Citations

Background Inadequate preventive dental care may contribute to inflammatory conditions such as periodontitis, increasing cardiovascular disease risk. Artificial intelligence–enabled electrocardiography (AI‐ECG) algorithms accurately estimate cardiovascular disease risk. We investigated the association between inadequate preventive dental care and the AI‐ECG–estimated risk of atrial fibrillation, aortic stenosis, low ejection fraction, cardiac amyloid, cardiovascular aging, and all‐cause mortality. Methods We retrospectively analyzed 266 637 Mayo Clinic patients (2018–2024) who completed a Social Determinants of Health Survey and had an ECG performed within 1 year of survey completion. AI‐ECG algorithms generated continuous risk scores (0 or 1) for atrial fibrillation, aortic stenosis, low ejection fraction, and amyloid. Cardiac aging was assessed by delta age, calculated as AI‐ECG–estimated physiological age minus the chronological age. Associations with preventive dental care were analyzed using logistic regression. Cox regression evaluated dental care predictiveness on all‐cause mortality. Results Among 266 637 individuals (mean age, 59.5±16.4 years; 51.1% women), 49 918 (18.7%) reported inadequate preventive dental care. These individuals had higher delta age and increased AI‐ECG–predicted risks of atrial fibrillation, aortic stenosis, amyloid, and reduced ejection fraction (all P <0.001). In multivariable logistic regression models including all AI‐ECG scores, inadequate preventive dental care was associated with higher probabilities of low ejection fraction (odds ratio [OR], 1.80 [95% CI, 1.69–1.91]), atrial fibrillation (OR, 1.10 [95% CI, 1.04–1.17]), aortic stenosis (OR, 1.90 [95% CI, 1.64–2.20]), amyloid (OR, 1.18 [95% CI, 1.12–1.25]), and higher delta age (OR, 1.03 [95% CI, 1.02–1.03]) (all P <0.001). Inadequate preventive dental care predicted all‐cause mortality after adjusting for demographics, risk factors, and AI‐ECG algorithms (HR, 1.45 [95% CI, 1.38–1.53], P <0.001). Conclusions Inadequate preventive dental care is associated with subclinical cardiovascular disease. AI‐ECG algorithms may facilitate cardiovascular risk stratification among individuals with limited preventive dental care access.

ABSTRACT HLA‐C*03:737 differs from HLA‐C*03:05:01:01 by one nucleotide substitution in exon 3.

The WHO 2019 classification of digestive tract tumors introduced a high-grade (G3) category for well-differentiated neuroendocrine tumors (NETs). These neoplasms appear to have a better prognosis than poorly differentiated neuroendocrine carcinomas (NECs) and may not respond to platinum-based chemotherapy, which is the treatment of choice for NECs, justifying the creation of this new category. Most existing data on G3 NETs are derived from pancreatic neuroendocrine neoplasms, as the majority of G3 neuroendocrine neoplasms (NENs) arise there. G3 NETs are rare at extrapancreatic sites, and their prognosis and behavior are not well studied. We collected and analyzed a multi-institutional cohort of 24 extrapancreatic primary gastrointestinal G3 NETs based on mitotic rate and/or Ki67 index (5 gastric, 13 small bowel, 6 colorectal/anal). Mean Ki67 index was 29.3% (range: 10.5% to 50.2%). Cases generally showed typical well-differentiated NET morphology. Nodal metastases were present in 17/17 (100%) patients; 13/24 (45%) had distant metastases at initial presentation, and 7 patients developed them on follow-up. With a median of 29 months of follow-up, only 2 patients (8%) were alive without disease. Outcomes of G3 NETs were compared with a separate cohort of 125 extrapancreatic G1/G2 gastrointestinal NETs. G3 tumors presented at a higher pT-category stage ( P <0.001) and overall stage ( P =0.002), and patients with G3 disease were more likely to die than those with G1/G2 disease ( P <0.001). Our data show that, like pancreatic G3 NETs, rare extrapancreatic gastrointestinal G3 NETs exhibit aggressive behavior compared with their G1/G2 counterparts (although small bowel NETs tend to recur irrespective of grade). In most cases, the G3 designation is driven by Ki67 index, though exceptions exist, emphasizing the need for reviewing mitotic count in every case.

Freezing of gait (FoG) affects 38–65% of advanced Parkinson’s disease patients, yet automated detection algorithms are often validated solely on laboratory datasets. This study quantifies the critical performance gap between laboratory and real-world performance—a prerequisite for clinical deployment. Using temporal convolutional networks (TCNs), we trained models on two public datasets representing ecological extremes: a daily living dataset (Figshare; n = 35, single-sensor) and a laboratory dataset (DAPHNET; n = 10, multi-sensor). We compared five training configurations to address class imbalance. Results showed that F1-based early stopping outperformed Area Under the Curve (AUC)-based stopping by 47% (F1: 0.55 vs. 0.37, p = 0.0008). Combining multiple imbalance corrections (focal loss, weighting, sampling) paradoxically degraded precision to 33% due to a ~60-fold over-weighting of the minority class. Most importantly, cross-dataset validation revealed an 83% performance gap: laboratory F1 reached 0.9999 ± 0.0002, whereas daily living F1 dropped to 0.55 ± 0.26 (p < 0.0001), with a 1299-fold increase in variance. These findings demonstrate that laboratory success does not guarantee real-world utility. We propose that the observed gap represents a “deployment gap” reflecting the combined influence of environmental complexity, sensor constraints, and physiological variability. These results provide an empirical framework for evaluating deployment readiness of wearable FoG detection systems and offer concrete training strategy recommendations for clinical translation.

BACKGROUND: While health systems have implemented resources to improve lipid management in people with atherosclerotic cardiovascular disease, the population-level effectiveness is unclear. METHODS: Electronic health record data from patients with atherosclerotic cardiovascular disease between January 1, 2021, and December 31, 2023, across 16 US academic health systems were analyzed. Atherosclerotic cardiovascular disease (defined by diagnosis and procedure codes) was established on or before the index date defined as the date of the most recent LDL-C (low-density lipoprotein-cholesterol) or the last day of the study period for those without an LDL-C available. Systems were surveyed to identify lipid management programs (eg, prevention/lipid clinic, electronic health record–based tools, and lipid management algorithms). Sequential multilevel multivariable logistic regression modeling was used to (1) assess the system-level association between an LDL-C <70 mg/dL and the presence of lipid management programs, adjusting for patient-level demographic and clinical characteristics, and (2) whether system-level uptake of statin or nonstatin lipid-lowering therapy was associated with an LDL-C <70 mg/dL. RESULTS: We included 1 738 042 patients with atherosclerotic cardiovascular disease (median, 88 365 per system; range, 17 086–2 48 371). About 81% of the health systems had prevention or lipid clinics. The proportion of those on statins ranged from 31.0% to 60.4% across health systems (median, 46.3%). Lower proportions were observed in the use of high-intensity statins (16.2%–33.1%), nonstatins (2.6%–8.1%), and combination lipid-lowering therapy (1.7%–6.3%). Among those with an LDL-C level available (n=890 521), 42.5% had an LDL-C <70 mg/dL, ranging 35.8% to 49.4% across systems. There was no association between the odds of achieving LDL-C <70 mg/dL and health system lipid management algorithm, prevention clinics/lipid clinics, or electronic health record–based tools. Neither system-level high-intensity statin utilization nor nonstatin use was associated with an LDL-C <70 mg/dL. CONCLUSIONS: Despite variability in lipid management resources and lipid-lowering therapy use across systems, many did not reach an LDL-C <70 mg/dL, and there was low uptake of lipid-lowering therapy. Innovative implementation strategies are needed for system-level lipid management.

ABSTRACT Background and Aims Artificial intelligence (AI) is increasingly integrated into gastrointestinal (GI) endoscopy, yet limited data exist on how patients and endoscopists perceive its use. This study aimed to evaluate users' values and preferences regarding AI in endoscopy to support effective implementation and inform guideline development. Methods As part of the World Endoscopy Organization (WEO) AI committee initiatives, two structured international surveys were conducted—one for patients and one for practicing endoscopists. Thirteen AI‐related statements were presented to patients via an established online platform, while 23 statements were shared with endoscopists through professional networks. Responses were captured using 5‐point Likert scales and analyzed with non‐parametric tests, including subgroup comparisons by age, gender, and endoscopic experience. Results A total of 1237 patients and 476 endoscopists participated. Most patients supported AI in image analysis (75.5%) but emphasized the need for endoscopist oversight (92.3%). Among endoscopists, 90.3% believed AI improves endoscopy quality, and 85.3% believed it benefits outcomes. Concerns were raised about liability (47%), operator dependency (34.8%), and procedure time (49%). Most respondents felt primary responsibility for AI‐related errors should rest with the endoscopist. Younger and male patients reported greater trust in AI. Conclusions Patients and endoscopists are generally supportive of AI in GI endoscopy, especially as an adjunct to human expertise. However, key concerns—including accountability, trust, and clinical integration—must be addressed to ensure responsible and effective adoption.

ABSTRACT Interactions between clinical pharmacists and industry partners continue to evolve as health care increases in complexity and technology assumes a more dominant role. To address the corresponding ethical challenges, the 2025 Industry Relations Committee of the American College of Clinical Pharmacy (ACCP) was charged with updating the 2008 ACCP position statement, Pharmacists and Industry: Guidelines for Ethical Interactions . This update expands its perspective, reflecting the broader and more influential roles pharmacists play across the health care ecosystem. All clinical pharmacist–industry interactions should be grounded in improving patient care. Through this lens, six recommendations are provided: (1) Pharmacists should not engage in industry partnerships or collaborations that might unduly influence objectivity, independence, or fairness in clinical and professional judgments; (2) Pharmacists should disclose and mitigate financial, consulting, or other relationships that constitute potential conflicts of interest and should not participate in activities where an existing conflict cannot be resolved; (3) Pharmacists who participate in industry‐sponsored research should follow accepted ethical, regulatory, and scientific standards; (4) Pharmacists providing continuing education programs or developing drug information materials should maintain control of content; ensure their content is fair, balanced, and free from bias; and disclose and mitigate any conflicts; (5) Formal instruction on ethical interactions with industry professionals should begin early and continue through all stages of pharmacist education, training, and professional development; (6) Pharmacists should maintain the confidentiality and privacy of patients and other health care professionals when working with industry partners. Questions to guide personal industry interactions are provided. This paper does not detail every emerging area (e.g., artificial intelligence, social media), but recommendations are applicable wherever clinical pharmacists leverage their professional expertise. By maintaining a critical, discerning, and ethical approach to industry collaborations, clinical pharmacists can uphold professional obligations while contributing to the advancement of evidence‐based practice, ultimately prioritizing patient welfare.

ABSTRACT Hospital‐at‐Home (HaH) programs provide acute care in patients' homes, reducing hospitalization and improving patient experience. While crucial in traditional settings, antimicrobial stewardship (AMS) in HaH programs remains understudied. This manuscript explores the potential benefits of AMS in HaH and the role of infectious diseases (ID) pharmacists in optimizing antimicrobial therapy. Key challenges in implementing AMS in HaH include medication delivery logistics, treatment regimen adjustments, and unique administration techniques. ID pharmacists can address these challenges by providing expertise in antimicrobial selection, dosing, and duration, as well as identifying opportunities for de‐escalation and intravenous (IV) to oral conversions. By integrating AMS into HaH programs, health care systems can improve patient outcomes, reduce antimicrobial resistance, and optimize resource utilization. Further research and training programs are needed to fully realize the potential of AMS in this emerging model of care.

Introduction Cerebellar transcranial direct current stimulation (tDCS) combined with language therapy can aid in chronic aphasia recovery, but the neural mechanisms and biomarkers of treatment efficacy remain uncertain. Methods In this secondary analysis of data from a previously conducted clinical trial, we used a randomized, double-blind, sham-controlled, within-subject crossover design with a study sample of 19 participants with post-stroke aphasia. We assessed the degree to which baseline properties of cerebro-cerebellar white matter tracts can predict or moderate longitudinal treatment effects at three time points: post-treatment, 2 weeks post-treatment, and 2 months post-treatment. Tract properties were measured by fractional anisotropy (FA) and mean diffusivity (MD) from diffusion tensor imaging (DTI). We also tested whether there are differential effects between trained and untrained language tasks and between cerebellar tDCS polarity (anodal and cathodal). Results Baseline measures of tracts connecting the left lesioned cortex to the right posterolateral cerebellum (stimulation target) influenced treatment gains for untrained tasks, relative to sham control. In contrast, for the trained task, treatment gains were influenced by baseline measures of tracts connecting the non-stimulated left cerebellum with the contralateral right cerebral cortex. Although there were no consistent effects from cerebellar tDCS polarity, a highly consistent pattern emerged across all tasks and tracts. Specifically, language improvements were predicted by a baseline tract profile (i.e., higher FA and lower MD) typically associated with higher white matter integrity, especially within the context of stroke-induced white matter decline. Discussion These findings corroborate the potential for baseline tract properties as a biomarker of treatment efficacy and support the notion that adjuvant (cerebellar tDCS + language) therapy preferentially benefits individuals with relatively preserved structural connections within functionally relevant networks. Clinical trial registration ClinicalTrials.gov , identifier (NCT02901574).