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Record W4281401039 · doi:10.1155/2022/7972039

Differential Regulation of NK Cell Receptors in Acute Lymphoblastic Leukemia

2022· article· en· W4281401039 on OpenAlexfundno aff
Le Jie Lee, Norfarazieda Hassan, Siti Zuleha Idris, Suresh Kumar, Heng Fong Seow, Norhafizah Mohtaruddin, Kian Meng Chang, Raudhawati Osman, Hishamshah Mohd Ibrahim, Sheila Nathan, Maha Abdullah

Bibliographic record

VenueJournal of Immunology Research · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsnot available
FundersUniversiti Putra MalaysiaMinistry of Health, British ColumbiaMinistério da Ciência, Tecnologia e InovaçãoKementerian Sains, Teknologi dan Inovasi
KeywordsLymphoblastic LeukemiaReceptorDifferential (mechanical device)LeukemiaCancer researchImmunologyBiologyMedicineInternal medicinePhysics

Abstract

fetched live from OpenAlex

Cancer immunotherapies are preferred over conventional treatments which are highly cytotoxic to normal cells. Focus has been on T cells but natural killer (NK) cells have equal potential. Concepts in cancer control and influence of sex require further investigation to improve successful mobilization of immune cells in cancer patients. Acute lymphoblastic leukemia (ALL) is a hematological malignancy mainly of B cell (B-ALL) and T cell (T-ALL) subtypes. Influence of ALL on NK cell is still unclear. Targeted next-generation sequencing was conducted on 62 activating/inhibitory receptors, ligands, effector, and exhaustion molecules on T-ALL (6 males) and normal controls (NC) (4 males and 4 females). Quantitative PCR (q-PCR) further investigated copy number variation (CNV), methylation index (MI), and mRNA expression of significant genes in T-ALL (14 males), NC (12 males and 12 females), and B-ALL samples ( <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>N</a:mi> <a:mo>=</a:mo> <a:mn>12</a:mn> </a:math> males and 12 females). Bioinformatics revealed unique variants particularly rs2253849 (T&gt;C) in KLRC1 and rs1141715 (A&gt;G) in KLRC2 only among T-ALL (allele frequency 0.8-1.0). Gene amplification was highest in female B-ALL compared to male B-ALL (KLRC2, KLRC4, and NCR3, <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>p</c:mi> <c:mo>&lt;</c:mo> <c:mn>0.05</c:mn> </c:math> ) and lowest in male T-ALL cumulating in deletion of KLRD1 and CD69. MI was higher in male ALL of both subtypes compared to normal (KIR2DL1-2 and 4 and KIR2DS2 and 4, <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M3"> <e:mi>p</e:mi> <e:mo>&lt;</e:mo> <e:mn>0.05</e:mn> </e:math> ) as well as to female B-ALL (KIR3DL2 and KIR2DS2, <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" id="M4"> <g:mi>p</g:mi> <g:mo>&lt;</g:mo> <g:mn>0.05</g:mn> </g:math> ). mRNA expressions were low. Thus, ALL subtypes potentially regulated NK cell suppression by different mechanisms which should be considered in future immunotherapies for ALL.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0100.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.288
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2022
Admission routes1
Has abstractyes

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