Search

This Grounded Theory examines the in-depth dynamics of cybersecurity professionals working in financial organizations regarding stress management, risk handling, communication challenges, and perceived benefits from work. This study conducted a grounded theory approach with cybersecurity professionals through in-depth interviews to comprehensively understand these dynamics. The findings identify critical themes about emotional and psychological resilience, effective risk management strategies, overcoming communication challenges, and recognizing organizational and personal benefits from robust cybersecurity practices. The current research highlights that for better well-being and performance of cybersecurity teams, extensive support systems, proactive risk management, and communication are very important and should be embedded within organizations. By utilizing the grounded theory, this study offers a nuanced exploration of the complex and entwined factors that impact daily operations and the overall effectiveness of cybersecurity professionals, offering valuable insights for improving organizational policies and creating a supportive work environment. These findings suggest, most importantly, that robust support systems, risk assessments, clear communication protocols, and training opportunities should be developed and operationalized in financial organizations to help professionals reduce stress at work and strengthen resilience factors. Not only does this research project cover significant gaps in the literature, but it also really helps financial organizations further improve their currently existing cybersecurity strategies to better support personnel by using this framework.

Abstract Brain tumors, particularly Glioblastoma Multiforme (GBM) and Low-Grade Gliomas (LGG), present significant clinical challenges due to their aggressive nature and resistance to conventional treatments. Traditional therapies such as surgery, chemotherapy, and radiation are often limited in efficacy, necessitating novel therapeutic strategies. Nanotechnology, particularly the use of silver nanoparticles (Ag NPs), offers a targeted and potentially more effective approach. This study focuses on the green synthesis of Ag NPs using Podocarpus macrophyllus leaf extract as a reducing agent. The synthesized Ag NPs were characterized for their physicochemical properties, demonstrating a controlled particle size of 13 nm as determined by scanning electron microscopy (SEM). Fourier-transform infrared (FTIR) spectroscopy confirmed the presence of functional groups, and energy-dispersive X-ray (EDX) spectroscopy revealed that silver constituted approximately 90% of the nanoparticle composition. The Ag NPs exhibited promising biological activity, including 90% free radical scavenging (antioxidant) activity, 99.15% inhibition of protein denaturation (anti-inflammatory activity), and 90.56% inhibition of alpha-amylase (anti-diabetic activity). Additionally, the nanoparticles displayed significant anti-hemolytic (89.9% inhibition) and antimicrobial activities, with a 20 mm inhibition zone against Staphylococcus species. Computational analyses further indicated that the NOTCH2 gene, which is upregulated in LGG and GBM, may interact with Ag NPs, suggesting their potential in brain cancer therapy. The green synthesis approach offers a sustainable and bioactive method for producing Ag NPs, underscoring their therapeutic promise for treating GBM and LGG.

Abstract Background A new approach based on the Newton–Gauss method is used to find the Weibull parameters. Results A Python program was developed to employ the Newton–Gauss method. It is implemented to find Weibull parameters and wind potential of Pakistan’s eight cities (Hyderabad, Khuzdar, Multan, Quetta, Bahawalpur, Islamabad, Lahore, and Peshawar). Wind speed data recorded at an interval of ten minutes for 2016 is used to implement a Python program to calculate wind potential. To compare the values of the parameters, five known methods, the empirical method, method of moments, energy pattern factor method, maximum likelihood method, and modified maximum likelihood method, were also used to model and determine the wind potential. The root mean square error, mean absolute error, coefficient of determination, and Akaike information criterion were calculated to compare values of wind parameters and average wind speed. The correlation between recorded and modeled Weibull pdf was almost 99% for each city. Conclusions The new method only caters to those wind speeds that contribute to the wind potential; therefore, the average value of the wind speed is the least in the case of the new method. The maximum wind potential was observed for Hyderabad.

Understanding the eigenvalue distribution of sequence Toeplitz matrices has advanced significantly in recent years. Notable contributors include Bogoya, Grudsky, Böttcher, and Maximenko, who have derived precise asymptotic expansions for these eigenvalues under certain conditions related to the generating function as the matrix size increases. Building on this foundation, the Stefano Serra-Capizzano conjectured that, under certain assumptions about $ \Omega $ and $ \Phi $, a similar expansion may hold for the eigenvalues of a sequence of preconditioned Toeplitz matrices $ T_{n}^{-1}(\Phi) T_n(\Omega) $, given a monotonic ratio $ r = \Omega/\Phi $. In contrast to current eigenvalue solvers, this work presents a novel method for efficiently calculating the eigenvalues of a sequence of large preconditioned banded symmetric Toeplitz matrices (PBST). Our algorithm uses a higher-order spline fitting extrapolation technique to gather spectral data from a smaller sequence of PBST matrices in order to forecast the spectrum of bigger matrices.

Abstract Most vegetable crops are severely affected by the uptake of heavy metals from the soil. Heavy metals in vegetable bodies generate reactive oxygen species (ROS) that unbalance the antioxidant defense system. This study was initiated to determine the physiological and biochemical characteristics of spinach plants grown on soil contaminated with heavy metals and responding to Bacillus cereus and Bacillus aerius were isolated from soil contaminated with heavy metals. Heavy metal contamination led to a significant reduction in seed germination, seedling biomass, protein, and total nitrogen content of spinach plants grown in contaminated soils compared to control soils. In contrast, a significant increase in the content of metallothioneins and antioxidant enzymes was observed. Plants inoculated with B. cereus and B. aerius significantly reduced the oxidative stress induced by heavy metals by improving seed germination (%), seedling growth, nitrogen, and protein content. The content of metallothioneins and the activities of antioxidant enzymes were reduced in spinach plants grown from seeds inoculated with bacterial strains. In addition, plants inoculated with, B. cereus and B. aerius showed greater stomata opening than plants grown on soil contaminated with heavy metals, whose stomata were almost closed. These results suggested that both bacterial strains enhanced plant growth by reducing oxidative stress caused by metals.

Abstract Background The electronic transitions between two fine levels depend on the transition probability. The transition probability depends on spectral line strength and oscillator strength. The oscillator strength depends on the number of oscillators and their energies. In this research, we will find the oscillator strengths of hyperfine multiplets of the Tantalum atom. The oscillator strength of hyperfine multiplet investigation aims to enhance our understanding of Tantalum's spectral characteristics. This work provides valuable information in the spectroscopy of material, atomic/molecular, and astrophysics. Result Fourier transform spectra from ultraviolet to far infrared regions have been obtained from TUGRAZ. Fourier transform spectra give the most reliable position of the wavelength of hyperfine multiplets. The Fourier transform spectra of Tantalum contain thousands of Tantalum I and II spectral lines. Each spectral line can be characterized by its upper and lower levels and corresponding angular momenta and hyperfine constants. These properties of the spectral lines were collected from the literature. Hyperfine multiplets for each fine structure were calculated, and they revealed their spectroscopic behavior with high precision. Conclusion In this study, Tantalum's hyperfine multiplet oscillator strength was calculated using advanced computational techniques to address its atomic structure. The fine structure “gf” values were obtained from literature, and intensities of the multiplets were determined. They combined with the gf values to calculate the oscillator strengths of the hyperfine multiplets.

Enhancement in the visible light absorption and efficient interfacial charge transfer is crucial for optimizing photocatalytic efficiency in the degradation of pollutants such as methyl orange (MO) and formaldehyde. This study focuses on the properties of a TiO2/C/g-C3N4 hybrid efficient photocatalyst, which is developed using an air calcination method to deposit graphitic nitride (g-C3N4) onto a carbon-modified TiO2 surface. The characterization techniques, including high-resolution transmission electron Microscopy (HRTEM), X-ray diffraction (XRD), Raman spectroscopy, thermogravimetric analysis (TGA), Fourier-transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS), were used to provide a comprehensive understanding of the material’s structural, morphological, thermal, and chemical properties. This hybrid catalyst is specifically engineered for the efficient decomposition of methyl orange (MO) and formaldehyde, demonstrating a significant increase in photocatalytic activity. The TiO2/C/g-C3N4 photocatalyst also exhibits an enhanced specific surface area of 181.2 m2/g, which facilitates increased physical adsorption and photo-catalytic active sites. Experimental results confirm that this catalyst effectively adsorbs MO physically even in the dark without degradation. Combining physical and photo-catalytic functions, this catalyst degrades 94 % of MO within 180 min with the initial concentration 0.2 mol/L of MO, and achieves almost 100 % decolorization of MO under visible light irradiation. Notably, the catalyst retains its high activity after 4 cycles of MO degradation, underscoring its durability and consistent performance. Additionally, the hybrid catalyst features a staggered type-II energy level configuration, which effectively enhances charge separation and boosts photocatalytic efficacy. The incorporation of an ultrafine carbon layer further augments electron mobility towards the surface, crucial for effective catalytic reactions. This study paves the way for future development of highly efficient photocatalytic materials for environmental purification.

The potential of FinTech algorithms to decrease gender bias in credit decisions is limited by the impartiality of the data used to train them. If the data is partial or biased, the algorithmic decision-making process may also be discriminatory, exacerbating existing inequalities. In this study, the effect of FinTech Firms on reducing gender inequality in bank loans in the USA is examined using a loan application from 60 U.S. banks from 2012 to 2022. We use a two-step system GMM approach to estimate the effect of FinTech algorithms on gender bias in credit decisions, focusing on female loan approval rates. Our results show that by controlling the other factor, banks with credit algorithms significantly increased the loan approval rates and thus reduced gender inequality in bank loans. Specifically, the female loan approval rates increased by 8% after banks adopted FinTech algorithms. We also find that the effect is more substantial for banks with higher baseline gender bias in credit decisions. We also performed the Difference in Difference analysis to analyse the policy shocks and FinTech adoption on bank loans’ gender inequality. Results of the study show that FinTech adoption and policy implications have significantly increased the loans for female borrowers. Our findings suggest that FinTech algorithms can potentially mitigate gender bias in credit decisions and promote gender equality in financial services.

The development on entropy generation in fluid flows has applications in many medical equipment such as cryogenic devices and therapeutic heat apparatus. This study looks at how porous medium, multi-slips, and entropy formation affect the pumping of Jeffrey nanofluid flow through an asymmetric channel containing motile microorganims. A lubrication theory is used to neglect the fluctuation effects in the flow. Numerical simulations are utilized to generate data for specific physical features of the problem utilizing the Shooting approach on Mathematica. Following a thorough research, it is appropriate to conclude that the porous medium's permeability reduces flow speed along the walls while increases at the center of the flow region. Graphical representation of the results further reveals that entropy production can be decreased by including high thermal slip and low viscous slip elements. It is also worth noting that the Brinkman number reduces the thermal distribution in the flow while it helps in increasing the flow speed.

This study investigates the increasingly important issue of sustainable tourism development through the lens of Big Data analytics, economic considerations, and policy frameworks. Guided by the Theory of Planned Behavior, we formulated seven hypotheses to explore how these variables impact attitudes, subjective norms, perceived behavioral control, intentions, and actual engagement in sustainable tourism. Using a questionnaire-based survey we collected data from N=398 respondents and employing Structural Equation Modeling (SEM) for data analysis, we found substantial evidence supporting our hypotheses. Findings revealed that Big Data analytics significantly influence attitudes towards sustainable tourism. Economic factors shape societal norms and expectations regarding sustainable tourism, whereas policy frameworks impact individuals' perceived control over sustainable tourism activities. The study also confirmed that positive attitudes towards sustainable tourism led to higher intentions to engage in such practices, which in turn are predictive of actual engagement. Furthermore, societal norms and perceived behavioural control were found to be significant predictors of both the intention to engage in and the actual engagement with sustainable tourism practices. Overall, the results indicate that Big Data analytics play a significant role in shaping attitudes and behaviors towards sustainable tourism. Moreover, economic and policy considerations were also found to be key determinants. This research not only fills a scholarly gap but also provides actionable insights for policymakers and business stakeholders in the tourism sector.

In permissionless blockchain systems, Proof of Work (PoW) is utilized to address the issues of double-spending and transaction starvation. When an attacker acquires more than 50% of the hash power of the entire network, they gain the ability to engage in double-spending activities, posing a significant threat to the PoW consensus algorithm. This research focuses on the consensus algorithm employed in the Bitcoin system, explaining how it operates and the security challenges it faces. The proposed modification to the PoW algorithm imposes a restriction on miners: they are not allowed to accept consecutive blocks from the same miner into the final local blockchain to prevent the 51% attack problem. This modification supports transactions that require six confirmations. In the event an attacker attempts a 51% attack with a private chain that consists of fewer than 6 blocks, it becomes easier to detect a double-spending attack before accepting the attacker’s private chain. The modified algorithm introduces a “Safe Mode Detection Algorithm” that scrutinizes incoming blocks for adjustments at the top of the local blockchain. If inconsistencies are identified, the consensus algorithm proceeds cautiously by comparing the UTXO dictionaries from the attacker’s chain with those from the miner’s own blockchain. This meticulous comparison aims to detect instances of double-spending. If such instances are detected, the miner rejects the attacker’s chain, establishing a double-spend-free environment and thwarting 51% attacks.

Osteoarthritis (OA) is a prevalent musculoskeletal disorder, predominantly affecting the knee joint and resulting in significant pain and functional limitations. The Kellgren-Lawrence (KL) grading system, traditionally utilized by radiologists, assesses OA severity through radiographic evaluation of knee bones on both sides. Recent advancements in computer-aided methods have focused on enhancing diagnostic precision and efficiency, leveraging automated classification models with Knee X-rays which represent a cornerstone imaging modality for OA severity assessment. In this study, a novel hybrid model combining Convolutional Neural Networks (CNN) and VGG16 architectures is proposed to achieve accurate OA detection. Various neural networks, including CNN, VGG16, VGG19, ResNet50, and CNN-ResNet, are implemented and compared alongside the proposed method. Additionally, data augmentation techniques are employed to address class imbalance, leading to enhanced accuracies across all models. Analysis demonstrates robust performance of all models on the training set. The proposed hybrid method (CNN-ResNet50) outperforms other state-of-the-art approaches, providing precise results across all five stages of OA according to the KL grading system by achieving an accuracy exceeding 93% on training, validation, and testing datasets.

Over the past decade, an abundance of research has been conducted in the area of agricultural technology and innovations. The Internet of Things (IoT) has demonstrated its ability to connect numerous agricultural equipment, sensors, and specialists, boosting agricultural procedures in off-the-grid regions. Agriculture has experienced considerable improvements in production, cost reductions, service accessibility, and operational efficiency. With an emphasis on security, developments and trends in the sector, and technological implementation, this research paper offers an up-to-date analysis of existing and projected IoT applications in agriculture. In this article, enabling technologies, agricultural applications based on cutting-edge machine learning models, and services are all examined in relation to the development of IoT deployment in tackling diverse agricultural concerns. In the IoT-based agriculture system, potential challenges and limitations are also addressed. In its conclusion, this research provides an extensive review of the various aspects of IoT in agriculture, with the goal of empowering future researchers enthusiastic to make contributions to and advancement in their quest for a more in-depth comprehension of this field of study. A total of 96 papers were chosen for the selection from 2018 to 2023, and each was categorized using predetermined standards. The research’s results have been thoroughly examined, providing an overview of IoT in agriculture.

This study investigates AI bias in financial technology (FinTech)-based supply chain management in Pakistan. The study employs Structural Equation Modeling (SEM) to analyze data from diverse respondents. Hypotheses examine the relationships between AI integration, algorithm diversity, employee training, data quality, regulatory compliance, organizational culture, and AI bias. The findings reveal that higher AI integration leads to increased AI bias, Algorithm diversity reduces AI bias, while employee training decreases bias, Quality and diversity of data negatively correlate with AI bias, and regulatory compliance lowers bias. In addition, organizational culture mediates the relationship between AI integration and AI bias. This research contributes a holistic understanding of AI bias factors, guiding ethical AI adoption. Policymakers can use these insights to shape regulations, and industry practitioners can make informed decisions.

Agricultural systems are being revolutionized due to emerging technologies that aim to make improvements in the traditional agriculture system. The major goal is not just to enhance agricultural output per hectare but also to enhance crop quality while protecting the natural environment. Weeds pose a significant threat to crops as they consume nutrients, water, and light, thereby reducing crop productivity. Spraying the entire field uniformly to control weeds not only incurs high costs but also has adverse environmental effects. To address the limitations of conventional weed control methods, in this research, Machine Learning (ML) and Deep Learning (DL) based techniques are proposed to identify and categorize weeds in crops. For ML-based techniques, several statistical and texture-based features are extracted, including central image and Hu moments, mean absolute deviation, Shannon entropy, gray level co-occurrence matrix (GLCM) and local binary patterns (LBP), contrast, energy, homogeneity, dissimilarity, correlation, and summarized local binary pattern histogram. YOLOv8m is employed to identify weeds and for weed classification, features extracted from two standard benchmark datasets, CottonWeedID15 and Earlycrop-weed are fed to Support Vector Machine (SVM), Random Forest, and Artificial Neural Network (ANN) while employing Synthetic Minority Oversampling Technique (SMOTE) to balance the classes. In addition to ML-based techniques, Deep learners such as VGG16, VGG19, Xception, DenseNet121, DenseNet169, DenseNet201, and ConvNeXtBase are trained on raw data with balanced classes for automated feature extraction and classification. Among the ML-based techniques, SVM with a polynomial kernel achieves 99.5% accuracy on the early crop weed dataset, and Artificial neural network attains 89% accuracy on the Cottonweedid15 dataset. Meanwhile, the combined employment of ConvNeXt and Random Forest results in the highest accuracy among DLs, specifically 98% on the early crop weed dataset and 90% on the Cottonweedid15 dataset. The high accuracy achieved underscores the practical viability of these methods, offering a sustainable and cost-effective solution for modern agriculture.

Abstract Complex networks have been programmed to mimic the input and output functions in multiple biophysical algorithms of cortical neurons at spiking resolution. Prior research has demonstrated that the ineffectual features of membranes can be taken into account by discrete fractional commensurate, non-commensurate and variable-order patterns, which may generate multiple kinds of memory-dependent behaviour. Firing structures involving regular resonator chattering, fast, chaotic spiking and chaotic bursts play important roles in cortical nerve cell insights and execution. Yet, it is unclear how extensively the behaviour of discrete fractional-order excited mechanisms can modify firing cell attributes. It is illustrated that the discrete fractional behaviour of the Izhikevich neuron framework can generate an assortment of resonances for cortical activity via the aforesaid scheme. We analyze the bifurcation using fragmenting periodic solutions to demonstrate the evolution of periods in the framework’s behaviour. We investigate various bursting trends both conceptually and computationally with the fractional difference equation. Additionally, the consequences of an excitable and inhibited Izhikevich neuron network (INN) utilizing a regulated factor set exhibit distinctive dynamic actions depending on fractional exponents regulating over extended exchanges. Ultimately, dynamic controllers for stabilizing and synchronizing the suggested framework are shown. This special spiking activity and other properties of the fractional-order model are caused by the memory trace that emerges from the fractional-order dynamics and integrates all the past activities of the neuron. Our results suggest that the complex dynamics of spiking and bursting can be the result of the long-term dependence and interaction of intracellular and extracellular ionic currents.

Online transactions are still the backbone of the financial industry worldwide today. Millions of consumers use credit cards for their daily transactions, which has led to an exponential rise in credit card fraud. Over time, many variations and schemes of fraudulent transactions have been reported. Nevertheless, it remains a difficult task to detect credit card fraud in real-time. It can be assumed that each person has a unique transaction pattern that may change over time. The work in this article aims to (1) understand how deep reinforcement learning can play an important role in detecting credit card fraud with changing human patterns, and (2) develop a solution architecture for real-time fraud detection. Our proposed model utilizes the Deep Q network for real-time detection. The Kaggle dataset available online was used to train and test the model. As a result, a validation performance of 97.10% was achieved with the proposed deep learning component. In addition, the reinforcement learning component has a learning rate of 80%. The proposed model was able to learn patterns autonomously based on previous events. It adapts to the pattern changes over time and can take them into account without further manual training.

Abstract The multiple activities of neurons frequently generate several spiking-bursting variations observed within the neurological mechanism. We show that a discrete fractional-order activated nerve cell framework incorporating a Caputo-type fractional difference operator can be used to investigate the impacts of complex interactions on the surge-empowering capabilities noticed within our findings. The relevance of this expansion is based on the model’s structure as well as the commensurate and incommensurate fractional-orders, which take kernel and inherited characteristics into account. We begin by providing data regarding the fluctuations in electronic operations using the fractional exponent. We investigate two-dimensional Morris–Lecar neuronal cell frameworks via spiked and saturated attributes, as well as mixed-mode oscillations and mixed-mode bursting oscillations of a decoupled fractional-order neuronal cell. The investigation proceeds by using a three-dimensional slow-fast Morris–Lecar simulation within the fractional context. The proposed method determines a method for describing multiple parallels within fractional and integer-order behaviour. We examine distinctive attribute environments where inactive status develops in detached neural networks using stability and bifurcation assessment. We demonstrate features that are in accordance with the analysis’s findings. The Erdös–Rényi connection of asynchronization transformed neural networks (periodic and actionable) is subsequently assembled and paired via membranes that are under pressure. It is capable of generating multifaceted launching processes in which dormant neural networks begin to come under scrutiny. Additionally, we demonstrated that boosting connections can cause classification synchronization, allowing network devices to activate in conjunction in the future. We construct a reduced-order simulation constructed around clustering synchronisation that may represent the operations that comprise the whole system. Our findings indicate the influence of fractional-order is dependent on connections between neurons and the system’s stored evidence. Moreover, the processes capture the consequences of fractional derivatives on surge regularity modification and enhance delays that happen across numerous time frames in neural processing.

Background: Spinal injuries are highly prevalent and are a major cause of disability. Studying resilience, perceived social support, and psychological well-being in persons with spinal cord injury is important for better-managing patients with spinal cord injuries. Objectives: To analyze the correlation among resilience, perceived social support, and psychological well-being in persons with spinal cord injury. Methods: This cross-sectional correlational study was conducted at Riphah International University, Lahore Pakistan from September 2019 to February 2020. The study utilized a sample of 100 patients with spinal cord injury using purposive sampling. The sample included paraplegic patients 6 months following SCI, of both genders above 18 years of age who were active wheelchair users. A demographic sheet, scale of psychological well-being, resilience scale, and multi-dimensional scale of perceived social support (MSPSS) were used for data collection. SPSS Version 22 was utilized for data analysis & correlation was analyzed using bivariate statistics with p< 0.05 was considered statistically significant. Results: The study revealed a positive correlation (γ=0.57, p

Rabies is a neglected viral zoonotic disease with an almost 100% fatality rate. A pilot project was initiated by the National Institute of Health (NIH), Islamabad, in collaboration with Health Security Partners (HSP) to establish a real-time dog bite surveillance in humans in public sector secondary care hospitals of Islamabad. The main objective of this study was to analyze different characteristics of dog bite cases, identify gaps in surveillance, and recommend suggestions to improve and strengthen real-time dog bite surveillance systems. An observational cross-sectional study was conducted in two major public sector hospitals in Islamabad from December 2019 to July 2020. Data on demographic information, site of the dog bite, category of the dog bite, and treatment was collected via a WVS mobile-based application. Descriptive analysis was carried out for different variables at a p-value of

Due to inadequate treatment, diabetes mellitus, which is characterized by hyperglycemia, is a global health challenge that requires creative management techniques. This study explores the therapeutic potential of Abelmoschus esculentus L. Moench fruit extracts in diabetes, offering an ample approach includes phytochemical analysis, qualitative and quantitative assessments, antidiabetic efficacy, antioxidant potential, and in vivo and in vitro investigations. Thin-layer chromatography (TLC), high-performance liquid chromatography (HPLC), and gas chromatography–mass spectrometry (GC–MS) were utilized to disclose the phytochemical profile of the fruit extracts, which indicated a diverse range of bioactive compounds. Four polyphenolic compounds including chlorogenic acid, quercetin, rutin, and morin were confirmed by HPLC fingerprinting, with rutin being the most prevalent. Qualitative analysis reveals the presence of carbohydrates, emodin, terpenoids, lignin, glycosides, and anthraquinones, while indicating the absence of amino acids, anthocyanins, phlobatannins, lactones, and leucoanthocyanins. Quantitative analysis reveals total phenolic, tannin, and flavonoid. In vitro assessments demonstrate the ability of the extracts to inhibit alpha amylase. Radical scavenging activities were evaluated through DPPH assay, underscoring their antioxidant capacity with high effect in methanol extracts. Additionally, the methanol extract decreased the blood glucose level of Type 2 diabetic mice. Notably, test samples stabilized blood glucose levels, reversed deviations in blood profile parameters, blood biochemistry, and regulated organ weight. Based on its antioxidant and antidiabetic effects, as well as positive effects on the physiology of Type 2 diabetic mice, A. esculentus emerges as an important nutraceutical vegetable. As an important nutraceutical vegetable, it holds promise for diabetes management. A. esculentus can be incorporated into dietary interventions for individuals with diabetes, harnessing its natural antioxidant and antidiabetic properties. Our findings validate the traditional use of A. esculentus in green therapeutics. In conclusion, A. esculentus emerges as a valuable ally in the fight against diabetes, bridging the gap between ancient wisdom and modern science. Further research and practical implementation are warranted to fully realize its potential.

Recent advancements in AI, driven by big data technologies, have reshaped various industries, with a strong focus on data-driven approaches. This has resulted in remarkable progress in fields like computer vision, e-commerce, cybersecurity, and healthcare, primarily fueled by the integration of machine learning and deep learning models. Notably, the intersection of oncology and computer science has given rise to Computer-Aided Diagnosis (CAD) systems, offering vital tools to aid medical professionals in tumor detection, classification, recurrence tracking, and prognosis prediction. Breast cancer, a significant global health concern, is particularly prevalent in Asia due to diverse factors like lifestyle, genetics, environmental exposures, and healthcare accessibility. Early detection through mammography screening is critical, but the accuracy of mammograms can vary due to factors like breast composition and tumor characteristics, leading to potential misdiagnoses. To address this, an innovative CAD system leveraging deep learning and computer vision techniques was introduced. This system enhances breast cancer diagnosis by independently identifying and categorizing breast lesions, segmenting mass lesions, and classifying them based on pathology. Thorough validation using the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) demonstrated the CAD system's exceptional performance, with a 99% success rate in detecting and classifying breast masses. While the accuracy of detection is 98.5%, when segmenting breast masses into separate groups for examination, the method's performance was approximately 95.39%. Upon completing all the analysis, the system's classification phase yielded an overall accuracy of 99.16% for classification. The potential for this integrated framework to outperform current deep learning techniques is proposed, despite potential challenges related to the high number of trainable parameters. Ultimately, this recommended framework offers valuable support to researchers and physicians in breast cancer diagnosis by harnessing cutting-edge AI and image processing technologies, extending recent advances in deep learning to the medical domain.

In this study, a well-known Weakest Bound Electron Potential Model (WBEPM) was used to determine the exited-state ionization potential of lithium-like elements for different iso-spectrum series such as 1s2 2p1 P1/2, 1s2 3s2 S1/2 , 1s2 3d2 D1/2, 1s2 4s2 S1/2, 1s2 4p2 P1/2, and 1s2 4d2 D1/2 having nuclear charges from Z = 3 to Z = 18. On the other hand, to utilize relativistic correction, Briet-Pauli approximation has also been applied to the ionization potential using a fourth-order polynomial expression in the nuclear charge Z. The deviation within the range of 0.1% has been observed between estimated and experimental values that are quite remarkable. Furthermore, new ionization potentials were proposed for iso-series with Z ranging from 19 to 30.

Women LIS researchers in Pakistan have been publishing their research since 1977. This paper provides a bibliometric analysis of the 43 years (1977–2020) of their research. This study conducted a bibliometric analysis of the data collected from four data sources, the Web of Science Core Collection, Scopus, LISA, and LISTA, to include the maximum number of scholarly publications by women Pakistani LIS researchers working in Pakistan or abroad. The study findings highlighted an upward trend in publications since 1977. It appears to have gathered momentum after the year 2003. The University of the Punjab was the top-ranked organization in publishing research by woman LIS researchers, and the Department of Information Management at the University of the Punjab was identified as being the most prolific in this regard. The researchers had published over 40% of their research in national journals and the rest in international journals of good repute. Kanwal Ameen led the list of woman researchers with over 100 research publications to her name. The main areas of research were academic libraries, information literacy, and evolving trends in LIS. This is the first research paper highlighting the different dimensions of Pakistani LIS women researchers since they started publishing in 1977. The findings of the study would help future researchers to understand trends and various bibliometric aspects of publications originating from women LIS researchers in Pakistan. The findings could also help LIS researchers and funding organizations in promoting the profession.

This bibliometric study investigates the publishing trends and patterns along with top authors, countries, organizations, nature of collaboration, and sub-areas of library leadership literature published from 1959 to 2022. The Scopus database was used for data extraction, and 500 relevant records were selected. The data were analyzed using Microsoft Access, Microsoft Excel, VOSviewer, Biblioshiny, and CiteSpace software. The results highlighted that the United States of America was a global trendsetter in library leadership research, being the top contributing country (313, 62.6% publications). They also identified the top productive author (Martin, J.), top-cited author (Ole Pors, N), top organization (University of Punjab, Pakistan), most preferable sources (Journal of Library Administration) and top-cited article (“Supporting Digital Scholarship in Research Libraries: Scalability and Sustainability” by Vinopal J). The highest research productivity was recorded in 2019, with 42 publications (8.4%), followed by 37 (7.4%) publications in 2018 and 2021. Furthermore, 270 publications (54%) on library leadership were published during the last decade (2011–2020). This study informed that most of the published literature on library leadership was general, following a solo authorship trend (314), with less collaborative research ( n = 186) and a significant number ( n = 148) of non-cited documents. The critical areas of future research identified in this study, including transformational, ethical, participative, and humanistic leadership, need to be investigated. The study suggests that emerging digital and virtual leadership areas should also be examined along with the areas with limited literature. To the best of our knowledge, this is the first comprehensive bibliometric study to present a holistic picture of the library leadership literature.

Real mechanisms that are advancing in a complex network exhibit chaotic behaviour. This behaviour is crucial in physical and complex systems involving numerical modelling frameworks because it essentially determines the framework’s evolutionary process. In this context, notwithstanding its difficulty, the potential of intentional oversight of the phenomenon has feasible effects; this is why theoretical approaches are advantageous in such scenarios. This study investigates the functioning of a Willamowski–Rössler (W–R) mechanism, including the synchronization of two minimal W–R structures depending on the responsive suggestion technique for regulation, with the purpose of achieving chaos influence in chemical interactions. We investigate the reliability of the steady state at various fractional order (FO) factors. Employing maximum Lyapunov exponents (MLEs), phase depictions, bifurcation schematics, the 0–1 evaluation and approximated entropy, it is demonstrated that adjusting the FOs causes a system’s behavioural pattern to undergo a transition from steady to chaotic. In addition to demonstrating that the proposed scheme fits chaotically under certain circumstances, simulation outcomes demonstrate that mathematical modelling is used to illustrate theoretical debates. To verify that the community detects chaos, the MLE and bifurcation illustrations, whose hallmark factors are plotted, display erratic behaviour while effectively attempting to control the chaos.

Purpose: The main objective of the current study was to evaluate the top-cited articles, countries, journals, authors, and papers published related to dental polymers and their application. Research articles published from 1962 to 2021 on dental polymers and their application were identified using the Scopus database. Methodology: Bibliographical data related to the abstract, citations, keywords, and other relevant information was extracted using different combinations of keywords. Further evaluation and visualization of the selected data were performed with the help of various tools, including MS Excel, Microsoft Word, Google open refine, Biblioshiny, BibExcel, and VOS viewer. An initial search revealed 351 documents, of which 327 were chosen for further analysis. Results: A substantial increase in the number of publications related to this domain was observed. The United States was the most prolific country, while the Aristotle University of Thessaloniki from Greece was identified as the leading institute. Conclusion: This bibliometric analysis can guide researchers, funding agencies, industry, and institutions.

The integration of wireless antenna sensors for cyber-physical systems has become increasingly prevalent in various biosimilar applications due to the escalating need for monitoring techniques that are efficient, accurate, and reliable. The primary objective of this comprehensive investigation is to offer a scholarly examination of the present advancements, challenges, and potentialities in the realm of wireless antenna sensor technology for monitoring biosimilars. Specifically, the focus will be on the current state of the art in wireless antenna sensor design, manufacturing, and implementation along with the discussion of cyber security trends. The advantages of wireless antenna sensors, including increased sensitivity, real-time data gathering, and remote monitoring, will next be discussed in relation to their use in a variety of biosimilar applications. Furthermore, we will explore the challenges of deploying wireless antenna sensors for biosimilar monitoring, such as power consumption, signal integrity, and biocompatibility concerns. To wrap things off, there will be a discussion about where this subject is headed and why collaborative work is essential to advancing wireless antenna sensor technology and its applications in biosimilar monitoring. Providing an in-depth overview of the present landscape and potential developments, this article aims to be an asset for academics and professionals in the fields of antenna sensors, biosimilar development, wireless communication technologies, and cyber physical systems.

Skin cancer, particularly the malignant melanoma subtype, is widely recognized as a highly lethal form of cancer characterized by abnormal melanocyte cell growth. However, diagnosing and classifying skin lesions, as well as automatically recognizing malignant tumors from dermoscopy images, present significant challenges. To address this challenge, our study employs variants of Convolutional Neural Networks (CNNs) to effectively diagnose and classify various skin lesion types using the latest benchmark datasets ISIC 2019 and 2020. The dataset underwent rigorous preprocessing, which involves employing advanced Generative Artificial Intelligence (AI) techniques i.e., Generative Adversarial Networks (GANs) and Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN), for augmentation. These generative techniques are carefully evaluated and compared for their effectiveness. Our CNN-based approach involves aggregating results from multiple transfer learning models, including VGG16, VGG19, SVM along with a hybrid model in combination of VGG19 and SVM. On ISIC 2019, we have achieved promising accuracies of 92% for VGG16 and 93% for VGG19. Notably, the hybrid VGG19+SVM model exhibits the highest accuracy of 96%. On ISIC 2020, VGG16, VGG19, and SVM achieves accuracies of 90%, 92%, and 92%, respectively. Our findings underscore the potential of generative AI for augmentation, and the efficacy of CNN-based transfer learning models in improving skin cancer classification accuracy.

Purpose: This study aimed to investigate the current state of research on tooth extraction socket preservation. The main aim of this study was to consolidate the research published on extraction socket preservation from 1968 to 2020 in Scopus indexed journals. Methodology: The bibliometric method, a quantitative analysis investigating publishing trends and patterns, was used. Scopus database was used to retrieve the bibliographic records of published scholarly output. The analysis was performed using software and visualization tools like MS Excel, VOSviewer, Cite Space, Biblioshiny (RStudio), and BibExcel. Results: The result showed a gradual increase in research, whereby a substantial increase was observed from 2005 to 2006. Six hundred nineteen articles were published in 173 journals with total citations of 12091. Most published articles were from the USA, Italy, Germany, and China. The authorship pattern showed an interdisciplinary and collaborative approach among researchers. Conclusion: This bibliometric analysis can guide researchers, funding agencies, industry, and institutions.

Introduction: Spinal cord injury results in disability, limited participation in physical activities, and mental health problems which greatly affects the quality of life of the injured person. Engaging in physical activity is necessary for optimal recovery in individuals with spinal cord injury. Chronic spinal cord injury patients suffer from many secondary complications which become a challenge for the patient and the health care community to manage due to which recovery will be complex and difficult. The aim of this study is to find out the association of physical activity with depression among chronic spinal cord injury patients at Paraplegic Centre Peshawar. Material and methods: This study was a cross-sectional survey in which a consecutive sampling technique was used. Data was collected from n=109 spinal cord injury patients in which 85 (78.0%) were males and 24 (22.0%) were females. Physical activity was measured using the PARA-SCI scale and the CESD-R-10 questionnaire was used to assess depression. Results: The average minutes of participating in mild physical activity was 67.72 ± 17.98 minutes/week, moderate physical activity was 140.79 ± 33.47 minutes/week, heavy physical activity was 21.92 ± 9.18 minutes/week and total PA was 247.93 ± 55.76. P value= .004 for mild physical activity with depression, p value= .097 for moderate physical activity with depression, p value= .137 for heavy physical activity with depression and p value= .001 for total physical activity with depression. Conclusions: Mild and total physical activity was associated with depression. Moderate and heavy physical activity was not associated with depression.

Traumatic brain injury (TBI) is the major and leading cause of mortality and an alarming public health challenge. TBI leads to permanent cognitive, motor, sensory and psychotic disabilities. Patients suffering from the various and long-term repercussions of TBI currently have limited therapy choices. The current research work was designed to evaluate the beneficial and neuroprotective role of Troxerutin (Trox) (a natural flavonoid) in a closed brain injury mouse model. The male BALB/c 8-weeks old mice (n꞊150) were randomly distributed in three experimental groups. Control group of mice (n꞊50), TBI group (n꞊50) and Trox pre-treated mice group (Trox + TBI, n꞊50). The mice in Trox + TBI were pre-treated with Trox (150 mg/kg, 7 days) before TBI. The weight-drop mechanism was used to induce mild-moderate injury in mice in both the groups. Our results showed that the mice pre-treated with troxerutin significantly improved neurological severity score, blood glucose level, food intake and brain edema as compared to the mice in the TBI group. Furthermore, compared to the TBI group, the mice treated with troxerutin improved cognitive behavior as evaluated by Open field test, Shallow Water Maze and Y-Maze, decreased brain-infarct volume and blood-brain barrier (BBB) permeability, significantly decreased Reactive Oxygen Species (ROS), improved neuronal morphology and survival in the brain regions such as cortex and hippocampus. In summary, our data provided evidence that pre-treatment with troxerutin improved neurological functions, decreased the BBB permeability, improved behavior, reduced ROS and increased neuronal survival in the weight-drop close head traumatic injury mouse model.

In this research, we computed transition probabilities, line strength, and oscillator strengths of more than 5000 transitions in C IV. Very few values of these spectroscopic characteristics were previously known and reported. The calculation method, based on a combination of the weakest bound electron model and numerical approximation, shows reliable values because the correlation between known and calculated values is high. The transition probabilities calculated in this work are compared with known values of the NIST database and those found in literature, and a reasonably good agreement has been observed. The lifetime of Rydberg levels ns, np, nd, nf, ng has been reported up to n = 25. A general sixth-degree polynomial was developed, generating C IV lifetimes with reasonable accuracy. Most of the results presented are new.

Abstract Methicillin-resistant Staphylococcus aureus (MRSA) is a prevailing nosocomial pathogen that is increasingly isolated in community settings. It shows resistance against all beta-lactam drugs and has acquired mechanisms to resist other groups of antibiotics. To tackle this emerging issue of MRSA, there is an urgent need for antibiotic alternatives, and utilizing lytic bacteriophages is one of the most promising therapeutic approaches. In the present study, a lytic bacteriophage TSP was isolated from hospital wastewater against MRSA. The phage efficiently inhibited bacterial growth for up to 12 h at MOI of 1 and 10. TSP phage showed activity against various isolates of MRSA and MSSA, isolated from different clinical samples, with variable antibiotic susceptibility patterns. The bacteriophage TSP showed stability at varying temperatures (25 °C, 37 °C) and pH values (5–9), while its maximum storage stability was observed at 4 °C. It had a short latent period (20 min) and burst size of 103 ± 5pfu/infected cells. TSP genome sequence and restriction analysis revealed that its genome has a linear confirmation and length of 17,987 bp with an average GC content of 29.7%. According to comparative genomic analysis and phylogenetic tree,TSP phage can be considered a member of genus “P68viruses”. The strong lytic activity and short latent period in addition to its lytic nature makes it a good candidate for phage therapy against MRSA infections, if it proves to be effective in in-vivo studies.

Background: Trans-femoral amputation (TFA) is an undesirable transaction of lower limb at the femoral level necessitating the use of a prosthetic device essentially requiring prolonged rehabilitation to ensure ambulation of the patient and integration into daily routine Objectives: The objective of the study was to find the association of quality of life with body mass index in patients using trans femoral prosthesis after amputation Methodology: Current Cross-Sectional study using convenience sampling recruited N=400 trans-femoral prosthesis users from PIPOS. Sample included both genders with age range of 12-60 years, using prosthesis > 1 year while orthosis users were excluded. SF-36 Health Survey tool & Lower Extremity Functional Scale (LEFS) were utilized for collection of data & analyzed utilizing SPSS Version 21. Results: LEFS items revealed association (p

This study examined the impact of personality traits on the knowledge sharing behavior of academicians in the public sector. The data were collected from 237 respondents using a questionnaire. The results showed that the personality trait openness to experience had a significant and positive impact on the knowledge sharing behavior and its sub-dimensions such as written contributions, organizational communication, personal interactions, and communities of practice. Furthermore, the personality traits extraversion and agreeableness positively predicted the knowledge sharing behavior for the dimensions of communities of practice and organizational communication, respectively. The results of this study would be helpful for the administrative staff of universities to develop programs to promote a knowledge sharing culture in universities and improve collaborative learning, research, organizational effectiveness, and performance. It would also be a worthy contribution to the existing literature as only a limited number of studies have addressed the role of personality traits in the knowledge sharing behaviors in the academic environment of a non-western country.

Objectives: This study aims to determine the association of ambulatory and social performance status of transfemoral prosthetic users with their age, gender, and marital status. Methods: A cross-sectional study was conducted on 400 transfemoral prosthesis users. A sample was recruited from both genders aged 10-60 years using the prosthesis for at least one year. They were selected using the non-probability convenience sampling method from the Pakistan Institute of Prosthetic and Orthotic Sciences from July 2019 to December 2019. Lower extremity functional scale and short form-36 health survey questionnaire (SF-36) were used for data collection, followed by statistical analysis. Results: Ambulatory status (as measured by the total lower extremity functional scale) revealed significant association (P

This study has conducted a meta-literature review examining the past, present and possible future trends of Fintech research using 360 selected articles published between 2006 and June 2020. Both quantitative and qualitative techniques were applied. In the quantitative approach, a bibliometric citation analysis using HistCite and VOSviewer software was conducted and the qualitative analysis covered the identification of four main research streams related to (i) Bitcoin and digital currency, (ii) crowdfunding, (iii) mobile payment, and (iv) blockchain. The study results highlight the most influential aspects of the FinTech literature, such as the leading countries, institutions, journals, authors, and articles. Suggestions for the potential future direction of FinTech literature have also been made.

Bank lending to SMEs plays a vital role in economic growth, contributing significantly to employment and GDP. Access to bank lending is crucial for small- and medium-sized enterprises (SMEs), as they contribute significantly to global employment and GDP. New financial technologies promise better bank operations, fewer costs, and enhanced credit supply to SMEs. However, there is still a lack of empirical findings on how these technologies can solve demand-side bank lending problems for small- and medium-sized firms. This study gathered data from a sample of 381 respondents, comprising CEOs, managers, officers, loan managers, IT consultants, and other relevant stakeholders. The findings indicate that the adoption of blockchain technologies, as well as the adoption of Big Data technologies encompassing cloud computing, data analytics, algorithms, and programming, along with the adoption of mobile banking technologies, have had a substantial positive impact on bank credit supplies for small- and medium-sized enterprises (SMEs) in Pakistan. This novel study contributes to existing knowledge in two ways. First, it provides knowledge to SMEs looking to adopt new technologies; second, it provides knowledge to a manager looking to finance the SMEs with information asymmetries. This research also provides key findings for researchers and policymakers.

The present study was designed to evaluate the in vitro and in silico potential of the Schiff bases (Z)-4-ethoxy-N-((5-nitrothiophen-2-yl)methylene)benzenamine (1) and (Z)-2,4-diiodo-6-((2-methyl-3-nitrophenylimino)methyl)phenol (2). These Schiff bases were synthesized according to a reported method using ethanol as a solvent, and each reaction was monitored on a TLC until completion of the reaction. The structures of both compounds were elucidated using spectroscopic techniques such as UV–Vis, FTIR, 1H NMR and 13C NMR. Molecular structure was determined using single-crystal XRD, which revealed that compounds 1 and 2 were monoclinic and triclinic, respectively. Hirshfeld surface analysis (HS) and 2D fingerprint plots were used to determine the intermolecular interactions along the contact contribution in the crystalline molecules. The structures of both compounds were optimized through a hybrid functional method B3LYP using the 6-31G(d,p) basis set, and various structural parameters were studied. The experimental and theoretical parameters (bond angle and bond length) of the compounds were compared with each other and are in close agreement. The in vitro esterase potential of the synthesized compounds was checked using a spectrophotometric model, while in silico molecular docking studies were performed with AutoDock against two enzymes of the esterase family. The docking studies and the in vitro assessment predicted that such molecules could be used as enzyme inhibitors against the tested enzymes: acetylcholine esterase (AChE) and butyrylcholine esterase (BChE).

The present study was designed to investigate and compare the effects of plant-derived smoke (PDS) and auxin (IAA and IBA) on maize growth under the application of 2,3,5-triiodo benzoic acid (TIBA). For this purpose, indole-3-acetic acid (IAA) and indole-3-butyric acid (IBA), each at a concentration of 10 ppm, along with PDS at a ratio of 1:500 (v/v) were used alone and in combination with 10 ppm of TIBA. The results indicate that the germination percentage (%) of maize seeds was enhanced under IAA, IBA and PDS treatment. However, IAA and IBA resulted in reduced germination when applied in combination with TIBA. Importantly, the germination percentage (%) was improved by PDS under TIBA treatment. The analysis of seedling height, length of leaves, and number of primary, seminal and secondary/lateral roots showed improvement under individual treatments of IAA and IBA, PDS and PDS + TIBA treatment, while these values were reduced under IAA + TIBA and IBA + TIBA application. Chlorophyll content, total soluble sugars and antioxidative enzymatic activity including POD and SOD increased in seedlings treated with PDS alone or both PDS and TIBA, while in seedlings treated with IAA and TIBA or IBA and TIBA, their levels were decreased. APX and CAT responded in the opposite way—under IAA, IBA and PDS treatment, their levels were found to be lower than the control (simple water treatment), while TIBA treatment with either IAA, IBA or PDS enhanced their levels as compared to the control. These results reveal that PDS has the potential to alleviate the inhibitory effects of TIBA. This study highlights the role of PDS in preventing TIBA from blocking the auxin entry sites.

Objective: This literature research aimed to compare, contrast and quantify the innovations in the most commonly used dental biomaterials. Methodology: Original research articles based on experimental dental biomaterials published between 2007 and 2019 were retrieved and reviewed. A search of electronic databases, PubMed, Scopus, and Web of Science indexed dental/biomaterials journals, has been conducted. The inclusion criteria in this research were: synthesis of experimental dental materials, whereas commercial dental materials, review articles, and clinical trials (case reports) were excluded. Results: It was found that the amount of publications related to dental subgingival implants, computer-aided modeling ceramics, aesthetic restorative materials, adhesives cements, ceramics, bioceramics, endodontic materials, bioactive scaffolds, stem cells, and guided-tissue membranes had increased significantly from 2007. At the same time, the number of publications related to dental cements, silver amalgam, and dental alloys has decreased. For characterization of dental materials it was noted that mechanical properties were tested mostly for restorative materials. On the other hand, biological properties were most assessed for dental subgingival implants and endodontic materials, however, physical properties predominantly for bioceramics. Conclusion: It is concluded that to meet clinical demands there was more focus on restorative materials that provided better aesthetics, including resin composites, adhesive resin composites (luting cements), zirconia, and other ceramics. The boost in laboratory and animal research related to bioceramics was attributed to their regenerative potential. This current literature study will help growing researchers to consider and judge the direction to which research might be guided in order to plan prospective research projects.

OBJECTIVE: To find-out the association of dyslipidemia with anxiety and depression in patients with polycystic ovarian syndrome (PCOS). METHODS: This descriptive cross-sectional study was conducted at Mardan Medical Complex, Mardan, Pakistan from December-2019 to March-2020. One hundred & forty diagnosed cases of PCOS, ranging in age from 20-40 years, were selected through purposive sampling. Anxiety and depression were calculated using hospital anxiety and depression scale (HADS) score. Total cholesterol (TC), triglycerides, low-density-lipoproteins (LDL), high-density-lipoproteins (HDL) and testosterone were tested through fasting blood samples. RESULTS: Anxiety and depression were documented in 102 (72.9%) & 99 (70.7%) cases respectively. Primary infertility, oligomenorrhoea, Hirsutism & acne were present in 69 (49.3%), 111 (73.5%), 81 (57.9%) and 55 (39.3%) patients respectively. Twenty-two (15.7%) patients were overweight/obese. All patients had raised testosterone levels and polycystic ovaries on ultrasound. Raised triglyceride levels (≥150 mg/dl), Low levels of HDL (≤ 60mg/dl), raised levels of LDL (≥130 mg/dl), raised cholesterol levels (≥200 mg/dl) were reported in 71/99 (71.7%), 56/99 (56.6%), 38/99 (38.4%) & 10/99 (10.2%) cases with depression as compared to 27/41 (65.9%), 13/41 (31.7%), 21/41 (51.2%) & 14/41(34.1%) non-depressed patients respectively. Similarly raised triglyceride levels, low levels of HDL, raised levels of LDL, raised cholesterol levels were reported in 73/102 (71.6%), 58/102 (56.9%), 40/102 (39.2%) & 12/102 (11.76%) cases with anxiety as compared to 25/38 (65.8%), 11/38 (28.9%), 19/38 (50%) and 12/38 (31.6%) patients without anxiety respectively. CONCLUSION: Dyslipidemia, anxiety and depression are very common in PCOS. Dyslipidemia is associated with anxiety and depression in PCOS patients.

Salmonella Gallinarum causes fowl typhoid in poultry leading to a huge economic loss to the poultry industry. The large virulence plasmid of S. gallinarum has been associated with various systemic infections in poultry. A five-gene spanning region (spvRABCD) of 7.8 kb on the large plasmid mainly confers virulence to the bacteria. However, the exact role of these genes in virulence has not been elucidated yet. SpvB exhibits delayed cell death by preventing actin polymerization followed by apoptosis during intracellular infection. The specific role of SpvB in causing the disease is not known yet. In the current study, the SpvB gene was deleted through CRISPR/Cas9 method from a large virulent plasmid of locally isolated S. gallinarum strain (SG18). The homology-directed repair method was used for complete deletion of SpvB gene using the modified pCas9 plasmid. The SpvB-deleted S. gallinarum strain (ΔSpvB_SG18), when tested for its virulence in broiler chicken showed no diseases signs and mortality. In addition, the avirulent strain does not affect the bird’s weight and was rapidly cleared from the liver after infection. However, it cleared from the intestine only after 4–5 days, which suggests that the ΔSpvB_SG18 strain is unable to invade from the intestine to the liver. This is the first study to report a complete gene deletion from the S. gallinarum virulent plasmid and its effect. This method will be useful for the deletion of virulent genes from S. gallinarum, to study their role in pathogenesis, and to prepare an effective vaccine strain for controlling fowl typhoid in poultry.

Abstract Background Recently, we and other researchers reported that brain metabolic disorders are implicated in Alzheimer’s disease (AD), a progressive, devastating and incurable neurodegenerative disease. Hence, novel therapeutic approaches are urgently needed to explore potential and novel therapeutic targets/agents for the treatment of AD. The neuronal adiponectin receptor 1 (AdipoR1) is an emerging potential target for intervention in metabolic-associated AD. We aimed to validate this hypothesis and explore in-depth the therapeutic effects of an osmotin-derived adiponectin-mimetic novel nonapeptide (Os-pep) on metabolic-associated AD. Methods We used an Os-pep dosage regimen (5 μg/g, i.p., on alternating days for 45 days) for APP/PS1 in amyloid β oligomer-injected, transgenic adiponectin knockout (Adipo−/−) and AdipoR1 knockdown mice. After behavioral studies, brain tissues were subjected to biochemical and immunohistochemical analyses. In separate cohorts of mice, electrophysiolocal and Golgi staining experiments were performed. To validate the in vivo studies, we used human APP Swedish (swe)/Indiana (ind)-overexpressing neuroblastoma SH-SY5Y cells, which were subjected to knockdown of AdipoR1 and APMK with siRNAs, treated with Os-pep and other conditions as per the mechanistic approach, and we proceeded to perform further biochemical analyses. Results Our in vitro and in vivo results show that Os-pep has good safety and neuroprotection profiles and crosses the blood-brain barrier. We found reduced levels of neuronal AdipoR1 in human AD brain tissue. Os-pep stimulates AdipoR1 and its downstream target, AMP-activated protein kinase (AMPK) signaling, in AD and Adipo−/− mice. Mechanistically, in all of the in vivo and in vitro studies, Os-pep rescued aberrant neuronal metabolism by reducing neuronal insulin resistance and activated downstream insulin signaling through regulation of AdipoR1/AMPK signaling to consequently improve the memory functions of the AD and Adipo−/− mice, which was associated with improved synaptic function and long-term potentiation via an AdipoR1-dependent mechanism. Conclusion Our findings show that Os-pep activates AdipoR1/AMPK signaling and regulates neuronal insulin resistance and insulin signaling, which subsequently rescues memory deficits in AD and adiponectin-deficient models. Taken together, the results indicate that Os-pep, as an adiponectin-mimetic novel nonapeptide, is a valuable and promising potential therapeutic candidate to treat aberrant brain metabolism associated with AD and other neurodegenerative diseases.

Using first-principles calculations, we predicted different structures of lithium (Li) atoms with solid electrolytes (e.g., ethylene carbonate (EC)) and their coordination number (n = 3) for the first time. Additionally, we explored the unique properties of the EC absorption of the Li atom using both the equilibrium EC solvent and complex cluster of Li+. Vibrational spectral analysis confirmed that only one type of Li coordinates with EC molecules. The coordination number of EC molecules increases with decreasing minimization or optimization energy and the charge of Li ions. Due to electron affinity and ion charges, only the optimized energy investigation supports the coordination of the EC solvent, which can be calculated by Li+Bn n = 1–3, where B is the organic solvent (ethylene).