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

General General

Multi fragment melting analysis system (MFMAS) for one-step identification of lactobacilli.

In Journal of microbiological methods

The accurate identification of lactobacilli is essential for the effective management of industrial practices associated with lactobacilli strains, such as the production of fermented foods or probiotic supplements. For this reason, in this study, we proposed the Multi Fragment Melting Analysis System (MFMAS)-lactobacilli based on high resolution melting (HRM) analysis of multiple DNA regions that have high interspecies heterogeneity for fast and reliable identification and characterization of lactobacilli. The MFMAS-lactobacilli is a new and customized version of the MFMAS, which was developed by our research group. MFMAS-lactobacilli is a combined system that consists of i) a ready-to-use plate, which is designed for multiple HRM analysis, and ii) a data analysis software, which is used to characterize lactobacilli species via incorporating machine learning techniques. Simultaneous HRM analysis of multiple DNA fragments yields a fingerprint for each tested strain and the identification is performed by comparing the fingerprints of unknown strains with those of known lactobacilli species registered in the MFMAS. In this study, a total of 254 isolates, which were recovered from fermented foods and probiotic supplements, were subjected to MFMAS analysis, and the results were confirmed by a combination of different molecular techniques. All of the analyzed isolates were exactly differentiated and accurately identified by applying the single-step procedure of MFMAS, and it was determined that all of the tested isolates belonged to 18 different lactobacilli species. The individual analysis of each target DNA region provided identification with an accuracy range from 59% to 90% for all tested isolates. However, when each target DNA region was analyzed simultaneously, perfect discrimination and 100% accurate identification were obtained even in closely related species. As a result, it was concluded that MFMAS-lactobacilli is a multi-purpose method that can be used to differentiate, classify, and identify lactobacilli species. Hence, our proposed system could be a potential alternative to overcome the inconsistencies and difficulties of the current methods.

Kesmen Zülal, Kılıç Özge, Gormez Yasin, Çelik Mete, Bakir-Gungor Burcu

2020-Sep-03

High resolution melting (HRM), Lactobacilli, Logistic regression (LR), Machine learning, Multi-fragment melting analysis system (MFMAS), One-step identification

General General

Managing gestational diabetes mellitus using a smartphone application with artificial intelligence (SineDie) during the COVID-19 pandemic: Much more than just telemedicine.

In Diabetes research and clinical practice ; h5-index 50.0

We describe our experience in the remote management of women with gestational diabetes mellitus during the COVID-19 pandemic. We used a mobile phone application with artificial intelligence that automatically classifies and analyses the data (ketonuria, diet transgressions, and blood glucose values), making adjustment recommendations regarding the diet or insulin treatment.

Albert Lara, Capel Ismael, García-Sáez Gema, Martín-Redondo Pablo, Hernando M Elena, Rigla Mercedes

2020-Sep-03

Artificial intelligence, Gestational diabetes mellitus, Mobile phone application, Telemedicine, eHealth

Ophthalmology Ophthalmology

Predicting progression to advanced age-related macular degeneration from clinical, genetic and lifestyle factors using machine learning.

In Ophthalmology ; h5-index 90.0

OBJECTIVE : Current prediction models for advanced age-related macular degeneration (AMD) are based on a restrictive set of risk factors. The objective of this study was to develop a comprehensive prediction model, applying a machine learning algorithm allowing selection of the most predictive risk factors automatically.

DESIGN : Two population-based cohort studies PARTICIPANTS: The Rotterdam Study I (RS-I, training set) included 3838 participants aged 55 years or more, with a median follow-up period of 10.8 years and 108 incident cases of advanced AMD. The ALIENOR study (test set) included 362 participants aged 73 years or more, with a median follow-up period of 6.5 years and 33 incident cases of advanced AMD.

METHODS : The prediction model used the bootstrap lasso for survival analysis to select the best predictors of incident advanced AMD in the training set. Predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC).

MAIN OUTCOME MEASURES : incident advanced AMD (atrophic and/or neovascular), based on standardized interpretation of retinal photographs.

RESULTS : The prediction model retained i) age, ii) a combination of phenotypic predictors (based on the presence of intermediate drusen, hyper-pigmentation in one or both eyes and age-related eye disease study (AREDS) simplified score), iii) a summary genetic risk score based on 49 single nucleotide polymorphisms, iv) smoking, v) diet quality, vi) education, and vii) pulse pressure. The cross-validated AUC estimation in RS-I was 0.92 [0.88-0.97] at 5 years, 0.92 [0.90-0.95] at 10 years and 0.91 [0.88-0.94] at 15 years. In ALIENOR, the AUC reached 0.92 at 5 years [0.87-0.98]. In terms of calibration, the model tended to underestimate the cumulative incidence of advanced AMD for the high-risk groups, especially in ALIENOR.

CONCLUSIONS : This prediction model reached high discrimination abilities, paving the way towards making precision medicine for AMD patients a reality in the near future.

Ajana Soufiane, Cougnard-Grégoire Audrey, Colijn Johanna M, Merle Bénédicte Mj, Verzijden Timo, de Jong Paulus Tvm, Hofman Albert, Vingerling Johannes R, Hejblum Boris P, Korobelnik Jean-François, Meester-Smoor Magda A, Ueffing Marius, Jacqmin-Gadda Hélène, Klaver Caroline Cw, Delcourt Cécile

2020-Sep-02

Cardiology Cardiology

Prediction Power on Cardiovascular Disease of Neuroimmune Guidance Cues Expression by Peripheral Blood Monocytes Determined by Machine-Learning Methods.

In International journal of molecular sciences ; h5-index 102.0

Atherosclerosis is the underlying pathology in a major part of cardiovascular disease, the leading cause of mortality in developed countries. The infiltration of monocytes into the vessel walls of large arteries is a key denominator of atherogenesis, making monocytes accountable for the development of atherosclerosis. With the development of high-throughput transcriptome profiling platforms and cytometric methods for circulating cells, it is now feasible to study in-depth the predicted functional change of circulating monocytes reflected by changes of gene expression in certain pathways and correlate the changes to disease outcome. Neuroimmune guidance cues comprise a group of circulating- and cell membrane-associated signaling proteins that are progressively involved in monocyte functions. Here, we employed the CIRCULATING CELLS study cohort to classify cardiovascular disease patients and healthy individuals in relation to their expression of neuroimmune guidance cues in circulating monocytes. To cope with the complexity of human datasets featured by noisy data, nonlinearity and multidimensionality, we assessed various machine-learning methods. Of these, the linear discriminant analysis, Naïve Bayesian model and stochastic gradient boost model yielded perfect or near-perfect sensibility and specificity and revealed that expression levels of the neuroimmune guidance cues SEMA6B, SEMA6D and EPHA2 in circulating monocytes were of predictive values for cardiovascular disease outcome.

Zhang Huayu, Bredewold Edwin O W, Vreeken Dianne, Duijs Jacques M G J, de Boer Hetty C, Kraaijeveld Adriaan O, Jukema J Wouter, Pijls Nico H, Waltenberger Johannes, Biessen Erik A L, van der Veer Eric P, van Zonneveld Anton Jan, van Gils Janine M

2020-Sep-02

cardiovascular diseases, machine-learning methods, monocytes, neuroimmune guidance cues

oncology Oncology

Scholarly Publishing in the Wake of COVID-19.

In International journal of radiation oncology, biology, physics

The speed at which the COVID-19 pandemic spread across the globe and the accompanying need to rapidly disseminate knowledge have highlighted the inadequacies of the traditional research/publication cycle, particularly the slowness and the fragmentary access globally to manuscripts and their findings. Scholarly communication has slowly been undergoing transformational changes since the introduction of the Internet in the 1990s. The pandemic response has created an urgency that has accelerated these trends in some areas. The magnitude of the global emergency has strongly bolstered calls to make the entire research and publishing lifecycle transparent and open. The global scientific community has collaborated in rapid, open, and transparent means that are unprecedented. The general public has been reminded of the important of science, and trusted communication of scientific findings, in everyday life. In addition to COVID-19-driven innovation in scholarly communication, alternative bibliometrics and artificial intelligence tools will further transform academic publishing in the near future.

Miller Robert C, Tsai C Jillian

2020-Oct-01

oncology Oncology

Scholarly Publishing in the Wake of COVID-19.

In International journal of radiation oncology, biology, physics

The speed at which the COVID-19 pandemic spread across the globe and the accompanying need to rapidly disseminate knowledge have highlighted the inadequacies of the traditional research/publication cycle, particularly the slowness and the fragmentary access globally to manuscripts and their findings. Scholarly communication has slowly been undergoing transformational changes since the introduction of the Internet in the 1990s. The pandemic response has created an urgency that has accelerated these trends in some areas. The magnitude of the global emergency has strongly bolstered calls to make the entire research and publishing lifecycle transparent and open. The global scientific community has collaborated in rapid, open, and transparent means that are unprecedented. The general public has been reminded of the important of science, and trusted communication of scientific findings, in everyday life. In addition to COVID-19-driven innovation in scholarly communication, alternative bibliometrics and artificial intelligence tools will further transform academic publishing in the near future.

Miller Robert C, Tsai C Jillian

2020-Oct-01