Background: Cervical proprioception is critical in the maintenance of posture and movements, so its assessment in different cervical conditions has gained importance in recent clinical practice. Studies reporting this assessment in subjects with cervical spondylosis (CS) have not previously been investigated. The goals of the study are (1) comparison of joint position error (JPE) in subjects with CS to healthy control group. (2) Correlation of neck pain intensity to cervical proprioception in patients with CS.Methods: In a Cross-sectional study, 132 subjects with CS and 132 healthy age-matched control subjects were evaluated for cervical JPE with the cervical range of motion device. The subjects were blindfolded and repositioned their heads to a target position, which was determined by the examiner previously and their repositioning accuracy (absolute error in degrees) was measured in the frontal (flexion and extension) and transverse planes (left rotation and right rotation). The CS subjects resting neck pain intensity was assessed using visual analog scale (VAS).Results: CS subjects showed statistically significantly larger JPEs compared to healthy control subjects in all the directions tested (flexion - 95% CI = 2.38–3.55,p < 0.001, extension - 95% CI =3.26–4.33,p < 0.001, left rotation - 95% CI = 2.64 - 3.83,p < 0.001, right rotation − 95% CI = 3.77–4.76,p < 0.001). The mean JPE errors in the CS group ranged from 6.27° to 8.28° and in the control group ranged from 2.36° to 4.48°. Pearson’s correlation coefficient showed a significant and positive relationship between neck pain intensity and cervical proprioception (p ≤ 0.001).Conclusions: Proprioception is impaired in subjects with CS when compared to healthy control group. Higher pain intensity was associated with greater cervical JPE in patients with CS.

E-Learning has proven to be the only resort as a replacement of traditional face-to-face learning methods in the current global lockdown due to COVID-19 pandemic. Academic institutions across the globe have invested heavily into E-Learning and the majority of the courses offered in traditional classroom mode have been converted into E-Learning mode. The success of E-Learning initiatives needs to be ensured to make it a sustainable mode of learning. The objective of the current study is to propose a holistic E-Learning service framework to ensure effective delivery and use of E-Learning Services that contributes to sustainable learning and academic performance. Based on an extensive literature review, a proposed theoretical model has been developed and tested empirically. The model identifies a broad range of success determinants and relates them to different success measures, including learning and academic performance. The proposed model was validated with the response from 397 respondents involved with an E-Learning system in the top five public universities in the southern region of Saudi Arabia through the Partial Least Squares regression technique using SmartPLS software. Five main factors (Learner’s Quality, Instructor’s Quality, Information’s Quality, System’s Quality and Institutional Quality) were identified as a determinant of E-Learning service performance which together explains 48.7% of the variance of perceived usefulness of ELS, 71.2% of the variance of use of the E-Learning system. Perceived usefulness of ELS and use of ELS together explain 70.6% of learning and academic performance of students. Hence the framework will help achieve the sustainable and successful adoption of E-Learning services.

Dermatological conditions are primarily prevalent in humans and are primarily caused by environmental and climatic fluctuations, as well as various other reasons. Timely identification is the most effective remedy to avert minor ailments from escalating into severe conditions. Diagnosing skin illnesses is consistently challenging for health practitioners. Presently, they rely on conventional methods, such as examining the condition of the skin. State-of-the-art technologies can enhance the accuracy of skin disease diagnosis by utilizing data-driven approaches. This paper presents a Computer Assisted Diagnosis (CAD) framework that has been developed to detect skin illnesses at an early stage. We suggest a computationally efficient and lightweight deep learning model that utilizes a CNN architecture. We then do thorough experiments to compare the performance of shallow and deep learning models. The CNN model under consideration consists of seven convolutional layers and has obtained an accuracy of 87.64% when applied to three distinct disease categories. The studies were conducted using the International Skin Imaging Collaboration (ISIC) dataset, which exclusively consists of dermoscopic images. This study enhances the field of skin disease diagnostics by utilizing state-of-the-art technology, attaining exceptional levels of accuracy, and striving for efficiency improvements. The unique features and future considerations of this technology create opportunities for additional advancements in the automated diagnosis of skin diseases and tailored treatment.

The COVID-19 outbreak continues to spread worldwide at a rapid rate. Currently, the absence of any effective antiviral treatment is the major concern for the global population. The reports of the occurrence of various point mutations within the important therapeutic target protein of SARS-CoV-2 has elevated the problem. The SARS-CoV-2 main protease (Mpro) is a major therapeutic target for new antiviral designs. In this study, the efficacy of PF-00835231 was investigated (a Mpro inhibitor under clinical trials) against the Mpro and their reported mutants. Various in silico approaches were used to investigate and compare the efficacy of PF-00835231 and five drugs previously documented to inhibit the Mpro. Our study shows that PF-00835231 is not only effective against the wild type but demonstrates a high affinity against the studied mutants as well.

The development of entrepreneurial activities helps in realization of multiple goals of sustainable development. This study aims to inspect the applicability of the entrepreneurial intention model (EIM) to explain entrepreneurial intention in different cultural contexts. A survey was conducted using the Entrepreneurial Intention Questionnaire. Based on 535 valid responses received from business students in India (N = 285) and Saudi Arabia (N = 250), the model was analyzed using the partial least square regression method. The results show that the EIM is validated for the combined sample (R²_combined = 0.522) as well as for the individual samples (R²_India = 0.644; R²_Saudi = 0.412). Demographic variables and human capital variables were found to impact the entrepreneurial intention differently in the combined sample and the individual samples of the two countries. Multigroup analysis was performed to test if the country variable influences the hypothesized paths in the model. The results of the multigroup analysis show that many of the hypothesized relationships (gender-personal attitude; gender-subjective norm; gender-perceived behavioral control, role model-subjective norm, role model-perceived behavioral control, subjective norm-personal attitude, and subjective norm-entrepreneurial intention) are significantly different in the two countries. This study addresses the gap in comparative international entrepreneurship (CIE) research by testing the applicability of EIM in two different cultures. The findings are also significant for policymakers at both national and international levels for facilitating entrepreneurship.

Biogenic nanoparticles are considered effective alternatives to chemical pesticides for the management of pathogenic plant diseases. This study was focused on the synthesis of stable silver nanoparticles (AgNPs) to control challenging plant pathogenic bacteria in vitro and in planta. We synthesized AgNPs by reacting different proportions of silver nitrate and aqueous extract of Hedera nepalensis. The physicochemical properties of the synthesized AgNPs were determined by using various physical techniques. The TEM analysis revealed the AgNPs less than 50 nm in size and spherical shaped. For antibacterial assays, different concentrations (1000–15.62 µg/mL, 2-fold dilutions) of the extract-free AgNPs (Ef-AgNPs) or extract-mixed AgNPs (Em-AgNPs), and fruit extracts (FE) were used against plant pathogenic bacteria Erwinia carotovora subsp. carotovora, Erwinia carotovora subsp. atroseptica, and Ralstonia solanacearum. In the in vitro assays, we found significant inhibition of both bacterial species in response to maximum concentrations of AgNPs. Overall, Ef-AgNPs exhibited a higher percent inhibition of bacterial pathogens. In potato tubers assay, complete inhibition of Erwinia carotovora was observed, except for the lowest AgNPs concentration of 15.62 µg/mL. Similarly, exposure of tomato plants to Ralstonia solanacearum suspensions (OD600 = 0.2) in the soil-drenching experiment and post-exposure treatment with 1000 µg/mL and 125 µg/mL of AgNPs resulted in disease inhibition. This study provides the basis that biogenic nanoparticles prepared from Hedera nepalensis are one of the best substitutes to synthetic pesticide, having displayed better results to control the growth of phytopathogenic microbes. However, field studies need to be conducted in a controlled environment to scale up the current work and find out the efficacy of nanoparticles on a larger scale.