Citations

ABSTRACT Ischemic events after contemporary percutaneous coronary intervention (PCI) are uncommon but carry high morbidity/mortality. Identifying patients at highest risk is critical to guide dual antiplatelet therapy (DAPT) intensity while minimizing bleeding. Current machine learning (ML) model‐developed risk scores do not incorporate pharmacogenetic data. To develop and externally validate ML models that integrate clinical, demographic, and CYP2C19 genetic information to predict 1‐year ischemic outcomes post‐PCI to refine clinical decision making. We analyzed 8317 patients from TAILOR‐PCI trial ( n = 4572) and the Precision PCI registry ( n = 3745). The outcome was a composite of cardiovascular death, myocardial infarction, stroke, and stent thrombosis at 1 year. Boruta feature selection identified 11 predictors. Multiple ML algorithms were trained in TAILOR‐PCI and validated in Precision PCI using cross‐validation and synthetic minority oversampling (SMOTE). Model performance was assessed by area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The best external performance was achieved with a support vector machine (SVM, polynomial kernel) (AUC 0.667; sensitivity 0.871; specificity 0.282), while XGBoost provided a more balanced profile (AUC 0.619; sensitivity 0.442; specificity 0.688). Variable importance for the SVM polynomial model demonstrated that all 11 included Boruta feature selected predictors had relatively high importance (> 75). ML models trained on large clinical trial and real‐world registry datasets can help identify the small subset of patients at high ischemic risk after PCI. This distinction is of relevance because ischemic events are rare, and most patients may safely de‐escalate DAPT to reduce bleeding risk while maintaining ischemic protection through a multimodal approach to risk stratification. Trial Registration: TAILOR‐PCI URL: https://clinicaltrials.gov/ct2/show/NCT01742117

Abstract Background Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome comprising multiple pathophysiological phenotypes. HFpEF trials have largely enrolled diverse populations and reported average treatment effects, consistently yielding neutral results that may obscure drug-specific benefits within distinct subgroups. To address this issue, we employ an interaction-based that incorporates treatment-by-variable interactions to uncover drug-specific responses. Methods We leveraged four HFpEF clinical trials (TOPCAT, RELAX, NEAT-HFpEF, INDIE-HFpEF) and developed a framework comprising two complementary approaches. The first employed a prognostic responder model to evaluate whether conventional responder definitions reflect treatment-specific benefit or instead capture favorable clinical trajectories common to both treatment and placebo groups. The second used an interaction-based individual treatment effect (ITE) modeling to identify baseline variables that modify therapy effect, distinguishing drug-specific response from prognostic phenotypes. Results Although the prognostic responder model demonstrated good discrimination, further analisys suggested it primarily captured a prognostic signal associated with favorable clinical trajectories common to both treatment and placebo arms. In contrast, the ITE model identified distinct, drug-specific effect modifiers across trials (cardiorenal-inflammatory for spironolactone (TOPCAT), NO-mediated anti-inflammatory for isosorbide mononitrate (NEAT-HFpEF), afterload-reducing for inorganic nitrite (INDIE-HFpEF), and anti–volume-overload for sildenafil (RELAX). Each ITE model demonstrated significance only within its own trial suggesting drug-specific signal. Conclusions The proposed method identifies mechanism-specific effect modifiers, and uncovers clinically meaningful heterogeneity in treatment response, which is not captured by conventional MCID-based approaches. Although exploratory, these findings support phenotype-guided therapy in HFpEF and argue for phenotype-informed trial design to enhance treatment-effect detection and therapy targeting. Graphical Abstract

The NCCN Clinical Practice Guidelines for Breast Cancer include recommendations for clinical management of patients with carcinoma in situ, invasive breast cancer, Paget’s disease, Phyllodes tumor, inflammatory breast cancer, and management of breast cancer during pregnancy. The panel convenes annually to update recommendations based on a review of recently published clinical trials. This selection from the NCCN Guidelines for Breast Cancer focuses on the recommendations for the management of recurrent or stage IV (M1) metastatic breast cancer. It summarizes the treatment strategies stratified by tumor biology, including hormone receptor status and HER2 expression, as well as clinical factors such as prior therapy and disease-free interval. Throughout all treatment lines, supportive care, quality of life preservation, toxicity minimization, and clinical trial participation are emphasized as integral components of patient care.

Glioblastoma (GBM) is a devastating cancer with a dismal prognosis. Current treatment includes temozolomide (TMZ), which is more effective in about 50% of GBMs that have O 6 - methylguanine DNA methyltransferase ( MGMT ) promoter methylation. MGMT is a DNA repair protein that reverses TMZ-induced DNA damage. EGFR is a prime oncogene in GBM. Here, we report that EGFR inhibition induced the down-regulation of MGMT in GBM cells, revealing a previously unidentified link between EGFR signaling and response to TMZ. EGFR inhibition led to activation of two transcription factors, activator protein-1 (AP-1), which repressed MGMT transcription, and nuclear factor κB (NF-κB), which up-regulated MGMT transcription. EGFR inhibition also induced AP-1–mediated transcription of miR-616. miR-616 inhibited both MGMT translation and NF-κB activation. Thus, the overall effect of EGFR inhibition was down-regulation of MGMT expression. In addition, we provided an explanation for the prior failure of clinical trials that used concomitant EGFR tyrosine kinase inhibitors (TKIs) and TMZ. TMZ up-regulated MGMT, and concomitant treatment with EGFR TKIs and TMZ failed to down-regulate MGMT. However, pretreatment with EGFR TKIs followed by TMZ efficiently down-regulated MGMT and enhanced TMZ sensitivity in experimental models. Posttreatment tumor tissues from two clinical trials were used to validate these findings. We demonstrated that EGFR inhibitors induced down-regulation of MGMT in posttreatment resected tumor tissues from patients with GBM and the failure of EGFR inhibition to down-regulate MGMT if TMZ was used concomitantly. These data support using EGFR TKIs before TMZ treatment as a therapeutic approach in MGMT unmethylated GBM.

Ring-closing carbonyl-olefin metathesis (COM) reactions provide a straightforward method for producing cyclic alkenes from precursors containing carbonyl functional groups tethered to nucleophilic alkenes. Within the last decade, many methods for carrying out Lewis acid- or organocatalyzed COM reactions were reported; however, Brønsted–Lowry acid-catalyzed COM reactions are less developed. Herein, we report that phosphoric acid derivatives and N-triflylphosphoramides mediate COM reactions, although the substrate scope is limited to biaryl compounds that cyclize to produce phenanthrene products. We also disclose the synthesis and characterization of a previously non-fully characterized phosphoric acid derivative and a novel phosphoramide.

TAF7, a core subunit of the general transcription factor TFIID, regulates transcription and additionally functions as an RNA chaperone that directs nascent nuclear RNAs to cytoplasmic polysomes, thereby influencing protein synthesis. Here, we identify a set of novel cytoplasmic interactors and a previously unappreciated cytoplasmic role for TAF7 in oncogenesis. BioID-mediated proximity labeling identified STAT3 and the CCT and WASH complexes as prominent cytoplasmic interactors of TAF7. These interactions were validated by co-immunoprecipitation and proximity ligation assays. We further showed that cytoplasmic TAF7 levels increased during oncogenesis in proportion to cancer cell pathogenicity. Notably, TAF7 engagement with STAT3, CCT and WASH complexes is markedly enhanced during cellular transformation, consistent with their functions in cancer progression. Together, these findings reveal a cytoplasmic TAF7 interactome and implicate elevated cytoplasmic TAF7 as associated with oncogenesis.

Abstract Objective Genicular nerve radiofrequency (gRF) treatment is a widely used intervention for chronic pain from knee osteoarthritis (KOA). This systematic review and meta-analysis focuses on RCTs utilizing image guidance to address inconsistencies in previous reviews and provide higher-quality evidence. Design, Setting, and Methods This study included RCTs that compared gRF techniques (conventional, cooled, pulsed, bipolar) to non-gRF treatments, including sham procedures, intra-articular injections, ozone therapy, and oral medications. Primary and secondary outcomes were changes in pain and total WOMAC scores at 3, 6, and 12 months post-gRF treatments. Subgroup analyses explored differences by lesion size, use of prognostic blocks, industry sponsorship, and study location. Statistical heterogeneity was assessed via I 2 statistics. Results Eleven RCTs with 868 knee osteoarthritis patients (435 gRF, 433 non-gRF) were included. At 3 months, gRF significantly reduced pain (SMD -1.65, 95% CI: -3.20 to -0.10, I 2 = 98%, P = 0.040), but not at 6 months or 12 months. Secondary analyses showed no significant differences in total WOMAC scores between the gRF and control groups at 3, 6, and 12 months. Subgroup analysis showed that industry-sponsored studies reported significant 3-month pain reduction, whereas non-sponsored studies did not. This effect was independent of study location. Conclusions GRF provides short-term pain relief (up to 3 months) but neither functional improvement nor sustained pain reduction compared to non-gRF treatments. Our findings do not support the routine use of gRF for long-term chronic pain due to KOA.

Primary care is a language-intensive environment where clinicians frequently synthesize, document, and communicate large amounts of information. Given the amount of information in primary care, large language models (LLMs) have emerged as tools to support primary care clinician workflows, aiming to reduce administrative burden and augment patient care. LLMs are artificial intelligence tools that can generate text and assist with chart summarization, message drafting, and clinical decision support. However, because LLMs are probabilistic, identical inputs may yield inconsistent responses. The LLMs output can be significantly improved through prompt engineering. Prompt engineering is the intentional design of text entered into the LLM to help produce better LLM results. Prompting is best approached as an iterative process where a clinician defines the task, the LLM generates output, and the clinician critically reviews and refines the results. Low-quality prompts are often vague and produce generic or misleading responses. High-quality prompts are specific, context-rich, and well-structured. Using structured prompting frameworks can improve the LLMs response. Including core elements, such as the LLMs role, the clinical context, the requested task, and the desired output formatting, can help improve the LLMs response by providing situational background and framing response formatting. Additional frameworks, such as Ask-Context-Expectation and PICO+O, can be utilized for specific patient circumstances and cases. However, despite their promise, LLMs have important limitations. They may generate inaccurate or hallucinated information and produce inconsistent results. Given these limitations, LLMs should not be viewed as knowledge authorities but rather as cognitive assistants. Thoughtful prompt design can help reduce these limitations and improve response accuracy, always keeping in mind that LLMs should not replace human judgment or clinical reasoning. As LLMs continue to be integrated into primary care, prompt engineering will become an increasingly important clinical skill. Careful prompt design and critical appraisal of LLM responses will be key to maintaining excellent patient care in the AI era.

BACKGROUND: Aortic enlargement is a powerful predictor of dissection and rupture, yet it is rarely evaluated during routine myocardial perfusion imaging, despite the widespread availability of computed tomography (CT) attenuation correction scans. The aim of this study was to determine whether fully automated, opportunistically derived, artificial intelligence-based aortic measurements from myocardial perfusion imaging CT attenuation correction scans are associated with adverse outcomes in a large multicenter cohort. METHODS: CT attenuation correction scans from patients undergoing positron emission tomography/CT and single-photon emission CT/CT myocardial perfusion imaging across 10 centers were included. A deep learning model automatically segmented the thoracic aorta, and a postprocessing algorithm extracted maximum ascending and descending diameters. The aortic size index was calculated by indexing the diameter to body surface area. RESULTS: A total of 29 339 patients (56% men; median age, 66 years [interquartile range, 58–75 years]) were included. Over a median follow-up of 3.5 years (interquartile range, 1.9–5.0 years), 5083 (17.3%) patients died. Median ascending and descending aortic size index values were 1.8 cm/m 2 (interquartile range, 1.6–2.0) and 1.5 cm/m 2 (interquartile range, 1.4–1.6), respectively, with an increase with age and higher values in females. Elevated aortic size index thresholds (ascending >2.2 cm/m 2 ; descending >1.6 cm/m 2 ) were significantly associated with increased all-cause mortality (ascending: adjusted hazard ratio, 1.16 [95% CI, 1.07–1.26], P <0.001; descending: adjusted hazard ratio, 1.23 [95% CI, 1.14–1.31]; P <0.001). Notably, the prognostic value of an abnormal aortic size index persisted independent of age, sex, and perfusion abnormalities. CONCLUSIONS: Artificial intelligence can unlock previously unused information within routine myocardial perfusion imaging CT attenuation correction scans by rapidly and automatically quantifying aortic size at scale. Opportunistic aortic measurements derived from CT attenuation correction may serve as an adjunctive risk biomarker and could add prognostic value to standard myocardial perfusion imaging without additional imaging or radiation.