Analysis of post-market adverse events of istradefylline: a real-world study base on FAERS database.

Analyze the adverse event (AE) signals of istradefylline based on the FAERS database. By extracting large-scale data from the FAERS database, this study used various signal quantification techniques such as ROR, PRR, BCPNN, and MGPS to calculate and evaluate the ratio and association between istradefylline and specific AEs. In the FAERS database, this study extracted data from the third quarter of 2019 to the first quarter of 2023, totaling 6,749,750 AE reports. After data cleansing and drug screening, a total of 3633 AE reports related to istradefylline were included for analysis. Based on four calculation methods, this study unearthed 25 System Organ Class (SOC) AE signals and 82 potential preferred terms (PTs) related to istradefylline. The analysis revealed new AEs during istradefylline treatment, including reports of Parkinsonism hyperpyrexia syndrome (n = 3, ROR 178.70, PRR 178.63, IC 1.97, EBGM 165.63), Compulsions (n = 5, ROR 130.12, PRR 130.04, IC 2.53, EBGM 123.02), Deep brain stimulation (n = 10, ROR 114.42, PRR 114.27, IC 3.33, EBGM 108.83), and Freezing phenomenon (n = 60, ROR 97.52, PRR 96.76, IC 5.21, EBGM 92.83). This study provides new risk signals and important insights into the use of istradefylline, but further research and validation are needed, especially for those AE that may occur in actual usage scenarios but are not yet explicitly described in the instructions.

Holomics - a user-friendly R shiny application for multi-omics data integration and analysis.

An organism's observable traits, or phenotype, result from intricate interactions among genes, proteins, metabolites and the environment. External factors, such as associated microorganisms, along with biotic and abiotic stressors, can significantly impact this complex biological system, influencing processes like growth, development and productivity. A comprehensive analysis of the entire biological system and its interactions is thus crucial to identify key components that support adaptation to stressors and to discover biomarkers applicable in breeding programs or disease diagnostics. Since the genomics era, several other 'omics' disciplines have emerged, and recent advances in high-throughput technologies have facilitated the generation of additional omics datasets. While traditionally analyzed individually, the last decade has seen an increase in multi-omics data integration and analysis strategies aimed at achieving a holistic understanding of interactions across different biological layers. Despite these advances, the analysis of multi-omics data is still challenging due to their scale, complexity, high dimensionality and multimodality. To address these challenges, a number of analytical tools and strategies have been developed, including clustering and differential equations, which require advanced knowledge in bioinformatics and statistics. Therefore, this study recognizes the need for user-friendly tools by introducing Holomics, an accessible and easy-to-use R shiny application with multi-omics functions tailored for scientists with limited bioinformatics knowledge. Holomics provides a well-defined workflow, starting with the upload and pre-filtering of single-omics data, which are then further refined by single-omics analysis focusing on key features. Subsequently, these reduced datasets are subjected to multi-omics analyses to unveil correlations between 2-n datasets. This paper concludes with a real-world case study where microbiomics, transcriptomics and metabolomics data from previous studies that elucidate factors associated with improved sugar beet storability are integrated using Holomics. The results are discussed in the context of the biological background, underscoring the importance of multi-omics insights. This example not only highlights the versatility of Holomics in handling different types of omics data, but also validates its consistency by reproducing findings from preceding single-omics studies.

A novel comprehensive strategy for high-thoroughly studying released compounds during the combustion process of herbs. A case study for Artemisia argyi Levl. et Vant.

The comprehensive study of compound variations in released smoke during the combustion process is a great challenge in many scientific fields related to analytical chemistry like traditional Chinese medicine, environment analysis, food analysis, etc. In this work, we propose a new comprehensive strategy for efficiently and high-thoroughly characterizing compounds in the online released complex smokes: (i) A smoke capture device was designed for efficiently collecting chemical constituents to perform gas chromatography-mass spectrometry (GC-MS) based untargeted analysis. (ii) An advanced data analysis tool, AntDAS-GCMS, was used for automatically extracting compounds in the original acquired GC-MS data files. Additionally, a GC-MS data analysis guided instrumental parameter optimizing strategy was proposed for the optimization of parameters in the smoke capture device. The developed strategy was demonstrated by the study of compound variations in the smoke of traditional Chinese medicine, Artemisia argyi Levl. et Vant. The results indicated that more than 590 components showed significant differences among released smokes of various moxa velvet ratios. Finally, about 88 compounds were identified, of which phenolic compounds were the most abundant, followed by aromatics, alkenes, alcohols and furans. In conclusion, we may provide a novel approach to the studies of compounds in online released smoke.

A Review of Web-Based Metagenomics Platforms for Analysing Next-Generation Sequence Data.

Metagenomics has now evolved as a promising technology for understanding the microbial population in the environment. By metagenomics, a number of extreme and complex environment has been explored for their microbial population. Using this technology, researchers have brought out novel genes and their potential characteristics, which have robust applications in food, pharmaceutical, scientific research, and other biotechnological fields. A sequencing platform can provide a sequence of microbial populations in any given environment. The sequence needs to be analysed computationally to derive meaningful information. It is presumed that only bioinformaticians with extensive computational skills can process the sequencing data till the downstream end. However, numerous open-source software and online servers are available to analyse the metagenomic data developed for a biologist with less computational skills. This review is focused on bioinformatics tools such as Galaxy, CSI-NGS portal, ANASTASIA and SHAMAN, EBI- metagenomics, IDseq, and MG-RAST for analysing metagenomic data.

Creating a difference - a role for the arts in addressing child wellbeing in conflict-affected areas.

BACKGROUND: Details findings from a project on the potential for arts activities and art therapy to support the mental health and wellbeing of children living in Kashmir. METHODS: The intervention engaged 30 school children over the course of one year who produced various forms of artwork and performances. In this paper, we report on project impacts, drawing on some of our qualitative measures including observations and interviews. RESULTS: Our research details impacts and improvements in areas of emotional expression, belonging, and agency. We also found an important role for schools to create safe, secure, and caring spaces to allow students to express themselves and work through traumatic feelings in a non-judgemental way. CONCLUSIONS: School-based arts interventions can play an important role in the mental health and wellbeing of children. Critical here, however, are dedicated space, time, and resources to provide a supportive environment and to sustain activity in long-term.

Exploration of Decision-Making Methods Based on Syndrome Differentiation by “Data-Knowledge” Dual-Driven Models： A Case Study of Gastric Precancerous State / 中医杂志

Data analysis models may assist the transmission of traditional Chinese medicine （TCM） experience and clinical diagnosis and treatment， and the possibility of constructing a “data-knowledge” dual-drive model was explored by taking gastric precancerous state as an example. Data-driven is to make clinical decisions around data analysis， and its syndrome-differentiation decision-making research relies on hidden structural models and partially observable Markov decision-making processes to identify the etiology of diseases， syndrome elements， evolution of pathogenesis， and syndrome differentiation protocols； knowledge-driven is to make use of data and information to promote decision-making and action processes， and its syndrome-differentiation decision-making research relies on convolutional neural networks to improve the accuracy of local disease identification and syndrome differentiation. The “data-knowledge” dual-driven model can make up for the shortcomings of single-drive numerical simulation accuracy， and achieve a balance between local disease identification and macroscopic syndrome differentiation. On the basis of previous research， we explored the construction method of diagnostic assisted decision-making platform for gastric precancerous state， and believed that the diagnostic and decision-making ability of doctors can be extended through the assistance of machines and algorithms. Meanwhile， the related research methods were integrated and the core features of gastric precancerous state based on TCM syndrome differentiation and endoscopic pathology diagnosis and prediction were obtained， and the elements of endoscopic pathology recognition based on TCM syndrome differentiation were explored， so as to provide ideas for the in-depth research and innovative application of cutting-edge data analysis technology in the field of intelligent TCM syndrome differentiation.

Radiotranscriptomics of non-small cell lung carcinoma for assessing high-level clinical outcomes using a machine learning-derived multi-modal signature.

BACKGROUND: Multi-omics research has the potential to holistically capture intra-tumor variability, thereby improving therapeutic decisions by incorporating the key principles of precision medicine. The purpose of this study is to identify a robust method of integrating features from different sources, such as imaging, transcriptomics, and clinical data, to predict the survival and therapy response of non-small cell lung cancer patients. METHODS: 2996 radiomics, 5268 transcriptomics, and 8 clinical features were extracted from the NSCLC Radiogenomics dataset. Radiomics and deep features were calculated based on the volume of interest in pre-treatment, routine CT examinations, and then combined with RNA-seq and clinical data. Several machine learning classifiers were used to perform survival analysis and assess the patient's response to adjuvant chemotherapy. The proposed analysis was evaluated on an unseen testing set in a k-fold cross-validation scheme. Score- and concatenation-based multi-omics were used as feature integration techniques. RESULTS: Six radiomics (elongation, cluster shade, entropy, variance, gray-level non-uniformity, and maximal correlation coefficient), six deep features (NasNet-based activations), and three transcriptomics (OTUD3, SUCGL2, and RQCD1) were found to be significant for therapy response. The examined score-based multi-omic improved the AUC up to 0.10 on the unseen testing set (0.74 ± 0.06) and the balance between sensitivity and specificity for predicting therapy response for 106 patients, resulting in less biased models and improving upon the either highly sensitive or highly specific single-source models. Six radiomics (kurtosis, GLRLM- and GLSZM-based non-uniformity from images with no filtering, biorthogonal, and daubechies wavelets), seven deep features (ResNet-based activations), and seven transcriptomics (ELP3, ZZZ3, PGRMC2, TRAK1, ATIC, USP7, and PNPLA2) were found to be significant for the survival analysis. Accordingly, the survival analysis for 115 patients was also enhanced up to 0.20 by the proposed score-based multi-omics in terms of the C-index (0.79 ± 0.03). CONCLUSIONS: Compared to single-source models, multi-omics integration has the potential to improve prediction performance, increase model stability, and reduce bias for both treatment response and survival analysis.

Exploration on Anti-Depression Mechanism of Baihe Zhimu Decoction Based on Network Pharmacology and Molecular Docking.

OBJECTIVE: To analyze anti-depression mechanism of Baihe Zhimu decoction (BZD) based on network pharmacology method, which provides reference for the development of new drugs and the clinical application of classical prescriptions. METHOD: The main chemical components and targets of Baihe and Zhimu were obtained through traditional Chinese medicine pharmacology system technology platform (TCMSP) database, and the active components of TCM were filtered according to ADME; Major targets for anti-depression were get through Gencards, OMIM and DRUGBANK databases; Protein interaction analysis was performed using the String platform; Build PPI networks and mine potential protein functional modules in the network; The Metascape platform was used to analyze the "drug-ingredients-target" and its involved biological processes and pathways; Finally, the molecular docking validation was performed by Systems Dock Web Site. RESULTS: The core active ingredients of BZD treating depression are kaempferol and Stigmasterol, The core targets are AKT1, TNF, TP53, PTGS2, and CASP3. The biological pathway of the anti-depression mainly acts on Lipid and atherosclerosis, Chemical carcinogenesis and receptor activation. Molecular docking results showed that AKT1, TNF and TP53 have good affinity with components kaempferol and Stigmasterol. CONCLUSION: This study initially revealed the mechanism of multicomponent, multiple target and multiple pathway of anti-depression, which may be related to neuroactive ligand-receptor interaction, atherosclerotic, PI3K-Akt and TNF signaling pathway.

It takes two (seconds): decreasing encoding time for two-choice functional near-infrared spectroscopy brain-computer interface communication.

Significance: Brain-computer interfaces (BCIs) can provide severely motor-impaired patients with a motor-independent communication channel. Functional near-infrared spectroscopy (fNIRS) constitutes a promising BCI-input modality given its high mobility, safety, user comfort, cost-efficiency, and relatively low motion sensitivity. Aim: The present study aimed at developing an efficient and convenient two-choice fNIRS communication BCI by implementing a relatively short encoding time (2 s), considerably increasing communication speed, and decreasing the cognitive load of BCI users. Approach: To encode binary answers to 10 biographical questions, 10 healthy adults repeatedly performed a combined motor-speech imagery task within 2 different time windows guided by auditory instructions. Each answer-encoding run consisted of 10 trials. Answers were decoded during the ongoing experiment from the time course of the individually identified most-informative fNIRS channel-by-chromophore combination. Results: The answers of participants were decoded online with an accuracy of 85.8% (run-based group mean). Post-hoc analysis yielded an average single-trial accuracy of 68.1%. Analysis of the effect of number of trial repetitions showed that the best information-transfer rate could be obtained by combining four encoding trials. Conclusions: The study demonstrates that an encoding time as short as 2 s can enable immediate, efficient, and convenient fNIRS-BCI communication.

Patient-dental student provider communication in an academic dental clinic setting: A dyadic data analysis.

PURPOSE: Patient-dentist communication is an inherently dyadic social process; however, it is rarely regarded as such in research and pedagogy. This study utilizes a dyadic data analysis approach to study patient-dental student provider communication in an academic dental clinic. PROCEDURES: Using pairwise data collected from patient-dental student provider dyads, we conducted unadjusted and adjusted actor-partner interdependence models to examine the association of intrapersonal (actor) and interpersonal (partner) effects of three communication skills on the assessment of appointment interaction among patient-dental student provider dyads in a pre-doctoral comprehensive care academic dental clinic setting. MAIN FINDINGS: Actor effects were most evident among the associations in the study. Dental student providers' assessment of their own shared decision-making predicted positive changes in their overall interaction assessment in both unadjusted and fully adjusted models. Patients' ratings of their dental student provider's capability/confidence predicted positive changes in their overall interaction assessment in both unadjusted and adjusted models. CONCLUSIONS: This study suggests that dental students and their patients are primarily impacted by actor perspectives regarding dental student communication and its impact on the assessment of their respective overall appointment interaction. Findings suggest a need for the incorporation of interpersonal skill building in collaboration with patients to strengthen the communication skills and practice of dental students.

Identification and Quantitation of the Bioactive Components in Wasted Aralia elata Leaves Extract with Endothelial Protective Activity.

Aralia elata, a renowned medicinal plant with a rich history in traditional medicine, has gained attention for its potential therapeutic applications. However, the leaves of this plant have been largely overlooked and discarded due to limited knowledge of their biological activity and chemical composition. To bridge this gap, a comprehensive study was conducted to explore the therapeutic potential of the 70% ethanol extract derived from Aralia elata leaves (LAE) for the treatment of cardiovascular disease (CVD). Initially, the cytotoxic effects of LAE on human umbilical vein endothelial cells (HUVECs) were assessed, revealing no toxicity within concentrations up to 5 µg/mL. This suggests that LAE could serve as a safe raw material for the development of health supplements and drugs aimed at promoting cardiovascular well-being. Furthermore, the study found that LAE extract demonstrated anti-inflammatory properties in HUVECs by modulating the PI3K/Akt and MAPK signaling pathways. These findings are particularly significant as inflammation plays a crucial role in the progression of CVD. Moreover, LAE extract exhibited the ability to suppress the expression of adhesion molecules VCAM-1 and ICAM-1, which are pivotal in leukocyte migration to inflamed blood vessels observed in various pathological conditions. In conjunction with the investigation on therapeutic potential, the study also established an optimal HPLC-PDA-ESI-MS/MS method to identify and confirm the chemical constituents present in 24 samples collected from distinct regions in South Korea. Tentative identification revealed the presence of 14 saponins and nine phenolic compounds, while further analysis using PCA and PLS-DA allowed for the differentiation of samples based on their geographical origins. Notably, specific compounds such as chlorogenic acid, isochlorogenic acid A, and quercitrin emerged as marker compounds responsible for distinguishing samples from different regions. Overall, by unraveling its endothelial protective activity and identifying key chemical constituents, this research not only offers valuable insights for the development of novel treatments but also underscores the importance of utilizing and preserving natural resources efficiently.

Classification and quantification of sucrose from sugar beet and sugarcane using optical spectroscopy and chemometrics.

Sucrose, obtained from either sugar beet or sugarcane, is one of the main ingredients used in the food industry. Due to the same molecular structure, chemical methods cannot distinguish sucrose from both sources. More practical and affordable methods would be valuable. Sucrose samples (cane and beet) were collected from nine countries, 25% (w/w) aqueous solutions were prepared and their absorbances recorded from 200 to 1380 nm. Spectral differences were observable in the ultraviolet-visible (UV-Vis) region from 200 to 600 nm due to impurities in sugar. Linear discriminant analysis (LDA), classification and regression trees, and soft independent modeling of class analogy were tested for the UV-Vis region. All methods showed high performance accuracies. LDA, after selection of five wavelengths, gave 100% correct classification with a simple interpretation. In addition, binary mixtures of the sugar samples were prepared for quantitative analysis by means of partial least squares regression and multiple linear regression (MLR). MLR with first derivative Savitzky-Golay were most acceptable with root mean square error of cross-validation, prediction, and the ratio of (standard error of) prediction to (standard) deviation values of 3.92%, 3.28%, and 9.46, respectively. Using UV-Vis spectra and chemometrics, the results show promise to distinguish between the two different sources of sucrose. An affordable and quick analysis method to differentiate between sugars, produced from either sugar beet or sugarcane, is suggested. This method does not involve complex chemical analysis or high-level experts and can be used in research or by industry to detect the source of the sugar which is important for some countries' agricultural policies.

Capturing Semantic Relationships in Electronic Health Records Using Knowledge Graphs: An Implementation Using MIMIC III Dataset and GraphDB.

Electronic health records (EHRs) are an increasingly important source of information for healthcare professionals and researchers. However, EHRs are often fragmented, unstructured, and difficult to analyze due to the heterogeneity of the data sources and the sheer volume of information. Knowledge graphs have emerged as a powerful tool for capturing and representing complex relationships within large datasets. In this study, we explore the use of knowledge graphs to capture and represent complex relationships within EHRs. Specifically, we address the following research question: Can a knowledge graph created using the MIMIC III dataset and GraphDB effectively capture semantic relationships within EHRs and enable more efficient and accurate data analysis? We map the MIMIC III dataset to an ontology using text refinement and Protege; then, we create a knowledge graph using GraphDB and use SPARQL queries to retrieve and analyze information from the graph. Our results demonstrate that knowledge graphs can effectively capture semantic relationships within EHRs, enabling more efficient and accurate data analysis. We provide examples of how our implementation can be used to analyze patient outcomes and identify potential risk factors. Our results demonstrate that knowledge graphs are an effective tool for capturing semantic relationships within EHRs, enabling a more efficient and accurate data analysis. Our implementation provides valuable insights into patient outcomes and potential risk factors, contributing to the growing body of literature on the use of knowledge graphs in healthcare. In particular, our study highlights the potential of knowledge graphs to support decision-making and improve patient outcomes by enabling a more comprehensive and holistic analysis of EHR data. Overall, our research contributes to a better understanding of the value of knowledge graphs in healthcare and lays the foundation for further research in this area.

Obstructive Sleep Apnea, Metabolic Dysfunction, and Periodontitis-Machine Learning and Statistical Analyses of the Dental, Oral, Medical Epidemiological (DOME) Big Data Study.

This study aimed to analyze the associations of obstructive sleep apnea (OSA) with dental parameters while controlling for socio-demographics, health-related habits, and each of the diseases comprising metabolic syndrome (MetS), its consequences, and related conditions. We analyzed data from the dental, oral, and medical epidemiological (DOME) cross-sectional records-based study that combines comprehensive socio-demographic, medical, and dental databases of a nationally representative sample of military personnel for one year. Analysis included statistical and machine learning models. The study included 132,529 subjects; of these, 318 (0.2%) were diagnosed with OSA. The following parameters maintained a statistically significant positive association with OSA in the multivariate binary logistic regression analysis (descending order from highest to lowest OR): obesity (OR = 3.104 (2.178-4.422)), male sex (OR = 2.41 (1.25-4.63)), periodontal disease (OR = 2.01 (1.38-2.91)), smoking (OR = 1.45 (1.05-1.99)), and age (OR = 1.143 (1.119-1.168)). Features importance generated by the XGBoost machine learning algorithm were age, obesity, and male sex (located on places 1-3), which are well-known risk factors of OSA, as well as periodontal disease (fourth place) and delivered dental fillings (fifth place). The Area Under Curve (AUC) of the model was 0.868 and the accuracy was 0.92. Altogether, the findings supported the main hypothesis of the study, which was that OSA is linked to dental morbidity, in particular to periodontitis. The findings highlight the need for dental evaluation as part of the workup of OSA patients and emphasizes the need for dental and general medical authorities to collaborate by exchanging knowledge about dental and systemic morbidities and their associations. The study also highlights the necessity for a comprehensive holistic risk management strategy that takes systemic and dental diseases into account.

Topological data analysis of antibody dynamics of severe and non-severe patients with COVID-19.

The COVID-19 pandemic is a significant public health threat with unanswered questions regarding the immune system's role in the disease's severity level. Here, based on antibody kinetic data of severe and non-severe COVID-19 patients, topological data analysis (TDA) highlights that severity is not binary. However, there are differences in the shape of antibody responses that further classify COVID-19 patients into non-severe, severe, and intermediate cases of severity. Based on the results of TDA, different mathematical models were developed to represent the dynamics between the different severity groups. The best model was the one with the lowest average value of the Akaike Information Criterion for all groups of patients. Our results suggest that different immune mechanisms drive differences between the severity groups. Further inclusion of different components of the immune system will be central for a holistic way of tackling COVID-19.

RNA-Seq Study on the Longissimus thoracis Muscle of Italian Large White Pigs Fed Extruded Linseed with or without Antioxidants and Polyphenols.

The addition of n-3 polyunsaturated fatty acids (n-3 PUFAs) to the swine diet increases their content in muscle cells, and the additional supplementation of antioxidants promotes their oxidative stability. However, to date, the functionality of these components within muscle tissue is not well understood. Using a published RNA-seq dataset and a selective workflow, the study aimed to find the differences in gene expression and investigate how differentially expressed genes (DEGs) were implicated in the cellular composition and metabolism of muscle tissue of 48 Italian Large White pigs under different dietary conditions. A functional enrichment analysis of DEGs, using Cytoscape, revealed that the diet enriched with extruded linseed and supplemented with vitamin E and selenium promoted a more rapid and massive immune system response because the overall function of muscle tissue was improved, while those enriched with extruded linseed and supplemented with grape skin and oregano extracts promoted the presence and oxidative stability of n-3 PUFAs, increasing the anti-inflammatory potential of the muscular tissue.

Visualization of Traditional Chinese Medicine Formulas: Development and Usability Study.

BACKGROUND: Traditional Chinese medicine (TCM) formulas are combinations of Chinese herbal medicines. Knowledge of classic medicine formulas is the basis of TCM diagnosis and treatment and is the core of TCM inheritance. The large number and flexibility of medicine formulas make memorization difficult, and understanding their composition rules is even more difficult. The multifaceted and multidimensional properties of herbal medicines are important for understanding the formula; however, these are usually separated from the formula information. Furthermore, these data are presented as text and cannot be analyzed jointly and interactively. OBJECTIVE: We aimed to devise a visualization method for TCM formulas that shows the composition of medicine formulas and the multidimensional properties of herbal medicines involved and supports the comparison of medicine formulas. METHODS: A TCM formula visualization method with multiple linked views is proposed and implemented as a web-based tool after close collaboration between visualization and TCM experts. The composition of medicine formulas is visualized in a formula view with a similarity-based layout supporting the comparison of compositing herbs; a shared herb view complements the formula view by showing all overlaps of pair-wise formulas; and a dimensionality-reduction plot of herbs enables the visualization of multidimensional herb properties. The usefulness of the tool was evaluated through a usability study with TCM experts. RESULTS: Our method was applied to 2 typical categories of medicine formulas, namely tonic formulas and heat-clearing formulas, which contain 20 and 26 formulas composed of 58 and 73 herbal medicines, respectively. Each herbal medicine has a 23-dimensional characterizing attribute. In the usability study, TCM experts explored the 2 data sets with our web-based tool and quickly gained insight into formulas and herbs of interest, as well as the overall features of the formula groups that are difficult to identify with the traditional text-based method. Moreover, feedback from the experts indicated the usefulness of the proposed method. CONCLUSIONS: Our TCM formula visualization method is able to visualize and compare complex medicine formulas and the multidimensional attributes of herbal medicines using a web-based tool. TCM experts gained insights into 2 typical medicine formula categories using our method. Overall, the new method is a promising first step toward new TCM formula education and analysis methodologies.

An Integrative Data Analysis of Main and Moderated Crossover Effects of Parent-Mediated Interventions on Depression and Anxiety Symptoms in Youth in Foster Care.

Without preventative intervention, youth with a history of foster care (FC) involvement have a high likelihood of developing depression and anxiety (DA) symptoms. The current study used integrative data analysis to harmonize data across four foster and kinship parent-mediated interventions (and seven randomized control trials) designed to reduce youth externalizing and other problem behaviors to determine if, and for how long, these interventions may have crossover effects on youth DA symptoms. Moderation of intervention effects by youth biological sex, developmental period, number of prior placements, and race/ethnicity was also examined. Youth (N = 1891; 59% female; ages 4 to 18 years) behaviors were assessed via the Child Behavior Checklist, Parent Daily Report, and Eyberg Child Behavior Inventory at baseline, the end of the interventions (4-6 months post baseline), and two follow-up assessments (9-12 months and 18-24 months post baseline), yielding 4830 total youth-by-time assessments. The interventions were effective at reducing DA symptoms at the end of the interventions; however, effects were only sustained for one program at the follow-up assessments. No moderation effects were found. The current study indicates that parent-mediated interventions implemented during childhood or adolescence aimed at reducing externalizing and other problem behaviors had crossover effects on youth DA symptoms at the end of the interventions. Such intervention effects were sustained 12 and 24 months later only for the most at-risk youth involved in the most intensive intervention.

Discrimination of Olive Oil and Extra-Virgin Olive Oil from Other Vegetable Oils by Targeted and Untargeted HRMS Profiling of Phenolic and Triterpenic Compounds Combined with Chemometrics.

Extra-virgin olive oil (EVOO) and virgin olive oil (VOO) are valuable natural products of great economic interest for their producing countries, and therefore, it is necessary to establish methods capable of proving the authenticity of these oils on the market. This work presents a methodology for the discrimination of olive oil and extra-virgin olive oil from other vegetable oils based on targeted and untargeted high-resolution mass spectrometry (HRMS) profiling of phenolic and triterpenic compounds coupled with multivariate statistical analysis of the data. Some phenolic compounds (cinnamic acid, coumaric acids, apigenin, pinocembrin, hydroxytyrosol and maslinic acid), secoiridoids (elenolic acid, ligstroside and oleocanthal) and lignans (pinoresinol and hydroxy and acetoxy derivatives) could be olive oil biomarkers, whereby these compounds are quantified in higher amounts in EVOO compared to other vegetable oils. The principal component analysis (PCA) performed based on the targeted compounds from the oil samples confirmed that cinnamic acid, coumaric acids, apigenin, pinocembrin, hydroxytyrosol and maslinic acid could be considered as tracers for olive oils authentication. The heat map profiles based on the untargeted HRMS data indicate a clear discrimination of the olive oils from the other vegetable oils. The proposed methodology could be extended to the authentication and classification of EVOOs depending on the variety, geographical origin, or adulteration practices.

Discriminant Principal Component Analysis of ToF-SIMS Spectra for Deciphering Compositional Differences of MSC-Secreted Extracellular Matrices.

Identifying characteristic extracellular matrix (ECM) variants is a key challenge in mechanistic biology, bioengineering, and medical diagnostics. The reported study demonstrates the potential of time-of-flight secondary ion mass spectrometry (ToF-SIMS) to detect subtle differences between human mesenchymal stromal cell (MSC)-secreted ECM types as induced by exogenous stimulation or emerging pathology. ToF-SIMS spectra of decellularized ECM samples are evaluated by discriminant principal component analysis (DPCA), an advanced multivariate analysis technique, to decipher characteristic compositional features. To establish the approach, signatures of major ECM proteins are determined from samples of pre-defined mixtures. Based on that, sets of ECM variants produced by MSCs in vitro are analyzed. Differences in the content of collagen, fibronectin, and laminin in the ECM resulting from the combined supplementation of MSC cultures with polymers that induce macromolecular crowding and with ascorbic acid are detected from the DPCA of ToF-SIMS spectra. The results are verified by immunostaining. Finally, the comparative ToF-SIMS analysis of ECM produced by MSCs of healthy donors and patients suffering from myelodysplastic syndrome display the potential of the novel methodology to reveal disease-associated alterations of the ECM composition.