Artificial Intelligence in Pancreatic Image Analysis: A Review.

Pancreatic cancer is a highly lethal disease with a poor prognosis. Its early diagnosis and accurate treatment mainly rely on medical imaging, so accurate medical image analysis is especially vital for pancreatic cancer patients. However, medical image analysis of pancreatic cancer is facing challenges due to ambiguous symptoms, high misdiagnosis rates, and significant financial costs. Artificial intelligence (AI) offers a promising solution by relieving medical personnel's workload, improving clinical decision-making, and reducing patient costs. This study focuses on AI applications such as segmentation, classification, object detection, and prognosis prediction across five types of medical imaging: CT, MRI, EUS, PET, and pathological images, as well as integrating these imaging modalities to boost diagnostic accuracy and treatment efficiency. In addition, this study discusses current hot topics and future directions aimed at overcoming the challenges in AI-enabled automated pancreatic cancer diagnosis algorithms.

Comparative evaluation of 16S rRNA primer pairs in identifying nitrifying guilds in soils under long-term organic fertilization and water management.

Compared with 454 sequencing technology, short-read sequencing (e.g., Illumina) technology generates sequences of high accuracy, but limited length (<500 bp). Such a limitation can prove that studying a target gene using a large amplicon (>500 bp) is challenging. The ammonia monooxygenase subunit A (amoA) gene of ammonia-oxidizing archaea (AOA), which plays a crucial part in the nitrification process, is such a gene. By providing a full overview of the community of a functional microbial guild, 16S ribosomal ribonucleic acid (rRNA) gene sequencing could overcome this problem. However, it remains unclear how 16S rRNA primer selection influences the quantification of relative abundance and the identification of community composition of nitrifiers, especially AOA. In the present study, a comparison was made between the performance of primer pairs 338F-806R, 515F-806R, and 515F-907R to a shotgun metagenome approach. The structure of nitrifier communities subjected to different long-term organic matter amendment and water management protocols was assessed. Overall, we observed higher Chao1 richness diversity of soil total bacteria by using 515F-806R compared to 338F-806R and 515F-907R, while higher Pielou's evenness diversity was observed by using 515F-806R and 515F-907R compared to 338F-806R. The studied primer pairs revealed different performances on the relative abundance of Thaumarchaeota, AOB, and NOB. The Thaumarchaeota 16S rRNA sequence was rarely detected using 338F-806R, while the relative abundances of Thaumarchaeota detected using 515F-806R were higher than those detected by using 515F-907R. AOB showed higher proportions in the 338F-806R and 515F-907R data, than in 515F-806R data. Different primers pairs showed significant change in relative proportion of NOB. Nonetheless, we found consistent patterns of the phylotype distribution of nitrifiers in different treatments. Nitrosopumilales (NP) and Nitrososphaerales (NS) clades were the dominant members of the AOA community in soils subject to controlled irrigation, whereas Ca. Nitrosotaleales (NT) and NS clades dominated the AOA community in soils subject to flooding irrigation. Nitrospira lineage II was the dominant NOB phylotype in all samples. Overall, ideal 16S rRNA primer pairs were identified for the analysis of nitrifier communities. Moreover, NP and NT clades of AOA might have distinct environmental adaptation strategies under different irrigation treatments.

Closed Twisted Hydrogel Ribbons with Self-Sustained Motions under Static Light Irradiation.

Self-sustained motions are widespread in biological systems by harvesting energy from surrounding environments, which inspire scientists to develop autonomous soft robots. However, most-existing soft robots require dynamic heterogeneous stimuli or complex fabrication with different components. Recently, control of topological geometry has been promising to afford soft robots with physical intelligence and thus life-like motions. Reported here are a series of closed twisted ribbon robots, which exhibit self-sustained flipping and rotation under constant light irradiation. Both Möbius strip and Seifert ribbon robots are devised for the first time by using an identical hydrogel, which responds to light irradiation on either side. Experiment and simulation results indicate that the self-regulated motions of the hydrogel robots are related to fast and reversible response of muscle-like gel, self-shadowing effect, and topology-facilitated refresh of light-exposed regions. The motion speeds and directions of the hydrogel robots can be tuned over a wide range. These closed twisted ribbon hydrogels are further applied to execute specific tasks in aqueous environments, such as collecting plastic balls, climbing a vertical rod, and transporting objects. This work presents new design principle for autonomous hydrogel robots by benefiting from material response and topology geometry, which may be inspirative for the robotics community.

Proteomic Networks and Related Genetic Variants Associated with Smoking and Chronic Obstructive Pulmonary Disease.

Background: Studies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features. Methods: Leveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.0 platform to derive sparse networks of proteins associated with current vs. former smoking status, airflow obstruction, and emphysema quantitated from high-resolution computed tomography scans. We then used NetSHy, a dimension reduction technique leveraging network topology, to produce summary scores of each proteomic network, referred to as NetSHy scores. We next performed genome-wide association study (GWAS) to identify variants associated with the NetSHy scores, or network quantitative trait loci (nQTLs). Finally, we evaluated the replicability of the networks in an independent cohort, SPIROMICS. Results: We identified networks of 13 to 104 proteins for each phenotype and exposure in NHW and AA, and the derived NetSHy scores significantly associated with the variable of interests. Networks included known (sRAGE, ALPP, MIP1) and novel molecules (CA10, CPB1, HIS3, PXDN) and interactions involved in COPD pathogenesis. We observed 7 nQTL loci associated with NetSHy scores, 4 of which remained after conditional analysis. Networks for smoking status and emphysema, but not airflow obstruction, demonstrated a high degree of replicability across race groups and cohorts. Conclusions: In this work, we apply state-of-the-art molecular network generation and summarization approaches to proteomic data from COPDGene participants to uncover protein networks associated with COPD phenotypes. We further identify genetic associations with networks. This work discovers protein networks containing known and novel proteins and protein interactions associated with clinically relevant COPD phenotypes across race groups and cohorts.

A Generalized Higher-order Correlation Analysis Framework for Multi-Omics Network Inference.

Multiple -omics (genomics, proteomics, etc.) profiles are commonly generated to gain insight into a disease or physiological system. Constructing multi-omics networks with respect to the trait(s) of interest provides an opportunity to understand relationships between molecular features but integration is challenging due to multiple data sets with high dimensionality. One approach is to use canonical correlation to integrate one or two omics types and a single trait of interest. However, these types of methods may be limited due to (1) not accounting for higher-order correlations existing among features, (2) computational inefficiency when extending to more than two omics data when using a penalty term-based sparsity method, and (3) lack of flexibility for focusing on specific correlations (e.g., omics-to-phenotype correlation versus omics-to-omics correlations). In this work, we have developed a novel multi-omics network analysis pipeline called Sparse Generalized Tensor Canonical Correlation Analysis Network Inference (SGTCCA-Net) that can effectively overcome these limitations. We also introduce an implementation to improve the summarization of networks for downstream analyses. Simulation and real-data experiments demonstrate the effectiveness of our novel method for inferring omics networks and features of interest.

Photo-steered rapid and multimodal locomotion of 3D-printed tough hydrogel robots.

Hydrogels are an ideal material to develop soft robots. However, it remains a grand challenge to develop miniaturized hydrogel robots with mechanical robustness, rapid actuation, and multi-gait motions. Reported here is a facile strategy to fabricate hydrogel-based soft robots by three-dimensional (3D) printing of responsive and nonresponsive tough gels for programmed morphing and locomotion upon stimulations. Highly viscoelastic poly(acrylic acid-co-acrylamide) and poly(acrylic acid-co-N-isopropyl acrylamide) aqueous solutions, as well as their mixtures, are printed with multiple nozzles into 3D constructs followed by incubation in a solution of zirconium ions to form robust carboxyl-Zr4+ coordination complexes, to produce tough metallo-supramolecular hydrogel fibers. Gold nanorods are incorporated into ink to afford printed gels with response to light. Owing to the mechanical excellence and small diameter of gel fibers, the printed hydrogel robots exhibit high robustness, fast response, and agile motions when remotely steered by dynamic light. The design of printed constructs and steering with spatiotemporal light allow for multimodal motions with programmable trajectories of the gel robots. The hydrogel robots can walk, turn, flip, and transport cargos upon light stimulations. Such printed hydrogels with good mechanical performances, fast response, and agile locomotion may open opportunities for soft robots in biomedical and engineering fields.

Coupling coordination analysis of population, economy and grain in major grain-producing counties.

This study discusses the coupling and coordination relationship among population, economy, and grain production in the central primary grain-producing counties. It aims to find a dynamic balance between the responsibility of the grain-producing areas in ensuring food security and the development of the economy and population. This study focuses on the main grain-producing provinces of Jilin and Jiangsu in China. Based on county-level data on population, economy, and grain production, it constructs an index system for the population, economy, and grain systems. The study employs the entropy weighting method and coupling coordination model to analyze the coupling coordination degree and coordinated development of the three systems in Jilin and Jiangsu provinces from 2000 to 2020, covering a span of 21 years. The coupling coordination degree and coordinated development of the three systems in the main grain-producing areas have gradually moved towards high-quality coordination. In the economically underdeveloped province of Jilin, factors such as geographical environment, population size, and industrial structure impose constraints on system coordination. In the economically developed region of Jiangsu, there is a high labour force and better development of the secondary and tertiary industries, but relatively less investment in agriculture, which affects overall coordination. It is necessary to promote regions' development with high-quality coordination by leveraging their advantages in economic foundations, and further advance the construction of the main grain-producing areas. Additionally, efforts should be made to strengthen policy support for underdeveloped regions, clearly define the industrial types and positioning of counties, and focus on industrial transformation and upgrading.

Animating hydrogel knotbots with topology-invoked self-regulation.

Steering soft robots in a self-regulated manner remains a grand challenge, which often requires continuous symmetry breaking and recovery steps for persistent motion. Although structural morphology is found significant for robotic functions, geometric topology has rarely been considered and appreciated. Here we demonstrate a series of knotbots, namely hydrogel-based robots with knotted structures, capable of autonomous rolling and spinning/rotating motions. With symmetry broken by external stimuli and restored by self-regulation, the coupling between self-constraint-induced prestress and photothermal strain animates the knotbots continuously. Experiments and simulations reveal that nonequilibrium processes are regulated dynamically and cooperatively by self-constraints, active deformations, and self-shadowing effect of the photo-responsive gel. The active motions enable the knotbots to execute tasks including gear rotation and rod climbing. This work paves the way to devise advanced soft robots with self-regulated sustainable motions by harnessing the topology.

KLF2/PPARγ axis contributes to trauma-induced heterotopic ossification by regulating mitochondrial dysfunction.

Trauma-induced heterotopic ossification (HO) is a complex disorder after musculoskeletal injury and characterized by aberrant extraskeletal bone formation. Recent studies shed light on critical role of dysregulated osteogenic differentiation in aberrant bone formation. Krupel-like factor 2 (KLF2) and peroxisome proliferator-activated receptor gamma (PPARγ) are master adapter proteins that link cellular responses to osteogenesis; however, their roles and relationships in HO remain elusive. Using a murine burn/tenotomy model in vivo, we identified elevated KLF2 and reduced PPARγ levels in tendon stem/progenitor cells (TSPCs) during trauma-induced HO formation. Both KLF2 inhibition and PPARγ promotion reduced mature HO, whereas the effects of PPARγ promotion were abolished by KLF2 overexpression. Additionally, mitochondrial dysfunction and reactive oxygen species (ROS) production also increased after burn/tenotomy, and improvements in mitochondrial function (ROS scavenger) could alleviate HO formation, but were abolished by KLF2 activation and PPARγ suppression by affecting redox balance. Furthermore, in vitro, we found increased KLF2 and decreased PPARγ levels in osteogenically induced TSPCs. Both KLF2 inhibition and PPARγ promotion relieved osteogenesis by improving mitochondrial function and maintaining redox balance, and effects of PPARγ promotion were abolished by KLF2 overexpression. Our findings suggest that KLF2/PPARγ axis exerts regulatory effects on trauma-induced HO through modulation of mitochondrial dysfunction and ROS production in TSPCs by affecting redox balance. Targeting KLF2/PPARγ axis and mitochondrial dysfunction can represent attractive approaches to therapeutic intervention in trauma-induced HO.

SmCCNet 2.0: A Comprehensive Tool for Multi-omics Network Inference with Shiny Visualization.

Summary: Sparse multiple canonical correlation network analysis (SmCCNet) is a machine learning technique for integrating omics data along with a variable of interest (e.g., phenotype of complex disease), and reconstructing multi-omics networks that are specific to this variable. We present the second-generation SmCCNet (SmCCNet 2.0) that adeptly integrates single or multiple omics data types along with a quantitative or binary phenotype of interest. In addition, this new package offers a streamlined setup process that can be configured manually or automatically, ensuring a flexible and user-friendly experience. Availability: This package is available in both CRAN: https://cran.r-project.org/web/packages/SmCCNet/index.html and Github: https://github.com/KechrisLab/SmCCNet under the MIT license. The network visualization tool is available at https://smccnet.shinyapps.io/smccnetnetwork/.

Development and Validation of a Deep-Learning Model to Predict Total Hip Replacement on Radiographs: The Total Hip Replacement Prediction (THREP) Model.

BACKGROUND: There are few methods for accurately assessing the risk of total hip arthroplasty (THA) in patients with osteoarthritis. A novel and reliable method that could play a substantial role in research and clinical routine should be investigated. The purpose of the present study was to develop a deep-learning model that can reliably predict the risk of THA with use of radiographic images and clinical symptom data. METHODS: This retrospective, multicenter, case-control study assessed hip joints on weighted-bearing anteroposterior pelvic radiographs obtained from Osteoarthritis Initiative (OAI) participants. Participants who underwent THA were matched to controls according to age, sex, body mass index, and ethnicity. Cases and controls were uniformly split into training, validation, and testing data sets at proportions of 72% (n = 528), 14% (n = 104), and 14% (n = 104), respectively. Images and clinical symptom data were passed through a detection model and a deep convolutional neural network (DCNN) model to predict the probability of THA within 9 years as well as the most likely time period for THA (0 to 2 years, 3 to 5 years, 6 to 9 years). Model performance was assessed with use of the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity in the testing set. RESULTS: A total of 736 participants were evaluated, including 184 cases and 552 controls. The prediction model achieved an overall accuracy, sensitivity, and specificity of 91.35%, 92.59% and 86.96%, respectively, with an AUC of 0.944, for THA within 9 years. The AUC of the DCNN model for assessing the most likely time period was 0.907 for 0 to 2 years, 0.916 for 3 to 5 years, and 0.841 for 6 to 9 years. Gradient-weighted class activation mapping closely corresponded to regions affecting the prediction of the DCNN model. CONCLUSIONS: The proposed DCNN model is a reliable and valid method to predict the probability of THA-within limitations. It could assist clinicians in patient counseling and decision-making regarding the timing of the intervention. In the future, by increasing the size of the data set, enhancing the ethnic and socioeconomic diversity of the participants, and improving the follow-up rate, the quality of the conclusions can be further improved. LEVEL OF EVIDENCE: Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Surgical resection outcomes of post-traumatic elbow heterotopic ossification: multicenter case series at a minimum 5-year follow-up.

BACKGROUND: Heterotopic ossification (HO), a common complication after elbow trauma, causes severe limb disability, Surgical resection is usually performed for post-traumatic elbow HO (PTEHO) to regain mobility. Though it was heavily reported, there has been no long-term (minimum 5-year) follow-up. PATIENTS AND METHODS: 173 patients who underwent PTEHO resection were followed up for minimum 5 years in 4 hospitals between January/2015 and August/2016. Demographics, disease characteristics, preoperative and minimum 5-year assessments were collected. After controlling for potential variables when dividing long-term ROM into <120° and ≥120°, risk factors for ROM recovery to modern functional arc were identified through multivariable regression analysis. RESULTS: Clinically important improvements in ROM of 39°â†’124° were obtained at final follow-up, and 74.6% achieved modern functional arc (≥120°). Mayo Elbow Performance Index (MEPI) had clinically important increases of 69→93 points at final follow-up, and 96.5% reported excellent-to-good. Pain (Numerical Rating Scale, 1.9→0.6 points) and ulnar nerve symptoms were improved. Total complication rate was 15.6%, including new-onset ulnar nerve symptoms (5.8%), HO recurrence with clinical symptoms (6.9%), elbow instability (1.7%), and joint infection (1.2%). Previously reported high body mass index (BMI, p=0.002) and long disease duration (p=0.033) were equally identified as risk factors for not achieving modern functional arc, meanwhile tobacco use (p=0.024) and ankylosed HO (p<0.001) were found to be new risk factors. CONCLUSION: Surgical resection yields satisfactory outcomes for PTEHO at long-term of minimum 5 years. High BMI, tobacco use, long disease duration, and ankylosed HO would negatively affect ROM recovery to modern functional arc (≥120°). LEVEL OF EVIDENCE: Level IV, therapeutic study.

Dual-engineered cartilage-targeting extracellular vesicles derived from mesenchymal stem cells enhance osteoarthritis treatment via miR-223/NLRP3/pyroptosis axis: Toward a precision therapy.

Osteoarthritis (OA) is the most common disabling joint disease with no effective disease modifying drugs. Extracellular vesicles released by several types of mesenchymal stem cells could promote cartilage repair and ameliorate OA pathology in animal models, representing a novel therapeutic strategy. In this study, we demonstrated that extracellular vesicles derived from human umbilical cord mesenchymal stem cells (hUC-EVs) could maintain chondrocyte homeostasis and alleviate OA, and further revealed a novel molecular mechanism of this therapeutic effect. miR-223, which could directly bind with the 3'UTR of NLRP3 mRNA, was found to be a key miRNA for hUC-EVs to exert beneficial effects on inflammation inhibiting and cartilage protecting. For enhancing the effect on mitigating osteoarthritis, exogenous miR-223 was loaded into hUC-EVs by electroporation, and a collagen II-targeting peptide (WYRGRL) was modified onto the surface of hUC-EVs by genetic engineering to achieve a more targeted and efficient RNA delivery to the cartilage. The dual-engineered EVs showed a maximal effect on inhibiting the NLRP3 inflammasome activation and chondrocyte pyroptosis, and offered excellent results for the treatment of OA. This study provides a novel theoretical basis and a promising therapeutic strategy for the application of engineered extracellular vesicles in OA treatment.

Multifunctional Extracellular Matrix Hydrogel with Self-Healing Properties and Promoting Angiogenesis as an Immunoregulation Platform for Diabetic Wound Healing.

Treating chronic wounds is a global challenge. In diabetes mellitus cases, long-time and excess inflammatory responses at the injury site may delay the healing of intractable wounds. Macrophage polarization (M1/M2 types) can be closely associated with inflammatory factor generation during wound healing. Quercetin (QCT) is an efficient agent against oxidation and fibrosis that promotes wound healing. It can also inhibit inflammatory responses by regulating M1-to-M2 macrophage polarization. However, its limited solubility, low bioavailability, and hydrophobicity are the main issues restricting its applicability in wound healing. The small intestinal submucosa (SIS) has also been widely studied for treating acute/chronic wounds. It is also being extensively researched as a suitable carrier for tissue regeneration. As an extracellular matrix, SIS can support angiogenesis, cell migration, and proliferation, offering growth factors involved in tissue formation signaling and assisting wound healing. We developed a series of promising biosafe novel diabetic wound repair hydrogel wound dressings with several effects, including self-healing properties, water absorption, and immunomodulatory effects. A full-thickness wound diabetic rat model was constructed for in vivo assessment of QCT@SIS hydrogel, in which hydrogels achieved a markedly increased wound repair rate. Their effect was determined by the promotion of the wound healing process, the thickness of granulation tissue, vascularization, and macrophage polarization during wound healing. At the same time, we injected the hydrogel subcutaneously into healthy rats to perform histological analyses of sections of the heart, spleen, liver, kidney, and lung. We then tested the biochemical index levels in serum to determine the biological safety of the QCT@SIS hydrogel. In this study, the developed SIS showed convergence of biological, mechanical, and wound-healing capabilities. Here, we focused on constructing a self-healing, water-absorbable, immunomodulatory, and biocompatible hydrogel as a synergistic treatment paradigm for diabetic wounds by gelling the SIS and loading QCT for slow drug release.

Potassium-doped g-C3N4 enables efficient visible-light-driven dye degradation.

Element doping is recognized as an efficient method to boost the photocatalytic performance of photocatalysts. Here, a new potassium ion-doped precursor, potassium sorbate, was employed in melamine configuration during calcination process to prepare the potassium-doped g-C3N4 (KCN). By various characterization techniques and electrochemical measurements, the doping of K in g-C3N4 can efficiently modify the band structure to enhance the light absorption and greatly increase the conductivity to accelerate charge transfer and photogenerated carrier separation, ultimately achieving an excellent photodegradation of the organic pollutant (methylene blue, MB). These results have demonstrated that the approach of potassium incorporation in g-C3N4 has potential in fabricating high-performance photocatalysts for organic pollutant removal.

NetSHy: network summarization via a hybrid approach leveraging topological properties.

MOTIVATION: Biological networks can provide a system-level understanding of underlying processes. In many contexts, networks have a high degree of modularity, i.e. they consist of subsets of nodes, often known as subnetworks or modules, which are highly interconnected and may perform separate functions. In order to perform subsequent analyses to investigate the association between the identified module and a variable of interest, a module summarization, that best explains the module's information and reduces dimensionality is often needed. Conventional approaches for obtaining network representation typically rely only on the profiles of the nodes within the network while disregarding the inherent network topological information. RESULTS: In this article, we propose NetSHy, a hybrid approach which is capable of reducing the dimension of a network while incorporating topological properties to aid the interpretation of the downstream analyses. In particular, NetSHy applies principal component analysis (PCA) on a combination of the node profiles and the well-known Laplacian matrix derived directly from the network similarity matrix to extract a summarization at a subject level. Simulation scenarios based on random and empirical networks at varying network sizes and sparsity levels show that NetSHy outperforms the conventional PCA approach applied directly on node profiles, in terms of recovering the true correlation with a phenotype of interest and maintaining a higher amount of explained variation in the data when networks are relatively sparse. The robustness of NetSHy is also demonstrated by a more consistent correlation with the observed phenotype as the sample size decreases. Lastly, a genome-wide association study is performed as an application of a downstream analysis, where NetSHy summarization scores on the biological networks identify more significant single nucleotide polymorphisms than the conventional network representation. AVAILABILITY AND IMPLEMENTATION: R code implementation of NetSHy is available at https://github.com/thaovu1/NetSHy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A new prognostic nomogram for heterotopic ossification formation after elbow trauma : the Shanghai post-Traumatic Elbow Heterotopic Ossification Prediction (STEHOP) model.

AIMS: Heterotopic ossification (HO) is a common complication after elbow trauma and can cause severe upper limb disability. Although multiple prognostic factors have been reported to be associated with the development of post-traumatic HO, no model has yet been able to combine these predictors more succinctly to convey prognostic information and medical measures to patients. Therefore, this study aimed to identify prognostic factors leading to the formation of HO after surgery for elbow trauma, and to establish and validate a nomogram to predict the probability of HO formation in such particular injuries. METHODS: This multicentre case-control study comprised 200 patients with post-traumatic elbow HO and 229 patients who had elbow trauma but without HO formation between July 2019 and December 2020. Features possibly associated with HO formation were obtained. The least absolute shrinkage and selection operator regression model was used to optimize feature selection. Multivariable logistic regression analysis was applied to build the new nomogram: the Shanghai post-Traumatic Elbow Heterotopic Ossification Prediction model (STEHOP). STEHOP was validated by concordance index (C-index) and calibration plot. Internal validation was conducted using bootstrapping validation. RESULTS: Male sex, obesity, open wound, dislocations, late definitive surgical treatment, and lack of use of non-steroidal anti-inflammatory drugs were identified as adverse predictors and incorporated to construct the STEHOP model. It displayed good discrimination with a C-index of 0.80 (95% confidence interval 0.75 to 0.84). A high C-index value of 0.77 could still be reached in the internal validation. The calibration plot showed good agreement between nomogram prediction and observed outcomes. CONCLUSION: The newly developed STEHOP model is a valid and convenient instrument to predict HO formation after surgery for elbow trauma. It could assist clinicians in counselling patients regarding treatment expectations and therapeutic choices. Cite this article: Bone Joint J 2022;104-B(8):963-971.

Significant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification.

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death in the United States. COPD represents one of many areas of research where identifying complex pathways and networks of interacting biomarkers is an important avenue toward studying disease progression and potentially discovering cures. Recently, sparse multiple canonical correlation network analysis (SmCCNet) was developed to identify complex relationships between omics associated with a disease phenotype, such as lung function. SmCCNet uses two sets of omics datasets and an associated output phenotypes to generate a multi-omics graph, which can then be used to explore relationships between omics in the context of a disease. Detecting significant subgraphs within this multi-omics network, i.e., subgraphs which exhibit high correlation to a disease phenotype and high inter-connectivity, can help clinicians identify complex biological relationships involved in disease progression. The current approach to identifying significant subgraphs relies on hierarchical clustering, which can be used to inform clinicians about important pathways involved in the disease or phenotype of interest. The reliance on a hierarchical clustering approach can hinder subgraph quality by biasing toward finding more compact subgraphs and removing larger significant subgraphs. This study aims to introduce new significant subgraph detection techniques. In particular, we introduce two subgraph detection methods, dubbed Correlated PageRank and Correlated Louvain, by extending the Personalized PageRank Clustering and Louvain algorithms, as well as a hybrid approach combining the two proposed methods, and compare them to the hierarchical method currently in use. The proposed methods show significant improvement in the quality of the subgraphs produced when compared to the current state of the art.

Development and validation of a prognostic nomogram for open elbow arthrolysis : the Shanghai Prediction model for Elbow Stiffness Surgical Outcome.

AIMS: The aim of this study was to develop and internally validate a prognostic nomogram to predict the probability of gaining a functional range of motion (ROM ≥ 120°) after open arthrolysis of the elbow in patients with post-traumatic stiffness of the elbow. METHODS: We developed the Shanghai Prediction Model for Elbow Stiffness Surgical Outcome (SPESSO) based on a dataset of 551 patients who underwent open arthrolysis of the elbow in four institutions. Demographic and clinical characteristics were collected from medical records. The least absolute shrinkage and selection operator regression model was used to optimize the selection of relevant features. Multivariable logistic regression analysis was used to build the SPESSO. Its prediction performance was evaluated using the concordance index (C-index) and a calibration graph. Internal validation was conducted using bootstrapping validation. RESULTS: BMI, the duration of stiffness, the preoperative ROM, the preoperative intensity of pain, and grade of post-traumatic osteoarthritis of the elbow were identified as predictors of outcome and incorporated to construct the nomogram. SPESSO displayed good discrimination with a C-index of 0.73 (95% confidence interval 0.64 to 0.81). A high C-index value of 0.70 could still be reached in the interval validation. The calibration graph showed good agreement between the nomogram prediction and the outcome. CONCLUSION: The newly developed SPESSO is a valid and convenient model which can be used to predict the outcome of open arthrolysis of the elbow. It could assist clinicians in counselling patients regarding the choice and expectations of treatment. Cite this article: Bone Joint J 2022;104-B(4):486-494.