Medical Research Studies Research Articles

Abstract 2112: Reporting recommendations for translational cancer genomic studies

Abstract Background. Inaccuracy and widespread deficiencies in the reporting of translational cancer genomic studies is common and represents a major issue of cancer research. It challenges not only the study interpretation but also the ability to accurately compare studies. The REMARK (REporting recommendations for tumour MARKer prognostic studies) guideline was previously developed to improve the reporting of tumour biomarker prognostic studies. However, it presents several limitations as many aspects of omic-based research are missing. We developed a new guideline to ensure transparency and standardized reporting of clinical cancer genomic studies. Methods. The concept for this checklist was generated after extensive review of current literature which identified major gaps in study reporting, including: insufficient information about study design, population included, and treatments; inappropriate analysis methods; and widespread deficiencies in the reporting of results. Key elements to be addressed in the new checklist were identified by experts in methodology, biostatistics, and preclinical, translational, and clinical cancer genomics (proposing panel). Several reporting guidelines produced for other types of medical research studies were also reviewed. Results. A preliminary draft of the proposed checklist was discussed during the 18th workshop of the International Cancer Genome Consortium (ICGC) in Verona, Italy (13-14 June 2022). Building on that work, the checklist was then re-discussed and improved by the proposing panel. This will serve as a starting point for further discussion among the ICGC, REMARK, the Catalogue Of Somatic Mutations In Cancer (COSMIC), the International Quality Network for Pathology (IQN Path) and other organizations to collect any criticisms, make further revisions, and reach final consensus. Conclusions. The proposed guideline provides a comprehensive overview to educate on good reporting of translational cancer genomic studies. It represents a valuable reference to consider when designing, conducting, and reporting this kinds of studies. After ICGC, REMARK, COSMIC, and IQN Path discussion, revision, and approval, the proposed checklist will be the new standard to be adopted by researchers, editors and reviewers. It will significantly impact future cancer research and patient care. Citation Format: Raffaella Casolino, Philip A. Beer, Nicola Normanno, Zbyslaw Sondka, Andrew V. Biankin. Reporting recommendations for translational cancer genomic studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2112.

Eliminating Ambiguous Treatment Effects Using Estimands.

Most reported treatment effects in medical research studies are ambiguously defined, which can lead to misinterpretation of study results. This is because most authors do not attempt to describe what the treatment effect represents, and instead require readers to deduce this based on the reported statistical methods. However, this approach is challenging, because many methods provide counterintuitive results. For example, some methods include data from all patients, yet the resulting treatment effect applies only to a subset of patients, whereas other methods will exclude certain patients while results will apply to everyone. Additionally, some analyses provide estimates pertaining to hypothetical settings in which patients never die or discontinue treatment. Herein we introduce estimands as a solution to the aforementioned problem. An estimand is a clear description of what the treatment effect represents, thus saving readers the necessity of trying to infer this from study methods and potentially getting it wrong. We provide examples of how estimands can remove ambiguity from reported treatment effects and describe their current use in practice. The crux of our argument is that readers should not have to infer what investigators are estimating; they should be told explicitly.

From cause andeffect to causes and effects.

It is now-at least loosely-acknowledged that most health and clinical outcomes are influenced by different interacting causes. Surprisingly, medical research studies are nearly universally designed to study-usually in a binary way-the effect of a single cause. Recent experiences during the coronavirus disease 2019pandemic brought to the forefront that most of our challenges in medicine and healthcare deal with systemic, that is, interdependent and interconnected problems. Understanding these problems defy simplistic dichotomous research methodologies. These insights demand a shift in our thinking from 'cause and effect'to 'causes and effects'since this transcends the classical way of Cartesian reductionist thinking. We require a shift to a 'causes and effects'frame so we can choose the research methodology that reflects the relationships between variables of interest-one-to-one, one-to-many, many-to-oneor many-to-many. One-to-one (or cause and effect) relationships are amenable to the traditional randomized control trial design, while all others require systemic designs to understand 'causes and effects'. Researchers urgently need to re-evaluate their science models and embrace research designs that allow an exploration of the clinically obvious multiple 'causes and effects' on health and disease. Clinical examples highlight the application of various systemic research methodologies and demonstrate how 'causes and effects' explain the heterogeneity of clinical outcomes. This shift in scientific thinking will allow us to find the necessary personalized or precise clinical interventions that address the underlying reasons for the variability of clinical outcomesand will contributeto greater health equity.

An empirical evaluation of the impact scenario of pooling bodies of evidence from randomized controlled trials and cohort studies in medical research

BackgroundRandomized controlled trials (RCTs) and cohort studies are the most common study design types used to assess treatment effects of medical interventions. We aimed to hypothetically pool bodies of evidence (BoE) from RCTs with matched BoE from cohort studies included in the same systematic review.MethodsBoE derived from systematic reviews of RCTs and cohort studies published in the 13 medical journals with the highest impact factor were considered. We re-analyzed effect estimates of the included systematic reviews by pooling BoE from RCTs with BoE from cohort studies using random and common effects models. We evaluated statistical heterogeneity, 95% prediction intervals, weight of BoE from RCTs to the pooled estimate, and whether integration of BoE from cohort studies modified the conclusion from BoE of RCTs.ResultsOverall, 118 BoE-pairs based on 653 RCTs and 804 cohort studies were pooled. By pooling BoE from RCTs and cohort studies with a random effects model, for 61 (51.7%) out of 118 BoE-pairs, the 95% confidence interval (CI) excludes no effect. By pooling BoE from RCTs and cohort studies, the median I2 was 48%, and the median contributed percentage weight of RCTs to the pooled estimates was 40%. The direction of effect between BoE from RCTs and pooled effect estimates was mainly concordant (79.7%). The integration of BoE from cohort studies modified the conclusion (by examining the 95% CI) from BoE of RCTs in 32 (27%) of the 118 BoE-pairs, but the direction of effect was mainly concordant (88%).ConclusionsOur findings provide insights for the potential impact of pooling both BoE in systematic reviews. In medical research, it is often important to rely on both evidence of RCTs and cohort studies to get a whole picture of an investigated intervention-disease association. A decision for or against pooling different study designs should also always take into account, for example, PI/ECO similarity, risk of bias, coherence of effect estimates, and also the trustworthiness of the evidence. Overall, there is a need for more research on the influence of those issues on potential pooling.

A varying-coefficient partially linear transformation model for length-biased data with an application to HIV vaccine studies.

Prevalent cohort studies in medical research often give rise to length-biased survival data that require special treatments. The recently proposed varying-coefficient partially linear transformation (VCPLT) model has the virtue of providing a more dynamic content of the effects of the covariates on survival times than the well-known partially linear transformation (PLT) model by allowing flexible interactions between the covariates. However, no existing analysis of the VCPLT model has considered length-biased sampling. In this paper, we consider the VCPLT model when the data are length-biased and right censored, thereby extending the reach of this flexible and powerful tool. We develop a martingale estimating function-based approach to the estimation of this model, provide theoretical underpinnings, evaluate finite sample performance via simulations, and showcase its practical appeal via an empirical application using data from two HIV vaccine clinical trials conducted by the U.S. National Institute of Allergy and Infectious Diseases.

A systematic review on metformin rational pharmacotherapeutics

This systematic review of the different and wide-ranged studies on metformin rational pharmacotherapeutics was performed, for a better comprehension of multi-centre maintenance of rational pharmacotherapeutic aspects in the regular anti-diabetic treatment prescribed to the new type II diabetic patients. This systematic review contributed 3729 refined and relevant medical records, among total 4570 records obtained from the study databases search, thus providing a refined qualitatively synthesised conclusive medical research study literature, on metformin rational pharmacotherapeutics.

Risk, benefit, and social value in Covid-19 human challenge studies: pandemic decision making in historical context

During the Covid-19 pandemic, ethicists and researchers proposed human challenge studies as a way to speed development of a vaccine that could prevent disease and end the global public health crisis. The risks to healthy volunteers of being deliberately infected with a deadly and novel pathogen were not low, but the benefits could have been immense. This essay is a history of the three major efforts to set up a challenge model and run challenge studies in 2020 and 2021. The pharmaceutical company Johnson and Johnson, the National Institutes of Health in the United States, and a private-public partnership of industry, university, and government partners in Britain all undertook preparations. The United Kingdom’s consortium began their Human Challenge Programme in March of 2021.Beyond documenting each effort, the essay puts these scientific and ethical debates in dialogue with the social, epidemiological, and institutional conditions of the pandemic as well as the commercial, intellectual, and political systems in which medical research and Covid-19 challenge studies operated. It shows how different institutions understood risk, benefit, and social value depending on their specific contexts. Ultimately the example of Covid-19 challenge studies highlights the constructedness of such assessments and reveals the utility of deconstructing them retrospectively so as to better understand the interplay of medical research and research ethics with larger social systems and historical contexts.

Post-COVIDLMU: implementation and evaluation of an interdisciplinary and cross-sectoral healthcare and research network for evidence-based treatment of patients with severe post-COVID syndrome

Post-COVIDLMU ist ein interdisziplinäres und sektorenübergreifendes Versorgungs- und Forschungsnetzwerk des LMU(Ludwig-Maximilians-Universität)-Klinikums. Der Schwerpunkt liegt auf der Behandlung und Erforschung komplexer und schwerer Post-COVID(„coronavirus disease“)-Syndrom-Fälle bei Erwachsenen. Die Behandlung dieser Patientengruppe erfolgt interdisziplinär unter Einbezug zahlreicher Fachkliniken des LMU-Klinikums. Ergänzt wird das universitäre Behandlungsangebot durch moderne telemedizinische Sprechstunden, interdisziplinäre Fallkonferenzen unter Beteiligungsmöglichkeit der zuweisenden Kollegen*innen sowie der Möglichkeit für Patient*innen, an Studien der medizinischen Forschung zum Post-COVID-Syndrom teilzunehmen. Das LMU-Klinikum kooperiert eng mit niedergelassenen Ärzten*innen sowie verschiedenen Rehaeinrichtungen in Deutschland.

Evaluating agreement between bodies of evidence from randomized controlled trials and cohort studies in medical research: a meta-epidemiological study

BackgroundRandomized controlled trials (RCTs) and cohort studies are the most common study design types used to assess the treatment effects of medical interventions. To evaluate the agreement of effect estimates between bodies of evidence (BoE) from randomized controlled trials (RCTs) and cohort studies and to identify factors associated with disagreement.MethodsSystematic reviews were published in the 13 medical journals with the highest impact factor identified through a MEDLINE search. BoE-pairs from RCTs and cohort studies with the same medical research question were included. We rated the similarity of PI/ECO (Population, Intervention/Exposure, Comparison, Outcome) between BoE from RCTs and cohort studies. The agreement of effect estimates across BoE was analyzed by pooling ratio of ratios (RoR) for binary outcomes and difference of mean differences for continuous outcomes. We performed subgroup analyses to explore factors associated with disagreements.ResultsOne hundred twenty-nine BoE pairs from 64 systematic reviews were included. PI/ECO-similarity degree was moderate: two BoE pairs were rated as “more or less identical”; 90 were rated as “similar but not identical” and 37 as only “broadly similar”. For binary outcomes, the pooled RoR was 1.04 (95% CI 0.97–1.11) with considerable statistical heterogeneity. For continuous outcomes, differences were small. In subgroup analyses, degree of PI/ECO-similarity, type of intervention, and type of outcome, the pooled RoR indicated that on average, differences between both BoE were small. Subgroup analysis by degree of PI/ECO-similarity revealed high statistical heterogeneity and wide prediction intervals across PI/ECO-dissimilar BoE pairs.ConclusionsOn average, the pooled effect estimates between RCTs and cohort studies did not differ. Statistical heterogeneity and wide prediction intervals were mainly driven by PI/ECO-dissimilarities (i.e., clinical heterogeneity) and cohort studies. The potential influence of risk of bias and certainty of the evidence on differences of effect estimates between RCTs and cohort studies needs to be explored in upcoming meta-epidemiological studies.

Anthropological Research Study of Migrants at the First Aid and Reception Center (CPSA) of Lampedusa.

A medical anthropology research study was conducted in 2015 at the First Aid and Reception Center (CPSA) on the island of Lampedusa (Italy) as part of a larger health project carried out by the National Institute for Health, Migration and Poverty (INMP) in Rome. The study investigated the health conditions of migrants at the moment of their departure and on arrival, their migration journey, and their life plans and expectations for the future. The ethnographic method adopted for the study was based on participant observation and on data collection by means of a semi-structured interview (51 items simultaneously translated by cultural mediators into Tigrinya, Arabic, English, and French). Interviewed were 112 adults (82 men and 30 women) from the Gulf of Guinea and the Horn of Africa. The cooccurrence of forced migration and economic concerns was confirmed; violence and torture were constants throughout the migration journey in 81% of cases. Ethnographic data detailed the timing, countries, settings, perpetrators, and types of violence endured. A combination of qualitative and quantitative findings can both facilitate the identification of fragile health conditions and support clinicians in the diagnostic, therapeutic, and rehabilitation pathways. These data illustrate the importance and feasibility of multidisciplinary collaboration even in emergency contexts.

Economic studies in medical research: ‘Importance, targets, outcome evaluation’

Economic studies in healthcare are used to measure the cost and effectiveness of an intervention and are valuable in determining how healthcare resources can be distributed to achieve the greatest overall gain. Most economic studies in healthcare are cost-benefit analyses, cost-effectiveness analyses (CEAs), or cost-utility analyses (CUAs). CEAs and CUAs compare alternative interventions based on cost and effectiveness but are influenced by different methodologies and assumptions employed by researchers. The perspective from which an economic study is evaluated (the patient, the provider, the payor, or the society) should be carefully considered. The incremental cost effectiveness ratio (ICER) describes the difference between two interventions in cost and health outcomes and can be expressed in dollars per quality-adjusted life year (QALY). A threshold ICER <$50,000/QALY is often used to determine whether an intervention is cost-effective, in conjunction with patient factors, healthcare system factors, and opportunity cost associated with the intervention. The Consolidated Health Economic Evaluating Reporting Standards (CHEERS) statement provides guidelines for reporting healthcare economic studies. Key elements to be reported include the study design, target population and subgroups, time horizon, health outcomes, perspectives, comparison group, and sensitivity analyses performed. Economic studies are particularly important in orthopedics given the prevalence of musculoskeletal disease, high upfront costs, and potential quality of life improvements associated with orthopedic surgical procedures. An understanding of economic evaluations in healthcare is important to critically review the available literature.

Guidance on the need for contraception related to use of pharmaceuticals: the Japan Agency for Medical Research and Development Study Group for providing information on the proper use of pharmaceuticals in patients with reproductive potential

BackgroundThe U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA) have published guidelines on the use of cancer treatments in young people of reproductive potential. However, no such guideline is available in Japan. Therefore, this project aimed to gather relevant data and draft a respective guidance paper.MethodsFrom April 2019 to March 2021, the Study Group for Providing Information on the Proper Use of Pharmaceuticals in Patients with Reproductive Potential at the Japan Agency for Medical Research and Development gathered opinions from experts in reproductive medicine, toxicology, and drug safety measures. The group considered these opinions, the FDA and EMA guidelines, and relevant Japanese guidelines and prepared a guidance paper, which they sent to 19 related organizations for comment.ResultsBy November 2020, the draft guidance paper was completed and sent to the related organizations, 17 of which provided a total of 156 comments. The study group finalized the guidance paper in March 2021.ConclusionsThe “Guidance on the Need for Contraception Related to Use of Pharmaceuticals” (The report of the Study Group for Providing Information on the Proper Use of Pharmaceuticals in Patients with Reproductive Potential, Research on Regulatory Science of Pharmaceuticals and Medical Devices, Japan Agency for Medical Research and Development: JP20mk0101139) is expected to help Japanese healthcare professionals provide fertility-related care and advice to adolescents, and young adults with cancer and their families.

Cellular Interaction of Bone Marrow Mesenchymal Stem Cells with Polymer and Hydrogel 3D Microscaffold Templates.

Biomimicking biological niches of healthy tissues or tumors can be achieved by means of artificial microenvironments, where structural and mechanical properties are crucial parameters to promote tissue formation and recreate natural conditions. In this work, three-dimensional (3D) scaffolds based on woodpile structures were fabricated by two-photon polymerization (2PP) of different photosensitive polymers (IP-S and SZ2080) and hydrogels (PEGDA 700) using two different 2PP setups, a commercial one and a customized one. The structures’ properties were tuned to study the effect of scaffold dimensions (gap size) and their mechanical properties on the adhesion and proliferation of bone marrow mesenchymal stem cells (BM-MSCs), which can serve as a model for leukemic diseases, among other hematological applications. The woodpile structures feature gap sizes of 25, 50, and 100 μm and a fixed beam diameter of 25 μm, to systematically study the optimal cell colonization that promotes healthy cell growth and potential tissue formation. The characterization of the scaffolds involved scanning electron microscopy and mechanical nanoindenting, while their suitability for supporting cell growth was evaluated with live/dead cell assays and multistaining 3D confocal imaging. In the mechanical assays of the hydrogel material, we observed two different stiffness ranges depending on the indentation depth. Larger gap woodpile structures coated with fibronectin were identified as the most promising scaffolds for 3D BM-MSC cellular models, showing higher proliferation rates. The results indicate that both the design and the employed materials are suitable for further assays, where retaining the BM-MSC stemness and original features is crucial, including studies focused on BM disorders such as leukemia and others. Moreover, the combination of 3D scaffold geometry and materials holds great potential for the investigation of cellular behaviors in a co-culture setting, for example, mesenchymal and hematopoietic stem cells, to be further applied in medical research and pharmacological studies.

Bayesian model averaging: improved variable selection for matched case-control studies.

The problem of variable selection for risk factor modeling is an ongoing challenge in statistical practice. Classical methods that select one subset of exploratory risk factors dominate the medical research field. However, this approach has been criticized for not taking into account the uncertainty of the model selection process itself. This limitation can be addressed by a Bayesian model averaging approach: instead of focusing on a single model and a few factors, Bayesian model averaging considers all the models with non-negligible probabilities to make inference. This paper reports on a simulation study designed to emulate a matched case-control study and compares classical versus Bayesian model averaging selection methods. We used Matthews's correlation coefficient to measure the quality of binary classifications. Both classical and Bayesian model averaging were also applied and compared for the analysis of a matched case-control study of patients with methicillin-resistant Staphylococcus aureus infections after hospital discharge 2011-2013. Bayesian model averaging outperformed the classical approach with much lower false positive rates and higher Matthew's correlation scores. Bayesian model averaging also produced more reliable and robust effect estimates. Bayesian model averaging is a conceptually simple, unified approach that produces robust results. It can be used to replace controversial P-values for case-control study in medical research.

Reporting methodological issues of the mendelian randomization studies in health and medical research: a systematic review

BackgroundMendelian randomization (MR) studies using Genetic risk scores (GRS) as an instrumental variable (IV) have increasingly been used to control for unmeasured confounding in observational healthcare databases. However, proper reporting of methodological issues is sparse in these studies. We aimed to review published papers related to MR studies and identify reporting problems.MethodsWe conducted a systematic review using the clinical articles published between 2009 and 2019. We searched PubMed, Scopus, and Embase databases. We retrieved information from every MR study, including the tests performed to evaluate assumptions and the modelling approach used for estimation. Using our inclusion/exclusion criteria, finally, we identified 97 studies to conduct the review according to the PRISMA statement.ResultsOnly 66 (68%) of the studies empirically verified the first assumption (Relevance assumption), and 40 (41.2%) studies reported the appropriate tests (e.g., R2, F-test) to investigate the association. A total of 35.1% clearly stated and discussed theoretical justifications for the second and third assumptions. 30.9% of the studies used a two-stage least square, and 11.3% used the Wald estimator method for estimating IV. Also, 44.3% of the studies conducted a sensitivity analysis to illuminate the robustness of estimates for violations of the untestable assumptions.ConclusionsWe found that incompleteness of the justification of the assumptions for the instrumental variable in MR studies was a common problem in our selected studies. This may misdirect the findings of the studies.

Sickle cell trait, knowledge, attitudes, practices and perceptions regarding sickle cell disease among people living in Yaoundé, Cameroon

Sickle cell disease is the most common autosomal recessive disease in sub-Saharan Africa. Our study aimed to determine the sickle cell trait, to assess knowledge, attitudes, practices and perceptions regarding sickle cell disease of people living in Yaoundé. We carried out a cross-sectional and descriptive study at the Institute of Medical Research and Medicinal Plants studies in Yaoundé. We included a total of 191 participants of mixed sex and ages ranging from 10 to 70 years old, able to complete a questionnaire and having accepted a blood sample for the hemoglobin electrophoresis test. Data was collected using a questionnaire. We used the Chi-square test to assess the relationships between variables with p-value ˂ 0.05 for meaningful relationships. More than half of the participants were women (59.16%), and the representative age group was 20-29 years old (47.12%). The highest level of education was university level (71.73%). Most of the participants had heard of sickle cell disease (93.72%) and the most talked-about information channel was television (41.90%). The proportions in relation to knowledge about sickle cell disease were as follows: in the transmission mode, 71.73% had mentioned that it is an inherited disease; in prevention mode, 88.08% had mentioned the hemoglobin electrophoresis test. For the follow-up of the disease, 85.86% noted the hospital as the location. Compared to marriage with a sickle cell disease patient, 68.59% answered negatively. There is a significant relationship (p˂0.05) between the level of education and knowledge of sickle cell disease transmission. The results of our study showed that the university population of Yaoundé had a good knowledge of sickle cell disease. Young people are called upon to be tested for sickle cell disease before marriage. Studies on larger samples should be conducted to have a better appreciation of the sickle cell trait in the population of Yaoundé. Keywords: Sickle cell trait, knowledge, attitudes, practices, sickle cell disease, Center Region, Yaoundé Cameroon.

An Evidence-based Medical Research on the Comparative Quantification of TGF? and Telomerase Experimentations, with their Molecular Pharmacological Analyses as Targets of Oncoimmunotherapeutic Vaccines

The basic oncotherapeutic vaccines used are cell based vaccines including whole cell vaccines, genetically modified tumour cell vaccines and dendritic cell vaccines, anti-idiotype antibody based vaccines, protein or peptide based vaccines, heat shock proteinbased vaccines, viral, bacterial or yeast vectors based vaccines, mRNA or DNA nucleic acid based vaccines, vaccines based on tumour associated antigens like overexpressed proteins, differentiation antigens, cancer-testis antigens and oncofoetal antigens, and tumour specific antigens including oncogenic viral antigens, antigen presenting cells or molecular neoantigens based vaccines with specific CD8+ T cells and, CD4+ T cells, and nanoparticles vectors based vaccines. The objective of this evidence-based medical research was the comparative quantification of TGF? and telomerase experimentations, with their molecular pharmacological analyses as targets of oncoimmunotherapeutic vaccines. A molecular pharmacological multi-variate, qualitative, analytical study of the retrieved literature derived through a thorough literature review from various available literature databases, was performed, to record, review, thoroughly analyse and delineate the molecular pharmacological basis of oncoimmunotherapeutic vaccines from a wide-ranged study literature containing molecular pharmacological researches, reviews, case presentations and varied databases about the pharmacooncoimmunotherapeutic rationale of the clinical use of vaccines in the treatment of cancer patients, with a specific emphasis on telomerase and TGF?, as molecular pharmacological targets of oncoimmunotherapeutic vaccines. After that, a multivariate evidence-based medical research study of comparative quantification and analysis of the global heterogenous multidisciplinary experimentations and study literature on telomerase and TGF?, as molecular pharmacological targets of pharmaco-oncoimmuno-therapeutic vaccines, affecting global malignant and borderline malign

A comparative study of R functions for clustered data analysis

BackgroundClustered or correlated outcome data is common in medical research studies, such as the analysis of national or international disease registries, or cluster-randomized trials, where groups of trial participants, instead of each trial participant, are randomized to interventions. Within-group correlation in studies with clustered data requires the use of specific statistical methods, such as generalized estimating equations and mixed-effects models, to account for this correlation and support unbiased statistical inference.MethodsWe compare different approaches to estimating generalized estimating equations and mixed effects models for a continuous outcome in R through a simulation study and a data example. The methods are implemented through four popular functions of the statistical software R, “geese”, “gls”, “lme”, and “lmer”. In the simulation study, we compare the mean squared error of estimating all the model parameters and compare the coverage proportion of the 95% confidence intervals. In the data analysis, we compare estimation of the intervention effect and the intra-class correlation.ResultsIn the simulation study, the function “lme” takes the least computation time. There is no difference in the mean squared error of the four functions. The “lmer” function provides better coverage of the fixed effects when the number of clusters is small as 10. The function “gls” produces close to nominal scale confidence intervals of the intra-class correlation. In the data analysis and the “gls” function yields a positive estimate of the intra-class correlation while the “geese” function gives a negative estimate. Neither of the confidence intervals contains the value zero.ConclusionsThe “gls” function efficiently produces an estimate of the intra-class correlation with a confidence interval. When the within-group correlation is as high as 0.5, the confidence interval is not always obtainable.

Object detection for automatic cancer cell counting in zebrafish xenografts.

Cell counting is a frequent task in medical research studies. However, it is often performed manually; thus, it is time-consuming and prone to human error. Even so, cell counting automation can be challenging to achieve, especially when dealing with crowded scenes and overlapping cells, assuming different shapes and sizes. In this paper, we introduce a deep learning-based cell detection and quantification methodology to automate the cell counting process in the zebrafish xenograft cancer model, an innovative technique for studying tumor biology and for personalizing medicine. First, we implemented a fine-tuned architecture based on the Faster R-CNN using the Inception ResNet V2 feature extractor. Second, we performed several adjustments to optimize the process, paying attention to constraints such as the presence of overlapped cells, the high number of objects to detect, the heterogeneity of the cells’ size and shape, and the small size of the data set. This method resulted in a median error of approximately 1% of the total number of cell units. These results demonstrate the potential of our novel approach for quantifying cells in poorly labeled images. Compared to traditional Faster R-CNN, our method improved the average precision from 71% to 85% on the studied data set.

The development of health preservation technologies: from individual to family approach

The national and foreign electronic databases were investigated to find publications concerning transformation of matrimonial relations, effect of family on health and need in health preservation technologies at each stage of its development. It is demonstrated that implementation of family-oriented technologies of health preservation based on knowledge of trends in family health, matrimonial relations and demographic shifts in its structure is one of the priority directions of preserving and strengthening population health. Moreover, in modern socioeconomic conditions, the categories of families that are in special need of medical social protection and health preservation such as socially problematic families, families where violence is practiced, families of the unemployed and migrants are shown up. Hence, one of the priority scientific tasks is to substantiate conceptual model of health preservation technologies for various types of families. The family and its health should become priority object of medical observation and research studies from the standpoint of public health and health care.