tbuckley/cabot-search huggingface dataset:
Exploring the predictive potential of programmed death ligand 1 expression in healthy organs and lymph nodes as measured by<sup>18</sup>F-BMS-986192 PET: pooled analysis of data from four solid tumor types
Authors: Iris H C Miedema, Johanna E. E. Pouw, Anne Kwakman, Gerben J C Zwezerijnen, Marc C Huisman, Florentine E F Timmer, Rieneke van de Ven, Tanja D de Gruijl, Geke A P Hospers, Adrianus J de Langen, C Willemien Menke-van der Houven van Oordt
Journal: Journal for ImmunoTherapy of Cancer (2024)

Immune checkpoint inhibitors (ICIs) can elicit anticancer immune responses, but predictive biomarkers are needed. We measured programmed death ligand 1 (PD-L1) expression in organs and lymph nodes using
Remodeling of anti-tumor immunity with antibodies targeting a p53 mutant
Authors: Dafei Chai, Junhao Wang, Chunmei Fan, Jing-Ming Lim, Xu Wang, Praveen Neeli, Xinfang Yu, Ken H. Young, Yong Li
Journal: Journal of Hematology & Oncology (2024)

p53, the most frequently mutated gene in cancer, lacks effective targeted drugs. We developed monoclonal antibodies (mAbs) that target a p53 hotspot mutation E285K without cross-reactivity with wild-type p53. They were delivered using lipid nanoparticles (LNPs) that encapsulate DNA plasmids. Western blot, BLI, flow cytometry, single-cell sequencing (scRNA-seq), and other methods were employed to assess the function of mAbs in vitro and in vivo. These LNP-pE285K-mAbs in the IgG1 format exhibited a robust anti-tumor effect, facilitating the infiltration of immune cells, including CD8+ T, B, and NK cells. scRNA-seq revealed that IgG1 reduces immune inhibitory signaling, increases MHC signaling from B cells to CD8+ T cells, and enriches anti-tumor T cell and B cell receptor profiles. The E285K-mAbs were also produced in the dimeric IgA (dIgA) format, whose anti-tumor activity depended on the polymeric immunoglobulin receptor (PIGR), a membrane Ig receptor, whereas that of IgG1 relied on TRIM21, an intracellular IgG receptor. Targeting specific mutant epitopes using DNA-encoded and LNP-delivered mAbs represents a potential precision medicine strategy against p53 mutants in TRIM21- or PIGR-positive cancers.
Switching to tenofovir alafenamide in patients with virologically suppressed chronic hepatitis B and renal or hepatic impairment: final week 96 results from an open-label, multicentre, phase 2 study
Authors: Harry L A Janssen, Young-Suk Lim, Pietro Lampertico, Jeong Heo, Chi-Yi Chen, Claire Fournier, Tak Yin Owen Tsang, Ho Bae, Chien-Hung Chen, Carla S Coffin, Sang Hoon Ahn, Huy Trinh, John F Flaherty, Frida Abramov, Yang Zhao, Yang Liu, Audrey Lau, Polina German, Wan-Long Chuang, Kosh Agarwal, Edward Gane
Journal: The Lancet. Gastroenterology & hepatology (2024)
Metabolic Dysregulation and Cancer Risk Program (MeDOC): a transdisciplinary approach to obesity-associated cancers
Authors: Tram Kim Lam, Phil Daschner, Naoko Ishibe, Anil Wali, Kara Hall, Susan Czajkowski, Somdat Mahabir, Joanna M Watson, Linda Nebeling, Sharon Ross, Edward Sauter
Journal: JNCI Journal of the National Cancer Institute (2024)

With the escalating prevalence of obesity, the association between obesity and cancer is a growing public health concern. Obesity will soon surpass tobacco smoking as the most important preventable cause of cancer. Obesity-driven mechanisms can alter cell functions to induce metabolic changes, chronic inflammation, and insulin resistance that are believed to contribute to cancer risk and development; yet the specific underlying biological mechanisms of obesity-related cancer development are largely unknown. The Metabolic Dysregulation and Cancer Risk Program: a transdisciplinary approach to obesity-associated cancers (MeDOC) is a trans-National Cancer Institute research initiative supported by the Division of Cancer Control and Population Sciences, the Division of Cancer Biology, the Division of Cancer Prevention, and the Center to Reduce Cancer Health Disparities. The overall purpose of the MeDOC Program is to advance our understanding of the underlying mechanisms that connect obesity, metabolic dysregulation, and increased obesity cancer risk as well as identify markers that will enhance cancer risk prediction, improve screening for high-risk individuals, and identify targets for preventive and therapeutic interventions for cancer interception or treatment. This report describes the funded research projects, the Coordinating Center, and the goals of the MeDOC program.
Efficacy and safety of personalized optimal PD-(L)1 combinations in advanced NSCLC: a network meta-analysis
Authors: Xianjing Chu, Wentao Tian, Jiaoyang Ning, Rongrong Zhou
Journal: JNCI Journal of the National Cancer Institute (2024)

Programmed death 1 (PD-1)/programmed death 1 ligand 1 (PD-L1)-directed immunotherapy has revolutionized the treatments for advanced non-small cell lung cancer (NSCLC), whereas the optimal therapeutic combinations remain uncertain.
From wearable sensor data to digital biomarker development: ten lessons learned and a framework proposal
Authors: Paola Daniore, Vasileios Nittas, Christina Haag, Jürgen Bernard, Roman Gonzenbach, Viktor von Wyl
Journal: npj Digital Medicine (2024)

Wearable sensor technologies are becoming increasingly relevant in health research, particularly in the context of chronic disease management. They generate real-time health data that can be translated into digital biomarkers, which can provide insights into our health and well-being. Scientific methods to collect, interpret, analyze, and translate health data from wearables to digital biomarkers vary, and systematic approaches to guide these processes are currently lacking. This paper is based on an observational, longitudinal cohort study, BarKA-MS, which collected wearable sensor data on the physical rehabilitation of people living with multiple sclerosis (MS). Based on our experience with BarKA-MS, we provide and discuss ten lessons we learned in relation to digital biomarker development across key study phases. We then summarize these lessons into a guiding framework (DACIA) that aims to informs the use of wearable sensor data for digital biomarker development and chronic disease management for future research and teaching.
Modulation of cholesterol metabolism with Phytoremedies in Alzheimer’s disease: A comprehensive review
Authors: Sushruta Koppula, Nitu L. Wankhede, Shivkumar S. Sammeta, Prajwali V. Shende, Rupali S. Pawar, Niyamat Chimthanawala, Mohit D. Umare, Brijesh G. Taksande, Aman B. Upaganlawar, Milind J. Umekar, Spandana Rajendra Kopalli, Mayur B. Kale
Journal: Ageing Research Reviews (2024)
Multiepitope recognition technology promotes the in-depth analysis of antibody‒drug conjugates
Authors: Yutian Lei, Yuan Shen, Feng Chen, Rui He, Zhang Zhang, Ying Zhou, Jin-Chen Yu, Jacques Crommen, Zhengjin Jiang, Qiqin Wang
Journal: Acta Pharmaceutica Sinica B (2024)

The dynamic tracking of antibody‒drug conjugates (ADCs) in serum is crucial. However, a versatile bioanalytical platform is lacking due to serious matrix interferences, the heterogeneity and complex biotransformation of ADCs, and the recognition deficiencies of traditional affinity technologies. To overcome this, a multiepitope recognition technology (MERT) was developed by simultaneously immobilizing CDR and non-CDR ligands onto MOF@AuNPs. MERT's excellent specificity, ultrahigh ligand density, and potential synergistic recognition ability enable it to target the different key regions of ADCs to overcome the deficiencies of traditional technologies. The binding capacity of MERT for antibodies is ten to hundred times higher than that of the mono-epitope or Fc-specific affinity technologies. Since MERT can efficiently capture target ADCs from serum, a novel bioanalytical platform based on MERT and RPLC‒QTOF-MS has been developed to monitor the dynamic changes of ADCs in serum, including the fast changes of drug-to-antibody ratio from 3.67 to 0.22, the loss of payloads (maytansinol), and the unexpected hydrolysis of the succinimide ring of the linker, which will contribute to clarify the fate of ADCs and provide a theoretical basis for future design. In summary, the MERT-based versatile platform will open a new avenue for in-depth studies of ADCs in biological fluids.
Development and validation of AI-assisted transcriptomic signatures to personalize adjuvant chemotherapy in patients with pancreatic ductal adenocarcinoma
Authors: N. Fraunhoffer, P. Hammel, T. Conroy, R. Nicolle, J.-B. Bachet, A. Harlé, V. Rebours, A. Turpin, M. Ben Abdelghani, E. Mitry, J. Biagi, B. Chanez, M. Bigonnet, A. Lopez, L. Evesque, T. Lecomte, E. Assenat, O. Bouché, D.J. Renouf, A. Lambert, L. Monard, M. Mauduit, J. Cros, J. Iovanna, N. Dusetti
Journal: Annals of Oncology (2024)

After surgical resection of pancreatic ductal adenocarcinoma (PDAC), patients are predominantly treated with adjuvant chemotherapy, commonly consisting of gemcitabine (GEM)-based regimens or the modified FOLFIRINOX (mFFX) regimen. While mFFX regimen has been shown to be more effective than GEM-based regimens, it is also associated with higher toxicity. Current treatment decisions are based on patient performance status rather than on the molecular characteristics of the tumor. To address this gap, the goal of this study was to develop drug-specific transcriptomic signatures for personalized chemotherapy treatment.
Precision medicine for asthma treatment: Unlocking the potential of the epigenome and microbiome
Authors: Javier Perez-Garcia, Andres Cardenas, Fabian Lorenzo-Diaz, Maria Pino-Yanes
Journal: Journal of Allergy and Clinical Immunology (2024)

Asthma is a leading worldwide biomedical concern. Patients can experience life-threatening worsening episodes (exacerbations) usually controlled by anti-inflammatory and bronchodilator drugs. However, substantial heterogeneity in treatment response exists, and a subset of patients with unresolved asthma carry the major burden of this disease. The study of the epigenome and microbiome might bridge the gap between human genetics and environmental exposure to partially explain the heterogeneity in drug response. This review aims to provide a critical examination of the existing literature on the microbiome and epigenetic studies examining associations with asthma treatments and drug response, highlight convergent pathways, address current challenges, and offer future perspectives. Current epigenetic and microbiome studies have shown the bilateral relationship between asthma pharmacologic interventions and the human epigenome and microbiome. These studies, focusing on corticosteroids and to a lesser extent on bronchodilators, azithromycin, immunotherapy, and mepolizumab, have improved the understanding of the molecular basis of treatment response and identified promising biomarkers for drug response prediction. Immune and inflammatory pathways (eg, IL-2, TNF-α, NF-κB, and C/EBPs) underlie microbiome-epigenetic associations with asthma treatment, representing potential therapeutic pathways to be targeted. A comprehensive evaluation of these omics biomarkers could significantly contribute to precision medicine and new therapeutic target discovery.
Blockade of endolysosomal acidification suppresses TLR3-mediated proinflammatory signaling in airway epithelial cells
Authors: Gunnar Pejler, Xinran O. Zhao, Ella Fagerström, Aida Paivandy
Journal: Journal of Allergy and Clinical Immunology (2024)

Endolysosomal compartments are acidic and contain low pH-dependent proteases, and these conditions are exploited by respiratory viruses, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza virus, for escaping into the cytosol. Moreover, endolysosomes contain various pattern recognition receptors (PRRs), which respond to virus-derived pathogen-associated molecular patterns (PAMPs) by production of proinflammatory cytokines/chemokines. However, excessive proinflammatory responses can lead to a potentially lethal cytokine storm.
Pemphigoid gestationis is associated with an increased risk for adverse pregnancy outcomes: A large-scale propensity-matched retrospective cohort study
Authors: Sophie L Preuß, Artem Vorobyev, Eva Lotta Moderegger, Patrick Terheyden, Katja Bieber, Khalaf Kridin, Pascal Joly, Ralf J Ludwig
Journal: Journal of the American Academy of Dermatology (2024)
An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs
Authors: Richard H. Chapple, Xueying Liu, Sivaraman Natarajan, Margaret I. M. Alexander, Yuna Kim, Anand G. Patel, Christy W. LaFlamme, Min Pan, William C. Wright, Hyeong-Min Lee, Yinwen Zhang, Meifen Lu, Selene C. Koo, Courtney Long, John Harper, Chandra Savage, Melissa D. Johnson, Thomas Confer, Walter J. Akers, Michael A. Dyer, Heather Sheppard, John Easton, Paul Geeleher
Journal: Genome biology (2024)

Abstract Background Neuroblastoma is a common pediatric cancer, where preclinical studies suggest that a mesenchymal-like gene expression program contributes to chemotherapy resistance. However, clinical outcomes remain poor, implying we need a better understanding of the relationship between patient tumor heterogeneity and preclinical models. Results Here, we generate single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We develop an unsupervised machine learning approach (“automatic consensus nonnegative matrix factorization” (acNMF)) to compare the gene expression programs found in preclinical models to a large cohort of patient tumors. We confirm a weakly expressed, mesenchymal-like program in otherwise adrenergic cancer cells in some pre-treated high-risk patient tumors, but this appears distinct from the presumptive drug-resistance mesenchymal programs evident in cell lines. Surprisingly, however, this weak-mesenchymal-like program is maintained in PDX and could be chemotherapy-induced in our GEMM after only 24 h, suggesting an uncharacterized therapy-escape mechanism. Conclusions Collectively, our findings improve the understanding of how neuroblastoma patient tumor heterogeneity is reflected in preclinical models, provides a comprehensive integrated resource, and a generalizable set of computational methodologies for the joint analysis of clinical and pre-clinical single-cell RNA-seq datasets.
Impact of frailty on patients hospitalized with myelodysplastic syndrome: A nationwide analysis.
Authors: Olivia Del Castillo, Saad Javaid, Kelly Frasier, Vivian Li, Julia Vinagolu-Baur, Evadne Rodriguez, Nataly Ortega Yaguachi, Raquel Batista, Kenlee Jonas, Laura Palma
Journal: Journal of Clinical Oncology (2024)

6569 Background: Myelodysplastic Syndrome (MDS) is a complex group of neoplasms that arises from abnormalities in the bone marrow's hematopoietic stem cells, leading to ineffective blood cell production. Frailty signifies increased vulnerability to internal and external stressors, with its impact on survival in MDS gaining recognition. We conducted a retrospective analysis to explore the impact of frailty on hospitalized MDS patients and its association with unfavorable hospital outcomes. Methods: National Inpatient Sample (NIS) 2019 and 2020 was utilized to identify the patients admitted with a primary diagnosis of MDS and had a concurrent diagnosis of Frailty. The Primary outcome was Mortality, and secondary outcomes included length of stay, total cost of hospitalization, and other adverse in-hospital outcomes. Multivariate logistic regression analysis was used to calculate the outcomes after adjusting for baseline sociodemographic characteristics. Results: A total of 17,000 patients were admitted with a primary diagnosis of MDS, among whom 4,285 (25.20%) patients had a concurrent diagnosis of Frailty. The mean age of patients with and without Frailty was 75.75 (+/-14.01) and 71.54 (+/-14.68), respectively, p&lt;0.001. After adjusting for confounding variables, Frailty was considered an independent predictor of mortality in MDS patients (OR=1.80 (1.22-2.64); p=0.003). Patients with Frailty had a prolonged length of stay in the hospital (8.62 days(3.2-9.6) without and 11.56 days (4.6-12.1) with Frailty, P&lt;0.001), as well as an increased total cost of hospitalization ($ 132159 (3256-23651) without and $173898 (5317-389146) with Frailty, p&lt;0.001). Additionally, Frailty was associated with an increased likelihood of sepsis (OR=2.02 (1.33-3.06); p=0.001), acute kidney injury (OR=1.22 (1.02-1.50); p=0.04), major depressive disorder (OR=1.51 (1.19-1.91); p=0.001), and a higher association with chemotherapy (OR=3.46 (1.21-9.91); p=0.021) and palliative care (OR=2.23 (1.72-2.90); p&lt;0.001). However, frail patients were less likely to receive blood transfusions (OR=0.73 (0.61-0.88); p=0.001), and no difference was noted in the risk of platelet transfusions (OR=0.95 (0.73-1.25); p=0.763). Conclusions: Frailty is considered an independent predictor of mortality and is associated with increased healthcare resource utilization along with other adverse in-hospital outcomes. Careful and thorough identification of frailty and its effective management is crucial for improving outcomes, reducing mortality, and enhancing the quality of life for patients with debilitating MDS. [Table: see text]
Association of triglyceride glucose index with stroke: from two large cohort studies and Mendelian randomization analysis
Authors: Yong’An Jiang, Jing Shen, Peng Chen, JiaHong Cai, YangYang Zhao, JiaWei Liang, JianHui Cai, ShiQi Cheng, Yan Zhang
Journal: International Journal of Surgery (2024)

Introduction: The triglyceride glucose index (TyG) is associated with cardiovascular diseases; however, its association with stroke remains unclear. This study aimed to elucidate this relationship by examining two extensive cohort studies using two-sample Mendelian randomization (MR). Methods: Using data from the 1999–2018 National Health and Nutrition Examination Survey (NHANES) and the Medical Information Mart for Intensive Care (MIMIC)-IV, the correlation between TyG (continuous and quartile) and stroke was examined using multivariate Cox regression models and sensitivity analyses. Two-sample MR was employed to establish causality between TyG and stroke using the inverse variance weighting method. Genome-wide association study catalog queries were performed for single nucleotide polymorphism-mapped genes, and the STRING platform used to assess protein interactions. Functional annotation and enrichment analyses were also conducted. Results: From the NHANES and MIMIC-IV cohorts, we included 740 and 589 participants with stroke, respectively. After adjusting for covariates, TyG was linearly associated with the risk of stroke death (NHANES: hazard ratio [HR] 0.64, 95% CI: 0.41–0.99, P =0.047; Q3 vs. Q1, HR 0.62, 95% CI: 0.40–0.96, P =0.033; MIMIC-IV: HR 0.46, 95% CI: 0.27–0.80, P =0.006; Q3 vs. Q1, HR 0.32, 95% CI: 0.12–0.86; Q4 vs. Q1, HR 0.30, 95% CI: 0.10–0.89, P =0.030, P for trend=0.017). Two-sample MR analysis showed genetic prediction supported a causal association between a higher TyG and a reduced risk of stroke (odds ratio 0.711, 95% CI: 0.641–0.788, P =7.64e -11 ). Conclusions: TyG was causally associated with a reduced risk of stroke. TyG is a critical factor for stroke risk management.
The mediating effect of blood biomarkers in the associations between inflammatory bowel disease and incident psychiatric disorders: a prospective cohort study
Authors: Jing Feng, Yanjun Wu, Meijun Meng, Ruijie Zeng, Yuying Ma, Dongling Luo, Lijun Zhang, Yajie Zhang, Yang Li, Wentao Huang, Felix W Leung, Chongyang Duan, Weihong Sha, Hao Chen
Journal: International Journal of Surgery (2024)

Despite the growing evidence of an association between inflammatory bowel disease (IBD) and psychiatric disorders, there has been limited research exploring the underlying mediating role of blood biomarkers on the gut-brain axis. This study aimed to examine the association between IBD and the risk of incident psychiatric disorders and investigate whether and how blood biomarkers mediate this association.
Three-Dimensional (3D) deep learning model complements existing models for preoperative disease-free survival prediction (DFS) in localized clear cell renal cell carcinoma (ccRCC): A multicenter retrospective cohort study
Authors: Yingjie Xv, Zongjie Wei, Qing Jiang, Xuan Zhang, Yong Chen, Bangxin Xiao, Siwen Yin, Zongyu Xia, Ming Qiu, Yang Li, Hao Tan, Mingzhao Xiao
Journal: International Journal of Surgery (2024)

Background: Current prognostic models have limited predictive abilities for the growing number of localized (stage I-III) ccRCCs. It is therefore crucial to explore novel preoperative recurrence prediction models to accurately stratify patients and optimize clinical decisions. This purpose of this study was to develop and externally validate a CT-based deep learning (DL) model for pre-surgical disease-free survival (DFS) prediction. Methods: Patients with localized ccRCC were retrospectively enrolled from six independent medical centers. Three-dimensional (3D) tumor regions from CT images were utilized as input to architect a ResNet 50 model, which outputted DL computed risk score (DLCR) of each patient for DFS prediction later. The predictive performance of DLCR was assessed and compared to the radiomics model (Rad-Score), clinical model we built and two existing prognostic models (UISS and Leibovich). The complementary value of DLCR to the UISS, Leibovich, as well as Rad-Score were evaluated by stratified analysis. Results: 707 patients with localized ccRCC were finally enrolled for models’ training and validating. The DLCR we established can perfectly stratify patients into low-, intermediate- and high-risks, and outperformed the Rad-Score, clinical model, UISS and Leibovich score in DFS prediction, with a C-index of 0.754 (0.689-0.821) in the external testing set. Furthermore, the DLCR presented excellent risk stratification capacity in subgroups defined by almost all clinic-pathological features. Moreover, patients in the UISS/Leibovich score/Rad-Score stratified low-risk but DLCR-defined intermediate- and high-risk groups were significantly more likely to experience ccRCC recurrences than those of intermediate- and high-risk in DLCR determined low-risk (all Log-rank P values＜0.05). Conclusions: Our deep learning model, derived from preoperative CT, is superior to radiomics and current models in precisely DFS predicting of localized ccRCC, and can provide complementary values to them, which may assist more informed clinical decisions and adjuvant therapies adoptions.
OpenAI’s Sora in intensive care medicine: opportunities and challenges
Authors: Nan Zhang, Qipeng Shao, Wanqing Li, Kunming Cheng, Haiyang Wu, Cheng Li
Journal: International Journal of Surgery (2024)

Zhang, Nan MD; Shao, Qipeng MD; Li, Wanqing MD; Cheng, Kunming MD; Wu, Haiyang MD; Li, Cheng PhD Author Information
Optimizing lymph node dissection can better for accurate assessment and management of lymph node recurrence in postoperative thoracic esophageal squamous cell carcinoma
Authors: Kexun Li, Jie Zhao, Xueqiang Wei, Xiaobo Chen, Jie Mao, Yunchao Huang, Jiapeng Yang
Journal: International Journal of Surgery (2024)

Li, Kexun MD, PhD; Zhao, Jie MD, PhD; Wei, Xueqiang MD, PhD; Chen, Xiaobo MD, PhD; Mao, Jie MD; Huang, Yunchao MD, PhD; Yang, Jiapeng MD, PhD Author Information