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Multi-center real world analysis of treatment outcomes of non-COVALENT btki, pirtobrutinib, in patients with COVALENT btki relapsed/refracdtory Mantle Cell Lymphoma
Nedved, Adrienne, Chen, Lu , Roua, Arian, Abidoye, Seyi, Choi, Yun Young , Pongas, Georgios , Zhuang, Tony , Clement, Jean, Sears-Smith, Megan , Sun, Laura , Willis, H., Munoz, Javier, Lossos, Izidore S., Wang, Michael , Cohen, Jonathon B. , Martin, Peter , Beaven, Anne , Joseph, Ashlee , Shafagati, Nazila , Rosenlind, Kira, Hill, Brian T. , Wagner‐Johnston, Nina, Kumar, Anita , Lunning, Matthew A. , Pak, Stacy, Danilov, Alexey V. , Lopedote, Paolo , Watkins, Marcus P. , Moustafa, Muhamad Alhaj , Clement, Jean, Willis, Hannah, Kahl, Brad S. , Bond, David A. , Munoz, Javier, Lossos, Izidore S. , Portell, Craig A. , Ghosh, Nilanjan , Abramson, Jeremy S. , Reagan, Patrick Michael , Ermann, Daniel A. , Brooks, Taylor , McLoughlin, Daniel E. , Farooq, Umar , Karmali, Reem , Shree, Tanaya , Wang, Yucai , Phillips, Tycel

Blood | December 4, 2025 | Volume: 146 | Issue: Supplement 1 | Pages: 5351-5351

10.1182/blood-2025-5351 Get It

Abstract Introduction: cBTKi have made a substantial impact in pts w/ MCL, but pts who develop PD while on treatment w/ cBTKi have historically had poor responses to subsequent lines of therapy (LOT). Brexucel, lisocel, and the non-covalent BTKi pirto have been approved in this population. While two RW studies have substantiated the benefit of brexucel, similar data for pirto are lacking. Accordingly, we aimed to evaluate the outcomes of pts treated w/ pirto in the RW setting. Methods: We performed a multi-center retrospective cohort study of adult pts w/ a diagnosis of MCL who experienced PD after any approved cBTKi and subsequently received pirto. Pts who received pirto w/ bridging intent and those who switched to pirto due to intolerance of cBTKi were excluded. The 95% exact binomial confidence intervals (CI) were calculated for overall response rate (ORR) and complete response rate (CRR). Progression-free survival (PFS) and overall survival (OS) were defined as the time from pirto start to PD or death, whichever occurred first, and the time from pirto start to death, respectively, and calculated w/ the Kaplan-Meier method. The log-rank test was used to compare survival curves. A multivariate (MV) Cox proportional hazard model was built to calculate adjusted hazard ratio (aHR). Analyses were performed w/ SAS, version 9.4. Results: Data on 111 pts w/ MCL treated across 21 institutions were collected. Median (mdn) age was 72 (range 33 – 93) years. Twenty-seven pts (24%) were female, 3 (3%) were Asian, 5 (5%) Black, 9 (8%) Hispanic, 86 (77%) White. Among the 53 (48%) pts w/ available MIPI-c at diagnosis, 28 (53%) pts were high-risk. Ki67 at diagnosis was available in 84 (76%) pts: of those, 58 (69%) had a Ki67 ≥30%, and 41 (49%) ≥50%. TP53 mutational status was available in 70 (63%) pts: 33 (47%) had documented TP53 mutation and 10/52 (19%) pts had a disease w/ complex karyotype. Pts had a mdn of 3 (range 1 – 9) prior LOT (n=106). Ninety-one pts (86%) received chemo-immunotherapy (CIT), 16 (15%) autologous stem cell transplant, 19 (18%) venetoclax (ven), 29 (27%) chimeric antigen receptor T-cell, 4 (4%) allogeneic stem cell transplant. For cBTKi, 39 (35%) pts received ibrutinib, 56 (50%) pts acalabrutinib, 33 (30%) pts zanubrutinib, and 17 (15%) pts received more than one cBTKi. The mdn time from the last dose of cBTKi to pirto start was 55 (range 0 – 2587) days, w/ 51 (46%) pts who switched from a cBTKi directly to pirto. Thirty (27%) pts had no response (stable disease or PD as best response) to cBTKi, and 40 (36%) pts had no response to the last LOT. Two (2%) pts received pirto in combination w/ CIT, 8 (7%) w/ an anti-CD20 monoclonal antibody (mAb), 11 (10%) w/ ven ± a mAb, while 86 (77%) pts as single agent. In 104 (94%) pts, pirto was given at full dose, 10 (9%) pts had a subsequent dose reduction. A total of 35 serious (grade ≥3) adverse events (AE) were documented in 27 (24%) pts: 4 (4%) pts had a serious bleeding AE, 5 (5%) pts a serious cardiac AE, 10 (9%) a serious GI AE, and 16 (14%) a serious infectious AE. Ninety-eight (88%) pts were evaluable for response. The ORR was 40% (95% CI 30% – 50%) w/ a CRR of 29% (95% CI 20%– 39%). At the last follow-up, 29 (26%) pts remained on pirto w/ a mdn treatment duration of 15.1 months (range 0.4 – 26). Fifty-eight (52%) pts died, 35 (78%) because of PD. At a mdn follow-up of 15.8 (range 0.4 – 26) months among survivors, the mdn PFS was 4.7 months (95% CI 3.3 – 8.2) and the mdn OS was 13 months (95% CI 9.2 – not available). Using the log-rank test, age (quartile groups), number of LOT (1-2 vs. 3-4 vs. 5+), time from cBTKi to pirto (quartile groups), Ki67≥30%, and high MIPI-C did not result in a statistically significant difference in PFS (p>0.05). Presence of TP53 mutation was associated w/ a non-statistically significant adverse trend in PFS (p=0.0585). No response to cBTKi and Ki67≥50% were associated w/ a statistically significant inferior PFS in log-rank test (p=<0.05), which persisted in a MV Cox regression model (aHR 2.4, 95% CI 1.4-4.2, and aHR 2.6, 95% CI 1.6-4.4, respectively). Conclusions: This is the first RW report on pirto in MCL pts. In this high-risk population, ORR to pirto was comparable to the BRUIN trial, while PFS was significantly worse, especially in pts w/ no response to cBTKi and w/ a high Ki67. However, prolonged remissions were seen in a subset of pts, suggesting that a better characterization of this population w/ durable benefit from pirto is needed.
Evaluation of Interventions for Cognitive Symptoms in Long COVID
Koltai, Deborah, Laskowitz, Daniel T. , Wisnivesky, Juan P., Federman, Alex D. , Silverstein, Adam M. , Lokhnygina, Yuliya, Haddad, Michelle M., Mahncke, Henry W. , Vleet, Tom Van, Huang, Rong , Cox, Wendy C., Terry, Diana, Karwowski, Jeannie, McCray, Netia, Lin, Jenny , Singh, Upinder , Geng, Linda N. , Reece, Rebecca , Moy, James N., Arvanitakis, Zoe , Parthasarathy, Sairam , Patterson, Thomas F., Gupta, Aditi , Parsonnet, Jeffrey, Kiriakopoulos, Elaine T. , Mullington, Janet , Jolley, Sarah E. , Shah, Nirav S. , Morimoto, Sarah Shizuko , Lee‐Iannotti, Joyce , Isache, Carmen, Frontera, Jennifer , O’Steen, Ashley, James, Melissa, Harper, Barrie, Zimmerman, Kanecia O. , Lawler, Andrew , Johnson, Brandon M. , Pitsch, Chloe E. , Feuerriegel, Elen , McKeehan, Jeffrey, Molden, Joie, Reid, Kayleigh M., Dowling, Sarah , Wodushek, Thomas, Canton, Diara, Turut, Dilara, Sheppard, Ethan , Scott‐Sutherland, Jennifer, Shaughnessy, Lynn, Beck, Michelle L. , Heisey, Sabrina, Katragadda, Vaishanvi, Yusuff, Amidat, Perlack, Amy, Merisme, Christie, Krause, David, Schulte, Duncan, Dupre, Fabiola, Sanchez, Gina A. Montealegre , Asupoto, Olanike, Simmons, Quinneil, Schroeder, Ryan W. , Timlin, Shannon, Devis, Ycar, Carter, Della, Caceres, Leomaris Caba, Rodríguez, Miguel Grande, Alvarez, Zariya , Yakubov, Amin , Engelson, Celia, Reyes, Michael, Youkhana, Alvin, Jennings, Cheryl , Naquiallah, Dina, Behun, Dylan , Stephen, Ellen, Timm, Emily , Bilimoria, Farrah, Li, Grace , Stricker, Thomas, Michelle, Kang, Minnie, Suarez, Rebeca Tojo, Carmona, Tania, Ramírez, Ana, Kadubek, Grace, Baker, Aileen, Romo-Serna, Andrea, Predki, Barbara, Leman, Cindy, Panhans, Cristina, Roth, Genevieve E., Rudraraju, Gowrisree , Miller, Hannah , DeLisa, Julie A., Williams, Kat, Woodson, Kimberly S., Jennette, Kyle J. , Villanueva, Laura, Norwick, Maria, Martínez, M. T., Rowley, Matt, Carrithers, Michael D. , Pliskin, Neil H. , Pérez, Nicolás , Durkin, Nicole M., Maniar, Nikita, Beety, Rachel, Daspgupta, Raktima, Ramchandran, Ramaswamy, Jaramillo, R., Hasek, Sharon, Macías, S Aguirre, Schuh, Tina , Ialacci, Sarah, Bailey, Christopher , Labbato, Danielle, Hautmann, Amanda , Smith, Beth, Nath, Dia, Meyer, Jessica , DiFrancesco, Kathryn, Pettinato, Kimberly, Curtis, Lynette, Daher, Joviane , Koberssy, Ziad , Mavar, Caleb, Andrefsky, John, Merheb, Alexander, Hernandez, Carla, Baissary, Jhony , Atieh, Ornina , Pereira, Carol, Carpenter, Deborah, Raynor, Jennifer , Brunton, Kristine, Podgoreanu, Andreea, Bunch, P. C., O’Brien, Sean M. , Dickey, Sheila, McNulty, Steven E., Gajjar, Varsha, Neale, Alec C. , Cohen, Cari, Geary, Elizabeth, Campanella, Franco, Anderson, Kirsten, Prestige, Laura, Guidotti-Breting, Leslie, Biewer, Megan, Morton, Melissa , Mathenia, Nicholas, Adeyemi, Oluwadamilola A. , Hengst, Samanike, Ning, Wei , Vanderloo, Adam, Caplan, Amanda, Chen, July, Acree, Mary Ellen , Nakhate, Mukund, Dan, Zaw Nu, Pk, Switzer, Aryal, Shambhu , Sanchez, Jessica, Sims, Luke, Ferdinand, Rebecca , Mammadova, Mehriban, Grimes, Carolyn Z. , Vidales, Elizabeth, Pham, Hau , Nielsen, Laura, Keller, Maryjane, Uruba, Syeda, Hernandez, Maria, Patel, Bela , Umana-Sanchez, Virginia, Pak, Chong, Gainer, Jeffery, BURR, R, Trapuzzano, Ashleigh, Celso, Brian G. , Bienkowski, Grace, Bowman, Jennifer, Verdecia, Jorge , Zerwekh, Kathryn, Serrano, Liliana , Marotz, Meghan, Propp, Piper, Sarig, Yonatan , Murphy, Annie, Comfort, Brandon, Torres, Iri, Klein, Jeffrey A. , Walker, John, Moore, John J., Tempest-Browning, Lisa, Arain, Mudassar, Kemp, Pamela, Sullivan, Samantha, Way, Sonja, Chevalier, Jessica, Sykes, Carly, Kruck, Carrie L., Sackett, L. W., Bang, Claire, Hynes, Mary, Gallagher, Torrey L., Childers-Kakos, Margaret, Zia, Ahmad, South, AP, Oravets, Anne, Ferreyra, Carolyn, Glaze, Chad, Villanueva, Daphne-Dominique , Blystone, Erica, Pritchard, Jane, Alyward, Laura, Cooper, Melissa, Chidester, Michelle, Alderson, R. Matt , Clark, Renee D., Hodder, Sally , Sarwari, Arif R, Juskowich, Joy, Welch, Shelly, Civil, Alyssa, Bendl, Ashley, Dicker, Elisa, Hanson, Hannah, Viola, Heather, Morinigo, Janice, Pantaleon, Karisma, Marcon, Kathryn, Stone, Kimberly, Stewart, Kiwan, Evo-Ortega, Lorraine, McVeety, Megan, Guliyeva, Sabina , Watson, Eric , Pehel, Shayna, Ko, Tim, Caropolo, Leah, Kocas, H. Deniz , O’Donnell, Andrew, Saperia, Corey , Thai, Daniel, Pathak, Divya , Keller, Jennifer M. , Olszewski, Kelly, Clinton, Kimberly, Cherian, Kirsten , Dutton, L M, Elepano, Ma, Mohapatra, Sukanya , Chen, Ting , Wroolie, Tonita, Leang, Zenita, Shashidhar, Sumana, Nguyen, T L, Ravisankar, Ashwath, Weiden, Brycie, Bolliger, Dawn, Glesne, Heather, Dixon, Jennifer , Chebrolu, Praneeth, Gazali, Zarine, Hurst, Whitney, Fry, Avery, Murthy, Ganesh, Grischo, Garrett, Erickson, Heidi L. , Gomez, Isaias, Copeland, Jacquelynn, Galaz, Judy, Autry, Lynn, DiLise-Russo, Marjorie, Marshell, Nikki, Borwege, Sabine, Vadovicky, Sheila, Marsh, Stephanie, Hartley, William, Murtagh, Linda, Kennedy, Anna, Meltzer-Christensen, Annie, Stringham, Caitlyn, Gaskin, Ella, Judd, Melissa, Côté, Sarah , Mahasenan, Shreya, Bjorklund, Todd, Farner, Cheryl E., Taylor, Barbara S. , Soileau, Bridgette, Cote, Cyrena , Hromas, Gabrielle A., Scholler, Irma, Longoria, Lisa, Heard, Patricia, Jones, Johnnie, Tragus, Robin , Harris, Maya, Pinones, Alexis, Anderson, Alyssa , Hastings, Gabrielyd, Courtright, jessica, Jayakumar, Madhumita, Fischer, M, Solis, Pamela, Harteloo, Alex, Elias-Warren, Anna, Nguyen, Daniel , McDonald, Dylan , Rau, Holly , Mernaugh, Jean, Pagulayan, Kathleen F. , Watanabe, Kino, Huden, Kristen, Peabody, Laurel, Mei, Tiffany, Ogunwuyi, Catherine, Cobbina, Esther, Sankar, Ian, Nguyen, Lisa , Fromm, N., Arnold, R. Douglas, Ogu, Stephanie, Yuan, Ying , Ibrahim, Homam , Szum, Izabella, Patel, Jignesh, Sultan, Sonia, Jazayeri, Yasmin, Anderson, Betty Lise, Muñoz, Isabel , Rhys, Logan, Vane, Ryan, Kim, Jessica , Diaz, Leticia M., Yee, Nathan, Gruhn, Robert, Browngoetz, Sarah, Johnson, Elisha, Chand, Arista , Bottrill, Kenneth , Henderson, Kymberly, Johnson, DeArmond, Richard C., Hughes, Trina, Silverstein, A , Silverstein, Adam P , Frontera, Jennifer , Geng, Linda N. , Knopman, David S. , Becker, Jacqueline , Charvet, Leigh , Pilloni, Giuseppina , Huang, Rong, Cox, Wendy, Karwowski, Jeannie, McComsey, Grace A. , Chu, Helen Y. , Ostrosky‐Zeichner, Luis , Fong, Tamara G. , Killgore, William D. S. , Dwyer, Brigid C. , Stringer, William W. , Krishnan, Jerry A , James, Melissa, Johnson, Brandon, Erlandson, Kristine M. , Dowling, Sarah, Hauser, Kristine , Doyle, Michael, Beck, Michelle, Tran, Anh, Altaras, Caroline , Sanchez, Gina, Krengel, Maxine , Ly, Monica T. , Asupoto, Olanike, Schroeder, Ryan, Rodriguez, Miguel, González, David Andrés , Torres, Jimena, García, Maria Luisa , Carrera, Laura, Rutherford, Melissa, Perez, Nicolas, Jaramillo, Ruthie, Smith, Beth, Walkup, Kevin, Scott, Sarah , Mavar, Caleb B., Carpenter, Deborah, Bunch, Pyper, Brown, Richard, Escobar, Carolina, Campanella, Franco, Mathenia, Nicholas, Glickman, Michael S. , Sims, Luke A., Pham, Hoai Phuang, Lopez, John, Hernandez, Maria, Lin, Matthew Junda , Gainer, Jeffery, Brown, Gina , Klein, Jeffrey, Reed, Jessica, Walker, John, Sackett, Lisa, Kaden, Sarah J. , Childers-Kakos, Margaret G, South, Amy, Blystone, Erica, Cooper, Melissa, Alderson, Rachael, Clark, Renee, Bardes, James M. , Hanson, Hannah, Loizos, Maria , Caropolo, Leah J., Fernandez, Luis, Keller, Jennifer, Lin, Rebecca, Brown, Daniel, Dixon, Jennifer, James, Michelle, Kala, Mrinalini , Smith, Ryan S. , Mahasenan, Shreya, Johnson, Hillary, Anderson, Alyssa, Hernandez, Jessica, Sullivan, Michael, Nguyen, Helen , Logue, Jennifer, Mei, Tiffany, Arnold, Rowan, Szum, Izabella , Hatlen, Timothy J. , Anderson, Betty, Munoz, Isabel, Wright, Matthew, Diaz, Leticia, DeCato, Thomas W. , Ortiz, Ernesto, Hildebrand, Lindsey L. , DeArmond, Richard

JAMA Neurology | November 10, 2025 | Volume: 83 | Issue: 1 | Pages: 49

10.1001/jamaneurol.2025.4415 | PMID 41212544 Get It

Importance Treatment for cognitive dysfunction due to postacute sequelae of long COVID (ie, symptoms of fatigue, malaise, weakness, confusion that persist beyond 12 weeks after an initial COVID infection) remains a significant unmet need. Objective To test evidence-based rehabilitation strategies for improving cognitive symptoms in persons with long COVID. Design, Setting, and Participants This was a 5-arm, multicenter, randomized clinical trial of 3 remotely delivered interventions conducted between August 17, 2023, and June 10, 2024. The study took place at 22 trial sites and included the screening of individuals with cognitive long COVID. Interventions Participants were randomized to 1 of 5 arms: adaptive computerized cognitive training (BrainHQ [Posit Science]), cognitive-behavioral rehabilitation involving both group and individual counseling sessions (PASC-Cognitive Recovery [PASC-CoRE]) paired with BrainHQ, and transcranial direct current stimulation (tDCS) paired with BrainHQ. Two comparator arms were included as follows: unstructured computer puzzles and games (active comparator) and sham tDCS paired with BrainHQ. The interventions occurred 5 times per week over 10 weeks. Main Outcomes and Measures Cognitive and behavioral in-person assessments were performed at baseline, midintervention, at the end of intervention, and 3 months after the end of the intervention. The primary outcome measure was the modified Everyday Cognition Scale 2 (ECog2) completed at the end of the intervention compared to the baseline visit based on participant self-report looking back over the prior 7 days. Results A total of 378 individuals were screened, from which there were 328 participants (median [IQR] age, 48.0 [37.0-58.0] years; 241 female [73.5%]; race: 15 Asian [4.6%], 47 Black [14.3%], and 235 White [71.6%]; ethnicity: 52 Hispanic [15.9%]). None of the 3 active interventions demonstrated benefits on the modified ECog2 in the intention-to-treat population by the end of the intervention period. The adjusted differences in mean change were 0.0 (95% CI, −0.2 to 0.2) for BrainHQ vs active comparator, 0.1 (95% CI, −0.1 to 0.3) for PASC-CoRE + BrainHQ vs active comparator, 0.0 (95% CI, −0.2 to 0.2) for tDCS-active + BrainHQ vs tDCS-sham + BrainHQ, and 0.1 (95% CI, −0.1 to 0.3) for PASC-CoRE + BrainHQ vs BrainHQ alone. Secondary participant-reported outcomes and neuropsychological tests showed no differential benefits for any treatment arm. All 5 arms demonstrated some improvements over time on the modified ECog2 and on secondary outcomes. There were no serious adverse events attributable to the interventions. Conclusions and Relevance This phase 2 randomized clinical trial failed to demonstrate differential benefits for online cognitive training, a structured cognitive rehabilitation program, and tDCS for cognitive long COVID. Trial Registration ClinicalTrials.gov Identifier: NCT05965739 .
Identification of a distinctive gene signature associated with disease activity in granulomatous myositis
Ruffer, Nikolas , Pak, Katherine, Preuße, Corinna , Kirou, Raphael A. , Kinder, Travis B. , Orso, Stefania Del, Naz, Faiza , Islam, Shamima , Gutierrez-Cruz, Gustavo, Milone, Margherita , Selva-O’Callaghan, Albert , Mammen, Andrew L. , Kleefeld, Felix , Schneider, Udo , Liewluck, T , Selva O'Callaghan, Albert , Pinal‐Fernandez, Iago , Casal-Domínguez, María , Milisenda, Jose César , Liewluck, Teerin , Stenzel, Werner

Rheumatology | October 17, 2025 | Volume: 65 | Issue: 1 | Pages: No page available

10.1093/rheumatology/keaf543 | PMID 41132132 Get It

Abstract Objectives Granulomatous myositis (GM) is defined by focal collections of activated macrophages that fuse to form multinucleated cells that aggregate into granulomas within skeletal muscle. This study aimed to elucidate the pathophysiology of GM by defining its specific transcriptomic profile. Methods Bulk RNA sequencing was performed on 722 muscle biopsies, including 38 from patients with GM, other myopathies and healthy comparators. Spatial transcriptomics and immunohistochemistry assays were used to identify the location of specific transcripts and proteins within the tissue. Results We conducted a systematic analysis of the inflammatory pathways implicated in GM. This analysis revealed that GM muscle biopsies are characterized by elevated levels of IFNγ, IFNγ-inducible genes and proinflammatory cytokine genes including IL1B, TNF and TGFB1. Additionally, we identified a specific gene signature in GM, comprising 1293 genes that were significantly upregulated and 256 significantly downregulated. These genes were strongly correlated with transcriptomic markers of disease activity. Using this gene signature, support vector machine models accurately identified GM, achieving an area under the curve of 99.6% (99.0–99.9%) and an accuracy of 98.6% (98.2–98.9%). Among the identified genes, CHIT1 emerged as the most significantly upregulated gene, with its expression strongly correlating with disease severity. Furthermore, CHIT1 was detected at both the RNA and protein levels in granulomas and giant cells. Conclusion GM is transcriptomically characterized by a strong IFNγ signature and overexpression of proinflammatory cytokines, including IL1B, TNF and TGFB1. Additionally, it exhibits a unique transcriptomic profile, including CHIT1, which correlates with disease activity.
Same-Day Hospital Discharge Is Feasible for a Variety of Urologic Surgeries When Using a Virtual Hybrid Care Hotel
Hampton, Sarah, Perry, Alan G., Kumar, Neal, Hochwald, Alex, Ma, R. , Pak, Raymond W., Petrou, Steven P. , Thiel, David D. , Nazario, Maura, Pathak, Ram A. , Hochwald, Alex P, Ma, Renny, Dora, Chandler , Ma, Renny, Broderick, Gregory A. , Lyon, Timothy D. , Chadha, Ryan

Urology Practice | July 25, 2025 | Volume: 12 | Issue: 6 | Pages: 683-690

10.1097/upj.0000000000000879 | PMID 40709932 Get It

Transferable Variational Feedback Network for Vendor and Sequence Generalization in Accelerated MRI
Paul, Riti, Vora, Sahil , Ding, Pak Lun Kevin, Patel, Ameet, Hu, Leland , Li, Baojie , Zhou, Yuxiang, Patel, Ameet C. , Li, Baoxin, Zhou, Yuxiang

Big Data Mining and Analytics | July 14, 2025 | Volume: 8 | Issue: 5 | Pages: 1092-1111

10.26599/bdma.2025.9020013 Get It

Distinct cytokine and cytokine receptor expression patterns characterize different forms of myositis
Kirou, Raphael A. , Pak, Katherine, Preuße, Corinna , Dari, Dilbe, Orso, Stefania Del, Naz, Faiza , Islam, Shamima, Gutierrez-Cruz, Gustavo, Stenzel, Werner , Selva-O’Callaghan, Albert, Mammen, Andrew L. , Selva-O’Callaghan, Albert , Naddaf, Elie , Liewluck, T , Selva O'Callaghan, Albert , Stenzel, Werner , Pinal‐Fernandez, Iago , Casal-Domínguez, María , Naddaf, Elie , Liewluck, Teerin , Milisenda, Jose César

Rheumatology | June 19, 2025 | Volume: 64 | Issue: 11 | Pages: 5751-5760

10.1093/rheumatology/keaf346 | PMID 40579223 Get It

Abstract Objective Myositis is a heterogeneous family of inflammatory myopathies. We sought to define the differential expression of cytokines, cytokine receptors and immune checkpoint genes in muscle biopsies from patients with different forms of myositis in order to characterize patterns of inflammation in each. Methods Bulk RNA-sequencing was performed on muscle biopsy samples from 669 patients, including 105 with DM, 80 with immune-mediated necrotizing myopathy (IMNM), 65 with anti-synthetase syndrome, 53 with IBM, 19 with anti-PM/Scl myositis, 310 with other inflammatory or genetic myopathies and 37 controls with normal tissue (NT). Myositis clinical groups and autoantibody subgroups were analysed separately. Expression data were analysed for 338 genes encoding cytokines, cytokine receptors and immune checkpoints. Myositis group-specific genes were identified from this list by finding genes that were specifically differentially expressed in one group compared with all samples and compared with NT (α < 0.001). Results IBM patients had the most differentially overexpressed genes (71) among all clinical groups, including 37 that were IBM-specific. Among the top genes were several involved in type 1 inflammation, including CCL5, CXCR3, CCR5, CXCL9 and IFNG. Anti-Jo1 and anti-PM/Scl patients exhibited differential overexpression of a similar set of genes, while DM patients exhibited differential overexpression of a different set of genes involved in type 1 inflammation. IMNM patients had the least number of differentially overexpressed genes with no predominant inflammatory pattern. Conclusion Each myositis clinical group and autoantibody subgroup had differentially overexpressed inflammatory mediators, including a strong type 1 inflammatory gene signature in IBM.
Peritoneal Window Technique: A Novel Sutureless Technique to Decrease the Rate of Symptomatic Lymphocele after Robot-Assisted Radical Prostatectomy with Extended Pelvic Lymph Node Dissection
Qualkenbush, Eric , Lee, Yeonsoo Sara , Kahn, Amanda E. , Qosja, Neda, Hochwald, Alex, Zganjar, Andrew , Pathak, Ram , Pak, Raymond , Qualkenbush, Eric , Pak, Raymond W. , Lee, Yeonsoo S, Kahn, Amanda , Sara Lee, Yeonsoo , Pak, Ray , Myers, Amanda A.

Journal of Endourology | June 10, 2025 | Volume: 39 | Issue: 10 | Pages: 1028-1032

10.1089/end.2025.0093 | PMID 40491420 Get It

Introduction: Robot-assisted radical prostatectomy (RARP) with extended pelvic lymph node dissection (PLND) represents the gold standard surgical treatment for high-risk localized prostate cancer. PLND carries the risk of lymphocele formation, which can lead to complications such as deep vein thrombosis (DVT), infection, and voiding dysfunction. Many techniques have been described to minimize lymphocele formation, yet no method has seen widespread use or been described as sutureless. The objective of this study is to describe the novel, sutureless peritoneal window (PWIN) technique and assess its efficacy in lymphocele prophylaxis for extended PLND. Methods: This was a retrospective review of patients with high-risk prostate cancer undergoing RARP with extended PLND by a single surgeon from 2018 to 2024. PLND was carried out in the usual manner with bipolar cautery. PWIN technique is performed by excising a 3 × 5 cm PWIN over the lymphadenectomy bed to maintain lymphatic communication with the abdominal cavity. The primary outcome was a comparison between the incidence of symptomatic lymphoceles (SLs) with PWIN vs standard technique. SLs were defined as lymphoceles presenting with pain, DVT, voiding dysfunction, or infectious symptoms. Patients with asymptomatic lymphoceles were excluded. Results: Our study included 199 patients who underwent RARP with extended PLND. There were 46 patients (23.1%) in the standard approach group and 153 patients (76.9%) in the PWIN group. We identified 5/46 (10.9%) SLs in the standard technique, which were significantly higher than the 3/153 (2.0%) observed in the PWIN group (p = 0.017). There was no difference between the standard and PWIN cohorts regarding symptomology or treatment of their lymphocele. Conclusion: This study describes a novel, sutureless PWIN technique that significantly decreases SL after extended PLND. This technique offers a practical, time-efficient, and cost-effective alternative for lymphocele prevention. Future prospective studies are warranted to validate these findings.
MEDL: Unsupervised Multi-Stage Ensemble Deep Learning With Diffusion Models for Denoising MRI Scans
Vora, Sahil , Paul, Riti, Ding, Pak Lun Kevin, Patel, Ameet, Hu, Leland , Zhou, Yuxiang, Li, Baoxin , Patel, Ameet C. , Sharpe, Richard E. , Zhou, Yuxiang

2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI) | May 12, 2025 | Volume: No volume available | Issue: No issue available | Pages: 1-5

10.1109/isbi60581.2025.10981026 Get It

Artificial intelligence–derived electrocardiographic aging and risk of atrial fibrillation: a multi-national study
Cho, Seunghoon , Eom, Sujeong , Kim, Daehoon , Kim, Tae‐Hoon , Uhm, Jae‐Sun , Pak, Hui‐Nam , Lee, Moon‐Hyoung , Yang, Pil‐Sung , Lee, Eunjung , Attia, Zachi I. , Friedman, Paul A. , You, Seng Chan , Yu, Hee Tae , Joung, Boyoung , Attia, Zachi I , Friedman, Paul A , Joung, Boyoung

European Heart Journal | December 3, 2024 | Volume: 46 | Issue: 9 | Pages: 839-852

10.1093/eurheartj/ehae790 | PMID 39626169 Get It

Abstract Background and Aims Artificial intelligence (AI) algorithms in 12-lead electrocardiogram (ECG) provides promising age prediction methods. This study investigated whether the discrepancy between ECG-derived AI-predicted age (AI-ECG age) and chronological age, termed electrocardiographic aging (ECG aging), is associated with atrial fibrillation (AF) risk. Methods An AI-ECG age prediction model was developed using a large-scale dataset (1 533 042 ECGs from 689 639 participants) and validated with six independent and multi-national datasets (737 133 ECGs from 330 794 participants). The AI-ECG age gap was calculated across two South Korean cohorts [mean (standard deviation) follow-up: 4.1 (4.3) years for 111 483 participants and 6.1 (3.8) years for 37 517 participants], one UK cohort [3.0 (1.6) years; 40 973 participants], and one US cohort [12.9 (8.6) years; 90 639 participants]. Participants were classified into two groups: normal group (age gap < 7 years) and ECG-aged group (age gap ≥ 7 years). The predictive capability of ECG aging for new- and early-onset AF risk was assessed. Results The mean AI-ECG ages were 51.9 (16.2), 47.4 (12.5), 68.4 (7.8), and 56.7 (14.6) years with age gaps of .0 (6.8), −.1 (6.0), 4.7 (8.7), and −1.4 (8.9) years in the two South Korean, UK, and US cohorts, respectively. In the ECG-aged group, increased risks of new-onset AF were observed with hazard ratios (95% confidence intervals) of 2.50 (2.24–2.78), 1.89 (1.46–2.43), 1.90 (1.55–2.33), and 1.76 (1.67–1.86) in the two South Korean, UK, and US cohorts, respectively. For early-onset AF, odds ratios were 2.89 (2.47–3.37), 1.94 (1.39–2.70), 1.58 (1.06–2.35), and 1.79 (1.62–1.97) in these cohorts compared with the normal group. Conclusions The AI-derived ECG aging was associated with the risk of new- and early-onset AF, suggesting its potential utility to identify individuals for AF prevention across diverse populations.