化学最新文献

Objective: The COVID-19 pandemic has disrupted educational systems worldwide, raising concerns about its impact on academic performance, particularly among developmental age students.

Methods: A systematic review with meta-analysis aimed to evaluate the association between the COVID-19 pandemic and the academic performance in this population was performed according to PRISMA 2020 guidelines. PubMed/MEDLINE, Scopus and Embase were searched on December 2023 to identify relevant studies. Both fixed and random effect models were performed. The Effect size was reported as Cohen's d with a 95% Confidence Interval. Studies' quality was assessed using the Newcastle-Ottawa scale. The protocol was registered in PROSPERO.

Results: A total of 30 studies met the inclusion criteria, but only 13 could be combined in the meta-analysis. Based on a sample size of 4,893,499 students, pooled Cohen's d was -0.07 [(95% CI = -0.10; -0.03); p-value <0.001]. Subgroup analyses by subject suggested that performance in math was affected the most, Cohen's d= -0.14 [(-0.18; -0.10); p-value <0.001].

Conclusion: The findings revealed a significant negative association between the COVID-19 pandemic and academic performance among developmental age students. Interventions to mitigate the adverse effects of the pandemic on educational outcomes in this population are needed.

In this work, three different dyes have been tested for the determination of gaseous ammonia. This gas is one of the products of microbial degradation and therefore its presence is an indicator of deterioration and could be used as a food freshness indicator. Three different sensors have been prepared and tested, two of them using the natural pigments curcumin and anthocyanin and the other one using bromothymol blue. All of them are biocompatible and therefore allowed to use in contact with food. Different compositions, materials for deposition, stability and reversibility for ammonia gas detection have been studied under high humidity conditions simulating real packaged food conditions. Colorimetry is the technique used to obtain the analytical parameter, the H coordinate of the HSV colour space, simply using a camera, avoiding the use of complex instrumentation. Sensibility, toxicity grade and stability found show that the sensor could be implemented in packaged food and form the basis of a freshness indicator for the food industry.

Lysine (Lys), Cu²⁺ and Fe³⁺ ions and biothiols are essential to a myriad of biological and pathological pathways, and their dysregulation is implicated in a variety of diseases. Development of fluorescent probes capable of detecting multiple analytes may be of great significance for early and accurate diagnosis of diseases and remains a huge challenge. In this context, a novel coumarin-dicyanoisophorone-based probe, engineered for the concurrent sensing of Lys, Cu²⁺, Fe³⁺ and biothiols was developed. The probe exhibited turn-on response to Lys, colorimetric and turn-off response to Cu²⁺ by formation of the probe-Cu²⁺ complex, and ratiometric sensing of Fe³⁺. In addition, the probe-Cu²⁺ complex served colorimetric and fluorescence turn-on sensor for biothiols. The limit of detection (LOD) values for the analytes were in the range of 0.30-4.40 μM. Sensing mechanisms based on intramolecular charge transfer (ICT) and iron-mediated hydrolysis of Schiff base were proposed and substantiated through density functional theory (DFT) calculations. Application of the probe for living cell bioimaging was demonstrated.

Gas-chromatography hyphenated with low-resolution mass spectrometry is a very flexible tool for the cost-effective identification and quantification of volatile compounds in complex matrices. In some analytical fields, criteria for the agreement between retention time and mass spectra of the analyte in calibrators and samples are defined based on the general understanding of the performance of these parameters. However, since this harmonisation is not based on experimental performance observed for specific GC-MS conditions and analyte it leads to false identifications. This research proposes a novel and robust tool for defining statistically sound criteria for the identification of compounds by GC-MS and LC-MS using experimental data. The Monte Carlo Method (MCM) simulation of the correlated abundance of characteristic ions of analyte mass spectrum allows simulating the abundance ratio difference of the analyte in a calibrator and sample used for statistically sound identifications. The Cholesky decomposition of the covariance matrix of ion abundances for MCM simulations allows the reliable use of many ion abundance ratios in identifications. The developed methodology was implemented in a user-friendly Excel spreadsheet and applied to the identification of tear gas agents in tear gas sprays. Criteria defined by SANTE for identifying pesticide residues in foodstuffs were compared with the developed tool. The cross-validation of computational and SANTE tools allowed concluding that the statistical control of retention time and mass spectra performs according to the defined confidence level. On the other hand, the SANTE criteria can produce up to 92 % false identifications for being too strict considering signal dispersion.

Previous studies have stated that both objective and subjective cognitive abilities and mental health symptoms are associated with community participation poststroke. However, there is a need to understand the direct and indirect associations among these variables in persons with stroke. The objective of this study was to investigate whether mental health symptoms mediate the associations of subjective and objective cognitive abilities with community participation poststroke. We built regression-based mediation models with 74 participants with mild to moderate stroke. Independent variables were objective and subjective cognitive abilities. The dependent variable was community participation. Mediators were mental health symptoms including depression, apathy, and anxiety. The results indicated that depression (b = .093), apathy (b = .134), and anxiety (b = .116) fully mediated the association between subjective cognitive ability (p < .05), but not objective cognitive ability (p > .05), and community participation poststroke. Our findings suggest that poor subjective cognitive ability combined with mental health symptoms should be addressed together to promote community participation poststroke.

Objectives: Research considered patient outcomes primarily over caregivers in end-of-life care settings. The importance of family caregivers (FCs) in end-of-life care draws growing awareness, evidenced by an increasing number of evaluations of caregiver-targeted interventions. Little is known of FCs' collateral benefits in patient-oriented home-based end-of-life care. The study aims to investigate FC outcomes and change mechanisms in patient-oriented care. Methods: A pre-post-test study. We recruited FCs whose patients with a life expectancy ≤ 6 months enrolled in home-based end-of-life care provided by service organizations in Hong Kong. Patients' symptoms, dimensions of caregiving strain (ie, perception of caregiving, empathetic strain, adjustment demands), and aspects of FCs' wellbeing (ie, perceived health, positive mood, life satisfaction, spiritual well-being) were measured at baseline (T₀) and 3 months later (T₁). Results: Of the 345 FCs at T₀, 113 provided T₁ measures. Three months after the service commenced, FCs' caregiving strain significantly reduced, and their positive mood improved. Alleviation of the patient's physical symptoms predicted FC better outcomes, including the perception of caregiving, empathetic strain, and wellbeing. Changes in perception of caregiving mediated the effects of changes in patients' physical symptoms on FCs' changes in life satisfaction and spiritual wellbeing. Changes in empathetic strain mediated the changes between patient's physical symptoms and FCs' positive mood. Conclusions: Collateral benefits of patient-oriented home-based end-of-life care were encouraging for FCs. Patient's physical symptom management matters to FCs' caregiving strain and wellbeing. The active ingredients modifying FCs' perception of caregiving and addressing empathetic strain may amplify their benefits in wellbeing.

Cannabis consumption among aging adults in Canada is increasing. The aims of the study were to examine cannabis consumption patterns before and after non-medical cannabis legalization and assess whether these patterns differ between men and women. Data were analyzed from Canadian respondents in a repeat cross-sectional survey conducted in 2018-2021. Analyses were conducted among adults aged 55-65 (n = 18,177) who had consumed cannabis in the past 12-month (n = 4119). Past 12-month cannabis consumption significantly increased among 55-65-year-olds from 2018 (19.3%) to the first-year post-legalization in 2019 (24.5%; p < .001), but remained stable thereafter (24.3%, and 25.6% in 2020 and 2021). More men reported past 12-month consumption than women (28.4% vs. 21.4%; p < .001). A substantial number of cannabis consumers consumed to manage a physical or mental health condition. Targeted messaging might be beneficial for this age group, including possible interactions with other medications. This research may be helpful for informing age-adapted cannabis education.

Different varieties of Gastrodia elata Blume (G. elata Bl.) have different qualities and different contents of active ingredients, such as polysaccharide and gastrodin, and it is generally believed that the higher the active ingredients, the better the quality of G. elata Bl. and the stronger the medicinal effects. Therefore, effective identification of G. elata Bl. species is crucial and has important theoretical and practical significance. In this study, first unsupervised PCA and t-SNE are established for data visualisation, follow by traditional machine learning (PLS-DA, OPLS-DA and SVM) models and deep learning (ResNet) models were established based on the fourier transform infrared (FTIR) and near infrared (NIR) spectra data of three G. elata Bl. species. The results show that PLS-DA, OPLS-DA and SVM models require complex preprocessing of spectral data to build stable and reliable models. Compared with traditional machine learning models, ResNet models do not require complex spectral preprocessing, and the training and test sets of ResNet models built based on raw NIR and low-level data fusion (FTIR + NIR) spectra reach 100 % accuracy, the external validation set based on low-level data fusion reaches 100 % accuracy, and the external validation set based on NIR has only one sample classification error and no overfitting.

This study introduces a novel approach for the sensitive and accurate detection of small molecule metabolites, employing metal-phenolic network (MPN) functionalized AuNPs as both adsorbent and matrix to enhance laser desorption/ionization mass spectrometry (LDI-MS) performance. The MPN comprising tannic acid (TA) and transition metal ions (Fe³⁺, Co²⁺, Ni²⁺, Cu²⁺, or Zn²⁺) was coated on the surface of AuNPs, forming metal-TA network-coated AuNPs (M-TA@AuNPs). The M-TA@AuNPs provided a tunable surface for specific interactions with analytes, displaying distinct enrichment efficacies for different amino acids, especially for Cu-TA@AuNPs exhibiting the highest affinity for histidine (His). Under the optimized condition, the proposed method enabled ultrasensitive detection of His, with good linearity (R² = 0.9627) in the low-concentration range (50 nM-1 μM) and a limit of detection (LOD) as low as 0.9 nM. Furthermore, the method was successfully applied to detect His from human urine samples, showcasing its practical applications in clinical diagnostics, particularly in the realm of amino acid-based targeted metabolomics.

Immunosensors based on electrical impedance spectroscopy allow for label-free, real-time detection of biologically relevant molecules and pathogens, without requiring electro-active materials. Here, we investigate the influence of bare gold nanoparticles (AuNPs), synthesized via laser ablation in solution, on the performance of an impedimetric immunosensor for detecting severe acute respiratory syndrome coronavirus (SARS-CoV-2). Graphene acetic acid (GAA) was used in the active layer for immobilizing anti-SARS-CoV-2 antibodies, owing to its high density of carboxylic groups. Immunosensors incorporating AuNPs exhibited superior performance compared to those relying solely on GAA, achieving a limit of detection (LoD) of 3 x 10^-20 g/mL to detect the Spike Receptor Binding Domain (RBD) protein of SARS-CoV-2 and of 2 PFU/mL for inactivated virus. Moreover, these immunosensors presented high selectivity against the H1N1 influenza virus. We anticipate that this platform will be versatile and applicable in the early diagnosis of various diseases and viral infections, thereby facilitating Point-of-Care testing.