SEARCH · PubMed Health

Results for “Next Generation Science Standards”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

6 recordsLinked to original sources

PubMed2016

DNA barcoding in diverse educational settings: five case studies.

Despite 250 years of modern taxonomy, there remains a large biodiversity knowledge gap. Most species remain unknown to science. DNA barcoding can help address this gap and has been used in a variety of educational contexts to incorporate original research into school curricula and informal education programmes. A growing body of evidence suggests that actively conducting research increases student engagement and retention in science. We describe case studies in five different educational settings in Canada and the USA: a programme for primary and secondary school students (ages 5-18), a year-long professional development programme for secondary school teachers, projects embedding this research into courses in a post-secondary 2-year institution and a degree-granting university, and a citizen science project. We argue that these projects are successful because the scientific content is authentic and compelling, DNA barcoding is conceptually and technically straightforward, the workflow is adaptable to a variety of situations, and online tools exist that allow participants to contribute high-quality data to the international research effort. Evidence of success includes the broad adoption of these programmes and assessment results demonstrating that participants are gaining both knowledge and confidence. There are exciting opportunities for coordination among educational projects in the future.This article is part of the themed issue 'From DNA barcodes to biomes'.

Biodiversity↗

PubMed2026

Circulating Tumor DNA in Breast Cancer: A Liquid Biopsy Revolution for Non-Invasive Genomic Profiling and Clinical Decision-Making.

Breast cancer remains the most frequently diagnosed cancer and a leading cause of cancer-related mortality among women worldwide, underscoring the need for accurate, minimally invasive biomarkers to support precision oncology. Conventional tissue biopsy remains the standard for molecular characterization but is limited by its invasiveness, inability to capture spatial and temporal tumor heterogeneity, and challenges in serial monitoring. Circulating tumor DNA (ctDNA), a tumor-derived fraction of cell-free DNA, has emerged as a promising liquid biopsy biomarker capable of providing real-time genomic information throughout disease progression. This narrative review examines recent advances in ctDNA biology, analytical technologies, clinical applications, current limitations, and future directions in breast cancer management. A structured literature search of PubMed/MEDLINE, Scopus, Embase, Web of Science, and Google Scholar identified relevant English-language publications from 2015 to 2026. Current evidence indicates that highly sensitive platforms, including digital PCR, BEAMing, and next-generation sequencing, can detect clinically actionable alterations in genes such as PIK3CA, ESR1, TP53, ERBB2, AKT1, and BRCA1/2. ctDNA has demonstrated particular utility in identifying minimal residual disease, monitoring therapeutic response, detecting emerging resistance mechanisms, and guiding targeted treatment selection in advanced breast cancer. However, applications in early cancer detection, population screening, and artificial intelligence-assisted clinical decision-making remain investigational. Widespread clinical implementation is constrained by low ctDNA abundance in early-stage disease, analytical variability, limited assay standardization, and cost considerations. Continued technological innovation, prospective multicenter validation, standardized testing protocols, and evidence-based clinical guidelines are essential to fully integrate ctDNA into routine precision breast cancer care.

breast cancer↗

PubMed2026

AI In Leukemia Diagnostics: Complementing the Pathologist's Role.

Artificial intelligence (AI) is reshaping every stage of leukemia diagnostics, from digital morphology and multiparameter flow cytometry to next-generation sequencing, multi-omics analysis, and emerging computational frontiers such as quantum-inspired feature selection. This review outlines how contemporary AI tools can automate labor-intensive quantitation, flag diagnostically salient patterns, and standardize interpretation, while the pathologist or hematologist retains authority over validation, context-specific integration, and clinical decision-making. We present an illustrative "human-in-the-loop" workflow that embeds AI modules within current laboratory information systems, emphasizing points where expert oversight mitigates algorithmic bias and resolves discordant findings. We further map the validator-integrator role across morphology, flow cytometry, and genomic/multi-omic interpretation and provide practical training competencies and use cases for AI-assisted hematopathology. Beyond technical deployment, the article addresses the educational transformation required for sustainable adoption. Drawing on international competency frameworks, including the Digital Health Competencies in Medical Education Framework and recently proposed AI-specific Entrustable Professional Activities, we map core skills that future hematopathologists must master: data-science literacy, critical appraisal of AI outputs, and ethical governance. We highlight evaluated training models such as the Pathology Informatics Essentials for Residents curriculum, Stanford Artificial Intelligence in Machine and Imaging workshops, and College of American Pathologists bootcamps and propose integration strategies adaptable across resource settings. By pairing rigorous validation with targeted education, AI can elevate rather than eclipse the diagnostic role of the leukemia specialist, enabling more timely, reproducible, and personalized patient care.

Humans↗

PubMed2026

Beyond da Vinci: a systematic review of next-generation multiport robotic platforms in pediatric surgery.

As robotic surgery expands beyond the da Vinci platform, the relevance of new-generation systems to pediatric patients remains uncertain. This review examined the technical characteristics, applications, and perioperative outcomes of alternative multiport robotic platforms in children. PubMed/MEDLINE, Web of Science, Scopus, and the Cochrane Library were searched through 28 February 2026 in accordance with PRISMA 2020. Owing to clinical heterogeneity, findings were synthesized narratively, with Wilson 95% confidence intervals for key binary outcomes. Six studies reported 166 patients across 27 procedure types. Senhance accounted for 164 patients, while Hugo RAS and Hinotori were each represented by one patient. Ages ranged from 15 days to 17 years and weights from 3.8 to more than 100 kg. Senhance was the only platform used with 3-mm robotic instruments. Conversion or planned escalation occurred in 19 patients (11.4%; 95% CI, 7.5-17.2%). Four intraoperative complications were reported (2.4%; 95% CI, 0.9-6.0%). Across all reports, 20 patients experienced postoperative complications (12.0%; 95% CI, 7.9-17.9%). In the largest cohort, seven patients required reintervention (4.6%; 95% CI, 2.2-9.2%) and seven were readmitted (4.6%; 95% CI, 2.2-9.2%). No deaths or comparative pediatric studies were reported. Published experience remains sparse and is almost entirely limited to Senhance. Current evidence describes early clinical use without establishing comparative safety, effectiveness, or platform superiority. Prospective multicenter studies with standardized reporting are needed.

Humans↗

PubMed1992

Approaches to evaluating the toxicity and carcinogenicity of man-made fibers: summary of a workshop held November 11-13, 1991, Durham, North Carolina.

The Workshop on Approaches to Evaluating the Toxicity and Carcinogenicity of Man-Made Fibers (MMF) was held in Durham, North Carolina, on November 11-13, 1991. The goal of the workshop was to reach a consensus, or to determine the extent to which a consensus existed, in two areas. Participants were asked to identify scientifically sound approaches for evaluating the toxicity and carcinogenicity of man-made fibers based on today's science and to determine research appropriate for study during the next 5 years that can provide an improved scientific basis for future revisions of approaches used to evaluate man-made fiber toxicity and carcinogenicity. During the first day, a series of "state of knowledge" presentations were made to provide all participants with a common data base from which to interact and discuss scientific issues. The workshop participants were assigned to one of four discussion groups, which met separately in three half-day sessions following the first day of presentations. All groups discussed the same topics: exposure assessment, hazard identification, and dose-response information needed to integrate to characterize risk in the first session; approaches to obtaining the needed information in the second session; and recommended approaches and guidelines for evaluating the toxicity and carcinogenicity of MMF and research needs in the third session. The workshop participants reconvened as a whole after each discussion session, and one member from each group reported the group's conclusions. A closure period was also included at the end of the workshop for review and discussion of items that had been considered during the workshop. The primary conclusions reached were the following: -All fiber types capable of depositing in the thorax are not alike in their pathogenic potential. -Only fiber samples with dimensions similar to those to which humans can inhale should be tested. -A complete characterization (i.e., dimensions, fiber number, mass, and aerodynamic diameter) of the fiber aerosol and retained dose is essential. -Appropriate aerosol generation methods must be used for inhalation studies in order to preserve fiber lengths. -A tiered approach to toxicity evaluation is recommended that includes: 1. In vitro screening for durability, surface properties, cytotoxicity, and similar properties, etc; 2. Short-term inhalation or other in vivo studies; 3. That chronic inhalation studies are the "gold standard" (i.e., provide most appropriate data for risk characterization). -The rat is the most appropriate species for inhalation studies. -In chronic inhalation studies, animals should be retained to at least 20% survival after 2-year exposure. -Serial lung burden analyses are an essential component of inhalation studies and are essential for understanding exposure-dose-response relationships. -Studies oriented to understanding mechanisms of toxicity and carcinogenicity are important adjuncts to traditional toxicity studies. -Histopathological analyses of tissues of the respiratory tract represent primary endpoints for evaluating effects of inhaled fibers. Major effects include pulmonary fibrosis, lung tumors, and mesotheliomas. Experimental tissues should be archived for future studies; wherever possible, handling and preservation of tissues should be done in a way that maximizes their future use in mechanistic studies. -Potential human exposures throughout the entire life-cycle of the fiber must be considered and fibrous material for toxicologic studies prepared accordingly. -Intracavity studies are inappropriate for risk characterization but can play a useful screening role in assessing fiber toxicity.(ABSTRACT TRUNCATED AT 400 WORDS)

Animals↗

PubMed2025

Accurate identification of abnormal ploidy using an artificial intelligence model in preimplantation genetic testing.

STUDY QUESTION: Can ultra-low-coverage whole-genome sequencing (ulc-WGS) accurately identify abnormal ploidy during preimplantation genetic testing (PGT)? SUMMARY ANSWER: The artificial intelligence (AI)-based PGT-Plus model demonstrates high accuracy in ploidy detection, offering a cost-effective solution that enhances clinical utility of PGT. WHAT IS KNOWN ALREADY: The predominant PGT for aneuploidy can identify chromosomal aneuploidies but cannot determine ploidy status. Transferring embryos with ploidy abnormalities can result in miscarriage and molar pregnancy. On the other hand, in ART, fertilization is assessed by morphological pronuclear assessment at the zygote stage. However, it has a low specificity in the prediction of abnormal ploidy status and embryos deemed abnormally fertilized can yield healthy pregnancies. Accurately identified abnormal ploidy in PGT-A can resolve current limitations and expand the utility range of PGT-A. Several studies have identified ploidy abnormalities; however, they were mainly based on single-nucleotide polymorphism (SNP) arrays or needed to combine additional targeted-next-generation sequencing (NGS) information. Studies based on ulc-WGS remain scarce. STUDY DESIGN SIZE DURATION: The study consisted of two stages: methodology establishment and validation. An AI model, named PGT-Plus, was developed using 653 samples with known ploidy status, which was further validated using 792 different ploidy status samples. In the clinical application stage, the approach was used to analyse the ploidy status of 19 103 normally fertilized PGT blastocysts and 140 single pronucleus (1PN)-derived blastocysts collected between May 2022 and December 2023. All blastocysts were tested using trophectoderm biopsy and NGS. PARTICIPANTS/MATERIALS SETTING METHODS: The methodology is based on the ulc-WGS data. First, based on samples with known ploidy status: the heterozygosity rate of high-frequency biallelic SNPs, the likelihood ratio (LLR) of alleles was calculated under different assumptions ('both parental homologs' [BPH] from a single parent, 'single parental homolog' [SPH] from each parent, disomy, and monosomy) by leveraging allele frequencies and linkage disequilibrium (LD) measured in the 1000 genomes project database. Twenty-three continuous candidate features derived from heterozygosity rates and LLRs of chromosomes or selected windows were included to establish the ploidy prediction AI model. Gini importance analysis and multicollinearity mitigation was performed for feature selection, then the performance of Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression for modelling was compared. Subsequently, the parameter optimization was performed based on the RF model. Ploidy constitution concordance was evaluated in known ploidy status samples. The frequency of abnormal ploidy in normal fertilized PGT blastocysts and 1PN-derived blastocysts (including conventional IVF and ICSI) was evaluated. MAIN RESULTS AND THE ROLE OF CHANCE: Eleven features were collected for model architecture compared to SVM and Logistic Regression; RF achieved superior performance for ploidy detection. The AI model achieved an AUC of 1 for genome-wide-uniparental diploidy (GW-UPD), 1 for triploidy, and 0.99 for diploidy. For the 792 validation samples, 99.5% of samples were successfully detected using the AI model, and the model showed 100% accuracy for ploidy classification. In the clinical application stage, out of 19 103 PGT samples, 19 069 were successfully analysed using the model, with 110 (0.57%) identified as having abnormal ploidy embryos. Among these, 12.7% (14/110) were identified as GW-UPD, and 87.3% (96/110) were triploid. Among 5563 diploid blastocysts transferred, 3478 clinical pregnancies were achieved. Subsequent ploidy analysis was performed for 217 spontaneous abortion and 935 prenatal diagnostic samples, and no abnormal ploidy was identified. Furthermore, of the 140 1PN embryos tested, 40 (28.6%) exhibited GW-UPD, 3 (2.1%) exhibited triploidy, and 97 (69.3%) were determined to be biparental and normally fertilized. Among the 97 biparental embryos, 46 were diploid, 11 were mosaic, and 40 were aneuploid. In terms of the insemination pattern, the percentage of abnormal ploidy in ICSI was significantly higher than in conventional IVF (P < 0.01, 37.1% vs. 2.9%, respectively). With full informed consent, 20 patients without euploidy from normal fertilization chose 1PN-derived biparental and diploid blastocysts to transfer, resulting in 10 clinical pregnancies and 9 ongoing pregnancies. LARGE-SCALE DATA: N/A. LIMITATIONS REASONS FOR CAUTION: Some rare ploidy abnormalities, such as polyploidy with an equal number of identical sets of chromosomes and ploidy mosaicism cannot be accurately identified. Moreover, the origin of abnormal ploidy was not identified due to the unavailability of DNA from both parents. WIDER IMPLICATIONS OF THE FINDINGS: The PGT-Plus AI model provides a ploidy evaluation method based on the conventional PGT-A data and integrates directly into standard PGT-A workflows. Clinical utility results suggest that the model is a valuable tool for identifying embryos with abnormal ploidy in PGT-A and rescuing normal diploid embryos from abnormally fertilized embryos. These findings demonstrate that PGT-Plus significantly enhances the diagnostic accuracy of PGT. STUDY FUNDING/COMPETING INTERESTS: This study was supported by grants from Major Scientific Program of CITIC Group (No. 2023ZXKYB34100, to Ge.L.), Hunan Provincial Grant for Innovative Province Construction (2019SK4012), Hunan Xiangjiang New District (Changsha High-tech Zone) key core technology research project in 2023, and Science Foundation of Hunan Province (Grant 2023JJ30422). All authors declared no conflicts of interest..

artificial intelligence↗