Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge.

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway structures remains prohibitively time-consuming. While significant efforts have been made towards enhancing automatic airway modelling, current public-available datasets predominantly concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for mortality prediction, a strong airway-derived biomarker (Hazard ratio>1.5, p < 0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.

A multi-center performance assessment for automated histopathological classification and grading of glioma using whole slide images.

Accurate pathological classification and grading of gliomas is crucial in clinical diagnosis and treatment. The application of deep learning techniques holds promise for automated histological pathology diagnosis. In this study, we collected 733 whole slide images from four medical centers, of which 456 were used for model training, 150 for internal validation, and 127 for multi-center testing. The study includes 5 types of common gliomas. A subtask-guided multi-instance learning image-to-label training pipeline was employed. The pipeline leveraged "patch prompting" for the model to converge with reasonable computational cost. Experiments showed that an overall accuracy of 0.79 in the internal validation dataset. The performance on the multi-center testing dataset showed an overall accuracy to 0.73. The findings suggest a minor yet acceptable performance decrease in multi-center data, demonstrating the model's strong generalizability and establishing a robust foundation for future clinical applications.

In situ analysis of surface composition and meteorology at the Zhurong landing site on Mars.

The Zhurong rover of the Tianwen-1 mission landed in southern Utopia Planitia, providing a unique window into the evolutionary history of the Martian lowlands. During its first 110 sols, Zhurong investigated and categorized surface targets into igneous rocks, lithified duricrusts, cemented duricrusts, soils and sands. The lithified duricrusts, analysed by using laser-induced breakdown spectroscopy onboard Zhurong, show elevated water contents and distinct compositions from those of igneous rocks. The cemented duricrusts are likely formed via water vapor-frost cycling at the atmosphere-soil interface, as supported by the local meteorological conditions. Soils and sands contain elevated magnesium and water, attributed to both hydrated magnesium salts and adsorbed water. The compositional and meteorological evidence indicates potential Amazonian brine activities and present-day water vapor cycling at the soil-atmosphere interface. Searching for further clues to water-related activities and determining the water source by Zhurong are critical to constrain the volatile evolution history at the landing site.

Histopathological auxiliary system for brain tumour (HAS-Bt) based on weakly supervised learning using a WHO CNS5-style pipeline.

PURPOSE: Classification and grading of central nervous system (CNS) tumours play a critical role in the clinic. When WHO CNS5 simplifies the histopathology diagnosis and places greater emphasis on molecular pathology, artificial intelligence (AI) has been widely used to meet the increased need for an automatic histopathology scheme that could liberate pathologists from laborious work. This study was to explore the diagnosis scope and practicality of AI. METHODS: A one-stop Histopathology Auxiliary System for Brain tumours (HAS-Bt) is introduced based on a pipeline-structured multiple instance learning (pMIL) framework developed with 1,385,163 patches from 1038 hematoxylin and eosin (H&E) slides. The system provides a streamlined service including slide scanning, whole-slide image (WSI) analysis and information management. A logical algorithm is used when molecular profiles are available. RESULTS: The pMIL achieved an accuracy of 0.94 in a 9-type classification task on an independent dataset composed of 268 H&E slides. Three auxiliary functions are developed and a built-in decision tree with multiple molecular markers is used to automatically formed integrated diagnosis. The processing efficiency was 443.0 s per slide. CONCLUSION: HAS-Bt shows outstanding performance and provides a novel aid for the integrated neuropathological diagnostic workflow of brain tumours using CNS 5 pipeline.

Deep learning derived automated ASPECTS on non-contrast CT scans of acute ischemic stroke patients.

Ischemic stroke is the most common type of stroke, ranked as the second leading cause of death worldwide. The Alberta Stroke Program Early CT Score (ASPECTS) is considered as a systematic method of assessing ischemic change on non-contrast CT scans (NCCT) of acute ischemic stroke (AIS) patients, while still suffering from the requirement of experts' experience and also the inconsistent results between readers. In this study, we proposed an automated ASPECTS method to utilize the powerful learning ability of neural networks for objectively scoring CT scans of AIS patients. First, we proposed to use the CT perfusion (CTP) from one-stop stroke imaging to provide the golden standard of ischemic regions for ASPECTS scoring. Second, we designed an asymmetry network to capture features when comparing the left and right sides for each ASPECTS region to estimate its ischemic status. Third, we performed experiments in a large main dataset of 870 patients, as well as an independent testing dataset consisting of 207 patients with radiologists' scorings. Experimental results show that our network achieved remarkable performance, as sensitivity and accuracy of 93.7 and 92.4% in the main dataset, and 95.5 and 91.3% in the independent testing dataset, respectively. In the latter dataset, our analysis revealed a high positive correlation between the ASPECTS score and the prognosis of patients in 90DmRs. Also, we found ASPECTS score is a good indicator of the size of CTP core volume of an infraction. The proposed method shows its potential for automated ASPECTS scoring on NCCT images.

Verification of Volume Similarity Between Unilateral Mammary Gland and Autologous Omentum in Adult Women by Measuring Cylinder Method.

OBJECTIVE: To verify the volume similarity between unilateral mammary gland and autologous omentum in adult females. METHODS: A total of 63 patients diagnosed with stage 0-II breast cancer and partial non-lactating multi-fistula mastitis in the breast surgery department of Inner Mongolia Xing'an League People's Hospital from 2007 to 2020 were enrolled in the study, including 52 cases of stage 0-II breast cancer and 11 cases of non-lactating multi-fistula mastitis. The volume of the resected mammary gland and the omentum were measured by a "soft tissue measuring cylinder" and recorded. The appearance of the reconstructed breast was compared with that of the healthy side. The correlation between unilateral mammary gland volume and autologous omentum volume was analyzed by linear regression. RESULTS: Valid data were obtained for 60 cases. Affected breast size, curve, texture, nipple, and inframammary fold after omentum breast reconstruction were similar and symmetrical to those of the unaffected side. Postoperative complications occurred in most patients; the majority of these (76.67%) involved numbness of the nipple, and other complications were few. Patient satisfaction with postoperative appearance, feel, and movement of the breast, as well as total treatment costs, was over 75.0%. Linear regression analysis indicates a linear relationship between subcutaneous gland volume (x) and autologous omentum volume (y): y = 0.9847x - 1.2132, R 2 = 0.9742. CONCLUSION: Only when the dissociated pedicled omentum is completely obtained under laparoscopy can the whole subcutaneous residual cavity of the mammary gland be filled to the same volume. This study verifies that the volume of the unilateral mammary gland is similar to that of the autologous omentum in adult females.

Identification of tissue-specific and cold-responsive lncRNAs in Medicago truncatula by high-throughput RNA sequencing.

BACKGROUND: Long non-coding RNAs (lncRNAs) play important roles in the regulation of plant responses to environmental stress by acting as essential regulators of gene expression. However, whether and how lncRNAs are involved in cold acclimation-dependent freezing tolerance in plants remains largely unknown. Medicago truncatula is a prominent model for studies of legume genomics, and distinguished by its cold-acclimation characteristics. To determine the roles of lncRNAs in plant cold stress response, we conducted genome-wide high-throughput sequencing in the legume model plant M. truncatula. RESULTS: RNA-seq data were generated from twelve samples for the four treatments, i.e., non-cold treated leaves and roots, cold-treated leaves and roots of M. truncatula Jemalong A17 seedlings. A total of 1204 million raw reads were generated. Of them, 1150 million filtered reads after quality control (QC) were subjected to downstream analysis. A large number of 24,368 unique lncRNAs were identified from the twelve samples. Among these lncRNAs, 983 and 1288 were responsive to cold treatment in the leaves and roots, respectively. We further found that the intronic-lncRNAs were most sensitive to the cold treatment. The cold-responsive lncRNAs were unevenly distributed across the eight chromosomes in M. truncatula seedlings with obvious preferences for locations. Further analyses revealed that the cold-responsive lncRNAs differed between leaves and roots. The putative target genes of the lncRNAs were predicted to mainly involve the processes of protein translation, transport, metabolism and nucleic acid transcription. Furthermore, the networks of a tandem array of CBF/DREB1 genes that were reported to be located in a major freezing tolerance QTL region on chromosome 6 and their related lncRNAs were dissected based on their gene expression and chromosome location. CONCLUSIONS: We identified a comprehensive set of lncRNAs that were responsive to cold treatment in M. truncatula seedlings, and discovered tissue-specific cold-responsive lncRNAs in leaves and roots. We further dissected potential regulatory networks of CBF Intergenic RNA (MtCIR1) and MtCBFs that play critical roles in response and adaptation of M. truncatula to cold stress.

Glutamate Receptor Homolog3.4 is Involved in Regulation of Seed Germination Under Salt Stress in Arabidopsis.

Seed germination is sensitive to salt stress. ABA and Ca2+ are involved in the regulation of seed germination under salt stress. Ca2+ influx mediated by glutamate receptors (GLRs) plays important roles in many physiological processes in plants. Here, we investigated the correlation of GLRs, Ca2+ and ABA during seed germination in response to salt stress by using Arabidopsis thaliana wild-type and T-DNA insertion knockout mutants of glutamate receptor homolog3.4. We demonstrated that atglr3.4-1 and atglr3.4-2 mutants were more sensitive to NaCl during seed germination and post-germination growth than wild-type plants. Treatments of wild-type seedlings with NaCl evoked a marked elevation in cytosolic Ca2+ activity ([Ca2+]cyt), and the elevation was inhibited by antagonists of GLRs, while the NaCl-induced elevation in [Ca2+]cyt was impaired in atglr3.4-1 and atglr3.4-2 mutants. Moreover, the mutants exhibited a lower expression of SOS3, SOS2 and SOS1, and greater accumulation of Na+ than wild-type seeds in the presence of NaCl. Mutation of AtGLR3.4 rendered the mutants more sensitive to ABA, while overexpression of AtGLR3.4 made the transgenic lines more tolerant to ABA in terms of seed germination. However, there was no difference in ABA content between atglr3.4 mutants and wild-type seeds, accompanied by lower expression of ABI3 and ABI4 in atglr3.4 mutants when challenged with NaCl. These results demonstrate that AtGLR3.4-mediated Ca2+ influx may be involved in the regulation of seed germination under salt stress by modulating Na+ accumulation through the SOS pathway.

Changes in Methane Flux along a Permafrost Thaw Sequence on the Tibetan Plateau.

Permafrost thaw alters the physical and environmental conditions of soil and may thus cause a positive feedback to climate warming through increased methane emissions. However, the current knowledge of methane emissions following thermokarst development is primarily based on expanding lakes and wetlands, with upland thermokarst being studied less often. In this study, we monitored the methane emissions during the peak growing seasons of two consecutive years along a thaw sequence within a thermo-erosion gully in a Tibetan swamp meadow. Both years had consistent results, with the early and midthaw stages (3 to 12 years since thaw) exhibiting low methane emissions that were similar to those in the undisturbed meadow, while the emissions from the late thaw stage (20 years since thaw) were 3.5 times higher. Our results also showed that the soil water-filled pore space, rather than the soil moisture per se, in combination with the sand content, were the main factors that caused increased methane emissions. These findings differ from the traditional view that upland thermokarst could reduce methane emissions owing to the improvement of drainage conditions, suggesting that upland thermokarst development does not always result in a decrease in methane emissions.

Complex Network Analysis for Characterizing Global Value Chains in Equipment Manufacturing.

The rise of global value chains (GVCs) characterized by the so-called "outsourcing", "fragmentation production", and "trade in tasks" has been considered one of the most important phenomena for the 21st century trade. GVCs also can play a decisive role in trade policy making. However, due to the increasing complexity and sophistication of international production networks, especially in the equipment manufacturing industry, conventional trade statistics and the corresponding trade indicators may give us a distorted picture of trade. This paper applies various network analysis tools to the new GVC accounting system proposed by Koopman et al. (2014) and Wang et al. (2013) in which gross exports can be decomposed into value-added terms through various routes along GVCs. This helps to divide the equipment manufacturing-related GVCs into some sub-networks with clear visualization. The empirical results of this paper significantly improve our understanding of the topology of equipment manufacturing-related GVCs as well as the interdependency of countries in these GVCs that is generally invisible from the traditional trade statistics.

Interaction between IGFBP7 and insulin: a theoretical and experimental study.

Insulin-like growth factor binding protein 7 (IGFBP7) can bind to insulin with high affinity which inhibits the early steps of insulin action. Lack of recognition mechanism impairs our understanding of insulin regulation before it binds to insulin receptor. Here we combine computational simulations with experimental methods to investigate the interaction between IGFBP7 and insulin. Molecular dynamics simulations indicated that His200 and Arg198 in IGFBP7 were key residues. Verified by experimental data, the interaction remained strong in single mutation systems R198E and H200F but became weak in double mutation system R198E-H200F relative to that in wild-type IGFBP7. The results and methods in present study could be adopted in future research of discovery of drugs by disrupting protein-protein interactions in insulin signaling. Nevertheless, the accuracy, reproducibility, and costs of free-energy calculation are still problems that need to be addressed before computational methods can become standard binding prediction tools in discovery pipelines.

Interaction of P-glycoprotein with anti-tumor drugs: the site, gate and pathway.

Understanding the mechanism and pathway of anti-cancer drugs to be pumped out by P-glycoprotein (P-gp) in cancer cell is very important for the successful chemotherapy. P-gp is a member of ATP-binding cassette (ABC) transporters. In this study, random accelerated molecular dynamics (RAMD) simulation was used to explore the potential egress pathway of ligands from the binding pocket. This could be considered as a reverse process of drug binding. The most possible portal of drugs to dissociate is TM4/TM6, which is almost the same for different drugs, such as paclitaxel and doxorubicin. The interactions in the binding site are found to be remarkably stronger than that outside of the binding site. The results were suggested by the free energy calculation between P-gp and different drugs from metadynamics simulation. All the results indicate that the flexibility of inner residues, especially the residue Phe339, is very important for the drugs to access the binding site.

Drug promiscuity of P-glycoprotein and its mechanism of interaction with paclitaxel and doxorubicin.

P-glycoprotein (P-gp) pumps a broad range of structurally diverse anti-cancer drugs out of cancer cells. Therefore, multi-drug resistance (MDR) in chemotherapy closely correlates with P-gp. However, how this single transport system recognizes different substrates remains unclear. In this study, we attempt to uncover the mechanism of substrate promiscuity of P-gp by atomistic molecular dynamics simulations. Results indicate that different drugs like paclitaxel and doxorubicin approach the putative binding site of P-gp, and the inner residues are found to be important in this process. An obstacle-overcoming process was observed, illustrating that the inner residues are flexible. Interaction energy calculations suggest that the inner residues possess high affinity toward substrates. The cavity of adaptability to accommodate different drugs would help explain why P-gp has so many different substrates.

A molecular dynamics study on pH response of protein adsorbed on peptide-modified polyvinyl alcohol hydrogel.

The interactions between proteins and functional biomaterials under different physical and environmental conditions need to be understood when designing biomedical devices. Herein, we present a molecular dynamics simulation study of the fragment antigen-binding (Fab) of trastuzumab (a monoclonal antibody) and its complex with a peptide-modified polyvinyl alcohol (PVA) hydrogel at different pH values. Consistent with experiments, PVA when modified by charged ligands does shrink as a direct response to a drop in the pH. The protein maintains a stable conformation when adsorbed on the hydrogel matrix with a varied pH, showing no signs of denaturation in all simulated systems, suggesting that peptide-grafted PVA is a good biocompatible material. Under neutral conditions, the hydrogel alone stabilizes the interactions between the protein and the peptide ligands. Strikingly under acidic conditions the protein-ligand interactions are disrupted by a collective protonation of ligands. A sharp decrease in the interaction energies, accompanied by the sudden increase of the protein-ligand distance, indicates a rapid pH response in the protein-hydrogel complex. This will be important in protein delivery and purification. The effect of pH on the interactions and the dynamics of the protein and the sudden pH response of the hydrogel at the atomic level present a new functional perspective in developing new hydrogels with desirable properties.

Adhesion of lactoferrin and bone morphogenetic protein-2 to a rutile surface: dependence on the surface hydrophobicity.

Binding of the proteins human lactoferrin (LF) and human bone morphogenetic protein-2 (BMP2) to a hydroxylated TiO2 rutile (110) surface has been modeled using molecular dynamics (MD) simulations. In order to study the effect of the hydrophobicity of the rutile surface on the protein binding process, the rutile surface was made more hydrophilic or more hydrophobic by adjusting the rutile atomic charges. The binding of LF and BMP2 to the hydrophobic rutile surface occurred through direct contact between the protein and rutile via both hydrophobic and hydrophilic amino acids. This forced the proteins to undergo structural rearrangements, observed primarily in BMP2. Binding to the hydrophilic rutile surface was largely indirect via the hydration layer of water on the surface of rutile. Both LF and BMP2 had a higher binding strength to the hydrophobic rutile surfaces than to the hydrophilic surfaces, as seen in the larger amplitude of the binding energies.

Trastuzumab-Peptide interactions: mechanism and application in structure-based ligand design.

Understanding of protein-ligand interactions and its influences on protein stability is necessary in the research on all biological processes and correlative applications, for instance, the appropriate affinity ligand design for the purification of bio-drugs. In this study, computational methods were applied to identify binding site interaction details between trastuzumab and its natural receptor. Trastuzumab is an approved antibody used in the treatment of human breast cancer for patients whose tumors overexpress the HER2 (human epidermal growth factor receptor 2) protein. However, rational design of affinity ligands to keep the stability of protein during the binding process is still a challenge. Herein, molecular simulations and quantum mechanics were used on protein-ligand interaction analysis and protein ligand design. We analyzed the structure of the HER2-trastuzumab complex by molecular dynamics (MD) simulations. The interaction energies of the mutated peptides indicate that trastuzumab binds to ligand through electrostatic and hydrophobic interactions. Quantitative investigation of interactions shows that electrostatic interactions play the most important role in the binding of the peptide ligand. Prime/MM-GBSA calculations were carried out to predict the binding affinity of the designed peptide ligands. A high binding affinity and specificity peptide ligand is designed rationally with equivalent interaction energy to the wild-type octadecapeptide. The results offer new insights into affinity ligand design.

Contribution of water molecules in the spontaneous release of protein by graphene sheets.

Applications of graphene sheets in the fields of biosensors and biomedical devices are limited by the aqueous solubility of graphene. Consequently, understanding the role of water molecules in the aggregation or dispersion of graphene in aqueous solution with a biomolecule is of vital importance to its application. Herein, protein is spontaneously released by the layer-to-layer aggregation of two single-layer graphene sheets due to van der Waals force between the sheets. The properties of water molecules, including density and dynamics, are discussed in detail. The dynamic behavior of aggregation of graphene sheets is triggered by the dynamics of water molecules. To stabilize dispersed graphene sheets in aqueous solution, the density of water molecules between the graphene sheets should be larger than 0.83 g cm(-3), and graphene modified by hydroxyl groups could be a good choice. The stability of a model protein on the graphene sheet is studied to investigate the biological compatibility of graphene sheets. To be a material with good biocompatibility, graphene should be functionalized by hydrophilic groups. The results presented herein could be helpful in the research and application of graphene sheets in the fields of biomaterials, biosensors, and biomedical devices.