UC Irvine

CLINICAL RELEVANCE: Although diabetes is associated with a classic microvascular disease of the retina, it is also increasingly being recognized as a cause of retinal neuropathy. Preclinical evidence suggests that retinal neuropathy in diabetes manifests in part as photoreceptor dysfunction, preceding the development of vascular features in experimental models. It remains unknown whether such findings are relevant to patients with diabetes. METHODS: Here, we review 4 lines of clinical evidence suggesting that diabetes-associated photoreceptor pathology is linked to the development of retinal microvascular disease. RESULTS: First, a major population-based investigation of susceptibility loci for diabetic retinopathy (DR) implicated a photoreceptor protein product as a protective factor. Next, electroretinography and other studies of visual function collectively show that rod and/or cone-derived abnormalities occur decades before the development of vascular features of DR. Third, protection from DR seemingly develops in patients with coincident retinitis pigmentosa, as suggested by several case series. Finally, based on anatomic features, we propose that the beneficial effect of macular laser in DR occurs via ablation of diseased photoreceptors. CONCLUSIONS: The evidence we present is limited due to the small patient populations used in the studies we cite and due to the lack of methodologies that allow causative relationships to be inferred. Collectively, however, these clinical observations suggest that photoreceptors are involved in early diabetic retinal disease and may in fact give rise to the classic features of DR. FINANCIAL DISCLOSURES: Proprietary or commercial disclosures may be found in the Footnotes and Disclosures at the end of this article.

Inspired by recent human studies, this paper investigates the benefits of employing varying navigation strategies in robot teams. We explore how mixed navigation strategies impact task completion time, environment exploration, and overall system effectiveness in multi-robot systems. Experiments were conducted in a simulated rectangular environment using Clearpath PR2 robots and evaluated different navigation strategies observed in humans: 1) Route (RT) knowledge where agents follow a predefined path, 2) Survey (SW) knowledge where agents take the shortest path while avoiding obstacles, 3) Mixed strategies with varying proportions, such as 40% RT and 60% SW (0.4RT 0.6SW) and 60% RT and 40% SW (0.6RT 0.4SW), and 4) An additional strategy where agents switch from RT to SW 10% of the time (0.9RT 0.1SW). While SW strategy is the most time-efficient, RT strategy covers more of the environment. Mixed strategies offer a balanced trade-off. These findings highlight the advantages of variability in navigation strategies, suggesting benefits in both biological and robotic populations. Additionally, we have observed that human participants in a similar study would start on a route, and then 10% of the time switch to survey. Therefore, we investigate a 90% Route 10% Survey (0.9RT 0.1SW) strategy for individual team members. While a pure Survey strategy is the most efficient regarding time taken and a pure Route strategy covers more of the environment, a mixture of strategies appears to be a beneficial tradeoff between time taken to complete a mission and area coverage. These results highlight the advantages of population variability, suggesting potential benefits in both biological and robotic populations.

In this case report, we describe a diagnosis of papillary renal cell carcinoma in a 76-year-old male patient who was incidentally found to have a left adrenal mass during routine aneurysm surveillance. Computed tomography demonstrated a left adrenal mass and left renal structure which was concerning for renal cell carcinoma. He underwent left adrenalectomy and initial histopathology demonstrated papillary renal cell carcinoma. He subsequently underwent left radical nephrectomy with lymph node dissection. Histopathological analysis of the removed left renal and nodal specimens revealed papillary renal cell carcinoma with lymph node metastasis. However, re-review of the adrenal pathology slides determined the specimen as represented by primary kidney tumor and not adrenal metastasis. This report reviews the presentation and radiological findings of synchronous papillary renal cell carcinoma and differential diagnosis for indeterminate adrenal mass on computed tomography.

Despite ART, we detected occasional microglia containing cell-associated HIV RNA and HIV DNA integrated into open regions of the hosts genome (∼0.005%) should be corrected to: Despite ART, we detected occasional microglia containing cell-associated HIV RNA and HIV DNA integrated into open regions of the hosts genome (∼0.5%).

Aberration of the "gut-liver axis" contributes to the development and progression of metabolic dysfunction-associated steatotic liver disease (MASLD). Here, we use multi-omics to analyze the gut microbiota composition and metabolic profile of patients with type-2 diabetes mellitus (T2DM). T2DM patients were screened for liver disease by blood tests, ultrasound, and liver stiffness measurements. Stool microbiota was analyzed by 16S rRNA gene sequencing; metabolomic profiling by Nuclear Magnetic Resonance spectroscopy and Ultra-High Performance-Mass Spectrometry. Microbiome and metabolic signatures were analyzed in the whole cohort and in matched subsets to identify signatures specific for steatosis (MASLD±) or fibrosis (Fibrosis±). Gut permeability was assessed in-vitro using monolayers of MDCK cells and trans-epithelial electric resistance (TEER). Cytokine profile was assessed in serum and stools.Overall, 285 patients were enrolled: 255 serum, 252 urine and 97 stool samples were analyzed. Anaeroplasma and Escherichia/Shigella ASVs were higher, while Butyricicoccus ASVs were lower in those with normal liver. In MASLD±, Butyricicoccus ASV was significantly higher in those with steatosis. In the Fibrosis±, Butyricicoccus ASV was significantly lower in those with fibrosis. Glycochenodeoxycholic acid-3-sulfate (G-UDCA-3S) appeared to be higher in MASLD with fibrosis. Fecal water from patients with MASLD and fibrosis caused the greatest drop in the TEER vs those with normal liver; this was reversed with protease inhibitors. Finally, fecal IL-13 was lower in MASLD with fibrosis. We identified microbiome signatures which were specific for steatosis and fibrosis and independent of other metabolic risk factors. Moreover, we conclude that protease-related gut permeability plays a role in those MASLD patients with fibrosis, and that disease progression is linked to a gut-liver axis which is at least partially independent of T2DM.

OBJECTIVE: This study aimed to assess patients interest in education content delivered through electronic modalities and identify trends in internet access and use among emergency department patients of various socioeconomic statuses. METHODS: A prospective, cross-sectional survey with 50 questions was completed by 241 English and Spanish-speaking patients in 2014 and repeated with 253 participants in 2019 at the University of California, Irvine Medical Centers Emergency Department (UCIMCED). RESULTS: Internet access increased from 83.8 % in 2014 to 88.1 % in 2019. Most internet-using patients owned smartphones (80.1 % in 2014, 89.7 % in 2019). Patients used electronic devices, such as fit bits and activity trackers, to obtain health information. Email was the preferred method for receiving discharge instructions. CONCLUSIONS: As of 2019, 88.1 % of UCIMCED patients have access to the internet or email, making electronic media a reasonable venue for patient education. Given that we have a predominantly low-income patient population-61 % and 32 % of respondents in 2014 and 2019, respectively, reporting an income of less than $25,000-these results are provide new avenues to reach patients of all socioeconomic statuses. INNOVATION: The implications of this study can be used to develop electronic resources tailored to educate emergency department patients about their healthcare beyond the confines of a hospital.

Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably drive this patient-centered transformation in healthcare. Through the provision of various services, including diagnosis, personalized lifestyle recommendations, dynamic scheduling of follow-ups, and mental health support, the objective is to substantially augment patient health outcomes, all the while mitigating the workload burden on healthcare providers. The life-critical nature of healthcare applications necessitates establishing a unified and comprehensive set of evaluation metrics for conversational models. Existing evaluation metrics proposed for various generic large language models (LLMs) demonstrate a lack of comprehension regarding medical and health concepts and their significance in promoting patients’ well-being. Moreover, these metrics neglect pivotal user-centered aspects, including trust-building, ethics, personalization, empathy, user comprehension, and emotional support. The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare. Subsequently, we present a comprehensive set of evaluation metrics designed to thoroughly assess the performance of healthcare chatbots from an end-user perspective. These metrics encompass an evaluation of language processing abilities, impact on real-world clinical tasks, and effectiveness in user-interactive conversations. Finally, we engage in a discussion concerning the challenges associated with defining and implementing these metrics, with particular emphasis on confounding factors such as the target audience, evaluation methods, and prompt techniques involved in the evaluation process.

Charged particle reconstruction in the presence of many simultaneous proton–proton (pp) collisions in the LHC is a challenging task for the ATLAS experiment’s reconstruction software due to the combinatorial complexity. This paper describes the major changes made to adapt the software to reconstruct high-activity collisions with an average of 50 or more simultaneous pp interactions per bunch crossing (pile-up) promptly using the available computing resources. The performance of the key components of the track reconstruction chain and its dependence on pile-up are evaluated, and the improvement achieved compared to the previous software version is quantified. For events with an average of 60pp collisions per bunch crossing, the updated track reconstruction is twice as fast as the previous version, without significant reduction in reconstruction efficiency and while reducing the rate of combinatorial fake tracks by more than a factor two.

The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques.

Bone metastasis remains a clinical challenge and is still considered incurable. While nanoparticles-based drug delivery and photothermal therapy (PTT) show promise in treating subcutaneous solid tumor, their therapeutic outcome in treating bone metastasis is limited, due to the inaccessibility of bone metastatic site and the complexity of bone metastasis. Herein, we reported a simple nanoplatform composed of thermo-sensitive liposomes (TSL) and gold nanorods (GNR) to treat bone metastasis through improved chemotherapy combined with GNR-assisted PTT. Lipid combination of TSL was firstly tailored to regulate its stability under physiological condition as well as its sensitivity in responding to PTT-caused mild hyperthermia. The obtained TSL with loaded drug was then combined with GNR to form the nanoplatform through unsophisticated incubation. Cell experiments revealed that upon near-infrared (NIR) irradiation, the nanoplatform effectively inhibited the viability and migration ability of tumor cells through PTT, PTT-triggered thermo-sensitive drug release, and PTT-augmented sensitivity of tumor cells to drug. In a murine model of bone metastasis, the nanoplatform enabled effective delivery of loaded drug and GNR to bone metastatic site for rapid drug release upon local NIR irradiation. Through killing tumor cells and rebalancing the turnover of osteoclasts and osteoblasts, the nanoplatform largely preserved bone structure for pain relief and survival extension. Inspired by the simplicity of nanoplatform acquirement and treatment operation, the strategy of liposomes-based thermo-sensitive drug delivery in combination with GNR-assisted PTT is considered greatly promising in treating bone metastasis.