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Background: Signs of feeding intolerance, such as gastrointestinal (GI) symptoms, are frequently observed in otherwise healthy formula-fed infants in the first months of life. The primary objective of this observational study was to examine GI tolerance in formula-fed infants (FFI) vs. breastfed infants (BFI) in a real-world setting with a secondary objective being the comparison of infants fed formula with pre- and/or probiotics (FFI_PP) and those fed formula without any pre- and/or probiotics (FFI_noPP) as well as BFI. Methods: A six-country, cross-sectional study in full-term exclusively/predominantly FFI (n = 2036) and BFI (n = 760) aged 6–16 weeks was conducted using the validated Infant Gastrointestinal Symptom Questionnaire (IGSQ) and a Feeding Practice and Gut Comfort Questionnaire. Results: The IGSQ composite score in FFI was non-inferior compared to BFI (mean difference [95%CI]: 0.17 [-0.34, 0.67]; non-inferiority p-value < 0.0001) and scores for BFI and FFI were below the threshold of 23, indicating no GI discomfort. Adjusted mean IGSQ scores ± SE were similar in FFI_PP (22.1 ± 0.2) and BFI (22.3 ± 0.3) while FFI_noPP (23.4 ± 0.3) was significantly higher and above 23 indicating some GI discomfort (mean differences [95%CI] FFI_noPP minus FFI_PP and FFI_noPP minus BFI were 1.28 [0.57, 1.98] and 1.09 [0.38, 1.80], respectively; both p < 0.01). Hard stools and difficulty in passing stool were more common in FFI compared to BFI (p < 0.01) but were less common in FFI_PP compared to FFI_noPP (p < 0.01). FFI_PP showed significantly less crying than FFI_noPP and was similar to BFI. Significantly fewer physician-confirmed colic episodes (Rome IV criteria) were reported in FFI_PP compared with FFI_noPP or BFI. Conclusions: In this real-world observational study, FFI had non-inferior overall GI tolerance compared to BFI. Within FFI, infants receiving formulas with pre- and/or probiotics had a better GI tolerance, improved stooling and less infantile colic compared to those receiving formula without any pre- and/or probiotics and were more similar to BFI. Trial registration: NCT03703583, 12/10/2018 (https://clinicaltrials.gov/ct2/show/NCT03703583). [ABSTRACT FROM AUTHOR]

Summary: Diseases can be caused by molecular perturbations that induce specific changes in regulatory interactions and their coordinated expression, also referred to as network rewiring. However, the detection of complex changes in regulatory connections remains a challenging task and would benefit from the development of novel nonparametric approaches. We develop a new ensemble method called BoostDiff (boosted differential regression trees) to infer a differential network discriminating between two conditions. BoostDiff builds an adaptively boosted (AdaBoost) ensemble of differential trees with respect to a target condition. To build the differential trees, we propose differential variance improvement as a novel splitting criterion. Variable importance measures derived from the resulting models are used to reflect changes in gene expression predictability and to build the output differential networks. BoostDiff outperforms existing differential network methods on simulated data evaluated in four different complexity settings. We then demonstrate the power of our approach when applied to real transcriptomics data in COVID-19, Crohn's disease, breast cancer, prostate adenocarcinoma, and stress response in Bacillus subtilis . BoostDiff identifies context-specific networks that are enriched with genes of known disease-relevant pathways and complements standard differential expression analyses., Availability and Implementation: BoostDiff is available at https://github.com/scibiome/boostdiff&#95;inference., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press.)

Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)

Traditional drug discovery faces a severe efficacy crisis. Repurposing of registered drugs provides an alternative with lower costs and faster drug development timelines. However, the data necessary for the identification of disease modules, i.e. pathways and sub-networks describing the mechanisms of complex diseases which contain potential drug targets, are scattered across independent databases. Moreover, existing studies are limited to predictions for specific diseases or non-translational algorithmic approaches. There is an unmet need for adaptable tools allowing biomedical researchers to employ network-based drug repurposing approaches for their individual use cases. We close this gap with NeDRex, an integrative and interactive platform for network-based drug repurposing and disease module discovery. NeDRex integrates ten different data sources covering genes, drugs, drug targets, disease annotations, and their relationships. NeDRex allows for constructing heterogeneous biological networks, mining them for disease modules, prioritizing drugs targeting disease mechanisms, and statistical validation. We demonstrate the utility of NeDRex in five specific use-cases., (© 2021. The Author(s).)

Responding quickly to unknown pathogens is crucial to stop uncontrolled spread of diseases that lead to epidemics, such as the novel coronavirus, and to keep protective measures at a level that causes as little social and economic harm as possible. This can be achieved through computational approaches that significantly speed up drug discovery. A powerful approach is to restrict the search to existing drugs through drug repurposing, which can vastly accelerate the usually long approval process. In this Review, we examine a representative set of currently used computational approaches to identify repurposable drugs for COVID-19, as well as their underlying data resources. Furthermore, we compare drug candidates predicted by computational methods to drugs being assessed by clinical trials. Finally, we discuss lessons learned from the reviewed research efforts, including how to successfully connect computational approaches with experimental studies, and propose a unified drug repurposing strategy for better preparedness in the case of future outbreaks., (© 2021. Springer Nature America, Inc.)

Coronavirus Disease-2019 (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Various studies exist about the molecular mechanisms of viral infection. However, such information is spread across many publications and it is very time-consuming to integrate, and exploit. We develop CoVex, an interactive online platform for SARS-CoV-2 host interactome exploration and drug (target) identification. CoVex integrates virus-human protein interactions, human protein-protein interactions, and drug-target interactions. It allows visual exploration of the virus-host interactome and implements systems medicine algorithms for network-based prediction of drug candidates. Thus, CoVex is a resource to understand molecular mechanisms of pathogenicity and to prioritize candidate therapeutics. We investigate recent hypotheses on a systems biology level to explore mechanistic virus life cycle drivers, and to extract drug repurposing candidates. CoVex renders COVID-19 drug research systems-medicine-ready by giving the scientific community direct access to network medicine algorithms. It is available at https://exbio.wzw.tum.de/covex/.