Citations

(1) Background: Large language models (LLMs) show promise in healthcare but are constrained by hallucinations, static knowledge, and limited domain specificity. Fine-tuning (FT) and retrieval-augmented generation (RAG) offer complementary solutions, with FT embedding domain reasoning and RAG enabling dynamic, up-to-date knowledge access. Hybrid FT + RAG frameworks have been proposed to improve factual accuracy and clinical reliability. This scoping review synthesizes current evidence on such hybrids in healthcare AI. (2) Methods: The search across PubMed, IEEE Xplore, Google Scholar, and Embase identified studies implementing explicit FT + RAG hybrids in healthcare or biomedical tasks. Eligible studies reported empirical evaluations of LLM performance or behavior. Data were extracted on base models, FT strategies, RAG architectures, applications, and performance outcomes. (3) Results: Seven studies met inclusion criteria. FT + RAG systems consistently outperformed FT-only or RAG-only approaches across QA, clinical summarization, report generation, and decision support tasks. Parameter-efficient FT methods (e.g., LoRA) were common, while RAG implementations varied (dense, hybrid, hierarchical, multimodal, federated). Reported benefits included improved accuracy, reduced hallucination, and greater clinician preference and feasibility in protected settings. (4) Conclusions: FT + RAG frameworks represent a promising direction for clinically grounded healthcare AI, combining domain-specific reasoning with transparent, up-to-date retrieval. Future work should prioritize standardized evaluation, workflow integration, and governance to enable safe deployment.

Abstract Diet profoundly influences gut microbial composition and metabolism, yet mechanistic pathways linking dietary exposures to human health remain fragmented across the literature. To systematically organize and integrate this evidence, we constructed an evidence-weighted Diet-Microbiome-Health Knowledge Graph (DMH-KG) from 1,309 curated PubMed abstracts (2023–2025), using a standardized schema spanning 11 entity categories and 12 relationship types with explicit polarity labels. Entities and relationships were identified through manual annotation, yielding 10,270 entity mentions and 4,866 relationships. Expert-guided consolidation of synonymous and lexically variant terms, along with pruning of disconnected concepts, resulted in 4,766 unique entities connected by 4,772 polarity-weighted edges. To prioritize robust biological signals, a composite edge-weighting function was applied, integrating both relationship frequency and polarity. Network analysis revealed a modular small-world structure centered on microbial and inflammatory mediators. High-confidence pathways emerged, including the probiotic-SCFA-immunity axis (34 supporting documents) and the high-fat diet-LPS-endotoxemia cascade (8 documents), both of which are central to age-related immune modulation and metabolic health. Quantitative validation against five KEGG and Reactome pathways demonstrated high biological fidelity: the DMH-KG recovered all reference diet-microbiome-outcome edges for Butanoate metabolism and Secondary Bile Acid biosynthesis (100% coverage) and achieved a mean pathway-level entity coverage of 92.0%, measured as the proportion of predefined pathway components represented in the graph. A comparative pilot study further demonstrated that DMH-KG augmentation improves mechanistic specificity inference across Diet-Microbiome-Health interactions. These features position the DMH-KG as a scalable platform for mechanistic inference across Diet-Microbiome-Health interactions, with direct relevance to immune regulation, muscle health, metabolic aging, and chronic disease prevention. The framework preserves evidence provenance, relationship polarity, and biological direction, supporting both discovery science and AI-driven nutritional reasoning.

Background/Objectives: Antigen presenting cells (APCs) and immune cells have unique properties to drive or suppress immune responses. They are therefore key targets for the expression of vaccine antigens or transgene proteins. To better determine the utility of different molecular therapies to modify these cells, mRNA and DNA-based molecular therapy vectors were compared for their ability to genetically modify immune cells after intradermal injections in mice. DNA-based vectors included naked plasmid DNA, plasmid packaged in lipid nanoparticles (LNPs), and replication-defective adenovirus (Ad) vectors. mRNA delivery was mediated by packaging into LNPs like those used in COVID-19 vaccines. Methods: Each vector was used to deliver Cre recombinase into Cre reporter mice whose cells were activated to express green fluorescent protein (GFP) and firefly luciferase after Cre recombination. The mice were injected intradermally (ID) near the base of their tail at a site that drains into the inguinal lymph node. Luciferase activity was imaged in the living mice 1 or 4 days after vector injection. The animals were then euthanized, and luciferase activity was imaged in the draining inguinal lymph node. Cells were prepared from the intradermal injection site and from the draining lymph node to determine which immune cells were genetically modified by phenotyping CD45, CD3, and CD11b GFP-positive cells by flow cytometry. Given that the skin uniquely contains Langerhans dendritic cells, these CD207+ cells were also phenotyped in skin samples and in the draining lymph node. Results: In both the skin and in the draining lymph node, the rank order of luciferase and GFP activation by the vectors were: (1) Ad; (2) mRNA-LNP; (3) DNA-LNP; and (4) naked DNA. Only mRNA-LNP and Ad vectors mediated obvious luciferase activity in the living animals and in the draining lymph nodes by imaging. Notably, both vectors appeared to leak from the ID injection site and not only modify the draining lymph node but also strongly modify the livers of the mice. Naked DNA and DNA-LNP mediated detectable GFP activation in the skin and draining lymph node in some mice, but this activity was low and did not reach statistical significance when compared to PBS-treated animals. mRNA-LNPs and Ad both mediated significant Cre delivery in CD45+, CD3+, CD11b+, and CD207+ immune cells in the skin and in the lymph node, with adenovirus mediating consistently higher levels of expression in all of the tested cells. Conclusions: These data indicate that mRNA-LNP and Ad vectors mediate stronger modification of skin and lymph node immune cells after intradermal injections. Naked DNA and DNA-LNPs were markedly less potent at this activity than the other vectors. These data are consistent with the higher vaccine potency of mRNA-LNP and Ad vectors and suggest that approaches that increase targeting of immune cell subsets may have utility to increase efficacy while also reducing off-target modification of tissues like the liver.

Background: Indications for TAVR explant have been established, although limited data exist regarding pre-TAVR baseline characteristics that predict eventual explantation. Methods: The TriNetX network, a database comprising medical records from over 105 institutions, was used. Two cohorts were created: those who underwent TAVR without explant and those requiring subsequent TAVR explant and SAVR. Predictors of explantation were analyzed by multivariate models. Results: Among the 63,377 patients undergoing TAVR, 273 (0.4%) required explantation. Patients in the explant group were younger (69.1 ± 11.3 vs. 78.1 ± 8.8 years; p < 0.001), more likely to have a thoracic aortic aneurysm (TAA) (10.6% vs. 4.7%; p < 0.001) and had higher LDL levels (88.2 ± 41.3 vs. 80.7 ± 34.6 mg/dL; p = 0.011). They also had increased post-TAVR rates of acute kidney injury (9.2% vs. 5.2%; p = 0.004) and paravalvular leak (5.9% vs. 0.9%; p < 0.001). Age at TAVR (HR 1.04; CI 1.03–1.06), baseline TAA (HR 1.69; CI 1.09–2.63), history of infective endocarditis (HR 1.92; CI 1.10–3.35), and higher LDL (HR 1.02; 95% CI 1.00–1.03) were independent predictors for explantation. Conclusions: Younger age at TAVR, TAA, history of endocarditis, and elevated baseline LDL were notable predictors of explantation. These findings highlight the necessity of pre-procedural assessment and follow-up in high-risk patients to optimize TAVR durability.

Background: Acute tubulointerstitial nephritis (ATIN) is a significant yet under-monitored cause of U.S. mortality, particularly among the elderly. This study anrackalyzed national trends and demographic disparities in age-adjusted mortality rates (AAMRs) from 1999 to 2020 to identify high-risk populations and inform public health policy. Methods: Using the CDC WONDER database, we conducted a retrospective analysis of 6872 ATIN-related deaths. AAMRs (per 100,000) were stratified by sex, race, and census region. Temporal shifts were quantified using Joinpoint regression to determine annual percentage changes (APC) and 95% confidence intervals (CIs). Results: The analysis revealed a distinct “V-shaped” mortality trend across the 22-year period. Following an initial decline from 1999 to 2013, AAMRs rose sharply through 2020. Males experienced a slightly steeper recent increase (9.90%) compared to females (9.50%). While Black/African American individuals initially had higher mortality rates, a significant surge in deaths among Non-Hispanic White individuals after 2013 (APC 10.42%) led to a convergence of mortality rates between the two groups by 2020. Geographically, the Midwest (APC 12.08%) and the South saw the most pronounced recent increases, whereas the West showed a sustained upward trend beginning as early as 2008. Conclusions: There has been a concerning reversal in ATIN-related mortality trends in the United States over the last decade. The convergence of racial mortality rates and significant regional variations suggest that shifting healthcare access, environmental factors, or medication exposure patterns (such as polypharmacy) warrant urgent investigation to mitigate this rising public health burden.

The treatment paradigm for deficient mismatch repair (dMMR) colorectal cancer (CRC) has evolved substantially in recent years. Universal mismatch repair testing and broad use of next-generation sequencing have increased the detection of dMMR tumours, which exhibit microsatellite instability-high (MSI-H), hypermutation and abundant neoantigens. The presence of dMMR/MSI-H serves as a robust predictive biomarker for immune checkpoint inhibitor (ICI) therapy, which has demonstrated superior efficacy to cytotoxic chemotherapy in the metastatic setting and led to the first tumour-agnostic Food and Drug Administration approval in 2017 for metastatic dMMR/MSI-H solid tumours. Recent evidence also supports the benefit of ICIs in non-metastatic dMMR CRC. Neoadjuvant immunotherapy has produced high rates of pathological response in both colon and rectal cancers. In locally advanced dMMR rectal cancer, ICI therapy has enabled omission of chemoradiation and surgery in most patients. In resected node-positive dMMR colon cancer, the addition of ICI therapy to chemotherapy has substantially improved disease-free survival, expanding its role earlier in the disease course. Despite these advances, the optimal treatment strategy for non-metastatic dMMR CRC remains undefined due to the lack of direct comparative studies. Importantly, a subset of patients derives limited or no benefit from ICIs despite dMMR/MSI-H status, underscoring the need to further elucidate resistance mechanisms and to develop strategies to overcome them.

Coronary CT angiography (CCTA) offers excellent negative predictive value for ruling out obstructive coronary artery disease (CAD); however, several interpretative pitfalls can lead to diagnostic errors including false-negative, false-positive results or inaccurate estimation of degree of stenosis. These errors may influence patient management, prompt unnecessary or missed downstream testing, and ultimately affect clinical outcomes. This two-part article reviews the most common sources of diagnostic inaccuracy in CCTA through case-based examples. Part I highlights factors that contribute to missed or overdiagnosed coronary lesions while Part II focuses on pitfalls that lead to underestimation or overestimation of stenosis severity and outlines practical strategies to mitigate these errors. Recognizing the inherent limitations of CCTA and employing a systematic, structured interpretive approach are essential for preserving its high diagnostic accuracy and ensuring optimal patient care.

Accurate quantification of coronary stenosis on coronary CT angiography (CCTA) is essential for appropriate risk stratification and clinical decision-making, yet several technical and interpretative pitfalls can compromise diagnostic accuracy. This second part of a two-part review examines common factors that lead to overestimation or underestimation of stenosis severity, with attention to how these errors influence CAD-RADS categorization, downstream testing, and patient management. Using case-based examples, we describe key contributors to misinterpretation and present practical strategies to mitigate these pitfalls. By recognizing these nuances and employing a structured, standardized interpretive approach, readers can minimize errors in stenosis assessment and enhance the reliability of CCTA as a noninvasive tool for evaluating coronary artery disease.