Citations

Abstract Chronic widespread pain (CWP) remains challenging due to its heterogeneous causes and complex mechanisms. A total of 2920 plasma proteins are analyzed from 29,254 UK Biobank participants. A total of 256 proteins are identified as cross‐sectionally correlated with CWP. A simple (top 10 proteins) and comprehensive (all significant proteins) proteomic‐based score (ProtS) is created for CWP diagnosis, both outperforming and improving the existing clinical score (area under the curve, AUC: 0.801, 0.723, and 0.791 alone, and 0.856 and 0.880 in combination). In addition, the protein score predicted 13‐years risk of pain‐related traits over the body, including pain onset, progression, and intensity; Moreover, it has stronger associations with nociplastic pain and fibromyalgia compared to nociceptive and neuropathic pain, implying a unique protein signature of different pain mechanisms. Finally, among 434 candidate proteins prioritized in the observational analysis, 18 are corroborated with causal relevance by Mendelian randomization, and importantly, four (CA14, DPEP1, LGALS3, and TNF) showed potential as novel drug targets repurposed for treating CWP.

Background and Aims: Cholangiocarcinoma (CCA) is one of the most lethal cancers, characterized by molecular heterogeneity and treatment resistance. To uncover new biological signals and therapeutic opportunities, we employed multiomic characterization combined with machine learning. Approach and Results: We profiled all anatomical CCA subtypes using whole exome sequencing, mRNA sequencing, and proteome/phosphoproteome analysis. Integrative dimensional reduction revealed RNA, protein, and phosphoprotein features driving tumor heterogeneity, enabling clustering. Machine learning algorithms identified molecular features for each cluster and mapped external datasets and patient-derived xenograft (PDX) models onto these clusters. Kinase enrichment analysis highlighted targetable kinases active in each cluster. In vivo validation was performed in cluster-specific PDX models using the selective TNK1 inhibitor, TP-5801. We identified 3 molecular clusters with distinct pathway characterization: immunomodulatory (cluster 1), metabolic (cluster 2), and gene regulation/cellular fate (cluster 3). Cluster assignment was independent of anatomic subtype but correlated with overall survival following curative-intent resection. We also identified multiomic features and pathways linked to overall survival and lymph node metastases, crucial for patient treatment selection. Kinase enrichment analysis pinpointed TNK1 as a highly active kinase in the metabolic cluster. Treatment with TP-5801 significantly reduced tumor growth in a metabolic PDX model, but not in models representing the other clusters. Combining internal data with publicly available datasets, we identified the immunomodulatory cluster as most responsive to gemcitabine/cisplatin therapy, confirmed in vivo using cluster-specific PDX models. Conclusions: Integrated multiomic characterization provides translational insights by defining unique molecular subtypes associated both with therapeutic response and overall clinical outcomes. This approach identified TNK1 as a previously unrecognized therapeutic target in a defined subset of CCA tumors.

Abstract Background Unresectable hepatocellular carcinoma (uHCC) is a leading cause of cancer death. FDA-approved first-line systemic therapies include atezolizumab/bevacizumab (atezo/bev) and durvalumab/tremelimumab (durva/treme); however, there is a lack of comparative data. Methods We reviewed outcomes of patients with uHCC who initiated atezo/bev or durva/treme between 2017 and 2024, across six institutions. Overall survival (OS) and time to treatment discontinuation (TTD) were analyzed using the Kaplan–Meier and Cox models, adjusting for baseline characteristics. Results Four hundred fifty-two uHCC pts were included. Median age: 68 years; 77% male; 81% white. Most common etiologies were viral hepatitis (38.9%) and metabolic dysfunction-associated steatohepatitis (19.5%). Disease progression was the primary reason for treatment discontinuation, atezo/bev (56%) and durva/treme (42%). Outcomes were not statistically significant (median OS [month, m]: 14.0 vs 14.6 [P = .66]; median TTD [m]: 4.9 vs 3.9 [P = .42] for atezo/bev vs durva/treme). Outcomes were significantly different between Child-Pugh classes (CP: A, B7, B8/9, C) respectively, median OS(m): 19.0, 6.1, 5.1, 2.0 (P < .001); median TTD(m): 6.1, 2.3, 3.0, 1.3 (P < .001). Conclusions In this real-world study of uHCC, no significant difference in clinical outcomes was observed between atezo/bev and durva/treme in the first-line setting. CP scores were a key prognostic variable with both regimens.

ABSTRACT Traumatic brain injury (TBI), particularly among military personnel, poses significant challenges in acute management and long-term outcomes. Difficult-to-diagnose autoregulatory and inflammatory changes lead to neurocognitive impairment, anxiety, sleep disturbance, and chronic pain. To optimize readiness and test therapeutics for both diagnostic and future TBI treatments, this feasibility study introduces a novel wearable multimodal platform that integrates photoplethysmography and infra-red (IR) sensing from a camera and a wearable sensor to detect vital signs and then leverages machine learning (ML) to predict the need for acute treatment. This prospective observational study involving 28 healthy volunteers simulated a triage emergency room workflow to test sensor accuracy and mobile app functionality. The wearable device demonstrated high accuracy in pulse rate (mean error 7.3%) and oxygen saturation (mean error −1.2%) compared to standard-of-care devices. Camera-based pulse and respiratory rate, and wearable pulse, oxygen, and temperature sensing were tested in a mock triage setting to simulate activation of light-based treatment upon detection of physiological changes of concern after acute TBI. Preliminary ML models to predict hypotension and pain based on retrospective data achieved 98.4% and 93% accuracy but showed limitations in prospective real-time applications. This study provided insights to prepare for future iterations that will integrate near-IR spectroscopy to detect cerebral oximetry, and quantum dot light-emitting diodes to activate light-based treatment (photobiomodulation) from within a wearable head dressing. The findings underscore the potential of this platform to bridge critical gaps in acute TBI care through early detection and future therapeutic intervention. Further development is needed to address environmental challenges, improve accuracy across diverse populations, and refine the device for real-time clinical use. This innovative approach has the potential to revolutionize TBI care by bridging current gaps in diagnostic and therapeutic management.

Background Endovascular thrombectomy (EVT) has transformed acute ischemic stroke (AIS) care, with onset-to-puncture (OTP) time widely recognized as a critical determinant of outcome. However, emerging evidence suggests that in-hospital procedure time (PT)—from arterial puncture to final recanalization—may have an equally or more significant impact. This study examines the relative contribution of PT versus OTP to functional outcomes in patients with AIS undergoing EVT. Methods A retrospective analysis was conducted of 6644 patients with AIS treated at 44 international stroke centers from the Stroke Thrombectomy and Aneurysm Registry (STAR; 2016–2023). Multivariable regression, time-equivalence analysis, and marginal effects modeling were used to assess associations between PT, OTP, and 90-day modified Rankin Scale (mRS) outcomes. Centers were stratified by procedural efficiency and compared using propensity score matching (PSM). Mediation analysis evaluated whether PT accounted for inter-center differences. Results PT and OTP were independently associated with functional outcomes; however, PT had a significantly stronger effect (adjusted OR for mRS 0–2: PT=0.56 vs OTP=0.96 per hour). Each 5 min increase in PT was equivalent to 78–100 min of additional OTP in outcome impact. Centers with faster average PT had higher rates of functional independence (number needed to treat (NNT)=10), fewer complications, and lower symptomatic intracranial hemorrhage rates. PT significantly mediated the relationship between center tier and outcomes (Sobel’s P<0.001). Conclusion While minimizing OTP remains important, PT exerts a greater influence on outcomes after EVT. Procedural efficiency should be emphasized in stroke systems of care and included in center performance metrics to improve patient outcomes.