Previously, we reported that heat shock protein (HSP)65 impairs the effects of high-density lipoprotein on macrophages. We also showed that immune response activation adversely affects reverse cholesterol transport (RCT). In this study, we investigated the effects of the Src family kinase lymphocyte-specific protein tyrosine kinase (Lck) and elucidated the mechanism underlying HSP65-regulated cholesterol efflux in T cells. We evaluated cell proliferation, Lck expression, and inflammatory cytokine production in Jurkat cells and CD4⁺ T cells. HSP65-mediated inhibition of RCT was assessed by evaluating ABCA1, ABCG1, SR-BI, PPAR-γ, and liver X receptor-α expression. A dose-dependent relationship was found between the levels of these proteins and the suppression of cholesterol efflux. Stimulation of Lck-silenced T cells with ionomycin resulted in a decrease in intracellular calcium levels. Treatment of Jurkat cells with PP2, an inhibitor of Src family kinase, inhibited calcium-induced, but not PMA-induced, ERK phosphorylation. NF-κB activation in response to PMA was minimally inhibited in cells stimulated with PP2. HSP65 failed to trigger downstream ERK or JNK phosphorylation or to activate NF-κB or protein kinase C-γ in Lck-silenced cells. Additionally, elevation of intracellular calcium was also impaired. However, HSP65 significantly enhanced cholesterol efflux and decreased cellular cholesterol content by inducing the expression of cholesterol transport proteins in Lck-silenced cells. The treatment of Jurkat cells with PP2 also inhibited cell proliferation and promoted RCT. In conclusion, Lck is a key molecule in the TCR signaling cascade that inhibits cholesterol efflux and upregulates intracellular cholesterol ester content in T cells. Our results demonstrate that the immune response plays a previously unrecognized role in RCT.

European law now requires AI to be explainable in the context of adverse decisions affecting the European Union (EU) citizens. At the same time, we expect increasing instances of AI failure as it operates on imperfect data. This paper puts forward a neurally inspired theoretical framework called “decision stacks” that can provide a way forward in research to develop Explainable Artificial Intelligence (X-AI). By leveraging findings from the finest memory systems in biological brains, the decision stack framework operationalizes the definition of explainability. It then proposes a test that can potentially reveal how a given AI decision was made.

Utilizing a randomized control design, this mixed method study aimed to assess the impact of a personalized music intervention on mood, agitation level, and psychotropic drug use in individuals with moderate to advanced dementia residing in long-term care facilities. The sample comprised of 261 participants, with 148 in the intervention group and 113 in the control group. Data were collected from three sources: quantitative data from the Minimum Data Set and the Cohen-Mansfield Agitation Inventory, observational data of music-listening sessions, and an administrator survey regarding the lead staff person’s perceptions of the intervention. Findings, based on Mixed Effect Models and content analyses, revealed positive impacts of the personalized music intervention on residents living with dementia. This low-cost, easily implementable intervention, requiring no special licensure for administration, can significantly enhance the quality of life for nursing facility residents.

Background: Peer review remains central to ensuring research quality, yet it is constrained by reviewer fatigue and human bias. The rapid rise in scientific publishing has worsened these challenges, prompting interest in whether large language models (LLMs) can support or improve the peer review process. Objective: This study aimed to address critical gaps in the use of LLMs for peer review of papers in the field of organ transplantation by (1) comparing the performance of 5 recent open-source LLMs; (2) evaluating the impact of author affiliations—prestigious, less prestigious, and none—on LLM review outcomes; and (3) examining the influence of prompt engineering strategies, including zero-shot prompting, few-shot prompting, tree of thoughts (ToT) prompting, and retrieval-augmented generation (RAG), on review decisions. Methods: A dataset of 200 transplantation papers published between 2024 and 2025 across 4 journal quartiles was evaluated using 5 state-of-the-art open-source LLMs (Llama 3.3, Mistral 7B, Gemma 2, DeepSeek r1-distill Qwen, and Qwen 2.5). The 4 prompting techniques (zero-shot prompting, few-shot prompting, ToT prompting, and RAG) were tested under multiple temperature settings. Models were instructed to categorize papers into quartiles. To assess fairness, each paper was evaluated 3 times: with no affiliation, a prestigious affiliation, and a less prestigious affiliation. Accuracy, decisions, runtime, and computing resource use were recorded. Chi-square tests and adjusted Pearson residuals were used to examine the presence of affiliation bias. Results: RAG with a temperature of 0.5 achieved the best overall performance (exact match accuracy: 0.35; loose match accuracy: 0.78). Across all models, LLMs frequently assigned manuscripts to quartile 2 and quartile 3 while avoiding extreme quartiles (quartile 1 and quartile 4). None of the models demonstrated affiliation bias, though Gemma 2 (P=.08) and Qwen 2.5 (P=.054) were substantially biased. Each model displayed unique “personalities” in quartile predictions, influencing consistency. Mistral had the highest exact match accuracy (0.35) despite having both the lowest average runtime (1246.378 seconds) and computing resource use (7 billion parameters). While accuracy was insufficient for independent review, LLMs showed value in supporting preliminary triage tasks. Conclusions: Current open-source LLMs are not reliable enough to replace human peer reviewers. The largely absent affiliation bias suggests potential advantages in fairness, but these benefits do not offset the low decision accuracy. Mistral demonstrated the greatest accuracy and computational efficiency, and RAG with a moderate temperature emerged as the most effective prompting strategy. If LLMs are used to assist in peer review, their outputs require nonnegotiable human supervision to ensure correct judgment and appropriate editorial decisions.

During the COVID-19 pandemic, the value of palliative care has become more evident than ever. The current study quantitatively investigated the perceptions of palliative care emerging from the pandemic experience by analyzing a total of 26,494 English Tweets collected between 1 January 2020 and 1 January 2022. Such an investigation was considered invaluable in the era of more people sharing and seeking healthcare information on social media, as well as the emerging roles of palliative care. Using a web scraping method, we reviewed 6000 randomly selected Tweets and identified four themes in the extracted Tweets: (1) Negative Impact of the Pandemic on Palliative Care; (2) Positive Impact of the Pandemic on Palliative Care; (3) Recognized Benefits of Palliative Care; (4) Myth of Palliative Care. Although a large volume of Tweets focused on the negative impact of COVID-19 on palliative care as expected, we found almost the same volume of Tweets that were focused on the positive impact of COVID-19 on palliative care. We also found a smaller volume of Tweets associated with myths about palliative care. Using these manually classified Tweets, we trained machine learning (ML) algorithms to automatically classify the remaining tweets. The automatic classification of Tweets was found to be effective in classifying the negative impact of the COVID-19.