Clinical stratification improves the diagnostic accuracy of small omics datasets within machine learning and genome-scale metabolic modelling methods

Background: Recently, multi-omic machine learning architectures have been proposed for the early detection of cancer. However, for rare cancers and their associated small datasets, it is still unclear how to use the available multi-omics data to achieve a mechanistic prediction of cancer onset and progression, due to the limited data available. Hepatoblastoma is the most frequent liver cancer in infancy and childhood, and whose incidence has been lately increasing in several developed countries. Even though some studies have been conducted to understand the causes of its onset and discover potential biomarkers, the role of metabolic rewiring has not been investigated in depth so far.Methods: Here, we propose and implement an interpretable multi-omics pipeline that combines mechanis-tic knowledge from genome-scale metabolic models with machine learning algorithms, and we use it to characterise the underlying mechanisms controlling hepatoblastoma.Results and Conclusions: While the obtained machine learning models generally present a high diagnostic classification accuracy, our results show that the type of omics combinations used as input to the machine learning models strongly affects the detection of important genes, reactions and metabolic pathways linked to hepatoblastoma. Our method also suggests that, in the context of computer-aided diagnosis of cancer, optimal diagnostic accuracy can be achieved by adopting a combination of omics that depends on the patient's clinical characteristics

Photonic Torque Microscopy of the Nonconservative Force Field for Optically Trapped Silicon Nanowires

We measure, by photonic torque microscopy, the nonconservative rotational motion arising from the transverse components of the radiation pressure on optically trapped, ultrathin silicon nanowires. Unlike spherical particles, we find that nonconservative effects have a significant influence on the nanowire dynamics in the trap. We show that the extreme shape of the trapped nanowires yields a transverse component of the radiation pressure that results in an orbital rotation of the nanowire about the trap axis. We study the resulting motion as a function of optical power and nanowire length, discussing its size-scaling behavior. These shape-dependent nonconservative effects have implications for optical force calibration and optomechanics with levitated nonspherical particles

Polarization-dependent optomechanics mediated by chiral microresonators.

Chirality is one of the most prominent and intriguing aspects of nature, from spiral galaxies down to aminoacids. Despite the wide range of living and non-living, natural and artificial chiral systems at different scales, the origin of chirality-induced phenomena is often puzzling. Here we assess the onset of chiral optomechanics, exploiting the control of the interaction between chiral entities. We perform an experimental and theoretical investigation of the simultaneous optical trapping and rotation of spherulite-like chiral microparticles. Due to their shell structure (Bragg dielectric resonator), the microparticles function as omnidirectional chiral mirrors yielding highly polarization-dependent optomechanical effects. The coupling of linear and angular momentum, mediated by the optical polarization and the microparticles chiral reflectance, allows for fine tuning of chirality-induced optical forces and torques. This offers tools for optomechanics, optical sorting and sensing and optofluidics

Inter-society consensus document on treatment and prevention of bronchiolitis in newborns and infants

Acute bronchiolitis is the leading cause of lower respiratory tract infection and hospitalization in children less than 1 year of age worldwide. It is usually a mild disease, but some children may develop severe symptoms, requiring hospital admission and ventilatory support in the ICU. Infants with pre-existing risk factors (prematurity, bronchopulmonary dysplasia, congenital heart diseases and immunodeficiency) may be predisposed to a severe form of the disease. Clinical diagnosis of bronchiolitis is manly based on medical history and physical examination (rhinorrhea, cough, crackles, wheezing and signs of respiratory distress). Etiological diagnosis, with antigen or genome detection to identify viruses involved, may have a role in reducing hospital transmission of the infection. Criteria for hospitalization include low oxygen saturation (<90-92%), moderate-to-severe respiratory distress, dehydration and presence of apnea. Children with pre-existing risk factors should be carefully assessed. To date, there is no specific treatment for viral bronchiolitis, and the mainstay of therapy is supportive care. This consists of nasal suctioning and nebulized 3% hypertonic saline, assisted feeding and hydration, humidified O2 delivery. The possible role of any pharmacological approach is still debated, and till now there is no evidence to support the use of bronchodilators, corticosteroids, chest physiotherapy, antibiotics or antivirals. Nebulized adrenaline may be sometimes useful in the emergency room. Nebulized adrenaline can be useful in the hospital setting for treatment as needed. Lacking a specific etiological treatment, prophylaxis and prevention, especially in children at high risk of severe infection, have a fundamental role. Environmental preventive measures minimize viral transmission in hospital, in the outpatient setting and at home. Pharmacological prophylaxis with palivizumab for RSV bronchiolitis is indicated in specific categories of children at risk during the epidemic period. Viral bronchiolitis, especially in the case of severe form, may correlate with an increased incidence of recurrent wheezing in pre-schooled children and with asthma at school age. The aim of this document is to provide a multidisciplinary update on the current recommendations for the management and prevention of bronchiolitis, in order to share useful indications, identify gaps in knowledge and drive future research

Roadmap for Optical Tweezers 2023

Optical tweezers are tools made of light that enable contactless pushing, trapping, and manipulation of objects ranging from atoms to space light sails. Since the pioneering work by Arthur Ashkin in the 1970s, optical tweezers have evolved into sophisticated instruments and have been employed in a broad range of applications in life sciences, physics, and engineering. These include accurate force and torque measurement at the femtonewton level, microrheology of complex fluids, single micro- and nanoparticle spectroscopy, single-cell analysis, and statistical-physics experiments. This roadmap provides insights into current investigations involving optical forces and optical tweezers from their theoretical foundations to designs and setups. It also offers perspectives for applications to a wide range of research fields, from biophysics to space exploration

OUP accepted manuscript

Motivation: High-throughput biological data, thanks to technological advances, have become cheaper to collect, leading to the availability of vast amounts of omic data of different types. In parallel, the in silico reconstruction and modeling of metabolic systems is now acknowledged as a key tool to complement experimental data on a large scale. The integration of these model- and data-driven information is therefore emerging as a new challenge in systems biology, with no clear guidance on how to better take advantage of the inherent multisource and multiomic nature of these data types while preserving mechanistic interpretation. Results: Here, we investigate different regularization techniques for high-dimensional data derived from the integration of gene expression profiles with metabolic flux data, extracted from strain-specific metabolic models, to improve cellular growth rate predictions. To this end, we propose ad-hoc extensions of previous regularization frameworks including group, view-specific and principal component regularization and experimentally compare them using data from 1143 Saccharomyces cerevisiae strains. We observe a divergence between methods in terms of regression accuracy and integration effectiveness based on the type of regularization employed. In multiomic regression tasks, when learning from experimental and model-generated omic data, our results demonstrate the competitiveness and ease of interpretation of multimodal regularized linear models compared to data-hungry methods based on neural networks. Availability and implementation: All data, models and code produced in this work are available on GitHub at https://github.com/Angione-Lab/HybridGroupIPFLasso_pc2Lasso. Supplementary information: Supplementary data are available at Bioinformatics online

Clinical Evaluation of Specific Oral Manifestations in Pediatric Patients with Ascertained versus Potential Coeliac Disease: A Cross-Sectional Study

Patients involved on coeliac disease (CD) have atypical symptoms and often remain undiagnosed. Specific oral manifestations are effective risk indicators of CD and for this reason an early diagnosis with a consequent better prognosis can be performed by the dentist. There are not researches analysing the frequency of these oral manifestations in potential coeliac patients. The aim of this study is to investigate the oral hard and soft tissue lesions in potential and ascertained coeliac children in comparison with healthy controls. 50 ascertained children, 21 potential coeliac patients, and 54 controls were recruited and the oral examination was performed. The overall oral lesions were more frequently present in CD patients than in controls. The prevalence of oral soft tissue lesions was 62% in ascertained coeliac, 76.2% in potential coeliac patients, and 12.96% in controls (P<0.05). Clinical dental delayed eruption was observed in 38% of the ascertained coeliac and 42.5% of the potential coeliac versus 11.11% of the controls (P<0.05). The prevalence of specific enamel defects (SED) was 48% in ascertained coeliac and 19% in potential coeliac versus 0% in controls (P<0.05; OR=3.923). The SED seem to be genetically related to the histological damage and villous atrophy

A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling

Complex, distributed, and dynamic sets of clinical biomedical data are collectively referred to as multimodal clinical data. In order to accommodate the volume and heterogeneity of such diverse data types and aid in their interpretation when they are combined with a multi-scale predictive model, machine learning is a useful tool that can be wielded to deconstruct biological complexity and extract relevant outputs. Additionally, genome-scale metabolic models (GSMMs) are one of the main frameworks striving to bridge the gap between genotype and phenotype by incorporating prior biological knowledge into mechanistic models. Consequently, the utilization of GSMMs as a foundation for the integration of multi-omic data originating from different domains is a valuable pursuit towards refining predictions. In this chapter, we show how cancer multi-omic data can be analyzed via multimodal machine learning and metabolic modeling. Firstly, we focus on the merits of adopting an integrative systems biology led approach to biomedical data mining. Following this, we propose how constraint-based metabolic models can provide a stable yet adaptable foundation for the integration of multimodal data with machine learning. Finally, we provide a step-by-step tutorial for the combination of machine learning and GSMMs, which includes: (i) tissue-specific constraint-based modeling; (ii) survival analysis using time-to-event prediction for cancer; and (iii) classification and regression approaches for multimodal machine learning. The code associated with the tutorial can be found at https://github.com/Angione-Lab/Tutorials_Combining_ML_and_GSMM

Integrating genome-scale metabolic modelling and transfer learning for human gene regulatory network reconstruction

Gene regulation is responsible for controlling numerous physiological functions and dynamically responding to environmental fluctuations. Reconstructing the human network of gene regulatory interactions is thus paramount to understanding the cell functional organisation across cell types, as well as to elucidating pathogenic processes and identifying molecular drug targets. Although significant effort has been devoted towards this direction, existing computational methods mainly rely on gene expression levels, possibly ignoring the information conveyed by mechanistic biochemical knowledge. Moreover, except for a few recent attempts, most of the existing approaches only consider the information of the organism under analysis, without exploiting the information of related model organisms. We propose a novel method for the reconstruction of the human gene regulatory network, based on a transfer learning strategy that synergically exploits information from human and mouse, conveyed by gene-related metabolic features generated in silico from gene expression data. Specifically, we learn a predictive model from metabolic activity inferred via tissue-specific metabolic modelling of artificial gene knockouts. Our experiments show that the combination of our transfer learning approach with the constructed metabolic features provides a significant advantage in terms of reconstruction accuracy, as well as additional clues on the contribution of each constructed metabolic feature