Deciphering the role of HLF in idiopathic orbital inflammation: integrative analysis via bioinformatics and machine learning techniques.

Idiopathic orbital inflammation, formerly known as NSOI (nonspecific orbital inflammation), is characterized as a spectrum disorder distinguished by the polymorphic infiltration of lymphoid tissue, presenting a complex and poorly understood etiology. Recent advancements have shed light on the HLF (Human lactoferrin), proposing its critical involvement in the regulation of hematopoiesis and the maintenance of innate mucosal immunity. This revelation has generated significant interest in exploring HLF's utility as a biomarker for NSOI, despite the existing gaps in our understanding of its biosynthetic pathways and operational mechanisms. Intersecting multi-omic datasets-specifically, common differentially expressed genes between GSE58331 and GSE105149 from the Gene Expression Omnibus and immune-related gene compendiums from the ImmPort database-we employed sophisticated analytical methodologies, including Lasso regression and support vector machine-recursive feature elimination, to identify HLF. Gene set enrichment analysis and gene set variation analysis disclosed significant immune pathway enrichment within gene sets linked to HLF. The intricate relationship between HLF expression and immunological processes was further dissected through the utilization of CIBERSORT and ESTIMATE algorithms, which assess characteristics of the immune microenvironment, highlighting a noteworthy association between increased HLF expression and enhanced immune cell infiltration. The expression levels of HLF were corroborated using data from the GSE58331 dataset, reinforcing the validity of our findings. Analysis of 218 HLF-related differentially expressed genes revealed statistically significant discrepancies. Fifteen hub genes were distilled using LASSO and SVM-RFE algorithms. Biological functions connected with HLF, such as leukocyte migration, ossification, and the negative regulation of immune processes, were illuminated. Immune cell analysis depicted a positive correlation between HLF and various cells, including resting mast cells, activated NK cells, plasma cells, and CD8 T cells. Conversely, a negative association was observed with gamma delta T cells, naive B cells, M0 and M1 macrophages, and activated mast cells. Diagnostic assessments of HLF in distinguishing NSOI showed promising accuracy. Our investigation delineates HLF as intricately associated with NSOI, casting light on novel biomarkers for diagnosis and progression monitoring of this perplexing condition.

AbdomenAtlas: A large-scale, detailed-annotated, & multi-center dataset for efficient transfer learning and open algorithmic benchmarking.

We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-dimensional CT volumes sourced from 112 hospitals across diverse populations, geographies, and facilities. AbdomenAtlas provides 673 K high-quality masks of anatomical structures in the abdominal region annotated by a team of 10 radiologists with the help of AI algorithms. We start by having expert radiologists manually annotate 22 anatomical structures in 5,246 CT volumes. Following this, a semi-automatic annotation procedure is performed for the remaining CT volumes, where radiologists revise the annotations predicted by AI, and in turn, AI improves its predictions by learning from revised annotations. Such a large-scale, detailed-annotated, and multi-center dataset is needed for two reasons. Firstly, AbdomenAtlas provides important resources for AI development at scale, branded as large pre-trained models, which can alleviate the annotation workload of expert radiologists to transfer to broader clinical applications. Secondly, AbdomenAtlas establishes a large-scale benchmark for evaluating AI algorithms-the more data we use to test the algorithms, the better we can guarantee reliable performance in complex clinical scenarios. An ISBI & MICCAI challenge named BodyMaps: Towards 3D Atlas of Human Body was launched using a subset of our AbdomenAtlas, aiming to stimulate AI innovation and to benchmark segmentation accuracy, inference efficiency, and domain generalizability. We hope our AbdomenAtlas can set the stage for larger-scale clinical trials and offer exceptional opportunities to practitioners in the medical imaging community. Codes, models, and datasets are available at https://www.zongweiz.com/dataset.

Domain Generalization with Correlated Style Uncertainty.

Domain generalization (DG) approaches intend to extract domain invariant features that can lead to a more robust deep learning model. In this regard, style augmentation is a strong DG method taking advantage of instance-specific feature statistics containing informative style characteristics to synthetic novel domains. While it is one of the state-of-the-art methods, prior works on style augmentation have either disregarded the interdependence amongst distinct feature channels or have solely constrained style augmentation to linear interpolation. To address these research gaps, in this work, we introduce a novel augmentation approach, named Correlated Style Uncertainty (CSU), surpassing the limitations of linear interpolation in style statistic space and simultaneously preserving vital correlation information. Our method's efficacy is established through extensive experimentation on diverse cross-domain computer vision and medical imaging classification tasks: PACS, Office-Home, and Camelyon17 datasets, and the Duke-Market1501 instance retrieval task. The results showcase a remarkable improvement margin over existing state-of-the-art techniques. The source code is available https://github.com/freshman97/CSU.

Osteoporotic osseointegration: therapeutic hallmarks and engineering strategies.

Osteoporosis is a systemic skeletal disease caused by an imbalance between bone resorption and formation. Current treatments primarily involve systemic medication and hormone therapy. However, these systemic treatments lack directionality and are often ineffective for locally severe osteoporosis, with the potential for complex adverse reactions. Consequently, treatment strategies using bioactive materials or external interventions have emerged as the most promising approaches. This review proposes twelve microenvironmental treatment targets for osteoporosis-related pathological changes, including local accumulation of inflammatory factors and reactive oxygen species (ROS), imbalance of mitochondrial dynamics, insulin resistance, disruption of bone cell autophagy, imbalance of bone cell apoptosis, changes in neural secretions, aging of bone cells, increased local bone tissue vascular destruction, and decreased regeneration. Additionally, this review examines the current research status of effective or potential biophysical and biochemical stimuli based on these microenvironmental treatment targets and summarizes the advantages and optimal parameters of different bioengineering stimuli to support preclinical and clinical research on osteoporosis treatment and bone regeneration. Finally, the review addresses ongoing challenges and future research prospects.

The roles of IRF8 in nonspecific orbital inflammation: an integrated analysis by bioinformatics and machine learning.

BACKGROUND: Nonspecific Orbital Inflammation (NSOI) represents a persistent and idiopathic proliferative inflammatory disorder, characterized by polymorphous lymphoid infiltration within the orbit. The transcription factor Interferon Regulatory Factor 8 (IRF8), integral to the IRF protein family, was initially identified as a pivotal element for the commitment and differentiation of myeloid cell lineage. Serving as a central regulator of innate immune receptor signaling, IRF8 orchestrates a myriad of functions in hematopoietic cell development. However, the intricate mechanisms underlying IRF8 production remain to be elucidated, and its potential role as a biomarker for NSOI is yet to be resolved. METHODS: IRF8 was extracted from the intersection analysis of common DEGs of GSE58331 and GSE105149 from the GEO and immune- related gene lists in the ImmPort database using The Lasso regression and SVM-RFE analysis. We performed GSEA and GSVA with gene sets coexpressed with IRF8, and observed that gene sets positively related to IRF8 were enriched in immune-related pathways. To further explore the correlation between IRF8 and immune-related biological process, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate TME characteristics of each sample and confirmed that high IRF8 expression might give rise to high immune cell infiltration. Finally, the GSE58331 was utilized to confirm the levels of expression of IRF8. RESULTS: Among the 314 differentially expressed genes (DEGs), some DEGs were found to be significantly different. With LASSO and SVM-RFE algorithms, we obtained 15 hub genes. For biological function analysis in IRF8, leukocyte mediated immunity, leukocyte cell-cell adhesion, negative regulation of immune system process were emphasized. B cells naive, Macrophages M0, Macrophages M1, T cells CD4 memory activated, T cells CD4 memory resting, T cells CD4 naive, and T cells gamma delta were shown to be positively associated with IRF8. While, Mast cells resting, Monocytes, NK cells activated, Plasma cells, T cells CD8, and T cells regulatory (Tregs) were shown to be negatively linked with IRF8. The diagnostic ability of the IRF8 in differentiating NSOI exhibited a good value. CONCLUSIONS: This study discovered IRF8 that are linked to NSOI. IRF8 shed light on potential new biomarkers for NSOI and tracking its progression.

Elucidating the multifaceted roles of GPR146 in non-specific orbital inflammation: a concerted analytical approach through the prisms of bioinformatics and machine learning.

Background: Non-specific Orbital Inflammation (NSOI) is a chronic idiopathic condition marked by extensive polymorphic lymphoid infiltration in the orbital area. The integration of metabolic and immune pathways suggests potential therapeutic roles for C-peptide and G protein-coupled receptor 146 (GPR146) in diabetes and its sequelae. However, the specific mechanisms through which GPR146 modulates immune responses remain poorly understood. Furthermore, the utility of GPR146 as a diagnostic or prognostic marker for NSOI has not been conclusively demonstrated. Methods: We adopted a comprehensive analytical strategy, merging differentially expressed genes (DEGs) from the Gene Expression Omnibus (GEO) datasets GSE58331 and GSE105149 with immune-related genes from the ImmPort database. Our methodology combined LASSO regression and support vector machine-recursive feature elimination (SVM-RFE) for feature selection, followed by Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) to explore gene sets co-expressed with GPR146, identifying a significant enrichment in immune-related pathways. The tumor microenvironment's immune composition was quantified using the CIBERSORT algorithm and the ESTIMATE method, which confirmed a positive correlation between GPR146 expression and immune cell infiltration. Validation of GPR146 expression was performed using the GSE58331 dataset. Results: Analysis identified 113 DEGs associated with GPR146, with a significant subset showing distinct expression patterns. Using LASSO and SVM-RFE, we pinpointed 15 key hub genes. Functionally, these genes and GPR146 were predominantly linked to receptor ligand activity, immune receptor activity, and cytokine-mediated signaling. Specific immune cells, such as memory B cells, M2 macrophages, resting mast cells, monocytes, activated NK cells, plasma cells, and CD8+ T cells, were positively associated with GPR146 expression. In contrast, M0 macrophages, naive B cells, M1 macrophages, activated mast cells, activated memory CD4+ T cells, naive CD4+ T cells, and gamma delta T cells showed inverse correlations. Notably, our findings underscore the potential diagnostic relevance of GPR146 in distinguishing NSOI. Conclusion: Our study elucidates the immunological signatures associated with GPR146 in the context of NSOI, highlighting its prognostic and diagnostic potential. These insights pave the way for GPR146 to be a novel biomarker for monitoring the progression of NSOI, providing a foundation for future therapeutic strategies targeting immune-metabolic pathways.

Bioinformatic validation and machine learning-based exploration of purine metabolism-related gene signatures in the context of immunotherapeutic strategies for nonspecific orbital inflammation.

Background: Nonspecific orbital inflammation (NSOI) represents a perplexing and persistent proliferative inflammatory disorder of idiopathic nature, characterized by a heterogeneous lymphoid infiltration within the orbital region. This condition, marked by the aberrant metabolic activities of its cellular constituents, starkly contrasts with the metabolic equilibrium found in healthy cells. Among the myriad pathways integral to cellular metabolism, purine metabolism emerges as a critical player, providing the building blocks for nucleic acid synthesis, such as DNA and RNA. Despite its significance, the contribution of Purine Metabolism Genes (PMGs) to the pathophysiological landscape of NSOI remains a mystery, highlighting a critical gap in our understanding of the disease's molecular underpinnings. Methods: To bridge this knowledge gap, our study embarked on an exploratory journey to identify and validate PMGs implicated in NSOI, employing a comprehensive bioinformatics strategy. By intersecting differential gene expression analyses with a curated list of 92 known PMGs, we aimed to pinpoint those with potential roles in NSOI. Advanced methodologies, including Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), facilitated a deep dive into the biological functions and pathways associated with these PMGs. Further refinement through Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) enabled the identification of key hub genes and the evaluation of their diagnostic prowess for NSOI. Additionally, the relationship between these hub PMGs and relevant clinical parameters was thoroughly investigated. To corroborate our findings, we analyzed expression data from datasets GSE58331 and GSE105149, focusing on the seven PMGs identified as potentially crucial to NSOI pathology. Results: Our investigation unveiled seven PMGs (ENTPD1, POLR2K, NPR2, PDE6D, PDE6H, PDE4B, and ALLC) as intimately connected to NSOI. Functional analyses shed light on their involvement in processes such as peroxisome targeting sequence binding, seminiferous tubule development, and ciliary transition zone organization. Importantly, the diagnostic capabilities of these PMGs demonstrated promising efficacy in distinguishing NSOI from non-affected states. Conclusions: Through rigorous bioinformatics analyses, this study unveils seven PMGs as novel biomarker candidates for NSOI, elucidating their potential roles in the disease's pathogenesis. These discoveries not only enhance our understanding of NSOI at the molecular level but also pave the way for innovative approaches to monitor and study its progression, offering a beacon of hope for individuals afflicted by this enigmatic condition.

New Insight into the Effects of Endogenous Protein and Lipids on the Enzymatic Digestion of Starch in Sorghum Flour.

The effects of endogenous lipids and protein in sorghum flour on starch digestion were studied following the depletion of lipids and/or protein and after the reconstitution of separated fractions. The removal of protein or lipids moderately increases the digestibility of starch in raw (uncooked) sorghum flour to values close to those for purified starch. Rapid Visco Analyzer data (as a model for the cooking process) show that cooked sorghum flours with lipids have a lower starch digestibility than those without lipids after RVA processing, due to the formation of starch-lipid complexes as evidenced by their higher final viscosity and larger enthalpy changes. Additionally, the formation of a starch-lipid-protein ternary complex was identified in cooked sorghum flour, rather than in a reconstituted ternary mixture, according to the unique cooling stage viscosity peak and a greater enthalpy of lipid complexes. After heating, the sorghum flour showed a lower digestibility than the depleted flours and the reconstituted flours. The results indicate that the natural organization of components in sorghum flour is an important factor in facilitating the interactions between starch, lipids, and protein during RVA processing and, in turn, reducing the starch digestion.

Adaptive Ion Channels Formed in Ultrathin and Semicrystalline Polymer Interphases for Stable Aqueous Batteries.

Aqueous Zn batteries have recently emerged as promising candidates for large-scale energy storage, driven by the need for a safe and cost-effective technology with sufficient energy density and readily accessible electrode materials. However, the energy density and cycle life of Zn batteries have been limited by inherent chemical, morphological, and mechanical instabilities at the electrode-electrolyte interface where uncontrolled reactions occur. To suppress the uncontrolled reactions, we designed a crystalline polymer interphase for both electrodes, which simultaneously promotes electrode reversibility via fast and selective Zn transport through the adaptive formation of ion channels. The interphase comprises an ultrathin layer of crystalline poly(1H,1H,2H,2H-perfluorodecyl acrylate), synthesized and applied as a conformal coating in a single step using initiated chemical vapor deposition (iCVD). Crystallinity is optimized to improve interphase stability and Zn-ion transport. The optimized interphase enables a cycle life of 9500 for Zn symmetric cells and over 11,000 for Zn-MnO2 full-cell batteries. We further demonstrate the generalizability of this interphase design using Cu and Li as examples, improving their stability and achieving reversible cycling in both. The iCVD method and molecular design unlock the potential of highly reversible and cost-effective aqueous batteries using earth-abundant Zn anode materials, pointing to grid-scale energy storage.

Evaluation of Head and Cervical Spine Posture after Therapy with Maxillary Protraction Appliances / Evaluación de la Postura de la Cabeza y la Columna Cervical Después de la Terapia con Aparatos de Protracción Maxilar

SUMMARY: The objective of this study was to evaluate the changes of head and cervical spine posture of skeletal class malocclusion in adolescent with maxillary protraction. Thirty cases of skeletal class malocclusion were randomly selected from the Stomatological Hospital of Shanxi Medical University. High-quality lateral cephalograms were collected including pre- and posttreatment to compare the changes of head and cervical spine posture. Data were processed using SPSS 26.0 statistical software. The paired-t test was used to compare pre- and posttreatment mean angular measurements.A significant difference in the SNA(p<0.001), SNB(p<0.01), and ANB(p<0.001) between T1 and T2 showed an improvement in the sagittal relationships. A significant change was observed in middle cervical spine posture, while upper cervical spine posture variables showed no significant difference after treatment. Skeletal class with maxillary protraction appliance not only led to the improvement of sagittal relationship, but also changed the middle cervical spine posture.

El objetivo de este estudio fue evaluar los cambios en la postura de la cabeza y la columna cervical debido a la maloclusión clase esquelética en adolescentes con protracción maxilar. Treinta casos de maloclusión de clase esquelética fueron seleccionados al azar del Hospital Estomatológico de la Universidad Médica de Shanxi. Se recogieron cefalogramas laterales de alta calidad, incluidos el tratamiento previo y posterior, para comparar los cambios en la postura de la cabeza y la columna cervical. Los datos se procesaron con el software estadístico SPSS 26.0. Se utilizó la prueba t pareada para comparar las medidas angulares medias antes y después del tratamiento. Una diferencia significativa en SNA (p <0,001), SNB (p <0,01) y ANB (p <0,001) entre T1 y T2 mostró una mejora en las relaciones sagitales. Se observó un cambio significativo en la postura de la columna cervical media, mientras que las variables de postura de la columna cervical superior no mostraron diferencias significativas después del tratamiento. La clase esquelética con aparato de protracción maxilar no solo condujo a la mejora de la relación sagital, sino que también cambió la postura de la columna cervical media.

Radiomics Boosts Deep Learning Model for IPMN Classification.

Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant pancreas lesions, and they can progress into pancreatic cancer. Therefore, detecting and stratifying their risk level is of ultimate importance for effective treatment planning and disease control. However, this is a highly challenging task because of the diverse and irregular shape, texture, and size of the IPMN cysts as well as the pancreas. In this study, we propose a novel computer-aided diagnosis pipeline for IPMN risk classification from multi-contrast MRI scans. Our proposed analysis framework includes an efficient volumetric self-adapting segmentation strategy for pancreas delineation, followed by a newly designed deep learning-based classification scheme with a radiomics-based predictive approach. We test our proposed decision-fusion model in multi-center data sets of 246 multi-contrast MRI scans and obtain superior performance to the state of the art (SOTA) in this field. Our ablation studies demonstrate the significance of both radiomics and deep learning modules for achieving the new SOTA performance compared to international guidelines and published studies (81.9% vs 61.3% in accuracy). Our findings have important implications for clinical decision-making. In a series of rigorous experiments on multi-center data sets (246 MRI scans from five centers), we achieved unprecedented performance (81.9% accuracy). The code is available upon publication.

Planted forest is catching up with natural forest in China in terms of carbon density and carbon storage.

Over the last several decades, China has taken multiple measures for afforestation and natural forest protection, including setting the goal of carbon neutrality by the middle of 21th century. In order to support the practice of relevant policies from the scientific perspective, it is essential to precisely estimate the carbon storage of arbor forest, as it plays an important role in the carbon cycle of ecosystems. In this study, we first used the latest four phases of national forest inventory data to investigate the variation of carbon storage for both natural and planted arbor forest in China during the covered period (1999-2018). Then we used machine leaning methods to simulate the carbon density based on various kinds of environmental factors and analyzed the contribution of each influencing factor. Our results demonstrate that the total carbon storage for arbor forest in China kept increasing over the last two decades, but this increment was mainly brought about by the continuous expansion of forest land. The gap of carbon sequestration between natural forest and planted forest showed a significant trend of reduction. Additionally, tree age was identified as the dominant factor for influencing the spatiotemporal variation of carbon density among all the independent variables while the impact of climatic factor was limited. Therefore, the future improvement of carbon sequestration of arbor forest in should mainly rely on additional projects of afforestation, reforestation, green space conservation and reduction of emissions in China. Conclusions of this study have important implications for policy makers and other stakeholders to evaluate the previous achievement of environmental projects and can also help to set future plans and finally realize the goals of carbon neutrality.