Citations

Background Subjective cognitive decline (SCD) is a preclinical condition associated with an increased risk of dementia and Alzheimer’s disease, with no consensus regarding effective therapeutic interventions to date. As the global burden of aging-related neurodegenerative diseases continues to rise, early intervention has become increasingly critical. Cognitive training has been proposed as a promising behavioral intervention; however, few studies have integrated multimodal assessments to elucidate its underlying neurobiological and psychological mechanisms, particularly within the educational system that shapes cognitive reserve and learning strategies across the lifespan. This pilot study aims to estimate effect sizes for future definitive trials, assess the feasibility and acceptability of the intervention and multimodal evaluations, and generate hypotheses regarding the relationship between recitation practice and clinical biomarkers. Methods A total of 60 individuals with subjective cognitive decline will be enrolled in a randomized, assessor-blinded, controlled pilot study. Participants will be randomly assigned to either an intervention group or a non-active control group prior to baseline assessment. The intervention group will undergo a six-month structured recitation training program. Both groups will undergo short-term follow-up after the intervention, and long-term follow-up annually post-intervention. Multimodal assessments will include neuropsychological testing, functional magnetic resonance imaging (fMRI), electroencephalography (EEG), blood biomarker profiling, gut microbiota analysis, and fecal metabolomics. Discussion This study protocol outlines a randomized, assessor-blinded, controlled pilot study designed to advance understanding of the feasibility of Chinese Classics recitation training on both physiological and psychological outcomes in individuals with subjective cognitive decline. The findings are expected to provide mechanistic insights into non-pharmacological interventions and to address the current underrepresentation of Asian populations in neuroscience research. Clinical trial registration We registered this randomized controlled trial on ClinicalTrials.gov on March 11, 2026, under the identifier NCT07463391.

Background/Objectives: Appointment volume is a primary measure of provider workload and a key contributor to provider burnout in primary care settings. It is highly driven by the size and patient mix of patient panels assigned to providers. To ensure that workload is balanced among providers, an effective patient panel assignment strategy is essential. Methods: A patient panel assignment framework was developed and demonstrated using a year of data from a large family medicine practice to assign patients evenly among providers with the following steps: 1. Predicting patient appointment needs using patient characteristics and Adjusted Clinical Group (ACG) scores; 2. Placing patients in appointment groups based on predictive appointment needs; 3. Determining the patient cap of each appointment group for each provider group; 4. Allocating patients from each appointment group to each provider group without exceeding the patient cap. Results: The results were demonstrated across 100 simulated reassignments to a retrospective dataset. The variations in the number of appointments among providers within a provider group were compared between current practice and the proposed framework. For the largest provider group with 14 providers, the proposed framework with appointment prediction using all features reduced the standard deviation and range by 85% and 84%, respectively. While the proposed framework improved appointment workload balance among providers, 11.7% of patients required reassignment. Conclusions: The proposed framework demonstrates its utility in supporting workload balance. Future work is needed to minimize patient reassignments while accounting for patient care continuity and to incorporate other contributing factors into the framework.

Artificial intelligence (AI) is transforming gastrointestinal (GI) endoscopy into a more standardized, data-driven, and workflow-integrated field. Advances in computer-assisted detection (CADe), diagnosis (CADx), quality assessment (CAQ), natural language processing (NLP), and multimodal deep learning have expanded AI applications across colonoscopy, upper endoscopy, endoscopic ultrasound (EUS), ERCP, cholangioscopy, and capsule endoscopy. These systems have demonstrated improvements in lesion detection, procedural quality assessment, workflow efficiency, and diagnostic support. However, current evidence remains largely focused on surrogate outcomes rather than patient-centered clinical benefits, while challenges related to generalizability, explainability, regulatory oversight, automation bias, and workflow integration continue to limit widespread adoption. Future progress will depend on prospective real-world validation, diverse datasets, explainable AI frameworks, and careful integration of human–AI interaction into clinical practice. Overall, AI is evolving from a supportive adjunct into an increasingly integrated component of gastrointestinal endoscopy with the potential to improve procedural quality, diagnostic consistency, and clinical efficiency.

Acute myeloid leukemia (AML) pathogenesis is deeply rooted in the concept of clonal evolution, in which hematopoietic stem cells experience an initial genetic insult, followed by the sequential acquisition of mutations leading to various disease phenotypes. Through natural selection, a malignant clone can diversify and propagate with time. Aside from clonal evolution, a number of time-honored principles of molecular oncology account for the initiation and maintenance of AML. These principles include loss of heterozygosity, genomic instability, epigenomic disruption, deregulation of signaling pathways, anti-apoptotic mechanisms, and oncogene addiction. Such tenets of cancer biology not only explain AML cellular behavior but also may inform target validation in AML. Herein, we highlight the current state of targeted therapeutics, including the 14 novel agents (beyond 7+3 chemotherapy) approved by the Food and Drug Administration, in the context of these tenets and discuss future prospects by leveraging current knowledge of AML genomics. Such therapeutic concepts, including synthetic lethality, are especially relevant to TP53-mutant AML, as there are no unique therapies that have received regulatory approval for this subset to date. We emphasize the emerging role of single-cell genomics and multi-omics toward better understanding of AML biology and development of future successful AML therapies.