Search Results (86)

This study aims to explore the associations between menstrual health, lifestyle behaviors, biological traits, and coping strategies among female students at a Vietnamese medical university. A cross-sectional survey was conducted among 884 female students across five academic majors. Data on demographics, menstrual patterns, biological characteristics, lifestyle behaviors, and coping mechanisms were collected. Statistical analyses included descriptive statistics, correlation, and logistic regression to identify significant predictors of self-reported menstrual changes post university admission. Of the 884 participants, 49.8% reported menstrual changes after entering university. Among the lifestyle-related factors, increased daily electronic use (mean = 5.83 h) and later bedtimes (mean = 23:58) were associated with menstrual change (p < 0.01). Older age and higher academic year emerged as significant predictors of menstrual changes (p < 0.001). Additionally, students with blood groups A and B exhibited a higher risk compared to those with group O (p < 0.05), and Rh-positive status was also significantly associated with menstrual changes (p = 0.05). In terms of knowledge and coping practices, students who had premenstrual syndrome awareness since school were significantly less likely to report menstrual changes (p = 0.003). Although use of pain relief, particularly painkillers, correlated with higher reported pain severity, it was not directly linked to menstrual change. On the other hand, clinic consultations were positively associated with menstrual changes (p = 0.003), while students who relied on their mothers as counselors exhibited a protective association (p = 0.001). Menstrual health in university-aged women is influenced by a complex interplay of lifestyle behaviors, biological traits, and menstrual knowledge. Early education and structured coping support may serve as protective factors. The findings call for targeted menstrual health programs in university settings. Full article

In emerging markets like Vietnam, where student borrowers often lack traditional credit histories, accurately predicting loan eligibility remains a critical yet underexplored challenge. While machine learning and deep learning techniques have shown promise in credit scoring, their comparative performance in the context of student loans has not been thoroughly investigated. This study aims to evaluate and compare the predictive effectiveness of four supervised learning models—such as Random Forest, Gradient Boosting, Support Vector Machine, and Deep Neural Network (implemented with PyTorch version 2.6.0)—in forecasting student credit eligibility. Primary data were collected from 1024 university students through structured surveys covering academic, financial, and personal variables. The models were trained and tested on the same dataset and evaluated using a comprehensive set of classification and regression metrics. The findings reveal that each model exhibits distinct strengths. Deep Learning achieved the highest classification accuracy (85.55%), while random forest demonstrated robust performance, particularly in providing balanced results across classification metrics. Gradient Boosting was effective in recall-oriented tasks, and support vector machine demonstrated strong precision for the positive class, although its recall was lower compared to other models. The study highlights the importance of aligning model selection with specific application goals, such as prioritizing accuracy, recall, or interpretability. It offers practical implications for financial institutions and universities in developing machine learning and deep learning tools for student loan eligibility prediction. Future research should consider longitudinal data, behavioral factors, and hybrid modeling approaches to further optimize predictive performance in educational finance. Full article

COVID-19-associated invasive fungal infections (CAIFIs) contribute to increased mortality and morbidity rates. This study explores the epidemiology, laboratory parameters, radiological characteristics, treatments, and 30-day mortality risks of CAIFI in critically ill intubated patients while also evaluating factors associated with prolonged mechanical ventilation (PMV) in this population. Adults admitted to a tertiary hospital from 1 April 2021 to 31 March 2022 who were diagnosed with severe COVID-19, required invasive mechanical ventilation, and developed invasive fungal infection (IFI) during hospitalization were analyzed in this retrospective cohort study. Among 150 patients, 65 (43.3%) required PMV, with an in-hospital mortality rate of 64%. Candida albicans (47%) and Aspergillus fumigatus (27%) were the most prevalent pathogens. Multivariate analysis revealed that COVID-19 vaccination (adjusted odds ratio, aOR = 0.155, 95% confidence interval, 95% CI = 0.029–0.835, p = 0.030) and higher serum protein levels (aOR = 0.900, 95% CI = 0.819–0.989, p = 0.028) were significantly associated with a reduced risk of PMV. Meanwhile, elevated glucose levels (hazard ratio, HR = 1.047, 95% CI = 1.003–1.093, p = 0.036) and an increased neutrophil-to-lymphocyte ratio (HR = 1.024, 95% CI = 1.009–1.039, p = 0.002) were correlated with a greater 30-day mortality risk. Tracheostomy emerged as a protective factor, significantly reducing the risk of 30-day mortality (HR = 0.273, 95% CI = 0.127–0.589, p = 0.001). In this single-center study, patients with CAIFI exhibit a high mortality rate. Clinicians should maintain vigilance for IFI in critically ill COVID-19 patients with mechanical ventilation. Full article

This paper aims to comprehensively investigate the axial and flexural performance of circularized square reinforced concrete (CSRC) columns discontinuously and continuously confined with carbon fiber-reinforced polymer (CFRP). The test results of twenty reinforced concrete (RC) columns, consisting of twelve CSRC columns and eight square RC (SRC) columns, are presented to compare the axial and flexural performance of discontinuously CFRP-confined CSRC (CFRPC-CSRC) columns with those of continuously CFRPC-CSRC and CFRPC-SRC columns. It was found that to enhance the load-bearing capacity (LBC) of SRC columns, circularizing the SRC columns before applying either discontinuous or continuous FRP confinement was more effective than applying continuous FRP confinement alone. Additionally, the theoretical strength interaction diagrams of test columns were developed using the strip-by-strip method, showing a strong agreement with the experimental results. Full article

The evaporation dynamics of sessile droplets on re-entrant microstructures are critical for applications in microfluidics, thermal management, and self-cleaning surfaces. Re-entrant structures, such as mushroom-like shapes with overhanging features, trap air beneath droplets to enhance non-wettability. The present study examines the evaporation of a water droplet on silicon carbide (SiC) and silicon dioxide (SiO₂) re-entrant structures, focusing on the effects of material composition and solid area fraction on volume reduction, contact angle, and evaporation modes. Using surface free energy (SFE) as an indicator of wettability, we find that the low SFE of SiC promotes quick depinning and contact line retraction, resulting in shorter CCL phases across different structures. For instance, the CCL phase accounts for 55–59% of the evaporation time on SiC surfaces, while on SiO₂ it extends to 51–68%, reflecting a 7–23% increase in duration due to stronger pinning effects. Additionally, narrower pillar gaps, which increase the solid area fraction, further stabilize droplets by extending both CCL and constant contact angle (CCA) phases, while wider gaps enable faster depinning and evaporation. These findings illustrate how hydrophobicity (via SFE) and structural geometry (via solid area fraction) influence microscale interactions, offering insights for designing surfaces with optimized liquid management properties. Full article

Surface wettability, the interaction between a liquid droplet and the surface it contacts, plays a key role in influencing droplet behavior and flow dynamics. There is a growing interest in designing surfaces with tailored wetting properties across diverse applications. Advanced fabrication techniques that create surfaces with unique wettability offer significant innovation potential. This study investigates the wettability transition of laser-textured anisotropic surfaces featuring shark skin-inspired microstructures using four post-processing methods: spray coating, isopropyl alcohol (IPA) treatment, silicone oil treatment, and silanization. The impact of each method on surface wettability was assessed through water contact angle measurements, scanning electron microscopy (SEM), and laser scanning microscopy. The results show a transition from superhydrophilic behavior on untreated laser-textured surfaces to various (super)hydrophobic states following surface treatment. Chemical treatments produced different levels of hydrophobicity and anisotropy, with silanization achieving the highest hydrophobicity and long-term stability, persisting for one year post-treatment. This enhancement is attributed to the low surface energy and chemical properties of silane compounds, which reduce surface tension and increase water repellence. In conclusion, this study demonstrates that post-processing techniques can effectively tailor surface wettability, enabling a wide range of wetting properties with significant implications for practical applications. Full article

Breast cancer remains a prevalent global health challenge, with tumor-removal surgeries being among the most common treatments but often leading to aesthetic defects. Adipose-derived stem cell (ADSC)-enriched fat grafting in breast reconstruction offers promising therapeutic benefits. However, concerns about its oncological safety persist, particularly regarding the potential risks of promoting cancer recurrence. This study investigated the effects of ADSCs on breast cancer progression by coculturing ADSCs with the MCF-7 breast cancer cell line for a short cell cultivation period of 3 days. We performed an RNA-seq analysis to identify significant transcriptomic changes in cocultured MCF-7 cells and carried out functional enrichment analyses to uncover key biological pathways influenced by ADSCs. Our findings revealed that transcriptomic alterations in MCF-7 cells are linked to aggressive cancer traits, including the upregulation of epithelial–mesenchymal transition (EMT) and the HIF-1 signaling pathway, which indicate a shift toward aerobic glycolysis. Some of the observed gene expression changes also correlated with relapse risk and mortality. These findings underscore the need for further research to explore the implications of these genes and pathways in driving aggressive cancer phenotypes and assess the safety of ADSCs in clinical settings. Full article

Microstructured surfaces are renowned for their unique properties, such as waterproofing and low adhesion, making them highly applicable in the biomedical field. These surfaces play a crucial role in influencing cell response by mimicking the native microenvironment of biological tissues. In this study, we engineered a series of biomimetic micropatterned surfaces using polydimethylsiloxane (PDMS) to explore their effects on primary breast cancer cell lines, contrasting these effects with those observed on conventional flat surfaces. The surface topography was varied to direct cells’ attachment, growth, and morphology. Our findings elucidate that surface-free energy is not merely a background factor but plays a decisive role in cell dynamics, strongly correlating with the spreading behaviour of breast cancer cells. Notably, on micropillar surfaces with high surface-free energy, an increase in the population of cancer cells was observed. Conversely, surfaces characterised by lower surface-free energies noted a reduction in cell viability. Moreover, the structural parameters, such as the gaps and diameters of the pillars, were found to critically influence cellular dispersion and adherence, underscoring the importance of the microstructures’ topography in biomedical applications. These insights pave the way for designing advanced microstructured surfaces tailored to specific cellular responses, opening new avenues for targeted cancer therapies and tissue engineering. Full article

Candida albicans is an opportunistic fungus that becomes pathogenic and problematic under certain biological conditions. C. albicans may cause painful and uncomfortable symptoms, as well as deaths in immunocompromised patients. Therefore, early detection of C. albicans is essential. However, conventional detection methods are costly, slow, and inaccessible to women in remote or developing areas. To address these concerns, we have developed a wearable and discrete naked-eye detectable colorimetric platform for C. albicans detection. With some modification, this platform is designed to be directly adhered to existing feminine hygiene pads. Our platform is rapid, inexpensive, user-friendly, and disposable and only requires three steps: (i) the addition of vaginal fluid onto sample pads; (ii) the addition of gold nanoparticle gel and running buffer, and (iii) naked eye detection. Our platform is underpinned by selective thiolated aptamer-based recognition of 1,3-β-D glucan molecules—a hallmark of C. albicans cell walls. In the absence of C. albicans, wearable sample pads turn bright pink. In the presence of C. albicans, the wearable pads turn dark blue due to significant nanoparticle target-induced aggregation. We demonstrate naked-eye colorimetric detection of 4.4 × 10⁶ C. albicans cells per ml and nanoparticle stability over a pH range of 3.0–8.0. We believe that this proof-of-concept platform has the potential to have a significant impact on women’s health globally. Full article

Micro elastofluidics is a transformative branch of microfluidics, leveraging the fluid–structure interaction (FSI) at the microscale to enhance the functionality and efficiency of various microdevices. This review paper elucidates the critical role of advanced computational FSI methods in the field of micro elastofluidics. By focusing on the interplay between fluid mechanics and structural responses, these computational methods facilitate the intricate design and optimisation of microdevices such as microvalves, micropumps, and micromixers, which rely on the precise control of fluidic and structural dynamics. In addition, these computational tools extend to the development of biomedical devices, enabling precise particle manipulation and enhancing therapeutic outcomes in cardiovascular applications. Furthermore, this paper addresses the current challenges in computational FSI and highlights the necessity for further development of tools to tackle complex, time-dependent models under microfluidic environments and varying conditions. Our review highlights the expanding potential of FSI in micro elastofluidics, offering a roadmap for future research and development in this promising area. Full article

The problem of data enrichment to train visual SLAM and VO construction models using deep learning (DL) is an urgent problem today in computer vision. DL requires a large amount of data to train a model, and more data with many different contextual and conditional conditions will create a more accurate visual SLAM and VO construction model. In this paper, we introduce the TQU-SLAM benchmark dataset, which includes 160,631 RGB-D frame pairs. It was collected from the corridors of three interconnected buildings comprising a length of about 230 m. The ground-truth data of the TQU-SLAM benchmark dataset were prepared manually, including 6-DOF camera poses, 3D point cloud data, intrinsic parameters, and the transformation matrix between the camera coordinate system and the real world. We also tested the TQU-SLAM benchmark dataset using the PySLAM framework with traditional features such as SHI_TOMASI, SIFT, SURF, ORB, ORB2, AKAZE, KAZE, and BRISK and features extracted from DL such as VGG, DPVO, and TartanVO. The camera pose estimation results are evaluated, and we show that the ORB2 features have the best results ($Er{r}_d$ = 5.74 mm), while the ratio of the number of frames with detected keypoints of the SHI_TOMASI feature is the best ($r_d=98.97\%$). At the same time, we also present and analyze the challenges of the TQU-SLAM benchmark dataset for building visual SLAM and VO systems. Full article

This first study investigated the presence of dioxins and furans in river sediments around a craft village in Vietnam, focusing on Secondary Steel Recycling. Sediment samples were collected from various locations along the riverbed near the Da Hoi Secondary Steel Recycling village in Bac Ninh province. The analysis was conducted using a HRGC/HRMS-DFS device, detecting a total of 17 dioxin/furan isomers in all samples, with an average total concentration of 288.86 ng/kg d.w. The concentrations of dioxin/furan congeners showed minimal variation among sediment samples, ranging from 253.9 to 344.2 ng/kg d.w. The predominant compounds in the dioxin group were OCDD, while in the furan group, they were 1,2,3,4,6,7,8-HpCDF and OCDF. The chlorine content in the molecule appeared to be closely related to the concentration of dioxins and their percentage distribution. However, the levels of furan isomers did not vary significantly. The distribution of these compounds was not dependent on the flow direction, as they were mainly found in solid waste and are not water-soluble. Although the hepta and octa congeners had high concentrations, when converted to TEQ values, the tetra and penta groups (for dioxins) and the penta and hexa groups (for furans) contributed more to toxicity. Furthermore, the source of dioxins in sediments at Da Hoi does not only originate from steel recycling production activities but also from other combustion sites. The average total toxicity was 10.92 ng TEQ/kg d.w, ranging from 4.99 to 17.88 ng TEQ/kg d.w, which did not exceed the threshold specified in QCVN 43:2017/BTNMT, the National Technical Regulation on Sediment Quality. Nonetheless, these levels are still concerning. The presence of these toxic substances not only impacts aquatic organisms in the sampled water environment but also poses potential health risks to residents living nearby. Full article

Menstruation and menstrual-related symptoms, stress, and lifestyle factors can significantly affect the health and academic lives of university students. This study aimed to explore menstruation-related symptoms and associated factors among female university students in Vietnam. This study used a cross-sectional design. Data from 349 students were analyzed for menstrual and menstrual-related symptoms using the Menstrual Distress Questionnaire (MDQ). We found that 92.0% and 98.9% of the students had menstrual-related symptoms in the pre- and intra-menstrual phases, respectively. The most common symptoms in each phase were backache (71.1%) in the pre-menstrual phase and fatigue (90.3%) in the intra-menstrual phase. Menstruation-related symptoms were associated with stress. These results contribute significantly to our understanding of menstrual health among Vietnamese university students. These findings imply the need to strengthen support systems for university students, especially by providing proper information about menstruation and managing stress. Full article

Fungal pathogens such as Candida albicans have significant impacts on women’s health and the economy worldwide. Current detection methods often require access to laboratory facilities that are costly, inconvenient, and slow to access. This often leads to self-diagnosis, self-treatment and eventual antifungal resistance. We have created a rapid (within five minutes), cost-effective, and user-friendly method for the early detection of Candida albicans. Our platform utilises aptamer-tagged-gold-core-shell nanoparticles for Candida albicans detection based on the presence of 1,3-β-d glucan molecules. Nanoparticle aggregation occurs in the presence of Candida albicans fungal cells, causing a redshift in the UV-visible absorbance, turning from pink/purple to blue. This colour change is perceptible by the naked eye and provides a “yes”/“no” result. Our platform was also capable of detecting Candida albicans from individual yeast colonies without prior sample processing, dilution or purification. Candida albicans yeast cells were detected with our platform at concentrations as low as 5 × 10⁵ cells within a 50 μL sample volume. We believe that this technology has the potential to revolutionise women’s health, enabling women to test for Candida albicans accurately and reliably from home. This approach would be advantageous within remote or developing areas. Full article