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Recent advances in microfluidic technology and biomaterial science have augmented the use of organ-on-chip (OoC) technology to closely mimic the human pathophysiology. Thus, it is now established that the pericellular micro-environment plays a key role on cell behaviour, such as response to drug compounds. OoC technology have been shown to enable the fabrication of micro-physiological systems recapitulating the key features of the in vivo tissues including, mechanical forces (matrix stiffness, fluid shear stress, and compressive/tensile stress), gradients of key chemical substances (oxygen and nutrients), cell-cell biochemical communication, and cell-matrix interactions. Although a plethora of works describes the use of biomatrices in OoC applications, the specific role of cell-matrix interaction in guiding cell behaviour has been overlooked. This review wants to fill this gap, by systematically analysing the matrix characteristics that guide cell functions including differentiation, proliferation, migration, and homing. Furthermore, a comprehensive classification of biomatrices, along with their use for building tissue-specific OoC is here provided. Finally, a prospect on novel OoC platform development comprising 3D bioprinting and Artificial Intelligence technologies is proposed, providing a new panorama on the future of OoC for drug development.

3D bioprinting is at the forefront of tissue engineering to fabricate complex constructs resembling functional tissues. However, the inability to produce heterogeneous tissues and the lack of spatio-temporal control over the release of bioactive factors are greatly limiting clinical translation. Herein, the combination of 3D bioprinting with high-throughput dispensing using a custom microfluidic system and nanoclay-based inks is presented. This approach was found to enhance printability, retention, and controlled release of bioactive factors. Advanced tissue models were developed to resemble cancer and skeletal tissue, while studying the effect of anti-cancer (Doxorubicin) and pro-osteogenic growth factors (bone morphogenetic protein-2, BMP-2), respectively. The engineering of a new nanoclay ink allowed the sustained release, making it suitable for long-term applications. These findings suggest that by combining 3D bioprinting and high-throughput delivery of nanoclay-based inks a new platform for the engineering of functional tissue constructs can be assembled, offering significant advancements in regenerative medicine.

Deep-Manager is a software platform that enables efficient selection of features with lower sensitivity to unspecified disturbances across sets of similar experiments in bioimaging.

The Common Workflow Language (CWL) is a platform-independent description language for the representation of data science workflows consisting of a set of tasks that interact with each other to perform scientific analysis. The tasks can be packaged as Linux containers. On the one hand, using containers ensures the reproducibility and portability of workflows. Still, on the other hand, it limits each task to exploiting, at most, the resources of the host where its container runs. In this paper, we propose CWL-PLAS, an extension of CWL that allows a task to instantiate and temporarily use a supporting cloud platform for parallel computing, which is specialized for the task’s activity. In this way, tasks can leverage the resources of multiple hosts in parallel, reducing the duration of the workflow. We implemented an open-source workflow manager that supports CWL-PLAS workflows and exploits a Kubernetes back-end. We used this workflow manager to evaluate the performance of CWL-PLAS in a couple of machine learning workflows.

Objectives Women of low socioeconomic status have been described as having suboptimal prenatal care, which in turn has been associated with poor pregnancy outcomes. Many types of conditional cash transfer (CCT) programmes have been developed, including programmes to improve prenatal care or smoking cessation during pregnancy, and their effects demonstrated. However, ethical critiques have included paternalism and lack of informed choice. Our objective was to determine if women and healthcare professionals (HPs) shared these concerns.Design Prospective qualitative research.Setting We included economically disadvantaged women, as defined by health insurance data, who participated in the French NAITRE randomised trial assessing a CCT programme during prenatal follow-up to improve pregnancy outcomes. The HP worked in some maternities participating in this trial.Participants 26 women, 14 who received CCT and 12 who did not, mostly unemployed (20/26), and - 7 HPs.Interventions We conducted a multicentre cross-sectional qualitative study among women and HPs who participated in the NAITRE Study to assess their views on CCT. The women were interviewed after childbirth.Results Women did not perceive CCT negatively. They did not mention feeling stigmatised. They described CCT as a significant source of aid for women with limited financial resources. HP described the CCT in less positive terms, for example, expressing concern about discussing cash transfer at their first medical consultation with women. Though they emphasised ethical concerns about the basis of the trial, they recognised the importance of evaluating CCT.Conclusions In France, a high-income country where prenatal follow-up is free, HPs were concerned that the CCT programme would change their relationship with patients and wondered if it was the best use of funding. However, women who received a cash incentive said they did not feel stigmatised and indicated that these payments helped them prepare for their baby’s birth.Trial registration number NCT02402855

Abstract High-throughput phenotyping is becoming increasingly available thanks to analytical and bioinformatics approaches that enable the use of very high-dimensional data and to the availability of dynamic models that link phenomena across levels: from genes to cells, from cells to organs, and through the whole organism. The combination of phenomics, deep learning, and machine learning represents a strong potential for the phenotypical investigation, leading the way to a more embracing approach, called machine learning phenomics (MLP). In particular, in this work we present a novel MLP platform for phenomics investigation of cancer-cells response to therapy, exploiting and combining the potential of time-lapse microscopy for cell behavior data acquisition and robust deep learning software architectures for the latent phenotypes extraction. A two-step proof of concepts is designed. First, we demonstrate a strict correlation among gene expression and cell phenotype with the aim to identify new biomarkers and targets for tailored therapy in human colorectal cancer onset and progression. Experiments were conducted on human colorectal adenocarcinoma cells (DLD-1) and their profile was compared with an isogenic line in which the expression of LOX-1 transcript was knocked down. In addition, we also evaluate the phenotypic impact of the administration of different doses of an antineoplastic drug over DLD-1 cells. Under the omics paradigm, proteomics results are used to confirm the findings of the experiments.

Abstract The dynamic contrast-enhanced MR imaging plays a crucial role in evaluating the effectiveness of neoadjuvant chemotherapy (NAC) even since its early stage through the prediction of the final pathological complete response (pCR). In this study, we proposed a transfer learning approach to predict if a patient achieved pCR (pCR) or did not (non-pCR) by exploiting, separately or in combination, pre-treatment and early-treatment exams from I-SPY1 TRIAL public database. First, low-level features, i.e., related to local structure of the image, were automatically extracted by a pre-trained convolutional neural network (CNN) overcoming manual feature extraction. Next, an optimal set of most stable features was detected and then used to design an SVM classifier. A first subset of patients, called fine-tuning dataset (30 pCR; 78 non-pCR), was used to perform the optimal choice of features. A second subset not involved in the feature selection process was employed as an independent test (7 pCR; 19 non-pCR) to validate the model. By combining the optimal features extracted from both pre-treatment and early-treatment exams with some clinical features, i.e., ER, PgR, HER2 and molecular subtype, an accuracy of 91.4% and 92.3%, and an AUC value of 0.93 and 0.90, were returned on the fine-tuning dataset and the independent test, respectively. Overall, the low-level CNN features have an important role in the early evaluation of the NAC efficacy by predicting pCR. The proposed model represents a first effort towards the development of a clinical support tool for an early prediction of pCR to NAC.