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Abstract Background Skin cancer continues to be an imperative global health issue, urging continuous exploration of treatment methodologies. Conventional treatments for skin cancer include surgical interventions, immunotherapy, targeted therapy, chemotherapy, and radiation therapy. However, these methods often present obstacles like treatment resistance, systemic toxicity, limited effectiveness in advanced stages, infection risk, pain, long recovery, and impact on healthy tissue. Main body of the abstract Nanomedicine holds promise by facilitating precise drug administration, early detection, and heightened therapeutic efficiency via targeted and localized delivery systems. The integration of nanomedicine into skin cancer alleviation therapies demonstrates optimistic outcomes, including refined drug delivery, augmented bioavailability, minimized adverse effects, and potential theranostic applications. Recent breakthroughs in nanomedicine have propelled advancements in skin cancer treatment, showing significant potential in transforming the treatment paradigm. The presents review provides comprehensive aspects of existing skin cancer treatments and their challenges, spotlighting recent breakthroughs propelled by nanomedicine. Short conclusion This abstract delineates the present landscape of skin cancer treatments, underscores their constraints, and highlights recent strides in nanomedicine that have the potential to transform the paradigm of skin cancer treatment, ultimately elevating patient prognosis. Importantly, the present review emphasizes substantial challenges that hinder the clinical translation of nanomedicines and suggests possible remedies to surpass them. Graphic abstract

Humans' indiscriminate usage of heavy metals has a negative impact on the world's natural ecosystem. One of the main causes of contaminating the fresh and healthy atmosphere in rural Pakistan is the use of coal and soil in an improper manner by brick kilns. Different heavy metal concentrations and their impact on the surrounding areas of Pakistan's District Lakki Marwat were investigated in this study. Acid digestion methods were used to prepare samples, which were then examined using an Atomic Absorption Spectrophotometer. The concentrations of cadmium, iron, and lead in certain samples exceeded the WHO's permitted level, whereas nickel and zinc concentrations were within the limit. It was also discovered that the supply of raw materials is the key determinant in the buildup of heavy metals in the areas near brick not the age of the kiln.

In today’s digital world, app stores have become an essential part of software distribution, providing customers with a wide range of applications and opportunities for software developers to showcase their work. This study elaborates on the importance of end-user feedback for software evolution. However, in the literature, more emphasis has been given to high-rating & popular software apps while ignoring comparatively low-rating apps. Therefore, the proposed approach focuses on end-user reviews collected from 64 low-rated apps representing 14 categories in the Amazon App Store. We critically analyze feedback from low-rating apps and developed a grounded theory to identify various concepts important for software evolution and improving its quality including user interface (UI) and user experience (UX), functionality and features, compatibility and device-specific, performance and stability, customer support and responsiveness and security and privacy issues. Then, using a grounded theory and content analysis approach, a novel research dataset is curated to evaluate the performance of baseline machine learning (ML), and state-of-the-art deep learning (DL) algorithms in automatically classifying end-user feedback into frequently occurring issues. Various natural language processing and feature engineering techniques are utilized for improving and optimizing the performance of ML and DL classifiers. Also, an experimental study comparing various ML and DL algorithms, including multinomial naive Bayes (MNB), logistic regression (LR), random forest (RF), multi-layer perception (MLP), k-nearest neighbors (KNN), AdaBoost, Voting, convolutional neural network (CNN), long short-term memory (LSTM), bidirectional long short term memory (BiLSTM), gated recurrent unit (GRU), bidirectional gated recurrent unit (BiGRU), and recurrent neural network (RNN) classifiers, achieved satisfactory results in classifying end-user feedback to commonly occurring issues. Whereas, MLP, RF, BiGRU, GRU, CNN, LSTM, and Classifiers achieved average accuracies of 94%, 94%, 92%, 91%, 90%, 89%, and 89%, respectively. We employed the SHAP approach to identify the critical features associated with each issue type to enhance the explainability of the classifiers. This research sheds light on areas needing improvement in low-rated apps and opens up new avenues for developers to improve software quality based on user feedback.

App stores allow users to search, download, and purchase software applications to accomplish daily tasks. Also, they enable crowd-users to submit textual feedback or star ratings to the downloaded software apps based on their satisfaction. Recently, crowd-user feedback contains critical information for software developers, including new features, issues, non-functional requirements, etc. Previously, identifying software bugs in low-star software applications was ignored in the literature. For this purpose, we proposed a natural language processing-based (NLP) approach to recover frequently occurring software issues in the Amazon Software App (ASA) store. The proposed approach identified prevalent issues using NLP part-of-speech (POS) analytics. Also, to better understand the implications of these issues on end-user satisfaction, different machine learning (ML) algorithms are used to identify crowd-user emotions such as anger, fear, sadness, and disgust with the identified issues. To this end, we shortlisted 45 software apps with comparatively low ratings from the ASA Store. We investigated how crowd-users reported their grudges and opinions against the software applications using the grounded theory & content analysis approaches and prepared a grounded truth for the ML experiments. ML algorithms, such as MNB, LR, RF, MLP, KNN, AdaBoost, and Voting Classifier, are used to identify the associated emotions with each captured issue by processing the annotated end-user data set. We obtained satisfactory classification results, with MLP and RF classifiers having 82% and 80% average accuracies, respectively. Furthermore, the ROC curves for better-performing ML classifiers are plotted to identify the best-performing under or oversampling classifier to be selected as the final best classifier. Based on our knowledge, the proposed approach is considered the first step in identifying frequently occurring issues and corresponding end-user emotions for low-ranked software applications. The software vendors can utilize the proposed approach to improve the performance of low-ranked software apps by incorporating it into the software evolution process promptly.

Abstract Etravirine (ERVN) is a potential NNRTI (non-nucleoside reverse transcriptase inhibitor) in treating HIV infection. It possesses extremely low oral bioavailability. The present research aims to optimize the formulation and characterization of TPGS-enriched ERVN-loaded lipid-based nanocarriers (NLCs) for HIV-infected patients. The formulation, ERVN–TPGS–NLCs, was optimized by central composite rotational design using a modified-solvent emulsification process. Various characterization parameters of NLCs were evaluated, including globule size of 121.56 ± 2.174 nm, PDI of 0.172 ± 0.042, the zeta potential of − 7.32 ± 0.021 mV, %EE of 94.42 ± 8.65% of ERVN and %DL was 8.94 ± 0.759% of ERVN and spherical shape was revealed by TEM. PXRD was also performed to identify the crystallinity of the sample. In-vitro drug release showed % a cumulative drug release of 83.72 ± 8.35% at pH 1.2 and 90.61 ± 9.11% at pH 6.8, respectively, whereas the % cumulative drug release from drug suspension (ERVN-S) was found to be 21.13 ± 2.01% at pH 1.2 and 24.84 ± 2.51 at pH 6.8 at the end of 48 h. Further, the intestinal permeation study and confocal microscope showed approximately three-fold and two-fold increased permeation in ERVN–TPGS–NLCs and ERVN-NLCs across the gut sac compared to ERVN-S. Hemolysis compatibility and lipolysis studies were performed to predict the in-vivo fate of the formulation. The pharmacokinetic study revealed a 3.13-fold increment in the relative bioavailability, which agrees with the ex-vivo studies, and lymphatic uptake was validated by using cycloheximide along with designed formulation, which showed the impact of lymphatic uptake in AUC. This study ensures that ERVN–TPGS–NLCs take lymphatic uptake to minimize the first-pass metabolism followed by improved oral bioavailability of ERVN. Thus, the enhanced bioavailability of ERVN can reduce the high dose of ERVN to minimize the adverse effects related to dose-related burden. Graphical abstract

Viral and non-viral hepatocellular carcinoma (HCC) is becoming predominant in developing countries. A major issue linked to HCC-related mortality rate is the late diagnosis of cancer development. Although traditional approaches to diagnosing HCC have become gold-standard, there remain several limitations due to which the confirmation of cancer progression takes a longer period. The recent emergence of artificial intelligence tools with the capacity to analyze biomedical datasets is assisting traditional diagnostic approaches for early diagnosis with certainty. Here we present a review of traditional HCC diagnostic approaches versus the use of artificial intelligence (Machine Learning and Deep Learning) for HCC diagnosis. The overview of the cancer-related databases along with the use of AI in histopathology, radiology, biomarker, and electronic health records (EHRs) based HCC diagnosis is given.

The graphical user interface (GUI) in mobile applications plays a crucial role in connecting users with mobile applications. GUIs often receive many UI design smells, bugs, or feature enhancement requests. The design smells include text overlap, component occlusion, blur screens, null values, and missing images. It also provides for the behavior of mobile applications during their usage. Manual testing of mobile applications (app as short in the rest of the document) is essential to ensuring app quality, especially for identifying usability and accessibility that may be missed during automated testing. However, it is time-consuming and inefficient due to the need for testers to perform actions repeatedly and the possibility of missing some functionalities. Although several approaches have been proposed, they require significant performance improvement. In addition, the key challenges of these approaches are incorporating the design guidelines and rules necessary to follow during app development and combine the syntactical and semantic information available on the development forums. In this study, we proposed a UI bug identification and localization approach called Mobile-UI-Repair (M-UI-R). M-UI-R is capable of recognizing graphical user interfaces (GUIs) display issues and accurately identifying the specific location of the bug within the GUI. M-UI-R is trained and tested on the history data and also validated on real-time data. The evaluation shows that the average precision is 87.7% and the average recall is 86.5% achieved in the detection of UI display issues. M-UI-R also achieved an average precision of 71.5% and an average recall of 70.7% in the localization of UI design smell. Moreover, a survey involving eight developers demonstrates that the proposed approach provides valuable support for enhancing the user interface of mobile applications. This aids developers in their efforts to fix bugs.

Emotion detection (ED) involves the identification and understanding of an individual’s emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person’s emotional state, recognizing these actions as physical functions integrating motor and cognitive processes. The study proposes an attention-based transformer model as an innovative approach to identify emotions from handwriting and drawing samples, thereby advancing the capabilities of ED into the domains of fine motor skills and artistic expression. The initial data obtained provides a set of points that correspond to the handwriting or drawing strokes. Each stroke point is subsequently delivered to the attention-based transformer model, which embeds it into a high-dimensional vector space. The model builds a prediction about the emotional state of the person who generated the sample by integrating the most important components and patterns in the input sequence using self-attentional processes. The proposed approach possesses a distinct advantage in its enhanced capacity to capture long-range correlations compared to conventional recurrent neural networks (RNN). This characteristic makes it particularly well-suited for the precise identification of emotions from samples of handwriting and drawings, signifying a notable advancement in the field of emotion detection. The proposed method produced cutting-edge outcomes of 92.64% on the benchmark dataset known as EMOTHAW (Emotion Recognition via Handwriting and Drawing).

Abstract Small and Medium Enterprises (SMEs) are steadily moving in the direction of implementing digital and smart technologies, including the Industrial Internet of Things (IIoT) for improving their products and services. The adoption of IIoT allows manufactures and producers to make quick decisions for improving productivity and quality in real‐time. For this purpose, the era of digital industrial revolution from IR 1.0 to IR 5.0 is briefly explained. In this research study, the authors have reviewed and analysed the existing reviews, surveys and technical research studies on IIoT technologies for the manufacturing and production SMEs to highlight the concern raised. Forty‐seven (47) influencing factors are identified and classified into four groups based on the TOEI framework. Based on the identified influencing factors, IIoT adoption model is proposed for the manufacturing and production SMEs to adopt the new IIoT technologies in their business environments. Furthermore, a comparative analysis of the influencing factors has been done for the adoption of IIoT to increase efficiency, productivity and competitiveness for the manufacturing and production SMEs in developing countries. The proposed IIoT adoption model will help future policymakers and stakeholders to develop policies and strategies for the successful adoption and implementation of IIoT in manufacturing and production SMEs in developing countries. Also, recommendations are suggested to encourage IIoT adoption in production and manufacturing environments so that manufacturers and producers can respond easily and quickly to highly changing demands, product trends, skills gaps and other unexpected challenges in the future.

Phishing constitutes a form of social engineering that aims to deceive individuals through email communication. Extensive prior research has underscored phishing as one of the most commonly employed attack vectors for infiltrating organizational networks. A prevalent method involves misleading the target by employing phishing URLs concealed through hyperlink strategies. PhishTank, a website employing the concept of crowd-sourcing, aggregates phishing URLs and subsequently verifies their authenticity. In the course of this study, we leveraged a Python script to extract data from the PhishTank website, amassing a comprehensive dataset comprising over 190,0000 phishing URLs. This dataset is a valuable resource that can be harnessed by both researchers and practitioners for enhancing phish- ing filters, fortifying firewalls, security education, and refining training and testing models, among other applications.

Data security and privacy are considered to be the biggest problems faced by service providers who have worked with public data for a long time. A key element of modern encryption that is utilized to increase textual confusion is the Substitution box (S-box) and the algebraic strength of the S-box has a significant impact on how secure the encryption method is. In this article, we present a unique method that uses a linear fractional transformation on a finite field to produce cryptographically robust S-boxes. Firstly, we choose a specific irreducible polynomial of degree 8 in Z2[x] to construct GF(28). Later, we used the action of PGL(2,GF(28)) on GF(28) to generate a robust S-box. The effectiveness of the built-in S-box was evaluated using several criteria including non-linearity, differential uniformity, strict avalanche criteria, linear approximation probability, and bit independence criterion. The proposed S-box's characteristics are compared to those of most recent S-boxes to confirm the higher performance. Additionally, the S box was used to encrypt images to show its usefulness for multimedia security applications. We performed several tests, including contrast, correlation, homogeneity, entropy, and energy, to evaluate the success of the encryption technique. The proposed method for ciphering an image is very effective, as proven by its comparison with several S boxes.

Natural hazards such as hurricanes, floods, and wildfires cause devastating socio-economic impacts on communities. In South Florida, most of these hazards are becoming increasingly frequent and severe because of the warming climate, and changes in vulnerability and exposure, resulting in significant damage to infrastructure, homes, and businesses. To better understand the drivers of these impacts, we developed a bottom-up impact-based methodology that takes into account all relevant drivers for different types of hazards. We identify the specific drivers that co-occurred with socio-economic impacts and determine whether these extreme events were caused by single or multiple hydrometeorological drivers (i.e., compound events). We consider six types of natural hazards: hurricanes, severe storm/thunderstorms, floods, heatwaves, wildfire, and winter weather. Using historical, socio-economic loss data along with observations and reanalysis data for hydrometeorological drivers, we analyze how often these drivers contributed to the impacts of natural hazards in South Florida. We find that for each type of hazard, the relative importance of the drivers varies depending on the severity of the event. For example, wind speed is a key driver of the socio-economic impacts of hurricanes, while precipitation is a key driver of the impacts of flooding. We find that most of the high-impact events in South Florida were compound events, where multiple drivers contributed to the occurrences and impacts of the events. For example, more than 50% of the recorded flooding events were compound events and these contributed to 99% of total property damages and 98% of total crop damages associated with flooding in Miami-Dade County. Our results provide valuable insights into the drivers of natural hazard impacts in South Florida and can inform the development of more effective risk reduction strategies for improving the preparedness and resilience of the region against extreme events. Our bottom-up impact-based methodology can be applied to other regions and hazard types, allowing for more comprehensive and accurate assessments of the impacts of compound hazards.

Clofibric acid (CFA), an important blood-lipid regulatory drug is an emerging organic pollutant and widely reported in water resources. A novel bimetallic, bismuth/zero valent cupper (Bi/Cu0) catalyst was prepared which showed better physiological, structural, and catalytic properties than Cu0. The Bi/Cu0 effectively catalyzed persulfate (S2O82−) and caused 85% degradation of CFA. The Bi coupling improved reusability and stability of Cu0. The use of alcoholic and anionic radical scavengers and analyzing change in [S2O82−]0 proved that Bi/Cu0/S2O82− yield hydroxyl radicals (●OH) and sulfate radicals (SO4●–). The ●OH and SO4●– showed faster reaction with CFA, i.e., 4.65 × 109 and 3.82 × 109 M−1 s−1 and degraded CFA into four degradation products. Under optimal conditions of [Bi/Cu0]0 = 1.0 g/L and [S2O82−]0 = 40 mg/L, 99.5% degradation of the 10 mg/L of CFA was achieved at 65 min. Temperature showed promising effects on the removal of CFA by Bi/Cu0/S2O82− and caused 98% removal at 323 K than 75% at 298 K at 32 min. The temperature effects were used to calculate activation energy, enthalpy, and rate constant of CFA degradation. The Bi/Cu0/S2O82− showed effective removal of CFA in real water samples also. The ecotoxicity study confirmed non-toxic product formation which suggests high capability of the proposed technology in the treatment of CFA.

AbstractCyclophosphamide (CP) is one of the most extensively used antineoplastic drug, but the nephrotoxicity caused by this drug is a major limiting factor for its use. Nerolidol (NERO) is a natural bioactive compound with diverse pharmacological actions. In Vitro and in vivo study was performed using HK-2 renal cells and Swiss Albino mice. Cell lines and animals were treated with NERO 25 and 50 µM + 30 µM CP (in vitro), 200 and 400 mg/kg, p.o. NERO from day 1 to day 15 + 200 mg/kg, i.p. CP on day 17 as single intraperitoneal injection (in vivo). The makers of oxidative stress, renal-specific injury markers, inflammation, apoptosis, fibrosis, and histopathological changes were studied. The study’s outcome showed a significant reduction in the level of malonaldehyde and interleukin-6 (p

IntroductionRecently, 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. MethodThis 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.ResultsThe 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.DiscussionThe 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.

Modern cryptographic systems use substitution boxes (S-boxes) throughout the encryption process to enhance the security of the plaintext. The integrity of the communication process is ensured by these S-boxes, which are vital in converting the ciphertext back into the original plaintext during the decryption phase. The cryptographic strength of a certain S-box has a substantial impact on the overall security of a given cipher. As a result, many researchers have used innovative construction techniques to create robust S-boxes. The method used in this paper is a novel combination of a specific map on the direct product of cyclic groups of order $16 \times 16$ and an inversion map of a Galois field with order 256. This strategy aims to produce dynamic S-boxes. The proposed method can produce a large number of strong S-boxes by making little changes to the map’s parameters. Four S-boxes were created and their performance was analyzed using industry standards such as bijectivity, the strict avalanche criterion (SAC), nonlinearity (NL), the bit independence criterion (BIC), linear probability (LAP), and differential probability (DAP). The performance of the recommended S-boxes was compared to that of state-of-the-art S-boxes to show their efficacy. The proposed S-box also exhibits considerable potential as a candidate for modern cryptosystems aiming at securing multimedia information, as shown by a suggested method for protecting the privacy of digital images using it. The effectiveness of the encryption method was then assessed using several tests including contrast, correlation, homogeneity, entropy, energy, Number of Pixel change rate (NPCR), and Unified Average changing intensity (UACI). We observed the efficacy of the suggested method for image encryption by comparing our results with different methods.

Hot topic trends have become increasingly important in the era of social media, as these trends can spread rapidly through online platforms and significantly impact public discourse and behavior. As a result, the scope of distributed representations has expanded in machine learning and natural language processing. As these approaches can be used to effectively identify and analyze hot topic trends in large datasets. However, previous research has shown that analyzing sequential periods in data streams to detect hot topic trends can be challenging, particularly when dealing with large datasets. Moreover, existing methods often fail to accurately capture the semantic relationships between words over different time periods, limiting their effectiveness in trend prediction and relationship analysis. This paper aims to utilize a distributed representations approach to detect hot topic trends in streaming text data. For this purpose, we build a sequential evolution model for a streaming news website to identify hot topic trends in streaming text data. Additionally, we create a visual display model and knowledge graph to further enhance our proposed approach. To achieve this, we begin by collecting streaming news data from the web and dividing it chronologically into several datasets. In addition, word2vec models are built in different periods for each dataset. Finally, we compare the relationship of any target word in sequential word2vec models and analyze its evolutionary process. Experimental results show that the proposed method can detect hot topic trends and provide a graphical representation of any raw data that cannot be easily designed using traditional methods.

Elliptic curve cryptography has gained attention due to its strong resilience against current cryptanalysis methods. Inspired by the increasing demand for reliable and secure cryptographic methods, our research investigates the relationship between complex mathematical structures and image encryption. A substitution box (S-box) is the single non-linear component of several well-known security systems. Mordell elliptic curves are used because of their special characteristics and the immense computational capacity of Galois fields. These S-boxes are dynamic, which adds a layer of complexity that raises the encryption process’s security considerably. We suggest an effective technique for creating S-boxes based on a class of elliptic curves over GF(2n),n≥8. We demonstrate our approach’s robustness against a range of cryptographic threats through thorough examination, highlighting its practical applicability. The assessment of resistance of the newly generated S-box to common attack methods including linear, differential, and algebraic attacks involves a thorough analysis. This analysis is conducted by quantifying various metrics such as non-linearity, linear approximation, strict avalanche, bit independence, and differential approximation to gauge the S-box’s robustness against these attacks. A recommended method for image encryption involves the use of built-in S-boxes to quickly perform pixel replacement and shuffling. To evaluate the efficiency of the proposed strategy, we employed various tests. The research holds relevance as it can provide alternative guidelines for image encryption, which could have wider consequences for the area of cryptography as a whole. We believe that our findings will contribute to the development of secure communication and data protection, as digital security is becoming increasingly important.

BackgroundCountries affected by armed conflict have higher maternal mortality than stable settings. South Sudan has one of the highest maternal mortality ratios in the world, with an estimated 789 maternal deaths per 100,000 live births. Long-term socio-political instability has contributed to significant challenges in its health system. To reduce maternal and newborn morbidity and mortality, South Sudan must increase the number of skilled midwives.MethodsA cross-sectional mixed methods study was conducted in 2022 to assess the midwifery education program at three schools receiving support from International Medical Corps in South Sudan, including in-depth interviews with 15 midwifery school graduates currently working as midwives, their supervisors, 16 school faculty (in dyads), and two Ministry of Health officials; and nine focus group discussions with women clients of graduate midwives.ResultsParticipants identified strengths of the schools, including being well equipped with trained and competent teaching staff, competency-based curriculum, including practical training which prepared graduate midwives to apply their skills in practice. Weaknesses of the program included its dependence on donor funding, inadequate mentorship and number of tutors, and insufficient practice for some services due to low client load at clinical sites. Additionally, participants identified challenges affecting midwives' ability to provide good quality care, including lack of equipment and supplies, low client load, low salaries, and insecurity due to conflict. Nevertheless, women in the community appreciated the immense work that midwives do. Midwives were respected by the community at large, and graduates expressed pride and satisfaction in their job, as well as the positive impact they have had in providing critical services to communities.DiscussionOverall, the quality of the midwifery education program appears to be strong, however gaps in the program and the provision of quality care remain. The findings highlight the need to ensure sustained funding for midwifery education, as well as health system strengthening to ensure midwives can practice their skills. Continued investment in midwifery education and training is critical to reduce high maternal mortality and morbidity in South Sudan.

IntroductionSports of all kinds even though have an alluring property of keeping their onlookers stuck to their place, the introduction of Technology, however, revolutionized it all together. Not only in legal sports but also the training and teaching methods have been reformed. The use of Information Communication and Technology (ICT) based technologies [Convolutional Neural Networks (CNN), Hawkeye, Computer vision, Artificial intelligence, etc.] has moderately increased the interactive nature of sports. Employing ICT-driven technologies have continuously been increasing performance levels due to which high effective performance levels have been achieved. In addition to offering information to the users, it also acts as a means for connecting and interacting with the remaining world. In this article, we provide a review of the studies considering the developments and impact of employing ICT technology on sports, especially cricket. The study has focussed on domain-specific developments in cricket sports: developments in the batting domain, bowling domain, and wicketkeeping as well.MethodsFor the study, the analysis has been done following the PRISMA guidelines.ResultsThe study found that even though the researchers have done justifiable work in employing technology in sports as a whole but the domain-specific contribution in sports like cricket is not at the level as is need of the hour. In addition to the mentioned domains in the study, the research should gain speed in other domains like domain-specific Talent Identification for both genders, different age groups, diverse sports, etc.Discussionundoubtedly, the sports domain is employing technology at a vast level but a few domains like sports talent identification especially related to the most famous games like cricket require an immediate and intense focus of the researchers. Since this domain is still carrying out a traditional coach-oriented approach. There is an acute need to revolutionize the domain by incorporating modern technologies into it.

Antipsychotics are accompanied by extrapyramidal side effects that deter treatment adherence and patient compliance. Paliperidone (PPD), an atypical (second-generation) antipsychotic recommended for managing schizophrenia presents biopharmaceutical challenges and pharmacological constraints which dissuade it from crossing the brain barrier. The present research aimed to assess paliperidone-loaded lipid nanoconstruct (PPD-LNC) for an improved antipsychotic activity for managing schizophrenia. High % cell viability in Neuro-2a cells (70–98%) exhibited the safety of PPD-LNC. The pharmacokinetic data showed a 3.46-fold improvement in the relative bioavailability in the brain for PPD-LNC compared to a drug suspension. The pharmacodynamic evaluation demonstrated a significant (p

Cancer is the second cause of mortality worldwide, and the currently available conventional treatment approach is associated with serious side effects and poor clinical outcomes. Based on the outcome of the exploratory preclinical and clinical studies, it was found that therapeutic response increases multiple folds when anticancer drugs are used in combination. However, the conventional combination of anticancer drugs was associated with various limitations such as increased cost of treatment, systemic toxicity, drug resistance, and reduced pharmacokinetic attributes. Hence, attempts were made to formulate nanocarrier fabricated combinatorial drugs (NFCDs) to effectively manage and treat cancer. This approach offers several advantages, such as improved stability, lower drug exposure, targeted drug delivery, low side effects, and improved clinical outcome. Hence, in this review, first time, we have discussed the recent advancement and various types of nano carrier-based combinatorial drug delivery systems in a different type of cancer and highlighted the personalized combinatorial theranostic medicine as a futuristic anticancer treatment approach.

Radiotherapy is one of the extensively used therapeutic modalities in glioblastoma and other types of cancers. Radiotherapy is either used as a first-line approach or combined with pharmacotherapy or surgery to manage and treat cancer. Although the use of radiotherapy significantly increased the survival time of patients, but its use has been reported with marked neuroinflammation and cognitive dysfunction that eventually reduced the quality of life of patients. Based on the preclinical and clinical investigations, the profound role of increased oxidative stress, nuclear translocation of NF-kB, production of proinflammatory cytokines such as TNF-α, IL-6, IL-β, increased level of MMPs, increased apoptosis, reduced angiogenesis, neurogenesis, and histological aberrations in CA1, CA2, CA3 and DG region of the hippocampus have been reported. Various pharmacotherapeutic drugs are being used as an adjuvant to counteract this neurotoxic manifestation. Still, most of these drugs suffer from systemic adverse effect, causes interference to ongoing chemotherapy, and exhibit pharmacokinetic limitations in crossing the blood-brain barrier. Therefore, various phytoconstituents, their nano carrier-based drug delivery systems and miRNAs have been explored to overcome the aforementioned limitations. The present review is focused on the mechanism and evidence of radiotherapy-induced neuroinflammation and cognitive dysfunction, pathological and molecular changes in the brain homeostasis, available adjuvants, their limitations. Additionally, the potential role and mechanism of neuroprotection of various nanocarrier based natural products and miRNAs have been discussed.

Despite several neuroscience tools existing, electroencephalography (EEG) is the most used and favoured tool among researchers because of its relatively low cost and high temporal resolution. Our study aimed to identify the global academic research trends of the empirical EEG studies in neuromarketing. This paper adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to identify relevant articles. A bibliometric analysis software (VOSviewer) was used to evaluate thirty open-access articles found in the Scopus database between 2016 and 2020. We found that the USA is the most productive country with five research articles that used the EEG tool in marketing studies, followed by Australia, Italy, and Malaysia with three articles each. According to the most prolific journals in neuromarketing, it has been found that Frontiers in Neuroscience journal (CiteScore 5.4) is the most prolific journal with two articles and 25 total citations, followed by Scientific reports (CiteScore 7.1) with two articles and eighteen total citations, which lead us to infer that the publications’ number does not necessarily reflect the citations’ number. The study provides a profound and comprehensive overview of academic research that used EEG in marketing research.

This paper aims to investigate the association between variables of intrinsic and extrinsic motivations, cyber-market mavenism, and information sharing behavior, particularly among social network sites (SNS) users in Iraq.This study depends on a quantitative method to test the variables. The sample contains 388 Iraqi customers or users using social network sites to share information about different products and services. The study conducts a purposive sample to determine the characteristics of users who have much information about different products and services. A questionnaire was prepared based on previous research and distributed to participants. The statistical analysis program (SPSS) for descriptive statistics and PLS-SEM were used to analyze the measurement and structural models to test the hypotheses. The study results showed that altruism, moral obligation, and self-efficacy as intrinsic motivations influence cyber-market mavenism. However, self-enjoyment appears to have no impact on cyber-market mavenism. Moreover, reputation and social interaction also influence cyber-market mavenism. In addition, reciprocity and belongingness have no effect among Iraqi users. Thus, cyber-market mavenism plays a significant role in information-sharing behavior. The reason for such an outcome may develop a competitive advantage for marketers through understanding the market maven behavior on social network sites.

Crop pests are to blame for significant economic, social, and environmental losses worldwide. Various pests have different control strategies, and precisely identifying pests has become crucial to pest control and is a significant difficulty in agriculture. Many agricultural professionals are interested in deep learning (DL) models since they have shown significant promise in image recognition. Pest identification approaches in literature have relatively low accuracy in pest recognition and classification due to the complexity of their algorithms and limited data availability. Misclassification of insect pests sometimes leads to using the wrong pesticides, causing harm to agricultural yields and the surrounding environment. It necessitates developing an automated system capable of more accurate pest identification and classification. This paper presents a novel end-to-end DeepPestNet framework for pest recognition and classification. The proposed model has 11 learnable layers, including eight convolutional and three fully connected (FC) layers. We used image rotations techniques to increase the size of the dataset and image augmentations techniques to test the generalizability of the proposed DeepPestNet approach. We used the popular Deng’s crops data set to assess the proposed DeepPestNet framework. We used the proposed method to recognize and classify crop pests into 10-class pests, i.e., Locusta migratoria, Euproctis pseudoconspersa strand, chrysochus Chinensis, empoasca flavescens, Spodoptera exigua, larva of laspeyresia pomonella, parasa lepida, acrida cinerea, larva of S. exigua, and L.pomonella types of insects pests. The proposed method achieved optimal accuracy of 100%. We compared the proposed DeepPestNet approach with traditional pre-trained deep learning (DL) models. To verify the general adaptability of this model, we tested the proposed model on the standard Kaggle dataset “Pest Dataset” to recognize nine types of pests: aphids, armyworm, beetle, bollworm, grasshopper, mites, mosquito, sawfly, and stem borer and achieved an accuracy of 98.92%. The proposed model can provide specialists and farmers with immediate and effective aid in recognizing pests, potentially reducing economic and crop yield losses.

[Context and Motivation] Before eliciting and gathering requirements for a software project, it is considered pivotal to know about concerned stakeholders. It becomes hard to elicit the actual system requirements without identifying relevant stakeholders, leading the software project to failure. Despite the paramount importance of stakeholder identification in requirement elicitation, it has been given less attention in the software engineering literature. [Method] For this purpose, we conducted a thorough Systematic Literature Review (SLR) on stakeholder identification (SI) and its methods in requirement elicitation. However, previously, a literature study on SI in the requirement elicitation was conducted. We found that no one has proposed any standard or baseline research method for stakeholder identification, stakeholder assessment, and stakeholder interaction up to date according to our knowledge. It provides an opportunity to update the current SLR on SI in requirements elicitation from 2011 till 2021 to search for a baseline methodology for the SI. For this purpose, we explored the existing literature research that involves the SI methods in requirements elicitation. [Principle Ideas/Results] Furthermore, we identify and capture seventeen research methodologies for SI, eight key stakeholders interaction methods, and ten stakeholders assessment methods in requirement elicitation. To further enhance the stakeholder identification process, we additionally identify pivotal information such as different potential stakeholder categories, stakeholder assessments methods, and stakeholder interaction methods. Also, based on the proposed SLR, we find out the existing gaps and new opportunities for SI methods in the requirement elicitation. [Contribution] These SI methodologies help requirements engineers and practitioners identify key stakeholders and efficiently improve the requirements quality. Moreover, this research study helps identify the effective practices used for the traditional and CrowdRE SI, recover consequences that can affect the effectiveness of SI, and recommend advisable SI practices to be employed in the future. This research study would help the software researchers and developers efficiently and accurately identify correct and concerned stakeholders to improve end-user satisfaction instead of considering it a self-evident task.

Social media has evolved at a rapid pace, influencing every aspect of the global community. The purpose of this paper is to use bibliometric analysis tools to evaluate valid works on the relationship between social media and information sharing. A review study of research papers on social media and information sharing published between 2009 and 2020 was conducted. The bibliometric analysis identified the following characteristics: (1) the primary articles are based on information sharing through social media; (2) the main authors deal with information sharing in social media; (3) tthe newest trend topics related to information sharing in social media are demographic characteristics, cognition, sentiment, healthcare, products and services recommendations, tourist recommendations, and COVID-19. The results show significant impact rates, in studies of information sharing in social media. Overall, this study serves as a basis for new scientific questions that will contribute to the further development of this research field.

Bioelectrochemical systems (BESs) are considered as the potential approaches to remediate the environments contaminated by hydrocarbons. This review addresses the application of BESs particularly microbial fuel cells (MFCs) in degradation of petroleum hydrocarbons, including BTEXs, from soil, water, wastewater and sediments. Details on reactor design and critical issues are discussed. Aspects on electrodes, redox mediators and membranes are evaluated, including economic feasibility. The microbial community is considered in detail. It can be concluded, that comparing to classic configurations, single-chamber air-cathode reactors are more cost-effective. Secondly, systems based on small-scale units are recommended for future developments.

The internet of things (IoT) is an emerging paradigm of educational applications and innovative technology in the current era. While capabilities are increasing day by day, there are still many limitations and challenges to utilizing these technologies within E-Learning in higher educational institutes (HEIs). The IoT is well-implemented in the United States of America (USA), United Kingdom (UK), Japan, and China but not in developing countries, including Saudi Arabia, Malaysia, Pakistan, Bangladesh, etc. Few studies have investigated the adoption of IoT in E-Learning within developing countries. Therefore, this research aims to examine the factors influencing IoT adoption for E-Learning to be utilized in HEIs. Further, an adoption model is proposed for IoT-based E-Learning in the contexts of developing countries and provides recommendations for enhancing the IoT adoption for E-Learning in HEIs. The IoT-based E-Learning model categorizes these influencing factors into four groups: individual, organizational, environmental, and technological. Influencing factors are compared along with a detailed description in order to determine which factors should be prioritized for efficient IoT-based E-Learning in HEIs. We identify the privacy (27%), infrastructure readiness (24%), financial constraints (24%), ease of use (20%), support of faculty (18%), interaction (15%), attitude (14%), and network and data security (14%), as the significant E-Learning influencing factors on IoT adoption in HEIs. These findings from the researcher's perspective will show that the national culture has a significant role in the individual, organizational, technological, and environmental behavior toward using new technology in developing countries.

Load balancing is a very important concept in cloud computing. In this work, studies are conducted on workload traces at Los Alamos National Lab (LANL). The jobs in this trace are asymmetric in nature as most of them have small time limit. Two hybrid algorithms, a Genetic Algorithm combined with First Come First Serve (GA_FCFS) and Genetic Algorithm combined with Round Robin (GA_RR), are proposed here. The results obtained are compared with the existing First Come First Serve (FCFS), Round Robin (RR) and Genetic Algorithm (GA). Makespan and Resource Utilization are used for the comparison of results. In terms of Makespan, it is observed that GA_RR outperforms the other methods for all the batch sizes. Although the performance of GA_FCFS is much better than that of the other three well-established algorithms FCFS, RR and GA, it is still worse than that of the GA_RR algorithm for all the cases. GA_RR performs best in terms of Resource Utilization also and GA_FCFS is a close competitor. Overall, GA_RR outperforms all the other algorithms.

Recently, its becomes easy to track down the data due to its availability in a large number. Although for data management, processing, and obtainability, cloud computing is considered a well-known approach for organizational development on the internet. Despite many advantages, cloud computing has still numerous security challenges that can affect the big-data usage on cloud computing. To find the security issues/challenges that are faced by software vendors’ organizations we conducted a systematic literature review (SLR) through which we have find out 103 relevant research publications by developing a search string that is inspired by the research questions. This relevant data was comprised from different databases e.g. Google Scholar, IEEE Explore, ScienceDirect, ACM Digital Library, and SpringerLink. Furthermore, for the detailed literature review, we have accomplished all the steps in SLR, for example, development of SLR protocol, Initials and final assortment of the relevant data, data extraction, data quality assessment, and data synthesis. We identified fifteen (15) critical security challenges which are: data secrecy, geographical data location, unauthorized data access, lack of control, lack of data management, network-level issues, data integrity, data recovery, lack of trust, data sharing, data availability, asset issues, legal amenabilities, lack of quality, and lack of consistency. Furthermore, sixty four (64) standard practices are identified for these critical security challenges using the proposed SLR that could help vendor organizations to overcome the security challenges for big data. The findings of our research study demonstrate the resemblances and divergences in the identified security challenges in different periods, continents, databases, and methods. The proposed SLR will also support software vendor organizations for securing big data on the cloud computing platforms. This paper has the following content: in Section II, we have describe the Literature review; in Section III, research methodology is specified; in Section IV, the findings of the SLR and the analysis of result are discussed; in Section V, the limitations of this research are given; in Section VI, we discussed our conclusions and future work.

Breast cancer causes hundreds of women’s deaths each year. The manual detection of breast cancer is time-consuming, complicated, and prone to inaccuracy. For Breast Cancer (BC) detection, several imaging methods are explored. However, sometimes misidentification leads to unnecessary treatment and diagnosis. Therefore, accurate detection of BC can save many people from unnecessary surgery and biopsy. Due to recent developments in the industry, deep learning’s (DL) performance in processing medical images has significantly improved. Deep Learning techniques successfully identify BC from ultrasound images due to their superior prediction ability. Transfer learning reuses knowledge representations from public models built on large-scale datasets. However, sometimes Transfer Learning leads to the problem of overfitting. The key idea of this research is to propose an efficient and robust deep-learning model for breast cancer detection and classification. Therefore, this paper presents a novel DeepBraestCancerNet DL model for breast cancer detection and classification. The proposed framework has 24 layers, including six convolutional layers, nine inception modules, and one fully connected layer. Also, the architecture uses the clipped ReLu activation function, the leaky ReLu activation function, batch normalization and cross-channel normalization as its two normalization operations. We observed that the proposed model reached the highest classification accuracy of 99.35%. We also compared the performance of the proposed DeepBraestCancerNet approach with several existing DL models, and the experiment results showed that the proposed model outperformed the state-of-the-art. Furthermore, we validated the proposed model using another standard, publicaly available dataset. The proposed DeepBraestCancerNet model reached the highest accuracy of 99.63%.

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.

The delivery of therapeutic molecules to the brain remains an unsolved problem to the researchers due to the existence of the blood–brain barrier (BBB), which halts the entry of unwanted substances to the brain. Central nervous system (CNS) disorders, mainly Parkinson’s disease, Alzheimer’s disease, schizophrenia, brain tumors, and stroke, are highly prevalent globally and are a growing concern for researchers due to restricting the delivery of pharmaceutical drugs to the brain. So effective treatment modalities are essential to combat the growing epidemic of CNS diseases. Recently, the growing attention in the field of nanotechnology has gained the faith of researchers for the delivery of therapeutics to the brain by targeting them to the specific target site. Polymeric nanoparticles (PNPs) emerge out to be an instrumental approach in drug targeting to the brain by overcoming the physiological barrier, biomedical barrier, and BBB. Preclinical discovery has shown the tremendous potential and versatility of PNPs in encapsulating several drugs and their targeting to the deepest regions of the brain, thus improving therapeutic intervention of CNS disorders. The current review will summarize advances in the development of PNPs for targeting therapeutics to the brain and the functional and molecular effects obtained in the preclinical model of most common CNS diseases. The advancement of PNPs in clinical practice and their prospect in brain targeting will also be discussed briefly.

Cannabis abuse is a common public health issue and may lead to considerable adverse effects. Along with other effects, the dependence on cannabis consumption is a serious problem which has significant consequences on biochemical and clinical symptoms. This study intends to evaluate the harmful effects of the use of cannabis on thyroid hormonal levels, cardiovascular indicators, and psychotic symptoms in the included patients. This prospective multicenter study was conducted on cannabis-dependent patients with psychotic symptoms (n = 40) vs. healthy control subjects (n = 40). All participants were evaluated for psychiatric, biochemical, and cardiovascular physiological effects. Patients were selected through Diagnostic and Statistical Manual of Mental Disorders (DSM)-IV criteria and urine samples, exclusively for the evaluation of cannabis presence. Serum thyroid stimulating hormone (TSH), T3, and T4 levels were measured using the immunoassay technique. Patients were assessed for severity of depressive, schizophrenic, and manic symptoms using international ranking scales. Various quantifiable factors were also measured for the development of tolerance by cannabis. Among the patients of cannabis abuse, 47.5% were found with schizophrenia, 20% with schizoaffective symptoms, 10% with manic symptoms, and 22.5% with both manic and psychotic symptoms. In the group–group and within-group statistical analysis, the results of thyroid hormones and cardiovascular parameters were non-significant. The psychiatric assessment has shown highly significant (p < 0.001) difference of positive, negative, general psychopathology, and total scores [through Positive and Negative Syndrome Scale (PANSS) rating scales] in patients vs. the healthy control subjects. The study revealed that cannabis abuse did not significantly alter thyroid hormones and cardiovascular parameters due to the development of tolerance. However, the cannabis abuse might have a significant contributing role in the positive, negative, and manic symptoms in different psychiatric disorders.

A new bisbenzylisoquinoline named as chondrofolinol (1) and four reported compounds (2–5) were isolated and characterized from the roots of Berberis glaucocarpa Stapf. Anti-inflammatory, anti-pyretic, and leishmanicidal studies were performed against carrageenan-induced paw edema, yeast-induced pyrexia, and the promastigotes of Leishmania tropica, respectively. The new compound significantly reduced the paw volume in carrageenan-induced paw edema and rectal temperature in yeast-induced pyrexia at 10 and 20 mg/ kg of body weight. Chondrofolinol caused almost 100% inhibition of the promastigotes of Leishmania tropica. All the compounds displayed minimal cytotoxicity against THP-1 monocytic cells. In order to ascertain the potential macromolecular targets of chondrofolinol responsible for the observed anti-inflammatory and anti-leishmanial activities, a molecular docking study was carried out on relevant protein targets of inflammation and Leishmania. Protein targets of human endoplasmic reticulum aminopeptidase 2 (ERAP2) and human matrix metalloproteinase-1 (MMP-1) for inflammation and protein targets of N-myristoyltransferase (NMT), tyrosyl-tRNA synthetase (TyrRS), and uridine diphosphate-glucose pyrophosphorylase (UGPase) for Leishmania major were selected after thorough literature search about protein targets responsible for inflammation and Leishmania major. Chondrofolinol showed excellent docking to ERAP2 and to MMP-1. The Leishmania major protein targets with the most favorable docking scores to chondrofolinol were NMT, TyrRS, and UGPase. The study indicated that bisbenzylisoquinoline and isoquinoline alkaloids possess anti-pyretic, anti-inflammatory, and anti-leishmanial properties with minimal cytotoxicity and therefore, need to be further explored for their therapeutic potential.

Gene expression profiling techniques, such as DNA microarray and RNA-Sequencing, have provided significant impact on our understanding of biological systems. They contribute to almost all aspects of biomedical research, including studying developmental biology, host-parasite relationships, disease progression and drug effects. However, the high-throughput data generations present challenges for many wet experimentalists to analyze and take full advantage of such rich and complex data. Here we present GeneCloudOmics, an easy-to-use web server for high-throughput gene expression analysis that extends the functionality of our previous ABioTrans with several new tools, including protein datasets analysis, and a web interface. GeneCloudOmics allows both microarray and RNA-Seq data analysis with a comprehensive range of data analytics tools in one package that no other current standalone software or web-based tool can do. In total, GeneCloudOmics provides the user access to 23 different data analytical and bioinformatics tasks including reads normalization, scatter plots, linear/non-linear correlations, PCA, clustering (hierarchical, k-means, t-SNE, SOM), differential expression analyses, pathway enrichments, evolutionary analyses, pathological analyses, and protein-protein interaction (PPI) identifications. Furthermore, GeneCloudOmics allows the direct import of gene expression data from the NCBI Gene Expression Omnibus database. The user can perform all tasks rapidly through an intuitive graphical user interface that overcomes the hassle of coding, installing tools/packages/libraries and dealing with operating systems compatibility and version issues, complications that make data analysis tasks challenging for biologists. Thus, GeneCloudOmics is a one-stop open-source tool for gene expression data analysis and visualization. It is freely available at http://combio-sifbi.org/GeneCloudOmics.

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.

Vaccine hesitancy among displaced populations is associated with inequitable access to services and mistrust of authorities, among other factors. This study evaluated variations in attitudes toward COVID-19 vaccines and factors associated with vaccine acceptance among refugees and Lebanese nationals accessing 60 International Medical Corps-supported health facilities through two cross-sectional surveys pre- (n = 3927; Survey 1) and post- (n = 4174; Survey 2) vaccine rollout. Logistic regression was used to assess predictors of vaccine acceptance using the health beliefs model. Refugees comprised 52.9% (Survey 1) and 54.2% (Survey 2) of respondents. Vaccine acceptance was low among both groups in Survey 1 (25.9% refugees vs. 23.1% Lebanese nationals), but higher in Survey 2 in Lebanese (57.6%) versus refugees (32.9%). Participants reported greater perceived benefits of vaccination, higher perceived COVID-19 susceptibility, and lower perceived vaccination barriers in Survey 2 versus Survey 1. Post-vaccine rollout, refugees had lower odds of vaccine acceptance compared to Lebanese (OR 0.50, 95%CI 0.41–0.60), while older age (OR 1.37, 95%CI 1.06–1.78, ≥51 years vs. 18–30 years) was associated with greater vaccine acceptance. Health beliefs model variables were associated with vaccine acceptance in both surveys. Tailored strategies to respond dynamically to changes in vaccine attitudes among vulnerable groups in Lebanon are essential for equitable vaccine uptake.

Human physical activity recognition from inertial sensors is shown to be a successful approach for monitoring elderly individuals and children in indoor and outdoor environments. As a result, researchers have shown significant interest in developing state-of-the-art machine learning methods capable of utilizing inertial sensor data and providing key decision support in different scenarios. This paper analyzes data-driven techniques for recognizing human daily living activities. Therefore, to improve the recognition and classification of human physical activities (for example, walking, drinking, and running), we introduced a model that integrates data preprocessing methods (such as denoising) along with major domain features (such as time, frequency, wavelet, and time–frequency features). Following that, stochastic gradient descent (SGD) is used to improve the performance of the extracted features. The selected features are catered to the random forest classifier to detect and monitor human physical activities. Additionally, the proposed HPAR system was evaluated on five benchmark datasets, namely the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE databases. The experimental results show that the HPAR system outperformed the present state-of-the-art methods with recognition rates of 90.18%, 91.25%, 91.83%, 90.46%, and 92.16% from the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE datasets, respectively. The proposed HPAR model has potential applications in healthcare, gaming, smart homes, security, and surveillance.

The solid self-nanoemulsifying drug delivery system (s-SNEDDS) is a growing platform for the delivery of drugs via oral route. In the present work, tamoxifen (TAM) was loaded in SNEDDS with resveratrol (RES), which is a potent chemotherapeutic, antioxidant, anti-inflammatory and P-gp inhibitor for enhancing bioavailability and to obtain synergistic anti-cancer effect against breast cancer. SNEDDS were developed using capmul MCM as oil, Tween 80 as surfactant and transcutol-HP as co-surfactant and optimized by central composite rotatable design. Neusilin US2 concentration was optimized for adsorption of liquid SNEDDS to prepare s-SNEDDS. The developed formulation was characterized and investigated for various in vitro and cell line comparative studies. Optimized TAM-RES-s-SNEDDS showed spherical droplets of a size less than 200 nm. In all in vitro studies, TAM-RES-s-SNEDDS showed significantly improved (p ˂ 0.05) release and permeation across the dialysis membrane and intestinal lumen. Moreover, TAM-RES-s-SNEDDS possessed significantly greater therapeutic efficacy (p < 0.05) and better internalization on the MCF-7 cell line as compared to the conventional formulation. Additionally, oral bioavailability of TAM from SNEDDS was 1.63 folds significantly higher (p < 0.05) than that of combination suspension and 4.16 folds significantly higher (p < 0.05) than TAM suspension. Thus, findings suggest that TAM- RES-s-SNEDDS can be the future delivery system that potentially delivers both drugs to cancer cells for better treatment.

Titanium dioxide is semiconductor metal oxide having many applications in photocatalytic activities, cosmetics and in the food industry. It exists in three major crystalline forms: anatase, rutile and brookite. The solvents play a major role in the synthesis, stability and morphology of the metal oxide nanoparticles. It affects both the phase and particle size of metal oxide. The main focus of the present study is to establish the effects of solvent on the phase of TiO2 nanoparticle. Titanium dioxide (TiO2) nanoparticles have been synthesized by the sol-gel method using different solvents. The XRD results showed that the average crystallite size of all the samples was in the range of 5-25 nm. TiO2 nanoparticles prepared in different solvents gave different FT-IR peaks. AFM data clarified that the majority of samples showed spherical shape with average particle size ranging from 10-68 nm. The nanoparticles prepared in diethanolamine, acetic acid, and propionic acid solvents showed comparatively good antibacterial activity due to the rutile phase of the nanoparticles. the pH of the solvent also influences titanium dioxide structure and antibacterial properties

About half of the people who develop heart failure (HF) die within five years of diagnosis. Over the years, researchers have developed several machine learning-based models for the early prediction of HF and to help cardiologists to improve the diagnosis process. In this paper, we introduce an expert system that stacks two support vector machine (SVM) models for the effective prediction of HF. The first SVM model is linear and L1 regularized. It has the capability to eliminate irrelevant features by shrinking their coefficients to zero. The second SVM model is L2 regularized. It is used as a predictive model. To optimize the two models, we propose a hybrid grid search algorithm (HGSA) that is capable of optimizing the two models simultaneously. The effectiveness of the proposed method is evaluated using six different evaluation metrics: accuracy, sensitivity, specificity, the Matthews correlation coefficient (MCC), ROC charts, and area under the curve (AUC). The experimental results confirm that the proposed method improves the performance of a conventional SVM model by 3.3%. Moreover, the proposed method shows better performance compared to the ten previously proposed methods that achieved accuracies in the range of 57.85%-91.83%. In addition, the proposed method also shows better performance than the other state-of-the-art machine learning ensemble models.

Different automated decision support systems based on artificial neural network (ANN) have been widely proposed for the detection of heart disease in previous studies. However, most of these techniques focus on the preprocessing of features only. In this paper, we focus on both, i.e., refinement of features and elimination of the problems posed by the predictive model, i.e., the problems of underfitting and overfitting. By avoiding the model from overfitting and underfitting, it can show good performance on both the datasets, i.e., training data and testing data. Inappropriate network configuration and irrelevant features often result in overfitting the training data. To eliminate irrelevant features, we propose to use χ2 statistical model while the optimally configured deep neural network (DNN) is searched by using exhaustive search strategy. The strength of the proposed hybrid model named χ2-DNN is evaluated by comparing its performance with conventional ANN and DNN models, another state of the art machine learning models and previously reported methods for heart disease prediction. The proposed model achieves the prediction accuracy of 93.33%. The obtained results are promising compared to the previously reported methods. The findings of the study suggest that the proposed diagnostic system can be used by physicians to accurately predict heart disease.

Purpose: Retinoblastoma (Rb) is the most common intraocular malignant tumor, which is not only unique but also has unraveled many novel aspects of tumor-suppressor genes. Genetic mutations of Rb, loss of phosphorylation, and many other factors resulted in uncontrolled cell division of the retinal cells resulting in tumor progression. In this study, we have analyzed the gene expression patterns of unilateral tumors (n = 11) in comparison to the normal-appearing retina (n = 2) from Rb patients who underwent enucleation for advanced Rb. With recent advances in the knowledge of the role of stem cells in these tumors, it is important to evaluate and understand the self-renewal signaling involved in these tumors. Here, in this study, we particularly aimed at evaluating the aberrant self-renewal signaling pathways in human Rb tumors and genes which show differential expression in cases with and without histologic risk factors (HRF). Materials and Methods: Freshly unfixed eyeballs (n = 11) were obtained. Normal-appearing retinas were pooled together (n = 2) and used as a control for microarray experiments. Total RNA was isolated from tumors and control tissues, and expression of genes was evaluated by hybridizing to expression arrays. Using real-time polymerase chain reaction (PCR), the results, thus, obtained were validated (for expression of N-Myc, HMGA2, LIN-28b, and Activin receptor 1C [ACVR1C]) in tissues compared to two control retinas latter obtained from enucleated Rb eyeballs without tumor. Furthermore, immunohistochemistry (IHC) was done on retrospective (n = 19) cases to confirm the expression of ACVR1C. Results: In Rb tumors, 5593 genes were upregulated and 4864 genes were downregulated (P ≤ 0.05 and fold change ≥1.5 folds). Changes in N-Myc, HMGA2, LIN28b, and ACVR1C expression detected by microarray were validated by real-time PCR. The analysis shows significant up-regulation of HMGA2 and its downstream regulator LIN-28b, which is involved in self-renewal pathway of fetal neural stem cells. ACVR1C is one of the markers, which shows differential expression between histological subtypes of tumors as evident in IHC. CBLB (P ≤ 0.05) and MAPK 8 (P ≤ 0.05) were shown to be highly upregulated in tumors without HRF compared to cases with HRF. Conclusions: This study showed up-regulation of genes involved in neural stem cell self-renewal and marginally in notch signaling. While other stem cell pathways such as Wnt and sonic hedgehog (SHH) pathways were upregulated in these tumors. Targeting these self-renewal pathways would aid in eliminating the resistant cells in this tumor and thus may help in treating the recurrence. Genes such as CBLB, MAPK 8, and ACVR1C can be used as potential biomarkers in this tumor to prognosticate cases with or without HRFs and differentiation of the tumors.

The suspected cases of COVID-19 must be detected quickly and accurately to avoid the transmission of COVID-19 on a large scale. Existing COVID-19 diagnostic tests are slow and take several hours to generate the required results. However, on the other hand, most X-rays or chest radiographs only take less than 15 min to complete. Therefore, we can utilize chest radiographs to create a solution for early and accurate COVID-19 detection and diagnosis to reduce COVID-19 patient treatment problems and save time. For this purpose, CovidDetNet is proposed, which comprises ten learnable layers that are nine convolutional layers and one fully-connected layer. The architecture uses two activation functions: the ReLu activation function and the Leaky Relu activation function and two normalization operations that are batch normalization and cross channel normalization, making it a novel COVID-19 detection model. It is a novel deep learning-based approach that automatically and reliably detects COVID-19 using chest radiograph images. Towards this, a fine-grained COVID-19 classification experiment is conducted to identify and classify chest radiograph images into normal, COVID-19 positive, and pneumonia. In addition, the performance of the proposed novel CovidDetNet deep learning model is evaluated on a standard COVID-19 Radiography Database. Moreover, we compared the performance of our approach with hybrid approaches in which we used deep learning models as feature extractors and support vector machines (SVM) as a classifier. Experimental results on the dataset showed the superiority of the proposed CovidDetNet model over the existing methods. The proposed CovidDetNet outperformed the baseline hybrid deep learning-based models by achieving a high accuracy of 98.40%.

Brain tumors are considered one of the most serious, prominent and life-threatening diseases globally. Brain tumors cause thousands of deaths every year around the globe because of the rapid growth of tumor cells. Therefore, timely analysis and automatic detection of brain tumors are required to save the lives of thousands of people around the globe. Recently, deep transfer learning (TL) approaches are most widely used to detect and classify the three most prominent types of brain tumors, i.e., glioma, meningioma and pituitary. For this purpose, we employ state-of-the-art pre-trained TL techniques to identify and detect glioma, meningioma and pituitary brain tumors. The aim is to identify the performance of nine pre-trained TL classifiers, i.e., Inceptionresnetv2, Inceptionv3, Xception, Resnet18, Resnet50, Resnet101, Shufflenet, Densenet201 and Mobilenetv2, by automatically identifying and detecting brain tumors using a fine-grained classification approach. For this, the TL algorithms are evaluated on a baseline brain tumor classification (MRI) dataset, which is freely available on Kaggle. Additionally, all deep learning (DL) models are fine-tuned with their default values. The fine-grained classification experiment demonstrates that the inceptionresnetv2 TL algorithm performs better and achieves the highest accuracy in detecting and classifying glioma, meningioma and pituitary brain tumors, and hence it can be classified as the best classification algorithm. We achieve 98.91% accuracy, 98.28% precision, 99.75% recall and 99% F-measure values with the inceptionresnetv2 TL algorithm, which out-performs the other DL algorithms. Additionally, to ensure and validate the performance of TL classifiers, we compare the efficacy of the inceptionresnetv2 TL algorithm with hybrid approaches, in which we use convolutional neural networks (CNN) for deep feature extraction and a Support Vector Machine (SVM) for classification. Similarly, the experiment’s results show that TL algorithms, and inceptionresnetv2 in particular, out-perform the state-of-the-art DL algorithms in classifying brain MRI images into glioma, meningioma, and pituitary. The hybrid DL approaches used in the experiments are Mobilnetv2, Densenet201, Squeeznet, Alexnet, Googlenet, Inceptionv3, Resnet50, Resnet18, Resnet101, Xception, Inceptionresnetv3, VGG19 and Shufflenet.