Recent advances in prime editing technologies using CRISPR modules fused with reverse transcriptase (RT) have enabled efficient and precise reprogramming of target genomic sequences. Twin prime editing using two coordinated prime editor complexes is a promising strategy for inducing extensive genomic modifications via reverse-transcribed complementary templates. However, current twin prime editing systems still require improvements in editing efficiency, accuracy, and intended edit predictability. Here, efficiency and precision of twin prime editing were enhanced via engineering and optimizing conventional SpCas9(H840A)-RT-based prime editor (twin-PE) components. A La-domain-fused prime editor (La-twin-PE) and optimized prime editing guide RNAs (pegRNAs) were developed, achieving a 1.75 ± 0.21-fold increase in gene editing efficiency at multiple genomic loci in human-derived cell lines without increasing unintended indel or inaccurate editing. La-twin-PE facilitated efficient ∼2.8 kb GFP transgene knockin at target loci and eliminated the expanded polyQ tract in ATXN3 in an engineered human-derived mutant cell line modeling spinocerebellar ataxia type 3. The optimized twin prime editing platform facilitates highly efficient and scalable genomic engineering through streamlined pegRNA design, offering substantial potential for a broad spectrum of biotechnological and therapeutic applications.
Large language models (LLMs) can become outdated or inconsistent over time, particularly when deployed across multiple knowledge domains. To address this, this paper studies multi-domain knowledge editing, focusing on the three core properties: reliability (accurate incorporation of new knowledge), generality (ability to correctly answer related queries), and locality (preservation of unrelated knowledge). In particular, two key issues are empirically analyzed: trade-offs among these properties and the phenomenon of catastrophic forgetting during editing. To address these issues, this study proposes anchor sparse memory (ASMem), which is a plug-and-play editing module that isolates edits into parallel memory-specific parameters. ASMem introduces a novel anchor prototype routing mechanism to enable precise query-to-memory alignment and reduce semantic interference during editing. Furthermore, an efficient partitioning strategy based on unsupervised clustering assigns memory modules to distinct semantic domains and thereby guides through clear knowledge boundaries. Extensive experiments on real-world hallucination correction and Chinese linguistic editing tasks reveal that ASMem considerably outperforms previous methods, with higher reliability and generalization while maintaining locality over multi-domain long edit sequences. Specifically, even at the longest edit sequence tested, ASMem's trade-off among reliability, generality, and locality remains within 0.05 of its peak, surpassing the strongest competing baseline by up to 0.197. Further experiments show that ASMem generalizes across the LLaMA and Qwen families at model scales from 0.5B to 8B and adapts to both English and Chinese settings.
Haploid induction coupled with genome editing (HI-Edit) enables direct modification of commercial crop varieties, bypassing the need for trait introgression or direct transformation of elite lines with CRISPR machinery. However, its widespread application has been constrained by low haploid editing rates (HER), the proportion of haploids carrying edits within the short window between double fertilization and uniparental chromosome elimination. Here, we report substantial improvements in maize HI-Edit efficiency through three complementary strategies: (1) driving an optimized LbCas12a variant (LbCas12aV) using promoters that are highly active in sperm cells and early zygotes; (2) applying a post-pollination heat treatment; and (3) fusing LbCas12aV with the UBA2 domain (ubiquitin-associated domain-2 of Arabidopsis thaliana RAD23) to enhance protein stability during haploid induction. Post-pollination heat treatment alone increased HER to 19.1% (up to 12-fold improvement depending on the target site), providing a simple and effective method to boost the yield of edited doubled haploid (DH) plants. UBA2 fusion improved HER by 6-fold at the Waxy1 (Wx1) locus and 4.5-fold at the Glossy2 (Gl2) locus under normal conditions. Strikingly, combining UBA2 fusion with heat treatment raised the average HER to 25% across multiple events targeting Wx1, with the highest HER reaching 33%. Collectively, these findings demonstrate that increasing CRISPR-Cas protein abundance and modulating environmental conditions can overcome key bottlenecks in HI-Edit. We establish a robust, scalable framework that is readily transferable to other crops for elite-line genome editing.
CRISPR/Cas9 based genome editing employing Homology Directed Repair (HDR) from template vector sequences is a widely used technique to enable precise insertions, deletions or modifications to genes. Here, we describe an undesired and highly frequent editing event when using conventional CRISPR/Cas9 plus HDR methods for Drosophila melanogaster germline genome editing. We find that the template vector employed for HDR repair unwantedly and commonly inserts into the genome. We observe this deviation from the desired edit at multiple genomic locations, with different HDR vectors and with multiple genome editing designs. To avoid these events, we have generated a novel HDR template vector that enables animals with these undesired insertions to be identified and excluded. Our results suggest that HDR based genome edited animals must be carefully screened for unwanted vector template genomic integration in order to avoid misleading interpretations of genome editing outcomes.
CRISPR-based prime editors (PEs) install precise edits into genomic DNA without generating double-strand breaks. Their editing efficiency is highly dependent on reverse transcriptases (RTs), but efficient RT candidates remain limited. Here, we identified 19 novel active RTs by screening 558 candidates. Among them, RERV-RT, derived from Rattus norvegicus, exhibited the highest activity. Through structure-guided engineering and deep mutational scanning, we developed an optimized variant, enRERV-RT, which outperforms conventional M-MLV-RT-based PE systems by 1.20-fold in mammalian and plant cells, and by 1.88-fold at hard-to-edit loci, while enabling precise multiplex editing of functionally relevant genes. Additionally, we developed a high-throughput platform, TRAP-seq-PE, to systematically evaluate prime editor performance. Across diverse mutation types, we found that PE systems based on enRERV-RT exhibited higher editing efficiencies than those based on M-MLV-RT. Collectively, our work establishes a versatile, high-efficiency PE system, thereby facilitating advances in clinical gene therapy and precise crop breeding.
Prime editing has not been established in filamentous fungi, which are major ecological contributors and industrial hosts with vast biosynthetic capacity. Here we develop fPE7max, a prime editing platform optimized for fungi, which supports different edit types, including base substitutions and defined small insertions or deletions, with an average editing efficiency approaching 90%, across diverse genomic loci and species. fPE7max further enables larger insertions of up to 1 kb and deletions of up to 10 kb. We perturb upstream open reading frames in the pleiotropic regulator gene, laeA, to modulate metabolic output across multiple fungal species. Metabolomic profiling reveals activation of previously lowly biosynthetic pathways, leading to the identification of 18 metabolites, including 8, to our knowledge, previously unreported structures, 3 of which with cytotoxic activity. These results establish fPE7max as an efficient platform for genome engineering in filamentous fungi and show upstream open reading frame editing as a strategy for modulating endogenous regulatory networks and accessing the fungal chemical repertoire.
The clustered regularly interspersed short palindromic repeats (CRISPR)/Cas9 system is an efficient and versatile genome engineering tool, which has been widely used for targeted mutagenesis and gene functional characterization in various organisms. This system is simple because it only requires a Cas9 enzyme serving as a nuclease and guide RNA (gRNA) containing a 20-nt sequence matching the target gene. Delivery of a vector expressing Cas9 and gRNA or the preassembled Cas9/gRNA complex as a ribonucleoprotein (RNP) into plant cells for gene targeting are usually via the biolistic- or Agrobacterium-mediated approach. However, most wheat genotypes suffer from low efficiency of callus induction and plant regeneration from explants receiving the vector or RNP delivered by the biolistic- or Agrobacterium-mediated transformation method, limiting the application of genome editing systems in many commercially grown wheat varieties. Here, we describe a stepwise protocol for targeted gene editing in wheat via wide hybridization with transgenic maize expressing Cas9 and gRNA. A binary vector expressing Cas9 and gRNA is constructed and used for Agrobacterium-mediated transformation to generate transgenic maize plants, which are used to pollinate emasculated spikes of wheat varieties. After fertilization, the maize chromosomes enter the transient hybrid zygote and the transgene (T-DNA) on a maize chromosome expresses the Cas9 enzyme and gRNA, which forms an RNP complex to edit the target gene in wheat genome. After several cell divisions, maize chromosomes in the hybrid zygote are eliminated, resulting in formation of haploid wheat embryos with the target gene edited, which can be rescued by embryo culture technique to produce haploid plants. Doubled haploid (DH) wheat plants with homozygous gene mutations are developed by chromosome doubling through colchicine treatment of the haploid plants. The wheat × maize hybridization combined with the CRISPR/Cas9 system provides a one-step approach for generating DH lines with the target gene edited from any wheat genotypes of interest.
Marsupials are a unique group of mammals widely used in clinical and evo-devo research for their unusual reproductive traits. The ability to edit marsupial genomes would vastly improve their utility as research models, as well as potentially alleviate the complex and pressing ecological challenges marsupials face. To date, however, genome editing has only been achieved for a single species (Monodelphis domestica), despite the recent influx of high-quality genomes, largely due to this species' similarity to rodent models. In this review, we outline the marsupial-specific challenges and opportunities of both zygote-based and germ cell transplantation approaches to genome editing and summarise current efforts to fill this critical gap in the marsupial research and conservation toolkit.
Influenza A virus (IAV) and influenza B virus (IBV) remain major global public health threats because of their rapid antigenic evolution and efficient human-to-human transmission. In contrast, influenza C virus (ICV) and influenza D virus (IDV) generally exhibit narrower host ranges and milder pathogenicity, yet their potential for interspecies transmission and zoonotic spillover still warrants attention. Conventional prevention strategies, such as inactivated and live-attenuated vaccines, suffer from prolonged development timelines and diminished efficacy against rapidly evolving viral strains. However, antiviral drugs are increasingly limited by the rapid emergence of drug-resistant variants. The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) gene-editing technology has emerged as a promising platform for influenza prevention and control owing to its programmability and precise targeting capability. In this paper, we summarize recent advances in CRISPR-based strategies for influenza prevention and control. The RNA-targeting CRISPR-associated protein 13 (Cas13) system can recognize conserved viral RNA sequences and suppress replication across influenza subtypes, whereas the DNA-targeting CRISPR-associated protein 9 (Cas9) system can edit host susceptibility genes and thereby reduce cellular permissiveness to infection. In addition, lipid nanoparticle (LNP)-based delivery systems have become important tools for improving the in vivo delivery and expression of CRISPR components by enhancing targeting efficiency and reducing immunogenicity. CRISPR-based diagnostics, such as Specific High-sensitivity Enzymatic Reporter unLOCKing (SHERLOCK), further expand the clinical utility of this technology by enabling rapid and sensitive detection of influenza viruses. Despite these advances, substantial challenges remain, including delivery inefficiency, off-target activity, long-term safety concerns, and the risk of viral escape. With continued technological refinement and careful translational development, CRISPR may become a versatile tool for influenza prevention, diagnosis, and therapy.
Over a decade of advances in Clustered Regularly Interspersed Short Palindromic Repeats (CRISPR) and CRISPR-associated protein 9 (Cas9)-based technologies have culminated in the first-ever FDA-approved CRISPR/Cas-based therapy. Aside from this approved therapy for sickle cell anemia, several CRISPR/Cas-based therapies are currently under development or testing for a range of chronic diseases, including viral diseases like human immunodeficiency virus type 1 (HIV-1) infection, genetic diseases like familial hypercholesterolemia, and cancer. The success of these therapies hinges on the effective delivery of CRISPR/Cas9 components to target regions, efficient Cas endonuclease editing, repair profiles generated, and their resulting outcomes. Here, we discuss the factors that influence the generation of CRISPR/Cas9-generated repair edits, the overall profiles, and outcome prediction(s), as well as the analytical tools that have been developed to date. Finally, how this technology has been used towards a functional HIV-1 cure is discussed.
Cell membranes are not mere platforms for signalling proteins; they can shape how receptor inputs are assembled into local responses. In membrane-rich microdomains, receptor identification and pathway mapping do not reveal the logic of a measured effect. That effect may arise from independent receptor activity, pairwise crosstalk or higher-order integration governed by membrane state. The membrane-encoded chemosensory system (MECS) is introduced as a conceptual framework for addressing the inferential gap between receptor co-expression mapping and mechanistic crosstalk claims in territories with cannabinoid GPCRs, ectopic olfactory GPCRs and TRP channels. Its operational method, MECS baseline-edit-rescue (MECS-BER), fixes one membrane prior, one locked proximal outcome and one three-arm candidate assembly. Eligibility gates test arm engagement and outcome competence. Combinatorial responses are analysed with κ, the third-order interaction under a pairwise-only null within a complete three-factor perturbation design, to distinguish lower-order explanation from higher-order interpretation and test edit-rescue reversibility. Deterministic matrices and simulations establish classification logic and tolerance handling; biological adjudication awaits fully compliant MECS-BER datasets. The workflow provides a prespecified and formalized methodological route, not biological proof of any specific receptor triad. It keeps nomination separate from adjudication and requires co-localization, distal phenotypes, shared downstream signals and nonlinear mixtures to be tested against a locked proximal outcome before biological interpretation. Renal micro-niches specify prospective deployment across renin, transport, barrier and flow-sensitive calcium control without claiming validated cannabinoid-olfactory-TRP assemblies. Membrane lipids may shape both the signalling vocabulary of individual receptors and the local language through which receptors communicate.
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We present SNIPSNP (crisprtools.org/snipsnp), a comprehensive bioinformatics pipeline for designing experiments for CRISPR-induced homology-directed repair (HDR). The tool addresses the critical challenge of Cas9 re-cleavage by simplifying the selection of "blocking" silent variants that are effective at inhibiting RNP binding upon donor-templated editing. SNIPSNP handles complex edits, including indels, and uses multi-objective optimization to balance editing efficiency with biological safety. From user-defined wild-type and desired HDR alleles, the pipeline identifies candidate guides, annotating them with integrated efficiency scores and genome-wide off-target assessments. Uniquely, SNIPSNP evaluates guide binding against the post-edit genome to determine whether the therapeutic variant alone disrupts repeated Cas9 recognition. When necessary, it introduces synonymous blocking variants, prioritizing PAM and seed regions to minimize re-cleavage probability and editing of the wild-type (WT) allele when editing heterozygous variants. All candidate modifications undergo safety profiling and prioritization of known benign variants from dbSNP. We experimentally validated SNIPSNP and benchmarked it on pathogenic inborn error of immunity variants in primary patient T-cells. Across loci, SNIPSNP-designed templates outperform standard "correction-only" strategies, demonstrating enhanced precision editing, and reduced re-cleavage, establishing SNIPSNP as a robust platform for genome editing and disease modeling.
Clinicians spend a substantial share of their working hours on documentation, contributing to workflow inefficiencies, reduced patient-facing time, and increased burnout. Artificial intelligence (AI) medical scribes have emerged as a promising solution to reduce this burden, yet real-world evidence remains limited and heterogeneous, and data from European health systems are especially scarce. This evaluation combines 2 complementary data sources: objective editing metadata from 236,153 notes generated by 1295 clinicians, describing operational editing behavior within the AI medical scribe, and paired self-reported survey responses from 177 fully onboarded clinicians, capturing perceived change in documentation time and clinician experience. This study aimed to evaluate the association of an AI medical scribe on documentation time and clinician experience. This observational real-world evaluation was conducted between April 26, 2024, and October 27, 2025, using retrospective paired ratings. The study was carried out across multiple specialties in primary, secondary, and hospital care within Capio Ramsay Santé, a large integrated health care provider operating in Sweden. Eligibility was limited to fully onboarded users, defined as clinicians who had used the scribe for at least 3 months, created more than 100 notes, generated at least 1 document or certificate, and used the conversational edit ("Add or adjust") feature at least once. Following the introduction of the AI medical scribe, the estimated time spent on documentation per note was lower than before (4.72 vs 6.69 minutes; -29%, P<.001). On a 5-point Likert scale, ratings for the ability to work without stress related to administrative tasks were higher after introduction than before (mean 3.14 vs 2.41; P<.001; median change 0 points, 95% CI 0-1), as were ratings for perceived presence with patients (mean 4.33 vs 3.73; P<.001; median change 0 points, 95% CI 0-1). The median editing time was 93 seconds, and it did not decrease significantly over continued use. Among sustained, fully onboarded adopters in a European health care system, use of an AI medical scribe was associated with reductions in self-reported documentation time, administrative stress, and increase of presence with patients, consistent with findings from prior US-based studies. Because the survey cohort represents a highly selected subgroup of users who adopted and continued using the tool mainly in general practice, these associations may not generalize to clinicians who discontinued use or never fully adopted the scribe, and the generalizability across specialties remains unverified. The single-arm observational design and reliance on retrospective self-report are important considerations when interpreting these associations. A limitation of this analysis is that 138,196 notes were excluded because their recorded editing time was 0; these notes may have been used as generated, used as a starting point and later modified in the medical record system, or discarded, which limits the operational interpretation of the editing-time findings.
Drug resistance is a constantly evolving challenge. The allosteric inhibitor asciminib is a novel therapy for chronic myelogenous leukemia (CML) that targets the myristoyl pocket of the BCR::ABL1 kinase. While it can overcome resistance to active-site inhibitors like imatinib, new resistance mutations to asciminib are emerging. The complete landscape of these mutations, particularly those outside the kinase domain or those arising from epistatic interactions between mutations, are not well understood. This study employed a dual functional genomics approach in CML cell line models. A high-throughput adenosine base editing (ABE) screen was used to identify broad hotspots of asciminib resistance across the entire BCR::ABL1 protein. Deep mutational scanning (DMS) was then used to create a high-resolution map of all possible amino acid changes within these hotspots. An "edit-on-edit" screen was performed to investigate epistasis by introducing a library of mutations into a cell line that was pre-edited to incorporate the common imatinib-resistance mutation, Y253H. Finally, a novel Förster resonance energy transfer (FRET) biosensor was developed to measure the conformational state of BCR::ABL1 in live cells and link it to drug sensitivity. The screens identified 279 asciminib resistance mutations and revealed resistance hotspots distributed across the SH3, SH2, and kinase domains, in contrast to imatinib resistance, which is largely confined to the kinase domain. The study uncovered a potent epistatic interaction between a mutation in the SH3 domain (V73A) and a mutation in the kinase domain P-loop (Y253H), which synergistically conferred high-level resistance. The FRET biosensor demonstrated that asciminib resistance mutations tend to destabilize the "closed" inactive conformation of the ABL1 kinase. The landscape of asciminib resistance is broader and more complex than previously appreciated, involving mutations across multiple domains that disrupt ABL1 autoinhibition. Epistasis between mutations acquired during sequential therapies can create unexpected and potent resistance. However, these diverse genetic resistance mechanisms converge on a single biophysical measurement of the openness of the active ABL1 conformation. This provides a unified framework for understanding asciminib resistance and underscores the need for routine clinical resistance monitoring to include the SH3 and SH2 domains in first line and later line therapy.
The ability to edit posttranslational modifications (PTMs) of endogenous proteins within cells is essential for precisely delineating the biological roles of PTMs and for developing targeted therapeutics. While the paradigm of chemically induced proximity (CIP) has advanced this field by enabling the recruitment of PTM enzymes to the proximity of proteins of interest (POIs), CIP requires small-molecule binders that are difficult to obtain for proteins without well-defined binding pockets. In principle, the use of biomolecular ligands that target disordered proteins should overcome this limitation. In this work, we developed the NanoBridge as a modular and generalizable platform to enable PTM editing of challenging POIs in live cells. The NanoBridge employs biologic binders to transiently direct the small protein tag FKBP12 F36V to unmodified target proteins, thereby enabling multiplex PTM editing upon use of heterobifunctional small molecules that recruit endogenous PTM enzymes. Compared with existing approaches, the NanoBridge offers greater flexibility to induce multiple types of PTMs on the same POI while providing precise temporal control and avoiding the introduction of exogenous PTM writers. Using eight protein binders targeting three structurally diverse and largely unstructured proteins - BCL11A (a hemoglobin regulator), KRAS (a cancer driver), and p53 (a tumor suppressor) - the NanoBridge mediated targeted degradation, phosphorylation, and acetylation in a rapid, reversible, and temporally controlled manner. As such, the NanoBridge represents a versatile strategy for the targeted modulation of endogenous proteins, particularly those lacking accessible small molecule ligands, and presents new opportunities for investigating the physiology of PTMs on challenging proteins.
Conventional data visualization techniques in single-cell analysis (such as two-dimensional dot plots, SPADE, PCA, t-SNE, or UMAP) often fall short in enabling an intuitive understanding of high-parameter flow cytometry data. These methods tend to oversimplify complex biological relationships, lack biologically meaningful interpretations, and offer no principled framework for downstream quantitative analysis. To address these limitations, we present a graph-based (network-based) visualization framework grounded in optimal transport theory. In this framework, cell populations are defined by their marker-expression profiles, and inter-population similarity is quantified using an efficiently computable optimal transport formulation known as the Sinkhorn distance. Our approach produces biologically consistent two-dimensional graph layouts using a phenotype-aware Hamming distance. Structural differences between sample graphs are characterized through a customized graph-edit distance that captures changes in population size, marker expression, and relationships between populations. We demonstrate our methods on two flow cytometry datasets: one from a clinical trial of dendritic cell-based immunotherapy in malignant peritoneal mesothelioma, involving 14 patients sampled at three time points with 14-color panels, and another from FlowCAP-II, which involved 43 acute myeloid leukemia patient samples analyzed with 7-color panels. Our framework produces robust, quantitative visual summaries of cell populations and supports statistical analysis based on graph edit distances, thereby offering new insights into disease progression and treatment response. Ultimately, our method bridges the gap between flow cytometry data visualization and biological interpretation.
Intracranial aneurysm clipping is technically demanding, with dynamic anatomy and evolving intraoperative decisions. Although AI has been applied to retrospective surgical phase recognition, near-future surgical step prediction remains largely unexplored in neurosurgery. This study evaluated the feasibility of fixed-horizon surgical step prediction in recorded microscope videos of middle cerebral artery aneurysm clipping operations. We retrospectively analyzed 25 uncomplicated MCA bifurcation aneurysm clipping surgeries by a single neurosurgeon, using 18 for training and 7 for independent testing. Cases were annotated into 12 standardized operative steps. A transformer-based prediction framework was evaluated with three input configurations: video features alone, prior human-annotated step labels alone, and combined video plus step-label inputs. With a sliding-window approach, each configuration used 1 min of input data to predict the operative step labels occurring during the subsequent 1 min. Performance was assessed by accuracy, weighted F1 score, and sequence-level agreement with ground truth. The multimodal model achieved the highest mean accuracy and weighted F1 score, 0.683 and 0.673, compared with 0.606 and 0.577 for the annotation-only model and 0.477 and 0.447 for the video-only model. The multimodal model also showed the best sequence-level alignment, with a normalized edit distance of 0.430 and edit score of 0.570. Fixed-horizon surgical step prediction during MCA aneurysm clipping was feasible under controlled input conditions. Multimodal modeling provided the strongest predictive performance. These findings represent upper-bound performance and require validation in fully automated recognition-to-prediction pipelines.
To evaluate resident versus attending operative notes using a two-phase approach combining natural language processing (NLP) diffing and structured assessment for evaluating operative reports (SAFE-OR) structured grading, with grader-severity adjustment to account for inter-rater differences. Operative notes document procedural conduct, support continuity of care, and have educational value. While prior studies show resident-authored notes often contain omissions or errors, their potential as a competency assessment tool remains untapped due to the difficulty of analyzing free text at scale. NLP and validated structured assessment tools offer new opportunities to objectively measure the quality and content of these notes. We extracted operative notes for common general surgery procedures (2010-2023) that had both a resident-authored and an attending-signed version for the same encounter. In Phase 1, an NLP-based "diffing" pipeline quantified insertions, deletions, and replacements between paired notes. In Phase 2, ten blinded graders (attendings and senior residents) evaluated de-identified notes with the SAFE-OR checklist and quality scale. To correct for inter-grader-severity differences, scores were z-normalized within each grader and rescaled to SAFE-OR ranges before resident versus attending comparisons. Paired t-tests, Wilcoxon signed-rank tests, and mixed-effects models were used for hypothesis testing; procedure-specific analyses were performed for common operations. A total of 2949 notes were analyzed with NLP diffing, revealing significant variation in edit burden across procedures (p < 0.001), with esophagogastroduodenoscopy with dilation showing the highest change rate. SAFE-OR grading included 139,439 grader-level scores across 75 matched note pairs. After grader-severity adjustment, resident-authored notes scored significantly lower than attending-authored notes in Section I (preoperative elements, mean diff -0.35, p = 0.005), Section III (postoperative/disposition, mean diff -1.70, p = 0.022), and overall score (mean diff -2.31, p = 0.029). Section II (intraoperative elements) differences were not statistically significant. Mixed-effects models confirmed these findings (overall coef -0.039, p < 0.001). Procedure-specific analysis showed significant differences for laparoscopic appendectomy but not for laparoscopic cholecystectomy or laparoscopic cholecystectomy with cholangiogram. Resident operative notes contained fewer key elements and scored lower in structured quality assessments compared with attending notes, particularly in preoperative and postoperative documentation. Combining NLP-based edit quantification with validated grading instruments enables high-throughput, objective assessment of operative note quality and offers a novel pathway for evaluating surgical trainees' cognitive understanding of operations. Future work will apply large language models for thematic analysis to identify the nature of content differences.
Building upon continuous pharmaceutical care (CPC) models led solely by clinical pharmacists, this study evaluated a collaborative pharmacy team model for reducing drug-related problems (DRPs) in coronary heart disease (CHD) patients during care transitions. In a randomized controlled trial, 60 hospitalized CHD patients were allocated equally to a CPC group or a usual-care control group. The CPC group received structured pharmaceutical interventions in which clinical pharmacists led hospital care and community pharmacists led post-discharge care during key care transition periods: within 24 hours of admission (T0), 24 hour pre-discharge (T1), 24-72 hour post-discharge (T2), and at 1-month follow-up (T3). DRPs were identified using the Pharmaceutical Care Network Europe (PCNE) classification. Mean DRPs per patient were comparable at T0 (Control: 3.03 ± 1.16 vs. CPC: 3.10 ± 1.24; p = 0.827). Subsequently, the CPC group showed significantly fewer DRPs than the control group at T1 (0.17 ± 0.59 vs. 2.13 ± 1.04), T2 (0.03 ± 0.18 vs. 1.97 ± 1.03), and T3 (0.67 ± 0.76 vs. 2.63 ± 1.13) (all p < 0.001). Pharmacist interventions in the CPC group had a high DRP acceptance rate 91.4% (181/198) and a resolution rate 89.1% (106/119). At 1-month follow-up, the CPC group demonstrated superior outcomes compared to the control group: LDL-C goal attainment (63.3% vs. 30.0%, p = 0.010), medication adherence (90.0% vs. 66.7%, p = 0.028), patient satisfaction (Numeric Rating Scale: 9.27 ± 0.52 vs. 8.59 ± 0.52, p < 0.001), and willingness to pay for pharmaceutical services (86.7% vs. 50.0%, p = 0.002). This collaborative pharmacy team model effectively reduced DRPs and improved lipid control and patient-reported outcomes in CHD patients, offering a scalable approach for integrated healthcare systems.Registered at the Chinese Clinical Trial Registry on July 1, 2024 (Registration number: ChiCTR2400086416; URL: https://www.chictr.org.cn/bin/project/edit?pid=235446).