Perceived Stress among Students in Medical/Dental and Allied Health Universities in Pakistan due to COVID-19 Pandemic

  Objective: Aim of the study was to explore the perceived stress in students at various medical and dental universities across Pakistan during the COVID 19 pandemic, using a validated scale.Materials and Methods: The study took place at the Institute of Psychiatry (IOP) Rawalpindi Medical University (RMU). Results: About 400 medical students participated countrywide. The final analysis was conducted on 333 participants who completed the survey form. Study participants comprised 69.1% female and 30.9% male students. About 74.5% of the participants represented Punjab province, 1.2% were from Sindh, another 1.2% belonged to Baluchistan, 2.4% were from KPK, and 1.5% were from AJK while 19.2% of them resided in Islamabad. The majority of participants were enrolled in MBBS (78.4%) while the rest were from BDS (3%), Allied Health Sciences (12.9%), Clinical Psychology (3.6%), and Pharm D (2.1%).The mean perceived stress score was 21.34, SD=4.90 suggesting high perceived stress levels. Approximately 4.5% of students perceived low levels of stress, 80.2% perceived moderate stress, whereas 15.3% scored high on the perceived stress scale. Male students had statistically significant (p=0.38) lower stress levels (M=19.99, SD=5.91) as compared to females (M= 21.95, SD= 4.26). Conclusions: Perceived stress level in medical students was alarmingly high and requires urgent intervention by the Medical and Dental Universities for immediate action and policy guidance for early identification and effective management. This can be achieved by delivering targeted e-workshops and evidence-based e-trainings for stress management like psychological first aid and mindfulness techniques

A novel approach to intrusion detection using zero-shot learning hybrid partial labels

Computer networks have become the backbone of our interconnected world in today's technologically driven landscape. Unauthorized access or malicious activity carried out by threat actors to acquire control of network resources, exploit vulnerabilities, or undermine system integrity are examples of network intrusion. ZSL(Zero-Shot Learning) is a machine learning paradigm that addresses the problem of detecting and categorizing objects or concepts that were not present in the training data. . Traditional supervised learning algorithms for intrusion detection frequently struggle with insufficient labeled data and may struggle to adapt to unexpected assault patterns. In this article We have proposed a unique zero-shot learning hybrid partial label model suited to a large image-based network intrusion dataset to overcome these difficulties. The core contribution of this study is the creation and successful implementation of a novel zero-shot learning hybrid partial label model for network intrusion detection, which has a remarkable accuracy of 99.12%. The suggested system lays the groundwork for future study into other feature selection techniques and the performance of other machine learning classifiers on larger datasets. Such research can advance the state-of-the-art in intrusion detection and improve our ability to detect and prevent the network attacks. We hope that our research will spur additional research and innovation in this critical area of cybersecurity

Synthesis, characterization and biological evaluation of three new Schiff bases derived from amino acids and their Ag(I) complexes

ABSTRACT. Three Schiff base ligands which were derived from glycine, asparagine and alanine L1, L2 and L3 were designed and used to synthesized their Ag(I) complexes. 1H NMR, FT-IR, UV-Visible and conductance techniques were used to characterize the ligands and their metal complexes. The synthesized compounds showed antioxidant activity against 2,2-diphenyl-1-picrylhydrazyl (DPPH). Schiff bases and their Ag(I) complexes were screened for antimicrobial activity, in vitro antibacterial activity against three gram negative bacteria Escherichia coli, Pseudomonas aeruginosa, and Salmonella typhi with two gram positive bacteria Staphylococcus aureus and Bacillus subtilis by micro plate almar blue assay (MABA), antifungal activity against Candida albicans and Candida glabrata by agar tube dilution protocol. In vitro anti-inflammatory activity was performed by heat induce denaturation method and in vivo anti-inflammatory activity was performed by induced paw edema method. Cytotoxicity of the synthesized compounds was recorded against cyclohexamide by MTT assay. Ag(I) metal complexes showed more significant biological activities as compared to ligands.                 KEY WORDS: Schiff bases, Metal complexes, Cytotoxicity, Antifungal, Antibacterial, Anti-inflammatory Bull. Chem. Soc. Ethiop. 2022, 36(1), 45-56.                                                                    DOI: https://dx.doi.org/10.4314/bcse.v36i1.5                                                       &nbsp

Process optimization for enhanced production of cellulases form locally isolated fungal strain by submerged fermentation

Cellulase has myriad applications in various sectors like pharmaceuticals, textile, detergents, animal feed and bioethanol production, etc. The current study focuses on the isolation, screening and optimization of fungal strain through one factor at a time technique for enhanced cellulase production.  In current study sixteen different fungal cultures were isolated and the culture which quantitatively exhibits higher titers of cellulase activity was identified both morphologically and molecularly by 18S rDNA and designated as Aspergillus niger ABT11. Different parameters like fermentation medium, volume, temperature, pH and nutritional components were optimized. The highest CMCase and FPase activities  was achieved in 100ml of M5 medium in the presence of 1% lactose and sodium nitrate at 30 oC, pH5 after 72 hours. The result revealed A. niger can be a potential candidate for scale up studies

Multi-Digit Handwritten Sindhi Numerals Recognition using SOM Neural Network

International audienceIn this research paper a multi-digit Sindhi handwritten numerals recognition system using SOM Neural Network is presented. Handwritten digits recognition is one of the challenging tasks and a lot of research is being carried out since many years. A remarkable work has been done for recognition of isolated handwritten characters as well as digits in many languages like English, Arabic, Devanagari, Chinese, Urdu and Pashto. However, the literature reviewed does not show any remarkable work done for Sindhi numerals recognition. The recognition of Sindhi digits is a difficult task due to the various writing styles and different font sizes. Therefore, SOM (Self-Organizing Map), a NN (Neural Network) method is used which can recognize digits with various writing styles and different font sizes. Only one sample is required to train the network for each pair of multi-digit numerals. A database consisting of 4000 samples of multi-digits consisting only two digits from 10-50 and other matching numerals have been collected by 50 users and the experimental results of proposed method show that an accuracy of 86.89% is achieved

Synthesis, spectroscopic characterization, crystal structure, interaction with DNA, CTAB as well as evaluation of biological potency, docking and Molecular Dynamics studies of N-(3,4,5-trimethoxybenzylidene)-2, 3-dimethylbenzenamine

A novel N-substituted Schiff base ligand: N-(3,4,5-trimethoxybenzylidene)-2,3-dimethylbenzenamine was designed and successfully characterized by several spectroscopic techniques. The formation of the desired compound was confirmed by the appearance of C[dbnd]N peak at 1691 cm−1 in FT-IR spectrum. Similarly, in 1H and 13C NMR spectra, the peaks at 8.22 ppm for azomethine proton (HC[dbnd]N) and 158.8 ppm for azomethine carbon (C[dbnd]N) confirm the formation of the synthesized compound. DNA interaction of the compound was screened by using UV–visible spectroscopy and viscometry measurements confirming an intercalation mode. The interaction of compound with CTAB (Cetyl trimethylammonium bromide) was also studied by conductometric method showing a strong interaction with CTAB. The IC50 value of the current compound was highly efficacious upon comparison with the standard Glucantime used. This activity represents a higher multitude interaction, which might be a cause of enhanced antileishmanial activity. Cytotoxicity results showed that this compound is highly active even at lower concentrations and is biocompatible, making it a promising drug candidate for further investigations in this field. The experimental data were auxiliary supported by molecular docking studies in order to explore their binding behavior and the stability of the molecule due to its interaction within the receptor active site

Deep Feature Fusion of Fingerprint and Online Signature for Multimodal Biometrics

The extensive research in the field of multimodal biometrics by the research community and the advent of modern technology has compelled the use of multimodal biometrics in real life applications. Biometric systems that are based on a single modality have many constraints like noise, less universality, intra class variations and spoof attacks. On the other hand, multimodal biometric systems are gaining greater attention because of their high accuracy, increased reliability and enhanced security. This research paper proposes and develops a Convolutional Neural Network (CNN) based model for the feature level fusion of fingerprint and online signature. Two types of feature level fusion schemes for the fingerprint and online signature have been implemented in this paper. The first scheme named early fusion combines the features of fingerprints and online signatures before the fully connected layers, while the second fusion scheme named late fusion combines the features after fully connected layers. To train and test the proposed model, a new multimodal dataset consisting of 1400 samples of fingerprints and 1400 samples of online signatures from 280 subjects was collected. To train the proposed model more effectively, the size of the training data was further increased using augmentation techniques. The experimental results show an accuracy of 99.10% achieved with early feature fusion scheme, while 98.35% was achieved with late feature fusion scheme

Assessment of musculoskeletal disorders among cricketers playing in domestic clubs of Lahore

Objective: To assess musculoskeletal disorders among male cricketers in an urban centre. Method: The cross-sectional study was conducted from October to November 2020 in Lahore, Pakistan, and comprised male cricket players aged 10-25 years playing in four domestic clubs. Data was collected about musculoskeletal disorders experienced during the preceding 12 months using the Extended Nordic Musculoskeletal Questionnaire. Data was analysed using SPSS 22. Results: Of the 89 players with a mean age of 19.24+3.12 years, 35(39.3%) were bowlers, 26(29.2%) were batsmen, 17(19.1%) were all-rounders, and 11(12.4%) were wicketkeepers. The anatomical distribution of disorder was lower-back 68(76.4%), shoulder 40(44.9%), neck 39(43.8%), upper-back 37(41.6%), knees 31(34.8%), ankle/feet 29(32.6%), thighs 27(30.3%), wrist/hands 18(20.2%), and elbows 17(19.1%). There were 22(24.7%) players who had at any time seen a doctor or a physiotherapist, while 24(27%) players had a history of taking sick leave. Conclusion: The most affected anatomical segments were lower-back, shoulder, knee, ankle and upper-back. Key Words: Musculoskeletal disorders, MSDs, Occupational health, Nordic musculoskeletal questionnaire extended version, NMQ-E