Laboratory medicine as a data science (2)

 Recent evolutions of machine learning applications in clinical laboratory medicine

You'll find an interesting review about laboratory medicina and machine learning in this article, with applicatons to chemical chemistry, hematology and microbiology.

There has been a recent rise in various ML applications in the field of clinical laboratory medicine. Despite the potential of ML to ameliorate the efficiency of laboratory processes and optimize diagnostic workflows, translation into routine practice is still slow-going. There is a need to raise more awareness about the vast ML landscape among laboratory professionals. Educational programs dealing with theoretical ML concepts as well as their associated challenges and opportunities could stimulate wider acceptance and exploitation in the clinical laboratory. It is important to realize that ML will not  immediately function as a surrogate of the laboratory professional’s neural networks, but will rather act as a valuable supportive tool with the capability of increasing the odds on optimal outcomes for patients accessing health care.

 Margaret Huntington Boehner

Bioethics for lab medicine

 Ethics for Laboratory Medicine

Key issues:

Table 1.Ethical Issues of particular importance in Laboratory medicine.

Informed consent 

Use of leftover specimens 

Biobanking 

Genetic testing 

Equity and access to laboratory testing 

Incidental findings and medically actionable results 

DTC testing 

Transfusion medicine and religious or ethical restrictions 

Disclosing medical error 

Emerging infectious diseases 

Test utilization 

The unique role of laboratorians, who care for patients but interact mainly with their samples rather than the person, creates distinct ethical dilemmas. In addition, laboratories function as critical parts of complex health systems, and the interaction of the laboratory with the greater healthcare system creates additional points of ethical friction (45). Clinical laboratory professionals are ethically bound to use our voices to advocate for excellence in patient care in the realms of respect for persons, beneficence, and justice, even in the face of technological, administrative, and, perhaps, clinical pressures to do otherwise.

Ethics represents moral principles based on cultural norms and values. Sometimes these moral values have been turned into federal or state laws or into local rules and regulations. However, laws and rules may be absent or difficult to apply to a given situation. When faced with ethical decisions, laboratorians should seek the input from other clinicians and laboratory colleagues. In addition, most hospitals have ethics boards comprising multidisciplinary teams of clinicians, lay people, and clergy to help guide decision-making.

 

Laboratory medicine as a data science

Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation

Artificial intelligence (AI) and data science are rapidly developing in healthcare, as is their translation into laboratory medicine.

These are the four areas that the authors consider that AI will have impact:

• Processes and care pathways
• Laboratory test ordering and interpretation
• Data mining, early diagnosis, and proactive disease monitoring
• Personalized treatment and clinical trials

Meanwhile there is a long way ahead.

Jacob Lawrence, This is Harlem, 1943. Gouache and pencil on paper. Hirshhorn Museum and Sculpture Garden, Smithsonian Institution, Gift of Joseph H. Hirshhorn, 1966. Artwork © The Jacob and Gwendolyn Knight Lawrence Foundation, Seattle / Artists Rights Society (ARS), New York; photograph by Cathy Carver

Regulatory uncertainty in "home-brew" lab testing

Laboratory-Developed Tests: A Legislative and Regulatory Review

In vitro diagnostics regulation requires continuous adaptation to technologic innovation. Unfortunately, there is a lack of understanding that such a crucial task should be performed efficiently. Europe has waited 23 years for a new regulation!. Anyway, US is under the same trend. Laboratory developed tests were initially regulated 25 years ago and there are still pending issues in the new draft legislation. If you want to know the details, an article in Clinical Chemistry explains the whole issue.

A quarter of a century after the FDA first asserted regulatory authority over LDTs in a draft guidance document, rules and/or guidance regarding LDT oversight have not been implemented. As such, legal questions regarding MDA authority over LDTs and the FDA draft guidance approach have neither been escalated to nor resolved by the judiciary. In addition, many questions central to this debate have not been answered. Are clinical laboratories manufacturers? Should laboratory devices and procedures be regulated similarly? Are there always clear limits between laboratory operations and the practice of laboratory medicine? Any future LDTregulatory or legislative efforts will need to balance and address these concerns if they are to be successful. It is unlikely that interpretation of current statutes and regulations can fully resolve these issues.

 Josep Moscardó, Barcelona landscape

Risks and benefits of self-testing (2)

 Direct to Consumer Testing: The Role of Laboratory Medicine

A specific issue on the topic has been released in Clinics in Laboratory Medicine. Inside the issue, you'll find this article: Direct-to-Consumer Tests on the Market Today: Identifying Valuable Tests from Those with Limited Utility 13. This is a key topic. It says:

Debate exists between the consumer and the health care provider when it comes to the value of direct-to-consumer (DTC) testing. At the heart of the issue is the observation that consumers are identifying value in DTC testing as evidenced by an expanding market, and health care providers are skeptical of their value from an analytical and clinical utility perspective. The aim of this article is to briefly review the subject of DTC testing with a specific focus on value from the perspective of the consumer and the health care provider.

 Paul Strand at KBr Barcelona

 

How to identify lab tests of low effectiveness?

Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests

A new approach to understand the size of useless lab tests is to apply: "regularized logistic regression, regress and round, naive Bayes, neural network multilayer perceptrons, decision tree, random forest, AdaBoost, and XGBoost". This means that machine learning has its own space in laboratory medicine. In the article, they show high level of prediction for useless tests.

The best performing machine learning models predicted normal results with an AUROC of 0.90 or greater for 12 stand-alone laboratory tests (eg, sodium AUROC, 0.92 [95%CI, 0.91-0.93]; sensitivity, 98%; specificity, 35%; PPV, 66%; NPV, 93%; lactate dehydrogenase AUROC, 0.93 [95%CI, 0.93-0.94]; sensitivity, 96%; specificity, 65%; PPV, 71%; NPV, 95%; and troponin I AUROC, 0.92 [95%CI, 0.91- 0.93]; sensitivity, 88%; specificity, 79%; PPV, 67%; NPV, 93%) and 10 common laboratory test components (eg, hemoglobin AUROC, 0.94 [95%CI, 0.92-0.95]; sensitivity, 99%; specificity, 17%; PPV, 90%; NPV, 81%; creatinine AUROC, 0.96 [95%CI, 0.96-0.97]; sensitivity, 93%; specificity, 83%; PPV, 79%; NPV, 94%; and urea nitrogen AUROC, 0.95 [95%CI, 0.94, 0.96]; sensitivity, 87%; specificity, 89%; PPV, 77%; NPV 94%).

 This approach goes further than this book:

Diagnostic testing and outcomes

When diagnostic testing leads to harm: a new outcomes-based approach for laboratory medicine

There are five causes of testing-related diagnostic error:

• An inappropriate test is ordered
• An appropriate test is not ordered
• An appropriate test result is misapplied
• An appropriate test is ordered, but a delay occurs somewhere in the total testing process
• The result of an appropriately ordered test is inaccurate

If we know that that these are the causes, are there any measures available?

In Lundberg’s model, the value of laboratory results is influenced by events
that occur before the sample reaches the laboratory and after the results are released
from it. His model encompasses the physician’s cognitive involvement at the start of
the process and at the end.

Medicine as a data science (2)

The Evolution of Patient Diagnosis: From Art to Digital Data-Driven Science

Currently medical diagnosis is driven by a standard way to proceed. We could say that the pattern of the decision flow has not changed for years.

A physician takes a history, performs an examination, and matches each patient to the traditional taxonomy of medical conditions. Symptoms, signs, family history, and laboratory reports are interpreted in light of clinical experience and scholarly interpretation of the medical literature.

Data availability, and specifically genetic data could change completely diagnostic process.

Initiatives to develop genetic reference data at the population level could be grouped into 3 categories.First are well-known databases of genotype-phenotype relationships
as observed and submitted by researchers (eg, Online Mendelian Inheritance in Man, ClinVar, and the National Human Genome Research Institute’s Genome-Wide Association Study [GWAS] Catalog). Second are databases, such as the Genome Aggregation Database (gnomAD), the next iteration of the ExomeAggregation Consortium (ExAC) database, and the 1000 Genomes Project, that aggregate sequences
collected from other studies for secondary use. Third, patients and other study participants are invited to donate data to registries like GenomeConnect or enroll
in cohorts like the National Institutes of Health All of Us initiative, which is recruiting 1 million patients to contribute biological samples and EHR data for research.

The reference to these databases is crucial to understand what's going on in US medicine, and how european medicine stands behind.
JAMA article develops the concept of Clinical Information Commons:

There should be a new compact between patients and the health system, such that captured data and biospecimen by- products of the care deliverysystem should be aggregated and linked to build a clinical information commons (CIC) to aid diagnosis

I agree. Saluscoop started as an alternative focused in this approach. As usual, the big question is: who is going to invest in a digital commons?. Unless governments take this initiative as a whole, the future of a data driven medicine is uncertain.

Adrian Piper: A Synthesis of Intuitions, 1965–2016

MoMA, New York, New York

Sat 31 Mar 2018 to Sun 22 Jul 2018

L'embut perfectament dissenyat i la necessària redefinició de les professions sanitàries

The political economy of corporatism in medicine: Self-regulation or cartel management?

When Pathology and Laboratory Medicine Becomes a Commodity and Health Care Becomes Both Its Customer and Owner

Fa més de tres dècades que en Peter Zweifel i en Rein Eichenberger van escriure un article significatiu sobre el corporativisme en medicina. En concret es fixaven en una qüestió que passa sovint desapercebuda, la delegació de les tasques regulatòries del govern en les organitzacions mèdiques. Fonamentalment, se centrava en les qüestions d'accés a la professió, nombre de professionals en formació sanitària especialitzada i tipus, o la definició de les especialitats. Deia que tot plegat, en els set països que analitzava, havia donat lloc a una densitat de metges (número de metges per càpita)que era diferent segons el nivell de corporativisme en medicina del país. I després afegia que un fort corporativisme reduïa l'impacte negatiu d'una major oferta de professionals en els ingressos econòmics dels metges.

Agafem un exempre recent. La setmana passada es va crear una nova especialitat que en diuen de Laboratori Clínic, a partir de la fusió de dues especialitats existents, Análisis Clíniques i Bioquímica. L'argumentació és magnífica, 

La evolución tecnológica va a seguir condicionando este tipo de estructuras organizativas en los centros sanitarios de mayor tamaño, donde se forman la mayoría de los especialistas, pero también va a condicionar el trabajo en centros comarcales, que van a requerir de especialistas más polivalentes.

El trabajo conjunto, en cualquiera de estas estructuras organizativas, no solo evita la fragmentación de la atención y el conocimiento, si no que genera el efecto contrario, unificando la atención a los y las pacientes e integrando conocimientos.

Con este proyecto, se persigue de esta forma mejorar la formación de los especialistas en Ciencias de la Salud, a la vez que se facilita la gestión de los recursos humanos en salud.

La tecnologia condiciona l'organització, punt. L'organització acaba rendint-se a la tecnologia. Però si és així, jo em pregunto per què han mantingut l'Anatomia Patològica al marge? L'anàlisi d'ADN circulant fa confluir també amb l'activitat dels patòlegs. Molts països ja ho han fet. I la fusió i redefinició d'especialitats hauria de continuar assenyadament en molts més casos. Ara bé, dir-ne Laboratorio Clínico és un disbarat per als annals de la història de la medicina. Aquest nom no és una especialitat, una disciplina mèdica, és tant sols un tros d'un edifici de l'hospital o d'una organització en general. La disciplina és la Medicina de Laboratori, el nom convencional europeu per aquesta especialitat. I quin hauria de ser el model d'especialitat i d'organització? . Doncs com a exemple mirar cap al Canadà i cap Alberta en particular donaria pistes del que cal fer. Aquesta taula ho resumeix:

Segurament a data d'avui caldria reescriure l'article de Zweifel i redefinir els papers de cadascú. Han passat moltes coses d'ençà d'aleshores. Però n'hi ha una que roman, l'escassetat de metges. I la pregunta que molts es fan, és per què hi ha escassetat?. Doncs que mirin qui decideix les places de forma efectiva, qui accepta que hi hagi determinada oferta i la resposta serà immediata. Molts diuen que el govern no ha planificat, i és cert. Però encara que hagués planificat té delegada la regulació parcialment en comissions de les especialitats i en la comissió nacional, així com els degans d'universitat, i també cal recordar-ho, hi tenim les decisions arbitràries de les comunitats autònomes. Unes comisions que en diuen assessores però que a la pràctica exerceixen com a lobby d'influència determinant i decisor. Mentre no es modifiqui el corporativisme i l'estructura legal que facilita la delegació de la regulació que deia Zweifel no hi haurà solució als problemes d'accés i qualitat a l'assistència per part de la població. 

Fa cinc anys Eric Topol va fer un informe per al NHS sobre com la digitalització modificaria la tasca dels professionals de la salut al NHS. Allà no l'hi han fet massa cas, però aquí ni tant sols hi ha un informe.

Resum. Hi ha un embut perfectament dissenyat. No hi ha el nombre ni tipus de metges que es necessiten per afrontar les tasques i disposar d'un accés digne als serveis. Tot el que observem com a resultat no és un error de planificació del nombre de metges, és exactament tal com s'ha previst que sigui, possiblement per deixadesa.

PS I sobretot recordeu sempre què va passar el 27 de febrer al Parlament, el PSC va votar amb PP i VOX per rebutjar la reserva de places universitàries de medicina i infermeria per estudiants catalans. A hores d'ara hi ha un 40% d'estudiants forasters.

Hospital de la Santa Creu

The Theranos contretemps as a serious scandal

Last Thursday WSJ released a long article on Theranos clinical lab. In this blog you may check my February and July posts on this firm under the title: A closely guarded secret. As you may imagine, such a title was not coincidental. There were some clues that justified it, something unusual was happening. And WSJ has contributed to shed light on the issue. All the details in it. Basically, the summary is that analytic validity and clinical validity is under compromise. This is an exemple:



If you want to read a first person account, you'll find it here and here. Some additional articles: Wired, New Yorker, Clinical Chemistry and Laboratory Medicine (CCLM), Forbes, NYT, WP,...
This is not only a contretemps, it is a serious scandal and a huge problem to credibility for this start-up.
From Wired:

Theranos got a lot of traction by tapping into the frustration—both from consumers and the medical community—that diagnostic testing is too painful, too slow, and too expensive. “Their problem is they tried to do it with existing diagnostic instrumentation, instead of innovating new diagnostic instrumentation,”

Theranos is a black box that has touted results rather than process. “The ability of the lab medicine community to police and correct itself depends on that flow of information,” says Master. Instead, Theranos’ research was internal, and rather than submit their work to peer review the company cited their FDA approvals as evidence that the technology worked.

At least in the USA there is a regulator, the FDA, lab regulation in Europe was enacted in 1998, completely outdated under a third party scheme, not a direct public regulator. Therefore, there is a pressing motive to speed up new and different rules in Europe. Microfluidics and nanotechnologies are calling for and urgent overhaul.


 PS. An statement from WSJ:

In 2005, Ms. Holmes hired Ian Gibbons, a British biochemist who had researched systems to handle and process tiny quantities of fluids. His collaboration with other Theranos scientists produced 23 patents, according to records filed with the U.S. Patent and Trademark Office. Ms. Holmes is listed as a co-inventor on 19 of the patents.

The patents show how Ms. Holmes’s original idea morphed into the company’s business model. But progress was slow. Dr. Gibbons “told me nothing was working,” says his widow, Rochelle. In May 2013, Dr. Gibbons committed suicide. Theranos’s Ms. King says the scientist “was frequently absent from work in the last years of his life, due to health and other problems.” Theranos disputes the claim that its technology was failing.

MABS in history of medicine

The Lock and Key of Medicine Monoclonal Antibodies and the Transformation of Healthcare

While reading FT this summer I came across an article quoting a unique book on history of monoclonal antibodies (MABS). Right now there are more than 30 drugs in the market based on hybridoma technology that was created in 1975.
The birth of MABS is explained with full details, how the creators finally didn't patented it and why, the difficulties for research in an unconnected world, etc... An exciting story that is worth reading. Right now, it would be completely different, commercialization of research and medicine has raised considerably.

That a British company spearheaded the first marketing of Mabs, a technology devised in a British laboratory by an émigré Argentinian scientist with his German colleague, highlights the international nature of biotechnology commercialization. Sera- Lab’s venture to sell Mabs took place in the midst of the excitement generated by the founding of Genentech in 1976. The emergence of Genentech, which had been set up
to market recombinant DNA products, galvanized numerous alliances among academics, entrepreneurs, and venture capitalists to launch new companies to commercialize biotechnology. Most of the early enterprises set up in the wake of Genentech’s birth were dedicated to exploiting recombinant DNA for the mass production of natural products such as interferon and insulin for drugs. But the early germination of the modern biotechnology industry did not rest solely on recombinant DNA. By the 1970s a number of pioneering companies were developing Mab products, including Sera- Lab and two startups: Hybritech in San Diego and Centocor in Philadelphia. Entrepreneurs who risked entry into the field had no guarantee of success and were entering totally uncharted
territory. Such individuals faced major fi nancial, personal, professional, and regulatory challenges as well as a great deal of hostility, pessimism, and litigation.

Milstein with Köhler at the time of their receiving the Nobel Prize in 1984 together with Nils Jerne.

Mabs have had their strongest therapeutic impact in the field of cancer. The first Mab to reach the market for cancer was edrecolomab (Panorex), which was granted German regulatory approval in 1995 for the treatment of postoperative colorectal cancer. Developed by Centocor in partnership with the Wistar Institute, it was withdrawn in 2001 because of its poor effi cacy in comparison with other drugs. Since 1997, however, the U.S. Food and Drug Administration (FDA) has approved twelve Mab drugs for cancer treatment, including rituximab (Rituxan), approved in 1998 for the treatment of non- Hodgkin’s lymphoma. By 2012 there were over 160 candidates in clinical trials for cancer, with seventy of them in phase III trials, the stage before a drug is submitted for regulatory approval.

Mabs have enabled the identification and characterization of cancerous tumors previously difficult to detect and diff erentiate from other tumors, thereby providing a better understanding of cancer. They have also opened a path to more personalized medical treatment. Trastuzumab (Herceptin), for example, was specifically developed to target HER2/neu, a protein overexpressed by tumors found in 25 percent of newly diagnosed breast- cancer patients

Clinical utility of genomic sequencing

 Clinical utility of genomic sequencing: a measurement toolkit

From Genomic Medicine:

For a diagnostic test such as WGS (whole genome sequencing) to be accepted into practice, commissioned in a health system, or receive coverage and reimbursement through health insurance, evidence of clinical utility and cost-effectiveness is generally required. Unlike prospective clinical research where the ‘effectiveness’ of an intervention can be easily tied to a predefined health outcome, the concept of clinical utility in genetic medicine is rarely uniformly defined nor necessarily directly tied to a specific health outcome. As such, generating and evaluating evidence of clinical utility is complex. The challenge in defining clinical utility today is compounded by the extraordinary heterogeneity of rare diseases, as well as the polygenic nature of more common conditions for which WGS is expected to be relevant. In this paper, we aim to extend earlier conceptualizations of clinical utility as applied to the diagnostic use of WGS and suggest that this framework not only be used as a tool for evidence review

 The application of this model to WGS includes six levels of efficacy: technical efficacy, diagnostic accuracy efficacy, diagnostic thinking efficacy, therapeutic efficacy, patient outcome efficacy, and societal efficacy (Table 1, Fig. 1). The model is hierarchical; achieving a given level of efficacy is often but not always contingent upon a demonstration of efficacy at the preceding level. As described in Fig. 1, levels 1–3 are necessarily contingent but beyond level 3, a genetic test can achieve therapeutic, patient outcome, and/or societal impact in ways that are contingent upon one another or independent of one another. We retain the levels of technical and diagnostic accuracy efficacy (i.e., levels 1 and 2) as essential starting points in our guiding framework as they are fundamental precursors to achieving clinical utility. However, since these laboratory-based components of efficacy are well-debated and described in the WGS literature and in recent guidelines published by members of our group27, we focus here on four levels of the efficacy model (i.e., levels 3–6) that align most directly with a broad definition of clinical utility and extend beyond laboratory-based components of efficacy. In emphasizing these four levels of efficacy as components of clinical utility, our intent is to encourage the use of a broad set of health and non-health-related indicators of value to bolster the state of evidence in this area, rather than to convey that all aspects of clinical utility need to be achieved for WGS adoption and reimbursement.

 

 

All you need to know about molecular diagnostics

Molecular Diagnostics Fundamentals, Methods, and Clinical Applications

Current advances in health sciences are available at the same time that diagnostic technology and knowledge provide new tools. This book is specially relevant because it summarises all the current state of the art on molecular diagnostics. Therefore a good suggestion for those who want to practice precision medicine.

Table of contents:
I. Fundamentals of Molecular Biology: An Overview
1. Nucleic Acids and Proteins
2. Gene Expression and Epigenetics
II. Common Techniques in Molecular Biology
3. Nucleic Acid Extraction Methods
4. Resolution and Detection of Nucleic Acids
5. Analysis and Characterization of Nucleic Acids and Proteins
6. Nucleic Acid Amplification
7. Chromosomal Structure and Chromosomal Mutations
8. Gene Mutations
9. DNA Sequencing
III. Techniques in the Clinical Laboratory
10. DNA Polymorphisms and Human Identification
11. Detection and Identification of Microorganisms
12. Molecular Detection of Inherited Diseases
13. Molecular Oncology
14. DNA-Based Tissue Typing
15. Quality Assurance and Quality Control in the Molecular Laboratory
Appendices
A. Study Questions Answers
B. Answers to Case Studies
Glossary
Index

Validesa i utilitat de l'òmica

Evolution of Translational Omics: Lessons Learned and the Path Forward

 L'"Òmica" és un terme que abasta múltiples disciplines moleculars, que impliquen la caracterització dels conjunts globals de molècules biològiques, com ara ADN, ARN, proteïnes, i metabòlits. Per exemple, la genòmica investiga milers de seqüències d'ADN, la  transcriptòmica investiga totes o moltes transcripcions de gens, la proteòmica investiga un gran nombre de proteïnes, i metabolòmica investiga grans conjunts de metabòlits.

Així comença el llibre de l'IOM sobre una qüestió fonamental de la medicina dels nostres dies.  I el més interessant és com explica la diferència entre l'òmica translacional i els biomarcadors. Malgrat la dificultat que presenta l'avaluació d'un biomarcador, els reptes al que s'enfronta l'òmica són molt superiors. Diu clarament a l'inici:

The complexity of omics research also makes data provenance more challenging and makes sharing of the complex data sets and computational models difficult, which limits the ability of other scientists to replicate and verify the findings and conclusions of omics research studies. Database repositories for genomic data sets are available, but data sharing is not routine, and  without access to the data sets or a precisely defined computational model, replication and  verification are more difficult than for single biomarker tests. While independent confirmation studies are expensive, the need for replication is beneficial in the omics field given the data  complexities that can lead to errors, from simple data management errors to incorrectly  designed computational models. This level of complexity does not exist for single-biomarker  test research, development, and validation.

Massa sovint es vol fer passar aquesta complexitat com inadvertida. I afegeix:

Many hope that the promise that omics science holds for medicine and public health will be realized. With the creation of high-throughput measurement technologies, it is now feasible to take a snapshot of a patient’s molecular profile at specific stages in the progression of disease pathology or at a given location in the body. However, the complexity of these technologies and of the resulting high-dimensional data introduces major challenges for the scientific community, as rigorous statistical, bioinformatics, laboratory, and clinical procedures are required to develop and validate these tests and evaluate their clinical usefulness.

Sobre el tipus de dades òmiques heu d'anar a la pàgina 40 i llegir-ho amb deteniment. Quan un acaba de comprendre el que s'explica de forma planera, aleshores s'adona que els que venen genoma i prou s'han quedat curts, la complexitat és notòria. I en especial la referent a l'epigenoma, del que ja n'he parlat repetidament en aquest blog. El capítol sobre avaluació de les proves esdevé clau. Només fa referència a validesa analítica i clínica, però és el principi sense el qual tots aquells que es plantegin fer cost-efectivitat no podran treballar. I cap al final trobo aquesta conclusió:

 A well-designed test development plan addresses a clinically meaningful question and employs rigorous test discovery, development, and validation procedures. This includes locking down all aspects of an omicsbased test prior to evaluation for clinical utility and use and avoiding overlap between discovery and validation specimens. Choosing an appropriate clinical/biological validation strategy and interacting with FDA prior to initiation of validation studies also reflect a well-designed test development plan. Making data and code available are critical aspects of test development because it enables external verification of the results and generation of additional insights that can advance science and patient care.

El rigor s'imposa i traduir la recerca en aplicacions obliga a comprendre el valor que aporten a la societat. El camí és llarg malgrat sovint apareix als diaris com que és bufar i fer ampolles.

PS. Es poden patentar les proves genòmiques? Avui un tribunal decideix, ho trobareu a WSJ.

PS. Ekaizer a 8TV, fonamental. I també a RAC1

PS. Al Diccionario RAE queda més clar encara: macarra. 1. adj. Dicho de una persona: Agresiva, achulada.

Eliseu Meifren, podeu veure'l a Sant Feliu de Guixols, paga la pena.