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

General General

A network-based analysis detects cocaine-induced changes in social interactions in Drosophila melanogaster.

In PloS one ; h5-index 176.0

Addiction is a multifactorial biological and behavioral disorder that is studied using animal models, based on simple behavioral responses in isolated individuals. A couple of decades ago it was shown that Drosophila melanogaster can serve as a model organism for behaviors related to alcohol, nicotine and cocaine (COC) addiction. Scoring of COC-induced behaviors in a large group of flies has been technologically challenging, so we have applied a local, middle and global level of network-based analyses to study social interaction networks (SINs) among a group of 30 untreated males compared to those that have been orally administered with 0.50 mg/mL of COC for 24 hours. In this study, we have confirmed the previously described increase in locomotion upon COC feeding. We have isolated new network-based measures associated with COC, and influenced by group on the individual behavior. COC fed flies showed a longer duration of interactions on the local level, and formed larger, more densely populated and compact, communities at the middle level. Untreated flies have a higher number of interactions with other flies in a group at the local level, and at the middle level, these interactions led to the formation of separated communities. Although the network density at the global level is higher in COC fed flies, at the middle level the modularity is higher in untreated flies. One COC specific behavior that we have isolated was an increase in the proportion of individuals that do not interact with the rest of the group, considered as the individual difference in COC induced behavior and/or consequence of group influence on individual behavior. Our approach can be expanded on different classes of drugs with the same acute response as COC to determine drug specific network-based measures and could serve as a tool to determinate genetic and environmental factors that influence both drug addiction and social interaction.

Petrović Milan, Meštrović Ana, Andretić Waldowski Rozi, Filošević Vujnović Ana

2023

Radiology Radiology

Domain-guided data augmentation for deep learning on medical imaging.

In PloS one ; h5-index 176.0

While domain-specific data augmentation can be useful in training neural networks for medical imaging tasks, such techniques have not been widely used to date. Our objective was to test whether domain-specific data augmentation is useful for medical imaging using a well-benchmarked task: view classification on fetal ultrasound FETAL-125 and OB-125 datasets. We found that using a context-preserving cut-paste strategy, we could create valid training data as measured by performance of the resulting trained model on the benchmark test dataset. When used in an online fashion, models trained on this hybrid data performed similarly to those trained using traditional data augmentation (FETAL-125 F-score 85.33 ± 0.24 vs 86.89 ± 0.60, p-value 0.014; OB-125 F-score 74.60 ± 0.11 vs 72.43 ± 0.62, p-value 0.004). Furthermore, the ability to perform augmentations during training time, as well as the ability to apply chosen augmentations equally across data classes, are important considerations in designing a bespoke data augmentation. Finally, we provide open-source code to facilitate running bespoke data augmentations in an online fashion. Taken together, this work expands the ability to design and apply domain-guided data augmentations for medical imaging tasks.

Athalye Chinmayee, Arnaout Rima

2023

General General

Programming 3D curved mesosurfaces using microlattice designs.

In Science (New York, N.Y.)

Cellular microstructures form naturally in many living organisms (e.g., flowers and leaves) to provide vital functions in synthesis, transport of nutrients, and regulation of growth. Although heterogeneous cellular microstructures are believed to play pivotal roles in their three-dimensional (3D) shape formation, programming 3D curved mesosurfaces with cellular designs remains elusive in man-made systems. We report a rational microlattice design that allows transformation of 2D films into programmable 3D curved mesosurfaces through mechanically guided assembly. Analytical modeling and a machine learning-based computational approach serve as the basis for shape programming and determine the heterogeneous 2D microlattice patterns required for target 3D curved surfaces. About 30 geometries are presented, including both regular and biological mesosurfaces. Demonstrations include a conformable cardiac electronic device, a stingray-like dual mode actuator, and a 3D electronic cell scaffold.

Cheng Xu, Fan Zhichao, Yao Shenglian, Jin Tianqi, Lv Zengyao, Lan Yu, Bo Renheng, Chen Yitong, Zhang Fan, Shen Zhangming, Wan Huanhuan, Huang Yonggang, Zhang Yihui

2023-Mar-24

Radiology Radiology

Current and Advanced Applications of Gadoxetic Acid-enhanced MRI in Hepatobiliary Disorders.

In Radiographics : a review publication of the Radiological Society of North America, Inc

Gadoxetic acid is an MRI contrast agent that has specific applications in the study of hepatobiliary disease. After being distributed in the vascular and extravascular spaces during the dynamic phase, gadoxetic acid is progressively taken up by hepatocytes and excreted to the bile ducts during the hepatobiliary phase. The information derived from the enhancement characteristics during dynamic and hepatobiliary phases is particularly relevant in the detection and characterization of focal liver lesions and in the evaluation of the structure and function of the liver and biliary system. The use of new MRI sequences and advanced imaging techniques (eg, relaxometry, multiparametric imaging, and analysis of heterogeneity), the introduction of artificial intelligence, and the development of biomarkers and radiomic and radiogenomic tools based on gadoxetic acid-enhanced MRI findings will play an important role in the future in assessing liver function, chronic liver disease, and focal liver lesions; in studying biliary pathologic conditions; and in predicting treatment responses and prognosis. © RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Baleato-González Sandra, Vilanova Joan C, Luna Antonio, Menéndez de Llano Rafael, Laguna-Reyes Juan Pablo, Machado-Pereira Diogo M, Bermúdez-Naveira Anaberta, Osorio-Vázquez Iria, Alcalá-Mata Lidia, García-Figueiras Roberto

2023-Apr

General General

Computational Approaches for Peroxisomal Protein Localization.

In Methods in molecular biology (Clifton, N.J.)

Computational approaches are practical when investigating putative peroxisomal proteins and for sub-peroxisomal protein localization in unknown protein sequences. Nowadays, advancements in computational methods and Machine Learning (ML) can be used to hasten the discovery of novel peroxisomal proteins and can be combined with more established computational methodologies. Here, we explain and list some of the most used tools and methodologies for novel peroxisomal protein detection and localization.

Anteghini Marco, Martins Dos Santos Vitor A P

2023

Cellular compartments, Machine learning, Peroxisome targeting signal, Sub-organelle localization, Subcellular localization

oncology Oncology

The Application of Artificial Intelligence to Investigate Long-Term Outcomes and Assess Optimal Margin Width in Hepatectomy for Intrahepatic Cholangiocarcinoma.

In Annals of surgical oncology ; h5-index 71.0

BACKGROUND : Intrahepatic cholangiocarcinoma (ICC) is associated with poor long-term outcomes, and limited evidence exists on optimal resection margin width. This study used artificial intelligence to investigate long-term outcomes and optimal margin width in hepatectomy for ICC.

METHODS : The study enrolled patients who underwent curative-intent resection for ICC between 1990 and 2020. The optimal survival tree (OST) was used to investigate overall (OS) and recurrence-free survival (RFS). An optimal policy tree (OPT) assigned treatment recommendations based on random forest (RF) counterfactual survival probabilities associated with each possible margin width between 0 and 20 mm.

RESULTS : Among 600 patients, the median resection margin was 4 mm (interquartile range [IQR], 2-10). Overall, 379 (63.2 %) patients experienced recurrence with a 5-year RFS of 28.3 % and a 5-year OS of 38.7 %. The OST identified five subgroups of patients with different OS rates based on tumor size, a carbohydrate antigen 19-9 [CA19-9] level higher than 200 U/mL, nodal status, margin width, and age (area under the curve [AUC]: training, 0.81; testing, 0.69). The patients with tumors smaller than 4.8 cm and a margin width of 2.5 mm or greater had a relative increase in 5-year OS of 37 % compared with the entire cohort. The OST for RFS estimated a 46 % improvement in the 5-year RFS for the patients younger than 60 years who had small (<4.8 cm) well- or moderately differentiated tumors without microvascular invasion. The OPT suggested five optimal margin widths to maximize the 5-year OS for the subgroups of patients based on age, tumor size, extent of hepatectomy, and CA19-9 levels.

CONCLUSIONS : Artificial intelligence OST identified subgroups within ICC relative to long-term outcomes. Although tumor biology dictated prognosis, the OPT suggested that different margin widths based on patient and disease characteristics may optimize ICC long-term survival.

Alaimo Laura, Moazzam Zorays, Endo Yutaka, Lima Henrique A, Butey Swatika P, Ruzzenente Andrea, Guglielmi Alfredo, Aldrighetti Luca, Weiss Matthew, Bauer Todd W, Alexandrescu Sorin, Poultsides George A, Maithel Shishir K, Marques Hugo P, Martel Guillaume, Pulitano Carlo, Shen Feng, Cauchy François, Koerkamp Bas Groot, Endo Itaru, Kitago Minoru, Kim Alex, Ejaz Aslam, Beane Joal, Cloyd Jordan, Pawlik Timothy M

2023-Mar-23