Development of novel drugs or drug delivery systems has attracted much attention of researchers. In this study, we aimed to prepare 2 new Pt(II) complexes with thiosemicarbazone and their ...nanoformulations for cancer treatment application. Pt(II)-camphor thiosemicarbazone/P1 and Pt(II)-camphor 4-phenyl thiosemicarbazone/P2) were successfully prepared and structurally confirmed by MS, IR, 1H-NMR, UV–vis spectroscopies and thermal analysis. From the complexes, 2 PLGA-based nanoformulations (nP1 and nP2) were synthesised with the average size of 50 nm by emulsification/evaporation method and investigated for their toxicity against Hep-G2, LU-1 and RD cancer cell lines. The results show that the Pt(II) complexes and their nanoformulations were potential for chemotherapy.
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To date, the prevalence of commonly used plastics like Polyethylene terephthalate (PET), polylactic acid (PLA), and polybutylene terephthalate (PBT) extends across diverse industries, ...from textiles to beverage bottles and daily packaging applications. Originally designed for up to 50 years of durable shelf life, these plastics face accelerated disposal challenges due to the pervasive “throw-away” culture. The rapid expansion of single-use plastic manufacturing, notably PET, has led to an astonishing global output of one million tons of plastic each year, highlighting the urgent requirement for efficient solutions in managing plastic waste. Carbon-based nanomaterials derived from PET are synthesized using chemical reactions in solution or high-temperature environments. This review discusses molten salt, hydrothermal, and one-step solvent-based synthesis techniques. We investigate advances in converting PET plastic into nanostructured materials, revealing their potential for energy storage, adsorption, supercapacitors, and sensors. As we navigate the challenges of plastic waste, this review scrutinizes the environmental impact by bridging the gap between plastic pollution and the utilization of upcycled nanomaterials of these pioneering methods, offering insights into their sustainability.
Mortality from dengue infection is mostly due to shock. Among dengue patients with shock, approximately 30% have recurrent shock that requires a treatment change. Here, we report development of a ...clinical rule for use during a patient's first shock episode to predict a recurrent shock episode.
The study was conducted in Center for Preventive Medicine in Vinh Long province and the Children's Hospital No. 2 in Ho Chi Minh City, Vietnam. We included 444 dengue patients with shock, 126 of whom had recurrent shock (28%). Univariate and multivariate analyses and a preprocessing method were used to evaluate and select 14 clinical and laboratory signs recorded at shock onset. Five variables (admission day, purpura/ecchymosis, ascites/pleural effusion, blood platelet count and pulse pressure) were finally trained and validated by a 10-fold validation strategy with 10 times of repetition, using a logistic regression model.
The results showed that shorter admission day (fewer days prior to admission), purpura/ecchymosis, ascites/pleural effusion, low platelet count and narrow pulse pressure were independently associated with recurrent shock. Our logistic prediction model was capable of predicting recurrent shock when compared to the null method (P < 0.05) and was not outperformed by other prediction models. Our final scoring rule provided relatively good accuracy (AUC, 0.73; sensitivity and specificity, 68%). Score points derived from the logistic prediction model revealed identical accuracy with AUCs at 0.73. Using a cutoff value greater than -154.5, our simple scoring rule showed a sensitivity of 68.3% and a specificity of 68.2%.
Our simple clinical rule is not to replace clinical judgment, but to help clinicians predict recurrent shock during a patient's first dengue shock episode.
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•Carbon dots (CDs) were synthesized from glucose and acid citric via the hydrothermal method.•The Raman signal of hydrogen bonding network was recorded on the photoluminescence (PL) ...spectra of the prepared CDs.•The hydrogen bonding network reduced the PL intensity of CDs in weak alkaline conditions.•A new hypothesis based on the interaction between the hydrogen bonding network and CD particles was proposed to explain the pH-sensing mechanism of the CDs.
Recently, carbon dots (CDs) have received huge attention from scientists around the globe due to their unique properties, including excellent optical properties, good photobleaching, good biocompatibility, and ease of production. Notably, CDs exhibit novel results in pH sensing applications in water and intracellular environments due to their excellent anti-interfering ability and photostability. Herein, we successfully synthesized CDs by hydrothermal method using glucose and citric acid as precursors. The formation and breaking of hydrogen bonding play an important role in pH-sensing mechanisms of CDs. By using Raman signal of the hydrogen bonding in the photoluminescence (PL) spectra of CDs, we investigate here the effect of hydrogen bonding on the pH-sensing of CDs. It is expected that the mechanism of pH-sensing on the PL signal could be unraveled. As the results, the hydrogen bonding significantly reduced the PL intensity of CDs in a pH range of 7–11 but it did not influence the PL of CDs in higher pH conditions. Based on the interaction between hydrogen bonding and CDs, a new model for the pH-sensing mechanism of CDs was proposed. This work sheds new light on the mechanism of pH-sensing on the PL signal and suggests a new application of CDs in moisture and water sensors for air, soil, food, and commercial chemicals.
Artificial intelligence (AI)-based techniques have been widely applied in depression research and treatment. Nonetheless, there is currently no systematic review or bibliometric analysis in the ...medical literature about the applications of AI in depression. We performed a bibliometric analysis of the current research landscape, which objectively evaluates the productivity of global researchers or institutions in this field, along with exploratory factor analysis (EFA) and latent dirichlet allocation (LDA). From 2010 onwards, the total number of papers and citations on using AI to manage depressive disorder have risen considerably. In terms of global AI research network, researchers from the United States were the major contributors to this field. Exploratory factor analysis showed that the most well-studied application of AI was the utilization of machine learning to identify clinical characteristics in depression, which accounted for more than 60% of all publications. Latent dirichlet allocation identified specific research themes, which include diagnosis accuracy, structural imaging techniques, gene testing, drug development, pattern recognition, and electroencephalography (EEG)-based diagnosis. Although the rapid development and widespread use of AI provide various benefits for both health providers and patients, interventions to enhance privacy and confidentiality issues are still limited and require further research.
Multi-target drug development has become an attractive strategy in the discovery of drugs to treat of Alzheimer's disease (AzD). In this study, for the first time, a rule-based machine learning (ML) ...approach with classification trees (CT) was applied for the rational design of novel dual-target acetylcholinesterase (AChE) and
-site amyloid-protein precursor cleaving enzyme 1 (BACE1) inhibitors. Updated data from 3524 compounds with AChE and BACE1 measurements were curated from the ChEMBL database. The best global accuracies of training/external validation for AChE and BACE1 were 0.85/0.80 and 0.83/0.81, respectively. The rules were then applied to screen dual inhibitors from the original databases. Based on the best rules obtained from each classification tree, a set of potential AChE and BACE1 inhibitors were identified, and active fragments were extracted using Murcko-type decomposition analysis. More than 250 novel inhibitors were designed in silico based on active fragments and predicted AChE and BACE1 inhibitory activity using consensus QSAR models and docking validations. The rule-based and ML approach applied in this study may be useful for the in silico design and screening of new AChE and BACE1 dual inhibitors against AzD.
The elemental concentration and grain size composition (sand, silt and clay) of 63 bulk estuarine sediment samples from the bank of the Hau River in Mekong Basin were analyzed by neutron activation ...analysis and laser-diffraction particle size analyzer in this study. Principal component analysis was used to investigate the association between elemental concentration and particle size composition in sediment samples. The first principal component (PC) represents the alluvial composition in the sediment. Correlation tests showed that the elemental concentrations in the first PC (As, Co, Cr, Cs, Fe, Rb, Sb, Sc and Zn) have been strongly positively correlated with clay and silt contents and negatively correlated with sand content. The second PC represents the minerals composition of the sediment. The elemental concentration in the second PC (Ce, Eu, Hf, La, Sm, Ta, Tb, Th, U, and Yb) had a lower correlation with the grain size than the elemental concentration in the first PC. The third PC, which includes Br and Na, shows deposits coming from marine organisms and chemical precipitation in the ocean that had not correlated with the grain size.
The number of pollen-bearing honey bees serves as a vital indicator for assessing colony balance and health. Despite its significance, prevailing detection techniques still rely heavily on manual ...observation and annotation, leading to time-consuming processes that cannot sustain long-term, continuous monitoring efforts. To facilitate automatic beehive monitoring, this study introduces an efficient method for pollen-bearing bee detection. Initially, we furnish a comprehensive dataset, dubbed VnPollenBee, meticulously annotated for pollen-bearing honey bee detection and classification. The dataset comprises 60,826 annotated boxes that delineate both pollen-bearing and non-pollen-bearing bees in 2051 images captured at the entrances of beehives under various environmental conditions. To the best of our knowledge, this represents the first dedicated dataset for pollen-bearing bee detection. The VnPollenBee dataset is publicly accessible to the research community at https://comvis-hust.github.io/datasets/pollenbee.html. Subsequently, we propose the incorporation of diverse techniques into two baseline models, namely YOLOv5 and Faster RCNN, to effectively address the imbalance that arises during the detection of pollen-bearing bees due to their number being typically much lower than the total number of bees present at hive entrances. The experimental results demonstrate that our proposed method outperforms the baseline models on the VnPollenBee dataset, yielding Precision, Recall, and F1 score of 99%, 93%, and 95%, respectively. Specifically, the improvements obtained are 3% and 2% in Recall and F1 score when using YOLOv5, and 3%, 2%, and 2% in Precision, Recall, and F1 score when using Faster RCNN. These findings confirm the potential of our approach to facilitate bee foraging behavior analysis and automated bee monitoring.
•An image dataset, dubbed VnPollenBee, for pollen-bearing bees detection.•Tackling imbalance issue in pollen-bearing bee detection provides better detection results.•Tracking is intergrated to count the number of pollen-bearing bees from images captured at entrance of beehives
The rate of exposure to second-hand smoke (SHS) is relatively high in several countries, including Vietnam, and health issues related to SHS have worsened in recent years, especially for pregnant ...women and their infants. Enhancement of knowledge, attitude, and practice (KAP) scores of pregnant women in Vietnam could raise practical interventions to protect their health and reduce complications of SHS. A cross-sectional study of 432 pregnant women who came to the Obstetrics Department of Bach Mai Hospital, Hanoi, Vietnam for antenatal care was conducted in 2016 to collect information about their KAP related to SHS. Composite mean scores from survey questions assessing their KAP were calculated on a 10-point scale, finding mean scores of 4.19, 7.45, and 4.30, respectively. Higher scores indicated better knowledge, attitude, and practice. Generalized linear models identified that age, occupation, living place, and sources of information were associated with SHS-related KAP. Findings from this study indicate that suitable programs related to SHS should be implemented to improve and reinforce health literacy to both mothers and smokers to reduce the harmfulness of smoking on women and their infants' health.
Stigma and discrimination are among the greatest challenges that people living with human immunodeficiency virus (HIV) face, and both are known to negatively affect quality of life as well as ...treatment outcomes. We analyzed the growing research and current understanding of HIV-related stigma and contextual factors in HIV/AIDS (human Immunodeficiency virus/ acquired immunodeficiency syndrome) bibliography. A total of 5984 publications published from 1991 to 2017 were retrieved from the Web of Science database. The number of papers and their impacts have been considerably grown in recent years. Research landscapes related to stigma and discrimination include clinical, physical and mental health outcomes, risk behaviors of most-at-risk populations, and HIV-related services. We found a lack of empirical studies not only on social, cultural and economic contexts, but also on specific interventions for particular settings and sub-populations. This study highlights certain gaps and provides a basis for future studies and interventions on this critical issue given the changing drivers of HIV epidemics.
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