bluuebunny/crossref_metadata_2025 huggingface dataset:
Locating Edge Domination Number of Some Classes of Claw-Free Cubic Graphs
Authors: Sardar Muhammad Shoaib , Choudhry Hamna , Liu Jia-Bao
Published: March 2024

Let G=V;E be a simple graph with vertex set V and edge set E. In a graph G, a subset of edges denoted by M is referred to as an edge-dominating set of G if every edge that is not in M is incident to at least one member of M. A set M⊆E is the locating edge-dominating set if for every two edges e1,e2∈E−M, the sets Ne1∩M and Ne2∩M are nonempty and different. The edge domination number γLG of G is the minimum cardinality of all edge-dominating sets of G. The purpose of this study is to determine the locating edge domination number of certain types of claw-free cubic graphs.
Optimizing the Dry Sliding Wear Behavior of Stir-Casted Al6061/Nano-Al2O3/Quartz Hybrid Metal Matrix Composite Using Taguchi Method
Authors: Tirfe Dagim , Debebe Woldeyohannes Abraham , Regassa Hunde Bonsa , Batu Temesgen , Geleta Eaba
Published: March 2024

The increased demand for wear-resistant and low-weight components in the automobile industry has led to the utilization of aluminum metal matrix composite parts due to their improved performance. The current work focused on optimizing the tribological factors such as applied load, rotational speed, and percentage weight fraction of the reinforcing particles for minimum dry sliding wear rate of Al6061/nano-Al2O3/quartz hybrid composite fabricated by stir casting. The optimization is performed by using the Taguchi L27 orthogonal array experimental plan, and the result is analyzed by the help of analysis of variance (ANOVA). The result of the optimization reveals that the optimum levels of factors for the minimum wear rate are 10 N, 200 rpm, and 12.5%. Furthermore, the ANOVA result depicts that the applied load has the highest impact (87.83%) on the wear rate, followed by rotational speed (10.06%) and percentage weight fraction of the reinforcement (1.60%). The developed linear regression model reveals that the applied load and the rotational speed have a positive relation with the sliding wear rate. However, the percentage weight fraction of the reinforcement has a negative relation. The confirmation test proves that the predicted value of wear rate using the regression equation at optimum levels has a closer agreement with the experimental result having a 6.934% error. Furthermore, the physical property test reveals that the rise in the weight percentage of quartz particles results in a corresponding increase in the percentage porosity of the hybrid composite.
Wind Farm Layout Optimization Based on Dynamic Opposite Learning‐Enhanced Sparrow Search Algorithm
Authors: Zhu Yun , Guo Yahui , Hu Tianyu , Wu Chengke , Zhang Lidong
Published: March 2024

In recent years, the proportion of wind power in new energy generation has gradually increased. The natural wind in wind farms is subject to velocity attenuation by the wake effect, so improving the efficiency of wind farm power generation has become a problem that must be solved for wind power generation. Considering the uncertainty of wind farms, we regard wind farm layout optimization (WFLO) as a strongly nonlinear problem. In this paper, we improve the sparrow search algorithm (SSA) using dynamic opposite learning (DOL) strategy. Twenty‐eight benchmark test results prove that compared with other algorithms, the improved algorithm DOLSSA has excellent robustness and the ability of searching for a better solution when solving a strongly nonlinear optimization problem, and the DOL strategy effectively improves the shortcomings of the original algorithm which is prone to local optimization and space limitation. In this paper, the authors establish the dynamic rotational coordinates of wind farms and set six different physical scenarios by considering the wind direction and wind speed variables, and the results prove that the performance of DOLSSA is optimal.
Metastatic Melanoma to the Urinary Bladder: A Rare Cause of Visible Haematuria
Authors: Ogunremi Olawale , Sirjuesingh Dinelle , Deshpande Aniket
Published: March 2024

Bladder metastasis from cutaneous melanoma is a rare pathology. A 79-year-old woman presented to the haematuria clinic on account of painless visible haematuria. Ten years prior to this index presentation, she was diagnosed with melanoma on her right thigh following a total excision of the skin lesion. Cystoscopy showed a pigmented bladder tumour, and the histology report following a transurethral resection was consistent with metastatic melanoma, and further imaging revealed metastasis to the lungs, adrenals, and lymph nodes.
Length-Weight and Length-Length Relationships of 39 Demersal Fish Species of an Estuarine-Coastal Ecosystem from the Northwestern of the Baja California Peninsula, Mexico
Authors: González-Acosta Adrián F. , Rábago-Quiroz Carlos H. , Ruiz-Campos Gorgonio , García-Borbón Juan Antonio , Alejo-Plata María del Carmen , Barrón-Barraza Francisco J.
Published: March 2024

The length-weight (LW) and length-length (LL) relationships of 39 demersal fish species belonging to 19 families were calculated. Fish samples were collected monthly during the open (September–February) and close (March–August) shrimp fishing seasons from 2014 to 2022 by the artisanal fishery in the Bahía Magdalena-Almejas lagoon system (BMA), using small boats and a shrimp bottom trawl with 16–17 m top rope and 38 mm mesh opening. Total (TL) and standard (SL) lengths and total weight (TW) were measured for all fish specimens. The b parameter of the LW relationship ranged from the minimum of 2.483 for Cynoscion parvipinnis to 3.775 as the maximum for Bagre panamensis while, for the LL relationship, the parameter b ranged from 0.685 for Balistes polylepis to 0.994 for Orthopristis cantharinus. Both LW and LL relationships were highly correlated (r2>0.95; P<0.05. The parameter b of the LW relationship indicated that 59% of the fish species showed isometric and 41% allometric growth. Information on LW and LL relationships is updated for thirteen species, and four are reported for the first time: Citharichthys xanthostigma, Ophidion galeoides, Pleuronichthys guttulatus, and P. ritteri. This study will be useful to update the LW relationship parameters for data-poor fish species and contribute to the accuracy of fish stock assessment in the BMA fisheries.
Impact of Early Rejection Treatment on Infection Development in Kidney Transplant Recipients: A Propensity Analysis
Authors: Gupta Simran , Gea-Banacloche Juan , Heilman Raymond L. , Yaman Reena N. , Me Hay Me , Zhang Nan , Vikram Holenarasipur R. , Kodali Lavanya
Published: March 2024

Introduction. The impact of renal allograft rejection treatment on infection development has not been formally defined in the literature. Methods. We conducted a retrospective cohort study of 185 rejection (case) and 185 nonrejection (control) kidney transplant patients treated at our institution from 2014 to 2020 to understand the impact of rejection on infection development. Propensity scoring was used to match cohorts. We collected data for infections within 6 months of rejection for the cases and 18 months posttransplant for controls. Results. In 370 patients, we identified 466 infections, 297 in the controls, and 169 in the cases. Urinary tract infections (38.9%) and cytomegalovirus viremia (13.7%) were most common. Cumulative incidence of infection between the case and controls was 2.17 (CI 1.54–3.05); p<0.001. There was no difference in overall survival (HR 0.90, CI 0.49–1.66) or graft survival (HR 1.27, CI 0.74–2.20) between the groups. There was a significant difference in overall survival (HR 2.28, CI 1.14–4.55; p=0.019) and graft survival (HR 1.98, CI 1.10–3.56; p=0.023) when patients with infection were compared to those without. Conclusions. As previously understood, rejection treatment is a risk factor for subsequent infection development. Our data have defined this relationship more clearly. This study is unique, however, in that we found that infections, but not rejection, negatively impacted both overall patient survival and allograft survival, likely due to our institution’s robust post-rejection protocols. Clinicians should monitor patients closely for infections in the post-rejection period and have a low threshold to treat these infections while also restarting appropriate prophylaxis.
Anti-Inflammatory Effects and Immunomodulatory Efficacy of Unitein (Fermented Glycine max, Panax ginseng, and Chenpi Mixture) in RAW 264.7 Macrophages and Mice Splenocytes along with Component Analysis
Authors: Lee Kyunghwa , Pan Yanni , Lee YeonJun , Song WoonSeo , Park Kun-Young
Published: March 2024

Inflammation and the immune system are intricately linked, with the immune system serving as a vital defense mechanism in the human body. Consequently, there is great emphasis placed on the regulation of both the body’s inflammatory response and the immune system. This study investigated the anti-inflammatory properties and immunomodulatory effects of Unitein (fermented Glycine max, Panax ginseng, and chenpi mixture) in both RAW 264.7 macrophages and mice splenocytes while also comprehensively analyzing its components. To this end, the 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide (MTT) assay was employed to evaluate the cytotoxicity of the samples to RAW 264.7 cells. The Griess assay was utilized to determine nitrite concentrations, while the enzyme-linked immunosorbent assay (ELISA) was employed to assess the levels of inflammation-related cytokines in RAW 264.7 cells and isolated mouse splenocytes. Additionally, quantitative polymerase chain reaction was employed to quantify the mRNA expression of inflammation-related genes in the cells, while the lactate dehydrogenase assay was used to analyze natural killer (NK) cell activity. Our results revealed that Unitein exhibited no toxicity to RAW 264.7 cells at concentrations below 1.5 mg/mL. However, Unitein effectively suppressed the release of proinflammatory cytokines tumor necrosis factor (TNF)-α, interleukin (IL)-1β, IL-6, and interferon (IFN)-γ, as well as enzymes COX-2 and iNOS from RAW 264.7 cells, while concurrently promoting the release of anti-inflammatory cytokines IκB-α, IL-10, and IL-4. Furthermore, Unitein inhibited the increase in TNF-α, IL-1β, IFN-γ, IL-12, nuclear factor (NF)-κB p50, NF-κB p65, and iNOS induced by lipopolysaccharides (LPSs) in isolated mouse splenocytes. Notably, Unitein also exhibited the potential to stimulate NK cell activity. Metabolite analysis indicated that Unitein contained more active compounds with anti-inflammatory and immunity-enhancing properties than did unfermented Unitein. This study highlights the potential therapeutic role of Unitein in mitigating inflammation and enhancing immune responses, thereby deepening our understanding of its biological activities and underlying mechanisms. These findings underscore the significance of Unitein as a potential candidate for the development of novel anti-inflammatory and immunomodulatory interventions.
Supplementary Table S1 from Maternal Prenatal Use of Alcohol, Tobacco, and Illicit Drugs and Associations with Childhood Cancer Subtypes
Authors: Wimberly Courtney E. , Gulrajani Natalie B. , Russ Jeffrey B. , Landi Daniel , Wiemels Joseph L. , Towry Lisa , Wiencke John K. , Walsh Kyle M.
Published: March 2024

Comparison of demographic characteristics of respondents reporting any maternal substance use during pregnancy vs. those reporting no substance use
Supplementary Table 1 from Evaluation of Algorithms Using Automated Health Plan Data to Identify Breast Cancer Recurrences
Authors: Aiello Bowles Erin J. , Kroenke Candyce H. , Chubak Jessica , Bhimani Jenna , O'Connell Kelli , Brandzel Susan , Valice Emily , Doud Rachael , Theis Mary Kay , Roh Janise M. , Heon Narre , Persaud Sonia , Griggs Jennifer J. , Bandera Elisa V. , Kushi Lawrence H. , Kantor Elizabeth D.
Published: March 2024

Supplementary Table 1 showing TP, FN, FP, and TN for each recurrence algorithm among the chart abstracted population and weighted to the overall population
Supplementary Table 1. from Marital Status, Living Arrangement, and Survival among Individuals with Advanced Prostate Cancer in the International Registry for Men with Advanced Prostate Cancer
Authors: Chen Naiyu , McGrath Colleen B. , Ericsson Caroline I. , Vaselkiv Jane B. , Rencsok Emily M. , Stopsack Konrad H. , Guard Hannah E. , Autio Karen A. , Rathkopf Dana E. , Enting Deborah , Bitting Rhonda L. , Mateo Joaquin , Githiaka Charles W. , Chi Kim N. , Cheng Heather H. , Davis Ian D. , Anderson Simon G. , Badal Simone Ann Marie , Bjartell Anders , Russnes Kjell M. , Heath Elisabeth I. , Pomerantz Mark M. , Henegan John C. , Hyslop Terry , Esteban Emilio , Omlin Aurelius , McDermott Ray , Fay Andre P. , Popoola Ademola Alabi , Ragin Camille , Nowak Joel , Gerke Travis , Kantoff Philip W. , George Daniel J. , Penney Kathryn L. , Mucci Lorelei A.
Published: March 2024

A table showing baseline characteristics at enrollment of individuals with advanced prostate cancer in IRONMAN, by outcome status (N=2,119).
Supplementary Table S5 from Lung Cancer in Ever- and Never-Smokers: Findings from Multi-Population GWAS Studies
Authors: Li Yafang , Xiao Xiangjun , Li Jianrong , Han Younghun , Cheng Chao , Fernandes Gail F. , Slewitzke Shannon E. , Rosenberg Susan M. , Zhu Meng , Byun Jinyoung , Bossé Yohan , McKay James D. , Albanes Demetrios , Lam Stephen , Tardon Adonina , Chen Chu , Bojesen Stig E. , Landi Maria T. , Johansson Mattias , Risch Angela , Bickeböller Heike , Wichmann H-Erich , Christiani David C. , Rennert Gad , Arnold Susanne M. , Goodman Gary E. , Field John K. , Davies Michael P.A. , Shete Sanjay , Marchand Loïc Le , Liu Geoffrey , Hung Rayjean J. , Andrew Angeline S. , Kiemeney Lambertus A. , Sun Ryan , Zienolddiny Shanbeh , Grankvist Kjell , Johansson Mikael , Caporaso Neil E. , Cox Angela , Hong Yun-Chul , Lazarus Philip , Schabath Matthew B. , Aldrich Melinda C. , Schwartz Ann G. , Gorlov Ivan , Purrington Kristen S. , Yang Ping , Liu Yanhong , Bailey-Wilson Joan E. , Pinney Susan M. , Mandal Diptasri , Willey James C. , Gaba Colette , Brennan Paul , Xia Jun , Shen Hongbing , Amos Christopher I.
Published: March 2024

Supplementary Table S5 shows the signals for the variants in stratified analysis by smoking behavior
Supplementary Table S3 from Lung Cancer in Ever- and Never-Smokers: Findings from Multi-Population GWAS Studies
Authors: Li Yafang , Xiao Xiangjun , Li Jianrong , Han Younghun , Cheng Chao , Fernandes Gail F. , Slewitzke Shannon E. , Rosenberg Susan M. , Zhu Meng , Byun Jinyoung , Bossé Yohan , McKay James D. , Albanes Demetrios , Lam Stephen , Tardon Adonina , Chen Chu , Bojesen Stig E. , Landi Maria T. , Johansson Mattias , Risch Angela , Bickeböller Heike , Wichmann H-Erich , Christiani David C. , Rennert Gad , Arnold Susanne M. , Goodman Gary E. , Field John K. , Davies Michael P.A. , Shete Sanjay , Marchand Loïc Le , Liu Geoffrey , Hung Rayjean J. , Andrew Angeline S. , Kiemeney Lambertus A. , Sun Ryan , Zienolddiny Shanbeh , Grankvist Kjell , Johansson Mikael , Caporaso Neil E. , Cox Angela , Hong Yun-Chul , Lazarus Philip , Schabath Matthew B. , Aldrich Melinda C. , Schwartz Ann G. , Gorlov Ivan , Purrington Kristen S. , Yang Ping , Liu Yanhong , Bailey-Wilson Joan E. , Pinney Susan M. , Mandal Diptasri , Willey James C. , Gaba Colette , Brennan Paul , Xia Jun , Shen Hongbing , Amos Christopher I.
Published: March 2024

Supplementary Table S3 shows the signals at rs7985487 in Firth test validation.
Supplementary Table S2 from Lung Cancer in Ever- and Never-Smokers: Findings from Multi-Population GWAS Studies
Authors: Li Yafang , Xiao Xiangjun , Li Jianrong , Han Younghun , Cheng Chao , Fernandes Gail F. , Slewitzke Shannon E. , Rosenberg Susan M. , Zhu Meng , Byun Jinyoung , Bossé Yohan , McKay James D. , Albanes Demetrios , Lam Stephen , Tardon Adonina , Chen Chu , Bojesen Stig E. , Landi Maria T. , Johansson Mattias , Risch Angela , Bickeböller Heike , Wichmann H-Erich , Christiani David C. , Rennert Gad , Arnold Susanne M. , Goodman Gary E. , Field John K. , Davies Michael P.A. , Shete Sanjay , Marchand Loïc Le , Liu Geoffrey , Hung Rayjean J. , Andrew Angeline S. , Kiemeney Lambertus A. , Sun Ryan , Zienolddiny Shanbeh , Grankvist Kjell , Johansson Mikael , Caporaso Neil E. , Cox Angela , Hong Yun-Chul , Lazarus Philip , Schabath Matthew B. , Aldrich Melinda C. , Schwartz Ann G. , Gorlov Ivan , Purrington Kristen S. , Yang Ping , Liu Yanhong , Bailey-Wilson Joan E. , Pinney Susan M. , Mandal Diptasri , Willey James C. , Gaba Colette , Brennan Paul , Xia Jun , Shen Hongbing , Amos Christopher I.
Published: March 2024

Supplementary Table S2 shows the signals at known susceptibility loci from ever- and never-smoking lung cancer.
Figure S4 from Lung Cancer in Ever- and Never-Smokers: Findings from Multi-Population GWAS Studies
Authors: Li Yafang , Xiao Xiangjun , Li Jianrong , Han Younghun , Cheng Chao , Fernandes Gail F. , Slewitzke Shannon E. , Rosenberg Susan M. , Zhu Meng , Byun Jinyoung , Bossé Yohan , McKay James D. , Albanes Demetrios , Lam Stephen , Tardon Adonina , Chen Chu , Bojesen Stig E. , Landi Maria T. , Johansson Mattias , Risch Angela , Bickeböller Heike , Wichmann H-Erich , Christiani David C. , Rennert Gad , Arnold Susanne M. , Goodman Gary E. , Field John K. , Davies Michael P.A. , Shete Sanjay , Marchand Loïc Le , Liu Geoffrey , Hung Rayjean J. , Andrew Angeline S. , Kiemeney Lambertus A. , Sun Ryan , Zienolddiny Shanbeh , Grankvist Kjell , Johansson Mikael , Caporaso Neil E. , Cox Angela , Hong Yun-Chul , Lazarus Philip , Schabath Matthew B. , Aldrich Melinda C. , Schwartz Ann G. , Gorlov Ivan , Purrington Kristen S. , Yang Ping , Liu Yanhong , Bailey-Wilson Joan E. , Pinney Susan M. , Mandal Diptasri , Willey James C. , Gaba Colette , Brennan Paul , Xia Jun , Shen Hongbing , Amos Christopher I.
Published: March 2024

Supplementary Figure S4 shows the results of eQTL analysis of rs968516 in GTEx.
Figure S3 from Lung Cancer in Ever- and Never-Smokers: Findings from Multi-Population GWAS Studies
Authors: Li Yafang , Xiao Xiangjun , Li Jianrong , Han Younghun , Cheng Chao , Fernandes Gail F. , Slewitzke Shannon E. , Rosenberg Susan M. , Zhu Meng , Byun Jinyoung , Bossé Yohan , McKay James D. , Albanes Demetrios , Lam Stephen , Tardon Adonina , Chen Chu , Bojesen Stig E. , Landi Maria T. , Johansson Mattias , Risch Angela , Bickeböller Heike , Wichmann H-Erich , Christiani David C. , Rennert Gad , Arnold Susanne M. , Goodman Gary E. , Field John K. , Davies Michael P.A. , Shete Sanjay , Marchand Loïc Le , Liu Geoffrey , Hung Rayjean J. , Andrew Angeline S. , Kiemeney Lambertus A. , Sun Ryan , Zienolddiny Shanbeh , Grankvist Kjell , Johansson Mikael , Caporaso Neil E. , Cox Angela , Hong Yun-Chul , Lazarus Philip , Schabath Matthew B. , Aldrich Melinda C. , Schwartz Ann G. , Gorlov Ivan , Purrington Kristen S. , Yang Ping , Liu Yanhong , Bailey-Wilson Joan E. , Pinney Susan M. , Mandal Diptasri , Willey James C. , Gaba Colette , Brennan Paul , Xia Jun , Shen Hongbing , Amos Christopher I.
Published: March 2024

Supplementary Figure S3 displays the Validation of the association effect of rs6757077 at IKZF2 using data from six independent study sites in China.
Supplementary Methods from Genome-Wide Gene–Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk
Authors: Stern Mariana C. , Sanchez Mendez Joel , Kim Andre E. , Obón-Santacana Mireia , Moratalla-Navarro Ferran , Martín Vicente , Moreno Victor , Lin Yi , Bien Stephanie A. , Qu Conghui , Su Yu-Ru , White Emily , Harrison Tabitha A. , Huyghe Jeroen R. , Tangen Catherine M. , Newcomb Polly A. , Phipps Amanda I. , Thomas Claire E. , Kawaguchi Eric S. , Lewinger Juan Pablo , Morrison John L. , Conti David V. , Wang Jun , Thomas Duncan C. , Platz Elizabeth A. , Visvanathan Kala , Keku Temitope O. , Newton Christina C. , Um Caroline Y. , Kundaje Anshul , Shcherbina Anna , Murphy Neil , Gunter Marc J. , Dimou Niki , Papadimitriou Nikos , Bézieau Stéphane , van Duijnhoven Franzel J.B. , Männistö Satu , Rennert Gad , Wolk Alicja , Hoffmeister Michael , Brenner Hermann , Chang-Claude Jenny , Tian Yu , Le Marchand Loïc , Cotterchio Michelle , Tsilidis Konstantinos K. , Bishop D. Timothy , Melaku Yohannes Adama , Lynch Brigid M. , Buchanan Daniel D. , Ulrich Cornelia M. , Ose Jennifer , Peoples Anita R. , Pellatt Andrew J. , Li Li , Devall Matthew A.M. , Campbell Peter T. , Albanes Demetrius , Weinstein Stephanie J. , Berndt Sonja I. , Gruber Stephen B. , Ruiz-Narvaez Edward , Song Mingyang , Joshi Amit D. , Drew David A. , Petrick Jessica L. , Chan Andrew T. , Giannakis Marios , Peters Ulrike , Hsu Li , Gauderman W. James
Published: March 2024

File contains the supplementary methods with appropriate citations and bibliography.
Supplementary Figure 5 from Genome-Wide Gene–Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk
Authors: Stern Mariana C. , Sanchez Mendez Joel , Kim Andre E. , Obón-Santacana Mireia , Moratalla-Navarro Ferran , Martín Vicente , Moreno Victor , Lin Yi , Bien Stephanie A. , Qu Conghui , Su Yu-Ru , White Emily , Harrison Tabitha A. , Huyghe Jeroen R. , Tangen Catherine M. , Newcomb Polly A. , Phipps Amanda I. , Thomas Claire E. , Kawaguchi Eric S. , Lewinger Juan Pablo , Morrison John L. , Conti David V. , Wang Jun , Thomas Duncan C. , Platz Elizabeth A. , Visvanathan Kala , Keku Temitope O. , Newton Christina C. , Um Caroline Y. , Kundaje Anshul , Shcherbina Anna , Murphy Neil , Gunter Marc J. , Dimou Niki , Papadimitriou Nikos , Bézieau Stéphane , van Duijnhoven Franzel J.B. , Männistö Satu , Rennert Gad , Wolk Alicja , Hoffmeister Michael , Brenner Hermann , Chang-Claude Jenny , Tian Yu , Le Marchand Loïc , Cotterchio Michelle , Tsilidis Konstantinos K. , Bishop D. Timothy , Melaku Yohannes Adama , Lynch Brigid M. , Buchanan Daniel D. , Ulrich Cornelia M. , Ose Jennifer , Peoples Anita R. , Pellatt Andrew J. , Li Li , Devall Matthew A.M. , Campbell Peter T. , Albanes Demetrius , Weinstein Stephanie J. , Berndt Sonja I. , Gruber Stephen B. , Ruiz-Narvaez Edward , Song Mingyang , Joshi Amit D. , Drew David A. , Petrick Jessica L. , Chan Andrew T. , Giannakis Marios , Peters Ulrike , Hsu Li , Gauderman W. James
Published: March 2024

Functional annotation plots for the two genome-wide gene-interaction main results.
Figure S1 from Lung Cancer in Ever- and Never-Smokers: Findings from Multi-Population GWAS Studies
Authors: Li Yafang , Xiao Xiangjun , Li Jianrong , Han Younghun , Cheng Chao , Fernandes Gail F. , Slewitzke Shannon E. , Rosenberg Susan M. , Zhu Meng , Byun Jinyoung , Bossé Yohan , McKay James D. , Albanes Demetrios , Lam Stephen , Tardon Adonina , Chen Chu , Bojesen Stig E. , Landi Maria T. , Johansson Mattias , Risch Angela , Bickeböller Heike , Wichmann H-Erich , Christiani David C. , Rennert Gad , Arnold Susanne M. , Goodman Gary E. , Field John K. , Davies Michael P.A. , Shete Sanjay , Marchand Loïc Le , Liu Geoffrey , Hung Rayjean J. , Andrew Angeline S. , Kiemeney Lambertus A. , Sun Ryan , Zienolddiny Shanbeh , Grankvist Kjell , Johansson Mikael , Caporaso Neil E. , Cox Angela , Hong Yun-Chul , Lazarus Philip , Schabath Matthew B. , Aldrich Melinda C. , Schwartz Ann G. , Gorlov Ivan , Purrington Kristen S. , Yang Ping , Liu Yanhong , Bailey-Wilson Joan E. , Pinney Susan M. , Mandal Diptasri , Willey James C. , Gaba Colette , Brennan Paul , Xia Jun , Shen Hongbing , Amos Christopher I.
Published: March 2024

Supplementary Figure S1 displays the distribution of histology subtypes in each population in ever- and never-smokers.
Table S5 from Mitochondrial DNA Haplogroups and SNPs: Risk Factors in Multiple Cancers Based on a Cross-Tumor Analysis in Chinese Population
Authors: Chen Dongbo , Yan Zeyu , Yuan Qing , Xie Fanfan , Liu Yang , Feng Zehui , Wang Zhenni , Zhou Feng , Xing Jinliang , Zhang Zhaohui , Wang Fuli , Guo Xu
Published: March 2024

Analysis of association between mtDNA haplogroups and occurrence risk in single type of cancer when referring to haplogroup D.
Supplementary Figure 2 from Genome-Wide Gene–Environment Interaction Analyses to Understand the Relationship between Red Meat and Processed Meat Intake and Colorectal Cancer Risk
Authors: Stern Mariana C. , Sanchez Mendez Joel , Kim Andre E. , Obón-Santacana Mireia , Moratalla-Navarro Ferran , Martín Vicente , Moreno Victor , Lin Yi , Bien Stephanie A. , Qu Conghui , Su Yu-Ru , White Emily , Harrison Tabitha A. , Huyghe Jeroen R. , Tangen Catherine M. , Newcomb Polly A. , Phipps Amanda I. , Thomas Claire E. , Kawaguchi Eric S. , Lewinger Juan Pablo , Morrison John L. , Conti David V. , Wang Jun , Thomas Duncan C. , Platz Elizabeth A. , Visvanathan Kala , Keku Temitope O. , Newton Christina C. , Um Caroline Y. , Kundaje Anshul , Shcherbina Anna , Murphy Neil , Gunter Marc J. , Dimou Niki , Papadimitriou Nikos , Bézieau Stéphane , van Duijnhoven Franzel J.B. , Männistö Satu , Rennert Gad , Wolk Alicja , Hoffmeister Michael , Brenner Hermann , Chang-Claude Jenny , Tian Yu , Le Marchand Loïc , Cotterchio Michelle , Tsilidis Konstantinos K. , Bishop D. Timothy , Melaku Yohannes Adama , Lynch Brigid M. , Buchanan Daniel D. , Ulrich Cornelia M. , Ose Jennifer , Peoples Anita R. , Pellatt Andrew J. , Li Li , Devall Matthew A.M. , Campbell Peter T. , Albanes Demetrius , Weinstein Stephanie J. , Berndt Sonja I. , Gruber Stephen B. , Ruiz-Narvaez Edward , Song Mingyang , Joshi Amit D. , Drew David A. , Petrick Jessica L. , Chan Andrew T. , Giannakis Marios , Peters Ulrike , Hsu Li , Gauderman W. James
Published: March 2024

Quantile-quantile plots of the genome-wide interaction scans for red meat and processed meat.
Figure S1 from Mitochondrial DNA Haplogroups and SNPs: Risk Factors in Multiple Cancers Based on a Cross-Tumor Analysis in Chinese Population
Authors: Chen Dongbo , Yan Zeyu , Yuan Qing , Xie Fanfan , Liu Yang , Feng Zehui , Wang Zhenni , Zhou Feng , Xing Jinliang , Zhang Zhaohui , Wang Fuli , Guo Xu
Published: March 2024

Schematic representation of experimental procedures.
Data from Mitochondrial DNA Haplogroups and SNPs: Risk Factors in Multiple Cancers Based on a Cross-Tumor Analysis in Chinese Population
Authors: Chen Dongbo , Yan Zeyu , Yuan Qing , Xie Fanfan , Liu Yang , Feng Zehui , Wang Zhenni , Zhou Feng , Xing Jinliang , Zhang Zhaohui , Wang Fuli , Guo Xu
Published: March 2024

AbstractBackground:
Mitochondrial DNA's (mtDNA) haplogroups and SNPs were associated with the risk of different cancer. However, there is no evidence that the same haplogroup or mitochondrial SNP (mtSNP) exhibits the pleiotropic effect on multiple cancers.
Methods:
We recruited 2,489 participants, including patients with colorectal, hepatocellular, lung, ovarian, bladder, breast, pancreatic, and renal cell carcinoma. In addition, 715 healthy individuals from Northern China served as controls. Next, cross-tumor analysis was performed to determine whether mtDNA variation is associated with multiple cancers.
Results:
Our results revealed a significant decrease in the occurrence risk of multiple cancers among individuals belonging to haplogroup A [OR = 0.553, 95% confidence interval (CI) = 0.375–0.815, P = 0.003]. Furthermore, we identified 11 mtSNPs associated with multiple cancers and divided the population into high-risk and low-risk groups. Low-risk groups showed a significantly reduced risk of occurrence compared with high-risk groups (OR = 0.614, 95% CI = 0.507–0.744, P < 0.001). Furthermore, using interaction analysis, we identified a special group of individuals belonging to haplogroup A/M7 and the low-risk population, who exhibit a lower risk of multiple cancers compared with other populations (OR = 0.195, 95% CI = 0.106–0.359, P < 0.001). Finally, gene set enrichment analysis confirmed that haplogroup A/M7 patients had lower expression levels of cancer-related pathway genes compared with haplogroup D patients.
Conclusions:
We found that specific mtDNA haplogroups and mtSNPs may play a role in predicting multiple cancer predisposition in Chinese populations.
Impact:
This may provide a potential tool for early screening in clinical settings for individuals in the Chinese population.
Data from Hematocrit Is Associated with Cancer-Related Fatigue in Colorectal Cancer Survivors: A Longitudinal Analysis
Authors: Kiebach Joann , de Vries-ten Have Judith , van Duijnhoven Fränzel J.B. , Kok Dieuwertje E. , van Lanen Anne-Sophie , Kouwenhoven Ewout A. , de Wilt Johannes H.W. , Schrauwen Ruud W.M. , Kampman Ellen , Winkels Renate M. , Wesselink Evertine
Published: March 2024

AbstractBackground:
Cancer-related fatigue (CRF) is a frequent symptom in colorectal cancer survivors. It is unknown to what extent anemia may contribute to CRF in colorectal cancer survivors. This study aimed to investigate the association between hematocrit, as marker for anemia, and CRF among colorectal cancer survivors from diagnosis until two years thereafter.
Methods:
The study population included 1,506 newly diagnosed colorectal cancer survivors at any stage of disease from a prospective cohort study. Hematocrit and CRF (EORTC QLQ-C30) were assessed at diagnosis, six months, and two years after diagnosis. Multivariable logistic regression or multivariable linear mixed models were used to assess the associations of hematocrit with CRF prevalence, or CRF severity over time, respectively.
Results:
A low hematocrit (levels <40% men/<36% women) was present in a third of the survivors at diagnosis and six months thereafter, and among 16% two years after diagnosis. The prevalence of CRF was 15% at diagnosis, peaked at 27% at six months, and was 14% two years after diagnosis. Hematocrit was associated with the prevalence of CRF at diagnosis [OR, 0.92; confidence interval (CI), 0.88–0.95], 6 months (OR, 0.89; 95% CI, 0.86–0.92), and 2 years (OR, 0.91; CI, 0.87–0.96) after diagnosis. Lower hematocrit was associated with higher severity of CRF over time (beta-coefficient = 1.3; CI, 1.5–1.1).
Conclusions:
Lower hematocrit levels were longitudinally associated with a higher prevalence and severity of CRF in colorectal cancer.
Impact:
Our findings emphasize the importance of long-term anemia monitoring and a potential role of anemia in CRF among colorectal cancer survivors.
Supplementary Figure S2 from GATA-3–dependent Gene Transcription is Impaired upon HDAC Inhibition
Authors: Geng Xiangrong , Wang Chenguang , Abdelrahman Suhaib , Perera Thilini , Saed Badeia , Hu Ying S. , Wolfe Ashley , Reneau John , Murga-Zamalloa Carlos , Wilcox Ryan A.
Published: March 2024

HDACi alters GATA-3 target genes expression in independent datasets
Supplementary Figure S8 from Surface and Global Proteome Analyses Identify ENPP1 and Other Surface Proteins as Actionable Immunotherapeutic Targets in Ewing Sarcoma
Authors: Mooney Brian , Negri Gian Luca , Shyp Taras , Delaidelli Alberto , Zhang Hai-Feng , Spencer Miko Sandra E. , Weiner Amber K. , Radaoui Alexander B. , Shraim Rawan , Lizardo Michael M. , Hughes Christopher S. , Li Amy , El-Naggar Amal M. , Rouleau Melanie , Li Wei , Dimitrov Dimiter S. , Kurmasheva Raushan T. , Houghton Peter J. , Diskin Sharon J. , Maris John M. , Morin Gregg B. , Sorensen Poul H.
Published: March 2024

CDH11 expression in three publicly available datasets.
Supplementary Figure S7 from Surface and Global Proteome Analyses Identify ENPP1 and Other Surface Proteins as Actionable Immunotherapeutic Targets in Ewing Sarcoma
Authors: Mooney Brian , Negri Gian Luca , Shyp Taras , Delaidelli Alberto , Zhang Hai-Feng , Spencer Miko Sandra E. , Weiner Amber K. , Radaoui Alexander B. , Shraim Rawan , Lizardo Michael M. , Hughes Christopher S. , Li Amy , El-Naggar Amal M. , Rouleau Melanie , Li Wei , Dimitrov Dimiter S. , Kurmasheva Raushan T. , Houghton Peter J. , Diskin Sharon J. , Maris John M. , Morin Gregg B. , Sorensen Poul H.
Published: March 2024

Characteristics of proteins in the scoring schema.
Supplementary Figure S2 from Surface and Global Proteome Analyses Identify ENPP1 and Other Surface Proteins as Actionable Immunotherapeutic Targets in Ewing Sarcoma
Authors: Mooney Brian , Negri Gian Luca , Shyp Taras , Delaidelli Alberto , Zhang Hai-Feng , Spencer Miko Sandra E. , Weiner Amber K. , Radaoui Alexander B. , Shraim Rawan , Lizardo Michael M. , Hughes Christopher S. , Li Amy , El-Naggar Amal M. , Rouleau Melanie , Li Wei , Dimitrov Dimiter S. , Kurmasheva Raushan T. , Houghton Peter J. , Diskin Sharon J. , Maris John M. , Morin Gregg B. , Sorensen Poul H.
Published: March 2024

Sample clustering of surface proteome data.
Supplementary Data S1 from Surface and Global Proteome Analyses Identify ENPP1 and Other Surface Proteins as Actionable Immunotherapeutic Targets in Ewing Sarcoma
Authors: Mooney Brian , Negri Gian Luca , Shyp Taras , Delaidelli Alberto , Zhang Hai-Feng , Spencer Miko Sandra E. , Weiner Amber K. , Radaoui Alexander B. , Shraim Rawan , Lizardo Michael M. , Hughes Christopher S. , Li Amy , El-Naggar Amal M. , Rouleau Melanie , Li Wei , Dimitrov Dimiter S. , Kurmasheva Raushan T. , Houghton Peter J. , Diskin Sharon J. , Maris John M. , Morin Gregg B. , Sorensen Poul H.
Published: March 2024

Log2 fold change values for proteins in EwS xenografts vs SuperMix.
Supplementary Table S7 from Spatial Gene-Expression Profiling Unveils Immuno-oncogenic Programs of NF1-Associated Peripheral Nerve Sheath Tumor Progression
Authors: Mitchell Dana K. , Burgess Breanne , White Emily E. , Smith Abbi E. , Sierra Potchanant Elizabeth A. , Mang Henry , Hickey Brooke E. , Lu Qingbo , Qian Shaomin , Bessler Waylan , Li Xiaohong , Jiang Li , Brewster Kylee , Temm Constance , Horvai Andrew , Albright Eric A. , Fishel Melissa L. , Pratilas Christine A. , Angus Steven P. , Clapp D. Wade , Rhodes Steven D.
Published: March 2024

Pairwise differential gene expression analysis comparing PNF (n=10), NF with atypia (n=6), ANNUBP (n=10), and MPNST (n=6).
Supplementary Table S6 from Spatial Gene-Expression Profiling Unveils Immuno-oncogenic Programs of NF1-Associated Peripheral Nerve Sheath Tumor Progression
Authors: Mitchell Dana K. , Burgess Breanne , White Emily E. , Smith Abbi E. , Sierra Potchanant Elizabeth A. , Mang Henry , Hickey Brooke E. , Lu Qingbo , Qian Shaomin , Bessler Waylan , Li Xiaohong , Jiang Li , Brewster Kylee , Temm Constance , Horvai Andrew , Albright Eric A. , Fishel Melissa L. , Pratilas Christine A. , Angus Steven P. , Clapp D. Wade , Rhodes Steven D.
Published: March 2024

Log2 transformed normalized count matrix. Probes with raw counts below threshold were excluded.
Supplementary Table S12 from Spatial Gene-Expression Profiling Unveils Immuno-oncogenic Programs of NF1-Associated Peripheral Nerve Sheath Tumor Progression
Authors: Mitchell Dana K. , Burgess Breanne , White Emily E. , Smith Abbi E. , Sierra Potchanant Elizabeth A. , Mang Henry , Hickey Brooke E. , Lu Qingbo , Qian Shaomin , Bessler Waylan , Li Xiaohong , Jiang Li , Brewster Kylee , Temm Constance , Horvai Andrew , Albright Eric A. , Fishel Melissa L. , Pratilas Christine A. , Angus Steven P. , Clapp D. Wade , Rhodes Steven D.
Published: March 2024

Differentially expressed genes comparing GTEx Normal Nerve and TCGA MPNST obtained from XenaBrowser.net
Supplementary Figure S9 from Spatial Gene-Expression Profiling Unveils Immuno-oncogenic Programs of NF1-Associated Peripheral Nerve Sheath Tumor Progression
Authors: Mitchell Dana K. , Burgess Breanne , White Emily E. , Smith Abbi E. , Sierra Potchanant Elizabeth A. , Mang Henry , Hickey Brooke E. , Lu Qingbo , Qian Shaomin , Bessler Waylan , Li Xiaohong , Jiang Li , Brewster Kylee , Temm Constance , Horvai Andrew , Albright Eric A. , Fishel Melissa L. , Pratilas Christine A. , Angus Steven P. , Clapp D. Wade , Rhodes Steven D.
Published: March 2024

Supplemental Figure 9. Upregulation of DNA damage repair signatures in MPNST and Cluster 3 tumors.
Supplementary Figure S8 from Spatial Gene-Expression Profiling Unveils Immuno-oncogenic Programs of NF1-Associated Peripheral Nerve Sheath Tumor Progression
Authors: Mitchell Dana K. , Burgess Breanne , White Emily E. , Smith Abbi E. , Sierra Potchanant Elizabeth A. , Mang Henry , Hickey Brooke E. , Lu Qingbo , Qian Shaomin , Bessler Waylan , Li Xiaohong , Jiang Li , Brewster Kylee , Temm Constance , Horvai Andrew , Albright Eric A. , Fishel Melissa L. , Pratilas Christine A. , Angus Steven P. , Clapp D. Wade , Rhodes Steven D.
Published: March 2024

Supplemental Figure 8. Core molecular features of human ANNUBP and MPNST are retained in two independent genetically engineered mouse models driven by combined inactivation of Nf1 and Ink4a/Arf.
Supplementary Figure S7 from Spatial Gene-Expression Profiling Unveils Immuno-oncogenic Programs of NF1-Associated Peripheral Nerve Sheath Tumor Progression
Authors: Mitchell Dana K. , Burgess Breanne , White Emily E. , Smith Abbi E. , Sierra Potchanant Elizabeth A. , Mang Henry , Hickey Brooke E. , Lu Qingbo , Qian Shaomin , Bessler Waylan , Li Xiaohong , Jiang Li , Brewster Kylee , Temm Constance , Horvai Andrew , Albright Eric A. , Fishel Melissa L. , Pratilas Christine A. , Angus Steven P. , Clapp D. Wade , Rhodes Steven D.
Published: March 2024

Supplemental Figure 7. Gene expression analysis of harmonized RNAseq data from normal nerve and MPNST samples obtained from GTEx and TCGA.
Supplementary Figure S5 from Spatial Gene-Expression Profiling Unveils Immuno-oncogenic Programs of NF1-Associated Peripheral Nerve Sheath Tumor Progression
Authors: Mitchell Dana K. , Burgess Breanne , White Emily E. , Smith Abbi E. , Sierra Potchanant Elizabeth A. , Mang Henry , Hickey Brooke E. , Lu Qingbo , Qian Shaomin , Bessler Waylan , Li Xiaohong , Jiang Li , Brewster Kylee , Temm Constance , Horvai Andrew , Albright Eric A. , Fishel Melissa L. , Pratilas Christine A. , Angus Steven P. , Clapp D. Wade , Rhodes Steven D.
Published: March 2024

Supplemental Figure 5. scRNAseq analysis of human NF1-associated MPNST.
Supplementary Figure S1 from Spatial Gene-Expression Profiling Unveils Immuno-oncogenic Programs of NF1-Associated Peripheral Nerve Sheath Tumor Progression
Authors: Mitchell Dana K. , Burgess Breanne , White Emily E. , Smith Abbi E. , Sierra Potchanant Elizabeth A. , Mang Henry , Hickey Brooke E. , Lu Qingbo , Qian Shaomin , Bessler Waylan , Li Xiaohong , Jiang Li , Brewster Kylee , Temm Constance , Horvai Andrew , Albright Eric A. , Fishel Melissa L. , Pratilas Christine A. , Angus Steven P. , Clapp D. Wade , Rhodes Steven D.
Published: March 2024

Supplemental Figure 1. Heatmap of raw counts.
Table S7 from CD46-Targeted Theranostics for PET and 225Ac-Radiopharmaceutical Therapy of Multiple Myeloma
Authors: Wadhwa Anju , Wang Sinan , Patiño-Escobar Bonell , Bidkar Anil P. , Bobba Kondapa Naidu , Chan Emily , Meher Niranjan , Bidlingmaier Scott , Su Yang , Dhrona Suchi , Geng Huimin , Sarin Vishesh , VanBrocklin Henry F. , Wilson David M. , He Jiang , Zhang Li , Steri Veronica , Wong Sandy W. , Martin Thomas G. , Seo Youngho , Liu Bin , Wiita Arun P. , Flavell Robert R.
Published: March 2024

Table S7: Biodistribution analysis % IA/g of [89Zr]Zr-DFO-YS5 in subcutaneous MM.1S CD46 (-/-) tumor model.
Table S30 from CD46-Targeted Theranostics for PET and 225Ac-Radiopharmaceutical Therapy of Multiple Myeloma
Authors: Wadhwa Anju , Wang Sinan , Patiño-Escobar Bonell , Bidkar Anil P. , Bobba Kondapa Naidu , Chan Emily , Meher Niranjan , Bidlingmaier Scott , Su Yang , Dhrona Suchi , Geng Huimin , Sarin Vishesh , VanBrocklin Henry F. , Wilson David M. , He Jiang , Zhang Li , Steri Veronica , Wong Sandy W. , Martin Thomas G. , Seo Youngho , Liu Bin , Wiita Arun P. , Flavell Robert R.
Published: March 2024

Table S30: [89Zr]Zr-DFO-YS5 %IA/Organ in the RPMI8266 systemic tumor model.
Table S3 from CD46-Targeted Theranostics for PET and 225Ac-Radiopharmaceutical Therapy of Multiple Myeloma
Authors: Wadhwa Anju , Wang Sinan , Patiño-Escobar Bonell , Bidkar Anil P. , Bobba Kondapa Naidu , Chan Emily , Meher Niranjan , Bidlingmaier Scott , Su Yang , Dhrona Suchi , Geng Huimin , Sarin Vishesh , VanBrocklin Henry F. , Wilson David M. , He Jiang , Zhang Li , Steri Veronica , Wong Sandy W. , Martin Thomas G. , Seo Youngho , Liu Bin , Wiita Arun P. , Flavell Robert R.
Published: March 2024

Table S3: Biodistribution analysis %IA/g of [89Zr]DFO-YS5 + blocking studies with 50 fold excess of cold YS5 in subcutaneous MM.1S tumor model.
Table S25 from CD46-Targeted Theranostics for PET and 225Ac-Radiopharmaceutical Therapy of Multiple Myeloma
Authors: Wadhwa Anju , Wang Sinan , Patiño-Escobar Bonell , Bidkar Anil P. , Bobba Kondapa Naidu , Chan Emily , Meher Niranjan , Bidlingmaier Scott , Su Yang , Dhrona Suchi , Geng Huimin , Sarin Vishesh , VanBrocklin Henry F. , Wilson David M. , He Jiang , Zhang Li , Steri Veronica , Wong Sandy W. , Martin Thomas G. , Seo Youngho , Liu Bin , Wiita Arun P. , Flavell Robert R.
Published: March 2024

Table S25: [89Zr]Zr-DFO-YS5+ 50 fold YS5 %IA/g in the MM.1S systemic tumor model.
Table S24 from CD46-Targeted Theranostics for PET and 225Ac-Radiopharmaceutical Therapy of Multiple Myeloma
Authors: Wadhwa Anju , Wang Sinan , Patiño-Escobar Bonell , Bidkar Anil P. , Bobba Kondapa Naidu , Chan Emily , Meher Niranjan , Bidlingmaier Scott , Su Yang , Dhrona Suchi , Geng Huimin , Sarin Vishesh , VanBrocklin Henry F. , Wilson David M. , He Jiang , Zhang Li , Steri Veronica , Wong Sandy W. , Martin Thomas G. , Seo Youngho , Liu Bin , Wiita Arun P. , Flavell Robert R.
Published: March 2024

Table S24: [89Zr]Zr-DFO-YS5 %IA/Organ in the MM.1S systemic tumor model.
Table S23 from CD46-Targeted Theranostics for PET and 225Ac-Radiopharmaceutical Therapy of Multiple Myeloma
Authors: Wadhwa Anju , Wang Sinan , Patiño-Escobar Bonell , Bidkar Anil P. , Bobba Kondapa Naidu , Chan Emily , Meher Niranjan , Bidlingmaier Scott , Su Yang , Dhrona Suchi , Geng Huimin , Sarin Vishesh , VanBrocklin Henry F. , Wilson David M. , He Jiang , Zhang Li , Steri Veronica , Wong Sandy W. , Martin Thomas G. , Seo Youngho , Liu Bin , Wiita Arun P. , Flavell Robert R.
Published: March 2024

Table S23: [89Zr]Zr-DFO-YS5 %IA/g in the MM.1S systemic tumor model.
Table S19 from CD46-Targeted Theranostics for PET and 225Ac-Radiopharmaceutical Therapy of Multiple Myeloma
Authors: Wadhwa Anju , Wang Sinan , Patiño-Escobar Bonell , Bidkar Anil P. , Bobba Kondapa Naidu , Chan Emily , Meher Niranjan , Bidlingmaier Scott , Su Yang , Dhrona Suchi , Geng Huimin , Sarin Vishesh , VanBrocklin Henry F. , Wilson David M. , He Jiang , Zhang Li , Steri Veronica , Wong Sandy W. , Martin Thomas G. , Seo Youngho , Liu Bin , Wiita Arun P. , Flavell Robert R.
Published: March 2024

Table S19: [18F]-FDG biodistribution %IA/g in the RPMI8226 subcutaneous xenograft models.
Table S18 from CD46-Targeted Theranostics for PET and 225Ac-Radiopharmaceutical Therapy of Multiple Myeloma
Authors: Wadhwa Anju , Wang Sinan , Patiño-Escobar Bonell , Bidkar Anil P. , Bobba Kondapa Naidu , Chan Emily , Meher Niranjan , Bidlingmaier Scott , Su Yang , Dhrona Suchi , Geng Huimin , Sarin Vishesh , VanBrocklin Henry F. , Wilson David M. , He Jiang , Zhang Li , Steri Veronica , Wong Sandy W. , Martin Thomas G. , Seo Youngho , Liu Bin , Wiita Arun P. , Flavell Robert R.
Published: March 2024

Table S18: [18F]-FDG %IA/Organ in the MM.1S subcutaneous xenograft model.
Table S16 from CD46-Targeted Theranostics for PET and 225Ac-Radiopharmaceutical Therapy of Multiple Myeloma
Authors: Wadhwa Anju , Wang Sinan , Patiño-Escobar Bonell , Bidkar Anil P. , Bobba Kondapa Naidu , Chan Emily , Meher Niranjan , Bidlingmaier Scott , Su Yang , Dhrona Suchi , Geng Huimin , Sarin Vishesh , VanBrocklin Henry F. , Wilson David M. , He Jiang , Zhang Li , Steri Veronica , Wong Sandy W. , Martin Thomas G. , Seo Youngho , Liu Bin , Wiita Arun P. , Flavell Robert R.
Published: March 2024

Table S16: [89Zr]Zr-DFO-IgG %IA/Organ in the ANBL6 subcutaneous xenograft models.
Table S15 from CD46-Targeted Theranostics for PET and 225Ac-Radiopharmaceutical Therapy of Multiple Myeloma
Authors: Wadhwa Anju , Wang Sinan , Patiño-Escobar Bonell , Bidkar Anil P. , Bobba Kondapa Naidu , Chan Emily , Meher Niranjan , Bidlingmaier Scott , Su Yang , Dhrona Suchi , Geng Huimin , Sarin Vishesh , VanBrocklin Henry F. , Wilson David M. , He Jiang , Zhang Li , Steri Veronica , Wong Sandy W. , Martin Thomas G. , Seo Youngho , Liu Bin , Wiita Arun P. , Flavell Robert R.
Published: March 2024

Table S15: [89Zr]Zr-DFO-IgG % IA/g biodistribution in the ANBL6 subcutaneous xenograft models.
Table S13 from CD46-Targeted Theranostics for PET and 225Ac-Radiopharmaceutical Therapy of Multiple Myeloma
Authors: Wadhwa Anju , Wang Sinan , Patiño-Escobar Bonell , Bidkar Anil P. , Bobba Kondapa Naidu , Chan Emily , Meher Niranjan , Bidlingmaier Scott , Su Yang , Dhrona Suchi , Geng Huimin , Sarin Vishesh , VanBrocklin Henry F. , Wilson David M. , He Jiang , Zhang Li , Steri Veronica , Wong Sandy W. , Martin Thomas G. , Seo Youngho , Liu Bin , Wiita Arun P. , Flavell Robert R.
Published: March 2024

Table S13: [89Zr]Zr-DFO-YS5 %IA/g in the ANBL6 subcutaneous xenograft models.
Table S11 from CD46-Targeted Theranostics for PET and 225Ac-Radiopharmaceutical Therapy of Multiple Myeloma
Authors: Wadhwa Anju , Wang Sinan , Patiño-Escobar Bonell , Bidkar Anil P. , Bobba Kondapa Naidu , Chan Emily , Meher Niranjan , Bidlingmaier Scott , Su Yang , Dhrona Suchi , Geng Huimin , Sarin Vishesh , VanBrocklin Henry F. , Wilson David M. , He Jiang , Zhang Li , Steri Veronica , Wong Sandy W. , Martin Thomas G. , Seo Youngho , Liu Bin , Wiita Arun P. , Flavell Robert R.
Published: March 2024

Table S11: [89Zr]Zr-DFO-IgG %IA/g in the RPMI8226 subcutaneous xenograft models.
Figure S9 from CD46-Targeted Theranostics for PET and 225Ac-Radiopharmaceutical Therapy of Multiple Myeloma
Authors: Wadhwa Anju , Wang Sinan , Patiño-Escobar Bonell , Bidkar Anil P. , Bobba Kondapa Naidu , Chan Emily , Meher Niranjan , Bidlingmaier Scott , Su Yang , Dhrona Suchi , Geng Huimin , Sarin Vishesh , VanBrocklin Henry F. , Wilson David M. , He Jiang , Zhang Li , Steri Veronica , Wong Sandy W. , Martin Thomas G. , Seo Youngho , Liu Bin , Wiita Arun P. , Flavell Robert R.
Published: March 2024

Figure S9: [89Zr]Zr-DFO-YS5 imaging in MM.1S xenograft models 120 days following initiation of fractionated [225Ac]Ac-DOTA-YS5 treatment.
Figure S4 from CD46-Targeted Theranostics for PET and 225Ac-Radiopharmaceutical Therapy of Multiple Myeloma
Authors: Wadhwa Anju , Wang Sinan , Patiño-Escobar Bonell , Bidkar Anil P. , Bobba Kondapa Naidu , Chan Emily , Meher Niranjan , Bidlingmaier Scott , Su Yang , Dhrona Suchi , Geng Huimin , Sarin Vishesh , VanBrocklin Henry F. , Wilson David M. , He Jiang , Zhang Li , Steri Veronica , Wong Sandy W. , Martin Thomas G. , Seo Youngho , Liu Bin , Wiita Arun P. , Flavell Robert R.
Published: March 2024

Figure S4: [225Ac]Ac-DOTA-YS5 shows high immunoreactivity in vitro assays.
Figure S2 from CD46-Targeted Theranostics for PET and 225Ac-Radiopharmaceutical Therapy of Multiple Myeloma
Authors: Wadhwa Anju , Wang Sinan , Patiño-Escobar Bonell , Bidkar Anil P. , Bobba Kondapa Naidu , Chan Emily , Meher Niranjan , Bidlingmaier Scott , Su Yang , Dhrona Suchi , Geng Huimin , Sarin Vishesh , VanBrocklin Henry F. , Wilson David M. , He Jiang , Zhang Li , Steri Veronica , Wong Sandy W. , Martin Thomas G. , Seo Youngho , Liu Bin , Wiita Arun P. , Flavell Robert R.
Published: March 2024

Figure S2: Regulation of CD46 expression in multiple myeloma.
Figure S11 from CD46-Targeted Theranostics for PET and 225Ac-Radiopharmaceutical Therapy of Multiple Myeloma
Authors: Wadhwa Anju , Wang Sinan , Patiño-Escobar Bonell , Bidkar Anil P. , Bobba Kondapa Naidu , Chan Emily , Meher Niranjan , Bidlingmaier Scott , Su Yang , Dhrona Suchi , Geng Huimin , Sarin Vishesh , VanBrocklin Henry F. , Wilson David M. , He Jiang , Zhang Li , Steri Veronica , Wong Sandy W. , Martin Thomas G. , Seo Youngho , Liu Bin , Wiita Arun P. , Flavell Robert R.
Published: March 2024

Figure S11: %ID/Tumor of [89Zr]Zr-DFO-YS5 in ANBL6 subcutaneous model is higher than RPMI8226 and MM.1S.