Metabolic syndrome and risk of subclinical hypothyroidism: a systematic review and meta-analysis.

Background: Subclinical hypothyroidism (SCH) is a common endocrine subclinical disorder, the main adverse consequences of which are the development of clinical hypothyroidism and the promotion of ischemic heart disease. Metabolic syndrome (MetS) is a collection of metabolic problems. The goal of this meta-analysis was to evaluate the relationship between MetS and SCH. Methods: Suitable publications were identified using PubMed, Embase, and the Cochrane Library. The meta-analysis included only studies in English that reported odds ratio (OR) data for MetS and SCH. Two researchers combined data using a random-effects model. OR and 95% confidence intervals (CIs) were used to present the results. Results: MetS was associated with an elevated risk of developing SCH (OR 2.56, 95% CI 1.44-4.55). However, the individual components of MetS were not associated with the risk of SCH. Subgroup analysis revealed that different definitions of MetS had varying effects on SCH. Sensitivity analysis confirmed that our results were robust. Conclusions: This meta-analysis indicates that patients with MetS have an increased risk of SCH, while there is no significant association between the five individual components of MetS and the risk of SCH. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023454415.

New challenge: Mitigation and control of antibiotic resistant genes in aquatic environments by biochar.

With an increase of diverse contaminants in the environment, particularly antibiotics, the maintenance and propagation of antibiotic resistance genes (ARGs) are promoted by co-selection mechanisms. ARGs are difficult to degrade, cause long-lasting pollution, and are widely transmitted in aquatic environments. Biochar is frequently used to remove various pollutants during environmental remediation. Thus, this review provides a thorough analysis of the current state of ARGs in the aquatic environment as well as their removal by using biochar. This article summarizes the research and application of biochar and modified biochar to remove ARGs in aquatic environments, in order to refine the following contents: 1) fill gaps in the research on the various ARG behaviors mediated by biochar and some influence factors, 2) further investigate the mechanisms involved in effects of biochar on extracellular ARGs (eARGs) and intracellular ARGs (iARGs) in aquatic environments, including direct and the indirect effects, 3) describe the propagation process and resistance mechanisms of ARGs, 4) propose the challenges and prospects of feasibility of application and subsequent treatment in actual aquatic environment. Here we highlight the most recent research on the use of biochar to remove ARGs from aquatic environments and suggest future directions for optimization, as well as current perspectives to guide future studies on the removal of ARGs from aquatic environments.

Incorporation of Zinc-Strontium Phosphate into Gallic Acid-Gelatin Composite Hydrogel with Multiple Biological Functions for Bone Tissue Regeneration.

Large bone defects resulting from fractures and diseases have become a significant medical concern, usually impeding spontaneous healing through the body's self-repair mechanism. Calcium phosphate (CaP) bioceramics are widely utilized for bone regeneration, owing to their exceptional biocompatibility and osteoconductivity. However, their bioactivities in repairing healing-impaired bone defects characterized by conditions such as ischemia and infection remain limited. Recently, an emerging bioceramics zinc-strontium phosphate (ZSP, Zn2Sr(PO4)2) has received increasing attention due to its remarkable antibacterial and angiogenic abilities, while its plausible biomedical utility on tissue regeneration is nonetheless few. In this study, gallic acid-grafted gelatin (GGA) with antioxidant properties was injected into hydrogels to scavenge reactive oxygen species and regulate bone microenvironment while simultaneously incorporating ZSP to form GGA-ZSP hydrogels. The GGA-ZSP hydrogel exhibits low swelling, and in vitro cell experiments have demonstrated its favorable biocompatibility, osteogenic induction potential, and ability to promote vascular regeneration. In an in vivo bone defect model, the GGA-ZSP hydrogel significantly enhanced the bone regeneration rates. This study demonstrated that the GGA-ZSP hydrogel has pretty environmentally friendly therapeutic effects in osteogenic differentiation and massive bone defect repair.

Serum exosomes lncRNAs: TCONS_I2_00013502 and ENST00000363624 are new diagnostic markers for rheumatoid arthritis.

The lack of diagnostic markers limits the window of effectiveness for rheumatoid arthritis (RA) therapies. Here, we isolated exosomes of serum samples from four distinct groups RA patients, according to disease activity and with/without medication. Then, total RNA of exosomes was extracted for whole-transcriptome sequencing. Focusing on lncRNA sequencing, gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were performed. We found that the number of upregulated lncRNAs were significantly higher than that of downregulated lncRNAs in each four RA groups. And most importantly, we identified two specific lncRNAs from differentially expressed lncRNAs, TCONS_I2_00013502 (up-regulated) and ENST00000363624 (down-regulated) in RA. Receiver Operating Characteristic (ROC) curve analysis showed that the two lncRNAs were promising biomarkers for RA diagnosis. These findings highlight lncRNAs of the serum exosome are important biomarkers and provide application potential for diagnosis of RA.

Identification of the circRNA-miRNA-mRNA network for treating methamphetamine-induced relapse and behavioral sensitization with cannabidiol.

AIMS: This study aims to investigate the pharmacological effects and the underlying mechanism of cannabidiol (CBD) on methamphetamine (METH)-induced relapse and behavioral sensitization in male mice. METHODS: The conditioned place preference (CPP) test with a biased paradigm and open-field test were used to assess the effects of CBD on METH-induced relapse and behavioral sensitization in male mice. RNA sequencing and bioinformatics analysis was employed to identify differential expressed (DE) circRNAs, miRNAs, and mRNAs in the nucleus accumbens (NAc) of mice, and the interaction among them was predicted using competing endogenous RNAs (ceRNAs) network analysis. RESULTS: Chronic administration of CBD (40 mg/kg) during the METH withdrawal phase alleviated METH (2 mg/kg)-induced CPP reinstatement and behavioral sensitization in mice, as well as mood and cognitive impairments following behavioral sensitization. Furthermore, 42 DEcircRNAs, 11 DEmiRNAs, and 40 DEmRNAs were identified in the NAc of mice. The circMeis2-miR-183-5p-Kcnj5 network in the NAc of mice is involved in the effects of CBD on METH-induced CPP reinstatement and behavioral sensitization. CONCLUSIONS: This study constructed the ceRNAs network for the first time, revealing the potential mechanism of CBD in treating METH-induced CPP reinstatement and behavioral sensitization, thus advancing the application of CBD in METH use disorders.

Emodin inhibits respiratory syncytial virus entry by interactions with fusion protein.

Introduction: Respiratory syncytial virus (RSV) fusion (F) protein is essential for facilitating virus entry into host cells, providing a hopeful path for combating viral diseases. However, F protein inhibitors can rapidly select for viral resistance. Thus, discovering new inhibitors of F-protein is necessary to enrich the RSV drug development pipeline. Methods: In this study, we screen 25 bioactive compounds from Chinese herbal medicines that exhibit a strong binding to the RSV-F protein using surface plasmon resonance. Results: After screening, we found emodin could strongly bind to RSV-F protein, and could effectively curb RSV infection. Further investigations certificated that emodin specifically disrupts the attachment and internalization phases of RSV infection by targeting the RSV-F protein. In vivo studies with mice infected with RSV demonstrated that emodin effectively reduces lung pathology. This therapeutic effect is attributed to emodin's capacity to diminish pro-inflammatory cytokine production and reduce viral load in the lungs. Discussion: In conclusion, our findings provide initial insights into the mechanism by which emodin counters RSV infection via engagement with the RSV-F protein, establishing it as a viable contender for the development of novel therapeutic agents aimed at RSV.

Recruitment and retention of adolescents for an ecological momentary assessment measurement burst mental health study: The MHIM engagement strategy.

INTRODUCTION: Recruitment and long-term retention of adolescent participants in longitudinal research are challenging and may be especially so in studies involving remote measurement and biosampling components. The ability to effectively recruit and retain participants can be supported by the use of specific evidence-based engagement strategies that are built in from the earliest stages. METHODS: Informed by a review of the evidence on effective engagement strategies and consultations with adolescents (via two Young Person Advisory Groups [YPAGs]; ages 11-13 and 14-17), the current protocol describes the planned participant engagement strategy for the Mental Health in the Moment Study: a multimodal measurement burst study of adolescent mental health across ages 11-19. RESULTS: The protocol incorporates engagement strategies in four key domains: consultations/co-design with the target population, incentives, relationship-building and burden/barrier reduction. In addition to describing general engagement strategies in longitudinal studies, we also discuss specific concerns regarding engagement in data collection methods such as biosampling and ecological momentary assessment where a paucity of evidence exists. CONCLUSION: Engagement strategies for adolescent mental health studies should be based on existing evidence and consultations with adolescents. We present our approach in developing the planned engagement strategies and also discuss limitations and future directions in engaging adolescents in longitudinal research. PATIENT OR PUBLIC CONTRIBUTION: The study design for this project places a strong emphasis on the active engagement of adolescents throughout its development. Specifically, the feedback and suggestions provided by the YPAGs have been instrumental in refining our strategies for maximising the recruitment and retention of participants.

Creation of a structured solar cell material dataset and performance prediction using large language models.

Materials scientists usually collect experimental data to summarize experiences and predict improved materials. However, a crucial issue is how to proficiently utilize unstructured data to update existing structured data, particularly in applied disciplines. This study introduces a new natural language processing (NLP) task called structured information inference (SII) to address this problem. We propose an end-to-end approach to summarize and organize the multi-layered device-level information from the literature into structured data. After comparing different methods, we fine-tuned LLaMA with an F1 score of 87.14% to update an existing perovskite solar cell dataset with articles published since its release, allowing its direct use in subsequent data analysis. Using structured information, we developed regression tasks to predict the electrical performance of solar cells. Our results demonstrate comparable performance to traditional machine-learning methods without feature selection and highlight the potential of large language models for scientific knowledge acquisition and material development.

Compatible camouflage for dual-band guided-laser radar and infrared via a metamaterial perfect absorber.

Laser-guided detector and infrared detection have attracted increasing attention in a wide range of research fields, including multispectral detection, radiative cooling, and thermal management. Previously reported absorbers presented shortcomings of lacking either tunability or compatibility. In this study, a metamaterial perfect absorber based on a Helmholtz resonator and fractal structure is proposed, which realizes tunable perfect absorptivity (α 1.06µ m >0.99,α 10.6µ m >0.99) of guided-laser radar dual operating bands (1.06 µm and 10.6 µm) and a low infrared average emissivity (ε¯3-5µ m =0.03,ε¯8-14µ m =0.31) in two atmospheric windows for compatible camouflage. The proposed perfect absorber provides a dynamically tunable absorptivity without structural changes and can be applied to optical communication, military stealth or protection, and electromagnetic detection.

Development and validation of the rheumatoid arthritis scale among the system of quality of life instruments for chronic diseases QLICD-RA (V2.0).

Rheumatoid Arthritis is a more serious threatening to people and suitable for QOL measurement. A few specific QOL instruments are available without considering Chinese culture. The present study was aimed to develop and validate the Rheumatoid Arthritis Scale among the System of Quality of Life Instruments for Chronic Diseases (QLICD-RA V2.0). The data collected from 379 patients with RA was used to evaluate the psychometric properties of the scale. The reliability was evaluated by the internal consistency Cronbach's α, test-retest reliability Pearson correlation r and intra-class correlation (ICC). We evaluated the construct validity and criteria-related validity by correlation analysis and structural equation modeling. We compared the differences in scores of QLICD-RA before and after treatment and used the Standard Response Mean (SRM) to assess the responsiveness. The results showed that the internal consistency coefficient Cronbach's α values were greater than 0.70. The correlations r and ICCs were greater than 0.80. The correlation analysis and structural equation modeling confirmed good construct validity and criterion-related validity. The SRM ranges from 0.07 to 0.27 for significant domains/facets. It concluded that QLICD-RA (2.0) is a reliable and valid instrument to measure QOL among patients with RA.

Network structure of adolescent social, emotional, and behavioral difficulties and their differential relationships with suicidality.

BACKGROUND: Social, emotional, and behavioral difficulties (SEBDs) tend to develop during adolescence. Their presence and especially co-occurrence induce numerous disrupting consequences, including suicidality. A recently developed network analysis is suitable to investigate the symptom-level structure of comorbid psychopathology. Rather than pairwise comorbidity networks, the current study investigated a comprehensive network of SEBDs at the symptom level and explored the differential relationships between symptoms of SEBDs and suicidality. METHODS: Recruited from four public schools in China, a sample of adolescents (N = 6974, mean age = 15.84, 50.1% boys) were assessed with the Strengths and Difficulties Questionnaire (SDQ) and one suicidality-related item. The cross-sectional network structure of the SEBD symptoms was investigated. The differential associations between individual symptoms of SEBDs and suicidality were also explored with a relative importance analysis. RESULTS: The results showed that constantly fidgeting, worry a lot, unhappy, down-hearted, tearful, and easily scared emerged as the most central symptoms in the network of SEBDs. Worry a lot, constantly fidgeting, lose my temper, and being bullied served as bridge symptoms, connecting various domains of SEBDs. In addition, the centrality of symptoms was positively associated with the variance shared with suicidality, with worry a lot and unhappy, down-hearted, and tearful explaining a large portion of the variance of suicidality. CONCLUSIONS: Taken together, the results were indicative of close connections among emotional, hyperactivity-inattention, peer, and conduct aspects of adolescent mental health difficulties, as well as the central role of emotional difficulties in the SEBDs network.

Synthetic approaches and clinical application of small-molecule inhibitors of sodium-dependent glucose transporters 2 for the treatment of type 2 diabetes mellitus.

Sodium-dependent glucose transporters 2 (SGLT2) inhibitors are a class of small-molecule drugs that have gained significant attention in recent years for their potential clinical applications in the treatment of type 2 diabetes mellitus (T2DM). These inhibitors function by obstructing the kidneys' ability to reabsorb glucose, resulting in a rise in the excretion of glucose in urine (UGE) and subsequently lowering blood glucose levels. Several SGLT2 inhibitors, such as Dapagliflozin, Canagliflozin, and Empagliflozin, have been approved by regulatory authorities and are currently available for clinical use. These inhibitors have shown notable enhancements in managing blood sugar levels, reducing body weight, and lowering blood pressure in individuals with T2DM. Additionally, they have exhibited potential advantages in decreasing the likelihood of cardiovascular incidents and renal complications among this group of patients. This review article focuses on the synthesis and clinical application of small-molecule SGLT2 inhibitors, which have provided a new therapeutic approach for the management of T2DM.

Checklist and distribution of Nepomorpha (Hemiptera: Heteroptera) from China.

An updated catalog of the infraorder Nepomorpha from China is provided based on literature reports, museum specimens, and field collections. In total, 214 species of Nepomorpha are listed in 6 superfamilies, 11 families, and 37 genera, including: Aphelocheiridae (1 genus, 27 species), Belostomatidae (3 genera, 7 species), Corixidae (9 genera, 52 species), Gelastocoridae (1 genus, 3 species), Helotrephidae (5 genera, 25 species), Micronectidae (1 genus, 28 species), Naucoridae (7 genera, 12 species), Nepidae (5 genera, 21 species), Notonectidae (4 genera, 32 species), Ochteridae (1 genus, 2 species) and Pleidae (1 genus, 5 species). Paraplea liturata (Fieber, 1844) is reported from mainland China for the first time. Distribution maps are provided for most species and are based on museum specimens and our field collections.

Immune checkpoint inhibitors or anti-claudin 18.2 antibodies? A network meta-analysis for the optimized first-line therapy of HER2-negative gastric cancer.

Background: Multiple anti-programmed cell death-1/programmed cell death-ligand 1 (PD-1/PD-L1) inhibitors and zolbetuximab, an anti-claudin 18.2 antibody, have shown efficacy in the first-line treatment of HER2-negative gastric cancers. How to choose the best regimen remains an unsolved question. Objectives: We aimed to conduct a comparative analysis of the therapeutic advantages between immunotherapy and anti-claudin-18.2-targeted therapies in the first-line treatment of HER2-negative, unresectable, or metastatic gastric cancers. Design: Network meta-analysis was employed to systematically compare efficacy and safety data derived from various clinical trials. Data sources and methods: We included phase III randomized controlled trials in PubMed, Embase, Web of Science, Cochrane Library, and major conference abstracts. Network meta-analysis was used to compare the efficacy of each first-line therapeutic agent and to indirectly compare immunotherapy with anti-claudin-18.2-targeted therapy. Results: Eight trials comprising a total of 6455 patients were included. For the overall survival (OS) analysis, no statistically significant differences were observed between pembrolizumab [hazard ratios (HR) = 1.00, 95% CI: 0.94-1.07], sintilimab (HR = 0.99, 95% CI: 0.89-1.09), sugemalimab (HR = 0.98, 95% CI: 0.87-1.10), tislelizumab (HR = 0.97, 95% CI: 0.87-1.09), zolbetuximab (HR = 0.98, 95% CI: 0.91-1.07), and nivolumab (HR = 1.00). For the progression-free survival (PFS) analysis, no statistically significant differences were observed between pembrolizumab (HR = 1.00, 95% CI: 0.93-1.06), sintilimab (HR = 0.91, 95% CI: 0.83-1.00), sugemalimab (HR = 0.92, 95% CI: 0.84-1.02), tislelizumab (HR = 0.93, 95% CI: 0.84-1.03), zolbetuximab (HR = 0.96, 95% CI: 0.88-1.05), and nivolumab (HR = 1.00). For the overall response rate analysis, all regimens presented similar effects on ORR. In addition, anti-claudin-18.2-targeted therapies presented similar OS (HR = 0.99, 95% CI: 0.95-1.04) and PFS (HR = 1.01, 95% CI: 0.91-1.12) compared to immunotherapy, although their toxicity profiles were distinct. Conclusions: Our network meta-analysis showed no significant difference in PFS, OS, or ORR between different checkpoint inhibitors or between immunotherapy and anti-claudin-18.2-targeted therapies in the first-line treatment of HER2-negative, unresectable, or metastatic gastric cancers.

Identification of novel biomarkers based on lipid metabolism-related molecular subtypes for moderately severe and severe acute pancreatitis.

BACKGROUND: Acute pancreatitis (AP) is an unpredictable and potentially fatal disorder. A derailed or unbalanced immune response may be the root of the disease's severe course. Disorders of lipid metabolism are highly correlated with the occurrence and severity of AP. We aimed to characterize the contribution and immunological characteristics of lipid metabolism-related genes (LMRGs) in non-mild acute pancreatitis (NMAP) and identify a robust subtype and biomarker for NMAP. METHODS: The expression mode of LMRGs and immune characteristics in NMAP were examined. Then LMRG-derived subtypes were identified using consensus clustering. The weighted gene co-expression network analysis (WGCNA) was utilized to determine hub genes and perform functional enrichment analyses. Multiple machine learning methods were used to build the diagnostic model for NMAP patients. To validate the predictive effectiveness, nomograms, receiver operating characteristic (ROC), calibration, and decision curve analysis (DCA) were used. Using gene set variation analysis (GSVA) and single-cell analysis to study the biological roles of model genes. RESULTS: Dysregulated LMRGs and immunological responses were identified between NMAP and normal individuals. NMAP individuals were divided into two LMRG-related subtypes with significant differences in biological function. The cluster-specific genes are primarily engaged in the regulation of defense response, T cell activation, and positive regulation of cytokine production. Moreover, we constructed a two-gene prediction model with good performance. The expression of CARD16 and MSGT1 was significantly increased in NMAP samples and positively correlated with neutrophil and mast cell infiltration. GSVA results showed that they are mainly upregulated in the T cell receptor complex, immunoglobulin complex circulating, and some immune-related routes. Single-cell analysis indicated that CARD16 was mainly distributed in mixed immune cells and macrophages, and MGST1 was mainly distributed in exocrine glandular cells. CONCLUSIONS: This study presents a novel approach to categorizing NMAP into different clusters based on LMRGs and developing a reliable two-gene biomarker for NMAP.

A Journey of Challenges and Victories: A Bibliometric Worldview of Nanomedicine since the 21st Century.

Nanotechnology profoundly affects the advancement of medicine. Limitations in diagnosing and treating cancer and chronic diseases promote the growth of nanomedicine. However, there are very few analytical and descriptive studies regarding the trajectory of nanomedicine, key research powers, present research landscape, focal investigative points, and future outlooks. Herein, articles and reviews published in the Science Citation Index Expanded of Web of Science Core Collection from first January 2000 to 18th July 2023 are analyzed. Herein, a bibliometric visualization of publication trends, countries/regions, institutions, journals, research categories, themes, references, and keywords is produced and elaborated. Nanomedicine-related academic output is increasing since the COVID-19 pandemic, solidifying the uneven global distribution of research performance. While China leads in terms of publication quantity and has numerous highly productive institutions, the USA has advantages in academic impact, commercialization, and industrial value. Nanomedicine integrates with other disciplines, establishing interdisciplinary platforms, in which drug delivery and nanoparticles remain focal points. Current research focuses on integrating nanomedicine and cell ferroptosis induction in cancer immunotherapy. The keyword "burst testing" identifies promising research directions, including immunogenic cell death, chemodynamic therapy, tumor microenvironment, immunotherapy, and extracellular vesicles. The prospects, major challenges, and barriers to addressing these directions are discussed.

Automatically Generated Datasets: Present and Potential Self-Cleaning Coating Materials.

The rise of urbanization coupled with pollution has highlighted the importance of outdoor self-cleaning coatings. These revolutionary coatings contribute to the longevity of various surfaces and reduce maintenance costs for a wide range of applications. Despite ongoing research to develop efficient and durable self-cleaning coatings, adopting systematic research methodologies could accelerate these advancements. In this work, we use Natural Language Processing (NLP) strategies to generate open- and traceable-sourced datasets about self-cleaning coating materials from 39,011 multi-disciplinary papers. The data are from function-based and property-based corpora for self-cleaning purposes. These datasets are presented in four different formats for diverse uses or combined uses: material frequency statistics, material dictionary, measurement value datasets for self-cleaning-related properties and optical properties, and sentiment statistics of material stability and durability. This provides a literature-based data resource for the development of self-cleaning coatings and also offers potential pathways for material discovery and prediction by machine learning.

Identification of potential hub genes associated with atopic dermatitis-like recombinant human epidermal model using integrated transcriptomic and proteomic analysis.

Atopic dermatitis (AD) is a severe inflammatory skin disorder, characterized by elevated levels of proinflammatory cytokines that fuel a vicious cycle of inflammation. While inflammatory recombinant human epidermal (RHE) models relevant to AD have been established, comprehensive understanding remains limited. To illuminate changes and identify potential hub genes involved in AD-related inflammation, RHE models, stimulated by an inflammatory cocktail including polyinosinic-polycytidylic acid, tumor necrosis factor alpha (TNF-α), interleukin 4 (IL-4) and interleukin 13 (IL-13), were constructed and examined using tandem mass tags-proteomic coupled with RNA-seq transcriptomic analyses. Principal component analysis (PCA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway functional enrichment were employed for the analysis of related genes and proteins. Protein-protein interaction networks helped identify hub genes, which were further confirmed by qPCR and western blot. We observed high expression of thymic stromal lymphopoietin in the inflammatory RHE. Our study identified 2369 differentially expressed genes and 880 differentially expressed proteins in the cocktail-induced group versus the normal control group. A total of 248 overlapping symbols were enriched in various biological processes and signaling pathways, including cornification envelope, cell-cell junction, calcium ion binding, extracellular matrix receptor, terpenoid backbone biosynthesis, and peroxisome proliferator-activated receptors signaling pathway, among others. Among the 248 overlapping symbols, CytoHubba identified 10 hub molecules, namely signal transducer and activator of transcription 3 (STAT3), integrin subunit beta 1 (ITGB1), filaggrin (FLG), involucrin (IVL), DEAD (Asp-Glu-Ala-Asp) box polypeptide 58 (DDX58), small proline rich protein 1B (SPRR1B), interferon induced with helicase C domain 1 (IFIH1), desmoglein 1 (DSG1), collagen type XVII alpha 1 chain (COL17A1), and integrin subunit alpha 6 (ITGA6), based on the degree. These integrated results offer valuable insights into the molecular mechanisms of AD and present potential tools for screening cosmetic formulations intended for the treatment of AD.

Engaging Adolescents in Contemporary Longitudinal Health Research: Strategies for Promoting Participation and Retention.

PURPOSE: High (and nonselective) recruitment and retention rates in longitudinal studies of adolescence are essential for illuminating health trajectories and determinants during this critical period. Knowledge of optimal recruitment and retention strategies must keep pace with emerging challenges and opportunities, such as the shifts towards digitally-based data collection. METHODS: We used a narrative review approach to synthesize research on promising recruitment and retention strategies for optimizing engagement in the next generation of longitudinal adolescent health studies. RESULTS: We identified a small number of well-evidenced strategies, emerging challenges and opportunities for recruitment and retention in contemporary studies, and key evidence gaps. Core recommendations include the use of well-evidenced strategies (e.g., incentivizing participation, reducing barriers and burden, and investing in building positive relationships with participants) and coproducing recruitment and retention strategies with adolescents and parents of adolescents. DISCUSSION: More research is needed into successful recruitment/retention strategies for digital/remote data collection methods, but initial evidence suggests that adopting principles and adapting well-evidenced strategies from traditional longitudinal studies is promising.

Opinion Mining by Convolutional Neural Networks for Maximizing Discoverability of Nanomaterials.

The scientific literature contains valuable information that can be used for future applications, but manual analysis presents challenges due to its size and disciplinary boundaries. The prevailing solution involves natural language processing (NLP) techniques such as information retrieval. Nonetheless, existing automated systems primarily provide either statistically based shallow information or deep information without traceability, thereby falling short of delivering high-quality and reliable insights. To address this, we propose an innovative approach of leveraging sentiment information embedded within the literature to track the opinions toward materials. In this study, we integrated material knowledge into text representation and constructed opinion data sets to hierarchically train deep learning models, named as Scientific Sentiment Network (SSNet). SSNet can effectively extract knowledge from the energy material literature and accurately categorize expert opinions into challenges and opportunities (94% and 92% accuracy, respectively). By incorporating sentiment features determined by SSNet, we can predict the ranking of emerging thermoelectric materials with a 70% correlation to experimental outcomes. Furthermore, our model achieves a commendable 68% accuracy in predicting suitable nanomaterials for atomic layer deposition (ALD) over time. These promising results offer a practical framework to extract and synthesize knowledge from the scientific literature, thereby accelerating research in the field of nanomaterials.