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Record W3208119767 · doi:10.5772/intechopen.99483

Genetic Polymorphism and Prostate Cancer: An Update

2021· book-chapter· en· W3208119767 on OpenAlexaff
S. Siddiqui, Sridevi I. Puranik, Aimen Akbar, Shridhar C. Ghagane

Bibliographic record

VenueIntechOpen eBooks · 2021
Typebook-chapter
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsProstate cancerMedicineCancerOncologyBioinformaticsBiologyInternal medicine

Abstract

fetched live from OpenAlex

Genetic polymorphism and prostate cancer (PC) are the most pernicious and recurrently malignancy worldwide. It is the most dominating cause of cancer related casualty among men in the US. Asian countries are inflicted with PC at an alarming rate though still the prevalence of PC is lower than European and American men. Some of the genetic and environmental factors that might play a role in PC risk include: age genetic predilection, family history, race/ethnicity, lifestyle, and dietary habits and non-dietary environmental risk factors such as smoking. Socio-economic factors including economic, scholastic and intellectual factors do not, intrinsically seem to straight away influence the risk of acquiring PC. Other genetic changes that may support an increased risk of developing PC include HPC1, HPC2, HPCX, CAPB, ATM,s HOXB13 and mismatch repair genes. PC occurrence rates are highly variable. Almost all PC mortalities are due to metastatic disease, generally through tumors the progress to be hormone refractory or castrate resistant. PC, developing research has acknowledged a number of candidate genes and biological pathways associated with PC. Indirect pathways such as P13K/AKT signaling pathway is one of most well known alternate pathway in PC Vascular endothelial growth factor (VEGF) is widely known to be potent stimulator of angiogenesis. The over expression of EGFR in a very large majority of cases is accompanied by the succession of PC, implying that this may play a mechanistic role. Numerous occupational factors have been proposed to cause PC. Some of the risk factors include; farmers/agricultural workers, pesticides, shift work and flight personnel. PC treatment can be done through surgery, radical prostatectomy is the main type of surgery. Risks of injury are many – reactions to anesthesia, loss of blood, blood clumps in the legs/lungs, injury to surrounding organs, infection at the site of surgery and many more. The other treatments are hormone therapy, chemotherapy and radio therapy chemotherapy. Chemotherapeutic drugs are typically used one at a time for PC such as transurethral resection of prostate (TURP). Some of the chemotherapeutic drugs are Docetaxel, Cabazitaxel, Mitoxantrone and Estramustine. Among the score of biomarkers being studied, numerous markers and techniques deserve awareness and acceptability for both patients and urologists in clinical practice.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.002

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 head0.030
GPT teacher head0.310
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2021
Admission routes1
Has abstractyes

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