Faculty Bibliography

BACKGROUND:Patients with breast cancer and limited English proficiency (LEP) experience barriers to care that may result in greater disease burden. We aim to evaluate breast cancer presentation and pathologic characteristics of patients with LEP compared with patients with English proficiency (EP). PATIENTS AND METHODS/METHODS:We performed an institutional review board (IRB)-approved, retrospective review of prospectively collected patient-reported data among a multilingual population evaluated within a New York City safety-net health system between 2018 and 2025. Comparative analysis and logistic regression were used. RESULTS:) compared with EP (31.2% versus 18.5%; p = 0.012) and was significantly associated with completion axillary lymph node dissection compared with EP (12.8% versus 3.6%; p = 0.0041). Patients with LEP had significantly longer time-to-therapy intervals from biopsy to first therapy compared with EP (52 versus 49 days; p = 0.041). On multivariate analysis, both LEP and delays to first therapy were significantly associated with axillary nodal metastasis. CONCLUSIONS:Patients with breast cancer and LEP were significantly more likely to have axillary nodal metastasis compared with patients with EP. Increased axillary nodal metastasis in this population may, in part, be due to lack of screening and delays to breast cancer treatment.

Saliva has emerged as a compelling liquid biopsy for the non-invasive diagnosis and monitoring of both oral and systemic diseases. Secreted by major and minor salivary glands and enriched by gingival crevicular fluid, epithelial turnover, immune mediators, microbial products, and plasma-derived constituents, saliva reflects a biologically integrated interface between local oral environments and systemic physiology. This dual origin enables detection of disease-associated signals across multiple molecular layers, including extracellular RNA, cell-free DNA, genomic and epigenetic material, proteins, metabolites, microbial signatures, and hormones. Advances in high-throughput sequencing, mass spectrometry, and ultrasensitive immunoassays have revealed that salivary analytes can capture dynamic pathological processes ranging from periodontal inflammation and dental caries to oral and head and neck cancers, as well as systemic conditions such as non-oral cancers and autoimmune, cardiometabolic, infectious, endocrine, and neurodegenerative diseases. In particular, extracellular vesicle-encapsulated RNA, cfDNA fragmentomics, and methylation patterns have expanded the diagnostic reach of saliva beyond conventional protein-based biomarkers, while integrated multi-omics approaches increasingly improve diagnostic performance. Despite this progress, clinical translation remains constrained by biological complexity, including variable glandular contributions, oral microbiome interactions, and pre-analytical variability in collection and processing. Emerging point-of-care technologies, microfluidic platforms, and artificial intelligence-driven multi-omic integration are beginning to address these limitations, enabling higher analytical sensitivity and improved disease stratification. Regulatory pathways, including in vitro diagnostic frameworks and laboratory-developed test structures, are progressively accommodating salivary assays, as demonstrated during the COVID-19 pandemic. However, widespread clinical adoption will require rigorous standardization, large-scale validation, and demonstration of clinical utility. Together, saliva-based liquid biopsy represents a rapidly evolving diagnostic paradigm with the potential to shift disease detection towards earlier, minimally invasive, and data-rich precision medicine approaches across dentistry and systemic healthcare.

This paper introduces a novel decomposition framework to explain heterogeneity in causal effects observed across different studies, considering both observational and randomized settings. We present a formal decomposition of between-study heterogeneity, identifying sources of variability in treatment effects across studies. The proposed methodology allows for robust estimation of causal parameters under various assumptions, addressing differences in pre-treatment covariate distributions, mediating variables, and the outcome mechanism. Our approach is validated through a simulation study and applied to data from the Moving to Opportunity (MTO) study, demonstrating its practical relevance. This work contributes to the broader understanding of causal inference in multi-study environments, with potential applications in evidence synthesis and policy-making.

Critical-sized bone defects remain a significant clinical challenge, and dipyridamole (DIPY)-coated 3D-tricalcium phosphate (β-TCP) scaffolds have shown promising osteogenic efficacy in preclinical models. However, the literature on the systematic physicochemical characterization of this scaffold system, including optimization of DIPY loading parameters, release kinetics, and surface properties, is lacking. This study addresses these gaps by characterizing DIPY-loaded 3D-printed β-TCP scaffolds across solid and porous architectures, three coating concentrations (10, 100, and 1000 µM), and three coating volumes (250, 500, and 1000 µL). Under static PBS conditions, drug release over 21 days was quantifiable only at 1000 µM, and release-kinetics modeling (zero-order, Higuchi, and Korsmeyer-Peppas) was therefore restricted to this highest concentration. At 1000 µM, both scaffold types showed biphasic release profiles, with standard empirical models reasonably approximating the overall kinetics, while not fully capturing the biphasic behavior over the entire duration. Porous scaffolds showed significant volume-dependent release (p = 0.002, η

BACKGROUND:Nearly half of global pregnancies are unintended. Contraceptive use is preference-sensitive, including concerns about convenience, privacy and side effects (eg, irregular bleeding, weight gain). We conducted a multi-country study examining women's preferences for attributes of current and potential future modern hormonal contraceptives using a discrete choice experiment (DCE). METHODS:Between April and October 2022, we recruited nationally representative samples of women of reproductive age from seven countries (Burkina Faso, Nigeria, Ethiopia, Kenya, Zambia, Pakistan and the USA) using multi-stage random cluster sampling from national census enumeration areas. The DCE comprised six attributes of hormonal contraceptives: format (ie, mode of administration: injection, implant, etc), dosing interval, provider-administration versus self-administration, effect on menstruation, effect on weight and time from discontinuation until return to fertility. Mixed logit models were fitted separately by country to estimate the relative preference weights (β) for each attribute level. RESULTS:Across 20 068 participants, format was the strongest driver of preferences. Injectables were most preferred in Kenya (β=0.44, 95% CI 0.25 to 0.63), Pakistan (β=0.55, 95% CI 0.29 to 0.81), Ethiopia (β=0.64, 95% CI 0.38 to 0.90) and Zambia (β=0.39, 95% CI 0.20 to 0.58). Oral pills were most preferred in the USA (β=0.78, 95% CI 0.58 to 0.98), Burkina Faso (β=0.43, 95% CI 0.27 to 0.59) and Nigeria (β=0.55, 95% CI 0.40 to 0.70). Intrauterine devices and vaginal rings were less preferred in all countries. Menstrual bleeding changes and dosing interval were secondary drivers, with longer-acting methods modestly preferred and ongoing irregular bleeding modestly non-preferred. Avoiding weight gain was significantly preferred in several countries, most strongly in Pakistan (β=0.56, 95% CI 0.44 to 0.67) and the USA (β=0.42, 95% CI 0.31 to 0.53). CONCLUSIONS:In this large, multi-country study, reproductive-age participants expressed strong, heterogeneous preferences for contraceptive format, and in some settings, avoiding weight gain side effects. These findings can support preference-aligned contraceptive product development.

Radiology is undergoing a global transformation across its workforce, workplace, and workflows. Imaging demand and examination complexity continue to increase, while workforce growth has lagged. Although these challenges are global, they manifest differently across regions and practice settings. Workforce shortages, burnout, and a weakening academic mission with less emphasis on research and teaching are systemic problems. In response to growing workloads, digital technologies, including artificial intelligence, have been introduced to increase capacity. Although these tools have the potential to improve efficiency, quality, and education, their effect on radiologist productivity has been mixed. Practice models such as teleradiology and hybrid work offer more flexibility for radiologists and have improved access to imaging services, especially in rural and underserved regions. However, remote work can lead to isolation, as well as lack of collaboration and exchange of ideas, which may contribute to burnout. In addition, declining reimbursement, rising costs, and growing unreimbursed noninterpretive tasks put a strain on the radiology workforce. This review from the International Society for Strategic Studies in Radiology (IS3R) examines international experiences on the challenges affecting the global workforce capacity, imaging demand, workplace, work design, and the work environment for radiology, and offers potential solutions.