El valor de p del test no es un ‘índice matemático’, es simplemente una frecuencia relativa / The p-value of the test is not a ‘mathematical index’, it is simply a relative frequency

Las revistas científicas más importantes en campos como medicina, biología y sociología publican reiteradamente artículos y editoriales denunciando que un gran porcentaje de médicos no entiende los conceptos básicos del análisis estadístico, lo que favorece el riesgo de cometer errores al interpretar los datos, los hace más vulnerables frente a informaciones falsas y reduce la eficacia de la investigación. Este problema se extiende a lo largo de toda su carrera profesional y se debe, en gran parte, a una enseñanza deficiente en estadística que es común en países desarrollados. En palabras de H. Halle y S. Krauss, ‘el 90% de los profesores universitarios alemanes que usan con asiduidad el valor de p de los test no entiende lo que mide ese valor’. Es importante destacar que los razonamientos básicos del análisis estadístico son similares a los que realizamos en nuestra vida cotidiana y que comprender los conceptos básicos del análisis estadístico no requiere conocimiento matemático alguno. En contra de lo que muchos investigadores creen, el valor de p del test no es un ‘índice matemático’ que nos permita concluir claramente si, por ejemplo, un fármaco es más efectivo que el placebo. El valor de p del test es simplemente un porcentaje.(AU)

Abstract. Leading scientific journals in fields such as medicine, biology and sociology repeatedly publish articles and editorials claiming that a large percentage of doctors do not understand the basics of statistical analysis, which increases the risk of errors in interpreting data, makes them more vulnerable to misinformation and reduces the effectiveness of research. This problem extends throughout their careers and is largely due to the poor training they receive in statistics – a problem that is common in developed countries. As stated by H. Halle and S. Krauss, ‘90% of German university lecturers who regularly use the p-value in tests do not understand what that value actually measures’. It is important to note that the basic reasoning of statistical analysis is similar to what we do in our daily lives and that understanding the basic concepts of statistical analysis does not require any knowledge of mathematics. Contrary to what many researchers believe, the p-value of the test is not a ‘mathematical index’ that allows us to clearly conclude whether, for example, a drug is more effective than a placebo. The p-value of the test is simply a percentage.(AU)

[Registration of national health registry-based research]. / Registrering av forskning baserad på nationella hälsoregister.

In medical research as a whole, frequent inaccurate or biased findings are of international concern. One measure against reporting biases is study registration before the start of data collection (preregistration), preferably together with the statistical analysis plan. This meta-research study systematically evaluated registration of Swedish observational research based on national health registries. In a random sample of registry-based observational studies published 2010-2022, very few were preregistered with a publicly available analysis plan (<1 procent). Ideas from the meta-research literature can be leveraged to strengthen the brand of Swedish registry-based observational studies and counteract reporting bias.

The role of correspondence analysis in medical research.

Correspondence analysis (CA) is a multivariate statistical and visualization technique. CA is extremely useful in analyzing either two- or multi-way contingency tables, representing some degree of correspondence between columns and rows. The CA results are visualized in easy-to-interpret "bi-plots," where the proximity of items (values of categorical variables) represents the degree of association between presented items. In other words, items positioned near each other are more associated than those located farther away. Each bi-plot has two dimensions, named during the analysis. The naming of dimensions adds a qualitative aspect to the analysis. Correspondence analysis may support medical professionals in finding answers to many important questions related to health, wellbeing, quality of life, and similar topics in a simpler but more informal way than by using more complex statistical or machine learning approaches. In that way, it can be used for dimension reduction and data simplification, clustering, classification, feature selection, knowledge extraction, visualization of adverse effects, or pattern detection.

A structured curriculum supporting biomedical trainees' transition into independent academic positions and early career success.

The United States government makes a substantial investment in biomedical training programs each year. However, for most trainees, these opportunities do not translate into career progression in academic research pathways. Only about one-fifth of postdoctoral fellows eventually secure a tenure-track faculty position, and even among these candidates, attrition is high. Although a number of factors govern career choices and career longevity, the transition from trainee to faculty is a challenging process and requires knowledge and skills that are not necessarily developed during a traditional university experience. Many postdoctoral fellows receive adequate training in research skills and scientific communication, but new faculty report not being sufficiently prepared for the job search process and for starting their labs. To address this critical training gap, the ITERT core (Interdisciplinary Translational Education and Research Training) and the Office of Postdoctoral Fellows at the University of Texas MD Anderson Cancer Center implemented a structured course for both postdoctoral fellows and senior PhD students to provide formalized training for successfully navigating academic positions in biomedical research. Here we report on the pilot Navigating Academic Careers course conducted in 2021-2022 for 30 PhD students and postdocs. The nine-module course was conducted over 13 weeks in 25.5 h instructional sessions. The key educational objectives included 1) navigating the job application and the interview/negotiation process, 2) hiring, leading, and mentoring lab personnel and program support staff, 3) project administration and financial stewardship, 4) managing time and work-life balance and 5) developing collaborations, branding, personalized niche, and networking. Survey-based analysis at the time of the course was used to capture the participants' assessment of the course content, organization, and delivery, with a follow-up survey conducted approximately 2 years post-course (2024) to evaluate longer-term impacts of the training. Initial in-course assessment revealed that 89.9% of respondents found the scope and instructional content appropriate, and 91.1% found the course relevant and applicable to their career needs. Longer-term post-course evaluation indicated that 80% of respondents applied the learnings of the course, that 80% reported feeling more confident in navigating an academic job search, and that 66.6% continued to report agreement with the course preparing them for their current role/ongoing job search, with 46.7% already securing jobs in academic research, including as independent faculty. The outcomes of this pilot course suggest that integrating this into the broader postdoctoral training curriculum can enhance both the transition and early-career success of talented scientists-in-training into working professionals in biomedical careers, as faculty and science-trained staff.

Aging-related biomarker discovery in the era of immune checkpoint inhibitors for cancer patients.

Older patients with cancer, particularly those over 75 years of age, often experience poorer clinical outcomes compared to younger patients. This can be attributed to age-related comorbidities, weakened immune function, and reduced tolerance to treatment-related adverse effects. In the immune checkpoint inhibitors (ICI) era, age has emerged as an influential factor impacting the discovery of predictive biomarkers for ICI treatment. These age-linked changes in the immune system can influence the composition and functionality of tumor-infiltrating immune cells (TIICs) that play a crucial role in the cancer response. Older patients may have lower levels of TIICs infiltration due to age-related immune senescence particularly T cell function, which can limit the effectivity of cancer immunotherapies. Furthermore, age-related immune dysregulation increases the exhaustion of immune cells, characterized by the dysregulation of ICI-related biomarkers and a dampened response to ICI. Our review aims to provide a comprehensive understanding of the mechanisms that contribute to the impact of age on ICI-related biomarkers and ICI response. Understanding these mechanisms will facilitate the development of treatment approaches tailored to elderly individuals with cancer.

Ending "domestic helicopter research".

"Helicopter research" refers to a practice where researchers from wealthier countries conduct studies in lower-income countries with little involvement of local researchers or community members. This practice also occurs domestically. In this Commentary, we outline strategies to curb domestic helicopter research and to foster equity-centered collaborations.

MicroRNAs in Pancreatic Cancer: Advances in Biomarker Discovery and Therapeutic Implications.

Pancreatic cancer remains a formidable malignancy characterized by high mortality rates, primarily attributable to late-stage diagnosis and a dearth of effective therapeutic interventions. The identification of reliable biomarkers holds paramount importance in enhancing early detection, prognostic evaluation, and targeted treatment modalities. Small non-coding RNAs, particularly microRNAs, have emerged as promising candidates for pancreatic cancer biomarkers in recent years. In this review, we delve into the evolving role of cellular and circulating miRNAs, including exosomal miRNAs, in the diagnosis, prognosis, and therapeutic targeting of pancreatic cancer. Drawing upon the latest research advancements in omics data-driven biomarker discovery, we also perform a case study using public datasets and address commonly identified research discrepancies, challenges, and limitations. Lastly, we discuss analytical approaches that integrate multimodal analyses incorporating clinical and molecular features, presenting new insights into identifying robust miRNA-centric biomarkers.

[Comparative pathology in oncology-Best practice]. / Vergleichende Pathologie in der onkologischen Forschung.

Comparative experimental pathology is a research field at the interface of human and veterinary medicine. It is focused on the comparative study of similarities and differences between spontaneous and experimentally induced diseases in animals (animal models) compared to human diseases. The use of animal models for studying human diseases is an essential component of biomedical research. Interdisciplinary teams with species-specific expertise should collaborate wherever possible and maintain close communication. Mutual openness, cooperation, and willingness to learn form the basis for a fruitful collaboration. Research projects jointly led by or involving both animal and human pathologists make a significant contribution to high-quality biomedical research. Such approaches are promising not only in oncological research, as outlined in this article, but also in other research areas where animal models are regularly used, such as infectiology, neurology, and developmental biology.

A medical big data access control model based on smart contracts and risk in the blockchain environment.

The rapid development of the Hospital Information System has significantly enhanced the convenience of medical research and the management of medical information. However, the internal misuse and privacy leakage of medical big data are critical issues that need to be addressed in the process of medical research and information management. Access control serves as a method to prevent data misuse and privacy leakage. Nevertheless, traditional access control methods, limited by their single usage scenario and susceptibility to single point failures, fail to adapt to the polymorphic, real-time, and sensitive characteristics of medical big data scenarios. This paper proposes a smart contracts and risk-based access control model (SCR-BAC). This model integrates smart contracts with traditional risk-based access control and deploys risk-based access control policies in the form of smart contracts into the blockchain, thereby ensuring the protection of medical data. The model categorizes risk into historical and current risk, quantifies the historical risk based on the time decay factor and the doctor's historical behavior, and updates the doctor's composite risk value in real time. The access control policy, based on the comprehensive risk, is deployed into the blockchain in the form of a smart contract. The distributed nature of the blockchain is utilized to automatically enforce access control, thereby resolving the issue of single point failures. Simulation experiments demonstrate that the access control model proposed in this paper effectively curbs the access behavior of malicious doctors to a certain extent and imposes a limiting effect on the internal abuse and privacy leakage of medical big data.

Genetic Analysis of 27 Y-STR Haplotypes in 11 Iranian Ethnic Groups.

BACKGROUND: The study of Y-chromosomal variations provides valuable insights into male susceptibility in certain diseases like cardiovascular disease (CVD). In this study, we analyzed paternal lineage in different Iranian ethnic groups, not only to identify developing medical etiology, but also to pave the way for gender-specific targeted strategies and personalized medicine in medical genetic research studies. METHODS: The diversity of eleven Iranian ethnic groups was studied using 27 Y-chromosomal short tandem repeat (Y-STR) haplotypes from Y-filer® Plus kit. Analysis of molecular variance (AMOVA) based on pair-wise RST along with multidimensional scaling (MDS) calculation and Network phylogenic analysis was employed to quantify the differences between 503 unrelated individuals from each ethnicity. RESULTS: Results from AMOVA calculation confirmed that Gilaks and Azeris showed the largest genetic distance (RST=0.35434); however, Sistanis and Lurs had the smallest considerable genetic distance (RST=0.00483) compared to other ethnicities. Although Azeris had a considerable distance from other ethnicities, they were still close to Turkmens. MDS analysis of ethnic groups gave the indication of lack of similarity between different ethnicities. Besides, network phylogenic analysis demonstrated insignificant clustering between samples. CONCLUSION: The AMOVA analysis results explain that the close distance of Azeris and Turkmens may be the effect of male-dominant expansions across Central Asia that contributed to historical and demographics of populations in the region. Insignificant differences in network analysis could be the consequence of high mutation events that happened in the Y-STR regions over the years. Considering the ethnic group affiliations in medical research, our results provided an understanding and characterization of Iranian male population for future medical and population genetics studies.

Natural language processing (NLP) to facilitate abstract review in medical research: the application of BioBERT to exploring the 20-year use of NLP in medical research.

BACKGROUND: Abstract review is a time and labor-consuming step in the systematic and scoping literature review in medicine. Text mining methods, typically natural language processing (NLP), may efficiently replace manual abstract screening. This study applies NLP to a deliberately selected literature review problem, the trend of using NLP in medical research, to demonstrate the performance of this automated abstract review model. METHODS: Scanning PubMed, Embase, PsycINFO, and CINAHL databases, we identified 22,294 with a final selection of 12,817 English abstracts published between 2000 and 2021. We invented a manual classification of medical fields, three variables, i.e., the context of use (COU), text source (TS), and primary research field (PRF). A training dataset was developed after reviewing 485 abstracts. We used a language model called Bidirectional Encoder Representations from Transformers to classify the abstracts. To evaluate the performance of the trained models, we report a micro f1-score and accuracy. RESULTS: The trained models' micro f1-score for classifying abstracts, into three variables were 77.35% for COU, 76.24% for TS, and 85.64% for PRF. The average annual growth rate (AAGR) of the publications was 20.99% between 2000 and 2020 (72.01 articles (95% CI: 56.80-78.30) yearly increase), with 81.76% of the abstracts published between 2010 and 2020. Studies on neoplasms constituted 27.66% of the entire corpus with an AAGR of 42.41%, followed by studies on mental conditions (AAGR = 39.28%). While electronic health or medical records comprised the highest proportion of text sources (57.12%), omics databases had the highest growth among all text sources with an AAGR of 65.08%. The most common NLP application was clinical decision support (25.45%). CONCLUSIONS: BioBERT showed an acceptable performance in the abstract review. If future research shows the high performance of this language model, it can reliably replace manual abstract reviews.

Biomedical semantic text summarizer.

BACKGROUND: Text summarization is a challenging problem in Natural Language Processing, which involves condensing the content of textual documents without losing their overall meaning and information content, In the domain of bio-medical research, summaries are critical for efficient data analysis and information retrieval. While several bio-medical text summarizers exist in the literature, they often miss out on an essential text aspect: text semantics. RESULTS: This paper proposes a novel extractive summarizer that preserves text semantics by utilizing bio-semantic models. We evaluate our approach using ROUGE on a standard dataset and compare it with three state-of-the-art summarizers. Our results show that our approach outperforms existing summarizers. CONCLUSION: The usage of semantics can improve summarizer performance and lead to better summaries. Our summarizer has the potential to aid in efficient data analysis and information retrieval in the field of biomedical research.

Training in the implementation of sex and gender research policies: an evaluation of publicly available online courses.

BACKGROUND: Recently implemented research policies requiring the inclusion of females and males have created an urgent need for effective training in how to account for sex, and in some cases gender, in biomedical studies. METHODS: Here, we evaluated three sets of publicly available online training materials on this topic: (1) Integrating Sex & Gender in Health Research from the Canadian Institutes of Health Research (CIHR); (2) Sex as a Biological Variable: A Primer from the United States National Institutes of Health (NIH); and (3) The Sex and Gender Dimension in Biomedical Research, developed as part of "Leading Innovative measures to reach gender Balance in Research Activities" (LIBRA) from the European Commission. We reviewed each course with respect to their coverage of (1) What is required by the policy; (2) Rationale for the policy; (3) Handling of the concepts "sex" and "gender;" (4) Research design and analysis; and (5) Interpreting and reporting data. RESULTS: All three courses discussed the importance of including males and females to better generalize results, discover potential sex differences, and tailor treatments to men and women. The entangled nature of sex and gender, operationalization of sex, and potential downsides of focusing on sex more than other sources of variation were minimally discussed. Notably, all three courses explicitly endorsed invalid analytical approaches that produce bias toward false positive discoveries of difference. CONCLUSIONS: Our analysis suggests a need for revised or new training materials that incorporate four major topics: precise operationalization of sex, potential risks of over-emphasis on sex as a category, recognition of gender and sex as complex and entangled, and rigorous study design and data analysis.

Recently implemented research policies requiring the inclusion of females and males have created an urgent need for effective training in how to account for sex, and in some cases gender, in biomedical studies. We evaluated three publicly available online trainings on this topic: (1) Integrating Sex & Gender in Health Research from the Canadian Institutes of Health Research; (2) Sex as a Biological Variable: A Primer from the United States National Institutes of Health; and (3) The Sex and Gender Dimension in Biomedical Research, developed as part of "Leading Innovative Measures to Reach Gender Balance in Research Activities (LIBRA)" from the European Commission. We reviewed each course with respect to their coverage of (1) What is required by the policy; (2) Rationale for the policy; (3) Handling of the concepts "sex" and "gender;" (4) Research design and analysis; and (5) Interpreting and reporting data. All three discussed the importance of including males and females to better generalize results, discover potential sex differences, and tailor treatments to men and women. The interconnectedness of sex and gender, how to operationalize sex, and potential downsides of focusing on sex more than other sources of variation were minimally discussed. Notably, all three courses explicitly endorsed invalid analytical approaches that lead to incorrect findings of differences. Our analysis suggests a need for revised or new training materials that cover four major topics: precise operationalization of sex, attention to potential risks of over-emphasizing sex, consideration of gender and sex as complex and intertwined, and rigorous study design and data analysis.

Definitions of digital biomarkers: a systematic mapping of the biomedical literature.

BACKGROUND: Technological devices such as smartphones, wearables and virtual assistants enable health data collection, serving as digital alternatives to conventional biomarkers. We aimed to provide a systematic overview of emerging literature on 'digital biomarkers,' covering definitions, features and citations in biomedical research. METHODS: We analysed all articles in PubMed that used 'digital biomarker(s)' in title or abstract, considering any study involving humans and any review, editorial, perspective or opinion-based articles up to 8 March 2023. We systematically extracted characteristics of publications and research studies, and any definitions and features of 'digital biomarkers' mentioned. We described the most influential literature on digital biomarkers and their definitions using thematic categorisations of definitions considering the Food and Drug Administration Biomarkers, EndpointS and other Tools framework (ie, data type, data collection method, purpose of biomarker), analysing structural similarity of definitions by performing text and citation analyses. RESULTS: We identified 415 articles using 'digital biomarker' between 2014 and 2023 (median 2021). The majority (283 articles; 68%) were primary research. Notably, 287 articles (69%) did not provide a definition of digital biomarkers. Among the 128 articles with definitions, there were 127 different ones. Of these, 78 considered data collection, 56 data type, 50 purpose and 23 included all three components. Those 128 articles with a definition had a median of 6 citations, with the top 10 each presenting distinct definitions. CONCLUSIONS: The definitions of digital biomarkers vary significantly, indicating a lack of consensus in this emerging field. Our overview highlights key defining characteristics, which could guide the development of a more harmonised accepted definition.

Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease.

One in ten severe acute respiratory syndrome coronavirus 2 infections result in prolonged symptoms termed long coronavirus disease (COVID), yet disease phenotypes and mechanisms are poorly understood1. Here we profiled 368 plasma proteins in 657 participants ≥3 months following hospitalization. Of these, 426 had at least one long COVID symptom and 233 had fully recovered. Elevated markers of myeloid inflammation and complement activation were associated with long COVID. IL-1R2, MATN2 and COLEC12 were associated with cardiorespiratory symptoms, fatigue and anxiety/depression; MATN2, CSF3 and C1QA were elevated in gastrointestinal symptoms and C1QA was elevated in cognitive impairment. Additional markers of alterations in nerve tissue repair (SPON-1 and NFASC) were elevated in those with cognitive impairment and SCG3, suggestive of brain-gut axis disturbance, was elevated in gastrointestinal symptoms. Severe acute respiratory syndrome coronavirus 2-specific immunoglobulin G (IgG) was persistently elevated in some individuals with long COVID, but virus was not detected in sputum. Analysis of inflammatory markers in nasal fluids showed no association with symptoms. Our study aimed to understand inflammatory processes that underlie long COVID and was not designed for biomarker discovery. Our findings suggest that specific inflammatory pathways related to tissue damage are implicated in subtypes of long COVID, which might be targeted in future therapeutic trials.

Elements of successful NIH grant applications.

Is there a formula for a competitive NIH grant application? The Serenity Prayer may provide one: "Grant me the serenity to accept the things I cannot change, the ability to change the things I can, and the wisdom to know the difference." But how to tell the difference? In this Perspective, we provide an inclusive roadmap-elements of NIH funding. Collectively, we have over 30 y of peer review experience as NIH Scientific Review Officers in addition to over 30 y of program experience as NIH Program Officers. This article distills our NIH experience. We use Euclid's 13-book landmark, The Elements, as our template to humbly share what we learned. We have three specific aims: inform, guide, and motivate prospective applicants. We also address ways that support diversity and inclusion among applicants and young investigators in biomedical research. The elements we describe come from a wide range of sources. Some themes will be general. Some will be specific. All will be candid. The ultimate goal is a competitive application, serenity, and hopefully both.