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Cross-cohort analysis of expression and splicing quantitative trait loci in TOPMed
Blackwell, Thomas W., Kachuri, Linda , Castaldi, Peter J. , Cho, Michael H. , Christenson, Stephanie A. , Durda, Peter , Gabriel, Stacey , Hersh, Craig P. , Huntsman, Scott , Hwang, S.-J., Joehanes, Roby, Johnson, Mari , Li, Xingnan , Lin, Honghuang , Liu, Ching‐Ti , Liu, Yongmei , Mak, Angel C. Y. , Manichaikul, Ani , Paik, David T. , Saferali, Aabida , Smith, Joshua D. , Taylor, Kent D. , Wang, Jiongming , Weinstock, Joshua S. , Weiss, Jeffrey, Wheeler, Heather E. , Zhou, Ying , Zoellner, Sebastian, Mestroni, Luisa , Graw, Sharon , Taylor, Matthew R.G. , Ortega, Victor E. , Johnson, Craig , Gan, Weiniu , Abecasis, Gonçalo , Nickerson, Deborah A. , Ardlie, Kristin G., Woodruff, Prescott G. , Bowler, Russell P. , Meyers, Deborah A. , Reiner, Alex, Kooperberg, Charles , Ziv, Elad , Vasan, Ramachandran S. , Silverman, Edwin K. , Rich, Stephen S., Heard‐Costa, Nancy L. , Tang, Hua, Rotter, Jerome I. , Smith, Albert V. , Aguet, François, Scott, Laura J. , Raffield, Laura M. , Parker, Stephen C.J. , Abe, Namiko, Almasy, Laura, Ament, Seth, Anugu, Pramod, Auer, Paul, Avramopoulos, Dimitrios, Balasubramanian, Adithya, Barr, R. Graham, Barwick, Lucas, Beaty, Terri, Becker, Diane, Becker, Lewis, Beitelshees, Amber, Benos, Takis, Bezerra, Marcos, Bis, Joshua, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Bunting, Karen, Buth, Erin, Carey, Vincent, Carty, Cara, Casaburi, Richard, Chaffin, Mark, Chang, Christy, Chang, Yi-Cheng, Chavan, Sameer, Chen, Bo-Juen, Chen, Wei-Min, Choi, Seung Hoan, Chuang, Lee-Ming, Chung, Ren-Hua, Conomos, Matthew, Cornell, Elaine, Crandall, Carolyn, Crapo, James, Curtis, Jeffrey, Damcott, Coleen, David, Sean, Fuentes, Lisa de las, Vries, Paul de, Deka, Ranjan, DeMeo, Dawn, Devine, Scott, Dinh, Huyen, Doddapaneni, Harsha, Duan, Qing, Duggirala, Ravi, Eaton, Charles, Ekunwe, Lynette, Boueiz, Adel El, Emery, Leslie, Farber, Charles, Farek, Jesse, Franceschini, Nora, Frazar, Chris, Fu, Mao, Fullerton, Stephanie M., Fulton, Lucinda, Gao, Shanshan, Gao, Yan, Gass, Margery, Geiger, Heather, Ghosh, Auyon, Gignoux, Chris, Glahn, David, Gogarten, Stephanie, Gong, Da-Wei, Goring, Harald, Grine, Daniel, Gu, C. Charles, Guan, Yue, Hall, Michael, Han, Yi, Harris, Daniel, Heavner, Ben, Herrington, David, Hobbs, Brian, Hong, Elliott, Hoth, Karin, Hsiung, Chao (Agnes), Hu, Jianhong, Hung, Yi-Jen, Huston, Haley, Hwu, Chii Min, Jackson, Rebecca, Jain, Deepti, Johnsen, Jill, Johnston, Rich, Jones, Kimberly, Kessler, Michael, Khan, Alyna, Khan, Ziad, Kim, Wonji, Kimoff, John, Kinney, Greg, Kramer, Holly, Lange, Christoph, Lange, Ethan, Laurie, Cathy, Laurie, Cecelia, LeBoff, Meryl, Lee, Sandra, Lee, Wen-Jane, Levine, David, Lewis, Joshua, Li, Yun, Lin, Xihong, Liu, Simin, Liu, Yu, Make, Barry, Manning, Alisa, Manson, JoAnn, Martin, Lisa, Marton, Melissa, Mathai, Susan, May, Susanne, McArdle, Patrick, McDonald, Merry-Lynn, McFarland, Sean, McGoldrick, Daniel, McHugh, Caitlin, Mei, Hao, Meigs, James, Menon, Vipin, Min, Nancy, Moll, Matt, Momin, Zeineen, Montasser, May, Mychaleckyj, Josyf C, Naik, Rakhi, Naseri, Take, Natarajan, Pradeep, Nelson, Sarah C., Neltner, Bonnie, Nessner, Caitlin, Nkechinyere, Osuji, O'Connell, Jeff, O’Connor, Tim, Ochs-Balcom, Heather, Okwuonu, Geoffrey, Pankow, James, Parker, Cora, Peloso, Gina, Peralta, Juan Manuel, Perez, Marco, Perry, James, Peters, Ulrike, Phillips, Lawrence S, Pollin, Toni, Becker, Julia Powers, Boorgula, Meher Preethi, Psaty, Bruce, Qiao, Dandi, Rafaels, Nicholas, Rajendran, Mahitha, Rasmussen-Torvik, Laura, Ratan, Aakrosh, Reed, Robert, Regan, Elizabeth, Reupena, Muagututi‘a Sefuiva, Robillard, Rebecca, Roselli, Carolina, Ruczinski, Ingo, Runnels, Alexi, Russell, Pamela, Ryan, Kathleen, Sabino, Ester Cerdeira, Salimi, Shabnam, Salvi, Sejal, Salzberg, Steven, Sandow, Kevin, Santibanez, Jireh, Schwander, Karen, Sciurba, Frank, Sériès, Frédéric, Shetty, Amol, Shetty, Aniket, Silver, Brian, Skomro, Robert, Smith, Tanja, Smoller, Sylvia, Snively, Beverly, Stilp, Adrienne M., Storm, Garrett, Streeten, Elizabeth, Su, Jessica Lasky, Sung, Yun Ju, Sylvia, Jody, Szpiro, Adam, Taub, Margaret, Taylor, Simeon, Thornton, Timothy A., Threlkeld, Machiko, Tinker, Lesley, Tirschwell, David, Tiwari, Hemant, Tong, Catherine, Tsai, Michael, Vaidya, Dhananjay, Walker, Tarik, Wallace, Robert, Walts, Avram, Wang, Fei Fei, Wang, Heming, Watson, Karol, Watt, Jennifer, Weng, Lu-Chen, Wessel, Jennifer, Williams, Kayleen, Wilson, Carla, Wilson, James, Winterkorn, Lara, Wong, Quenna, Wu, Baojun, Xu, Huichun, Yanek, Lisa, Yang, Ivana, Zekavat, Seyedeh Maryam, Zhao, Snow Xueyan, Zhao, Wei, Zhu, Xiaofeng, Rich, Stephen S. , Pankow, James S. , Reiner, Alex P. , Aguet, François , Johnson, W. Craig , Ardlie, Kristin , Orchard, Peter , Kachuri, Linda , Castaldi, Peter J , Cho, Michael H , Christenson, Stephanie A , Durda, Peter , Hersh, Craig P , Huntsman, Scott , Hwang, Seungyong , Joehanes, Roby , Johnson, Mari , Li, Xingnan , Lin, Honghuang , Liu, Ching-Ti , Liu, Yongmei , Mak, Angel C Y , Manichaikul, Ani W , Paik, David T , Smith, Joshua D , Taylor, Kent D , Tracy, Russell P , Wang, Jiongming , Wang, Mingqiang , Weinstock, Joshua S , Weiss, Jeffrey , Wheeler, Heather E , Zhou, Ying , Zöllner, Sebastian , Wu, Joseph C , Mestroni, Luisa , Graw, Sharon , Taylor, Matthew R G , Ortega, Victor E , Johnson, W Craig , Gan, Weiniu , Gupta, Namrata , Ardlie, Kristin , Woodruff, Prescott G , Bowler, Russell P , Reiner, Alexander P , Kooperberg, Charles , Ziv, Elad , Vasan, Ramachandran S , Silverman, Edwin K , Rich, Stephen S , Heard-Costa, Nancy , Tang, Hua , Rotter, Jerome I , Smith, Albert V , Levy, Daniel , Aguet, François , Scott, Laura J , Raffield, Laura M , Parker, Stephen C J , Pankow, James S , Tracy, Russell P. , Wu, Joseph C. , Larson, Martin G. , Cupples, L. Adrienne , Abe, Namiko, Almasy, Laura, Ament, Seth, Anugu, Pramod, Auer, Paul, Avramopoulos, Dimitrios, Balasubramanian, Adithya, Barr, R. Graham, Barwick, Lucas, Beaty, Terri, Becker, Diane, Becker, Lewis, Beitelshees, Amber, Benos, Takis, Bezerra, Marcos, Bis, Joshua, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Bunting, Karen, Buth, Erin, Carey, Vincent, Carty, Cara, Casaburi, Richard, Chaffin, Mark, Chang, Christy, Chang, Yi-Cheng, Chavan, Sameer, Chen, Bo-Juen, Chen, Wei-Min, Choi, Seung Hoan , Chuang, Lee-Ming, Chung, Ren-Hua, Conomos, Matthew, Cornell, Elaine, Crandall, Carolyn, Crapo, James, Curtis, Jeffrey, Damcott, Coleen, David, Sean, Fuentes, Lisa de las, Vries, Paul de, Deka, Ranjan, DeMeo, Dawn, Devine, Scott, Dinh, Huyen, Doddapaneni, Harsha, Duan, Qing, Duggirala, Ravi, Eaton, Charles, Ekunwe, Lynette, Boueiz, Adel El, Emery, Leslie, Farber, Charles, Farek, Jesse, Franceschini, Nora, Frazar, Chris, Fu, Mao, Fullerton, Stephanie M., Fulton, Lucinda, Gao, Shanshan, Gao, Yan, Gass, Margery, Geiger, Heather, Ghosh, Auyon, Gignoux, Chris, Glahn, David, Gogarten, Stephanie, Gong, Da-Wei, Goring, Harald, Grine, Daniel, Gu, C. Charles, Guan, Yue, Hall, Michael, Han, Yi, Harris, Daniel, Heavner, Ben, Herrington, David, Hobbs, Brian, Hong, Elliott, Hoth, Karin, Hsiung, Chao (Agnes), Hu, Jianhong, Hung, Yi-Jen, Huston, Haley, Hwu, Chii Min, Jackson, Rebecca, Jain, Deepti, Johnsen, Jill, Johnston, Rich, Jones, Kimberly, Kessler, Michael, Khan, Alyna, Khan, Ziad, Kim, Wonji, Kimoff, John, Kinney, Greg, Kramer, Holly, Lange, Christoph, Lange, Ethan, Laurie, Cathy, Laurie, Cecelia, LeBoff, Meryl, Lee, Sandra, Lee, Wen-Jane, Levine, David, Lewis, Joshua, Li, Yun, Lin, Xihong, Liu, Simin, Liu, Yu, Make, Barry, Manning, Alisa, Manson, JoAnn, Martin, Lisa, Marton, Melissa, Mathai, Susan, May, Susanne, McArdle, Patrick, McDonald, Merry-Lynn, McFarland, Sean, McGoldrick, Daniel, McHugh, Caitlin, Mei, Hao, Meigs, James, Menon, Vipin, Min, Nancy, Moll, Matt, Momin, Zeineen, Montasser, May, Mychaleckyj, Josyf C, Naik, Rakhi, Naseri, Take, Natarajan, Pradeep, Nelson, Sarah C., Neltner, Bonnie, Nessner, Caitlin, Nkechinyere, Osuji, O'Connell, Jeff, O’Connor, Tim, Ochs-Balcom, Heather, Okwuonu, Geoffrey, Parker, Cora, Peloso, Gina, Peralta, Juan Manuel, Perez, Marco, Perry, James, Peters, Ulrike, Phillips, Lawrence S, Pollin, Toni, Becker, Julia Powers, Boorgula, Meher Preethi, Psaty, Bruce, Qiao, Dandi, Rafaels, Nicholas, Rajendran, Mahitha, Rasmussen-Torvik, Laura, Ratan, Aakrosh, Reed, Robert, Regan, Elizabeth, Reupena, Muagututi‘a Sefuiva, Robillard, Rebecca, Roselli, Carolina, Ruczinski, Ingo, Runnels, Alexi, Russell, Pamela, Ryan, Kathleen, Sabino, Ester Cerdeira, Salimi, Shabnam, Salvi, Sejal, Salzberg, Steven, Sandow, Kevin, Santibanez, Jireh, Schwander, Karen, Sciurba, Frank, Sériès, Frédéric , Shetty, Amol, Shetty, Aniket, Silver, Brian, Skomro, Robert, Smith, Tanja, Smoller, Sylvia, Snively, Beverly, Stilp, Adrienne M., Storm, Garrett, Streeten, Elizabeth, Su, Jessica Lasky, Sung, Yun Ju, Sylvia, Jody, Szpiro, Adam, Taub, Margaret, Taylor, Simeon, Thornton, Timothy A., Threlkeld, Machiko, Tinker, Lesley, Tirschwell, David, Tiwari, Hemant, Tong, Catherine, Tsai, Michael, Vaidya, Dhananjay, Walker, Tarik, Wallace, Robert, Walts, Avram, Wang, Fei Fei, Wang, Heming, Watson, Karol, Watt, Jennifer, Weng, Lu-Chen, Wessel, Jennifer, Williams, Kayleen, Wilson, Carla, Wilson, James, Winterkorn, Lara, Wong, Quenna, Wu, Baojun, Xu, Huichun, Yanek, Lisa, Yang, Ivana, Zekavat, Seyedeh Maryam, Zhao, Snow Xueyan, Zhao, Wei, Zhu, Xiaofeng

Science | July 16, 2026 | Volume: 393 | Issue: 6808 | Pages: No page available

10.1126/science.adx2989 | PMID 42462027 Get It

Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood ( n = 6454) and lung ( n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank–derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.
Noninvasive electrical neuromodulation involving vagus nerve mechanism for functional constipation and irritable bowel syndrome with constipation
Liu, Jie , Lv, Chanlan, Zhu, Xiuli, Hong, Na, Huang, Yizhou , Sun, C J , Yan, Yan, Ys, Yu , Wang, Yichen, Huang, Yuting

Therapeutic Advances in Gastroenterology | July 16, 2026 | Volume: 19 | Issue: No issue available | Pages: No page available

10.1177/17562848261469142 | PMID 42491410 Get It

Functional constipation (FC) and irritable bowel syndrome with constipation (IBS-C) are prevalent disorders of gut–brain interaction with limited effective treatments. The vagus nerve exerts considerable influence on the gastrointestinal (GI) tract, indicating that noninvasive electrical neuromodulation targeting this nerve could provide a promising therapeutic avenue for both FC and IBS-C. This review explores several emerging noninvasive neuromodulation techniques involving the vagus nerve, such as transcutaneous auricular vagal nerve stimulation, percutaneous electrical nerve field stimulation, and transcutaneous electrical acustimulation. The clinical applications of these methods in managing FC and IBS-C are evaluated. Existing evidence supports the efficacy and safety of noninvasive neuromodulation in relieving symptoms of these conditions. Future investigations should aim to refine these techniques, advance device technology, and further clarify the mechanisms of action.
Modifiable Risk Factors and Attributable Ischemic Heart Disease Mortality in US States, 1990-2023
Benziger, Catherine P., Stark, Benjamin A., Johnson, Catherine O., Roth, Gregory A., Collaborators, GBD 2023 US IHD, Abohashem, Shady, Ahmed, Syed Anees, Al-Aly, Ziyad, Alsabri, Mohammed A., Antony, Catherine M., Aravkin, Aleksandr Y., Areda, Demelash, Bell, Michelle L., Brauer, Michael, Chi, Gerald, Criqui, Michael H., Dai, Xiaochen, Doshi, Ojas Prakashbhai, Doshi, Rajkumar Prakashbhai, E’mar, Abdel Rahman, Elhadi, Muhammed, Göbölös, Laszlo, Haile, Demewoz, Hebert, Jeffrey J., Hemmati, Mehdi, Ibrahim, Ramzi, Kankam, Samuel Berchi, Kantar, Rami S., Khubchandani, Jagdish, Kim, Min Seo, Kimokoti, Ruth W., Kisa, Adnan, Kokkorakis, Michail, Kumar, Ashish, Liu, Xuefeng, Lv, Lei, Mahmoudi, Morteza, Manla, Yosef, Martinez-Piedra, Ramon, Marzouk, Sammer, Mensah, George A., Mestrovic, Tomislav, Miller, Ted R., Mokdad, Ali H., Mougin, Vincent, Mustafa, Ahmad, Nassar, Mahmoud, Natto, Zuhair S., Nugen, Fred, Parikh, Romil R., Pasovic, Maja, Patil, Shankargouda, Puvvula, Jagadeesh, Ramasamy, Shakthi Kumaran, Rashid, Ahmed Mustafa, Root, Kevin T., Sawhney, Monika, Schuermans, Art, Shariff, Mariam, Shuval, Kerem, Simegn, Gizeaddis Lamesgin, Singh, Rohit, Stafford, Lauryn K., Taiba, Jabeen, Tanwar, Manoj, Teramoto, Masayuki, Thirunavukkarasu, Sathish, Tran, Thang Huu, Uppal, Dipan, Vinayak, Manish, Yuce, Deniz, Murray, Christopher J. L., Moran, Andrew E., Kimokoti, Ruth W , Liu, Xuefeng , Nassar, Mahmoud , Singh, Rohit , Tanwar, Manoj , Yu, Hairui , Taiba, Jabeen , Benziger, Catherine P. , Stark, Benjamin, Johnson, Catherine O., Roth, Gregory A. , Collaborators, GBD, Abohashem, Shady , Al‐Aly, Ziyad , Antony, Catherine M, Aravkin, Aleksandr Y. , Areda, Demelash, Bräuer, Michael , Chi, Gerald , Criqui, Michael H. , Doshi, Ojas Prakashbhai , Doshi, Rajkumar , E’mar, Abdel Rahman, Elhadi, Muhammed, Göbölös, László , Haile, Demewoz , Hébert, Jeffrey J. , Hemmati, Mehdi , Ibrahim, Ramzi , Kankam, Samuel Berchi , Kantar, Rami S. , Khubchandani, Jagdish , Kısa, Adnan , Kokkorakis, Michail , Lv, Lei , Mahmoudi, Morteza , Manla, Yosef , Martínez, Ramón , Marzouk, Sammer , Mensah, George A. , Meštrović, Tomislav , Miller, Ted R. , Mokdad, Ali H. , Mougin, Vincent , Mustafa, Ahmad , Nassar, Mahmoud, Nugen, Fred , Pasovic, Maja , Puvvula, Jagadeesh , Rashid, Ahmed Mustafa , Root, Kevin T., Sawhney, Monika, Schuermans, Art , Shariff, Mariam , Shuval, Kerem , Simegn, Gizeaddis Lamesgin , Stafford, Lauryn K , Teramoto, Masayuki , Thirunavukkarasu, Sathish, Uppal, Dipan , Vinayak, Manish , Yuce, Deniz, Murray, Christopher J. L., Moran, Andrew Edward

JAMA Cardiology | July 15, 2026 | Volume: No volume available | Issue: No issue available | Pages: No page available

10.1001/jamacardio.2026.2435 | PMID 42455550 Get It

Importance Ischemic heart disease (IHD), the leading cause of death in the US, is predominantly due to modifiable risk factors. Estimates of IHD mortality attributable to risk factors provide evidence for health policy decision-making. Objective To estimate the burden of IHD death attributable to risk factors in the US from 1990-2023. Design, Setting, and Participants The Global Burden of Disease Study 2023 (GBD 2023) used vital records and a broad set of epidemiologic data to estimate IHD death rates, risk factor exposure, and relative risk curves for risk-outcome pairs for 1990-2023 for the general population. Data analysis was performed from October 2024 to December 2025. Exposure Twelve metabolic, behavioral, and environmental risk factors. Main Outcomes and Measures The primary outcomes were IHD death rates per 100 000 persons, counts, and attributable risks from 1990-2023 by age, sex, and US state. Estimates include 95% uncertainty intervals (UI). IHD death rates were estimated using ensemble modeling methods. Risk exposures were estimated using bayesian meta-regression methods. Relative risks were estimated following the Burden of Proof framework. Results In 2023, there were 473 000 IHD deaths (95% UI, 414 000-510 000) in the US, a decrease of 58.7% (95% UI, 56.8%-61.0%) in age-standardized rate since 1990. Between 2010 and 2023, there was a 19.0% decrease (95% UI, 15.0%-22.9%) for males and a 24.5% decrease (95% UI, 20.3%-29.7%) for females in IHD death rates. High systolic blood pressure (SBP), dietary risks, and high low-density lipoprotein cholesterol (LDL-C) were the leading risk factors for IHD deaths in 2023, accounting for 47.2% (95% UI, 36.4%-57.0%), 38.6% (95% UI, 17.2%-56.8%), and 28.5% (95% UI, 19.3%-39.6%) of IHD deaths, respectively. Increased exposure to several risk factors substantially increased their attributable burden for IHD deaths in 2023, with high fasting plasma glucose (FPG) increasing 38.8% (95% UI, 11.5%-81.1%) and high body mass index (BMI) increasing 54.5% (95% UI, 41.8%-66.3%) since 1990. Smoking and particulate matter pollution had the greatest decrease in attributable IHD mortality since 1990, at 33.3% (95% UI, 23.6%-41.7%) and 74.9% (95% UI, 46.7%-88.8%), respectively. Conclusion and Relevance Per the results of this systematic analysis of GBD 2023, a total of 88.7% (95% UI, 83.4%-92.5%) of IHD deaths were attributable to modifiable risk factors in the US in 2023, with high SBP, dietary risks, and high LDL-C being the greatest contributors. High BMI and high FPG showed the largest attribution increases, while exposure to other risks did not increase significantly for the population.
Developmental Control of DNA Damage Responses in α- and β-Cells Shapes the Selective β-Cell Susceptibility in Diabetes
Varghese, Sneha S., Peña, Alessandro Giovanni Hernandez-De La, Wang, Liu, Ruiz, Victor, Tixi, Wilma, Pandey, Aparamita, Anchondo, Laura, Chiu, Amy, Wu, Xiwei, Georgia, Senta K. , Shih, Hung-Ping, Bhattacharya, Supriyo, Dhawan, Sangeeta , Wang, Liu , Wei, Zong , Dhawan, Sangeeta , Varghese, Sneha S., Peña, Alessandro Giovanni Hernandez-De La , Ruiz, Victor, Tixi, Wilma, Pandey, Aparamita, Anchondo, Laura M. , Chiu, Amy, Wu, Xiwei, Georgia, Senta K. , Shih, Hung-Ping, Bhattacharya, Supriyo

Diabetes | July 15, 2026 | Volume: 75 | Issue: 9 | Pages: 1612-1628

10.2337/db25-1027 | PMID 42455072 Get It

Accumulation of DNA damage drives β-cell dysfunction, senescence, and death in type 1 and type 2 diabetes. Although α-cell dysfunction also contributes to disease pathology, α-cells remain remarkably resistant to senescence and increased cell death under diabetogenic stress. The mechanisms underlying these differential responses to diabetogenic stress, particularly differences in their DNA damage vulnerability, remain unclear. We demonstrate that replication introduces a window of genomic vulnerability in both α- and β-cells during neonatal growth, with α-cells exhibiting higher replication rates and DNA damage. We show that neonatal β-cells resolve DNA damage more efficiently during mitosis and favor error-free repair, whereas α-cells compensate for their greater DNA-damage vulnerability through increased cellular turnover. Using mouse models of overnutrition and diabetes, we show that β-cells exhibit greater vulnerability to terminal DNA damage and impaired repair capacity under diabetogenic stress, with compensatory replication amplifying this vulnerability. We demonstrate that developmental and postnatal epigenetic programs shape the differential DNA damage vulnerability of postnatal β- and α-cells. Loss of de novo DNA methyltransferase Dnmt3a in pancreatic progenitors selectively increases the DNA damage vulnerability of β-cells from neonatal growth through adulthood. Complementing this developmental process, enhancer analyses reveal lineage-specific DNA damage response regulatory landscapes that are remodeled in β-cells under metabolic stress. Collectively, our findings uncover novel developmental mechanisms that shape the distinct DNA damage responses of postnatal β- and α-cells during growth and diabetes. Article Highlights Mechanisms underlying the differential susceptibility of pancreatic β- and α-cells to diabetogenic stress remain unclear. We investigated whether replication, repair fidelity, and developmental epigenetic programs determine the vulnerability of postnatal β- and α-cells to DNA damage, a key driver of β-cell failure in diabetes. Replication introduces DNA damage vulnerability in both neonatal β- and α-cells, yet β-cells resolve damage more efficiently. Loss of DNA methyltransferase 3a in pancreatic progenitors selectively heightens β-cell DNA damage vulnerability that persists into adulthood. Moreover, diabetogenic stress preferentially compromises β-cell repair fidelity. These findings reveal how developmental programs shape β-cell resilience and may influence lifelong diabetes risk.
Glioblastoma Subtypes Exhibit Distinct Migration Mechanics and Immune Responses
Shamsan, Ghaidan A. , Liu, Chao J. , Braman, Brooke C. , Li, Ruyi , Rathe, Susan K. , Sarver, Aaron L. , Ghaderi, Nima , McMahon, Mariah , Klank, Rebecca L. , McFarren, S Joseph. , Masopust, David , Clark, H. Brent , Rosenfeld, Steven S. , Largaespada, David A. , Tschida, Barbara R , McFarren, S Joseph , Rosato, Pamela C , Masopust, David , Sarkaria, Jann N , Clark, H Brent , Rosenfeld, Steven S , Largaespada, David A. , Odde, David J , Shamsan, Ghaidan A , Liu, Chao J. , Rathe, Susan K. , Sarkaria, Jann N.

Cancer Research Communications | July 14, 2026 | Volume: 6 | Issue: 8 | Pages: 1898-1915

10.1158/2767-9764.crc-25-0579 | PMID 42447444 Get It

Abstract Glioblastoma (GBM) remains a deadly cancer driven in part by invasion of tumor cells into the brain. Transcriptomic analyses have identified distinct molecular subtypes, but mechanistic differences that account for clinical differences are not clear. In this study, we show that, as predicted by the motor-clutch model of cell migration, mesenchymal glioma cells are more spread, generate larger traction forces, and migrate faster in brain tissue compared with proneural cells. Despite their rapid migration and comparable proliferation rates in vitro, mice with mesenchymal tumors survive longer than those with proneural tumors. This improved survival correlated with an immune response in mesenchymal tumors, including T cell–mediated. Consistently, inducing mesenchymal tumors in immunodeficient mice resulted in shorter survival, supporting a protective immune role in mesenchymal tumors. Thus, mesenchymal tumors have aggressive migration but are immunologically “hot,” which suppresses net proliferation. These two features counteract each other and may explain the lack of a strong survival difference between subtypes clinically, while also opening up new opportunities for subtype-specific therapies. Significance: This study highlights new mechanical and immunologic insights into GBM molecular subtypes using an integrated modeling–genome engineering strategy, which can potentially facilitate GBM subtype-specific therapeutic strategies.
Clinical Recommendations for Remote Robotic Assisted Surgery From the CRSA 2025 International Consensus Conference
Raptis, Dimitri Aristotle , Kavoussi, Louis, Wren, Sherry M. , Fowler, Dennis, Anvari, Mehran, Bianco, Francesco, Liang, Xiao, Li, Zheyong, Yin, Xiaoyu, Piccoli, Micaela, Marks, John, Shen, Baiyong, Ko, Clifford, Seeliger, Barbara, Hagendoorn, Jeroen, Essani, Rahila, Walsh, R. Matthew, Oleynikov, Dmitry, Jones, Stephanie B., Bindal, Vivek , Kim, Hyun Koo, Hyung, Woo Jin, Hashimoto, Daniel, Chen, Liaohai Leo, Burt, Bryan M., Lafaro, Kelly, Elli, Fernando, Wilson, Erik, Bianchi, Paolo Pietro, Morgan, Annette, Bartlett, Stephen T., Rashidi, Laila, Yu, Liu, Gawkins, Bill, Hinoul, Piet, Peine, Bill, Woo, Yanghee, Raoof, Mustafa, Jones, Daniel, Deshpande, Supriya, Bernard, Jordana , Wang, Yulun, Broering, Dieter C., Giulianotti, Pier Cristoforo, Fong, Yuman , Raptis, Dimitri , Wren, Sherry M , Aldousari, Saad , Ruurda, Jelle P. H. , Bindal, Vivek , Melstrom, Laleh , Celotto, Francesco , Kudsi, Yusef , Ratti, Francesca , Bernard, Jordana , Kavoussi, Louis R., Fowler, Dennis L. , Anvari, Mehran , Bianco, Francesco Maria , Piccoli, Micaela , Marks, John, Ko, Clifford, Seeliger, Barbara , Hagendoorn, Jeroen , Essani, Rahila, Walsh, R. Matthew , Oleynikov, Dmitry , Hyung, Woo Jin , Hashimoto, Daniel A. , Burt, Bryan M. , Lafaro, Kelly J. , Elli, Fernando, Bianchi, Paolo Pietro, Rashidi, Laila, Hinoul, Piet , Peine, Bill, Woo, Yanghee , Raoof, Mustafa , Jones, Daniel, Deshpande, Supriya , Bröering, Dieter C. , Giulianotti, Pier Cristoforo

World Journal of Surgery | July 14, 2026 | Volume: 50 | Issue: 8 | Pages: 2274-2288

10.1002/wjs.70468 | PMID 42444290 Get It

ABSTRACT Background Implementation of remote robotic‐assisted surgery (rRAS) has the potential to significantly improve access to high‐quality surgical care for patients worldwide. Advances in robotic systems and telecommunications now enable highly reliable, low latency, and cybersecure connectivity. These technological developments, together with the broader adoption of telemedicine, have driven an increasing interest in the implementation of rRAS. Methods The Clinical Robotic Surgery Association (CRSA) convened a conference to develop international clinical recommendations for rRAS using the Danish Model of Consensus. The society identified five key issue categories and invited a panel of experts to address specific questions for each. Panel members and a jury of 10 unbiased, non‐expert in rRAS professionals participated in a day‐long conference on November 19, 2025 (Los Angeles, USA). During the conference, participants presented, discussed, and reached consensus on specific guidelines relevant to each category. Immediately after the conference, the jury reviewed and approved the recommended guidelines. Results Multiple recommendations were approved for each of the following categories: Operating Models, Roles and Responsibilities (2 recommendations), Clinical Pathway (2 recommendations), Emergency Management (8 recommendations), Technical Considerations (5 recommendations), and Medico‐legal and Ethical Considerations (4 recommendations). Conclusions There was consensus that with recent advances in telecommunication and robotic technologies, rRAS can be offered safely and effectively if the identified safeguards are realized. Adoption of rRAS is expected to continue advancing rapidly.
Using a translational data platform to create clinical-grade genome-informed risk assessments
Morse, Jennifer L, He, Megan, Bangash, Hana , Cortopassi, Joshua, Forman, Sophie, Gordon, Adam S, Hamed, Marwan, Karlson, Elizabeth W. , Kiryluk, K, Kullo, Iftikhar J, Lewis, Adam, Limdi, Nita, Marathe, Priya, McGuffin, Kyle, Odgis, Jacqueline, Perez, Emma, Petrzelka, Jacob, Prows, Cynthia A. , Thomas, Ellis, Wiesner, Georgia L. , Wilson, Travis, Linder, Jodell E. , Liu, Cong , Rasmussen, Luke V , Morse, Jennifer L. , Chung, Wendy K. , Cortopassi, Josh , Gordon, Adam S, Harris, Paul A. , Kenny, Eimear E , Kiryluk, Krzysztof , Kullo, Iftikhar J. , Lewis, Adam J. , Liu, Cong , Marathe, Priya , McGuffin, Kyle, Odgis, Jacqueline A. , Perez, Emma , Rasmussen, Luke V. , Thomas, Ellis, Peterson, Josh F.

JAMIA Open | July 13, 2026 | Volume: 9 | Issue: 4 | Pages: No page available

10.1093/jamiaopen/ooag118 | PMID 42445300 Get It

Abstract Objective To describe the development and implementation of an automated platform for genomic risk prediction that integrates multiple data types. Materials and methods Using the REDCap infrastructure, we constructed the R4 (Recruitment, Results, and Risk Reduction) platform to intake data from clinical sites, partner laboratories, participant surveys, and electronic health record (EHR) data across 13 institutions. Results The R4 Portal successfully integrated data to generate genome-informed risk assessments (GIRAs) across 11 conditions for a 23 840 person cohort. Testing phases and quality control led to network-wide protocols ensuring consistency and accuracy. Discussion As the science of estimating disease risk evolves, standardized and high-throughput methods of collecting and manipulating complex data are required. Platforms should be open-source, modular, and reusable, ensuring flexibility, security, and integration across healthcare environments. Conclusion The electronic MEdical Records and GEnomics (eMERGE) network successfully generated and returned comprehensive risk profiles using logic and data specific to 11 conditions in a secure and semi-automated fashion employing a customized REDCap database.
mRNA COVID-19 Vaccination Following PD-1/PD-L1 Inhibitor Therapy and Survival in Liver Cancer
Liu, L N, Zou, Yuntao, Wang, Yichen, Yan, Yan, Huang, Y N , Sun, Chenyu , Wang, Yichen, Yang, Liu

EMJ Hepatology | July 13, 2026 | Volume: No volume available | Issue: No issue available | Pages: 74

10.33590/emjhepatol/q11x9311 Get It

Quantitative single photon emission computed tomography/computed tomography of Tc-99m-pyrophosphate scans: metrics for short-term risk stratification in transthyretin cardiac amyloidosis
Pleasure, Mitchell , Jackson, Candace L. , DeLuca, Albert, Kinkhabwala, Mona, Liu, Qi, Chernovolenko, Margarita , Wats, Karan , Brailovsky, Yevgeniy, Maurer, Mathew S., Johnson, Lynne L. , Poterucha, Timothy J. , Einstein, Andrew Jeffrey , Castillo, Michelle , Kinkhabwala, Mona, Ruberg, Frederick L. , Elias, Pierre , Poterucha, Timothy Joseph

Nuclear Medicine Communications | July 13, 2026 | Volume: No volume available | Issue: No issue available | Pages: No page available

10.1097/mnm.0000000000002199 | PMID 42438950 Get It

Background Radionuclide SPECT/computed tomography (CT) is standard for diagnosing transthyretin cardiac amyloidosis (ATTR-CM), but the prognostic value of quantitative metrics remains incompletely defined. We investigated their prognostic utility and additive value beyond existing staging systems. Methods Retrospective study of patients undergoing technetium-99m-pyrophosphate (PYP) SPECT/CT, including consecutive clinically referred patients (October 2023 to 2024) and trial participants with positive scans from January 2022. Myocardial PYP was quantified using standardized uptake values (SUV mean and SUV max ) and volumetric measures [percent injected dose (%ID), cardiac amyloid activity (CAA)]. Major adverse cardiovascular events (MACEs) comprised heart failure hospitalization or death. Cox and Kaplan–Meier analyses assessed associations; likelihood ratio testing and change in area under the curve (AUC) evaluated the additive prognostic value to existing staging systems. Results Among 43 patients with ATTR-CM, 16 experienced MACE over a median 518 days. AUCs ranged from 0.74 to 0.88 at 3 months and 0.56–0.62 at 12 months. All variables trended toward association with MACE: SUV mean ( P = 0.02), SUV max ( P = 0.03), %ID ( P = 0.06), and CAA ( P = 0.06). After age adjustment, cut-points for SUV max (5.8) and SUV mean (3.5) remained associated with MACE. Adding imaging variables to the National Amyloidosis Centre and Columbia staging systems did not significantly improve prognostic accuracy (Δ χ 2 : 0.40–2.48, P = 0.11–0.53). Interobserver reproducibility was high (intraclass correlation coefficients = 0.89–0.99). Conclusion Quantitative Tc-99m–PYP SPECT/CT metrics were associated with short-term outcomes in ATTR-CM, but did not improve established staging systems. Findings are hypothesis-generating and require validation in larger cohorts.
H&E-Referenced Multiplex Immunofluorescence Interpretation in TMA Cores: Spatial Co-localization, Cell Feature Validation, and Virtual H&E Generation
Jiang, J N, Moore, Raymond M. , Lou, Peiliang , Guo, Ruifeng, Liu, Yuanhang , Liu, H, Goode, Ellen L. , Novotny, Brenna C. , Fogarty, Zachary C. , Winham, Stacey J. , Markovic, Svetomir N. , Wang, Chen

No container title available | July 7, 2026 | Volume: No volume available | Issue: No issue available | Pages: No page available

10.21203/rs.3.rs-9802601/v1 | PMID 42466420 Get It

Abstract Background Multiplexed immunofluorescence (MxIF) enables high-dimensional immune cell phenotyping and detailed characterization of the tumor immune microenvironment (TIME), but complex cyclic staining can introduce signal degradation and uncertainty in cell-level measurements. In routine practice, pathologists rely on hematoxylin and eosin (H&E) staining as the primary reference for tissue morphology and frequently cross-reference H&E to interpret MxIF findings. Methods We present a framework for aligning H&E and MxIF images at the tissue microarray (TMA) core level to support cross-modal analysis. Cell nucleus detections from each modality are used as anchor points, formulating the task as a point-set alignment problem. Coherent Point Drift (CPD) is applied for global alignment, followed by graph-matching–based refinement to improve local consistency. Evaluation on ovarian TMAs demonstrates consistent alignment performance for both restained and serial sections. We further explore the use of restained H&E as a reference for MxIF interpretation and investigate virtual H&E generation from MxIF data as a complementary approach when restained H&E is unavailable. Results The aligned images enable quantitative assessment of cross-modal feature concordance between MxIF-derived measurements and H&E morphology. The generated virtual H&E demonstrated similar cell population compared to the real H&E. Conclusions Cell-centric alignment can facilitate integrative multimodal histopathology analysis for TMA cores in spatial co-localization, cell feature validation, and virtual H&E generation.