Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

oncology Oncology

Status and perspectives of biomarker validation for diagnosis, stratification, and treatment.

In Public health

OBJECTIVES : The aim of this study was to discuss the status of and perspective for biomarker validation in view of the challenges imposed on national healthcare systems due to an increasing number of citizens with chronic diseases and new expensive drugs with effects that are sometimes poorly documented. The demand for a paradigm shift toward stratification of patients or even 'personalized medicine' (PM) is rising, and the implementation of such novel strategies has the potential to increase patient outcomes and cost efficiency of treatments. The implementation of PM depends on relevant and reliable biomarkers correlated to disease states, prognosis, or effect of treatment. Beyond biomarkers of disease, personalized prevention strategies (such as individualized nutrition guidance) are likely to depend on novel biomarkers.

STUDY DESIGN : We discuss the current status of the use of biomarkers and the need for standardization and integration of biomarkers based on multi-omics approaches.

METHODS : We present representative cases from laboratory medicine, oncology, and nutrition, where present and emerging biomarkers have or may present opportunities for PM or prevention.

RESULTS : Biomarkers vary greatly in complexity, from single genomic mutations to metagenomic analyses of the composition of the gut microbiota and comprehensive analyses of metabolites, metabolomics. Using biomarkers for decision-making has previously often relied on measurements of single biomolecules. The current development now moves toward the use of multiple biomarkers requiring the use of machine learning or artificial intelligence. Still, the usefulness of biomarkers is often challenged by suboptimal validation, and the discovery of new biomarkers moves much faster than standardization efforts. To reap the potential benefits of personalization of treatment and prevention, healthcare systems and regulatory authorities need to focus on validation and standardization of biomarkers.

CONCLUSION : There is a great public health need for better understanding of the usefulness, but also limitations, of biomarkers among policy makers, clinicians, and scientists, and efforts securing effective validation are key to the future use of novel sets of complex biomarkers.

Skov J, Kristiansen K, Jespersen J, Olesen P


Biomarker, Genomics, Metabolomics, Metagenomics, Multi-omics, Nutrigenomics, Personalized medicine, Prevention

Public Health Public Health

Racialized algorithms for kidney function: Erasing social experience.

In Social science & medicine (1982)

The rise of evidence-based medicine, medical informatics, and genomics --- together with growing enthusiasm for machine learning and other types of algorithms to standardize medical decision-making --- has lent increasing credibility to biomedical knowledge as a guide to the practice of medicine. At the same time, concern over the lack of attention to the underlying assumptions and unintended health consequences of such practices, particularly the widespread use of race-based algorithms, from the simple to the complex, has caught the attention of both physicians and social scientists. Epistemological debates over the meaning of "the social" and "the scientific" are consequential in discussions of race and racism in medicine. In this paper, we examine the socio-scientific processes by which one algorithm that "corrects" for kidney function in African Americans became central to knowledge production about chronic kidney disease (CKD). Correction factors are now used extensively and routinely in clinical laboratories and medical practices throughout the US. Drawing on close textual analysis of the biomedical literature, we use the theoretical frameworks of science and technology studies to critically analyze the initial development of the race-based algorithm, its uptake, and its normalization. We argue that race correction of kidney function is a racialized biomedical practice that contributes to the consolidation of a long-established hierarchy of difference in medicine. Consequentially, correcting for race in the assessment of kidney function masks the complexity of the lived experience of societal neglect that damages health.

Braun Lundy, Wentz Anna, Baker Reuben, Richardson Ellen, Tsai Jennifer


Algorithms, Chronic kidney disease, Estimated glomerular filtration rate, Racialization

Radiology Radiology

Medical image segmentation and reconstruction of prostate tumor based on 3D AlexNet.

In Computer methods and programs in biomedicine

BACKGROUND : Prostate cancer is a disease with a high incidence of tumors in men. Due to the long incubation time and insidious condition, early diagnosis is difficult; especially imaging diagnosis is more difficult. In actual clinical practice, the method of manual segmentation by medical experts is mainly used, which is time-consuming and labor-intensive and relies heavily on the experience and ability of medical experts. The rapid, accurate and repeatable segmentation of the prostate area is still a challenging problem. It is important to explore the automated segmentation of prostate images based on the 3D AlexNet network.

METHOD : Taking the medical image of prostate cancer as the entry point, the three-dimensional data is introduced into the deep learning convolutional neural network. This paper proposes a 3D AlexNet method for the automatic segmentation of prostate cancer magnetic resonance images, and the general network ResNet 50, Inception -V4 compares network performance.

RESULTS : Based on the training samples of magnetic resonance images of 500 prostate cancer patients, a set of 3D AlexNet with simple structure and excellent performance was established through adaptive improvement on the basis of classic AlexNet. The accuracy rate was as high as 0.921, the specificity was 0.896, and the sensitivity It is 0.902 and the area under the receiver operating characteristic curve (AUC) is 0.964. The Mean Absolute Distance (MAD) between the segmentation result and the medical expert's gold standard is 0.356 mm, and the Hausdorff distance (HD) is 1.024 mm, the Dice similarity coefficient is 0.9768.

CONCLUSION : The improved 3D AlexNet can automatically complete the structured segmentation of prostate magnetic resonance images. Compared with traditional segmentation methods and depth segmentation methods, the performance of the 3D AlexNet network is superior in terms of training time and parameter amount, or network performance evaluation. Compared with the algorithm, it proves the effectiveness of this method.

Chen Jun, Wan Zhechao, Zhang Jiacheng, Li Wenhua, Chen Yanbing, Li Yuebing, Duan Yue


3D AlexNet, Convolutional Neural Network, Prostate Cancer, Three-dimensional reconstruction

Public Health Public Health

Combining Data, Machine Learning, and Visual Analytics to Improve Detection of Disease Re-emergence: The Re-emerging Disease Alert Tool.

In JMIR public health and surveillance

BACKGROUND : Currently, the identification of infectious disease re-emergence is performed without describing specific quantitative criteria that can be used to identify re-emergence events consistently. This practice may lead to ineffective mitigation. In addition, identification of factors contributing to local disease re-emergence and assessment of global disease re-emergence require access to data about disease incidence and a large number of factors at the local level for the entire world. This paper presents Re-emerging Disease Alert (RED Alert), a web-based tool designed to help public health officials detect and understand infectious disease re-emergence.

OBJECTIVE : Our objective is to bring together variety of disease-related data and analytics needed to help public health analysts answer following three primary questions for detecting and understanding disease re-emergence: Is there a potential disease re-emergence at the local (country) level? What are the potential contributing factors for this re-emergence? Is there a potential for global re-emergence?

METHODS : We collected and cleaned disease-related data (e.g., case counts, vaccination rates, and indicators related to disease transmission) from several data sources including the WHO, PAHO, World Bank, and Gideon. We combined these data with machine learning and visual analytics into a tool called RED Alert to detect re-emergence for following four diseases: measles, cholera, dengue, and yellow fever. We evaluated the performance of the machine learning models for re-emergence detection and reviewed the output of the tool through a number of case studies.

RESULTS : Our supervised learning models were able to identify 82-90% of the local re-emergence events, although with 18-31% (except 46% for dengue) false positives. This is consistent with our goal of identifying all possible re-emergences while allowing some false positives. The review of the web-based tool through case studies showed that local re-emergence detection was possible, the tool provided actionable information about potential factors contributing to the local disease re-emergence, and trends in global disease re-emergence.

CONCLUSIONS : To the best of our knowledge, this is the first tool that focuses specifically on disease re-emergence and addresses the important challenges mentioned above.


Parikh Nidhi Kiranbhai, Daughton Ashlynn Rae, Rosenberger William Earl, Aberle Derek Jacob, Chitanvis Maneesha Elizabeth, Altherr Forest Michael, Velappan Nileena, Fairchild Geoffrey, Deshpande Alina


General General

State bounding for fuzzy memristive neural networks with bounded input disturbances.

In Neural networks : the official journal of the International Neural Network Society

This paper investigates the state bounding problem of fuzzy memristive neural networks (FMNNs) with bounded input disturbances. By using the characters of Metzler, Hurwitz and nonnegative matrices, this paper obtains the exact delay-independent and delay-dependent boundary ranges of the solution, which have less conservatism than the results in existing literatures. The validity of the results is verified by two numerical examples.

Gao Yu, Zhu Song, Yang Chunyu, Wen Shiping


Bounded disturbances, Fuzzy systems, Memristor, Neural networks, State bounding

Public Health Public Health

Evaluating the impact of mobility on COVID-19 pandemic with machine learning hybrid predictions.

In The Science of the total environment

COVID-19 pandemic had expanded to the US since early 2020 and has caused nationwide economic loss and public health crisis. Until now, although the US has the most confirmed cases in the world and are still experiencing an increasing pandemic, several states insisted to re-open business activities and colleges while announced strict control measures. To provide a quantitative reference for official strategies, predicting the near future trend based on finer spatial resolution data and presumed scenarios are urgently needed. In this study, the first attempted COVID-19 case predicting model based on county-level demographic, environmental, and mobility data was constructed with multiple machine learning techniques and a hybrid framework. Different scenarios were also applied to selected metropolitan counties including New York City, Cook County in Illinois, Los Angeles County in California, and Miami-Dade County in Florida to assess the impact from lockdown, Phase I, and Phase III re-opening. Our results showed that, for selected counties, the mobility decreased substantially after the lockdown but kept increasing with an apparent weekly pattern, and the weekly pattern of mobility and infections implied high infections during the weekend. Meanwhile, our model was successfully built up, and the scenario assessment results indicated that, compared with Phase I re-opening, a 1-week and a 2-week lockdown could reduce 4%-29% and 15%-55% infections, respectively, in the future week, while 2-week Phase III re-opening could increase 16%-80% infections. We concluded that the mandatory orders in metropolitan counties such lockdown should last longer than one week, the effect could be observed. The impact of lockdown or re-opening was also county-dependent and varied with the local pandemic. In future works, we expect to involve a longer period of data, consider more county-dependent factors, and employ more sophisticated techniques to decrease the modeling uncertainty and apply it to counties nationally and other countries.

Kuo Cheng-Pin, Fu Joshua S


County-level, Forecasting, Lockdown, Pandemic, Re-opening