Citations

Abstract An individual participant data (IPD) meta-analysis can assess the predictive value of data on outcomes at the individual level, offering a potential tool for developing personalized pain management. Pretreatment quantitative sensory testing (QST) may stratify patient groups, which are then linked to treatment outcomes. Our objective was to determine if measures of QST at baseline are related to treatment outcomes (at any time point) for pain and disability in lower-limb osteoarthritis. We performed a systematic review with an IPD meta-analysis. Searches were conducted in 9 databases until May 5, 2023 for intervention studies that measured baseline QST and longitudinal measures of participant-reported pain and disability. We performed a 2-stage approach to analyse longitudinal data. Individual models were fitted to each study and combined using random effects multivariate meta-analytic models. Study quality was assessed using the Joanna Briggs Institute checklist, and certainty of the evidence was assessed using GRADE. We identified 3082 records and included 1 hip and 28 knee datasets consisting of 2522 participants from 40 studies. Local warm detection thresholds (P = 0.024) predicted knee osteoarthritis pain outcomes (very-low certainty). Local warm detection thresholds (P = 0.030), remote cold detection thresholds (P = 0.05), and remote pressure tolerance thresholds (P = 0.007) predicted knee osteoarthritis disability outcomes (very-low certainty). Other QST variables were associated with hip and knee osteoarthritis pain and disability levels (eg, pressure pain thresholds), but this relationship did not change over time. This review finds that mechanism-based, QST methodologies do not consistently predict pain or disability on an individual level in hip or knee osteoarthritis.

Abstract Background: Cholangiocarcinoma (CCA) is a diverse type of cancer. For several years, platinum-based chemotherapy (Gemcitabine and Cisplatin) has been the standard treatment and serves as the foundation for current immunotherapy combinations. The typical response rates are generally between 20% and 30%. In this study, we present the results of preclinical efficacy trials conducted on CCA patient-derived xenograft (PDX) models, correlating them with the baseline multiomics profiling of these models to identify molecular characteristics linked to treatment response. Methods: Tumor samples from twenty-eight CCA PDX were used to isolate RNA and protein to be included in multiomics analysis (Phosphoproteomics, Proteomics, and Transcriptomics). A subset of PDX models were treated with various doses of Gemcitabine and Cisplatin. The Median Efficacy Index (MEI) was calculated as the ratio of the difference in median of delta in tumor volume at the last day of the study between the treatment and the control arms divided by the median of delta in tumor volume of the control at the last day of the treatment (positive= growth, negative=inhibition, and 0=no efficacy). A Hierarchical All-Against-All algorithm was utilized to compare multiomics features to the MEI and pathway analysis completed for identified molecular features. Pathway enrichment was calculated using the KEGG database, and random-forest variable selection chose the pathways most influential in predicting sensitivity or resistance. Results: After conducting preclinical efficacy trials, the 11 tested CCA PDX models were classified into sensitive, moderately sensitive, and resistant models based on the MEI on each treatment arm. The top up-regulated genes were identified for Gemcitabine response (SRPK2, GLK2, and RIOK2) and Cisplatin response (NTRK1, PRPF4B, and SRPK2). The top up-regulated genes related to Gemcitabine resistance were NTRK1, PIP5K1C, and CSNK2A2, while those related to Cisplatin resistance were SRPK1, PRPF4B, and CSNK2A1. Downregulation of the anti-folate resistance pathway is a strong predictor of gemcitabine sensitivity, and the lysine degradation pathway is a strong predictor of cisplatin sensitivity. Conclusion: Multiomics signatures of cholangiocarcinoma can forecast responses to standard chemotherapy in preclinical models. New markers of resistance to Gemcitabine and Cisplatin were discovered, linked to NTRK1, SRPK1, and SRPK2. These resistance profiles will be tested using suitable targeted therapies on the resistant CCA patient-derived xenograft (PDX) models. Citation Format: Amro M. Abdelrahman, Danielle M. Carlson, Ellen L. Larson, Erik Jessen, Dong-Gi Mun, Isaac Lynch, Alessandro Fogliati, Stella Konadu Adjei Antwi, Aushinie Abeynayake, Akhilesh Pandey, Mark J. Truty, Gregory J. Gores, Rory L. Smoot. Baseline multiomics and chemotherapy efficacy in preclinical trials using patient-derived xenograft models of cholangiocarcinoma can reveal distinct signatures of chemotherapy response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6762.

Abstract Chimeric antigen receptor T (CART) cell therapy has shown clinical successes in B cell hematological malignancies. Two B cell maturation antigen (BCMA)-directed CART cell therapies treating multiple myeloma (MM) were approved by FDA but most patients relapse within 1-2 years. Ex vivo-expanded antigen specific T cells as cancer treatment rely on TCR-mediated killing of tumors. We hypothesized that arming ex vivo enriched tumor-specific T cells with CARs targeting a different antigen improves T cell functions. First, we expanded mucin 1 (MUC1)-activated T cells from patients with MM through ex vivo stimulation by MUC1 peptides-loaded autologous PBMCs for 19 days. Then, we engineered MUC1-activated T cells with 4-1BB-co-stimulated BCMA CAR to generate MUC1 TCR+BCMA CAR+ T cells. Baseline T cells without MUC1 stimulation were engineered with BCMA CAR as control. Baseline T cells, MUC1 T cells, baseline BCMA CART cells, or MUC1 BCMA CART cells were cocultured with luc+ RPMI-8226, a MUC1+BCMA+ MM cell line, for 3 days. MUC1 BCMA CART cells showed stronger tumor killing than BCMA CART or MUC1 T cells in all 3 patients (p<0.0001). MUC1 BCMA CART showed improved proliferation than BCMA CART or MUC1 T cells (p=0.0765, p<0.0001, respectively) after a 5-day stimulation by RPMI-8226. To exclude possibility of metabolic alterations by 19-day PBMC incubation leading to better T cell functions in MUC1 BCMA CART, baseline and MUC1-stimulated T cells from 3 healthy donors were processed for the same assays. Day-19 BCMA CART cells showed inferior tumor killing compared to baseline BCMA CART in all 3 donors (p<0.0001). Lower T cell proliferation was also observed in day-19 BCMA CART (p=0.0838). Our data indicate that improved T cell functions in MUC1 BCMA CART were unlikely due to metabolic changes and required stimulation by tumor antigens prior to CAR engineering likely through MUC1 TCR. Additionally, MUC1 T cells, BCMA CART, or MUC1 BCMA CART cells from tested patients were stimulated by RPMI-8226 for bulk RNA sequencing. Both BCMA CART and MUC1 T cells had distinct transcriptomic features from MUC1 BCMA CART cells. Differential gene expression analysis showed upregulation of T cell function-associated genes (RYR2, ITGA1, GNLY, GZMA, etc.) in MUC1 BCMA CART vs BCMA CART. Enrichment of T cell function-associated pathways was also observed in MUC1 BCMA CART. Analysis between MUC1 T cell and MUC1 BCMA CART showed upregulation of T cell function-associated genes (CSF2, CRTAM, IFNG, GZMB, etc.) and enrichment of immune response pathways in MUC1 BCMA CART. Our findings suggest that equipping ex vivo-enriched tumor-specific T cells with CARs targeting a different antigen improves T cell functions in vitro. Importantly, this provides a rationale for a therapeutic regimen where patients receive ex vivo-enriched tumor-induced T cells followed by CAR engineering. Persistence of the engineered T cells is being investigated Citation Format: Kun Yun, R. Leo Sakemura, Ismail Can, Omar Gutierrez Ruiz, Ekene J. Ogbodo, Truc N. Huynh, Claudia Manriquez Roman, Olivia Sirpilla, Carli M. Stewart, Jennifer M. Feigin, Grace E. DeFranco, Sophia Y. Goldberg, Dominic A. Skeele, Mariam Stein, Dara Missan, Latha Pathangey, Long Mai, Hong Xia, Brooke Kimball, Lionel A. Kankeu Fonkoua, Mehrdad Hefazi, Michael W. Ruff, Elizabeth L. Siegler, Leif Bergsagel, Michael Gustafson, Sandra Gendler, Saad S. Kenderian. BCMA CAR engineering arms T cells with superior functions post ex vivo stimulation by MUC1 peptide-loaded autologous PBMCs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6103.

Artificial intelligence (AI) has emerged as a promising adjunct in the diagnosis and management of infective endocarditis (IE), a disease characterized by diagnostic complexity and significant morbidity. Machine learning (ML) models such as SABIER and SYSUPMIE have demonstrated strong predictive accuracy for early IE diagnosis, embolic risk stratification, and postoperative mortality, surpassing traditional clinical scoring systems. In imaging, AI-enhanced echocardiography and advanced modalities like FDG-PET/CT offer improved sensitivity, specificity, and reduced inter-observer variability, potentially transforming clinical decision making. Additionally, AI-powered microbiological techniques, including MALDI-TOF mass spectrometry combined with ML and neural network-based metagenomic classifiers, show promise in rapidly identifying pathogens and predicting antimicrobial resistance. Despite encouraging early results, widespread adoption faces barriers, including data limitations, interpretability issues, ethical concerns, and the need for robust validation. Future directions include leveraging generative AI as clinical consultative tools, provided their capabilities and limitations are carefully managed. Ultimately, collaborative efforts addressing these challenges could transform IE care, enhancing diagnostic accuracy, clinical outcomes, and patient safety.

We report the case of a man in his 50s who presented with pleural and pericardial effusions, chylous ascites, lymphadenopathy, weight loss and fatigue. Extensive evaluation ruled out malignancy, infection and many autoimmune conditions, but test results were positive for antinuclear antibodies and anti–double-stranded DNA antibodies. A kidney biopsy showed evidence of lupus nephritis (class 5). He ultimately was diagnosed with systemic lupus erythematosus and lupus nephritis. The patient began immunosuppressive treatment, and the manifestations started to resolve. Recognition of systemic lupus erythematosus as the cause of chylous ascites was essential for identifying effective therapy.

Objective: Postoperative nausea and vomiting (PONV) is a frequent adverse event after bariatric surgery and is associated with patient dissatisfaction and increased health care burden. Aggressive multimodal antiemetic prophylaxis and the use of propofol infusion during anesthesia are associated with the reduction of PONV. In this study, we examined the use of rescue antiemetics following bariatric surgery performed with 3 different anesthetic strategies designed to reduce PONV: (1) primary volatile (VOL) anesthetic and propofol (PROP) infusion (VOL+PROP), (2) volatile anesthetic with propofol and dexmedetomidine (DEX) infusions (VOL+PROP+DEX), or (3) opioid-sparing total intravenous anesthesia (PROP+DEX). Methods: In this retrospective observational study, we included patients undergoing bariatric surgery from 2018-2022 who received 1 of 3 anesthetics: (1) VOL+PROP, (2) VOL+PROP+DEX, or (3) opioid-sparing PROP+DEX without a VOL. Inverse probability of treatment weighting analysis determined the association between the need for rescue-antiemetics in the postanesthesia care unit (PACU) and following PACU discharge. Results: Three hundred thirty-two patients received VOL+PROP, 354 VOL+PROP+DEX, and 166 PROP+DEX, and all received prophylactic antiemetics during surgery. After surgery, the PROP+DEX patients received fewer rescue antiemetics in the PACU compared with VOL+PROP (11% vs. 24%, P=0.002), and VOL+PROP+DEX fewer compared with VOL+PROP (16% vs. 24%, P=0.023). This differential antinausea effect was limited to PACU stay only. Rescue antiemetic use increased across all anesthetic management groups following PACU discharge until midnight on the day of surgery (ranging from 38% to 46% across groups, P=0.71) and through the first postoperative day (ranging from 47% to 57% across groups, P=0.20). Conclusions: The benefit associated with anesthetic strategies designed to reduce PONV was present but did not persist past PACU discharge. This finding suggests that aggressive perioperative multimodal antiemetic prophylaxis combined with anesthetic strategies designed to prevent PONV after bariatric surgery have only a short-lived effect, thus health care staff in hospital wards may expect to encounter high rates of PONV in these patients. There is a need for the development of novel antinausea treatments to reduce the rate of this frequent postoperative complication.