Development and validation of scale for self evaluation of soft skills in postgraduate dental students

Objective: To develop and validate a soft skills questionnaire, and to use it for self-evaluation by postgraduate dentistry students.Methods: The cross-sectional descriptive study was conducted at University College of Dentistry, , University of Lahore, Lahore, Pakistan, from February 2020 to April 2020 and comprised of residents from first to final year of training for either Masters in Dental Surgery or Fellowship of the College of Physicians and Surgeons Pakistan programmes. A soft skills questionnaire was generated and was validated through exploratory factor analysis of the elements and items of the questionnaire using SPSS 23.Results: Of the 60 subjects, 37(61.7%) were MDS residents and 23(38.3%) were Fellowship residents. The mean age of the sample was 29.650±2.815 years, and 26(43.3%) subjects were males. The questionnaire was validated (p\u3c0.001). Three domains measured the attitude of dentists, with 7 scenarios having 5 items per scenario. Both categories of trainees had high agreement in understanding and application of non-technical skills, with the exception of leadership skills. However, the difference between the groups was non-significant (p\u3e0.05).Conclusions: A self-generated questionnaire was successfully validated

Frequency of Different Ligament Tears in Knee Injury On Magnetic Resonance Imaging

Background: MRI had been useful in the diagnosis of ligament injuries and the tears were detected by non-invasive procedure. Objective: To determine the frequency of different ligament tears in knee injury on Magnetic Resonance Imaging. Methods: A descriptive cross-sectional was conducted with the sample size of 206 patients of both genders by selecting the convenient sampling from Ghurki Trust Teaching Hospital, Lahore. Out of 206 patients, 157 were males while 49 were females. Data was analyzed with the help of SPSS version 24. The results were derived by mean, frequency and standard deviation. Results: Findings shows that among 206 patients, with in age limit of 12 years to 70 years. 157(76.2%) were males and 49(23.7%) were female while 96(46.6%) were presented with ACL tear, 19(9.2%) were presented with PCL tears, 51(25.7%) were presented with MCL tears, 33(16.0%) patients were presented with LCL tears and all of these 206 patients were suffering from pain. Conclusion: We concluded that males are more prevalent than females and in this population the incidence of ACL tears is more than other ligament tears. Hence, ACL is proved to be the most injured ligament. Keywords: Anterior cruciate ligament, Posterior cruciate ligament, Medial collateral ligament, Lateral collateral ligament and Magnetic resonance imaging. DOI: 10.7176/JHMN/71-06 Publication date: February 29th 202

Balance Among Patients after Six Months of Total Knee Arthroplasty

Total knee arthroplasty (TKA) is an orthopedic surgical technique in which the knee joint surfaces, the femoral condyles and tibial plateau, are substituted with prosthetic components.TKA is found effective for improving perambulating purposes, but some other impairment such as proprioception, muscle strength, and balance may continue postoperatively. Balance is defined as an individual’s ability to maintain a desired position within their base of support. The aim of the study was to asses balance among patients who underwent total knee arthroplasty post six months. A cross sectional descriptive study was conducted for a period of 6 months from August 2018 till January 2019 at Ghurki Trust and Teaching hospital and Horizon Hospital Lahore, in which 26 subjects (7 Males and 19 Females) were included according to inclusion criteria. Permission from the Ethical Committee of the Lahore College of Physical Therapy was obtained. The sampling technique used was convenient sampling. Balance of each patient was assessed with Berg Balance Scale. The Berg Balance Scale grades execution on 14 different tasks, utilizing a scale of five points ranging from 0 to 4; the isolated scores are summed, for a total score of 56 where greater scores show enhanced stability of task execution. The results of the study showed that out of the total (n = 26) patients, 19 patients had low risk of fall, 6 patients had moderate risk of fall, while only one patient had high risk of fall. The study concluded that patients, who underwent total knee arthroplasty, had low risk of fall when balance was assessed with Berg Balance Scale after six months of Total knee Arthroplasty procedure

Automatic Detection of Offensive Language for Urdu and Roman Urdu

In recent years, unethical behavior in the cyber-environment has been revealed. The presence of offensive language on social media platforms and automatic detection of such language is becoming a major challenge in modern society. The complexity of natural language constructs makes this task even more challenging. Until now, most of the research has focused on resource-rich languages like English. Roman Urdu and Urdu are two scripts of writing the Urdu language on social media. The Roman script uses the English language characters while the Urdu script uses Urdu language characters. Urdu and Hindi languages are similar with the only difference in their writing script but the Roman scripts of both languages are similar. This study is about the detection of offensive language from the user's comments presented in a resource-poor language Urdu. We propose the first offensive dataset of Urdu containing user-generated comments from social media. We use individual and combined n-grams techniques to extract features at character-level and word-level. We apply seventeen classifiers from seven machine learning techniques to detect offensive language from both Urdu and Roman Urdu text comments. Experiments show that the regression-based models using character n-grams show superior performance to process the Urdu language. Character-level tri-gram outperforms the other word and character n-grams. LogitBoost and SimpleLogistic outperform the other models and achieve 99.2% and 95.9% values of F-measure on Roman Urdu and Urdu datasets respectively. Our designed dataset is publically available on GitHub for future research

Post-Treatment of Synthetic Polyphenolic 1,3,4 Oxadiazole Compound A3, Attenuated Ischemic Stroke-Induced Neuroinflammation and Neurodegeneration

Ischemic stroke is categorized by either permanent or transient blood flow obstruction, impeding the distribution of oxygen and essential nutrients to the brain. In this study, we examined the neuroprotective effects of compound A3, a synthetic polyphenolic drug product, against ischemic brain injury by employing an animal model of permanent middle cerebral artery occlusion (p-MCAO). Ischemic stroke induced significant elevation in the levels of reactive oxygen species and, ultimately, provoked inflammatory cascade. Here, we demonstrated that A3 upregulated the endogenous antioxidant enzymes, such as glutathione s-transferase (GST), glutathione (GSH), and reversed the ischemic-stroke-induced nitric oxide (NO) and lipid peroxidation (LPO) elevation in the peri-infarct cortical and striatal tissue, through the activation of endogenous antioxidant nuclear factor E2-related factor or nuclear factor erythroid 2 (Nrf2). In addition, A3 attenuated neuroinflammatory markers such as ionized calcium-binding adapter molecule-1 (Iba-1), cyclooxygenase-2 (COX-2), tumor necrotic factor-α (TNF-α), toll-like receptors (TLR4), and nuclear factor-κB (NF-κB) by down-regulating p-JNK as evidenced by immunohistochemical results. Moreover, treatment with A3 reduced the infarction area and neurobehavioral deficits. We employed ATRA to antagonize Nrf2, which abrogated the neuroprotective effects of A3 to further assess the possible involvement of the Nrf2 pathway, as demonstrated by increased infarction and hyperexpression of inflammatory markers. Together, our findings suggested that A3 could activate Nrf2, which in turn regulates the downstream antioxidants, eventually mitigating MCAO-induced neuroinflammation and neurodegeneration

BIO-SORPTION OF NICKEL ON MODIFIED PHOENIX DACTYLIFERA L. SEEDS

This contemporary study reveals the potential of modified Phoenix Dactylifera L. seeds as a biosorbent to adsorb Ni (II) ions present in simulated wastewater. The effect of initial pH and contact time on the uptake of Ni (II) ions were studied. Increase in pH from 3 to 5 increased the removal efficiency from 7.68 to 24.8 %. Pseudo 2ndorder(PSO)model was found most suitable in kinetic studies. Equilibrium was reached within first 20 minutes. Chemisorption was proved to be the rate limiting step(RLS) in biosorption of Ni (II) ions on modified Phoenix Dactylifera L. seeds

Classification of Pneumonia via a Hybrid ZFNet-Quantum Neural Network Using a Chest X-ray Dataset

INTRODUCTION: Deep neural networks (DNNs) have made significant contributions to diagnosing pneumonia from chest X-ray imaging. However, certain aspects of diagnosis and planning can be further enhanced through the implementation of a quantum deep neural network (QDNN). Therefore, we introduced a technique that integrates neural networks with quantum algorithms named the ZFNet-quantum neural network for detecting pneumonia using 5863 X-ray scans with binary cases. METHODS: The hybrid model efficiently pre-processes complex and high-dimensional data by extracting significant features from the ZFNet model. These significant features are given to the quantum circuit algorithm and further embedded into a quantum device. The parameterized quantum circuit algorithm using qubits, superposition theorem, and entanglement phenomena generates 4 features from 4098 features extracted from images via a deep transfer learning model. Moreover, to validate the outcome measures of the proposed technique, we used various PennyLane quantum devices to detect pneumonia and normal control images. By using the Adam optimizer, which exploits an adaptive learning rate that is fixed to 10-6 and six layers of a quantum circuit composed of quantum gates, the proposed model achieves an accuracy of 96.5%, corresponding to 25 epochs. RESULTS: The integrated ZFNet-quantum learning network outperforms the deep transfer learning network in terms of testing accuracy, as the accuracy gained by the convolutional neural network (CNN) is 94%. Therefore, we use a hybrid classical-quantum model to detect pneumonia in which a variational quantum algorithm enhances the outcomes of a ZFNet transfer learning method. CONCLUSION: This approach is an efficient and automated method for detecting pneumonia and could significantly enhance outcome measures related to the speed and accuracy of the network in the clinical and healthcare sectors

An efficient Parkinson's disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the quality of life of over 10 million individuals worldwide. Early diagnosis is crucial for timely intervention and better patient outcomes. Electroencephalogram (EEG) signals are commonly used for early PD diagnosis due to their potential in monitoring disease progression. But traditional EEG-based methods lack exploration of brain regions that provide essential information about PD, and their performance falls short for real-time applications. To address these limitations, this study proposes a novel approach using a Time-Frequency Representation (TFR) based AlexNet Convolutional Neural Network (CNN) model to explore EEG channel-based analysis and identify critical brain regions efficiently diagnosing PD from EEG data. The Wavelet Scattering Transform (WST) is employed to capture distinct temporal and spectral characteristics, while AlexNet CNN is utilized to detect complex spatial patterns at different scales, accurately identifying intricate EEG patterns associated with PD. The experiment results on two real-time EEG PD datasets: San Diego dataset and the Iowa dataset demonstrate that frontal and central brain regions, including AF4 and AFz electrodes, contribute significantly to providing more representative features compared to other regions for PD detection. The proposed architecture achieves an impressive accuracy of 99.84% for the San Diego dataset and 95.79% for the Iowa dataset, outperforming existing EEG-based PD detection methods. The findings of this research will assist to create an essential technology for efficient PD diagnosis, enhancing patient care and quality of life