Recent publications
Clinical observations indicate a pronounced exacerbation of Cardiovascular Diseases (CVDs) in individuals grappling with Alcohol Use Disorder (AUD), suggesting an intricate interplay between these maladies. Pinpointing shared risk factors for both conditions has proven elusive. To address this, we pioneered a sophisticated bioinformatics framework and network-based strategy to unearth genes exhibiting aberrant expression patterns in both AUD and CVDs. In heart tissue samples from patients battling both AUD and CVDs, our study identified 76 Differentially Expressed Genes (DEGs) further used for retrieving important Gene Ontology (GO) keywords and metabolic pathways, highlighting mechanisms like proinflammatory cascades, T-cell cytotoxicity, antigen processing and presentation. By using Protein-Protein Interaction (PPI) analysis, we were able to identify key hub proteins that have a significant impact on the pathophysiology of these illnesses. Several hub proteins were identified include PTGS2, VCAM1, CCL2, CXCL8, IL7R, among these only CDH1 was covered in 10 algorithms of cytoHubba plugin. Furthermore, we pinpointed several Transcription Factors (TFs), including SOD2, CXCL8, THBS2, GREM1, CCL2, and PTGS2, alongside potential microRNAs (miRNAs) such as hsa-mir-203a-3p, hsa-mir-23a-3p, hsa-mir-98–5p, and hsa-mir-7–5p, which exert critical regulatory control over gene expression… In vitro study investigates the effect of alcohol on E-cadherin (CDH1) expression in HepG2 and Hep3B cells, showing a significant decrease in expression following ethanol treatment. These findings suggest that alcohol exposure may disrupt cell adhesion, potentially contributing to cellular changes associated with cardiovascular diseases. Our innovative approach has unveiled distinctive biomarkers delineating the dynamic interplay between AUD and various cardiovascular conditions for future therapeutic exploration.
The aim of the study is to examine the relevant factors that influence employee job satisfaction and employee performance. The moderating effect of employee loyalty, or whether there is a link between job satisfaction and employee loyalty, is also an aim of this research. This study combines exploratory and descriptive research designs, enabling in-depth description and analysis of study variables. A representative sample of 300 employees from the pharmaceutical companies operating in Bangladesh was used in the study. The survey included managers, executives, supervisors, and all other types of employees. Regression analysis, correlation analysis, and reliability testing are all done to validate our theory. The present study considers employee satisfaction and organizational performance as an independent variable, while employee loyalty acts as a moderating effect. Survey results have shown that greater employee loyalty can help keep employees satisfied. According to the analysis of the study, beta (β) value of compensation and reward, leadership, and empowerment and facilitating condition is 0.366, 0.220, and 0.170, respectively, which indicates strong positive relationship with job satisfaction. More importantly, the relationship between job satisfaction and employee performance has the highest beta value (0.554) indicating a significant positive connection between the variables. In addition, the results also indicate that loyalty contributes significantly as a mediator of employee satisfaction and organizational performance. Hence, the results indicate a favorable link with numerous aspects examined in the study. Dedicated employees generally feel satisfied working for the company and make valuable contributions to its success. Maintaining and retaining the loyalty of knowledgeable and experienced employees is a major aspect for any firm to maintain organizational performance.
Together, ortho (o)-phenyldiamine and para (p)-hydroxy benzaldehyde generate a Schiff base (SB). In order to make transition metal complexes of Ni2+, Cu2+, Co2+ and Cd2+ ions, this Schiff base (SB) was employed as a ligand of choice. The generated transition metal complexes' chemical structure is examined using a variety of physical methods, such as fundamental analysis, conductivity (molar), susceptibility (magnetic), spectroscopy (IR), and electronic spin spectroscopy. According to the elemental data analysis, a 1:2 [M:2L] complex of the formula, is produced ( M2+ = Ni2+, Cu2+, Co2+ and Cd2+ ions and L= Schiff base). All of the complexes were shown to be electrolytic in nature, as demonstrated by the molar conductance (conductivity) experiment. The 1H NMR and infrared (IR) spectral studies were utilized to fix the Schiff base binding sites that the transition metal ions are attached to. The anticipated coordination geometry and magnetic characteristics, such as the magnet with paramagnetic or diamagentic of the complexes were validated by the magnetic susceptibility tests and electronic spectral data. While the Cd2+ ion creates tetrahedral structure with low spin, the Ni2+and Cu2+, Co2+ ions yield high spin tetrahedra geometry. Comparing the obtained results with common antibiotics as kanamycin and ampicillin, the Cu2+ and Ni2+complexes showed high activity, while the Co2+ and Cd2+ complexes showed week and occasionally moderate antimicrobial activity. The complex compounds of Schiff base showed more activity towards gram position and gram negative bacteria as compared to its Schiff base. This idea can be improved upon with more adjustments and used in the pharmaceutical or medical industries.
Mechatronics engineering is a creative and dynamic field that focuses on cutting-edge technology for a variety of applications including high-speed manufacturing systems used in many contemporary companies. Modern manufacturing incorporates sophisticated technology and cutting-edge software, tools, and goods that improve quality of life utilizing intelligent sensors and parameter controllers. Mechatronics has enhanced efficiency and the quality of the products while decreasing technical faults in smart autonomous manufacturing. A detailed overview of the recent mechatronics research with applications in manufacturing, agriculture, industrial automation, robotics, biomedical and assistive technology, human–machine interface, unmanned vehicles, energy, aerospace, and transportation is provided in this chapter. Production has significantly increased as a result of Industry 4.0, digitization, and artificial intelligence integration in mechatronics. A significant factor in the change of the automobile industry has been the development of mechatronics in electric and driverless vehicles. Numerous research and development initiatives have the potential to advance the use of mechatronics in line with forthcoming findings. Technology based on mechatronics focuses on practically all facets of intelligent industrial techniques and is useful for every area of a high-quality way of life for almost everyone.
Loom industry in Bangladesh is the foremost imperative & antiquated house industry with a decentralized setup. The industry is giving vocation for millions of individuals within the nation. The target populace includes weavers over northern Bangladesh. The ponder secured a sizable number of population i.e, 208 subjects in Enayetpur and Madhabpur village, Sirajganj. To ensure representativeness, a non-probability convenience technique was utilized to choose a test estimate of 208 members. To be qualified for the study, members were chosen who had minimum two years of experience. Male were predominant (96.2%) among the respondents. Monthly income showed that major part (48.1%) of the respondents fell in 10-15thousand BDT category. Majority (63.5%) of waste disposal strategy taken after by the wok-places was landfilled. What's really interesting is that many respondents reported having musculoskeletal problems; lower back pain (59.6%) was the most frequently reported one. These health issues were connected to weaving activities because they involve lots of repetitive movements with hands, shoulders, legs, and other parts. Most health concerns in this field came from ergonomic risk factors. Future research should aim to include diverse populations and explore interventions that can mitigate these ergonomic risks, ultimately improving the well-being of this vital workforce in Bangladesh's loom industry. Keywords: Bangladesh, Sirajganj, musculoskeletal, health, loom, weavers
The primary aim of this study is to identify the most relevant financial inclusion variables influencing gross domestic product (GDP) and to choose the optimal multiple linear regression model to quantify their contribution to economic growth. Finance is essential for every economic activity and financial inclusion is closely related to societal empowerment. It is observed in different nations that financial inclusion variables have a valuable impact on economic growth. For this, secondary data were gathered for this study from the IMF (International Monetary Fund) from 2011 to 2020. Graphical presentations were used to visualize the relationship between financial inclusion variables and economic growth. The most eligible variables and the best model among a collection of models based on empirical data were chosen using a stepwise backward elimination technique using multiple linear regression and model selection criteria. From the graph, it is seen that financial inclusion variables are proportionately related to GDP and the results demonstrate that the most eligible variables of financial inclusion that affect GDP are (1) the number of registered mobile money agent outlets, (2) the number of active mobile money accounts, (3) the number of mobile money transactions, and (4) outstanding balances on active mobile money accounts. These variables have a positive impact on economic growth, and the fitted model measures the more significant contribution to economic growth. As a result, it may be summarized that financial enclosure has to be enhanced for the development of rural and border areas of Bangladesh. This study will assist policymakers, development organizations, and scholars and make knowledgeable findings that will guide long-term improvements to minimize the gap between rich and poor, leading to the improvement of the community's wealth and welfare.
Pseudomonas aeruginosa is a complex nosocomial infectious agent responsible for numerous illnesses, with its growing resistance variations complicating treatment development. Studies have emphasized the importance of virulence factors OprE and OprF in pathogenesis, highlighting their potential as vaccine candidates. In this study, B-cell, MHC-I, and MHC-II epitopes were identified, and molecular linkers were active to join these epitopes with an appropriate adjuvant to construct a vaccine. Computational tools were employed to forecast the tertiary framework, characteristics, and also to confirm the vaccine’s composition. The potency was weighed through population coverage analysis and immune simulation. This project aims to create a multi-epitope vaccine to reduce P. aeruginosa–related illness and mortality using immunoinformatics resources. The ultimate complex has been determined to be stable, soluble, antigenic, and non-allergenic upon inspection of its physicochemical and immunological properties. Additionally, the protein exhibited acidic and hydrophilic characteristics. The Ramachandran plot, ProSA-web, ERRAT, and Verify3D were employed to ensure the final model’s authenticity once the protein’s three-dimensional structure had been established and refined. The vaccine model showed a significant binding score and stability when interacting with MHC receptors. Population coverage analysis indicated a global coverage rate of 83.40%, with the USA having the highest coverage rate, exceeding 90%. Moreover, the vaccine sequence underwent codon optimization before being cloned into the Escherichia coli plasmid vector pET-28a (+) at the EcoRI and EcoRV restriction sites. Our research has developed a vaccine against P. aeruginosa that has strong binding affinity and worldwide coverage, offering an acceptable way to mitigate nosocomial infections.
Email serves as the primary mode of communication in today’s interconnected digital world, encompassing business, education, and interpersonal relationships. However, email’s reliance on shared media makes it susceptible to interception and misuse of confidential data. Pretty Good Privacy (PGP) protects the privacy of email contents to address this problem. While PGP offers encryption, its key sharing has weaknesses. Blockchain technology is characterized by its immutability feature. Once information is stored in the blockchain, altering it becomes extremely difficult. This characteristic serves as a valuable defense against weaknesses in the PGP key sharing system. Furthermore, the implementation of smart contracts eliminates the need for a Man-in-the-Middle when sharing keys, thereby improving the security of key sharing and fostering trust among individuals. Blockchain and smart contracts improve security, but privacy remains a concern. To further bolster privacy protection, in this paper we propose the integration of Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) and blockchain into PGP key sharing mechanism. zk-SNARKs enable efficient verification of encrypted data without revealing sensitive information, thus preventing exposure of user privacy. Additionally, we employ Elliptic Curve Cryptography (ECC) in order to guarantee the confidentiality of the PGP key. Through this holistic integration, the security of the PGP key is enhanced, ensuring both confidentiality and integrity while safeguarding user privacy. Furthermore, gas consumption and transaction costs were evaluated with and without zk-SNARKs. The results demonstrate that the proposed mechanism minimizes gas consumption and transaction costs.
Cancer is one of the leading causes of mortality worldwide. Despite the advancement of cancer treatment by various means including surgery, chemotherapy, etc., cancer is still a challenging disease to manage. This study was undertaken to investigate extraction, purification, structural elucidation, and the potential anti‐cancer effects of Pleurotus ostreatus polysaccharide (POP). The anti‐cancer activities were performed on the Ehrlich Ascites Carcinoma Cell Line. The results demonstrated that the MW of POP was154649.8 Da with homopolysaccharide composed of D‐glucose units, featuring (1→6)‐α‐D‐Glcp backbone with O‐6 branches and T‐α‐D‐Glcp terminations. and the yield was 6.27%. was 6.27%, The antitumor activity assessment demonstrated significant cytotoxicity of POP against Ehrlich Ascites Carcinoma (EAC) cells, with an IC50 of 121.801 µg/mL, supported by LDH release analysis. POP inhibited cell migration, invasion, and colony formation, indicating its potential as an anti‐cancer agent. POP elicited the apoptotic activity with the upregulation of Caspase‐9 and Bax, and downregulation of Bcl‐2. The DNA fragmentation assay further confirmed apoptosis‐mediated DNA degradations. Additionally, POP‐induced cell cycle arrest at the G0/G1 phase, by altering the expression of p53, Cyclin D, and Cdk4 proteins. So, Pleurotus ostreatus polysaccharide (POP) showed significant cytotoxicity on Ehrlich Ascites Carcinoma cells, indicating potential as an anti‐cancer agent.
Background and Aims
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by a wide range of symptoms and challenges. While ASD is primarily associated with atypical social and communicative behaviors, increasing research has pointed towards the involvement of various brain regions, including the cerebellum. This review article aims to provide a comprehensive overview of the role of cerebellar lobules in ASD, highlighting recent findings and potential therapeutic implications.
Methods
Using published articles found in PubMed, Scopus, and Google Scholar, we extracted pertinent data to complete this review work. We have searched for terms including anatomical insights, neuroimaging, neurobiological, and autism spectrum disorder.
Results
The intricate relationship between the cerebellum and other brain regions linked to ASD has been highlighted by neurobiological research, which has shown abnormalities in neurotransmitter systems and cerebellar circuitry. The relevance of the cerebellum in the pathophysiology of ASD has been further highlighted by anatomical studies that have revealed evidence of cerebellar abnormalities, including changes in volume, morphology, and connectivity.
Conclusion
Thorough knowledge of the cerebellum's function in ASD may lead to new understandings of the underlying mechanisms of the condition and make it easier to create interventions and treatments that are more specifically targeted at treating cerebellar dysfunction in ASD patients.
This research investigates the Circle of Willis, a critical vascular structure vital for cerebral blood supply. A modified novel dual‐pathway multi‐scale hierarchical upsampling network (HUNet) is presented, tailored explicitly for accurate segmentation of Circle of Willis anatomical components from medical imaging data. Evaluating both the multi‐label magnetic resonance angiography region of interest and the multi‐label magnetic resonance angiography whole brain‐case datasets, HUNet consistently outperforms the convolutional U‐net model, demonstrating superior capabilities and achieving higher accuracy across various classes. Additionally, the HUNet model achieves an exceptional dice similarity coefficient of 98.61 and 97.95, along with intersection over union scores of 73.32 and 85.76 in both the multi‐label magnetic resonance angiography region of interest and the multi‐label magnetic resonance angiography whole brain‐case datasets, respectively. These metrics highlight HUNet's exceptional performance in achieving precise and accurate segmentation of anatomical structures within the Circle of Willis, underscoring its robustness in medical image segmentation tasks. Visual representations substantiate HUNet's efficacy in delineating Circle of Willis structures, offering comprehensive insights into its superior performance.
Pests are a significant challenge in paddy cultivation, resulting in a global loss of approximately 20% of rice yield. Early detection of paddy insects can help to save these potential losses. Several ways have been suggested for identifying and categorizing insects in paddy fields, employing a range of advanced, noninvasive, and portable technologies. However, none of these systems have successfully incorporated feature optimization techniques with Deep Learning and Machine Learning. Hence, the current research provided a framework utilizing these techniques to detect and categorize photos of paddy insects promptly. Initially, the suggested research will gather the image dataset and categorize it into two groups: one without paddy insects and the other with paddy insects. Furthermore, various pre-processing techniques, such as augmentation and picture filtering, will be applied to enhance the quality of the dataset and eliminate any unwanted noise. To determine and analyze the deep characteristics of an image, the suggested architecture will incorporate 5 pre-trained Convolutional Neural Network models. Following that, feature selection techniques, including Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Linear Discriminant Analysis (LDA), and an optimization tool called Lion Optimization, were utilized in order to further reduce the redundant number of features that were collected for the study. Subsequently, the process of identifying the paddy insects will be carried out by employing 7 ML algorithms. Finally, a set of experimental data analyses has been conducted to achieve the objectives, and the proposed approach demonstrates that the Extracted Feature Vectors of ResNet50 with Logistic Regression and PCA have achieved the highest accuracy, precisely 99.28%. However, the present idea will significantly impact how paddy insects are diagnosed in the field.
Automatic classification of colon and lung cancer images is crucial for early detection and accurate diagnostics. However, there is room for improvement to enhance accuracy, ensuring better diagnostic precision. This study introduces two novel dense architectures (D1 and D2) and emphasizes their effectiveness in classifying colon and lung cancer from diverse images. It also highlights their resilience, efficiency, and superior performance across multiple datasets. These architectures were tested on various types of datasets, including NCT-CRC-HE-100K (set of 100,000 non-overlapping image patches from hematoxylin and eosin (H&E) stained histological images of human colorectal cancer (CRC) and normal tissue), CRC-VAL-HE-7K (set of 7180 image patches from N = 50 patients with colorectal adenocarcinoma, no overlap with patients in NCT-CRC-HE-100K), LC25000 (Lung and Colon Cancer Histopathological Image), and IQ-OTHNCCD (Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases), showcasing their effectiveness in classifying colon and lung cancers from histopathological and Computed Tomography (CT) scan images. This underscores the multi-modal image classification capability of the proposed models. Moreover, the study addresses imbalanced datasets, particularly in CRC-VAL-HE-7K and IQ-OTHNCCD, with a specific focus on model resilience and robustness. To assess overall performance, the study conducted experiments in different scenarios. The D1 model achieved an impressive 99.80 % accuracy on the NCT-CRC-HE-100K dataset, with a Jaccard Index (J) of 0.8371, a Matthew's Correlation Coefficient (MCC) of 0.9073, a Cohen's Kappa (Kp) of 0.9057, and a Critical Success Index (CSI) of 0.8213. When subjected to 10-fold cross-validation on LC25000, the D1 model averaged (avg) 99.96 % accuracy (avg J, MCC, Kp, and CSI of 0.9993, 0.9987, 0.9853, and 0.9990), surpassing recent reported performances. Furthermore, the ensemble of D1 and D2 reached 93 % accuracy (J, MCC, Kp, and CSI of 0.7556, 0.8839, 0.8796, and 0.7140) on the IQ-OTHNCCD dataset, exceeding recent benchmarks and aligning with other reported results. Efficiency evaluations were conducted in various scenarios. For instance, training on only 10 % of LC25000 resulted in high accuracy rates of 99.19 % (J, MCC, Kp, and CSI of 0.9840, 0.9898, 0.9898, and 0.9837) (D1) and 99.30 % (J, MCC, Kp, and CSI of 0.9863, 0.9913, 0.9913, and 0.9861) (D2). In NCT-CRC-HE-100K, D2 achieved an impressive 99.53 % accuracy (J, MCC, Kp, and CSI of 0.9906, 0.9946, 0.9946, and 0.9906) with training on only 30 % of the dataset and testing on the remaining 70 %. When tested on CRC-VAL-HE-7K, D1 and D2 achieved 95 % accuracy (J, MCC, Kp, and CSI of 0.8845, 0.9455, 0.9452, and 0.8745) and 96 % accuracy (J, MCC, Kp, and CSI of 0.8926, 0.9504, 0.9503, and 0.8798), respectively, outperforming previously reported results and aligning closely with others. Lastly, training D2 on just 10 % of NCT-CRC-HE-100K and testing on CRC-VAL-HE-7K resulted in significant outperformance of InceptionV3, Xception, and DenseNet201 benchmarks, achieving an accuracy rate of 82.98 % (J, MCC, Kp, and CSI of 0.7227, 0.8095, 0.8081, and 0.6671). Finally, using explainable AI algorithms such as Grad-CAM, Grad-CAM++, Score-CAM, and Faster Score-CAM, along with their emphasized versions, we visualized the features from the last layer of DenseNet201 for histopathological as well as CT-scan image samples. The proposed dense models, with their multi-modality, robustness, and efficiency in cancer image classification, hold the promise of significant advancements in medical diagnostics. They have the potential to revolutionize early cancer detection and improve healthcare accessibility worldwide.
Objective: Academic resilience, a critical determinant of academic achievement, is affected by various factors. There is a paucity of large-scale international assessments of academic resilience among pharmacy students. Therefore, this study aimed to assess academic resilience among pharmacy students in 12 countries and to evaluate factors associated with their academic resilience levels.
Methods: A cross-sectional online survey-based study was conducted among randomly selected pharmacy students in 12 countries: Egypt, Türkiye, Indonesia, Pakistan, Bangladesh, Iraq, Jordan, Nigeria, Malaysia, Saudi Arabia, Sudan, and the United Arab Emirates. After pilot testing, the validated 30-item academic resilience scale (ARS) was used for the assessment. The data were collected between November 1, 2022 and April 15, 2023. Descriptive and inferential statistics were performed, as appropriate.
Results: A total of 3950 were received from the 12 participating countries. The mean age was 21.68 ± 2.62 years. About two-thirds of the responses were from female participants and those studying for Bachelor of Pharmacy degrees. Overall, the findings show moderate academic resilience, which varied across countries. The median (IQR) of the total ARS-30 was 114 (103-124). Females exhibited lower negative affective and emotional response subscale levels than males. There were significant cross-country variations in the ARS-30 and all subscales. The highest overall levels were reported for Sudan, Pakistan, and Nigeria and the lowest were reported for Indonesia and Türkiye. Students in private universities tended to have higher overall ARS levels than public university students. Higher academic performance was significantly associated with ARS levels, whereas those with excellent performance exhibited the highest ARS levels. Students with exercise routines had higher ARS levels than those without exercise routines. Finally, students who were engaged in extracurricular activities had higher ARS levels than those who did not participate in these activities.
Conclusion: The study offers insights into the factors affecting academic resilience in pharmacy students across several countries. The findings could guide interventions and support activities to improve resilience and academic outcomes.
According to the latest mental health survey, the prevalence of mental illness among children and adolescents is 12.6% in Bangladesh. Though in recent times child and adolescent mental health services have undergone significant development in our country still it is not sufficient to meet the needs of the population. Child and adolescent mental health resources both in manpower and facilities are extremely scarce and maldistributed. Additionally, there is a lack of standard training and research in this field. Lack of awareness, high level of stigma and unfavorable beliefs, attitudes and behavior make the condition more complicated. Affordable, culturally suitable, local resources-based services can help to combat the condition. This chapter discusses the epidemiology of child psychiatric disorders, history, current status and challenges of child and adolescent mental health services in Bangladesh.
A BSTRACT
Background
Psychological distress may worsen during cancer treatment and affect well-being. Information on the prevalence of distress and its associated variables in cancer patients undergoing chemotherapy in rural Bangladesh has not been thoroughly explored. To address this, we aimed to assess psychological distress and its associated factors in patients with cancer undergoing chemotherapy.
Methods
This cross-sectional study was conducted at a tertiary care hospital in rural Bangladesh. Only adult patients with cancer who were receiving chemotherapy were enrolled in this study. The validated Depression Anxiety Stress Scale was used to assess psychological distress. Frequency and percentages were used in descriptive analysis, and logistic regression analysis was performed to investigate potential associated factors for depression, anxiety, and stress.
Results
Participants comprised 415 patients with a mean age of 46.3 years. The prevalence of depression, anxiety, and stress was 61.5%, 55.4%, and 22.0%, respectively. In the multivariate logistic regression analysis, patients with more than five family members and smokeless tobacco users had a significant association with depression, anxiety, and stress. In contrast, participants aged >60 years had a protective association with depression.
Conclusions
Our findings show that patients with cancer receiving chemotherapy experience a high prevalence of depression and anxiety and that the use of smokeless tobacco and having six or more family members are associated with psychological distress. These findings will aid health professionals and policymakers in establishing and implementing improved care programs to ensure the greater mental health of cancer survivors, particularly in resource-limited settings.
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