Search Results (113)

Background: Snake envenomation is a major public health issue in Nigeria, primarily due to bites from Echis ocellatus, Naja nigricollis, and Bitis arietans. Understanding their venom composition is essential for effective antivenom development. This study characterizes and compares the venom proteomes of these snakes using iTRAQ-based proteomics, focusing on key toxin families and their relative abundances. Methods: Venom samples were ethically collected from adult snakes, pooled by species, lyophilized, and stored for proteomic analysis. Proteins were extracted, digested with trypsin, and labeled with iTRAQ. Peptides were analyzed via mass spectrometry, and data were processed using Mascot and IQuant for protein identification and quantification. Results: E. ocellatus and B. arietans venoms had similar profiles, rich in C-type lectins, serine proteases, and phospholipase A₂s. These comprised 17%, 11%, and 5% in E. ocellatus and 47%, 10%, and 7% in B. arietans, with metalloproteinases dominating both (53% and 47%). In N. nigricollis, three-finger toxins (9%) were most abundant, followed by metalloproteinases (3%). All species shared four core protein families, with N. nigricollis also containing four uncharacterized proteins. Conclusions: This study highlights venom compositional differences, advancing snake venom biology and informing targeted antivenom development. Full article

Microsporidia MB (MB), a promising biological control agent, suppresses Plasmodium falciparum transmission in Anopheles mosquitoes. This study examined the spatial distribution of MB infection in natural populations of An. gambiae s.l. mosquitoes collected in Nigeria and Niger Republic, and its association with insecticide susceptibility in the mosquitoes. Microsporidia MB has wide geographic distribution across Nigeria and Niger Republic. The overall prevalence of MB in F₀ mosquitoes was 12.25% (95% CI: 7.76–16.75%); 25 mosquitoes out of 204 were positive. Geographic variation was observed, with a higher prevalence (5/15 mosquitoes) in Ebonyi State (33.33%, CI: 9.48–57.19%, Fisher’s exact test, p = 0.008). Infection rates were higher in An. coluzzii mosquitoes (21/133 mosquitoes), estimated at 15.79% (CI: 9.59–21.99%) compared to An. gambiae s.s. mosquitoes (4/71), with approximately 5.63% (CI: 0.27–11.00%, χ² = 4.44; df = 1, p = 0.035). Resistant mosquitoes had a significantly higher prevalence of MB infection than susceptible mosquitos at 28.57% (CI: 16.74–40.40%) with an odds ratio of 3.33 (CI: 1.23–9.03, p = 0.017). These findings suggests that MB can be exploited as an alternative for vector control in Nigeria and Niger, but its possible association with pyrethroid resistance suggests that it should be taken into account as a potential confounder when designing insecticide resistance management strategies. Full article

Background: AI has been adopted in dentistry for diagnosis, decision making, and therapy prognosis prediction. This systematic review aimed to identify AI models in dentistry, assess their performance, identify their shortcomings, and discuss their potential for adoption and integration in dental practice in the future. Methodology: The sources of the papers were the following electronic databases: PubMed, Scopus, and Cochrane Library. A total of 20 out of 947 needed further studies, and this was encompassed in the present meta-analysis. It identified diagnostic accuracy, predictive performance, and potential biases. Results: AI models demonstrated an overall diagnostic accuracy of 82%, primarily leveraging artificial neural networks (ANNs) and convolutional neural networks (CNNs). These models have significantly improved the diagnostic precision for dental caries compared with traditional methods. Moreover, they have shown potential in detecting and managing conditions such as bone loss, malignant lesions, vertical root fractures, apical lesions, salivary gland disorders, and maxillofacial cysts, as well as in performing orthodontic assessments. However, the integration of AI systems into dentistry poses challenges, including potential data biases, cost implications, technical requirements, and ethical concerns such as patient data security and informed consent. AI models may also underperform when faced with limited or skewed datasets, thus underscoring the importance of robust training and validation procedures. Conclusions: AI has the potential to revolutionize dentistry by significantly improving diagnostic accuracy and treatment planning. However, before integrating this tool into clinical practice, a critical assessment of its advantages, disadvantages, and utility or ethical issues must be established. Future studies should aim to eradicate existing barriers and enhance the model’s ease of understanding and challenges regarding expense and data protection, to ensure the effective utilization of AI in dental healthcare. Full article

The building sector accounts for more than one-third of final energy consumption worldwide. Green retrofitting, which is part of the green building activities, is one of the main factors in achieving the target of zero carbon emissions by 2060. Green retrofitting is a viable way to reduce greenhouse gas (GHG) emissions and energy consumption. The risks in green retrofitting work activities have not been studied much, even though the risks in green retrofitting projects are likely to be greater and more complex than the risks in conventional projects. This is reflected in the small application of customization in developing countries, one of which is Indonesia. Through the calculation of the risk matrix between probability and impact, a high risk was obtained from the relationship between risk and correlated resources, and it had a positive impact on the work breakdown structure (WBS). The results obtained show that complexity factors consisting of labor, materials, equipment, work activities, work methods, and scope/work package affect the success of the project, then the risk handling strategy that needs to be implemented is to set the right priorities. A focused project team allocates resources wisely. Knowing the probability of events and impacts arising from the non-implementation of the WBS, we identified sources of risk factors and high risk in the implementation of green retrofitting work based on the WBS based on the Greenship Existing Building Rating Tools and PUPR RI Regulation Number 21 of 2021 applicable in Indonesia, its effect on resource planning, and cost accuracy. Full article

This study presents a comprehensive bibliometric analysis of identity studies over the past decade, focusing on global trends and scholarly impact. Identity is defined as a dynamic concept that reflects how individuals and groups construct their sense of self within diverse social, cultural, and political contexts. Using the Scopus database, this research analyzed 18,399 publications from 2013 to 2024, spanning multiple disciplines such as sociology, anthropology, and digital cultural studies. This study identified leading authors, collaboration patterns, and thematic evolutions within identity research, highlighted through citation analysis and network visualization. The findings reveal significant contributions from Western countries, particularly the United States and the United Kingdom, in shaping the global discourse on identity. Key topics frequently explored include social identity, cultural identity, and the influence of globalization. Additionally, emerging themes such as identity in the context of the COVID-19 pandemic and racial disparities are highlighted. This bibliometric analysis provides insights into global collaboration, research productivity, and the intellectual contributions of prominent scholars. Utilizing R software and the Bibliometric package, this study offers a deep understanding of the dynamics of identity research and suggests future directions to further explore the formation, negotiation, and expression of identity in an increasingly globalized world. Full article

Objective: This study investigated the effects of a supraphysiological dose of testosterone on uterine morphology and the regulation of the leukaemia inhibitory factor (LIF) signalling pathway during endometrial receptivity. Methods: In this study, 30 adult female Sprague–Dawley rats were divided into treatment and control groups. The treatment groups received subcutaneous injections of 1 mg/kg/day of testosterone from gestational day 1 to day 3, either testosterone alone or in combination with inhibitors (anastrozole, finasteride, or both). A control group of six untreated rats was maintained for comparison. Rats were euthanised on the evening of gestational day 4 to examine uterine morphological changes, gene expression and the distribution of proteins associated with the LIF signalling pathway (LIF, LIFR, JAK1 and STAT3) and MUC1 by quantitative polymerase chain reaction (qPCR) and immunohistochemistry (IHC), respectively. Results: The results of this study showed that the thickness of the endometrium and myometrium, as well as the number of glands, markedly decreased in all testosterone-treated rats. In addition, the mRNA levels of LIF, LIFR, JAK1 and STAT3 were significantly downregulated in response to supraphysiological testosterone treatment, while the mRNA of MUC1 was significantly upregulated. The IHC results were consistent with the mRNA data and confirmed the changes in protein distribution in all treatment groups. Conclusions: A supraphysiological dose of testosterone may impair endometrial receptivity through dysregulation of the LIF signalling pathway, potentially affecting fertility. Full article

Malaria, a devastating disease caused by Plasmodium parasites, continues to pose a significant threat to global health, with increasing resistance to current antimalarial drugs. In this study, we employed an in silico approach to design and evaluate novel 2-pyrazoline carboxamide derivatives as potential protease inhibitors against Plasmodium falciparum. Our results show that all the designed ligands exhibit good drug-like properties, satisfying Lipinski’s rule of five, and have low toxicity profiles. Molecular docking studies revealed that five newly designed ligands (P5, P6, P7, P11, and P13) exhibit promising binding affinities and interactions with key protease enzymes involved in the hemoglobin degradation pathway, including Falcipain-2, Falcipain-3, and Plasmepsin-2 with PDB (Protein Data Bank) codes of 6JW9, 3BWK, and 1LF3, respectively. Notably, ligand P13 showed the strongest binding affinity with Falcipain-2, forming an additional hydrogen bond with CYS42, an essential residue for the enzyme’s catalytic activity. The interactions between the ligands and the enzymes suggest a competitive inhibition mechanism, with the potential to disrupt the hemoglobin degradation pathway and halt the parasite’s lifecycle. The biological implications of these findings are significant, as they suggest that these novel ligands could be effective against Plasmodium parasites, particularly in the context of increasing resistance to current antimalarial drugs. Overall, this study provides valuable insights into the potential of novel 2-pyrazoline carboxamide derivatives to serve as protease inhibitors against Plasmodium parasites, highlights their potential as a promising strategy for antimalarial drug development, and demonstrates the importance of in silico approaches in the discovery of novel therapeutics. Full article

This study aims to investigate the effect of a supraphysiological dose of testosterone on the levels of sex steroid hormones and the expression and distribution of sex steroid receptors in the uterus during the endometrial receptivity development period. In this study, adult female Sprague–Dawley rats (n = 24) were subcutaneously administered 1 mg/kg/day of testosterone alone or in combination with the inhibitors (finasteride or anastrozole or both) from day 1 to day 3 post-coitus, while a group of six untreated rats served as a control group. The rats were sacrificed on the evening of post-coital day 4 of to measure sex steroid hormone levels by ELISA. Meanwhile, gene expression and protein distribution of sex steroid receptors were analysed by quantitative polymerase chain reaction (qPCR) and immunohistochemistry (IHC), respectively. In this study, treatment with a supraphysiological dose of testosterone led to a significant reduction in oestrogen and progesterone levels compared to the control. The mRNA expression of the androgen receptor increased significantly in all treatment groups, while the mRNA expression of both the progesterone receptor and the oestrogen receptor-α decreased significantly in all treatment groups. The IHC findings of all sex steroid receptors were coherent with all mRNAs involved. This study shows that a supraphysiological dose of testosterone was able to interrupt the short period of the implantation window. This finding could serve as a basis for understanding the role of testosterone in endometrial receptivity in order to develop further therapeutic approaches targeting androgen-mediated disorders of endometrial receptivity. Full article

In this study, we investigate the symmetry analysis and explicit solutions for the Estevez–Mansfield–Clarkson (EMC) equation. Our main objectives are to identify the Lie point symmetries of the EMC equation, construct an optimal system of one-dimensional subalgebras, and reduce the EMC equation to a set of ordinary differential equations (ODEs). We employ the Riccati–Bernoulli sub-ODE method (RBSODE) to solve these reduced ODEs and obtain explicit solutions for the EMC model. The obtained solutions are validated using numerical analyses, and corresponding figures are presented to illustrate the physical implications of the derived solutions. Full article

Electric vehicles (EVs) have seen significant growth due to the increasing awareness about environmental concerns and the negative impacts of internal combustion engine vehicles (ICEVs). The electric vehicle landscape is rapidly evolving, with EV policies, battery, and charging infrastructure and electric vehicle-to-everything (V2X) at its forefront. This review study used a bibliometric analysis of the Scopus database to investigate the development of EV technology. This bibliometric study specifically focuses on analyzing electric vehicle trends, policy implications, lithium-ion batteries, EV battery management systems, charging infrastructure, EV smart charging technologies, and V2X. Through this detailed bibliometric analysis discussion, we aim to provide a better understanding of holistic EV technology and inspire further research in electric vehicles. The analysis covers the period from 1990 to 2022. This bibliometric analysis underscores the interplay of electric vehicle policies, technology, and infrastructure, specifically focusing on developments in battery management and the possibility of V2X technology. In addition, this bibliometric analysis suggests the synchronization of international electric vehicle policy, advancement of battery technology, and promotion of the use of EV smart charging and V2X systems. This bibliometric analysis emphasizes that the expansion of EVs and sustainable mobility relies on a comprehensive strategy that encompasses policy, technology, and infrastructure. This bibliometric analysis recommends fostering collaboration between different sectors to drive innovation and advancements in electric vehicle technology. Full article

Diabetes mellitus (DM) is a chronic endocrine disorder that affects more than 20 million people in the United States. DM-related complications affect multiple organ systems and are a significant cause of morbidity and mortality among people with DM. Of the numerous acute and chronic complications, atherosclerosis due to diabetic dyslipidemia is a condition that can lead to many life-threatening diseases, such as stroke, coronary artery disease, and myocardial infarction. The nuclear erythroid 2-related factor 2 (Nrf2) signaling pathway is an emerging antioxidative pathway and a promising target for the treatment of DM and its complications. This review aims to explore the Nrf2 pathway’s role in combating diabetic dyslipidemia. We will explore risk factors for diabetic dyslipidemia at a cellular level and aim to elucidate how the Nrf2 pathway becomes a potential therapeutic target for DM-related atherosclerosis. Full article

Type 1 diabetes (T1D) affects gastrointestinal (GI) motility, favoring gastroparesis, constipation, and fecal incontinence, which are more prevalent in women. The mechanisms are unknown. Given the G-protein-coupled estrogen receptor’s (GPER) role in GI motility, we investigated sex-related diabetes-induced epigenetic changes in GPER. We assessed GPER mRNA and protein expression levels using qPCR and Western blot analyses, and quantified the changes in nuclear DNA methyltransferases and histone modifications (H3K4me3, H3Ac, and H3K27Ac) by ELISA kits. Targeted bisulfite and chromatin immunoprecipitation assays were used to evaluate DNA methylation and histone modifications around the GPER promoter by chromatin immunoprecipitation assays in gastric and colonic smooth muscle tissues of male and female control (CTR) and non-obese diabetic (NOD) mice. GPER expression was downregulated in NOD, with sex-dependent variations. In the gastric smooth muscle, not in colonic smooth muscle, downregulation coincided with differences in methylation ratios between regions 1 and 2 of the GPER promoter of NOD. DNA methylation was higher in NOD male colonic smooth muscle than in NOD females. H3K4me3 and H3ac enrichment decreased in NOD gastric smooth muscle. H3K4me3 levels diminished in the colonic smooth muscle of NOD. H3K27ac levels were unaffected, but enrichment decreased in NOD male gastric smooth muscle; however, it increased in the NOD male colonic smooth muscle and decreased in the female NOD colonic smooth muscle. Male NOD colonic smooth muscle exhibited decreased H3K27ac levels, not female, whereas female NOD colonic smooth muscle demonstrated diminished enrichment of H3ac at the GPER promoter, contrary to male NOD. Sex-specific epigenetic mechanisms contribute to T1D-mediated suppression of GPER expression in the GI tract. These insights advance our understanding of T1D complications and suggest promising avenues for targeted therapeutic interventions. Full article

Incidence data from 17-year veterinary neoplasm surveillance and registration were reviewed. Most of the neoplastic cases diagnosed in Nigerian veterinary teaching hospitals (VTHs) were in the avian (49%) and canine species (44%). Fewer cases were recorded in the equine (3.2%), bovine (2.4%), ovine (1.5%), caprine (0.3%) and porcine (0.15%) species. Marek’s disease was the most prevalently diagnosed neoplastic disease of domestic animals in Nigerian VTHs from 2000–2017. Also, the Nigerian local breed had a higher mean distribution than any other dog breed and this was statistically significant (p < 0.05). Nearly all of the neoplastic cases diagnosed, were found in females (60.4%) and so the mean distribution of sex was statistically significant (p < 0.05). The digestive system, with 296 (46.25%) cases, was the anatomic location where the majority of the neoplastic cases were found. However, the mean distribution of different neoplastic anatomic sites was not statistically significant (p > 0.05). In conclusion, little emphasis is given to the appropriate diagnosis and recording of neoplastic cases that are diagnosed. The study provides information regarding the prevalence and distribution of tumours in different animal species consulted in Nigeria veterinary teaching hospitals. To illustrate all of this, ArcGIS software was used. Veterinary clinicians, pathologists and epidemiologists from Nigeria may benefit from the results of this study by freely accessing some specific data regarding the breed, the age group or the gender of some animal species diagnosed with different tumours. Full article

This paper presents a meticulous exploration of advanced machine learning techniques for precise forest type classification using multi-temporal remote sensing data within a woodland environment. The study comprehensively evaluates a diverse range of models, ranging from advanced (ensemble) machine learning (ML) methods to several finely tuned support vector machine (SVM) variants, with a specific focus on Bayesian-optimized SVM with a radial basis function (RBF) kernel. Our findings highlight the robust performance of the Bayesian-optimized SVM, achieving a high accuracy of up to 94.27% and average precision and recall of 94.46% and 94.27%, respectively. Notably, this accuracy aligns with the levels attained by acclaimed ensemble techniques such as random forest and CatBoost while also surpassing those of XGBoost and LightGBM. These results highlight the potential of these methodologies to significantly enhance forest type mapping accuracy compared to traditional (linear) SVM and black-box neural networks. This, in turn, can enable the reliable identification and quantification of key services, including carbon storage and erosion protection, intrinsic to the forest ecosystem. The findings of our comparative study emphasize the profound impact of employing and fine-tuning ML approaches in the realm of remote sensing-based environmental analysis. Full article

Software fault prediction (SFP) is vital for ensuring software system reliability by detecting and mitigating faults. Machine learning has proven effective in addressing SFP challenges. However, extensive fault data from historical repositories often lead to dimensionality issues due to numerous metrics. Feature selection (FS) helps mitigate this problem by identifying key features. This research enhances the Whale Optimization Algorithm (WOA) by combining truncation selection with a single-point crossover method to enhance exploration and avoid local optima. Evaluating the enhancement on 14 SFP datasets from the PROMISE repository reveals its superiority over the original WOA and other variants, demonstrating its potential for improved SFP. Full article