Citations

Abstract Extracellular vesicles (EV) are phospholipid‐encapsulated nanoparticles secreted by cells into their surrounding environment. EVs can transfer a variety of biomolecules that mediate intercellular communication and play a key role in physiological and pathological processes. Therefore, EVs are emerging as new biomarkers for diseases, therapeutic targets, and drug delivery vehicles. The isolation and detection of EVs requires time‐consuming and labor‐intensive processes, first to extract EVs from biological and physiological fluids and then to detect EV‐associated molecules with high sensitivity. The methodologies and instruments commonly used for EV analysis are not widely accessible outside of dedicated research laboratories, creating practical barriers for the study of EV‐associated molecules in biology research and clinical applications. To bridge this gap, we developed a proof‐of‐concept digital microfluidic device that can automatedly extract EVs from 20 µL of culture and human plasma samples within 25 min and detect EV‐bound proteins (e.g PD‐L1) on‐chip using an electrochemical sensor. This work serves as a framework for the development of streamlined EV analysis in both research and diagnostics.

Comparative analysis of fibrin sealant and recombinant thrombin hydrogels reveals distinct microarchitectures and cellular responses, emphasizing recombinant thrombin’s potential to enhance cellular activity and enable vesicle delivery.

Inborn errors of immunity (IEI) presenting with immunodeficiency and autoimmunity can illuminate pathways essential for immunocompetence and self-tolerance. We recently characterized a new IEI named MAGIS (“Midline malformations of the brain, Anterior pituitary gland dysfunction, Growth retardation, Immunodysregulation/Immunodeficiency, and Skeletal defects”) caused by heterozygous germline-activating mutations in GNAI2 (encoding the heterotrimeric G protein, Gαi2). This disorder demonstrates the central role of Gαi2 regulation of chemotaxis in humans and a novel pathway by which G proteins regulate T cell activation. Here, we review the clinical features, current genetic and biochemical understanding, and future therapeutic considerations for this new syndromic immune dysregulation disorder.

Abstract Background Protein biomarkers are routinely measured for disease diagnosis and prognosis in clinical laboratories. Since most assays focus on protein quantity, information about proteoforms is often not acquired. Proteoforms of a protein represent the complex integration of genetic polymorphism, alternative splicing of RNA transcripts, and post-translational modifications (PTMs) on the amino-acid backbone. A detailed analysis of the post-translationally modified proteoforms (PTMPs), which are influenced by pathophysiological conditions, may lead to more precise diagnosis and prognosis. Content This article first discusses the methodologies used to accurately detect and characterize PTMPs, i.e., immunoassays, electrophoresis, chromatography, and intact and proteolysis-aided mass spectrometry techniques. Then it reviews specific examples of PTMP biomarkers that have been successfully translated from biomarker discovery to clinical use. The examples include β2–transferrin for cerebrospinal fluid leak diagnosis, phosphorylated tau proteoforms for Alzheimer disease diagnosis, and fucosylated alpha-fetoprotein for hepatocellular carcinoma prognosis. In addition, the article provides prospective views of novel analytical technologies and promising new PTMP biomarkers entering clinical practice. Summary In summary, PTMs are controlled by biochemical processes to modulate the functions of proteins by expanding their chemical diversity. PTM alterations in proteins can be indicators for pathophysiological conditions. Advances in analytical technologies are deepening our understanding of PTMPs and paving the way for their translation to clinical use. As research continues to discover the clinical meaning of PTMP biomarkers, they are poised to become valuable additions to the clinical testing menu for precision medicine.

BACKGROUND: Older adults value the maintenance of function as an outcome of surgery, which can be evaluated by subjective perception and objective performance. The aims of this study were to explore whether these perceptions correlate with a decline in function, as measured by decreased ability to perform activities of daily living (ADLs), and to identify risk factors associated with worsened 30-day postoperative perceived physical function (PPF) in patients aged 80 years and older. STUDY DESIGN: American College of Surgeons NSQIP Geriatric Surgery Verification Pilot Program data (2015 to 2017) were used for patients aged 80 years and older. Chi-square tests were used to examine the concordance between 30-day outcomes of PPF and the quantified performance of ADLs. Multivariable logistic regression models identified risks associated with worsened PPF 30 days postoperatively. RESULTS: A total of 5,464 patients from 16 hospitals were included. The mean age was 85.7 years and 63% of participants were women. At 30 days postoperatively, 33% reported functional decline, whereas 28% had worsened quantified ADL performance (K = 0.25). Twenty-one percent of patients without quantified decline reported worsened PPF. Risk factors for worsened PPF were found for patient (eg American Society of Anesthesiologists class IV and V (odds ratio [OR] 2.2, 95% CI 1.5 to 3.2)), procedure (eg orthopaedic [OR 1.7, 95% CI 1.2 to 2.4]), and older adult-specific characteristics (eg preoperative fall [OR 1.9, 95% CI 1.2 to 2.9]). CONCLUSIONS: At 30 days postsurgery, there was discordance between quantified ADL performance and PPF in patients aged 80 years and older. Although assessing ADLs is a common approach to detect functional decline and loss of independence, directly asking patients about their PPF can serve as an additional important method for identifying functional decline, ensuring that the evaluation remains patient centered.

Ultrasound localization microscopy (ULM) enables imaging of the microvasculature at spatial resolutions surpassing the limits of conventional ultrasound, while maintaining comparable penetration depth. This super-resolution technique relies on the localization and tracking of individual microbubbles (MBs) administered into the bloodstream, providing detailed visualization of microvascular structures and functions. However, clinical implementation of ULM faces significant challenges, such as low signal-to-noise ratio (SNR), tissue motion artifacts, and prolonged acquisition times. In this presentation, we will discuss advancements aimed at achieving robust and reproducible clinical implementation of ULM through a series of technical innovations and optimizations. These include signal enhancement techniques to boost MB SNR, advanced algorithms for accurate MB localization and tracking at clinically relevant dosages, optimized data acquisition strategies tailored to clinical workflows, and the development of 3-D imaging capabilities. Through pilot preclinical and clinical studies, we demonstrate the feasibility and efficacy of ULM in various applications, including chronic kidney disease (CKD), kidney transplant, liver disease evaluation, and beyond, in both animal models and human subjects under clinical scanning conditions. These findings underscore the transformative potential of super-resolution ultrasound imaging as a diagnostic tool, paving the way for widespread clinical adoption.

Mechanical stretch associated with respiratory support can impair airway development and function in neonates, but the mechanisms are not fully understood. Using developing human airway smooth muscle cells exposed to cyclic forces with static stretch to mimic continuous positive airway pressure, we found that stretch dysregulates long-term cell proliferation and inflammatory cytokine production, and mechanosensitive Piezo ion channels may play a role in the proliferation response.

Abstract Twenty percent of patients with follicular lymphoma relapse early with poor outcomes; however, the molecular mechanisms underlying this aggressive behavior are unknown. Using a multiomic approach, we show that patients with follicular lymphoma with elevated IRF4 expression (IRF4hi) have increased transformation risk, dysregulated immune signaling, and a suppressive tumor microenvironment. Loss- and gain-of-function experiments in IRF4hi lymphoma cells, along with chromatin profiling, demonstrate that IRF4 impairs their interaction with T cells by repressing antigen presentation and co-receptor gene modules while promoting the expression of cytokines that antagonize T-follicular helper cell and regulatory T-cell functions. Additionally, IRF4 rewires tumor metabolism and restricts glucose availability to immune cells. Silencing of IRF4 inhibits tumor cell growth and restores immune surveillance mechanisms, thus representing a promising target for therapy. Our data suggest that IRF4hi lymphoma cells co-opt a developmental mechanism used to exit the germinal center response in promoting a more aggressive cancer via engagement of multiple immune-evasive mechanisms. Significance: We show that IRF4 controls immune and metabolic signaling with functional effects on the cross-talk between B and T cells, resulting in a suppressive immune microenvironment when IRF4 is overexpressed in lymphoma. This underscores the importance of IRF4 as a pivotal therapeutic target to restore antitumor immunosurveillance for IRF4-associated lymphomas.

Abstract Background The radiation oncology clinical practice involves many steps relying on the dynamic interplay of abundant text data. Large language models have displayed remarkable capabilities in processing complex text information. But their direct applications in specific fields like radiation oncology remain underexplored. Purpose This study aims to investigate whether fine‐tuning LLMs with domain knowledge can improve the performance on Task (1) treatment regimen generation, Task (2) treatment modality selection (photon, proton, electron, or brachytherapy), and Task (3) ICD‐10 code prediction in radiation oncology. Methods Data for 15 724 patient cases were extracted. Cases where patients had a single diagnostic record, and a clearly identifiable primary treatment plan were selected for preprocessing and manual annotation to have 7903 cases of the patient diagnosis, treatment plan, treatment modality, and ICD‐10 code. Each case was used to construct a pair consisting of patient diagnostics details and an answer (treatment regimen, treatment modality, or ICD‐10 code, respectively) for the supervised fine‐tuning of these three tasks. Open source LLaMA2‐7B and Mistral‐7B models were utilized for the fine‐tuning with the Low‐Rank Approximations method. Accuracy and ROUGE‐1 score were reported for the fine‐tuned models and original models. Clinical evaluation was performed on Task (1) by radiation oncologists, while precision, recall, and F‐1 score were evaluated for Task (2) and (3). One‐sided Wilcoxon signed‐rank tests were used to statistically analyze the results. Results Fine‐tuned LLMs outperformed original LLMs across all tasks with p value ≤ 0.001. Clinical evaluation demonstrated that over 60% of the fine‐tuned LLMs‐generated treatment regimens were clinically acceptable. Precision, recall, and F1‐score showed improved performance of fine‐tuned LLMs. Conclusion Fine‐tuned LLMs demonstrated statistically significant improvements over original LLMs upon three clinically important tasks in radiation oncology. This study explored the feasibility of applying fine‐tuned LLMs in radiation oncology, inspiring further development of utilizing LLMs to assist with radiation oncology tasks.