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Ransomware attacks are becoming increasingly sophisticated, thereby rendering conventional detection methods less effective. Recognizing this challenge, this study reviews advanced detection mechanisms and explores the potential of artificial intelligence (AI) techniques to improve detection capabilities. This study reviews the recent literature, including journal articles, conference proceedings, and online resources since 2017, to offer insights into the current state of AI-based ransomware detection and suggests future research directions. This study contributes significantly to the development of a systematic evaluation framework that evaluates each component of the AI-based detection model framework using specific criteria and methodologies and analyzes how various AI algorithms respond to different ransomware attacks, thereby providing insights for more effective and robust detection methods. This review begins with an overview of AI and ransomware, and discusses various types of ransomware attacks, the process of an attack chain, and emerging trends. We then review the existing literature on the core components of AI-based ransomware detection models, including the datasets and challenges arising during data collection, data pre-processing, feature engineering techniques, model training, and performance evaluation for effective model training. This study assessed the detection performance of AI models using metrics such as accuracy, precision, recall, and F1-score. By synthesizing these findings, we identify gaps in the current research and suggest future directions for enhancing AI-based ransomware detection techniques. The insights provided aim to guide researchers and practitioners in developing more robust methods for detecting and mitigating ransomware attacks by using AI.

Federated learning (FL) is a distributed machine learning technique that enables multiple decentralized clients to develop a model collaboratively without exchanging their local data. Heightened privacy risks and the recent strict privacy laws make it even more precarious for the gathering and integration of data in a centralized location for full utilization. Federated learning is compatible with established privacy laws like General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), Health Insurance Portability and Accountability Act (HIPAA), and China’s Cybersecurity Law. Further, there are very few scenarios where centralized, properly labeled, and complete data are available. Federated learning provides a way to solve this problem. As a result, much research has been conducted in several areas within the emerging field of FL. This review paper focuses on decision tree-based FL systems due to their desirable properties of interpretability, parallelism, and high performance. We take a closer look at the motivations, design considerations, tree building algorithms, and security mechanisms used for these systems. We also present the various datasets used in these systems, demonstrated application areas, and the evidence of their benefits. The objective of this paper is to provide an informative overview about the characteristics of FL, privacy and security mechanisms used in them, available open source development frameworks for FL, and the decision tree-based systems developed in FL for researchers in academia and system architects in the industry.

Sleep apnea (SA) is one of the most prevalent sleep-related problems, impacting more than 100 million people worldwide. A full-night Polysomnography (PSG) is an effective SA diagnosis strategy. However, it requires multiple wearable devices and the patient staying overnight to collect the findings, rendering it both a time-consuming and costly option. Research attempts to develop non-invasive, sensor-based, or automated solutions for diagnosing SA are also made in recent years. In this study, we analyzed a total of 85 papers, shortlisted from an initial collection of 954 articles published in reputable scientific repositories, e.g., IEEE Xplore, PubMed Central (PMC), Springer, Elsevier etc., where each chosen study was thoroughly examined to determine its contribution and performance. A detailed analysis of data preprocessing, feature extraction and classification algorithm is also addressed. It is found that most of the studies are based on signal analysis for identifying sleep apnea, which yields results with substantial reliability, while contemporary research emphases have been on heart rate variability and pulse oximetry outcomes.

Decolonising the curriculum (DtC) is an emerging concept that continues to challenge higher education institutions globally, with educators and scholars increasingly recognising how traditional curricular structures, content, and pedagogical practices can reinforce colonial biases and marginalise certain student groups. DtC can be seen as a process rather than a phenomenon, one which is ongoing and multiplexed, consisting of elements that aim to dismantle the hierarchy between staff and students and question the ways in which knowledge is produced, valued, and disseminated. The expected outcome is to provide students with an education which is equitable, inclusive, and relevant to their personal lives and experiences. Efforts in DtC have continued to gain traction, but assessment practices remain overlooked as a crucial component, with most decolonising efforts focussed towards content and pedagogy. As such, this review aims to contribute to the conversation by exploring the roots and impacts of colonisation on higher education, particularly within the context of assessment in STEM subjects. We examine various elements of decolonised assessment methods and ultimately suggest project-based learning (PBL) as a comprehensive approach that brings together the aforementioned decolonial elements, illustrating this concept through the examination of a PBL assessment mode study conducted at the University of Sussex.

Project-based learning (PBL) is a widely used active learning approach in science, technology, engineering and mathematics (STEM) education, but concerns about unequal participation in group work can arise. To address this, we incorporated peer evaluation via the Buddycheck tool into a summative PBL assessment. In this study, students worked in small groups to create and present posters, with Buddycheck enabling them to evaluate each other’s contributions. This peer evaluation process generated an adjustment factor, which was applied to the final group mark to assign individual grades based on performance. Students reported that the peer evaluation promoted fairness in grading and increased their accountability, leading to higher engagement in group activities. They also found the exercise valuable for consolidating their knowledge and improving communication skills. Overall, the use of Buddycheck in PBL was well received, with students expressing greater satisfaction and a sense of equity in the grading process. The tool not only improved learning outcomes but also contributed to better group dynamics and an overall improvement in final grades.

Sustainable development through technical progress for middle-income countries (MICs) is overlooked in growth allied studies. Despite their crucial role in alleviating poverty and resource shortages, MICs encounter challenges in global economic competition, driving persistent efforts to find practical solutions. Therefore, this study explores the integrated impact of R&D expenditure and remittances on economic growth in MICs. Using data from 25 MICs between 1996 and 2021, this study employs the “2nd generation unit root” and “panel autoregressive distributed lag (ARDL)” methods. The “feasible generalized least square (FGLS)” techniques and the “Dumitrescu-Hurlin (D-H)” causality test are employed to verify the robustness of the panel ARDL estimation. The Westerlund cointegration tests confirm a long-term cointegration between variables. The findings of the panel ARDL approach show that R&D expenditure and remittances positively and significantly influence economic growth. The robustness of the panel ARDL results is confirmed by the FGLS estimation, which produces similar outcomes. The outcomes from the FGLS and the ARDL methods are additionally validated by the D-H causality check. Therefore, encouraging R&D and remittances is crucial to accelerate middle-income nations' economic growth. The study reveals a novel mechanism of R&D expenditures, remittances, and economic growth in MICs, shaping their mutual influence on this economic landscape. The study supports middle-income countries' policymakers in creating effective policies for their financial institutions regarding R&D expenditure and remittances.

Enzymes are biological molecules that act as catalysts and speed up the biochemical reactions. The world's biotechnological ventures are development of enzyme productiveness, and advancement of novel techniques for thriving their shelf existence. Nowadays, the most burning questions in enzyme technology are how to improve the enzyme productivity and reuse them. The immobilization of enzymes provides an excellent scope to reuse the enzymes several times to increase productivity. The main aim of the present study is the establishment of an immobilized multi-enzyme bio-system engineering process for the production of High-fructose corn syrup (HFCS) with an industrial focus. In this study, multi-enzyme such as α-amylase, glucoamylase and glucose isomerase were immobilized in various support matrices like sodium alginate, sawdust, sugarcane bagasse, rice bran and combination of alginate with cellulosic materials. The activities of the immobilized multi-enzyme system for the production of HFCS from the starch solution were determined. The multi-enzyme immobilized in sodium alginate shows better fructose conversion than free enzyme. Among the support matrices, multi-enzyme immobilized in sawdust produced total 80.74 mg/mL of fructose from starch solution and it was able to be used in several production cycles. On the other hand, multi-enzyme immobilized in combination of sodium alginate and sawdust produced the maximum amount of fructose (total 84.82 mg/mL). The free enzyme produced 17.25 mg/mL of fructose from the starch solution in only a single cycle. In this study a new fixed bed immobilized multi-enzyme bioreactor system was developed for the production of HFCS directly from starch. This finding will create a new opportunity for the application of immobilized multi-enzyme systems in many sectors of industrial biotechnology.

Current cardiac biomarkers, troponins and brain natriuretic peptide, are primarily used to assist in the diagnosis or exclusion of myocardial damage and congestive heart failure, respectively. The use of these biomarkers in chemotherapy-induced cardiotoxicity has been evaluated by various studies. However, neither biomarker provides early predictive value, leaving many cancer survivors with irreversible cardiac injury. Assessing endothelial dysfunction could be an effective measure of chemotherapy-induced cardiotoxicity at the vascular level. Risk profiling and detection of vascular toxicities may offer predictive biomarkers to prevent chronic manifestation of irreversible cardiotoxicities. Emerging interest has developed in finding biomarkers that could ideally provide earlier prognostic value. Thus, the aim of this review is to give an overview of current blood-based cardiac biomarkers and discuss the potential of endothelin-1 (ET-1) and more stable peptide fragments of ET-1 synthesis as biomarkers of endothelial dysfunction. For instance, endothelin-like domain peptide (ELDP) and C-terminal pro-endothelin-1 (CT-proET-1) demonstrated high-sensitivity and longer clearance rate than ET-1. Thus, investigating their biomarker role in chemotherapy-induced cardiotoxicity is important and could provide additional insights for identifying patients at risk. Also, additional research is required to fully understand ELDP-mediated vasoconstriction. This review will discuss the future development of ET-1, ELDP and CT-proET-1 as prospective predictive biomarkers.

Chronic diseases within Indigenous communities constitute the most compelling ill-health burdens and treatment inequalities, particularly in rural and remote Australia. In response to these vital issues, a systematic literature review of the adoption of wearable, Artificial Intelligence-driven, electrocardiogram sensors, in a telehealth Internet of Medical Things (IoMT) context was conducted to scale up rural Indigenous health. To this end, four preselected scientific databases were chosen for data extraction to align with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique. From the initially collected (n=4436) articles, a total of 32 articles were analysed, being synthesised from the review inclusion criteria, maintaining strict eligibility and eliminating duplicates. None of the various studies found on this innovative healthcare intervention has given a comprehensive picture of how this could be an effective method of care dedicated to rural Indigenous communities with cardiovascular diseases (CVDs). Herein, we presented the unique concepts of IoMT-driven wearable biosensors tailored for rural indigenous cardiac patients, their clinical implications, and cardiovascular disease management within the telehealth domain. This work contributes to understanding the adoption of wearable IoMT sensor-driven telehealth model, highlighting the need for real-time data from First Nations patients in rural and remote areas for CVD prevention. Pertinent implications, research impacts, limitations and future research directions are endorsed, securing long-term Wearable IoMT sensor-driven telehealth sustainability.

The ability of a cryptographic system to protect information from attacks depends on many factors, including the secrecy of the encryption key. A crucial aspect of any cryptosystem is how it manages the encryption keys. Encryption Key Management (EKM) spans the entire life cycle of the key, including the key’s generation, usage, distribution, renewal, and destruction. Given the security sensitivity, it is desirable to adopt a widely accepted standard when developing an encryption key management system. Through rigorous development of security requirements and following standardized validation, evaluation, and certification, the consumers’ confidence in the security of the EKM system will be enhanced. The Protection Profile (PP), defined in the Common Criteria for Information Technology Security Evaluation (often referred to as Common Criteria or CC), specifies the security functional and assurance requirements for a specific technology. In this work, we propose a PP Module that is the new evolution of the PP covering trusted security features for EKM, which is based on its compliance with the Network Device collaborative Protection Profile (NDcPP). In particular, by analyzing threats and vulnerabilities of EKM systems, corresponding security objectives and requirements are proposed in the PP, along with the specification of evaluation activities. The quantum-safe aspect of key distribution protocols is further investigated to support EKM products with quantum-resistant algorithms and quantum key distribution features. In addition to presenting the development methodology and implementation process for the PP Module of EKM, we distill lessons learned from developing and validating the PP Module to inspire future research efforts on defining security requirements with the CC.

Weft knit fabrics are popular for traditional wear because of their elastic and light structures, gentle smoothness, low production costs, and high productivity. However, due to asymmetrical loop formation, spirality causes serious dimensional problems in single jersey fabrics (SJF). This study investigates the effects of various parameters on spirality, including yarn count, yarn tension, tightness factor, stitch length, fabric weight per unit area, machine speed, needle gauge, and number of feeders. All the sample fabrics were knitted with Z-twisted, combed, 100% cotton yarns and went through a scouring, bleaching, and whitening process before being dried in the same conditions. Fabric spirality was found to increase as stitch length, number of feeders, yarn count, and twists per unit length in constituent yarn increased whereas the tightness factor and needle gauge, on the other hand, had opposite effects on spirality. The test results were analyzed statistically, and a significant impact was discovered.

The COVID-19 coronavirus, which primarily affects the lungs, is the source of the disease known as SARS-CoV-2. According to “Smoking and COVID-19: a scoping review,” about 32% of smokers had a severe case of COVID-19 pneumonia at their admission time and 15% of non-smokers had this case of COVID-19 pneumonia. We were able to determine which genes were expressed differently in each group by comparing the expression of gene transcriptomic datasets of COVID-19 patients, smokers, and healthy controls. In all, 37 dysregulated genes are common in COVID-19 patients and smokers, according to our analysis. We have applied all important methods namely protein-protein interaction, hub-protein interaction, drug-protein interaction, tf-gene interaction, and gene-MiRNA interaction of bioinformatics to analyze to understand deeply the connection between both smoking and COVID-19 severity. We have also analyzed Pathways and Gene Ontology where 5 significant signaling pathways were validated with previous literature. Also, we verified 7 hub-proteins, and finally, we validated a total of 7 drugs with the previous study.

Endothelin-1 (ET-1) plays a physiological role as a potent vasoconstrictor. It is implicated in an array of diseases, and its signalling is often found to be overactivated within cancers. ET-1 has been found to potentiate hallmarks of cancer progression such as cell proliferation, invasion and metastasis, as well as angiogenesis. ET-1 has also been implicated in inducing the epithelial–mesenchymal transition (EMT) and promoting resistance to anticancer drugs. Many preclinical efforts have been made to target ET-1 expression within cancer, such as by using ET-1 receptor antagonists, many of which have been approved for treating pulmonary hypertension. Targeting ET-1 has been shown to improve the response to various other cancer therapeutics, highlighting the potential benefits targeting this peptide may exert. Drug repurposing is an attractive strategy, and exploration of this avenue may be promising for targeting ET-1 in cancer. There are many clinical trials which have been completed and are currently undergoing involving the repurposing of ET-1 receptor antagonists for cancer treatment. In this review, the pathways through which ET-1 potentiates cancer will be discussed, as well as where the opportunity for therapeutic intervention lies in relation to cancer.

Abstract Background and Aims Cervical cancer is characterized by abnormal cell growth in the lining of cervix and it is the second major cause of cancer‐related deaths among females in Bangladesh. Interleukin‐6 (IL‐6) is a multifunctional cytokine that has been heavily linked with cervical cancer. Our aim was to investigate the association of two promoter single‐nucleotide polymorphisms (SNPs) of IL‐6 (rs1800795 and rs1800797) with the susceptibility of cervical cancer in Bangladeshi women. Methods DNA was extracted from venous blood samples from cervical cancer patients (n = 126) and healthy controls (n = 120). Polymerase chain reaction‐restriction fragment length polymorphism was used for genotyping of the selected SNPs. Logistic regression was performed to calculate the odds ratio (OR) with 95% confidence interval (CI) and p values. Results We found a significant association between rs1800795 and rs1800797 polymorphisms and cervical cancer. For, rs1800795 (G > C) the GC heterozygous genotype (OR = 2.80, 95% CI = 1.55–5.07, p = 0.0007) and CC mutant homozygous genotype (OR = 3.5, 95% CI = 1.29–9.51, p = 0.014) conferred an increased risk of cervical cancer. In case of rs1800797 (G > A) polymorphism, the AG heterozygous genotype (OR = 6.94, 95% CI = 3.76–12.81, p

Breast cancer is the second most prevalent malignancy affecting women. Postmenopausal women breast tumor is one of the top causes of death in women, accounting for 23% of cancer cases. Type 2 diabetes, a worldwide pandemic, has been connected to a heightened risk of several malignancies, although its association with breast cancer is still uncertain. In comparison to non-diabetic women, women with T2DM had a 23% elevated likelihood of developing breast cancer. It is difficult to determine causative or genetic susceptibility that connect T2DM and breast cancer. We created a large-scale network-based quantitative approach employing unbiased methods to discover abnormally amplified genes in both T2DM and breast cancer, to solve these issues. We performed transcriptome analysis to uncover identical genetic biomarkers and pathways to clarify the connection between T2DM and breast cancer patients. In this study, two RNA-seq datasets (GSE103001 and GSE86468) from the Gene Expression Omnibus (GEO) are used to identify mutually differentially expressed genes (DEGs) for breast cancer and T2DM, as well as common pathways and prospective medicines. Firstly, 45 shared genes (30 upregulated and 15 downregulated) between T2D and breast cancer were detected. We employed gene ontology and pathway enrichment to characterize prevalent DEGs' molecular processes and signal transduction pathways and observed that T2DM has certain connections to the progression of breast cancer. Using several computational and statistical approaches, we created a protein-protein interactions (PPI) network and revealed hub genes. These hub genes can be potential biomarkers, which may also lead to new therapeutic strategies for investigated diseases. We conducted TF-gene interactions, gene-microRNA interactions, protein-drug interactions, and gene-disease associations to find potential connections between T2DM and breast cancer pathologies. We assume that the potential drugs that emerged from this study could be useful therapeutic values. Researchers, doctors, biotechnologists, and many others may benefit from this research.

Precision agriculture represents the new age of conventional agriculture. This is made possible by the advancement of various modern technologies such as the internet of things. The unparalleled potential for data collection and analytics has resulted in an increase in multi-disciplinary research within machine learning and agriculture. However, the application of machine learning techniques to agriculture seems to be out of step with core machine learning research. This gap is further exacerbated by the inherent challenges associated with agricultural data. In this work, we conduct a systematic review of a large body of academic literature published between 2000 and 2022, on the application of machine learning techniques to agriculture. We identify and discuss some of the key data issues such as class imbalance, data sparsity and high dimensionality. Further, we study the impact of these data issues on various machine learning approaches within the context of agriculture. Finally, we identify some of the common pitfalls in the machine learning and agriculture research including the misapplication of machine learning evaluation techniques. To this end, this survey presents a holistic view on the state of affairs in the cross-domain of machine learning and agriculture and proposes some suitable mitigation strategies to address these challenges.

Advances in emerging Information and Communications Technology (ICT) technologies push the boundaries of what is possible and open up new markets for innovative ICT products and services. The adoption of ICT products and systems with security properties depends on consumers’ confidence and markets’ trust in the security functionalities and whether the assurance measures applied to these products meet the inherent security requirements. Such confidence and trust are primarily gained through the rigorous development of security requirements, validation criteria, evaluation, and certification. The Common Criteria for Information Technology Security Evaluation (often referred to as Common Criteria or CC) is an international standard (ISO/IEC 15408) for cyber security. Motivated by encouraging the adoption of the CC that is used for ICT security evaluation and certification, in this paper, we conduct a systematic review of the CC standard and its adoptions. Adoption barriers of the CC are investigated based on the analysis of current trends in cyber security evaluation. In addition, we share the experiences and lessons gained through the recent Development of Australian Cyber Criteria Assessment (DACCA) project on the development of the Protection Profile that defines security requirements with the CC. Best practices, challenges, and future directions on defining security requirements for trusted cyber security advancement are presented.

Cyber assurance, which is the ability to operate under the onslaught of cyber attacks and other unexpected events, is essential for organizations facing inundating security threats on a daily basis. Organizations usually employ multiple strategies to conduct risk management to achieve cyber assurance. Utilizing cybersecurity standards and certifications can provide guidance for vendors to design and manufacture secure Information and Communication Technology (ICT) products as well as provide a level of assurance of the security functionality of the products for consumers. Hence, employing security standards and certifications is an effective strategy for risk management and cyber assurance. In this work, we begin with investigating the adoption of cybersecurity standards and certifications by surveying 258 participants from organizations across various countries and sectors. Specifically, we identify adoption barriers of the Common Criteria through the designed questionnaire. Taking into account the seven identified adoption barriers, we show the recommendations for promoting cybersecurity standards and certifications. Moreover, beyond cybersecurity standards and certifications, we shed light on other risk management strategies devised by our participants, which provides directions on cybersecurity approaches for enhancing cyber assurance in organizations.

Smart grid technologies are based on the integration of the cyber network and the power grid into a cyber-physical power system (CPPS). The increasing cyber-physical interdependencies bring about tremendous opportunities for the modeling, monitoring, control, and protection of power grids, but also create new types of vulnerabilities and failure mechanisms threatening the reliability and resiliency of system operation. A major concern regarding the interdependent networks is the cascading failure (CF), where a small initial disturbance/failure in the network results in a seemingly unexpected large-scale failure. Although there has been a significant volume of recent work in the CF research of CPPS, a comprehensive review remains unavailable. This article aims to fill the gap by providing a systematic literature survey regarding the modeling, analysis, and mitigation of CF in CPPS. The open research questions for further research are also discussed. This article allows researchers to easily understand the state of the art of CF research in CPPS and fosters future work required towards full resolutions to the remaining questions and challenges.

COVID-19 has brought to light the systemic racism faced by ethnic minorities in the UK. During the pandemic, we saw an increase in anti-Asian hate crimes and a lack of support from the government given to both patients and healthcare workers from minority backgrounds on the front lines. This lack of support potentially contributed to the increased susceptibility of ethnic minorities to COVID-19 and also their hesitancy toward the vaccine, particularly the south Asian communities. In this paper we discuss potential reasons for COVID-19 vaccine hesitancy among south Asian groups. Additionally, we propose that introducing a decolonised curriculum in secondary school may enhance cultural awareness with historical context among the white British populations, allowing for more inclusion for south Asian communities. By exploring ways to decolonise specific subjects in the secondary curriculum, this paper aims to set out a guideline for teachers and education professionals on expanding secondary school pupils’ knowledge of racial issues and equality, to start the process of educating a new generation appropriately. We propose that decolonising the secondary school curriculum is a potential long-term solution to eradicating racism and discrimination.

Obesity is a chronic multifactorial disease characterized by the accumulation of body fat and serves as a gateway to a number of metabolic-related diseases. Epidemiologic data indicate that Obesity is acting as a risk factor for neuro-psychiatric disorders such as schizophrenia, major depression disorder and vice versa. However, how obesity may biologically interact with neurodevelopmental or neurological psychiatric conditions influenced by hereditary, environmental, and other factors is entirely unknown. To address this issue, we have developed a pipeline that integrates bioinformatics and statistical approaches such as transcriptomic analysis to identify differentially expressed genes (DEGs) and molecular mechanisms in patients with psychiatric disorders that are also common in obese patients. Biomarker genes expressed in schizophrenia, major depression, and obesity have been used to demonstrate such relationships depending on the previous research studies. The highly expressed genes identify commonly altered signalling pathways, gene ontology pathways, and gene-disease associations across disorders. The proposed method identified 163 significant genes and 134 significant pathways shared between obesity and schizophrenia. Similarly, there are 247 significant genes and 65 significant pathways that are shared by obesity and major depressive disorder. These genes and pathways increase the likelihood that psychiatric disorders and obesity are pathogenic. Thus, this study may help in the development of a restorative approach that will ameliorate the bidirectional relation between obesity and psychiatric disorder. Finally, we also validated our findings using genome-wide association study (GWAS) and whole-genome sequence (WGS) data from SCZ, MDD, and OBE. We confirmed the likely involvement of four significant genes both in transcriptomic and GWAS/WGS data. Moreover, we have performed co-expression cluster analysis of the transcriptomic data and compared it with the results of transcriptomic differential expression analysis and GWAS/WGS.

Type 2 diabetes (T2D) is a persistent medical condition characterized by insulin resistance and impaired insulin utilization. T2D is primarily observed in men and is strongly linked to obesity, which is considered a significant contributing factor to its onset. The underlying pathogenesis mechanism of T2D remains uncertain concerning potential sex differences. This study aimed to examine the differentially expressed genes (DEGs) and signaling pathways between males and females with T2D.For analysis of differentially expressed genes in this study, GSE86473 dataset was obtained from the NCBI's Gene Expression Omnibus (GEO). Utilizing Enrichr, we performed pathway enrichment analysis and gene ontology. Protein-protein interactions and hub genes are extracted using Cytoscape. Network-Analyst was employed to investigate transcription factors and microRNAs, protein-drug interactions, and gene-disease relationships.We identified 51 DEGs in men and 237 DEGs in women. We determined the leading 20 pathways and 15 GO terms for each gender. In male subjects, the following 15 hub genes were discovered: LDHA, FLT1, IGFBP3, LILRB4, CSF2, IL1A, HLA-C, UBD, C1QB, HLA-DMA, HLA-DPB1, CFB, HLA-DRB1, HLA-A, and CXCL8. The following genes were identified for female subjects: CPA1, CPB1, CEL, PNLIP, CPA2, CELA3A, SYCN, PRSS3P2, CTRB1, AMY2A, CTRC, PNLIPRP1, PRSS1, HLA-DRB1, and HLA-DQB1. We determined six TF genes shared by both males and females: ATF1, IRF1, MAZ, SSRP1, TFDP1, and MXD3. Mir-124-3p, mir-107, mir-16-5p, mir-23b-3p, mir-27a-3p, mir-103a-3p, mir-155-5p, and mir-1-3p were confirmed as microRNAs shared by males and females in our study. We analyzed protein-drug interactions to investigate the potential effects of a particular drug on the activity of target proteins in the body.The results of these analyses provided insights into the molecular mechanisms underlying the diseases under study and identified potential drug targets for further investigation. The hub genes identified in this study may have varying roles in the disease progression mechanisms of T2D between males and females, and they could serve as potential targets for treatment or prognostic markers for both sexes.

COVID-19 became a health emergency on January 30, 2020. SARS-CoV-2 is the causative agent of the coronavirus disease known as COVID-19 and can develop cardiometabolic and neurological disorders. Intracranial aneurysm (IA) is considered the most significant reason for hemorrhagic stroke,and it accounts for approximately 85% of all subarachnoid hemorrhages (SAH). Retinoid signaling abnormalities may explain COVID-19's pathogenesis with inhibition of AEH2, from which COVID-19 infection may enhance aneurysm formation and rupture due to abrupt blood pressure changes, endothelial cell injury, and systemic inflammation. The objective of this study was to investigate the potential biomarkers, differentially expressed genes (DEGs), and metabolic pathways associated with both COVID-19 and intracranial aneurysm (IA) using simulation databases like DIsGeNET. The purpose was to confirm prior findings and gain a comprehensive understanding of the underlying mechanisms that contribute to the development of these conditions. We combined the regulated genes to describe intracranial aneurysm formation in COVID-19. To determine DEGs in COVID-19 and IA patient tissues, we compared gene expression transcriptomic datasets from healthy and diseased individuals. There were 41 differentially expressed genes (DEGs) shared by both the COVID-19 and IA datasets (27 up-regulated genes and 14 down-regulated genes). Using protein-protein interaction analysis, we were able to identify hub proteins (C3, NCR1, IL10RA, OXTR, RSAD2, CD38, IL10RB, MX1, IL10, GFAP, IFIT3, XAF1, USP18, OASL, IFI6, EPSTI1, CMPK2, and ISG15), which were not described as key proteins for both COVID-19 and IA before. We also used Gene Ontology analysis (6 significant ontologies were validated), Pathway analysis (the top 20 were validated), TF-Gene interaction analysis, Gene miRNA analysis, and Drug-Protein interaction analysis methods to comprehend the extensive connection between COVID-19 and IA. In Drug-Protein interaction analysis, we have gotten the following three drugs: LLL-3348, CRx139, and AV41 against IL10 which was both common for COVID-19 and IA disease. Our study with different cabalistic methods has showed the interaction between the proteins and pathways with drug analysis which may direct further treatment development for certain diseases.

Background: Urinary tract infection (UTI) is one of the most recurrent infections in the community and healthcare settings. Although many studies related with microbial sensitivity (MS) of uropathogens (UPs) to antibiotics have been done in Bangladesh, no conclusive study has compared antibiotic sensitivity (AS) to UPs in diabetic and non-diabetic patients. The aim of the study is to find out whether there is a difference in AS in common UPs between diabetic and non-diabetic UTI patients. Methods: A retrospective review was conducted on 833 patients. The data was collected from different diagnostic centers located within Dhaka city in Bangladesh, and the data was analyzed using convenient statistical tools. Results: We have studied a total of 833 UTI patients. Out of 833 patients, 664 were diabetic and 169 were non-diabetic patients respectively. Among the studied population, females were found to be more inclined to have UTIs as compared to males. E. coli was found to be the leading UPs in our study. Patients within the age of 20–34 were more vulnerable to UTI in both groups. Imipenem and meropenem showed 100% sensitivity against E. coli, Staphylococcus and Klebsiella in non-diabetic patients, while both antibiotics showed lower sensitivity to the same organisms in diabetic patients. Antibiotics like nitrofurantoin (p ≤ 0.0002), ceftazidime (p ≤ 0.0124) and ceftriaxone (p ≤ 0.0168) showed less sensitivity to E. coli in diabetic UTI patients as compared to non-diabetic UTI patients. Overall sensitivity patterns elucidated that all the studied antibiotics, except ciprofloxacin and levofloxacin, showed lower sensitivity against UPs in diabetic while compared to non-diabetic UTI patients (p=

Despite modern treatment, infertility remains one of the most common gynecologic diseases causing severe health effects worldwide. The clinical and epidemiological data have shown that several cancerous risk factors are strongly linked to Female Infertility (FI) development, but the exact causes remain unknown. Understanding how these risk factors affect FI-affected cell pathways might pave the door for the discovery of critical signaling pathways and hub proteins that may be targeted for therapeutic intervention. To deal with this, we have used a bioinformatics pipeline to build a transcriptome study of FI with four carcinogenic risk factors: Endometrial Cancer (EC), Ovarian Cancer (OC), Cervical Cancer (CC), and Thyroid Cancer (TC). We identified FI sharing 97, 211, 87 and 33 differentially expressed genes (DEGs) with EC, OC, CC, and TC, respectively. We have built gene-disease association networks from the identified genes based on the multilayer network and neighbour-based benchmarking. Identified TNF signalling pathways, ovarian infertility genes, cholesterol metabolic process, and cellular response to cytokine stimulus were significant molecular and GO pathways, both of which improved our understanding the fundamental molecular mechanisms of cancers associated with FI progression. For therapeutic intervention, we have targeted the two most significant hub proteins VEGFA and PIK3R1, out of ten proteins based on Maximal Clique Centrality (MCC) value of cytoscape and literature analysis for molecular docking with 27 phytoestrogenic compounds. Among them, sesamin, galangin and coumestrol showed the highest binding affinity for VEGFA and PIK3R1 proteins together with favourable ADMET properties. We recommended that our identified pathway, hub proteins and phytocompounds may be served as new targets and therapeutic interventions for accurate diagnosis and treatment of multiple diseases.

The adsorption equilibrium of methyl blue (MB) at different temperatures was optimized using activated graphene (AG) as an adsorbent. The experimental data were compared using five linear and nonlinear adsorption isotherms, namely, Langmuir, Freundlich, Redlich–Peterson (R-P), Sips, and Toth, to estimate the best fit of the equilibrium data. Five distinct error functions were utilized to conduct nonlinear regression for the adsorption equilibrium: SSE, ARE, HYBRID, MPSD, and EABS. These functions offered a wide range of residuals for comparison. For a more accurate prediction of the isotherm model, two statistical techniques—SNE and CND—were applied. By using these techniques in conjunction, a more objective analysis of the error and deviation between the observed and predicted data was achieved, ultimately leading to improved accuracy in the error analysis. The sorption results demonstrated the highest MB removal of 691.89 mg g−1, which amounted to 98.32% within 120 min. The error analysis findings indicated that the SSE and HYBRID functions produced the smallest error residuals. Based on the “goodness of fit” criterion, the models in this study were ranked as R-P > Toth > Langmuir > Sips > Freundlich. Among these models, the R-P isotherm demonstrated the best fit for the data, exhibiting the lowest variance in residuals. Its CND value ranged between 0.0025 and 0.0048, which further supports its superior fit compared to the other models. The combination of multiple error functions and statistical methods allowed for a comprehensive and objective assessment of the nonlinear regression models. The results highlight the importance of using various techniques to improve the accuracy of error analysis and identify the best-fitting isotherms for adsorption.

Tumour metastasis to the brain is a complex process involving crosstalk between the circulating tumour cells and the blood brain barrier (BBB). Astrocytes, which reside in the abluminal surface of the microvasculature of the BBB, are now known to play an essential role in tumour cell migration and invasion into the brain parenchyma. For instance, pro-inflammatory astrocyte secretions, including TNF-α, IL-6, CXCL10 as well as polyunsaturated fatty acids interact with circulating tumour cells to promote migration and proliferation. Additionally, astrocyte and tumour cell derived MMPs play a vital role in tumour cell invasion through the BBB. Understanding these complex interactions between tumour cells and astrocytes in the tumour microenvironment may contribute to the development of novel therapeutics for brain metastasis. Therefore, in this review, we present key interactions within the neurovascular unit of the BBB in the tumour microenvironment that significantly aids cancer metastasis, focusing particularly on astrocytes.

Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide. The disease still remains incurable and highly lethal in the advanced stage, representing a global health concern. Therefore, it is essential to understand the causes and risk factors leading to its development. Because age-related cellular senescence and type 2 diabetes (T2D) have been recognised as risk factors for CRC development, the recent finding that type 2 diabetic patients present an elevated circulating volume of senescent cells raises the question whether type 2 diabetes facilitates the process of CRC tumorigenesis by inducing premature cell senescence. In this review, we will discuss the mechanisms according to which T2D induces cellular senescence and the role of type 2 diabetes-induced cellular senescence in the pathogenesis and progression of colorectal cancer. Lastly, we will explore the current therapeutic approaches and challenges in targeting senescence.

Advancements in adaptive educational technologies, specifically the adaptive learning system, have made it possible to automatically optimize the sequencing of the pedagogical instructions according to the needs of individual learners. The crux of such systems lies in the instructional sequencing policy, which recommends personalized learning material based on the learning experiences of the learner to maximize their learning outcomes. However, limited available information such as cognitive, affective states, and competence levels of the learners ongoing knowledge points servers critical challenges to optimizing individual-specific pedagogical instructions in real-time. Moreover, making such decisions policy for every learner with a unique knowledge profile demands a trade-off between learner current knowledge and curiosity to learn next knowledge point. To address these challenges, this paper proposes a personalized adaptability knowledge extraction strategy (PAKES) using cognitive diagnosis and reinforcement learning (RL). We apply the general diagnostic model to track the current knowledge state of the learners. Subsequently, an RL-based Q-learning algorithm is employed to recommend optimal pedagogical instructions for individuals to meet their learning objectives while maintaining equilibrium among the learner-control and teaching trajectories. The results indicate that the learning analytics of the proposed framework can fairly deliver the optimal pedagogical paths for the learners based upon their learning profiles. A 62% learning progress score was achieved with the pedagogical paths recommended by the PAKES, showing a 20% improvement compared to the baseline model.

Systemic Sclerosis (SSc) is an autoimmune disease associated with changes in the skin's structure in which the immune system attacks the body. A recent meta-analysis has reported a high incidence of cancer prognosis including lung cancer (LC), leukemia (LK), and lymphoma (LP) in patients with SSc as comorbidity but its underlying mechanistic details are yet to be revealed. To address this research gap, bioinformatics methodologies were developed to explore the comorbidity interactions between a pair of diseases. Firstly, appropriate gene expression datasets from different repositories on SSc and its comorbidities were collected. Then the interconnection between SSc and its cancer comorbidities was identified by applying the developed pipelines. The pipeline was designed as a generic workflow to demonstrate a premise comorbid condition that integrate regarding gene expression data, tissue/organ meta-data, Gene Ontology (GO), Molecular pathways, and other online resources, and analyze them with Gene Set Enrichment Analysis (GSEA), Pathway enrichment and Semantic Similarity (SS). The pipeline was implemented in R and can be accessed through our Github repository: https://github.com/hiddenntreasure/comorbidity. Our result suggests that SSc and its cancer comorbidities share differentially expressed genes, functional terms (gene ontology), and pathways. The findings have led to a better understanding of disease pathways and our developed methodologies may be applied to any set of diseases for finding any association between them. This research may be used by physicians, researchers, biologists, and others.

Global warming as a result of climate change has become a major concern for people all over the world. It has recently drawn the attention of the entire conscious community, with the fear that if not addressed properly, it will result in the extinction of numerous species around the world. At the same time, it will pose a threat to human health, food security, living environment and standard of living. Thereby, possible solutions are being explored accordingly; regulations have been imposed in places binding green production practices, limiting the emission of CO2 and emphasis is given on renewable resources along with the search for alternatives to carbon-positive materials. Cannabis sativa L. (hemp) has received a lot of attention because of its multipurpose usability, short production cycle, low capital demand in cultivation, possibility of carbon-negative transformation and easy carbon sequestering material. This paper reviews hemp as a very promising renewable resource including its potential uses in paper, textiles, composites, biofuel, and food industry.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection results in the development of a highly contagious respiratory ailment known as new coronavirus disease (COVID-19). Despite the fact that the prevalence of COVID-19 continues to rise, it is still unclear how people become infected with SARS-CoV-2 and how patients with COVID-19 become so unwell. Detecting biomarkers for COVID-19 using peripheral blood mononuclear cells (PBMCs) may aid in drug development and treatment. This research aimed to find blood cell transcripts that represent levels of gene expression associated with COVID-19 progression. Through the development of a bioinformatics pipeline, two RNA-Seq transcriptomic datasets and one microarray dataset were studied and discovered 102 significant differentially expressed genes (DEGs) that were shared by three datasets derived from PBMCs. To identify the roles of these DEGs, we discovered disease-gene association networks and signaling pathways, as well as we performed gene ontology (GO) studies and identified hub protein. Identified significant gene ontology and molecular pathways improved our understanding of the pathophysiology of COVID-19, and our identified blood-based hub proteins TPX2, DLGAP5, NCAPG, CCNB1, KIF11, HJURP, AURKB, BUB1B, TTK, and TOP2A could be used for the development of therapeutic intervention. In COVID-19 subjects, we discovered effective putative connections between pathological processes in the transcripts blood cells, suggesting that blood cells could be used to diagnose and monitor the disease’s initiation and progression as well as developing drug therapeutics.

BackgroundBacillus Calmette-Guérin (BCG) vaccination has recently been found to have beneficial effects among children infected other than Mycobacterium tuberculosis. Due to the paucity of data on the outcomes of children who had successful BCG vaccination following Expanded Programme on Immunization (EPI) schedule, we aimed to investigate the characteristics of such children and their outcomes who were hospitalized for severe malnutrition.MethodsA prospective observational study was conducted to determine the viral etiology of pneumonia in severely malnourished children those were admitted to the Dhaka Hospital of International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b) between April 2015 and December 2017, constituted the study population. Using a case-control design for the analysis, children having BCG vaccination prior hospital admission were treated as cases (n = 611) and those without vaccination, constituted as controls (n = 83). Bi-variate analysis was conducted using socio-demographic, clinical, laboratory, and treatment characteristics on admission and outcomes during hospitalization. Finally, log-linear binomial regression analysis was done to identify independent impact of BCG vaccination.ResultsThe cases more often presented with older age, have had lower proportion of maternal illiteracy, higher rate of breastfeeding, severe wasting and lower rate of hypoglycemia, compared to the controls. The cases were also found to have lower risk of severe sepsis and deaths, compared to the controls (for all, pConclusionBCG vaccination and breastfeeding were found to be protective for the development of severe sepsis in hospitalized severely malnourished under-five children which underscores the importance of continuation of BCG vaccination at birth and breastfeeding up to two years of age.

Alzheimer's disease and Huntington's disease are considered to be the most lethal illnesses that result in common human disorders. Alzheimer's disease (AD) is a progressive neurological illness distinguished by age-related dementia, mental abnormalities, and poor memory, among other symptoms. However, Huntington's disease (HD) is influenced by genetics as well as a generalized dysfunction of the motor system. Despite the fact that many similar genetic elements have been found in the literature as being interrelated between these two diseases, it is still unclear how people acquire infected with these two neurological disorders. Detecting biomarkers for Alzheimer's and Huntington's disease in brain tissue might help in drug development and treatment. The purpose of this research was to find brain cell transcripts that show levels of gene expression linked to the progression of Alzheimer's and Huntington's disease. A bioinformatics pipeline was used to study one RNA-Seq transcriptomic dataset and one microarray dataset, and 24 significant differentially expressed genes (DEGs) were discovered that were shared by two brain cell datasets. We uncovered disease-gene association networks and signaling pathways, as well as gene ontology (GO) investigations and hub protein identification, to determine the roles of these DEGs. The discovery of significant gene ontologies and molecular pathways increased our understanding of the pathophysiology of these two disorders, and the hub proteins B2M, HLA-A, HLA-E, HLA-B, HLA-C, HLA-F, CANX, HLA-DQA1, HLA-DRA, and HLA-DRB1 might be exploited to design therapeutic interventions. In neurological disorder subjects, we uncovered efficient hypothetical linkages between pathogenic processes in brain cells, implying that brain cells may be exploited to detect and monitor illness origin and development, as well as design pharmacological therapies.

Background In this study, we investigate the expression of markers of angiogenesis and microvessel density (MVD) in cases of microcystic, elongated and fragmented (MELF) pattern, with its prognostic role in the survival of endometrioid endometrial adenocarcinomas (EA) patients. Methods In this study, 100 cases of EA, 49 cases with MELF pattern and 51 without, were immunohistochemically stained for galectin-1, vascular endothelial growth factor (VEGF), and MVD. Morphometry and statistical (univariate and multivariate) analyses were performed to assess overall survival (OS) and disease-free survival. Results The expression of VEGF (p

Abstract Glioblastoma multiforme (GBM) is the most lethal primary intracranial malignant neoplasm in adults and most resistant to treatment. Integration of gene therapy and chemotherapy, chemovirotherapy, has the potential to improve treatment. We have introduced an intravenous bacteriophage (phage) vector for dual targeting of therapeutic genes to glioblastoma. It is a hybrid AAV/phage, AAVP, designed to deliver a recombinant adeno‐associated virus genome (rAAV) by the capsid of M13 phage. In this vector, dual tumor targeting is first achieved by phage capsid display of the RGD4C ligand that binds the αvβ3 integrin receptor. Second, genes are expressed from a tumor‐activated and temozolomide (TMZ)‐induced promoter of the glucose‐regulated protein, Grp78. Here, we investigated systemic combination therapy using TMZ and targeted suicide gene therapy by the RGD4C/AAVP‐Grp78. Firstly, in vitro we showed that TMZ increases endogenous Grp78 gene expression and boosts transgene expression from the RGD4C/AAVP‐Grp78 in human GBM cells. Next, RGD4C/AAVP‐Grp78 targets intracranial tumors in mice following intravenous administration. Finally, combination of TMZ and RGD4C/AAVP‐Grp78 targeted gene therapy exerts a synergistic effect to suppress growth of orthotopic glioblastoma.

Expanding data suggest that glioblastoma is accountable for the growing prevalence of various forms of stroke formation, such as ischemic stroke and moyamoya disease. However, the underlying deterministic details are still unspecified. Bioinformatics approaches are designed to investigate the relationships between two pathogens as well as fill this study void. Glioblastoma is a form of cancer that typically occurs in the brain or spinal cord and is highly destructive. A stroke occurs when a brain region starts to lose blood circulation and prevents functioning. Moyamoya disorder is a recurrent and recurring arterial disorder of the brain. To begin, adequate gene expression datasets on glioblastoma, ischemic stroke, and moyamoya disease were gathered from various repositories. Then, the association between glioblastoma, ischemic stroke, and moyamoya was established using the existing pipelines. The framework was developed as a generalized workflow to allow for the aggregation of transcriptomic gene expression across specific tissue; Gene Ontology (GO) and biological pathway, as well as the validation of such data, are carried out using enrichment studies such as protein–protein interaction and gold benchmark databases. The results contribute to a more profound knowledge of the disease mechanisms and unveil the projected correlations among the diseases.

Many individuals visit rural telemedicine centres to obtain safe and effective health remedies for their physical and emotional illnesses. This study investigates the antecedents of patients' satisfaction relating to telemedicine adoption in rural public hospitals settings in Bangladesh through the adaptation of Expectation Disconfirmation Theory extended by Social Cognitive Theory. This research advances a theoretically sustained prediction model forecasting patients' satisfaction with telemedicine to enable informed decision making. A research model explores four potential antecedents: expectations, performance, disconfirmation, and enjoyment; that significantly contribute to predicting patients' satisfaction concerning telemedicine adoption in Bangladesh. This model is validated using two-staged structural equation modeling and artificial neural network approaches. The findings demonstrate the determinants of patients' satisfaction with telemedicine. The presented model will assist medical practitioners, academics, and information systems practitioners to develop high-quality decisions in the future application of telemedicine. Pertinent implications, limitations and future research directions are endorsed securing long-term telemedicine sustainability.

Introduction To improve human resources for health (HRH) management in Bangladesh, the directorate general of health services (DGHS) introduced a new information and communications technology (ICT) tool, named ‘human resources information system (HRIS)’, to process real-time HRH data of all facilities under the DGHS. However, synchronisation is a major concern since multiple authorities are involved in the implementation of the tool at different tiers of the health system. Introducing ICT tools in healthcare organisations has always proved challenging as evidence from low-income and middle-income countries suggests. The knowledge gap in terms of factors that support or constrain the successful implementation of the HRIS in Bangladesh will be investigated in this exploratory study to identify ways of engaging the key stakeholders in a better way for an effective use of the tool.Methods and analysis Desk review and qualitative data collection methods will be used to address the study objectives. Key informant interviews and in-depth interviews will be conducted to explore perspectives of policy-makers, programme managers, service providers and other stakeholders to understand the barriers to implementing HRIS in the context of Bangladesh. We plan to organise stakeholder consultation workshops to validate the qualitative study findings and to seek suggestions for ensuring a successful implementation of the HRIS. Framework analysis will be applied to analyse qualitative data, and an outline with the definitions of a priori codes guided by the policy engagement framework will be prepared. Besides, emerging themes will also be identified. A data display matrix will be prepared to summarise and interpret the findings for policy review.Ethics and dissemination The research review committee and the ethical review committee of icddr,b have approved the research protocol. Findings from the study will be communicated through national and international forums, conferences, policy briefs and peer-reviewed journal publications.

Antimicrobial resistance has emerged as a global health crisis and, therefore, new drug discovery is a paramount need. Cannabis sativa contains hundreds of chemical constituents produced by secondary metabolism, exerting outstanding antimicrobial, antiviral, and therapeutic properties. This paper comprehensively reviews the antimicrobial and antiviral (particularly against SARS-CoV-2) properties of C. sativa with the potential for new antibiotic drug and/or natural antimicrobial agents for industrial or agricultural use, and their therapeutic potential against the newly emerged coronavirus disease (COVID-19). Cannabis compounds have good potential as drug candidates for new antibiotics, even for some of the WHO’s current priority list of resistant pathogens. Recent studies revealed that cannabinoids seem to have stable conformations with the binding pocket of the Mpro enzyme of SARS-CoV-2, which has a pivotal role in viral replication and transcription. They are found to be suppressive of viral entry and viral activation by downregulating the ACE2 receptor and TMPRSS2 enzymes in the host cellular system. The therapeutic potential of cannabinoids as anti-inflammatory compounds is hypothesized for the treatment of COVID-19. However, more systemic investigations are warranted to establish the best efficacy and their toxic effects, followed by preclinical trials on a large number of participants.

Background In this study, we hypothesized that microcystic, elongated, fragmented (MELF)-pattern, vascular endothelial growth factor (VEGF) expression by cancer cells and microvessel density of cancer stroma may be associated with progression of endometrioid adenocarcinoma. Methods The study used data from the Belarus Cancer Registry and archival histological material of 100 patients with retrospectively known good (survival) and poor (disease progression and death) outcomes. All cases were immunohistochemically stained for CD34 and VEGF. Two independent samples were compared for the characteristics of signs, and obtained results were analyzed by receiver operating characteristic analysis, Mann-Whitney U test, χ2 test (Yates correction), and Mantel-Cox test. Multivariate Cox hazard analysis and Spearman correlation test were used. A p-value of less than .05 was considered statistically significant. Results The observed survival rate of patients with endometrioid adenocarcinoma was significantly lower (p = .002) in MELF-pattern positive patients when compared with MELF-pattern negative patients. The overall survival rate of patients whose tumors had more than 114 vessels/mm2 of tissue was significantly low (p < .001). Interestingly, a similar observation was found in patients with increased vessel area, evidenced by VEGF expression in the glandular tumor component. Conclusions Our study suggests, for the first time, that these criteria may be used as risk factors of endometrioid adenocarcinoma progression during 5 years after radical surgical treatment. However, a large independent cohort of samples should be considered in the future to validate our findings.

Purpose: This study aims to examine the impact of knowledge and skills acquisition from youth leadership programs on the extent of training transfer. Additionally, it explores the role of self-esteem as a potential intervening mechanism in linking the acquired knowledge and skills to the transfer of training. Design/methodology/approach: Using a non-probability purposive sampling, data were gathered from participants of youth leadership programs in Brunei. The study hypotheses were validated using multiple linear regression analysis and Hayes PROCESS macro. Findings: The findings revealed that knowledge and skills acquisition positively affect the extent of training transfer among youth leadership program participants. Moreover, self-esteem is found to be a crucial mediator in the relationship between knowledge and skills acquired from youth leadership programs and the extent of training transfer. Practical implications: The study suggests that to optimize the transfer of training in the youth leadership programs, relevant stakeholders -- training providers, and practitioners -- must prioritize not only the knowledge and skills acquisition but also the cultivation of participants' self-esteem. To achieve this, a primary focus should be placed on the criticality of designing such programs to address these factors. Policymakers, particularly in Brunei, can enhance the leadership pipeline among the youth population and expedite progress toward achieving the national vision by aligning leadership development initiatives with the broader national development agenda. Originality/value: Collectively, this study enhances understanding of training transfer in youth leadership development, an often-overlooked area in the literature.

Purpose: Drawing on the job demands-resources (JD-R) model, the authors examine how working in the hybrid workplace model (telework and flexible work) affects job performance via the intervening role of work engagement. Design/methodology/approach: The authors adopted a quantitative approach and collected data from 277 employees working in universities in Nigeria. Partial least square structural equation modelling was used to analyse the data and test the hypotheses. Findings: The findings reveal that flexible work, not telework, has a significant and positive effect on job performance. It also emerges that flexible work positively affects work engagement, and work engagement significantly mediates the relationship between flexible work and job performance. However, the findings do not support the effect of telework on work engagement and the mediating role of work engagement in the proposed relation between telework and job performance. Originality/value: The paper provides fresh insights by linking the components of the hybrid workplace model with job performance and employee work engagement and extending the JD-R model to the hybrid workplace setting. The practitioners can benefit from the findings of this study by factoring in the importance of the hybrid workplace model in designing policies and procedures to promote job performance.

Epithelial ovarian cancer (EOC) is the leading cause of death from gynecologic malignancies and has a poor prognosis due to relatively unspecific early symptoms, and thus often advanced stage, metastasized cancer at presentation. Metastasis of EOC occurs primarily through the transcoelomic route whereby exfoliated tumor cells disseminate within the abdominal cavity, particularly to the omentum. Primary and metastatic tumor growth requires a pool of proangiogenic factors in the microenvironment which propagate new vasculature in the growing cancer. Recent evidence suggests that proangiogenic factors other than the widely known, potent angiogenic factor vascular endothelial growth factor may mediate growth and metastasis of ovarian cancer. In this review we examine the role of some of these alternative factors, specifically cathepsin D and cathepsin L.

Data mining plays an important role in analyzing the massive amount of data collected in today’s world. However, due to the public’s rising awareness of privacy and lack of trust in organizations, suitable Privacy Preserving Data Mining (PPDM) techniques have become vital. A PPDM technique provides individual privacy while allowing useful data mining. We present a novel noise addition technique called Forest Framework, two novel data quality evaluation techniques called EDUDS and EDUSC, and a security evaluation technique called SERS. Forest Framework builds a decision forest from a dataset and preserves all the patterns (logic rules) of the forest while adding noise to the dataset. We compare Forest Framework to its predecessor, Framework, and another established technique, GADP. Our comparison is done using our three evaluation criteria, as well as Prediction Accuracy. Our experimental results demonstrate the success of our proposed extensions to Framework and the usefulness of our evaluation criteria.

The paper outlines the performance of a modified large scale shear box apparatus, which is mainly used to execute full scale laboratory study of segmental retaining walls. A typical apparatus has already been adopted by the current ASTM and NCMA test protocols and by literature studying of those test protocols, it is found that protocols recommend a fixed vertical actuator with roller or airbag configuration as a proposed vertical loading assembly. Previous research study demonstrated that vertical loading arrangement greatly influences the interface shear capacity of block systems and fixed vertical actuator with flexible airbag shows better loading arrangement for the blocks which have dilatant behavior. However, airbag arrangement is strenuous and time-consuming loading assembly compared to fixed vertical actuator which increases normal load with shear displacement due to bending of vertical actuator locked with the top block during shear loading. For the drawbacks of fixed vertical loading arrangement, the apparatus used in this study was fully redesigned and modified in terms of normal loading arrangement specially. A moveable vertical loading assembly is used in the modified apparatus which allows the piston movement with the top blocks during shear testing. The results outlined in this paper report that normal load remains constant over the period of shear testing for a wide range of surcharge loading. It could easily be concluded that the modified apparatus might be a better alternative to the existing apparatus used in the test protocols.

In this paper we present two clustering techniques called ModEx and Seed-Detective. ModEx is a modified version of an existing clustering technique called Ex-Detective. It addresses some limitations of Ex-Detective. Seed-Detective is a combination of ModEx and Simple K-Means. Seed-Detective uses ModEx to produce a set of high quality initial seeds that are then given as input to K-Means for producing the final clusters. The high quality initial seeds are expected to produce high quality clusters through K-Means. The performances of Seed-Detective and ModEx are compared with the performances of Ex-Detective, PAM, Simple K-Means (SK), Basic Farthest Point Heuristic (BFPH) and New Farthest Point Heuristic (NFPH). We use three cluster evaluation criteria namely F-measure, Entropy and Purity and four natural datasets that we obtain from the UCI Machine learning repository. In the datasets our proposed techniques perform better than the existing techniques in terms of F-measure, Entropy and Purity. The sign test results suggest a statistical significance of the superiority of Seed-Detective (and ModEx) over the existing techniques.

Cancer gene therapy expanded and reached its pinnacle in research in the last decade. Both viral and non-viral vectors have entered clinical trials, and significant successes have been achieved. However, a systemic administration of a vector, illustrating safe, efficient, and targeted gene delivery to solid tumors has proven to be a major challenge. In this review, we summarize the current progress and challenges in the targeted gene therapy of cancer. Moreover, we highlight the recent developments of bacteriophage-derived vectors and their contributions in targeting cancer with therapeutic genes following systemic administration.

This study investigated the causes and consequences of child marriage in Meherpur District, Bangladesh. Researchers conducted in-depth interviews with 20 respondents to understand this complex issue. Poverty, lack of education, family instability, and societal pressure emerged as significant factors contributing to child marriage. The study also reveals how cultural factors, including superstitions, religious interpretations, and traditional practices, perpetuate this harmful practice. Findings indicate that child marriage negatively impacts girls' physical and mental health, often leading to health complications and psychological trauma. This research highlights the urgent need for interventions that empower girls, promote education, and address the root causes of child marriage in Meherpur District.