Applications of different types of heat pipes in solar desalinations: A comprehensive review.

Desalination processes are energy consuming and it is required to apply clean energy sources for supplying them to prevent environmental issues. Solar energy is one of the attractive clean energy sources for desalination. In solar thermal desalination systems, different thermal components could be used for heat transfer purpose. In solar desalination technologies, heat pipe as efficient heat transfer mediums could be employed to transfer absorbed and/or stored thermal energy. The objective of this study is to review applications of heat pipes in solar energy desalination systems. Regarding the performance dependency of these thermal systems on the variety of factors, scholars have investigated these systems by consideration of the effect of different influential factors. Based on the results, it is concluded that use of heat pipes could lead to proper performance of solar desalination systems. Aside from direct transfer of absorbed heat from solar radiation, heat pipes can be applied in the storage units of solar desalination systems to keep the systems active in night-hours or low solar irradiation conditions. The overall performance of the solar desalinations systems with heat pipes can be influenced by some factors such as filling ratio and operating fluid that affect the performance of heat pipes.

Enhanced As-COF nanochannels as a high-capacity anode for K and Ca-ion batteries.

Covalent organic frameworks can be used for next-generation rechargeable metal-ion batteries due to their controllable spatial and chemical architectures and plentiful elemental reserves. In this study, the arsenic-based covalent organic framework (As-COF) is designed by employing the geometrical symmetry of a semiconducting phosphazene-based covalent organic framework that uses p-phenylenediamine as a linker and hexachorocyclotriphosphazene as an As-containing monomer in a C3-like spatial configuration. The As-COF with engineered nanochannels demonstrates exceptional anodic behavior for potassium (K) and calcium (Ca) ion batteries. It exhibits a high storage capacity of about 914(2039) mA h g-1, low diffusion barriers of 0.12(0.26) eV, low open circuit voltage of 0.23(0.18) V, and a minimal volume expansion of 2.41(2.32)% for K (Ca) ions. These attributes collectively suggest that As-COF could significantly advance high-capacity rechargeable batteries.

ChestCovidNet: An Effective DL-based Approach for COVID-19, Lung Opacity, and Pneumonia Detection Using Chest Radiographs Images.

Currently used lung disease screening tools are expensive in terms of money and time. Therefore, chest radiograph images (CRIs) are employed for prompt and accurate COVID-19 identification. Recently, many researchers have applied Deep learning (DL) based models to detect COVID-19 automatically. However, their model could have been more computationally expensive and less robust, i.e., its performance degrades when evaluated on other datasets. This study proposes a trustworthy, robust, and lightweight network (ChestCovidNet) that can detect COVID-19 by examining various CRIs datasets. The ChestCovidNet model has only 11 learned layers, eight convolutional (Conv) layers, and three fully connected (FC) layers. The framework employs both the Conv and group Conv layers, Leaky Relu activation function, shufflenet unit, Conv kernels of 3×3 and 1×1 to extract features at different scales, and two normalization procedures that are cross-channel normalization and batch normalization. We used 9013 CRIs for training whereas 3863 CRIs for testing the proposed ChestCovidNet approach. Furthermore, we compared the classification results of the proposed framework with hybrid methods in which we employed DL frameworks for feature extraction and support vector machines (SVM) for classification. The study's findings demonstrated that the embedded low-power ChestCovidNet model worked well and achieved a classification accuracy of 98.12% and recall, F1-score, and precision of 95.75%.

Oral health behind the bars: oral health seeking behavior among jail prisoners at central jail of Peshawar, Pakistan: a cross-sectional study.

BACKGROUND: The oral health care-seeking behavior among prison inmates is an overlooked area, often leading to deteriorating general health due to the prisoners' limited awareness of oral hygiene practices. It is crucial to address this issue and understand the factors associated with oral healthcare-seeking behavior in prisons. OBJECTIVE: To assess the oral healthcare-seeking behavior of prison inmates at Central Prisoner Jail, Peshawar Pakistan and to look the factors associated with their dental care utilization. MATERIAL AND METHODS: This cross-sectional study was conducted at Central Prisoner Jail, Peshawar Khyber Pakhtunkhwa, Pakistan from November 2021 to April 2022. A consecutive sampling technique was used to collect data from both convicted and under-trial prisoners by using a pre-tested WHO Basic Oral Health Survey 2013 tool. Our outcome variable was "Visit to a dentist in the last 12 months (Never/Once or more than one visit). Chi-square test was used to determine univariate association with other explanatory variables while multivariable logistic regression was performed to adjust for potential confounders. RESULT: A total of 225 participants were recruited to the study with a mean (SD) age of 32.9(11.4). More than two-thirds of 200(88.9%) of the participants were males. One-third of the sample never visited the dentist75(33.3). Participants who completed college/university education and never visited the dentist in the last 12 months constituted a smaller proportion (17.6%) compared to those who visited the dentist once or more than once n = 28(82.4%, p-value = 0.003). Individuals who were using toothbrushes were most frequently visiting the dentist n = 130(72.6%=p value = 0.001) as compared to never visitors. Multivariate logistic regression analysis revealed that Participants who experienced teeth pain or discomfort had 0.42 times lower odds of visiting the dentist compared to those who did not experience any pain or discomfort [AOR 0.42 (95% CI 0.17-0.80), p = 0.04]. Similarly, Participants who do not use any denture have 4.06 times higher odds[AOR 4.06(95% CI 1.76-9.36), p = 0.001] of visiting the dentist compared to those who use a denture. CONCLUSION: Our result demonstrates that those prisoners who were experiencing tooth pain or discomfort and not using dentures were the strong predictors with lower dental visit frequency to seek oral health care.

Healthcare workers safety in the COVID-19 era: the impact of pre-pandemic personal protective equipment (PPE) training in Pakistan.

BACKGROUND: The COVID-19 pandemic has revealed vulnerabilities in healthcare systems worldwide, emphasizing the importance of healthcare worker safety through adequate utilization of personal protective equipment (PPE). This study aims to assess the impact of pre-pandemic PPE training on the practices and other associated factors among frontline healthcare workers during the COVID-19 pandemic in Pakistan and provide insights into the implications of such training programs for future initiatives. METHODS: A cross-sectional study from May 9th to June 5th, 2020 was conducted among the frontline healthcare workers against COVID-19 in Pakistan, utilizing an online structured questionnaire shared via WhatsApp and Facebook by using purposive sampling. Statistical analyses, including chi-square tests for proportion and logistic regression for the association while multi-logistic regression for potential confounders, were performed using SPSS version 22. RESULTS: A total of 453 healthcare staff participated, with 68.9% (n = 312) reporting no prior PPE training and 31.1% (n = 141) having received training. Significant associations were found between prior training and healthcare group distribution (p = 0.006), with doctors exhibiting the highest proportion of training 82 (37.61%), followed by nurses 50 (27.32%) and paramedics 9 (17.31%). Those who didn't receive any prior training in PPEs showed a higher perceived professional risk of 216 (69.23%) compared to those who received prior PPE training 96 (30.77%, p-value 0.005). Similarly, a higher frequency 137 (63.72%) of Perceived Personal risk was observed in those who didn't receive training, labeled as "high risk" compared to those who were trained 78 (36.28%, P value 0.02). Multi-logistic regression analysis identified paramedics as 0.26 times less likely to have received prior PPE training (Adjusted OR 0.26, 95% CI 0.10-0.65, p = 0.01) compared to medical doctors. Healthcare workers in tertiary care hospitals were 0.46 times less likely to undergo PPE training (Adjusted OR 0.46, 95% CI 0.25-0.87,p = 0.01) compared to those working at COVID-19 facilities/hospitals/quarantine centers. Likewise, individuals who doffed disposable gowns [Adjusted OR 3.86, (95% CI, 1.23-12.08, p = 0.02] were 3.86 times more interested in getting prior training in PPE compared to those who don't have skills to wear them. CONCLUSION: Our findings highlight that healthcare levels, type of healthcare, and doffing skills are important predictors of whether healthcare workers have taken prior training in PPE. These findings imply developing effective training programs for healthcare workers to ensure safety while providing care during pandemics like COVID-19.

An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model.

Introduction: Recently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant diseases is necessary to optimize output to prevent such destruction. The physical examination of plant diseases produced low accuracy, required a lot of time, and could not accurately anticipate the plant disease. Creating an automated method capable of accurately classifying to deal with these issues is vital. Method: This research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases. The proposed DeepPlantNet model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) and three fully connected (FC) layers. The framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, and a mix of 3×3 and 1×1 filters, making it a novel plant disease classification framework. The Proposed DeepPlantNet model can categorize plant disease images into many classifications. Results: The proposed approach categorizes the plant diseases into the following ten groups: Apple_Black_rot (ABR), Cherry_(including_sour)_Powdery_mildew (CPM), Grape_Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach_Bacterial_spot (PBS), Pepper_bell_Bacterial_spot (PBBS), Potato_Early_blight (PEB), Squash_Powdery_mildew (SPM), Strawberry_Leaf_scorch (SLS), bacterial tomato spot (TBS), and maize common rust (MCR). The proposed framework achieved an average accuracy of 98.49 and 99.85in the case of eight-class and three-class classification schemes, respectively. Discussion: The experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases.

Entropy production with the flow of nanomaterials through the permeable stretched surface with heterogeneous-homogenous chemical reaction.

In various thermodynamic procedures and the optimisation of thermal manipulation, nanofluids flowing through porous media represent an emerging perspective. The main objective of this study, from the perspective of thermal applications, was the investigation of the flow of nanofluid over a horizontal stretched surface embedded in a porous medium. The effects of the chemical reactions on the surface, magnetic field, and thermal radiations were invoked in the mathematical formulation. The non-Darcy model examines the fluid flow in the porous media. The principles of thermodynamics were employed to integrate entropy optimisation methods with the established theoretical approach to analyse the thermal behaviour of nanomaterials in the chemical reactive diffusion processes. Using the Tiwari-Das nanofluid model, the volume fraction of the nanomaterials was merged in the mathematical equation for the flow model. Water was taken as a base fluid and nanoparticles composed of aluminium oxide (Al2O3) and silver (Ag) were used. The significance of radiation, heat production, and ohmic heating were included in the energy equation. Furthermore, an innovative mathematical model for the diffusion of the autocatalytic reactive species in the boundary layer flow was developed for a linear horizontally stretched surface embedded in a homogeneous non-Darcy porous medium saturated with the nanofluid. The computer-based built-in bvp5c method was used to compute numerically these equations for varied parameters. It is clear that the magnetic parameter has a reversal influence on the entropy rate and velocity. Temperature and velocity are affected in the opposite direction from a higher volume fraction estimate. Thermal field and entropy were increased when the radiation action intensified. The inclusion of nanoparticle fraction by the volume fraction of nanoparticles and Brinkman number also enhances the system entropy. Entropy production can be minimized with the involvement of the porosity factor within the surface.

Assessment of renal function and prevalence of acute kidney injury following coronary artery bypass graft surgery and associated risk factors: A retrospective cohort study at a tertiary care hospital in Islamabad, Pakistan.

Acute kidney injury (AKI) is a sudden decline in renal function after cardiac surgery. It is characterized by a significant reduction in glomerular filtration rate, alterations in serum creatinine (S.Cr) levels, and urine output. This study aimed to retrospectively analyze a cohort of 704 patients selected using stringent inclusion and exclusion criteria. AKI was defined by an increase of 0.3 mg/dL in S.Cr levels compared to baseline. Data were collected from the hospital and analyzed using SPSS 16.0. Data analysis revealed that 22% (n = 155) of the patients developed AKI on the second post-operative day, accompanied by a substantial increase in S.Cr levels (from 1.064 ±â€…0.2504 to 1.255 ±â€…0.2673, P < .000). Age and cardiopulmonary bypass duration were identified as risk factors along with ejection fraction and days of hospital stay, contributing to the development of AKI. Early renal replacement therapy can be planned when the diagnosis of AKI is established early after surgery.

Mechanistic Study on Steric Activity Interplay of Olefin/Polar Monomers for Industrially Selective Late Transition Metal Catalytic Reactions.

A significant issue in developing metal-catalyzed plastic polymer materials is obtaining distinctive catalytic characteristics to compete with current plastics in industrial commodities. We performed first-principle DFT calculations on the key insertion steps for industrially important monomers, vinyl fluoride (VF) and 3,3,3-trifluoropropene (TFP), to explain how the ligand substitution patterns affect the complex's polymerization behaviors. Our results indicate that the favorable 2,1-insertion of TFP is caused by less deformation in the catalyst moiety of the complexes in contrast to the 1,2-insertion mode. In contrast to the VF monomer, the additional interaction between the fluorine atoms of 3,3,3-trifluoropropene and the carbons of the catalyst ligands also contributed to favor the 2,1-insertion. It was found that the regioselectivity of the monomer was predominated by the progressive alteration of the catalytic geometry caused by small dihedral angles that were developed after the ligand-monomer interaction. Based on the distribution of the 1,2- and 2,1-insertion products, the activity and selectivity were influenced by the steric environment surrounding the palladium center; thus, an increased steric bulk visibly improved the selectivity of the bulkier polar monomer (TFP) during the copolymerization mechanism. In contrast, better activity was maintained through a sterically less hindered Pd metal center; the calculated moderate energy barriers showed that a catalyst with less steric hindrance might provide an opportunity for a wide range of prospective industrial applications.

TumorDetNet: A unified deep learning model for brain tumor detection and classification.

Accurate diagnosis of the brain tumor type at an earlier stage is crucial for the treatment process and helps to save the lives of a large number of people worldwide. Because they are non-invasive and spare patients from having an unpleasant biopsy, magnetic resonance imaging (MRI) scans are frequently employed to identify tumors. The manual identification of tumors is difficult and requires considerable time due to the large number of three-dimensional images that an MRI scan of one patient's brain produces from various angles. Moreover, the variations in location, size, and shape of the brain tumor also make it challenging to detect and classify different types of tumors. Thus, computer-aided diagnostics (CAD) systems have been proposed for the detection of brain tumors. In this paper, we proposed a novel unified end-to-end deep learning model named TumorDetNet for brain tumor detection and classification. Our TumorDetNet framework employs 48 convolution layers with leaky ReLU (LReLU) and ReLU activation functions to compute the most distinctive deep feature maps. Moreover, average pooling and a dropout layer are also used to learn distinctive patterns and reduce overfitting. Finally, one fully connected and a softmax layer are employed to detect and classify the brain tumor into multiple types. We assessed the performance of our method on six standard Kaggle brain tumor MRI datasets for brain tumor detection and classification into (malignant and benign), and (glioma, pituitary, and meningioma). Our model successfully identified brain tumors with remarkable accuracy of 99.83%, classified benign and malignant brain tumors with an ideal accuracy of 100%, and meningiomas, pituitary, and gliomas tumors with an accuracy of 99.27%. These outcomes demonstrate the potency of the suggested methodology for the reliable identification and categorization of brain tumors.

Detection of Extended-spectrum ß-lactamase-producing Escherichia coli isolates by isothermal amplification and association of their virulence genes and phylogroups with extraintestinal infection.

Extraintestinal pathogenic Escherichia coli (ExPEC) producing extended-spectrum ß-lactamases (ESBL) cause serious human infections due to their virulence and multidrug resistance (MDR) profiles. We characterized 144 ExPEC strains (collected from a tertiary cancer institute) in terms of antimicrobial susceptibility spectrum, ESBL variants, virulence factors (VF) patterns, and Clermont's phylogroup classification. The developed multiplex recombinase polymerase amplification and thermophilic helicase-dependent amplification (tHDA) assays for blaCTX-M, blaOXA, blaSHV, and blaTEM detection, respectively, were validated using PCR-sequencing results. All ESBL-ExPEC isolates carried blaCTX-M genes with following prevalence frequency of variants: blaCTX-M-15 (50.5%) > blaCTX-M-55 (17.9%) > blaCTX-M-27 (16.8%) > blaCTX-M-14 (14.7%). The multiplex recombinase polymerase amplification assay had 100% sensitivity, and specificity for blaCTX-M, blaOXA, blaSHV, while tHDA had 86.89% sensitivity, and 100% specificity for blaTEM. The VF genes showed the following prevalence frequency: traT (67.4%) > ompT (52.6%) > iutA (50.5%) > fimH (47.4%) > iha (33.7%) > hlyA (26.3%) > papC (12.6%) > cvaC (3.2%), in ESBL-ExPEC isolates which belonged to phylogroups A (28.4%), B2 (28.4%), and F (22.1%). The distribution of traT, ompT, and hlyA and phylogroup B2 were significantly different (P < 0.05) between ESBL-ExPEC and non-ESBL-ExPEC isolates. Thus, these equipment-free isothermal resistance gene amplification assays contribute to effective treatment and control of virulent ExPEC, especially antimicrobial resistance strains.

Association and comparison of periodontal and oral hygiene status with serum HbA1c levels: a cross-sectional study.

BACKGROUND: Diabetes Mellitus and periodontitis are chronic diseases with known reciprocal association. Studies have shown that uncontrolled diabetes increases the risk of development and progression of periodontal disease. This study aimed to explore the association and severity of periodontal clinical parameters and oral hygiene with HbA1c levels in non-diabetics and T2DM patients. MATERIALS AND METHODS: In this cross-sectional study, the periodontal status of 144 participants, categorized into non-diabetics, controlled T2DM, and uncontrolled T2DM and were assessed via the Community Periodontal Index (CPI), Loss of Attachment Index (LOA index), and the number of missing teeth, while oral hygiene was measured by utilizing the Oral Hygiene Index Simplified (OHI-S). SPSS was used for data analysis. Chi-square test was used to find out the association of different independent variables with HbA1c groups, while ANOVA and post-hoc tests were run for inter-group and intra-group comparison respectively. RESULTS: Out of 144 participants, the missing dentition was prevalent in uncontrolled T2DM with mean 2.64 ± 1.97 (95% CI 2.07-3.21; p = 0.01) followed by controlled T2DM 1.70 ± 1.79 (95% CI 1.18-2.23; p = 0.01) and non-diabetics 1.35 ± 1.63 (95% CI 0.88-1.82; p = 0.01) respectively. Furthermore, non-diabetics had a higher proportion of CPI score 0 (Healthy) [30 (20.8%); p = 0.001] as compared to uncontrolled T2DM [6 (4.2%); p = 0.001], while CPI score 3 was more prevalent in uncontrolled T2DM in comparison to non-diabetics. Loss of attachment (codes-2,3 and 4) was also frequently observed in uncontrolled T2DM compared to non-diabetics (p = 0.001). Similarly, based on Oral Hygiene Index- Simplified (OHI-S), the result showed that poor oral hygiene was most commonly observed in uncontrolled T2DM 29 (20.1%) followed by controlled T2DM patients 22 (15.3%) and non-diabetic [14 (9.7%); p = 0.03]. CONCLUSION: This study showed that periodontal status and oral hygiene status were deteriorated in uncontrolled T2DM patients compared to non-diabetic participants and controlled T2DM.

Tunable metalensing based on plasmonic resonators embedded on thermosresponsive hydrogel.

Metalenses of adjustable power and ultrathin flat zoom lens system have emerged as a promising and key photonic device for integrated optics and advanced reconfigurable optical systems. Nevertheless, realizing an active metasurface retaining lensing functionality in the visible frequency regime has not been fully explored to design reconfigurable optical devices. Here, we present a focal tunable metalens and intensity tunable metalens in the visible frequency regime through the control of the hydrophilic and hydrophobic behavior of freestanding thermoresponsive hydrogel. The metasurface is comprised of plasmonic resonators embedded on the top of hydrogel which serves as dynamically reconfigurable metalens. It is shown that the focal length can be continuously tuned by adjusting the phase transition of hydrogel, the results reveal that the device is diffraction limited in different states of hydrogel. In addition, the versatility of hydrogel-based metasurfaces is further explored to design intensity tunable metalens, that can dynamically tailor the transmission intensity and confined it into the same focal spot under different states, i.e., swollen and collapsed. It is anticipated that the non-toxicity and biocompatibility make the hydrogel-based active metasurfaces suitable for active plasmonic devices with ubiquitous roles in biomedical imaging, sensing, and encryption systems.

Structural insights into the effect of mutations in the spike protein of SARS-CoV-2 on the binding with human furin protein.

The SARS COV-2 and its variants are spreading around the world at an alarming speed, due to its higher transmissibility and the conformational changes caused by mutations. The resulting COVID-19 pandemic has imposed severe health consequences on human health. Several countries of the world including Pakistan have studied its genome extensively and provided productive findings. In the current study, the mCSM, DynaMut2, and I-Mutant servers were used to analyze the effect of identified mutations on the structural stability of spike protein however, the molecular docking and simulations approaches were used to evaluate the dynamics of the bonding network between the wild-type and mutant spike proteins with furin. We addressed the mutational modifications that have occurred in the spike protein of SARS-COV-2 that were found in 215 Pakistani's isolates of COVID-19 patients to study the influence of mutations on the stability of the protein and its interaction with the host cell. We found 7 single amino acid substitute mutations in various domains that reside in spike protein. The H49Y, N74K, G181V, and G446V were found in the S1 domain while the D614A, V622F, and Q677H mutations were found in the central helices of the spike protein. Based on the observation, G181V, G446V, D614A, and V622F mutants were found highly destabilizing and responsible for structural perturbation. Protein-protein docking and molecular simulation analysis with that of furin have predicted that all the mutants enhanced the binding efficiency however, the V622F mutant has greatly altered the binding capacity which is further verified by the KD value (7.1 E-14) and therefore may enhance the spike protein cleavage by Furin and increase the rate of infectivity by SARS-CoV-2. On the other hand, the total binding energy for each complex was calculated which revealed -50.57 kcal/mol for the wild type, for G181V -52.69 kcal/mol, for G446V -56.44 kcal/mol, for D614A -59.78 kcal/mol while for V622F the TBE was calculated to be -85.84 kcal/mol. Overall, the current finding shows that these mutations have increased the binding of Furin for spike protein and shows that D614A and V622F have significant effects on the binding and infectivity.

Application of Recently used Green Solvents in Sample Preparation Techniques: A Comprehensive Review of Existing Trends, Challenges, and Future Opportunities.

Green solvents (GSs) has gained significant attention in recent years due to their potential as safer and more sustainable alternatives to traditional organic solvents. Solvents are used in a wide range of applications, from industrial processes to everyday products. Solvent emissions and losses can have a significant impact on the environment and human health, which is why many initiatives are being undertaken to get rid of or switch to eco-friendly alternatives. A key area of green chemistry that led to the concept of "green" solvents is the development of alternative solvents that are less toxic and more environmentally friendly than traditional organic solvents. The advantages of using green solvents over conventional ones are their environmental friendliness, biocompatibility, biodegradability, and simplicity of preparation. Different sample preparation techniques have successfully utilized green solvents to offer a sustainable separation media for the extraction of a variety of inorganic and organic compounds which are crucial for research in environmental samples. Recent developments in green analytical chemistry (GAC) have focused on how to prepare and use samples using environmentally sustainable solvents. The current study covers the advance and currently used green solvents with an emphasis on environmentally friendly sample preparation methods. This review aims to briefly summarize the current state of knowledge about the use of green solvents particularly ionic liquids, deep eutectic solvents and switchable solvents (SSs) with the perspective of GAC in sample preparation methods.

A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network.

Early and precise COVID-19 identification and analysis are pivotal in reducing the spread of COVID-19. Medical imaging techniques, such as chest X-ray or chest radiographs, computed tomography (CT) scan, and electrocardiogram (ECG) trace images are the most widely known for early discovery and analysis of the coronavirus disease (COVID-19). Deep learning (DL) frameworks for identifying COVID-19 positive patients in the literature are limited to one data format, either ECG or chest radiograph images. Moreover, using several data types to recover abnormal patterns caused by COVID-19 could potentially provide more information and restrict the spread of the virus. This study presents an effective COVID-19 detection and classification approach using the Shufflenet CNN by employing three types of images, i.e., chest radiograph, CT-scan, and ECG-trace images. For this purpose, we performed extensive classification experiments with the proposed approach using each type of image. With the chest radiograph dataset, we performed three classification experiments at different levels of granularity, i.e., binary, three-class, and four-class classifications. In addition, we performed a binary classification experiment with the proposed approach by classifying CT-scan images into COVID-positive and normal. Finally, utilizing the ECG-trace images, we conducted three experiments at different levels of granularity, i.e., binary, three-class, and five-class classifications. We evaluated the proposed approach with the baseline COVID-19 Radiography Database, SARS-CoV-2 CT-scan, and ECG images dataset of cardiac and COVID-19 patients. The average accuracy of 99.98% for COVID-19 detection in the three-class classification scheme using chest radiographs, optimal accuracy of 100% for COVID-19 detection using CT scans, and average accuracy of 99.37% for five-class classification scheme using ECG trace images have proved the efficacy of our proposed method over the contemporary methods. The optimal accuracy of 100% for COVID-19 detection using CT scans and the accuracy gain of 1.54% (in the case of five-class classification using ECG trace images) from the previous approach, which utilized ECG images for the first time, has a major contribution to improving the COVID-19 prediction rate in early stages. Experimental findings demonstrate that the proposed framework outperforms contemporary models. For example, the proposed approach outperforms state-of-the-art DL approaches, such as Squeezenet, Alexnet, and Darknet19, by achieving the accuracy of 99.98 (proposed method), 98.29, 98.50, and 99.67, respectively.

Mechanistic study to reveal steric and electronic aspects involved in the formation of microstructures during Pd-catalyzed olefin/divinyl formal copolymerization: reactivity to catalyst choice.

The advancement of metal-catalyzed copolymers is a formidable challenge for achieving distinct catalytic properties to compete with existing plastic polymers in industrial commodities. Herein, we reveal the roles of electronic and steric environments in the thermodynamic preference of microstructures in ethylene/divinyl formal (DVF) co-polymerization using a Pd catalyst under mild conditions to accommodate the respective industrial applicabilities. The insertion products of DVF result in the alteration of the steric crowding, ultimately favoring the efficient formation of cyclic units having potential applications in the manufacture of high-strength fibers. More specifically, to achieve an improved yield of the end copolymer, we tuned the catalytic activity and regioselectivity through a variety of catalysts during ethylene-DVF co-polymerization. The naphthalene-bridged (P^O)PdMe catalyst was found to be promising in terms of the least hindered (buried volume of 47.8%) environment with the thermodynamic preference of 2,1-insertion with an energy of 5.1 kcal mol-1 among all the Pd-metal based catalysts. The highest activity with moderate energy barriers of the proposed catalyst will open new avenues for achieving a variety of potential applications, which is typically not possible using existing polymerization techniques.

A critical review of selected preconcentration techniques used for selenium determination in analytical samples.

Selenium (Se) is considered to be an essential trace element needed for all living organisms. The importance, deficiency, and toxic effects of Se mainly depend on its quantity and chemical nature. It has been observed that the inorganic versions of Se are more hazardous than the organic versions. This review is mainly focused on the application of different extraction methods used for Se extraction and determination such as microextraction, solid-phase extraction (SPE), and their modified modes in the last 12 years. The use of different dispersive medium (magnetic field, ultrasonic radiation, and vortex agitator) to enhance Se separation is also part of this review. The usage of environmentally friendly solvents such as supramolecular solvents, hydrophobic deep eutectic solvents (DESs), and ionic liquids (ILs) are also the focus of attention in this review. This review is also emphasized the application of advanced microextraction methods, particularly liquid-phase microextraction (LPME). The most recent advances in LPME extraction techniques for Se in various environmental samples, as well as their prospects, are reviewed. Additionally, a summary of cheap, simple, and accurate techniques that have not yet been used to determine small amounts of Se has been provided.

A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images.

Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, and prone to error. Magnetic resonance imaging (MRI) scans are mostly used for tumor detection due to their non-invasive properties and also avoid painful biopsy. MRI scanning of one patient's brain generates many 3D images from multiple directions, making the manual detection of tumors very difficult, error-prone, and time-consuming. Therefore, there is a considerable need for autonomous diagnostics tools to detect brain tumors accurately. In this research, we have presented a novel TumorResnet deep learning (DL) model for brain detection, i.e., binary classification. The TumorResNet model employs 20 convolution layers with a leaky ReLU (LReLU) activation function for feature map activation to compute the most distinctive deep features. Finally, three fully connected classification layers are used to classify brain tumors MRI into normal and tumorous. The performance of the proposed TumorResNet architecture is evaluated on a standard Kaggle brain tumor MRI dataset for brain tumor detection (BTD), which contains brain tumor and normal MR images. The proposed model achieved a good accuracy of 99.33% for BTD. These experimental results, including the cross-dataset setting, validate the superiority of the TumorResNet model over the contemporary frameworks. This study offers an automated BTD method that aids in the early diagnosis of brain cancers. This procedure has a substantial impact on improving treatment options and patient survival.

Analysis of associated risk factors among recurrent cutaneous leishmaniasis patients: A cross-sectional study in Khyber Pakhtunkhwa, Pakistan.

BACKGROUND: Leishmaniasis is the second and fourth highest cause of mortality and morbidity respectively among all tropical diseases. Recurrence in the onset of leishmaniasis is a major problem that needs to be addressed to reduce the case fatality rate and ensure timely clinical intervention. Here we are investigating the association of risk factors with recurrent cutaneous leishmaniasis to address this issue. MATERIAL AND METHODS: Patients received by Nasser Ullah Khan Babar Hospital in Peshawar, Pakistan from March 2019 to July 2020 were enrolled in this study. Those patients who developed symptoms after completion of treatment were included in Group-A while those who had atypical scars like leishmaniasis but were negative for cutaneous leishmaniasis were included in the comparison group tagged as Group B. All those individuals who had completed six weeks of treatment for CL but had normal complete blood counts (CBC) were included to avoid other underlying immunological pathologies, while we excluded those participants who had co-morbidities like diabetes, liver disease, cardiac disease, and pregnant and lactating women through their history Association was tested between Group-A and Group-B with other explanatory variables through chi-square test. The regression model was proposed to determine the predictors. RESULT: A total of 48 participants of both sexes were included in the study with a mean age of 32.2 ± 15.10. The data suggest that females are overrepresented among the patients with recurrent leishmaniasis [21(53.8 %,); p = 0.07]. Compared to patients; healthy participants had a higher proportion of adults (19-59 years) versus adolescents (13-18 years) [26(66.7 %) vs 07(17.9), p = 0.004]. Multivariate logistic regression analysis shows that females are 2.1 times more prone to infections among cases as compared to healthy individuals [unadjusted OR 2.20, 95 % confidence interval (CI) 1.5-10.6, p = 0.02; adjusted OR 2.1, 95 % CI 1.50-10.69, p = 0.02]. We propose that patients receiving intradermal were less likely to be infected as compared to those receiving intralesional injections [unadjusted OR 0.07.0, 95 % confidence interval (CI) 1.18-3.37, p = 0.03; adjusted OR 0.06, 95 % CI 1.18-3.38, p = 0.03]. CONCLUSION: Old age (adults) and sex (females) were the strongest predictors to be associated with recurrent leishmaniasis. Similarly, the choice of intradermal as compared to intralesional injection and the prolonged treatment duration were strongly associated with greater chances of recurrence.