. According to WHO standard, the 700,000 humans are being with a brain tumor and around 86,000 are diagnosed since 2019. While the total number of deaths due to brain tumors is 16,830 since 2019 and the average survival rate is 35%. Therefore, automated techniques are needed to grade brain tumors precisely from MRI scans. In this work, a new deep learning-based method is proposed for microscopic brain tumor detection and tumor type classification. A 3D convolutional neural network (CNN) architecture is designed at the first step to extract brain tumor and extracted tumors are passed to a pretrained CNN model for feature extraction. The extracted features are transferred to the correlation-based selection method and as the output, the best features are selected. These selected features are validated through feed-forward neural network for final classification. Three BraTS datasets 2015, 2017, and 2018 are utilized for experiments, validation, and accomplished an accuracy of 98.32, 96.97, and 92.67%, respectively. A comparison with existing techniques shows the proposed design yields comparable accuracy.

Endoscopic thyroid lobectomy vs Conventional open thyroid lobectomy

BACKGROUND AND OBJECTIVE

Surgical managements for these suspicious nontoxic swellings requires open conventional method of thyroidectomy by neck incisions that can result in prominent scars and immediate risk usually hemorrhage. However new technological innovations came into practiced that include video assisted minimal invasive endoscopy by axillo-breast approach that gives very promising results with excellent cosmesis. In this study, we compared conventional open surgery with minimal invasive endoscopic techniques and associate various complaints and complications that were encountered in surgery.

METHODS

Sixty patients were enrolled in this comparative study. It was conducted from period February 2018 to February 2019. The patients were randomized alternatively in two groups. Group-I patients underwent conventional lobectomy while Group-II patients were operated endoscopically, Patients having nodules less than 3cm and Thy 1 and 2 were included in this study. Patient having nodules greater than 3cm, Multinodular goiter, recurrent nodule and Thy 3-6 were excluded from the study.

RESULTS

Patients who underwent endoscopic lobectomy were much more satisfied about scar marks whereas some developed post-operative complications. It included hoarseness of voice in Three (13.62%) patients, two patients developed seroma (9.08%), three patients (13.62%) erythema, whereas no postoperative complications were seen in patients who underwent open thyroid lobectomy. No signs of hypocalcemia noted in both approaches.

CONCLUSIONS

The complications with endoscopic approaches are higher but they are minor and resolved spontaneously within maximum period of six weeks. However scar mark satisfaction was much higher in endoscopic lobectomy group.

Profile and predictors of hepatitis and HIV infection in patients on hemodialysis of Quetta, Pakistan

Hemodialysis (HD) is the most commonly used treatment in patients with end-stage renal failure or disease (ESRD) worldwide. Blood-borne viral diseases are the major causes of mortality and morbidity in patients on HD. This study aims to analyze the prevalence and to concentrate on the key risk factors that are responsible for hepatitis B virus (HBV), hepatitis C virus (HCV), and human immunodeficiency virus (HIV) infection in patients on HD visiting two dialysis centers in the city of Quetta in southwestern Pakistan. The overall incidence of HBV was found to be 16.1%, the overall incidence of HCV was found to be 43.2%, and two patients (1.6%) were found to be positive for both HBV and HCV. HIV was not found among patients seen at both hospitals during the study period. The main risk factors for development of a viral infection were the length of time on HD (p = 0.007), number of sessions (p = 0.001), and level of education (p = 0.092). Biochemical and hematological parameters including urea, creatinine, uric acid, and calcium levels, red blood cell count, white blood cell count, hemoglobin levels, and platelet count were also studied in patients on HD. HD is becoming one of the major factors causing a viral infection because a patient can possibly become infected during an HD session via a blood transfusion, dialysis machines, instruments and/or other contaminated equipment. In order to control the spread of viral infections, increased public awareness, vaccinations, and health education programs for both health care providers and patients are needed, and proper screening programs should be instituted before dialysis is performed.

Microscopic melanoma detection and classification: A framework of pixel-based fusion and multilevel features reduction

The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel-based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean-based function is implemented and fed input to top-hat and bottom-hat filters which later fused for contrast stretching, (b) seed region growing and graph-cut method-based lesion segmentation and fused both segmented lesions through pixel-based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy-based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.

An automated nuclei segmentation of leukocytes from microscopic digital images

Leukemia is a life-threatening disease. So far diagnosing of leukemia is manually carried out by the Hematologists that is time-consuming and error-prone. The crucial problem is leukocytes' nuclei segmentation precisely. This paper presents a novel technique to solve the problem by applying statistical methods of Gaussian mixture model through expectation maximization for the basic and challenging step of leukocytes' nuclei segmentation. The proposed technique is being tested on a set of 365 images and the segmentation results are validated both qualitatively and quantitatively with current state-of-the-art methods on the basis of ground truth data (manually marked images by medical experts). The proposed technique is qualitatively compared with current state-of-the-art methods on the basis of ground truth data through visual inspection on four different grounds. Finally, the proposed technique quantitatively achieved an overall segmentation accuracy, sensitivity and precision of 92.8%, 93.5% and 98.16% respectively while an overall F-measure of 95.75%.

Modeling user rating preference behavior to improve the performance of the collaborative filtering based recommender systems

One of the main concerns for online shopping websites is to provide efficient and customized recommendations to a very large number of users based on their preferences. Collaborative filtering (CF) is the most famous type of recommender system method to provide personalized recommendations to users. CF generates recommendations by identifying clusters of similar users or items from the user-item rating matrix. This cluster of similar users or items is generally identified by using some similarity measurement method. Among numerous proposed similarity measure methods by researchers, the Pearson correlation coefficient (PCC) is a commonly used similarity measure method for CF-based recommender systems. The standard PCC suffers some inherent limitations and ignores user rating preference behavior (RPB). Typically, users have different RPB, where some users may give the same rating to various items without liking the items and some users may tend to give average rating albeit liking the items. Traditional similarity measure methods (including PCC) do not consider this rating pattern of users. In this article, we present a novel similarity measure method to consider user RPB while calculating similarity among users. The proposed similarity measure method state user RPB as a function of user average rating value, and variance or standard deviation. The user RPB is then combined with an improved model of standard PCC to form an improved similarity measure method for CF-based recommender systems. The proposed similarity measure is named as improved PCC weighted with RPB (IPWR). The qualitative and quantitative analysis of the IPWR similarity measure method is performed using five state-of-the-art datasets (i.e. Epinions, MovieLens-100K, MovieLens-1M, CiaoDVD, and MovieTweetings). The IPWR similarity measure method performs better than state-of-the-art similarity measure methods in terms of mean absolute error (MAE), root mean square error (RMSE), precision, recall, and F-measure.

Elemental Characterization of Medicinal Plants and Soils from Hazarganji Chiltan National Park and Nearby Unprotected Areas of Balochistan, Pakistan

The aim of this work was to evaluate the variability in elemental composition of seven medicinal plants and their respective soils belonging to protected and nearby unprotected sites of the Hazarganji Chiltan National Park. The medical plants under study were Achillea wilhelmsii C. Koch, Peganum harmala Linn, Sophora mollis (Royle) Baker, Perovskia atriplicifolia Benth, Seriphidium quettense (Podlech.) Ling, Hertia intermedia (Bioss) O. Ktze, and Nepeta praetervisa Rech. F. Macro (C, H, N, S, K, Ca), micro (Cl, Cu, Fe, Mn, and Zn), beneficial (Al, Co, Na), others (As, Br, Cr, Cs, Hf, Pb, Rb, Sb, Sr, Sn, V and Th) and rare earth elements (Ce, Eu, La, Lu, Nd Sc, Sm, Tb and Yb) were characterized by means of standard organic elemental and instrumental neutron activation methodologies and by flame atomic absorption spectroscopy. Results showed that, among macro nutrients, carbon concentration was the highest element in both plant and soil samples followed by H and K. Elements such as Cl, Na and Fe were detected in considerably good amounts; all the other elements were found in trace quantities. Principal component analysis (PCA) was applied to identify spatial variation in elemental composition of medicinal plants, in which 80-90% of the total variance in whole set of data was found. In particular, the findings highlighted the presence of essential and beneficial elements such as C, H, N, K, Ca, Fe, Mn and Na, in samples from protected sites, while potentially dangerous elements such as Al, As, Br and Cr were detected in samples from unprotected sites. These results emphasized on the need for rational exploitation of valuable medicinal plants and supporting protected areas as an excellent source of biodiversity conservation.

Sensitivity Enhancement of Silicon-on-Insulator CMOS MEMS Thermal Hot-Film Flow Sensors by Minimizing Membrane Conductive Heat Losses

Minimizing conductive heat losses in Micro-Electro-Mechanical-Systems (MEMS) thermal (hot-film) flow sensors is the key to minimize the sensors' power consumption and maximize their sensitivity. Through a comprehensive review of literature on MEMS thermal (calorimetric, time of flight, hot-film/hot-film) flow sensors published during the last two decades, we establish that for curtailing conductive heat losses in the sensors, researchers have either used low thermal conductivity substrate materials or, as a more effective solution, created low thermal conductivity membranes under the heaters/hot-films. However, no systematic experimental study exists that investigates the effect of membrane shape, membrane size, heater/hot-film length and M e m b r a n e (size) to H e a t e r (hot-film length) Ratio (MHR) on sensors' conductive heat losses. Therefore, in this paper we have provided experimental evidence of dependence of conductive heat losses in membrane based MEMS hot-film flow sensors on MHR by using eight MEMS hot-film flow sensors, fabricated in a 1 µm silicon-on-insulator (SOI) CMOS foundry, that are thermally isolated by square and circular membranes. Experimental results demonstrate that: (a) thermal resistance of both square and circular membrane hot-film sensors increases with increasing MHR, and (b) conduction losses in square membrane based hot-film flow sensors are lower than the sensors having circular membrane. The difference (or gain) in thermal resistance of square membrane hot-film flow sensors viz-a-viz the sensors on circular membrane, however, decreases with increasing MHR. At MHR = 2, this difference is 5.2%, which reduces to 3.0% and 2.6% at MHR = 3 and MHR = 4, respectively. The study establishes that for membrane based SOI CMOS MEMS hot-film sensors, the optimum MHR is 3.35 for square membranes and 3.30 for circular membranes, beyond which the gain in sensors' thermal efficiency (thermal resistance) is not economical due to the associated sharp increase in the sensors' (membrane) size, which makes sensors more expensive as well as fragile. This paper hence, provides a key guideline to MEMS researchers for designing the square and circular membranes-supported micro-machined thermal (hot-film) flow sensors that are thermally most-efficient, mechanically robust and economically viable.

Mobile-Health Applications for the Efficient Delivery of Health Care Facility to People with Dementia (PwD) and Support to Their Carers: A Survey

Dementia directly influences the quality of life of a person suffering from this chronic illness. The caregivers or carers of dementia people provide critical support to them but are subject to negative health outcomes because of burden and stress. The intervention of mobile health (mHealth) has become a fast-growing assistive technology (AT) in therapeutic treatment of individuals with chronic illness. The purpose of this comprehensive study is to identify, appraise, and synthesize the existing evidence on the use of mHealth applications (apps) as a healthcare resource for people with dementia and their caregivers. A review of both peer-reviewed and full-text literature was undertaken across five (05) electronic databases for checking the articles published during the last five years (between 2014 and 2018). Out of 6195 searches yielded articles, 17 were quantified according to inclusion and exclusion criteria. The included studies distinguish between five categories, viz., (1) cognitive training and daily living, (2) screening, (3) health and safety monitoring, (4) leisure and socialization, and (5) navigation. Furthermore, two most popular commercial app stores, i.e., Google Play Store and Apple App Store, were searched for finding mHealth based dementia apps for PwD and their caregivers. Initial search generated 356 apps with thirty-five (35) meeting the defined inclusion and exclusion criteria. After shortlisting of mobile applications, it is observed that these existing apps generally addressed different dementia specific aspects overlying with the identified categories in research articles. The comprehensive study concluded that mobile health apps appear as feasible AT intervention for PwD and their carers irrespective of limited available research, but these apps have potential to provide different resources and strategies to help this community.

Fatty Acids and Phenolic Profiles of Extravirgin Olive Oils from Selected Italian Cultivars Introduced in Southwestern Province of Pakistan

Mediterranean olive trees have been cultivated in Pakistan for decades to promote olive cultivation and use of olive oil. The qualitative characteristics of seven mono and one multi-varietal extra virgin olive oils (EVOOs) extracted from Italian cultivars grown in different areas of Balochistan a southwestern province of Pakistan were evaluated. Present study aims to assess the impact of bioclimatological change on biochemical profile of exotic cultivars. The dominating fatty acids found in analyzed EVOOs were oleic (65-72%), linoleic (10.61-18.33%) and palmitic acids (12-16%). The tocopherols α, (β+γ) and δ contents showed a great diversity which ranged from (60-408) mg/kg while, total phenol concentration ranged from (200-370) mg/kg. The analyses of phenolic compounds revealed the presence of phenolic acids, phenolic alcohols, secoiridoids, flavonoids, oleuropein and verbascosides. One-way ANOVA revealed significant differences (p < 0.05) regarding studied parameters. Principal component analysis (PCA) was applied to identify the main components and to classify samples into groups in terms of fatty acids and phenolic profiles. The first group (Frantoio, Moraiolo, Pendolino, Multi-varietal mixture) characterized by high amount of oleic acid and MUFAs/PUFAs ratio. The second group (Maurino and Leccino) correlates with SFAs and third (Ottobrattica, Coratina) with PUFAs. Based on the PCA of phenolic profile the studied cultivars were divided into two main groups. Morialo, Pendolino and Maurino correlated with (phenolic acids, hydroxytyrosol, flavonoids and secoiridoids). Frantoio, Ottobrattica, Coratina, multi-varietal and Leccino were correlated with oleuropein, tyrosol and ligstroside aglycon. The obtained data was compared with those obtained from same cultivars in their original and/or different growing area. Marked differences were observed in the composition of oleic, linoleic, palmitic acids, secoiridoids and total phenolic contents. These differences could be due to change in geographical location and climatical condition of Balochistan. The cultivar Moraiolo has shown best adaptation and preserved its biochemical composition among all studied cultivars.

Data hiding technique in steganography for information security using number theory

In the current era, due to the widespread availability of the Internet, it is extremely easy for people to communicate and share multimedia contents with each other. However, at the same time, secure transfer of personal and copyrighted material has become a critical issue. Consequently, secure means of data transfer are the most urgent need of the time. Steganography is the science and art of protecting the secret data from an unauthorised access. The steganographic approaches conceal secret data into a cover file of type audio, video, text and/or image. The actual challenge in steganography is to achieve high robustness and capacity without bargaining on the imperceptibility of the cover file. In this article, an efficient steganography method is proposed for the transfer of secret data in digital images using number theory. For this purpose, the proposed method represents the cover image using the Fibonacci sequence. The representation of an image in the Fibonacci sequence allows increasing the bit planes from 8-bit to 12-bit planes. The experimental results of the proposed method in comparison with other existing steganographic methods exhibit that our method not only achieves high embedding of secret data but also gives high quality of stego images in terms of peak signal-to-noise ratio (PSNR). Furthermore, the robustness of the technique is also evaluated in the presence of salt and pepper noise attack on the cover images.

A novel method for content-based image retrieval to improve the effectiveness of the bag-of-words model using a support vector machine

The advancements in the multimedia technologies result in the growth of the image databases. To retrieve images from such image databases using visual attributes of the images is a challenging task due to the close visual appearance among the visual attributes of these images, which also introduces the issue of the semantic gap. In this article, we recommend a novel method established on the bag-of-words (BoW) model, which perform visual words integration of the local intensity order pattern (LIOP) feature and local binary pattern variance (LBPV) feature to reduce the issue of the semantic gap and enhance the performance of the content-based image retrieval (CBIR). The recommended method uses LIOP and LBPV features to build two smaller size visual vocabularies (one from each feature), which are integrated together to build a larger size of the visual vocabulary, which also contains complementary features of both descriptors. Because for efficient CBIR, the smaller size of the visual vocabulary improves the recall, while the bigger size of the visual vocabulary improves the precision or accuracy of the CBIR. The comparative analysis of the recommended method is performed on three image databases, namely, WANG-1K, WANG-1.5K and Holidays. The experimental analysis of the recommended method on these image databases proves its robust performance as compared with the recent CBIR methods.

A hybrid technique for speech segregation and classification using a sophisticated deep neural network

Recent research on speech segregation and music fingerprinting has led to improvements in speech segregation and music identification algorithms. Speech and music segregation generally involves the identification of music followed by speech segregation. However, music segregation becomes a challenging task in the presence of noise. This paper proposes a novel method of speech segregation for unlabelled stationary noisy audio signals using the deep belief network (DBN) model. The proposed method successfully segregates a music signal from noisy audio streams. A recurrent neural network (RNN)-based hidden layer segregation model is applied to remove stationary noise. Dictionary-based fisher algorithms are employed for speech classification. The proposed method is tested on three datasets (TIMIT, MIR-1K, and MusicBrainz), and the results indicate the robustness of proposed method for speech segregation. The qualitative and quantitative analysis carried out on three datasets demonstrate the efficiency of the proposed method compared to the state-of-the-art speech segregation and classification-based methods.