Promote mentor-mentee relationships among academics

LETTERS: RECENT concerns about the publication of substandard research articles by Malaysian academics has heightened the concerns about the quality of higher education and academicians in the country. A contributing factor is insufficient mentorship between seasoned and emerging scholars. Most individuals who enter into the world of academia perceive teaching as the primary role of the profession. However, the challenging reality of publish or perish sinks in, a task for which early-career researchers are often inadequately prepared. Mentorship plays an important role in sustaining knowledge creation and professional growth of academics towards strengthening and enhancing research quality

The role of sales personnel empathy and customer-oriented behaviour on female consumers' emotions and satisfaction: an empirical analysis

Even though online shopping and self-service options are popular, salespeople still play a crucial role in influencing women's decisions when buying intimate apparel. This study looks into how sales staff influence Asian female consumers' choices in this industry. By studying 301 people who shop for intimate apparel, the research explores how salespeople's empathetic interactions affect customers' emotions, relationships and overall satisfaction. Drawing on empirical data from 301 participants engaged in intimate apparel shopping, the research examines how empathic interactions by salespersons impact customers' emotions, relational outcomes and overall satisfaction. Findings reveal that affective empathic interactions significantly enhance salesperson competencies and customer-oriented behaviours, leading to heightened levels of satisfaction. Moreover, the study elucidates the mediating roles of customer competence and hedonic value in shaping satisfaction levels. The implications of these findings for improving customer satisfaction in intimate apparel retail are discussed

A critical analysis of smartphone usage among undergraduate nursing students

By promoting responsible usage and digital literacy, nursing programmes can equip students to navigate the digital landscape effectively, thereby enhancing their education and future practice

Evaluation of the Immunogenicity of T and B Cell Epitopes from the S And M Proteins of Sars-Cov-2 Wuhan and Omicron Strains in Balb/C Mice

The coronavirus disease (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in numerous infections and deaths. The emergence of SARS-CoV-2 variants of concern (VOCs) resulted in reductions in the protective efficacies of current mRNA and viral-vectored vaccines targeting the spike (S) protein from the SARS-CoV-2 Wuhan strain. A more promising strategy involves targeting highly conserved and immunogenic sequences from SARS-CoV-2 structural proteins to produce immune responses against the Wuhan strain and circulating VOCs. Recombinant protein vaccines could serve as a valuable vaccine development platform based on their high stability, safety, and immunogenicity in clinical development. This research project aimed to develop a recombinant protein vaccine against SARS-CoV-2. Antigens were identified through literature mining and derived from the SARS-CoV-2 S and membrane (M) in the form of six peptides specifying highly conserved B cell and T cell epitopes. The expressed recombinant protein of interest, GST-6Phis, was purified through ammonium sulphate precipitation, gel filtration, immobilized metal affinity chromatography (IMAC), nickel-nitrilotriacetic acid (Ni-NTA) histidine affinity chromatography, and a protein concentrator. Four groups of 5 BALB/c mice each were intramuscularly or intranasally immunized with 10 µg GST-6Phis or with PBS. Cellular and humoral responses were evaluated at 42 days’ post-immunization. Intramuscular administration of GST-6Phis resulted in IFN-γ secreting CD4+ T cells, while intranasal administration produced IFN-γ secreting CD8+ T cells. Robust IgG antibody responses, as represented by absorbance values and mean reciprocal antibody titters, resulted from the intramuscular and intranasal administration of GST-6Phis. Sera obtained from mice immunized both intramuscularly and intranasally with GST-6Phis contained neutralizing antibodies against the SARS-CoV-2 Wuhan strain, while intramuscular administration produced neutralizing antibodies against Omicron. In conclusion, the recombinant protein vaccine demonstrated the promise of utilizing conserved and immunogenic epitopes to produce immune responses against both the SARS-CoV-2 Wuhan and Omicron strains

Revisiting the environmental Kuznets curve: assessing the impact of climate policy uncertainty in the Belt and Road Initiative

Climate change repercussions such as temperature shifts and more severe weather occurrences are felt globally. It contributes to larger-scale challenges, such as climate change and biodiversity loss in food production. As a result, the purpose of this research is to develop strategies to grow the economy without harming the environment. Therefore, we revisit the environmental Kuznets curve (EKC) hypothesis, considering the impact of climate policy uncertainty along with other control variables. We investigated yearly panel data from 47 Belt and Road Initiative (BRI) nations from 1998 to 2021. Pooled regression, fixed effect, and the generalized method of moment (GMM) findings all confirmed the presence of inverted U-shaped EKC in BRI counties. Findings from this paper provide policymakers with actionable ideas, outlining a framework for bringing trade and climate agendas into harmony in BRI countries. The best way to promote economic growth and reduce carbon dioxide emissions is to push for trade and climate policies to be coordinated. Moreover, improving institutional quality is essential for strong environmental governance, as it facilitates the adoption of environmentally friendly industrialization techniques and the efficient administration of climate policy uncertainties

Investigating the role of SIRT 1/Autophagy/NF-kB regulatory axis in chemoresistance in Hepatocellular Carcinoma (HCC)

Liver cancer is the fifth most common cancer in the world and the third leading cause of cancer-related death worldwide. Hepatocellular carcinoma (HCC) which originates from the main liver cells (hepatocytes), accounting for about 90% of total liver cancer cases. HCC remains a major challenge in health as patients are often detected in the late stage with high mortality. HCC therapy becomes even more challenging in patients with drug resistance. Sirtuin 1 (SIRT 1), a member of the sirtuin family is known to regulate various oncogenic events such as cell survival, apoptosis, autophagy, tumourigenesis, metastasis and drug resistance in various cancers, but its role in HCC, particularly in chemoresistance is underexplored. Besides SIRT 1, the event of autophagy and nuclear factor kappa B (NF-κB) signaling are also involved in cancer chemoresistance. However, the regulatory role of the SIRT 1/autophagy/NF-κB signaling in HCC chemoresistance remains unclear. In the present study, a multikinase inhibitor sorafenib, was selected as a chemotherapeutic agent to generate chemoresistance cell lines. Sorafenib stands out as the first-line systemic treatment for advanced unresectable HCC and has demonstrated a notable improvement in patient survival. Cell viability was measured using MTS assay. Clonogenic assays were performed to determine the colony-forming ability of the parental and its respective chemoresistant cells. The expression of ATP binding cassette (ABC) transporters and the expression of SIRT 1, autophagy, and NF-κB were examined by western blot. Sorafeni resistant HCC cells demonstrated increased IC50 values of sorafenib, a greater number of clones were formed, and elevated expression of ATP binding cassette (ABC) transporters includes ABCB1, ABCC1 and ABCG2, which confirmed the acquired sorafenib. resistance. Next, a high expression of SIRT 1 was seen in chemoresistant HepG2 and Huh-7 cells when compared to its respective parental cells. Activation of autophagy was seen in chemoresistant Huh-7 cells, evidenced by reduction LC3A-I and LC3B-I expression, and increased LC3A-II and LC3B-II expression were observed, suggesting the conversion of LC3-I to LC3-II and reflecting the progression of autophagy. However, the upregulation of autophagy was not seen in chemoresistant HepG2 cells, suggesting that this upregulation could be cell-dependent, which requires further investigations. Activation of autophagy was significantly inhibited by silencing SIRT 1 expression, while NF-κB remains active following SIRT 1 -knockdown in both parental and resistance cell. Overall, the results of the present study suggest that autophagy is highly regulated by SIRT 1, while regulation of NF-κB may involve other signaling pathways. SIRT/ autophagy / NF-κB signaling provides a novel perspective on regulation of chemoresistance in HCC

Methane plume localization with enhanced self-best reduction and Gaussian improved particle swarm optimization (GiPSO)

Swarm intelligence is a branch of artificial intelligence that studies the collective behavior of groups of social animals such as birds, fish, and bees. It has been used to solve various dynamic problems, including gas leak detection in drone-based leak detection platforms. However, gas plume dispersion has dynamical characteristics often influenced by external environmental factors such as wind direction, wind speed, dispersion rate, and gas density. To investigate the adaptation of swarm intelligence with dynamic modification to further enhance its capability to optimize gas plume dispersion. The research focuses on three questions to enhance the drone swarm optimization algorithm. These three questions steer the research in three separate domains, which helps the evaluation of the performance of our research. The research question, problems, and objectives will be the research directed toward modifying Particle Swarm Optimization (PSO), namely Gaussian improved Particle Swarm Optimization (GiPSO). Firstly, how can swarm intelligence aid in engaging dynamically challenging optimization problems such as gas plume dispersion? To investigate this, our research will investigate the adaptation of the Gaussian gas plume in the simulation. Adapting the Gaussian gas plume model in the simulation provides the experiment with a realistic optimization problem for GiPSO to optimize in the simulation, where we can test the engagement of dynamically challenging optimization problems such as gas plume dispersions. Secondly, our research questions how the Gaussian gas plume model can address the adaptation of swarm intelligence in drone-based gas leakage detection. To address swarm intelligence adaptation in drone-based gas leakage detection, we investigate the existing swarm intelligence capability in optimizing dynamical problems in gas plume detection. Our research employs Gaussian improved Particle Swarm Optimization (GiPSO), derived from modifications implemented on Particle Swarm Optimization (PSO) with Z-axis coefficient clamping and Self-Best reduction mechanism. Z-axis coefficient Clamping provides safety and reduction of drone swarm controlled by GiPSO risk, with the physical collision with the petroleum refinery exhaust. Finally, the third question of our research is how the gas leakage detection algorithm’s performance can be improved when the drone population is low. This guides the research investigating how population growth can impact GiPSO in Optimising Dynamic Problems. To enhance the performance of the population study in GiPSO, the GiPSO self-best reduction mechanism allows GiPSO to re-disperse the swarm when the same particle has been retained as the global best, as it achieves the limitation controlled by the operator. The highlight of our algorithm, GiPSO, exhibits improvement in optimizing the source of leakage in high precision Objective Function Value (OFV). As the experiment setup benchmark specification of DJI Phantom 4 available flight time, GiPSO shows improvement with high success in localizing the source of leakage with population performance peak with 14 particles used in the drone swarm. These further answer our third research question concerning the performance of GiPSO with a low particle population

Impact of UV radiation on Mxene-mediated tubulin dissociation and mitochondrial apoptosis in breast cancer cells.

Breast cancer is a global health concern that requires personalized therapies to prevent relapses, as conventional treatments may develop resistance over time. Photothermal therapy using spectral radiation or intense light emission is a broad-spectrum treatment that induces hyperthermia-mediated cancer cell death. MXene, a two-dimensional material, has been reported to have potential biological applications in photothermal therapy for cancer treatment. In this study, we investigated the apoptotic activity of MXene and UV-irradiated MXene in MCF-7 breast cancer cells by treating them with varying concentrations of MXene. The cytotoxicity of MXene and UV was evaluated by analyzing cellular morphology, nuclei condensation, caspase activation, and apoptotic cell death. We also assessed the effect of the combined treatment on the expression and cellular distribution of Tubulin, a key component of microtubules required for cell division. At low concentrations of MXene (up to 100 µg/ml), the level of cytotoxicity in MCF-7 cells was low. However, the combined treatment of MXene and UV resulted in a synergistic increase in cytotoxicity, causing rounded cellular morphology, condensed nuclei, caspase activation, and apoptotic cell death. Furthermore, the treatment reduced Tubulin protein expression and cellular distribution, indicating a potent inducer of cell death with potential application for cancer treatment. The study demonstrates that the combined treatment of MXene and UVB irradiation is a promising strategy for inducing apoptotic cell death in breast cancer cells, suggesting its potential as a therapeutic intervention for breast cancer

A simulation study on the radiosensitization properties of gold nanorods

Objective. Gold nanorods (GNRs) have emerged as versatile nanoparticles with unique properties, holding promise in various modalities of cancer treatment through drug delivery and photothermal therapy. In the rapidly evolving field of nanoparticle radiosensitization (NPRS) for cancer therapy, this study assessed the potential of gold nanorods as radiosensitizing agents by quantifying the key features of NPRS, such as secondary electron emission and dose enhancement, using Monte Carlo simulations.Approach. Employing the TOPAS track structure code, we conducted a comprehensive evaluation of the radiosensitization behavior of spherical gold nanoparticles and gold nanorods. We systematically explored the impact of nanorod geometry (in particular size and aspect ratio) and orientation on secondary electron emission and deposited energy ratio, providing validated results against previously published simulations.Main results. Our findings demonstrate that gold nanorods exhibit comparable secondary electron emission to their spherical counterparts. Notably, nanorods with smaller surface-area-to-volume ratios (SA:V) and alignment with the incident photon beam proved to be more efficient radiosensitizing agents, showing superiority in emitted electron fluence. However, in the microscale, the deposited energy ratio (DER) was not markedly influenced by the SA:V of the nanorod. Additionally, our findings revealed that the geometry of gold nanoparticles has a more significant impact on the emission of M-shell Auger electrons (with energies below 3.5 keV) than on higher-energy electrons.Significance. This research investigated the radiosensitization properties of gold nanorods, positioning them as promising alternatives to the more conventionally studied spherical gold nanoparticles in the context of cancer research. With increasing interest in multimodal cancer therapy, our findings have the potential to contribute valuable insights into the perspective of gold nanorods as effective multipurpose agents for synergistic photothermal therapy and radiotherapy. Future directions may involve exploring alternative metallic nanorods as well as further optimizing the geometry and coating materials, opening new possibilities for more effective cancer treatments

An advanced deep learning model for predicting water quality index

Predicting a water quality index (WQI) is important because it serves as an important metric for assessing the overall health and safety of water bodies. Our paper develops a new hybrid model for predicting the WQI. The study uses a combination of a convolutional neural network (CNN), clockwork recurrent neural network (Clockwork RNN), and M5 Tree (CNN-CRNN-M5T) to predict a WQI. The M5T model lacks advanced operators for extracting meaningful data from water quality parameters, so the new model enhances its ability to analyze intricate patterns. The general linear model analysis of variance (GLM-ANOVA) is an improved version of the ANOVA. Our study uses the GLM-ANOVA to determine significant inputs. As all input variables had p < 0.050, they were defined as significant variables. Results showed that NH-NL and PH had the highest and lowest impact, respectively. Our study used the CNN-CRNN-M5T, CNN-CRNN, CRNN-M5T, CNN-M5T, CRNN, CNN, and M5T models to predict the WQI of a large basin in Malaysia. The CNN-CRNN decreased testing mean absolute error (MAE) of the CRNN, CNN, and M5T models by 2.1 %, 12 %, and 15 %, respectively. The CNN-CRNN-M5T model increased Nash–Sutcliffe efficiency coefficient of the other models by 4–20 % and 2.1–19 %, respectively. The CNN-CRNN-M5T model was a reliable tool for spatial and temporal predictions of WQI