搜索结果：Research synthesis methods

Because of the novelty and complexity of the construct of enjoyment in preterm infants, an exploration of insights from clinical and research perspectives is warranted. The purpose of this article is threefold: (a) to describe the clinical and research-based indicators of enjoyment, (b) to promote clinical strategies that may facilitate enjoyment, and (c) to identify potential benefits of enjoyment for preterm infants in the NICU. We conducted a comprehensive literature review to explore the clinical and research perspectives of enjoyment in preterm infants in the NICU. Articles were selected for full-text review if they discussed the potential clinical and research-based indicators of enjoyment, clinical strategies that may facilitate enjoyment, or benefits of enjoyment. There is a limited description of this construct for preterm infants in the NICU. Our synthesis of the literature highlights the relative value of enjoyment in preterm infants and its clinical and research implications. A conceptual framework was developed to guide further exploration of the construct of enjoyment in preterm infants and aimed at sparking deeper discussions among neonatal researchers and clinicians. Enjoyment in preterm infants remains an underrecognized construct in the literature. Initiating discussions on its meaning and potential impact to transform NICU care and research is essential. Further research is warranted to develop and test validated measures to assess enjoyment in preterm infants. Clinicians are encouraged to reflect on their practice and consider strategies to foster enjoyable experiences for preterm infants in the NICU.

ObjectiveTo synthesise and analyse qualitative evidence relevant to the question: What are the experiences and perspectives of healthcare professionals on goal setting in stroke rehabilitation?Data sourcesPubMed, PsycINFO, MEDLINE and CINAHL were systematically searched in May 2025, supplemented by backward and forward citation searching.Review methodsThis systematic review was pre-registered on PROSPERO (CRD420251038210). Eligibility criteria included peer-reviewed qualitative or mixed methods studies with qualitative data from healthcare professionals outlining experiences of goal setting in stroke rehabilitation. Non-English publications were excluded. The Critical Appraisal Skills Programme (CASP) checklist was used to appraise quality. Data were analysed using thematic synthesis.ResultsEight studies, published between 1999 and 2020, were included. These comprised 108 clinicians of various rehabilitation disciplines, from multiple countries, working across acute, inpatient and community settings. Most data were collected via semi-structured interviews. Methodological rigour of identified studies was generally high. Nine descriptive themes emerged from the thematic synthesis. From these descriptive themes, three analytical themes were derived: (1) Who leads, who follows?, (2) Between hope and reality, (3) Starting with the person, not the problem. Eight of the descriptive themes were directly related to analytical themes, whereas one theme was a stand-alone theme. Confidence in the thematic synthesis findings was assessed as moderate.ConclusionThis synthesis of qualitative studies from various rehabilitation settings in stroke found that experiences of goal setting from the perspective of healthcare professionals describe directive and collaborative approaches, emotional aspects of goal setting in time-limited contexts and a commitment to person-centred care.

Conventional chemical and physical methods for nanoparticle synthesis often involve toxic reagents, high energy demands, and limited biocompatibility. As a result, the biosynthesis of precious metal nanoparticles (PMNPs) using green algal extracts has gained attention as an eco-friendly, low-cost alternative, particularly for biomedical applications. This review explores the synthesis of PMNPs, i.e., silver, gold, platinum, palladium, rhodium, iridium, osmium, and ruthenium, via green algae, emphasizing the role of algal metabolites and phytochemicals in nanoparticle reduction and stabilization. Biosynthesized PMNPs consistently exhibit strong anticancer properties, including dose-dependent cytotoxicity, reactive oxygen species generation, apoptosis induction, and selective activity against cancer cells, especially in breast, cervical, liver, and colorectal cancer models. However, challenges such as limited mechanistic understanding, variability in synthesis outcomes, and scalability constraints remain. This review highlights the cancer therapeutic promise of green algae-mediated PMNPs while outlining critical directions for future research in anticancer nanomedicine.

The exponential growth of digital information has led to an unprecedented expansion in the volume of unstructured text data. Efficient classification of these data is critical for timely evidence synthesis and informed decision-making in health care. Machine learning techniques have shown considerable promise for text classification tasks. However, multiclass classification of papers by study publication type has been largely overlooked compared to binary or multilabel classification. Addressing this gap could significantly enhance knowledge translation workflows and support systematic review processes. This study aimed to fine-tune and evaluate domain-specific transformer-based language models on a gold-standard dataset for multiclass classification of clinical literature into mutually exclusive categories: original studies, reviews, evidence-based guidelines, and nonexperimental studies. The titles and abstracts of McMaster's Premium Literature Service (PLUS) dataset comprising 162,380 papers were used for fine-tuning seven domain-specific transformers. Clinical experts classified the papers into four mutually exclusive publication types. PLUS data were split in an 80:10:10 ratio into training, validation, and testing sets, with the Clinical Hedges dataset used for external validation. A grid search evaluated the impact of class weight (CW) adjustments, learning rate (LR), batch size (BS), warmup ratio, and weight decay (WD), totaling 1890 configurations. Models were assessed using 10 metrics, including the area under the receiver operating characteristic curve (AUROC), the F1-score (harmonic mean of precision and recall), and Matthew's correlation coefficient (MCC). The performance of individual classes was assessed using a one-to-rest approach, and overall performance was assessed using the macro average. Optimal models identified from validation results were further tested on both PLUS and Clinical Hedges, with calibration assessed visually. Ten best-performing models achieved macro AUROC≥0.99, F1-score≥0.89, and MCC≥0.88 on the validation and testing sets. Performance declined on Clinical Hedges. Models were consistently better at classifying original studies and reviews. Biomedical Bidirectional Encoder Representations from Transformers (fine-tuned on biomedical text; BioBERT)-based models had superior calibration performance, especially for original studies and reviews. Optimal configurations for search included lower LRs (1 × 10-5 and 3 × 10-5), midrange BSs (32-128), and lower WD (0.005-0.010). CW adjustments improved recall but generally reduced performance on other metrics. Models generally struggled with accurately classifying nonexperimental and guideline studies, potentially due to class imbalance and content heterogeneity. This study used a comprehensive hyperparameter search to highlight the effectiveness of fine-tuned transformer models, notably BioBERT variants, for multiclass clinical literature classification. Although class weighting generally decreased overall performance, addressing class imbalance through alternative methods, such as hierarchical classification or targeted resampling, warrants future exploration. Hyperparameter configurations were crucial for robust performance, aligning with the previous literature. These findings support future modeling research and practical deployment in human-in-the-loop systems to support knowledge synthesis and translation workflows with the findings from this work.

Peer support has emerged as a developmentally appropriate approach to address the psychosocial and behavioral needs of adolescent and young adult childhood cancer survivors (AYA CCS). Current evidence on peer support for AYA CCS remains fragmented, with qualitative and quantitative findings rarely combined to explain how and why these interventions exert their effects. This mixed-methods systematic review aimed to investigate the effectiveness, mechanisms, and contextual influences of peer support interventions for AYA CCS. Searches across eight databases identified quantitative, qualitative, and mixed-methods studies involving peer support interventions for AYA CCS aged 15-39 years. Quantitative outcomes were integrated with qualitative findings using a convergent segregated approach. Twenty-six studies were included. Quantitative evidence indicated improvements in emotional well-being, social support, coping, self-efficacy, and selected health behaviors; however, the findings for quality of life and identity-related outcomes were heterogeneous. Qualitative synthesis identified core relational mechanisms such as belonging, normalization, emotional safety, reciprocity, shared meaning-making, and identity reconstruction, explaining the observed quantitative benefits and contextualized key divergences (such as a response shift phenomenon and high clinical demand despite null efficacy). One-on-one and structured peer mentoring programs had stronger and more consistent effects than asynchronous or app-based interventions. Peer support is a multifaceted and impactful strategy that supports psychosocial adaptation and health-related behaviors among AYA CCS. Programs fostering meaningful peer relationships and relational depth appear most effective. Future research should clarify the mechanisms of change and optimize hybrid or digitally supported models to enhance accessibility while preserving relational quality.

Chemoenzymatic dynamic kinetic resolution (DKR) offers a powerful bridge between chemocatalysis and biocatalysis for the preparation of chiral molecules. However, its broader application has been limited by the incompatibility between racemization and resolution catalysts, where mutual interference often compromises catalytic activity and/or enantioselectivity. Here, we introduce a membrane-modulated strategy that circumvents the mandatory requirement for strict rate matching, offering a significant conceptual advance in the design of chemoenzymatic DKR systems. By spatially separating racemization and resolution while enabling their collaborative operation within a two-stage, two-step process, this approach dramatically enhances the typically low efficiency of conventional DKR, allowing the efficient synthesis of tetra-substituted 3-hydroxyphthalide esters that are challenging to access by traditional methods, and greatly expanding the scope of chiral phthalide preparation. This membrane-modulated strategy not only streamlines the typically laborious optimization required in conventional DKR for developing an alternative chemoenzymatic DKR approach but also provides a useful platform with the potential for pharmaceutical synthesis.

Natural Language Processing (NLP) models show promise in enhancing interpretation and triage of outpatient referrals across diverse specialties. To conduct a systematic literature review and narrative synthesis of recent studies that utilized NLP-based models for triage-related tasks such as urgency prioritization, referral classification, and justification review. Medline, Embase, Web of Science, and CINAHL databases were searched for articles published up to February 17 2024, limiting searches to the last 5 years prior to the search. All citations were imported into Covidence for duplicate removal and screening. We included studies that utilized NLP techniques to triage outpatient referrals to a specialist (medical or surgical), and included comparison to human triage. Abstracts and full texts were each screened independently by two reviewers. Data from each study were extracted independently by two reviewers using a standardized extraction form, including fields such as study design, dataset size, specialty, models tested, and outcomes reported. Results were synthesized narratively, organized by key themes focused on data, model and clinical applicability. Quality and risk of bias assessment was performed using the PROBAST-AI and Technology Readiness scales. A total of 4,225 titles and abstracts were reviewed resulting in 26 full-text reviews. A total of 10 studies were used for data extraction and synthesis. These studies spanned a wide range of medical specialties including surgery, medical specialties, and radiology. Tasks included predicting condition and priority level. Most domains were assessed as low or uncertain risk of bias. Outcome measures varied across studies, but overall, 7 studies reported high levels of accuracy compared to manual workflows. We summarized key differences in dataset preprocessing and augmentation, triage model, and feasibility and clinical applicability. NLP shows promise in augmenting human triage of outpatient referrals to specialty care. To realize the full potential of NLP for triage, future work should prioritize standardized reporting and prospective validation to support safe and effective integration into healthcare systems.

Progesterone production by the corpus luteum is essential for embryo implantation and early pregnancy maintenance and is acutely stimulated by luteinizing hormone (LH). While LH signaling through protein kinase A (PKA) is well established, downstream regulatory networks that constrain or shape luteal steroidogenesis remain incompletely defined. Here, we identify a previously unrecognized role for the Hippo signaling pathway in regulating luteal progesterone production. Using primary bovine luteal cells isolated from corpora lutea, we examined the relationship between LH/PKA signaling and Hippo signaling pathway activity. Expression, phosphorylation, and subcellular localization of Hippo pathway components were assessed by immunoblotting and nuclear fractionation. Progesterone production was quantified by ELISA. LH-induced transcriptional responses were analyzed using upstream regulator prediction from RNA sequencing. Functional roles of YAP1 and TAZ were evaluated using adenoviral overexpression of constitutively active mutants and siRNA-mediated knockdown. Hippo pathway components were enriched in luteal cells relative to follicular precursors. LH rapidly increased phosphorylation and cytoplasmic sequestration of YAP1 and TAZ in small luteal cells through PKA. Pharmacologic inhibition of LATS1/2 did not alter LH-stimulated progesterone production, suggesting that LH-induced steroidogenesis is not limited by LATS-dependent regulation of YAP1/TAZ. Sustained activation of YAP1 or TAZ suppressed LH-induced progesterone synthesis, whereas depletion of either factor enhanced progesterone output. Consistently, RNA-seq analysis identified YAP1/TAZ, and TEAD transcription factors as inhibited upstream regulators following LH stimulation in small luteal cells. Our findings support a model in which LH, via PKA and activation of Hippo signaling promotes progesterone synthesis by restraining YAP1/TAZ transcriptional activity in small luteal cells. This work identifies Hippo signaling as an unrecognized regulatory layer in luteal steroidogenesis and highlights YAP1/TAZ as potential therapeutic target for luteal insufficiency and infertility.

Lassa fever remains a major public health threat in West Africa, requiring coordinated scientific, policy, and financing responses. Regional scientific convenings are increasingly used to connect research evidence with policy action, yet their contribution to epidemic preparedness is not well documented. We conducted a qualitative health systems and policy analysis of the 2nd ECOWAS Lassa Fever International Conference (ELFIC 2025) in Abidjan, Côte d'Ivoire. Data sources comprised 302 scientific abstracts, plenary and ministerial session records, and the official Ministerial Joint Communiqué. Using the conference's six thematic pillars as a deductive framework, we conducted a thematic content analysis and synthesized findings into four domains: scientific advances; surveillance and laboratory systems; policy and financing insights; and cross-cutting lessons for regional preparedness. Progress was noted in diagnostics, therapeutics, vaccine development, decentralized laboratory capacity, genomic surveillance, and digital reporting. Persistent gaps remain at sub-national and community levels, in surveillance coverage, workforce capacity, and operational readiness. A major outcome was the Ministerial Joint Communiqué endorsing regional co-financing for Lassa fever vaccine development. ELFIC 2025 demonstrates the role of regional scientific platforms in aligning evidence with policy commitments. Sustained impact will require institutionalized coordination, strengthened accountability, and targeted investments in frontline capacity.

Development of an efficient and stable catalytic system for the production of sustainable hydrogen has remained a pivotal challenge in photocatalysis and electrocatalysis. In this work, we have reported a facile calcination strategy for synthesizing n-type BaCeO3@g-C3N4 heterojunctions that synergistically integrate the strong visible-light response of g-C3N4 with the high ionic conductivity and redox versatility of BaCeO3 perovskite. The BaCeO3@g-C3N4 heterojunction formation was confirmed through XRD, SEM, HR-TEM, and XPS techniques. The BaCeO3 nanoparticles were found to be evenly anchored on g-C3N4 nanosheets post-calcination as compared to non-calcined BaCeO3@g-C3N4. The as-prepared heterostructures exhibited remarkable activity under different water-splitting conditions. The optimized 20% BaCeO3@g-C3N4 demonstrated enhanced photochemical (PC) H2 production performance (15.21 mmol gcat-1 h-1) compared to pristine g-C3N4 and BaCeO3. Electrochemical (EC) and photoelectrochemical (PEC) investigations also corroborated its advanced HER activity at low overpotentials. The superior H2 evolution activity is attributed to the optimized band alignment and the interfacial charge transfer between g-C3N4 and BaCeO3. This work presents a multimodal H2 production activity of BaCeO3@g-C3N4 heterojunctions via photochemical and photo-/electrochemical methods.

Generative artificial intelligence (GenAI) tools are increasingly used in scientific research to support literature searches, evidence synthesis, and manuscript preparation. While these systems promise substantial efficiency gains, concerns have emerged regarding their reliability, particularly their tendency to cite inaccurate, fabricated, or retracted literature. The unrecognized inclusion of retracted studies poses a serious risk to research integrity and evidence-based decision-making. Whether commonly used GenAI tools can reliably detect, exclude, or transparently communicate the retraction status of scientific publications remains unclear. This study aimed to evaluate the ability of freely available GenAI tools to correctly handle retracted scientific articles during literature searches. Primary and secondary outcomes focused on accuracy, reliability, and consistency in recognizing retracted literature. In this pragmatic trial, nine widely used free-access GenAI tools (ChatGPT 4, ChatGPT 5, Claude, Gemini, Perplexity, Microsoft Copilot, SciSpace, ScienceOS, and Consensus) were evaluated. Each tool was asked five predefined, standardized questions addressing topic overview, article identification, article summarization, and explicit assessment of retraction status. Overall, 15 retracted articles (the 10 most cited and 5 most recently retracted as of May 23, 2025) were selected from the Retraction Watch database. All questions were repeated twice to assess intratool consistency. Responses were independently rated as correct or incorrect by 2 researchers. Descriptive statistics summarized performance, and comparisons between general-purpose and research-focused AI tools were conducted using descriptive statistics. Interreviewer agreement was assessed using Cohen kappa coefficient. None of the evaluated AI tools consistently handled retracted articles correctly. No model achieved perfect accuracy across all question sets. ChatGPT 5 performed best, defined by the primary outcome of achieving fully correct responses to all five predefined tasks (5/5) for the highest number of retracted articles, correctly answering all five questions for 8 of 15 articles (53.3%). Research-focused tools (SciSpace, ScienceOS, and Consensus) failed to produce a single fully correct response set. Retracted articles were frequently included in topic overviews without warning, with error rates exceeding 40% in several tools. When specifically asked about retraction status, most systems failed to provide correct or complete information. OpenEvidence only reported data for a subset of our retracted articles as it is only used in health care literature. It demonstrated strong performance in topic overviews but low accuracy in identifying retracted articles. Freely available GenAI tools are currently not able to detect, exclude, or appropriately flag retracted scientific literature. The widespread and confident reproduction of retracted studies represents a substantial threat to research integrity, particularly in medical and evidence-based fields. Until retraction-aware verification mechanisms are systematically integrated, independent source checking remains essential when using AI-assisted literature tools.

Heterogeneous photocatalysis offers a sustainable alternative to many energy-intensive industrial processes; however, its scalability remains limited because common photoreactor designs rely on powder-based photocatalysts. This perspective explores the transition from traditional batch powder photocatalysis to scalable continuous-flow photocatalytic panels, with a focus on polymeric carbon nitride (CN) materials. Although CN has beneficial properties, such as ease of synthesis and stability, its use has been mainly limited to suspended powder systems for hydrogen production, hydrogen peroxide formation, CO2 reduction, and organic transformations. We review recent advancements in the development of CN-based photocatalytic panels (PCPs), highlighting scalable synthesis methods, including in situ growth techniques that enable direct polymerization onto substrates. The perspective covers photocatalytic system designs, PCP synthesis methods, structural characterization techniques, and applications in both batch and flow reactors. We highlight key challenges in transitioning from lab-scale to commercial-scale production and propose future research directions for CN photocatalytic panels, including learning opportunities from powder photocatalysis and photoelectrochemical systems. This analysis aims to connect laboratory demonstrations with future PCP implementation in industry.

The implantable cardioverter defibrillator (ICD) is an effective therapeutic option for hypertrophic cardiomyopathy (HCM). However, a comprehensive quantitative synthesis in this field remains limited. This study aims to analyze the research landscape of ICD application in HCM from 2000 to 2025. Publications related to ICD use in HCM were retrieved from the Web of Science Core Collection between January 1, 2000, and November 3, 2025. Data were visually analyzed using VOSviewer and CiteSpace. A total of 864 publications from 251 countries/regions met the inclusion criteria, with the United States contributing the most. *Circulation* was identified as the most frequently cited journal in this field. Keyword cluster analysis revealed that research hotspots primarily focused on "hypertrophic cardiomyopathy," "sudden cardiac death," and "implantable cardioverter defibrillator." Furthermore, keyword burst analysis indicated that current research frontiers center on terms such as "outcome" and "association." The field of ICD application in HCM has matured and is now on the verge of a paradigm shift. Future research should focus on refining risk prediction models, evaluating long-term patient outcomes, and addressing challenges posed by novel targeted therapies.

Rhubarb (Rheum spp.), a traditional herbal medicine, has attracted growing interest due to its anti-renal fibrosis effects in chronic kidney disease (CKD). This review systematically evaluates Rhubarb's botanical features, global distribution, and diverse processing methods, which influence its chemical composition and bioactivity. Major bioactive constituents, including anthraquinones, stilbenes, and polyphenols, are cataloged, and their potential roles in renal protection are elucidated. Traditional applications in nephropathy management are critically assessed alongside contemporary pharmacological evidence demonstrating Rhubarb's ability to attenuate renal fibrosis. Notably, this review highlights that multiple bioactive components in Rhubarb exert potent anti-fibrotic effects through complex, interactive modulation of multiple signaling pathways. Despite promising preclinical data, clinical translation remains limited by insufficient understanding of pharmacokinetics and potential herb-drug interactions. This synthesis identifies key research gaps, advocating for interdisciplinary studies to decipher multi-target mechanisms, refine pharmacokinetic profiles to enhance bioavailability, and translate preclinical findings into randomized controlled trials (RCTs). By integrating ethnopharmacological knowledge with modern drug discovery frameworks, this review underscores Rhubarb's potential as a multifaceted anti-fibrotic agent while calling for methodologically rigorous research to validate its therapeutic integration into CKD management protocols.

Breast cancer is one of the most prevalent cancers worldwide, with a 5-year survival rate exceeding 90%. Despite advances in treatment, survivors frequently experience persistent cancer- and treatment-related symptoms that negatively impact their quality of life. Body-oriented interventions (BOIs) have demonstrated effectiveness in symptom management; however, systematic reviews focused exclusively on BOIs for women who survived breast cancer (WSBC) remain limited. This systematic review protocol outlines the methodology for evaluating the scientific evidence on the effects of BOIs on cancer- and treatment-related symptoms in WSBC. The aim of this study is to examine the scientific evidence on the effects of BOIs on cancer- and treatment-related symptoms, well-being, and quality of life in WSBC. This protocol follows PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols) guidelines. We will conduct searches in 6 electronic databases: PubMed, Cochrane, Web of Science, Scopus, APA PsycNet, and Portal Regional da BVS. Studies will be considered for inclusion if they are written in English, French, Portuguese, or Spanish, with no restrictions on publication date; they consist of WSBC (aged 18 to 64 years); they are randomized controlled trials, quasi-randomized controlled trials, and pilot studies focusing on BOIs; they include a control group receiving no intervention, standard care, or a non-BOI; and the primary outcomes of interest include preintervention and postintervention measures of cancer- and treatment-related symptoms, well-being, and quality of life. The methodological quality of the studies and the risk of bias will be assessed using the PEDro scale and version 2 of the Cochrane risk-of-bias tool, respectively. The synthesis of results will be measured through the Best Evidence Synthesis. Two experienced independent reviewers will conduct study selection, data extraction, methodological quality assessment, and scientific evidence assessment. Disagreements will be resolved by a third reviewer. This protocol describes the prespecified methodology for the systematic review. At the time of publication of this protocol, the corresponding full systematic review manuscript was under peer review. The outcomes will synthesize the scientific evidence on the effects of BOIs on cancer- and treatment-related symptoms in WSBC. It is anticipated that this systematic review will identify benefits and directions for future research to support the integration of BOIs tailored to this population. Considering that previous systematic reviews focused on the effects of BOIs in survivors of all cancer types, challenges related to study risk of bias such as heterogeneity in intervention types, study designs, and outcome measures are anticipated, potentially leading to some inconsistency and imprecision. To mitigate these issues, PRISMA guidelines will be followed, and methodological quality and best evidence strength will be rigorously assessed. This review will systematically synthesize the effects of BOIs on cancer- and treatment-related symptoms in WSBC. These findings will provide health professionals with reliable evidence and methodological guidance for further research. PROSPERO CRD42023452519; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=452519. DERR1-10.2196/76858.

Resting metabolic rate (RMR) prediction equations used today often rely on the consideration of binary sex. Significant intrasex variability and a lack of data on diverse populations raise concerns about these equations' validity and generalizability. Existing systematic reviews have focused on specific populations like individuals with obesity or athletes, but none have systematically examined the demographic characteristics of participants used to derive these equations. Our central hypothesis is that the accuracy of RMR prediction is influenced by the demographic alignment between the equation's derivation population and the individual. We present a systematic review protocol to critically evaluate the literature and participant demographic profiles that underpin current RMR prediction equations. Our objectives are to (1) determine the characteristics of participant populations, including reporting on gender and sex diversity, used in RMR equation research; (2) critically appraise the methodologies, findings, and reporting practices of studies that developed RMR equations for binary populations; and (3) use the Sex and Gender Equity in Research guidelines to assess sex and gender terminology and variable inclusion in the generative RMR prediction literature. Following a PROSPERO-registered protocol (CRD420251084400), we will conduct a comprehensive search across multiple databases, including Academic Search Premier, PubMed, and Web of Science. The final search string will be: ((resting metab* rate) OR (RMR) OR (basal metab* rate) OR (BMR) OR (metabol*) OR (resting energy expenditure) OR (metab* rate)) AND ((predict* equation) OR (predict* model) OR (predict* algorithm) OR (formula) OR (estimation equation)) AND ((demograph*) OR (characterist*) OR (age) OR (race) OR (ethnicity) OR (sex) OR (gender)). We will include peer-reviewed, English-language articles reporting studies that generated RMR prediction equations and reported human participant demographic characteristics. Exclusion criteria include studies not generating prediction equations, without demographic data, or involving animals. Data extraction will include reported participant demographics (eg, sex, gender, race or ethnicity, age, and body composition), RMR test protocols, and reported reliability or validity metrics. Risk of bias will be assessed using PROBAST (Prediction Model Risk of Bias Assessment Tool). This study was funded in June 2025 by the University of Nevada, Las Vegas Sports Innovation Initiative Catalyst Grant Funding Program and in July 2025 by the National Association for Kinesiology in Higher Education Hellison Interdisciplinary Research Grant. The databases were searched using the final search string between August 1, 2025, and August 8, 2025. Training of team members began on September 3, 2025, and concluded on October 20, 2025. Findings will be disseminated through a narrative synthesis submitted for publication, adhering to the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) reporting guidelines. This review will identify gaps in the inclusivity and generalizability of current RMR prediction equations, informing future research and clinical applications. PROSPERO CRD420251084400; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251084400. PRR1-10.2196/82482.

Inflammatory bowel disease (IBD) is a chronic, relapsing condition associated with diagnostic delays, disease misclassification, and variable treatment response. Conventional diagnostic and monitoring tools remain limited in capturing the biological complexity of IBD, prompting growing interest in metabolomics as a complementary approach. This systematic review aimed to examine the role of metabolomics in enhancing the diagnosis and management of IBD across adult and pediatric populations. Systematic review. The review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. PubMed, Web of Science Core Collection, ScienceDirect, Cochrane Library, and Google Scholar were searched from inception to identify eligible studies. Observational studies and clinical trials assessing metabolomics in IBD diagnosis or management were included. Methodological quality was appraised using the Newcastle-Ottawa Scale, RoB 2, and ROBINS-I. Due to substantial heterogeneity, a narrative synthesis was performed. Fourteen studies involving approximately 3700 participants met the inclusion criteria. Metabolomic analyses of serum, feces, urine, and plasma consistently identified disease-associated metabolic perturbations, particularly in amino acids, bile acids, lipids, and short-chain fatty acids. Only two studies reported formal diagnostic performance, with sensitivity and specificity exceeding 80% for distinguishing IBD subtypes. Several studies demonstrated metabolomic changes associated with treatment response and remission; however, outcome definitions varied widely across studies. Metabolomics shows significant potential to enhance IBD diagnosis and management, particularly for disease differentiation and treatment monitoring. Nonetheless, clinical translation is constrained by methodological heterogeneity and limited diagnostic validation. Future research should prioritize standardized protocols and robust diagnostic accuracy studies. This review explores metabolomics’ role in enhancing IBD diagnosis and management for both adults and children. The systematic review followed PRISMA 2020 guidelines, searching PubMed, Web of Science, Cochrane Library, ScienceDirect, and Google Scholar. Two reviewers independently screened studies and assessed risk of bias using Cochrane’s RoB 2, ROBINS-I, and NOS. A narrative synthesis was conducted due to study heterogeneity. Out of 2,630 records screened, 14 studies met eligibility criteria. These included ten observational studies, one case-control, one longitudinal observational, one RCT, and one nonrandomized trial. Six observational studies were of high quality. Metabolomics shows potential for enhancing IBD diagnosis and treatment, but high heterogeneity and a lack of diagnostic accuracy studies limit practical insights. The identified biomarkers/metabolites are consistent with previous studies, showing metabolomics’ potential in diagnosing and treating IBD in both pediatric and adult populations. Recent observational studies report sensitivity and specificity, indicating progress. Comprehensive diagnostic protocols should be developed based on previously identified biomarkers/metabolites before conducting rigorous diagnostic accuracy studies to evaluate their accuracy and improve clinical application of research findings.

To systematically evaluate the research on amputation risk prediction models for patients with diabetic foot. PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database (VIP), and SinoMed were searched for studies on risk prediction tools for amputation in patients with diabetic foot. Two researchers screened 2134 articles, and 9 met the inclusion criteria. Included study basic information, model construction methods, predictive factors, and model efficacy indicators (AUC, sensitivity, specificity). The 9 studies included in the review described the construction of 15 prediction models and one prediction tool. Eight of the 9 studies had a high overall risk of bias; 2 had poor applicability in the field of predictive factors, and 6 had good applicability in all fields and overall. The main predictive factors included in the models were diabetes duration, glycated hemoglobin, white blood cell count, fibrinogen, and infection. The most common predictive factors were duration of diabetes (odds ratio=2.79; 95% CI: 1.65-3.93), white blood cell count (odds ratio=1.88; 95% CI: 0.78-2.98), and fibrinogen (odds ratio=0.10; 95% CI: 0.06-0.14). The predictive performance of current amputation risk prediction tools for patients with diabetic foot is good, but the literature has a high risk of bias and needs improved clinical applicability. Researchers should further validate and calibrate the existing tools or develop risk prediction tools with low bias risk and high clinical applicability based on local data.

Conversational agents (CAs) are increasingly used in mental health care to enhance access and engagement. However, their safe, ethical, and user-sensitive design remains a challenge. Despite growing attention to trauma-informed approaches in human-computer interaction, there is limited work on how the trauma-informed care (TIC) framework could be applied in the design of mental health CAs and no comprehensive synthesis to date. Guided by the Substance Abuse and Mental Health Services Administration's TIC framework, this scoping review explored how TIC principles (safety; trustworthiness and transparency; collaboration and mutuality; empowerment, voice, and choice; peer support; and cultural, historical, and gender issues) are currently represented in the design and evaluation of mental health conversational agents (MHCAs) and identified gaps and opportunities to promote more trauma-informed design practices. Online databases, as well as a secondary survey of citation lists from an initial search, were used to identify English-language journal articles and conference proceedings from 2000 to 2024 that empirically evaluated an independent, web- or app-based, unassisted CA used for mental health and included concepts from TIC. Our analysis included 38 publications (n=28, 73.7%, published in 2020 or later) covering 28 distinct MHCAs. Most studies used experimental methods (n=23, 60.6%) or user studies (n=11, 28.9%), with samples skewed toward female (men: mean 34.92%, SD 18.64%), young in age (mean 32.52, SD 14.6 y), and predominantly nonclinical (n=29, 76.3%). MHCAs were largely rule-based prototypes. No studies explicitly referenced the TIC framework as a guiding lens for MHCA design or evaluation. A total of 26 studies referenced terminology from TIC core principles but rarely defined them, while all 38 included language that could be linked to one or more principles. Overall, TIC-related concepts appeared most often within intervention design descriptions, qualitative assessments, or as items embedded in questionnaires evaluating broader constructs. Trustworthiness and transparency, safety, empowerment, voice and choice, and collaboration and mutuality were comparatively well addressed, while peer support and cultural, historical, and gender issues were largely absent. Design recommendations, where present, were relatively broad and emphasized secure, customizable, reliable, human-like, and context-sensitive MHCAs that offered multimodal interaction, goal setting and tracking, and transparency. Studies did not self-identify as using Substance Abuse and Mental Health Services Administration's framework for TIC, making it more difficult to identify its elements. The fragmented terms, disciplines, and metrics used make it difficult to draw more systematic conclusions about the current research landscape related to TIC, but our analysis indicates TIC to be a descriptive and potentially unifying framework and provides a starting point for the explicit trauma-informed MHCA research and design.