Despite publishing and securing research grants being obligatory in research universities, the literature on the factors influencing academic productivity is relatively scarce. Thus, in this study, we aimed to determine the personal and behavioural-related factors that influence the culture of publishing and securing research grants among academicians with lower research-related performance. This cross-sectional study was conducted among 49 academic staff members of Universiti Kebangsaan Malaysia (UKM). A self-administered questionnaire consisting of personal, attitude and behavioural (barriers, perceived stress scale, work extrinsic and intrinsic motivation scale, psychological well-being scale, and basic needs satisfaction scale) questions were distributed during a workshop and online. Simple linear regression (SLR) analyses were performed for each variable, followed by multiple linear regression (MLR) to identify the associated factors of research output. After adjusting for covariates, having a doctoral degree (β=0.396, 95% CI=0.221-2.146, p<0.05) and integrated regulation (β=0.574, 95% CI=0.036-3.612, p<0.05) were found to be associated with research grant acquisition (R2=0.273). Moreover, increasing age (β=0.426, 95% CI=0.088-0.397, p<0.05), living alone (β=0.331, 95% CI=0.944-6.626, p<0.05), having a doctoral degree (β=0.248, 95% CI=0.174-6.747, p<0.05), environmental mastery (β=0.318, 95% CI=0.013-0.347, p<0.05), self-acceptance (β=0.284, 95% CI=0.010-0.242, p<0.05), satisfaction incompetence (β=0.273, 95% CI=0.001-0.200, p<0.05) and relatedness (β=0.280, 95% CI=0.001-0.116, p<0.05) were found to be the factors that influence the publications produced among participants (R2 =0.423). The findings of this study could be used by management to formulate effective strategies to increase the productivity of academics in their research-related performance.

Patient's need for endodontic treatment in a short period is on the rise nowadays. Technological developments in instruments and materials are allowing single-visit endodontic treatment to be performed. The success of an endodontic treatment is also influenced by the restoration, in consideration of the remaining tooth structure. This becomes a challenge for the clinician if the cavities are large or if minimal tooth structure remains, so it needs a
minimally invasive restoration like morphology-driven preparation technique. This case report aims to describe the single-visit endodontic treatment and minimally invasive indirect restoration of the mandibular first molar. A 13-year-old girl came to the Dental Hospital of Hasanuddin University with complaints of cavities in the mandibular left posterior tooth and pain for one month. The patient had taken analgesic medication but was afforded no relief. Intraoral examination showed tooth #36 with extensive cavities reaching the proximal area, a positive thermal test, and no pain to percussion. The case diagnosis was asymptomatic irreversible pulpitis. The treatment plan was a single-visit endodontic treatment and minimally invasive indirect restoration.

Voltage-gated sodium channels (VGSCs) play pivotal roles in cancer progression and have emerged as promising therapeutic targets and biomarkers. VGSCs comprise multiple subtypes with distinct tissue distributions, influencing tumour characteristics in different ways. Among these, the tetrodotoxin-sensitive α-subunits and the β1 subunit, commonly found in breast cancer, have been implicated in metastasis and tumour aggressiveness. The NaV1.5 channel and its neonatal variant (nNaV1.5) are overexpressed in aggressive cancers such as breast, prostate, colorectal, and lung cancers, thereby enhancing their invasive capacity. nNaV1.5 is particularly significant due to its tumour-specific expression and strong association with poor prognosis, especially in breast cancer, where it regulates cell proliferation, invasion, and tumour microenvironment remodelling. This review highlights nNaV1.5 as a critical ion channel that drives metastasis through ion regulation, extracellular acidification, and cytoskeletal remodelling. We further evaluate current therapeutic strategies, including siRNA, monoclonal antibodies, and small-molecule inhibitors, while addressing translational challenges such as tumour heterogeneity, drug delivery limitations, and off-target cardiotoxicity due to its similarity with the adult isoform. In addition, we explore the potential of nNaV1.5 as a biomarker subject to epigenetic regulations by factors including RE1-silencing transcription factor (REST) and histone deacetylase 2 (HDAC2), which may facilitate patient stratification and treatment optimization. By integrating mechanistic insights, therapeutic opportunities, and translational challenges, this review goes beyond descriptive summaries to provide a framework for advancing nNaV1.5 research from preclinical studies toward clinical application in cancer therapy.

Objectives: Hypertension, commonly known as high blood pressure, is a prevalent and serious condition affecting a significant portion of the adult population globally. It is a chronic medical issue that, if left unaddressed, can lead to severe health complications, including kidney problems, heart disease, and stroke. This study aims to develop a feature selection model using the XGBoost algorithm to identify specific single nucleotide polymorphisms (SNPs) as biomarkers for detecting hypertension risk. Methods: We propose using the high dimensionality of genetic variations (i.e., SNPs) to build a classifier model for prediction. In this study, SNPs were used as markers for hypertension in patients. We utilized the OpenSNP dataset, which includes 19,697 SNPs from 2,052 samples. Extreme gradient boosting (XGBoost) is an ensemble machine learning method employed here for feature selection, which incrementally adjusts weights in a series of steps. Results: The experimental results identified 292 SNPs that exhibited high performance, with an F1-score of 98.55%, precision of 98.73%, recall of 98.38%, and overall accuracy of 98%. This study provides compelling evidence that the XGBoost feature selection method outperforms other representative feature selection methods, such as genetic algorithms, analysis of variance, chi-square, and principal component analysis, in predicting hypertension risk, demonstrating its effectiveness. Conclusions We developed a model for predicting hypertension using the SNPs dataset. The high dimensionality of SNP data was effectively managed to identify significant features as biomarkers using the XGBoost feature selection method. The results indicate high performance in predicting the risk of hypertension.

The effects of food-simulating liquids (FSLs) on the microhardness and surface roughness: Luna (LN, conventional nanohybrid), Aura Bulk Fill (AB, bulk-fill composite), and Stela Automix (SA, self-cured composite) were evaluated. The materials were exposed to air as control, artificial saliva and three FSLs: 50% ethanol-water solution, 0.02N citric acid, and heptane-over different time intervals to compare their performance. Microhardness and surface roughness were measured at baseline, seven days, and 30 days after immersion. Two-way ANOVA revealed significant interactions between FSLs and materials for both microhardness and surface roughness (p<0.001). All materials showed a decrease in microhardness and an increase in surface roughness over time, except when exposed to air. LN exhibited the highest microhardness and lowest surface roughness, while SA demonstrated the lowest microhardness and the highest surface roughness in all FSLs. SA's surface roughness decreased in citric acid, potentially due to its lower filler content, which may result in a less durable surface. Ethanol and citric acid had the most detrimental effects on the microhardness of all materials, while heptane caused the most significant surface roughness changes in SA. These results highlight the material-specific responses to FSLs, with important implications for material selection and restoration longevity in clinical practice.

Introduction: Conservative treatment in flexible flat foot (FFF) is preferred in most cases; only in symptomatic and conservative-refractory cases is surgical treatment proposed. Subtalar arthroereisis (STA) is a well-established technique, but there remains a paucity of information surrounding the effectiveness and outcome. The aim of this study was to evaluate radiological and functional outcome of STA in FFF. Materials and methods: This study is a case series of 15ft with symptomatic FFF treated by STA from a period of 2018-2024. Mean age at the time of surgery was 10.1±0.83 years. There was a significant increase in both American Orthopaedic Foot and Ankle Society score (AOFAS) and The Oxford Ankle Foot Questionnaire for Children (OxAFQ-C) subjective scoring with p-value of <0.001. Degree of valgus correction was statistically significant (p=0.001). There were significant differences between preand post-operative lateral radiographic measurements with p-value of <0.005. One patient with the lowest radiological angle of correction and increased femoral anteversion had loosened implant in two years follow-up. Results: Despite results in earlier studies, recent evidence, including randomised control trials, supports the reliability of STA in managing FFF, advocating its use over traditional osteotomies due to fewer complications and quicker rehabilitation periods. In our study, all subjects had significant improvement in lateral radiographic measurements and subjective scoring evaluation during follow-up. Conclusion: STA is an option for surgical treatment in symptomatic FFF patients. In this study, all presented subjects showed overall satisfying functional and radiological outcome.

Objectives: Hypertension, commonly known as high blood pressure, is a prevalent and serious condition affecting a significant portion of the adult population globally. It is a chronic medical issue that, if left unaddressed, can lead to severe health complications, including kidney problems, heart disease, and stroke. This study aims to develop a feature selection model using the XGBoost algorithm to identify specific single nucleotide polymorphisms (SNPs) as biomarkers for detecting hypertension risk. Methods: We propose using the high dimensionality of genetic variations (i.e., SNPs) to build a classifier model for prediction. In this study, SNPs were used as markers for hypertension in patients. We utilized the OpenSNP dataset, which includes 19,697 SNPs from 2,052 samples. Extreme gradient boosting (XGBoost) is an ensemble machine learning method employed here for feature selection, which incrementally adjusts weights in a series of steps. Results: The experimental results identified 292 SNPs that exhibited high performance, with an F1-score of 98.55%, precision of 98.73%, recall of 98.38%, and overall accuracy of 98%. This study provides compelling evidence that the XGBoost feature selection method outperforms other representative feature selection methods, such as genetic algorithms, analysis of variance, chi-square, and principal component analysis, in predicting hypertension risk, demonstrating its effectiveness. Conclusions We developed a model for predicting hypertension using the SNPs dataset. The high dimensionality of SNP data was effectively managed to identify significant features as biomarkers using the XGBoost feature selection method. The results indicate high performance in predicting the risk of hypertension.