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.

A comprehensive study of mobile-health based assistive technology for the healthcare of dementia and Alzheimer's disease (AD).

Assistive technology (AT) involvement in therapeutic treatment has provided simple and efficient healthcare solutions to people. Within a short span of time, mobile health (mHealth) has grown rapidly for assisting people living with a chronic disorder. This research paper presents the comprehensive study to identify and review existing mHealth dementia applications (apps), and also synthesize the evidence of using these applications in assisting people with dementia including Alzheimer's disease (AD) and their caregivers. Six electronic databases searched with the purpose of finding literature-based evidence. The search yielded 2818 research articles, with 29 meeting quantified inclusion and exclusion criteria. Six groups and their associated sub-groups emerged from the literature. The main groups are (1) activities of daily living (ADL) based cognitive training, (2) monitoring, (3) dementia screening, (4) reminiscence and socialization, (5) tracking, and (6) caregiver support. Moreover, two commercial mobile application stores i.e., Apple App Store (iOS) and Google Play Store (Android) explored with the intention of identifying the advantages and disadvantages of existing commercially available dementia and AD healthcare apps. From 678 apps, a total of 38 mobile apps qualified as per defined exclusion and inclusion criteria. The shortlisted commercial apps generally targeted different aspects of dementia as identified in research articles. This comprehensive study determined the feasibility of using mobile Health based applications for dementia including AD individuals and their caregivers regardless of limited available research, and these apps have capability to incorporate a variety of strategies and resources to dementia community care.

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.

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.

A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer's Disease Stages Using Resting-State fMRI and Residual Neural Networks.

Alzheimer's disease (AD) is an incurable neurodegenerative disorder accounting for 70%-80% dementia cases worldwide. Although, research on AD has increased in recent years, however, the complexity associated with brain structure and functions makes the early diagnosis of this disease a challenging task. Resting-state functional magnetic resonance imaging (rs-fMRI) is a neuroimaging technology that has been widely used to study the pathogenesis of neurodegenerative diseases. In literature, the computer-aided diagnosis of AD is limited to binary classification or diagnosis of AD and MCI stages. However, its applicability to diagnose multiple progressive stages of AD is relatively under-studied. This study explores the effectiveness of rs-fMRI for multi-class classification of AD and its associated stages including CN, SMC, EMCI, MCI, LMCI, and AD. A longitudinal cohort of resting-state fMRI of 138 subjects (25 CN, 25 SMC, 25 EMCI, 25 LMCI, 13 MCI, and 25 AD) from Alzheimer's Disease Neuroimaging Initiative (ADNI) is studied. To provide a better insight into deep learning approaches and their applications to AD classification, we investigate ResNet-18 architecture in detail. We consider the training of the network from scratch by using single-channel input as well as performed transfer learning with and without fine-tuning using an extended network architecture. We experimented with residual neural networks to perform AD classification task and compared it with former research in this domain. The performance of the models is evaluated using precision, recall, f1-measure, AUC and ROC curves. We found that our networks were able to significantly classify the subjects. We achieved improved results with our fine-tuned model for all the AD stages with an accuracy of 100%, 96.85%, 97.38%, 97.43%, 97.40% and 98.01% for CN, SMC, EMCI, LMCI, MCI, and AD respectively. However, in terms of overall performance, we achieved state-of-the-art results with an average accuracy of 97.92% and 97.88% for off-the-shelf and fine-tuned models respectively. The Analysis of results indicate that classification and prediction of neurodegenerative brain disorders such as AD using functional magnetic resonance imaging and advanced deep learning methods is promising for clinical decision making and have the potential to assist in early diagnosis of AD and its associated stages.

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%.

Common clinical features of pediatric multiple sclerosis in Pakistan - A report of 15 cases.

The aim of this note is to assess the common clinical features of paediatric multiple sclerosis (PMS) in Pakistan. For this purpose, 150 MS patients with the age range of (1-72) years and mean age (34.2±11.09) years were studied during the period 2010 to 2015 from MRI centers of Pakistan. We found 15 paediatric MS cases which had clinical course relapsing-remitting MS (11), secondary-progressive MS (3) and primary-progressive MS (1). Revised McDonald criteria 2010 of MRI was used to disseminate lesions in space and time. Sensory symptoms were found 27% in PMS patients and contributed brain area of corpus callosum, brain stem, periventricle, basal ganglia, white matter and cerebellum. Optic neuritis was the second clinical feature and its prevalence was reported 20% in paediatric patients. In conclusion, Paediatric multiple sclerosis is predicted 10 % with mean age 11.2 years in Pakistan. Sensory and optic neuritis are suggested the common clinical features of paediatric multiple sclerosis in Pakistan.

Review-Medicinal plants and management of Diabetes Mellitus: A review.

Diabetes is a metabolic disorder characterized by chronic hyperglycemia and associated with dysfunction and failure of various body organs. Alarming increase in prevalence rate has made this disorder a major health problem globally. The available treatment modalities are not sufficient to combat diabetes and associated complications. A number of medicinal plants have a significant antidiabetic potential against diabetes mellitus. We have listed the use of important medicinal herbs for the treatment and management of diabetes in this review.

Comparative study of antioxidant, metal chelating and antiglycation activities of Momordica charantia flesh and pulp fractions.

Momordica charantia is commonly used as a vegetable and folk medicine in most parts of South Asia. This study aims to determine and compare the antioxidant, metal chelating and antiglycation activities of aqueous extracts of M. charantia fruit flesh (MCF) and fruit pulp (MCP) fractions. Our results show that MCP has pronounced DPPH and ABTS radical scavenging potential compared to MCF. In the antiglycation assay both fractions illustrated considerable inhibitory activities against the formation of AGEs induced by glucose with an efficacy of 75 and 67% with 150 µl of MCP and MCF extracts respectively, almost equal to 0.3mM amino guanidine. Results for metal catalysed protein fragmentation and autoxidative and glycoxidation assays demonstrate that MCF and MCP inhibited metal catalysed protein fragmentation. The percentage of relative standard deviation for three replicate measurements of 150 µl of MCF and MCP was < 3.0% for antiglycation. The antioxidant assays with regression values of MCP (0.981 and 0.991) and MCF (0.967 and 0.999) were also recorded. We conclude that both extracts possess high antioxidant and antiglycation activities and are equally good sources of antioxidant and antiglycating agents.

Improved physicochemical characteristics of artemisinin using succinic acid.

Artemisinin (ARMN) is a potent antimalarial drug, which is effective against multidrug resistant strains of Plasmodium falciparum and produces rapid recovery even in patients with cerebral malaria. Being poorly soluble in water, artemisinin is incompletely absorbed after oral intake due to poor dissolution characteristics in the intestinal fluids. To enhance these properties, solid dispersions of artemisinin with succinic acid (SUC) were prepared using drug-carrier ratios 1 : 1, 1 : 4, 1 : 6, 1 : 8 and 1 : 10 by solvent evaporation and freeze drying methods. These solid dispersions were characterized by differential scanning calorimetery (DSC), Fourier transform infrared spectroscopy (FTIR), x-ray diffraction patterns (XRD), phase solubility and dissolution kinetics evaluated by applying zero order, first order, Higuchi, and Korsmeyer-Peppas models. Physical mixtures produced significantly higher aqueous solubility and rate of dissolution as compared to artemisinin alone. The dissolution profiles of all formulations followed Higuchi model and exhibited diffusion-controlled release of drug. Solvent evaporation method (SLVPs) exhibited improved solubility and freeze dried solid dispersions (FDSDs) produced highest solubility but stability constant was opposite. ARMN and SUC both were found completely crystalline as shown by their XRD patterns. Physical mixtures (PMs) showed reduced intensity in their XRD patterns while solid dispersions by SLVPs exhibited twice reduced intensity and much displaced angles, whereas FDSDs showed synergistic effects in some of ARMN and SUC peaks. DSC thermograms of FDSDs at drug-carrier ratios 1 : 1-1 : 4 showed lower melting temperature and enthalpy change (deltaH) values than respective SLVPs, whereas at higher ratios, a reverse was true. SLVPs showed displaced methyl stretching bands at lower drug-carrier ratios and exhibited O-H stretching characteristic bands of SUC at higher drug-carrier ratios. In addition, carbonyl group and C-O stretching vibrations characteristic of SUC (1307 cm(-1)) appeared prominently compared to PMs, whereas C-O stretching characteristic bands of ARMN disappeared at higher ratios. FDSDs exhibited distinct nature of bonding compared to respective SLVPs and PMs.

Preparation and investigation of Montmorillonite-K10 Polyaniline nanocomposites for optoelectronic applications.

One-dimensional polyaniline (PANI) nanostructures were synthesized in situ in the presence of two-dimensional (2D) Montmorillonite (MMT) clay nanosheets. Strong interactions between the polymer and MMT platelets in the nanocomposites were confirmed through spectroscopic studies. X-ray diffraction and scanning electron microscopic studies revealed the clay's profound effect on the polymer's crystallinity and morphology. The clay nanosheets induced higher crystallinity and well-defined nanorod morphology in the polymer structure. Consequently, the nanocomposite showed an electrical conductivity of 8.72 S/cm, closer to that of the pristine polymer (8.97 S/cm), despite the presence of highly insulting clay material. Surprisingly, a notable decrease in the optical bandgap of the polymer from 3.73 to 2.88 eV of the nanocomposite was also observed. This novel integration of a narrow band gap and high conductivity in PANI/MMT nanocomposites can expand their utility for visible light interactions in areas encompassing photocatalysis, photovoltaics, electro/photochromism, and related technologies.

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.

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.

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.

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.

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.

Stress Estimation Model for the Sustainable Health of Cancer Patients.

Good health is the most important and very necessary characteristic for stress-free, skillful, and hardworking people with a cooperative environment to create a sustainable society. Validating two algorithms, namely, sequential minimal optimization for regression (SMOreg) using vector machine and linear regression (LR) and using their predicted cancer patients' cases, this study presents a patient's stress estimation model (PSEM) to forecast their families' stress for patients' sustainable health and better care with early management by under-study cancer hospitals. The year-wise predictions (1998-2010) by LR and SMOreg are verified by comparing with observed values. The statistical difference between the predictions (2021-2030) by these models is analyzed using a statistical t-test. From the data of 217067 patients, patients' stress-impacting factors are extracted to be used in the proposed PSEM. By considering the total population of under-study areas and getting the predicted population (2021-2030) of each area, the proposed PSEM forecasts overall stress for expected cancer patients (2021-2030). Root mean square error (RMSE) (1076.15.46) for LR is less than RSME for SMOreg (1223.75); hence, LR remains better than SMOreg in forecasting (2011-2020). There is no significant statistical difference between values (2021-2030) predicted by LR and SMOreg (p value = 0.767 > 0.05). The average stress for a family member of a cancer patient is 72.71%. It is concluded that under-study areas face a minimum of 2.18% stress, on average 30.98% stress, and a maximum of 94.81% overall stress because of 179561 expected cancer patients of all major types from 2021 to 2030.

Fecal Loading at Caecum as a New Radiological Sign for Diagnosing Acute Appendicitis.

Background The present study aimed to address the importance of a new radiological sign - the presence of fecal loading at the caecum - for the diagnosis of acute appendicitis. Methodology A cross-sectional study was conducted at the Department of General Surgery, Jinnah Postgraduate Medical Centre, Karachi from January 2020 to June 2020. Patients who presented in the emergency with acute pain at the right iliac fossa fulfilling the criteria of acute appendicitis (AA) according to the Alvarado scoring system, and were planned for appendectomy were included. Before surgery plain abdominal radiographs were taken in anteroposterior view in the supine position and were evaluated for the presence of fecal loading at the caecum. After that all patients underwent surgery and radiologic findings were correlated with histopathologic findings. Results The mean age of patients was 32.19±7.34 years. There were 83 (55.3%) male and 67 (44.7%) female patients. Out of 150, there were 144 (96.0%) patients in whom fecal loading in the caecum was diagnosed on plain radiographs. On histopathology reporting, acute appendicitis was diagnosed in 143 (95.3%) patients. Regarding accuracy, fecal loading at the caecum was found to have a sensitivity of 98.6%, specificity of 83.3%, a positive predictive value of 99.3%, and a negative predictive value of 71.4%.  Conclusion According to the results of the present study and existing literature, we suggest using fecal loading at the caecum along with a clinical scoring system for the diagnosis of acute appendicitis. As per our findings, fecal loading at the caecum is a valuable sign on plain abdominal radiograph for the diagnosis of AA. It has a sensitivity of 98.6% and a specificity of 83.3%. This sign typically becomes undetectable after an appendectomy. It will help to improve the accuracy of diagnosis of acute appendicitis, and hence will reduce the chances of negative appendectomy.

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.

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.