PAICS antibody
- Known as:
- PAICS (anti-)
- Catalog number:
- orb101759
- Product Quantity:
- 200 ug
- Category:
- -
- Supplier:
- Biorb
- Gene target:
- PAICS antibody
Related genes to: PAICS antibody
- Gene:
- PAICS NIH gene
- Name:
- phosphoribosylaminoimidazole carboxylase and phosphoribosylaminoimidazolesuccinocarboxamide synthase
- Previous symbol:
- PAIS
- Synonyms:
- ADE2H1, AIRC
- Chromosome:
- 4q12
- Locus Type:
- gene with protein product
- Date approved:
- 1991-03-11
- Date modifiied:
- 2016-10-13
Related products to: PAICS antibody
Related articles to: PAICS antibody
- Indirect treatment comparisons (ITCs) are essential in the context of joint clinical assessments (JCAs) under Regulation (European Union [EU]) 2021/2282, bridging evidence gaps where head-to-head data are lacking and enabling assessment across diverse national patient, intervention, comparator, and outcome (PICO) requirements. This paper critically reviews the EU Health Technology Assessment Coordination Group's (HTACG) guidelines on direct and indirect comparisons, with particular focus on ITCs. While the guidelines promote transparency and rigorous evaluation of assumptions, they adopt a restrictive stance on assumption violations, the use of unanchored comparisons, and population-adjusted methods such as matching-adjusted indirect comparisons (MAIC) and simulated treatment comparisons (STC). The guidance shows limited support for Bayesian methods and undervalues meta-regression in favor of subgroup analyses. Operational implications for health technology developers (HTDs) are substantial, including new requirements for dual systematic reviews, multiple network structures, and shifted null hypothesis testing. Moreover, the guidelines effectively dissuade the use of non-randomized comparisons in rare or rapidly evolving indications and may inadvertently hinder access to effective treatments. Emerging practices such as external control arms (ECA) or target trial emulation are underdeveloped. Notably, there is no indication that the guidelines are grounded in systematic methodological validation studies. As JCAs evolve, greater methodological flexibility, empirical grounding, and clear operational guidance will be essential. Refining the guidelines along these principles would enhance their practical utility, mitigate intrinsic assessment variability, support consistent assessments across Member States (MS), and ultimately improve patient access to innovative therapies. - Source: PubMed
Publication date: 2026/05/07
Aballéa SamuelToumi MondherWojciechowski PiotrClay EmilieFalissard BrunoSimoens StevenAuquier PascalCapri StefanoBernardini RenatoRuof JoergFricke Frank-UlrichMorales Oriol SolaBoyer Laurent - In the original publication [...]. - Source: PubMed
Publication date: 2026/04/30
Huang Chin-ShengHsieh Ming-ShouYadav Vijesh KumarWu Yang-CheLiu Shao-ChengYeh Chi-TaiHuang Mao-Suan - Diffuse large B-cell lymphoma (DLBCL) features an immunosuppressive tumor microenvironment (TME), yet the molecular drivers connecting metabolic reprogramming to immune evasion remain poorly defined. Here, we deployed an integrative single-cell transcriptomic analysis combined with a machine learning (ML) framework to systematically identify key immune-suppressive hubs in DLBCL. Through ML-driven prioritization of a 33-gene panel, PAICS emerged as a central node within an immunosuppressive B-cell subgroup. Functional assays confirmed that PAICS promotes lymphoma proliferation, survival, and tumor growth while establishing an immunosuppressive TME-marked by reduced IFN‑γ, elevated TGF‑β and IL‑10, and enhanced CD8⁺ T cell exhaustion. Mechanistically, we uncovered the IRF4-PAICS-LDHA axis: IRF4 transcriptionally activates PAICS, which physically interacts with LDHA to augment its activity, thereby skewing the NAD⁺/NADH balance toward metabolic immunosuppression. Importantly, our AI-aided approach not only identified this axis but also predicted its vulnerability to metabolic intervention: both methotrexate treatment and LDHA knockdown restored metabolic balance, reversed T‑cell exhaustion, and suppressed tumor growth. These findings highlight the power of ML in uncovering multi-targetable metabolic-immune networks and in guiding therapeutic strategies to overcome immune evasion in DLBCL. - Source: PubMed
Publication date: 2026/04/16
Wang ZeyuanWang LiyeQian SiyuZhang YueYang QingYang ZhenzhenWu ShaoxuanDong MengZhang ZhiqiWei XufengYang MingleiMeng HuiLiu EnjieJiang GuozhongZhang XudongLi WencaiChen Qingjiang - Breast cancer metastasis remains a major clinical challenge due to its complex molecular mechanisms, highlighting the need to identify key regulatory factors. - Source: PubMed
Publication date: 2026/04/14
Guo JianCen LichaoQian XinyeYou Shengban - Advanced Therapy Medicinal Products (ATMPs) often present substantial clinical uncertainties at the time of reimbursement evaluation, particularly due to the lack of appropriate comparators and the absence of long-term clinical endpoints. This study primarily examined two methodological areas relevant to these challenges: indirect treatment comparisons (ITCs) and surrogate endpoints. In addition, the review was supplemented with an assessment of innovative trial designs to explore how emerging approaches may contribute to evidence generation for ATMPs. - Source: PubMed
Publication date: 2026/03/12
Delemarre LotteHuys IsabelleVan Dyck WalterSimoens Steven