Citations

Background: Autosomal dominant polycystic kidney disease (ADPKD) is the most common inherited disorder leading to kidney failure. Tolvaptan, the only FDA-approved therapy, modestly slows disease progression but is limited by significant adverse effects, highlighting the need for more effective and better-tolerated treatments. Ciliary polycystins are central to ADPKD pathogenesis. We previously demonstrated that a cilia base-localized pool of CDK6 restricts ciliary polycystin abundance by suppressing axonemal polyglutamylation. However, whether CDK6 inhibition can restore polycystin function and suppress ADPKD progression in vivo remains unknown. Methods: Abemaciclib was tested in in vitro 3D cyst assays and ex vivo metanephric organ cultures. In vivo efficacy of early- and late-stage intervention were assessed in F1(129/B6) Pkd1 R3277C/R3277C mice receiving daily abemaciclib. Tolvaptan served as a therapeutic control. Cyst burden, fibrosis in the kidneys, kidney function, and primary cilia of renal epithelial cells were analyzed at study endpoints. Results: CDK6 was prominently upregulated in cyst-lining cells in human and murine PKD kidneys. At doses substantially lower than those used in oncology, abemaciclib robustly suppressed cyst growth in vitro and ex vivo . In vivo , long-term early treatment arrested cyst progression more effectively than tolvaptan without detectable toxicity. Late intervention largely halted cyst growth in females and reversed cyst progression in males. Mechanistically, abemaciclib restored axonemal polyglutamylation and increased ciliary polycystin levels. Conclusions: Abemaciclib has greater therapeutic efficacy and tolerability than tolvaptan in this preclinical model, largely driven by CDK6 inhibition–mediated restoration of axonemal polyglutamylation and increased ciliary polycystin abundance.

Abstract Background Distal (ileal) metabolic bariatric surgeries (DMBS) have gained increasing adoption due to their enhanced metabolic effects. However, the absence of standardized definitions, classification, and clinical indications limits their consistent application. This consensus aimed to establish an international consensus on the definition, classification, indications, and postoperative management of DMBS. Methods A three-round modified Delphi study was conducted including international expert surgeons from 16 countries. A total of 98 statements were developed across three domains: procedural characteristics, patient selection, and postoperative follow-up. Consensus was defined as ≥75% agreement. Statements not reaching consensus were revised and resubmitted. Results Consensus was achieved on 81 statements. DMBS were defined as procedures involving gastro-ileal or duodeno-ileal bypass, emphasizing ileal stimulation as a key metabolic mechanism. Procedures with a common channel ≤350 cm, including single-anastomosis duodenal-ileal with SG (SADI-S), single anastomosis sleeve-ileal transit bipartition (SASI), sleeve gastrectomy with Roux-en-Y transit bipartition (SG+TB), and biliopancreatic diversion with duodenal switch (BPD-DS), as well as one-anastomosis gastric bypass (OAGB) with biliopancreatic limb > 50% of total small bowel length were classified as DMBS. Routine measurement of total small bowel length was strongly recommended. DMBS were considered appropriate as primary procedures in patients with BMI ≥40 kg/m² with comorbidities, a BMI ≥50–60 kg/m², irrespective of the presence of comorbidities, and as revisional options for suboptimal clinical response (SoCR), recurrent weight gain (RWG), or persistent metabolic disease. Comprehensive preoperative and multidisciplinary evaluation was deemed mandatory, and liver cirrhosis, heart failure, end-stage kidney disease, and inflammatory bowel disease were considered contraindications. Postoperative care requires lifelong multidisciplinary follow-up, with increased attention to nutritional deficiencies and supplementation. Conclusions This consensus provides a standardized framework for DMBS, supporting harmonization of practice and improved patient safety.

Objective Glaucoma and cancer are debilitating diseases with increasing global prevalence and morbidity. However, current literature lacks a systematic consolidation of their relationships and interactions. This systematic review aims to (A) summarize the association between intraocular/non-ocular cancers and their treatments with the prevalence and etiology of glaucoma, (B) explore glaucoma as a risk factor for complications or poor prognosis for intraocular cancer, and (C) explore the management of glaucoma secondary to cancer therapies. Methods A systematic search was performed from inception to July 2025 across three databases with variants of ‘cancer’ and ‘glaucoma’ as search terms. Findings were narratively synthesized. Results Of 8,103 identified studies, 44 were included in this review. Eighteen studies reported increased glaucoma prevalence amongst intraocular and non-ocular cancer patients. Fifteen studies suggested increased risk of regional invasion, metastases, and high-risk histopathologies when intraocular cancers co-occurred with glaucoma. Corticosteroids in cancer therapy have been linked to intraocular pressure (IOP) elevation, while androgen deprivation therapy was associated with lowered glaucoma risk. Conclusion Given the relationship between glaucoma and intraocular/non-ocular cancers, strategies to monitor and manage glaucoma risk following cancer diagnosis and/or therapy may be considered. Glaucoma may be an early sign of complications in intraocular cancers.

Abstract Background Ionizing radiation (IR) accelerates atherosclerosis through induction of cellular senescence, DNA damage, defective efferocytosis, and dysregulation of clonal hematopoiesis (CH) drivers. Although low-dose colchicine reduces ischemic cardiovascular events in coronary artery disease, the precise molecular mechanisms underlying its vasculoprotective effects remain incompletely defined, and whether it mitigates radiation-associated vascular injury is unknown. Methods Bone marrow–derived macrophages (BMDMs) were pretreated with low-dose colchicine and exposed to 2 Gy IR. Molecular effects were assessed by RNA-seq, immunoblotting, and molecular docking. In vivo effects were tested in a partial carotid ligation (PLCL) model using spatial proteomics. Human monocyte-derived macrophages (HMDMs) from thoracic malignancy patients were analyzed before and after radiation therapy (RT). Results Low-dose colchicine suppressed IR-induced macrophage senescence signaling while preserving NRF2 activity. In a cell-free assay, colchicine directly activated aldehyde dehydrogenase 2 (ALDH2) in a dose-dependent manner (EC 50 1–5 nM), identifying ALDH2 as a direct molecular target of colchicine. Following irradiation, colchicine restored ALDH2, reduced mitochondrial (mt)ROS - dependent p90 ribosomal S6 kinase ( p90RSK ) activation and lipid peroxidation, preserved TET2 and DNMT3A expression, and rescued impaired efferocytosis while preventing nicotinamide adenine dinucleotide (NAD⁺) and adenosine triphosphate (ATP) depletion. These protective effects were ALDH2-dependent, as they were lost with ALDH2 inhibition or depletion and were mimicked by pharmacologic ALDH2 activation. In vivo, colchicine attenuated radiation-induced atherosclerosis and macrophage senescence-associated stemness (SAS). Consistently, macrophages from patients after RT showed reduced ALDH2 with increased mtROS, lipid peroxidation, and senescence. Conclusion These findings identify ALDH2 as a previously unrecognized molecular target of colchicine that links mitochondrial redox control to suppression of radiation-induced macrophage senescence and atherosclerosis and may contribute to the efficacy of low-dose colchicine in cardiovascular disease.

Abstract Gastrointestinal (GI) cancers are rising in people < 50 and remain major causes of cancer mortality despite therapeutic advances. Chimeric antigen receptor T (CART) cell therapy has transformed treatment of hematological cancers. However, its efficacy in GI cancers has been hindered by antigen heterogeneity and immunosuppressive cancer associated fibroblasts (CAFs) in the tumor microenvironment (TME.) Here we aimed to 1) study CART-CAF interactions and 2) engineer CART cells capable of eliminating CAFs to overcome their inhibitory role and enhance their antitumor activity in GI cancers. We first analyzed primary CAFs from 20 GI cancer patients. CAFs showed high fibroblast activation protein (FAP) expression and significantly suppressed T cells proliferation (26-77%, p≤0.0001). We next generated CART cells capable of safely eliminating both GI cancer cells and CAFs. To target cancer cells, we developed a novel nanobody (VHH)-based CAR against Claudin18.2, a GI cancer specific target. To avoid off-tumor toxicity in healthy FAP+ tissues, we incorporated an inducible FAP-CAR using the synNotch platform, enabling conditional targeting upon engagement with Claudin18.2+ cells, restricting FAP targeting within the TME. VHH Claudin18.2 CART cells showed superior specificity and efficacy vs scFv-based CART cells (hu8E85 or zolbetuximab). In Claudin18.2+ SNU-C2B xenografts, tumors were 92mm3 (VHH) vs 48mm3 (huE85) vs 211mm3 (zolbetuximab) on day 23 post-CART, median survival undefined vs 17 days vs 23 days respectively; p=0005. In Claudin18.2+ LS411N xenografts, tumors were 315mm3 vs 106mm3 vs 382 mm3 respectively at 23 days post-CART, median survival undefined vs 19 days vs undefined respectively; p=0004. VHH Claudin18.2 CART cells showed no off-tumor effects, while hu8E85 CART induced significant toxicity. We next established a co-engraftment model. NSG mice received 1x106 SNU-C2B and 6x106 CAFs from a GI cancer patient. At 500 mm3 tumors, mice received 5x106 VHH Claudin18.2 or inducible Claudin18.2/FAP CART. Dual-targeting CART cells showed greater antitumor response (728mm3 vs 320mm3, p=0.0011 at day 20 post-CART), expansion (61 vs 2,350 CART/µl of blood, p=0.0037), and tumor infiltration (8.71 x 105 vs 1.03 x 106 CART/g of tumor, p<0.0001). This was also observed in gastric, colon, and pancreatic PDX models, with no observable off-tumor cytotoxic effects with inducible dual-targeting Claudin18.2/FAP CART. Our findings demonstrate that our novel VHH-based Claudin18.2 CART cells are highly specific and effective against GI malignancies, and that inducible Claudin18.2/FAP dual-targeting CART cells safely remodel the TME into an immune-permissive state. This approach represents a mechanistically innovative and translationally relevant strategy to overcome CAF-mediated immunosuppression, enhance CART cell efficacy, and improve clinical outcomes in refractory solid tumors. Citation Format: Omar L. Gutierrez Ruiz, Jennifer Feigin, Brooke Kimball, R. Leo Sakemura, Elizabeth L. Siegler, Dominic A. Skeele, Ateka Saleh, Claudia Manriquez Roman, Ismail Can, Olivia Sirpilla, Kun Yun, Carli M. Stewart, Mehrdad Hefazi, Yoh Yamaguchi, Lionel Aurelien Kankeu Fonkoua, Long K. Mai, Truc Huynh, Sophia Y. Goldberg, Grace E. DeFranco, Sara Foote, Ekene Johnkennedy Newton Ogbodo, Hong Xia, Michael Redig, Matthew L. Matthew, Tamiel Nichole Turley, Skyeler Klinge, John Alton Copland, Saad J. Kenderian. Optimized CART cell therapy to remodel the tumor microenvironment and eliminate GI malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1525.

Abstract Faciobrachial dystonic seizures (FBDS), paroxysmal dizziness spells, and thermal sensory attacks are highly frequent and stereotypic phenomena experienced in leucine‐rich glioma inactivated 1 (LGI1)‐antibody encephalitis. This study aims to describe the electrophysiologic mechanism underlying these pathognomonic symptoms. LGI1‐antibody encephalitis patients with active symptoms were enrolled from two separate centers in South Korea and the United States. Patients were evaluated with simultaneous magnetoencephalography (306 channels) and electroencephalography. Regional alterations in neuronal excitability represented by interictal epileptiform discharges were present in the faciobrachial area of the motor cortex, insula, and somatosensory cortex, somatotopically aligned with each of the ictal semiologies observed in patients. FBDS and other LGI1‐antibody encephalitis‐specific spells localized to cortical regions neuroanatomically corresponding to ictal semiologies: the faciobrachial homunculus (FBDS), insular cortex (paroxysmal dizziness spells), and somatosensory cortex (thermal sensory attacks). Our findings support the ictal hypothesis underlying these unique phenomena.

Abstract Generative artificial intelligence (AI) is rapidly populating medical records with synthetic or partially AI-generated content, creating a feedback loop where future models are increasingly at risk of training on uncurated AI-generated data. However, the clinical consequences of this AI-generated data contamination remain unexplored. Here, we show that in the absence of mandatory human verification, this self-referential cycle drives a rapid erosion of pathological variability and diagnostic reliability of medical data at population scale. By analysing more than 800,000 synthetic data points across clinical text generation, vision–language reporting, and medical image synthesis, we find that models progressively converge toward generic phenotypes regardless of the model architectures. Specifically, rare but critical findings, including pneumothorax and effusions, vanish from the synthetic content generated by AI models, while demographic representations skew heavily toward middle-aged male phenotypes. Crucially, this degradation is masked by false diagnostic confidence. Models continue to issue reassuring reports while failing to detect life-threatening pathology, with false reassurance rates tripling to 40%. Blinded physician evaluation confirms that this decoupling of confidence and accuracy renders AI-generated documentation clinically useless after just two generations. We systematically evaluate three mitigation strategies that can be easily integrated into existing clinical workflows, finding that while synthetic volume scaling fails to prevent collapse, mixing real data with quality-aware filtering effectively preserves diversity. Ultimately, our results suggest that without policy-mandated human oversight, the deployment of generative AI threatens to degrade the very healthcare data ecosystems it relies upon.

Abstract Generative artificial intelligence (AI) is rapidly populating medical records with synthetic or partially AI-generated content, creating a feedback loop where future models are increasingly at risk of training on uncurated AI-generated data. However, the clinical consequences of this AI-generated data contamination remain unexplored. Here, we show that in the absence of mandatory human verification, this self-referential cycle drives a rapid erosion of pathological variability and diagnostic reliability of medical data at population scale. By analysing more than 800,000 synthetic data points across clinical text generation, vision–language reporting, and medical image synthesis, we find that models progressively converge toward generic phenotypes regardless of the model architectures. Specifically, rare but critical findings, including pneumothorax and effusions, vanish from the synthetic content generated by AI models, while demographic representations skew heavily toward middle-aged male phenotypes. Crucially, this degradation is masked by false diagnostic confidence. Models continue to issue reassuring reports while failing to detect life-threatening pathology, with false reassurance rates tripling to 40%. Blinded physician evaluation confirms that this decoupling of confidence and accuracy renders AI-generated documentation clinically useless after just two generations. We systematically evaluate three mitigation strategies that can be easily integrated into existing clinical workflows, finding that while synthetic volume scaling fails to prevent collapse, mixing real data with quality-aware filtering effectively preserves diversity. Ultimately, our results suggest that without policy-mandated human oversight, the deployment of generative AI threatens to degrade the very healthcare data ecosystems it relies upon.