Climate change impacts of bioenergy technologies: A comparative consequential LCA of sustainable fuels production with CCUS.

The use of sustainable biomass can be a cost-effective way of reducing the greenhouse gas emissions in the maritime and aviation sectors. Biomass, however, is a limited resource, and therefore, it is important to use the biomass where it creates the highest value, not only economically, but also in terms of GHG reductions. This study comprehensively evaluates the GHG reduction potential of utilising forestry residue in different bioenergy technologies using a consequential LCA approach. Unlike previous studies that assess GHG impacts per unit of fuel produced, this research takes a feedstock-centric approach which enables comparisons across systems that yield diverse products and by-products. Three technologies-combined heat and power plant with carbon capture, hydrothermal liquefaction, and gasification-are assessed, while considering both carbon capture and storage (CCS) or carbon capture and utilisation (CCU). Through scenario analysis, the study addresses uncertainty, and assumptions in the LCA modelling. It explores the impact of energy systems, fuel substitution efficiency, renewable energy expansion, and the up/down stream supply chain. All technology pathways showed a potential for net emissions savings when including avoided emissions from substitution of products, with results varying from -111 to -1742 kgCO2eq per tonne residue. When combining the bioenergy technologies with CCU the dependency on the energy system in which they are operated was a significantly higher compared to CCS. The breakpoint was found to be 44 kg CO2eq/kWh electricity meaning that the marginal electricity mix has to be below this point for CCU to obtain lower GHG emissions. Furthermore, it is evident that the environmental performance of CCU technologies is highly sensitive to how it will affect the ongoing expansion of renewable electricity capacity.

Cluster Analysis of Cortical Amyloid Burden for Identifying Imaging-driven Subtypes in Mild Cognitive Impairment.

Over the past decade, Alzheimer's disease (AD) has become increasingly severe and gained greater attention. Mild Cognitive Impairment (MCI) serves as an important prodromal stage of AD, highlighting the urgency of early diagnosis for timely treatment and control of the condition. Identifying the subtypes of MCI patients exhibits importance for dissecting the heterogeneity of this complex disorder and facilitating more effective target discovery and therapeutic development. Conventional method uses clinical measurements such as cognitive score and neurophysical assessment to stratify MCI patients into two groups with early MCI (EMCI) and late MCI (LMCI), which shows their progressive stages. However, such clinical method is not designed to de-convolute the heterogeneity of the disorder. This study uses a data-driven approach to divide MCI patients into a novel grouping of two subtypes based on an amyloid dataset of 68 cortical features from positron emission tomography (PET), where each subtype has a homogeneous cortical amyloid burden pattern. Experimental evaluation including visual two-dimensional cluster distribution, Kaplan-Meier plot, genetic association studies, and biomarker distribution analysis demonstrates that the identified subtypes performs better across all metrics than the conventional EMCI and LMCI grouping.

PFERM: A Fair Empirical Risk Minimization Approach with Prior Knowledge.

Fairness is crucial in machine learning to prevent bias based on sensitive attributes in classifier predictions. However, the pursuit of strict fairness often sacrifices accuracy, particularly when significant prevalence disparities exist among groups, making classifiers less practical. For example, Alzheimer's disease (AD) is more prevalent in women than men, making equal treatment inequitable for females. Accounting for prevalence ratios among groups is essential for fair decision-making. In this paper, we introduce prior knowledge for fairness, which incorporates prevalence ratio information into the fairness constraint within the Empirical Risk Minimization (ERM) framework. We develop the Prior-knowledge-guided Fair ERM (PFERM) framework, aiming to minimize expected risk within a specified function class while adhering to a prior-knowledge-guided fairness constraint. This approach strikes a flexible balance between accuracy and fairness. Empirical results confirm its effectiveness in preserving fairness without compromising accuracy.

Interpretability Study for Long Interview Transcripts from Behavior Intervention Sessions for Family Caregivers of Dementia Patients.

Mental health challenges are significant global public health concerns, affecting millions of people and impacting individuals, families, and communities alike. Therapists play a crucial role in supporting those with mental health issues by providing emotional, practical, and financial assistance, as well as facilitating access to treatment and services. Utilizing one-to-one interviews is an effective approach that yields valuable transcripts for further study. In this paper, we focus on interview transcripts between therapists and caregivers with family members suffering from dementia. We propose a method to efficiently handle long interview transcripts for classification. Then we employ the Shapley-value based interpretability technique to identify important contents that significantly contribute to classification results and build a corpus containing sentences potentially beneficial to the therapy. This approach offers valuable insights for enhancing the treatment of mental health issues.

Multivariate mediation analysis with voxel-based morphometry revealed the neurodegeneration pathways from genetic variants to Alzheimer's Disease.

Neurodegenerative processes are increasingly recognized as potential causative factors in Alzheimer's disease (AD) pathogenesis. While many studies have leveraged mediation analysis models to elucidate the underlying mechanisms linking genetic variants to AD diagnostic outcomes, the majority have predominantly focused on regional brain measure as a mediator, thereby compromising the granularity of the imaging data. In our investigation, using the imaging genetics data from a landmark AD cohort, we contrasted both region-based and voxel-based brain measurements as imaging endophenotypes, and examined their roles in mediating genetic effects on AD outcomes. Our findings underscored that using voxel-based morphometry offers enhanced statistical power. Moreover, we delineated specific mediation pathways between SNP, brain volume, and AD outcomes, shedding light on the intricate relationship among these variables.

Topology-based Clustering of Functional Brain Networks in an Alzheimer's Disease Cohort.

Alzheimer's disease is a progressive neurodegenerative disease with many identifying biomarkers for diagnosis. However, whole-brain phenomena, particularly in functional MRI modalities, are not fully understood nor characterized. Here we employ the novel application of topological data analysis (TDA)-based methods of persistent homology to functional brain networks from ADNI-3 cohort to perform a subtyping experiment using unsupervised clustering techniques. We then investigate variations in QT-PAD challenge features across the identified clusters. Using a Wasserstein distance kernel with a variety of clustering algorithms, we found that the 0th-homology Wasserstein distance kernel and spectral clustering yielded clusters with significant differences in whole brain and medial temporal lobe (MTL) volume, thus demonstrating an intrinsic link between whole brain functional topology and brain morphometric structure. These findings demonstrate the importance of MTL in functional connectivity and the efficacy of using TDA-based machine learning methods in network neuroscience and neurodegenerative disease subtyping.

On Transforming Reinforcement Learning With Transformers: The Development Trajectory.

Transformers, originally devised for natural language processing (NLP), have also produced significant successes in computer vision (CV). Due to their strong expression power, researchers are investigating ways to deploy transformers for reinforcement learning (RL), and transformer-based models have manifested their potential in representative RL benchmarks. In this paper, we collect and dissect recent advances concerning the transformation of RL with transformers (transformer-based RL (TRL)) to explore the development trajectory and future trends of this field. We group the existing developments into two categories: architecture enhancements and trajectory optimizations, and examine the main applications of TRL in robotic manipulation, text-based games (TBGs), navigation, and autonomous driving. Architecture enhancement methods consider how to apply the powerful transformer structure to RL problems under the traditional RL framework, facilitating more precise modeling of agents and environments compared to traditional deep RL techniques. However, these methods are still limited by the inherent defects of traditional RL algorithms, such as bootstrapping and the "deadly triad". Trajectory optimization methods treat RL problems as sequence modeling problems and train a joint state-action model over entire trajectories under the behavior cloning framework; such approaches are able to extract policies from static datasets and fully use the long-sequence modeling capabilities of transformers. Given these advancements, the limitations and challenges in TRL are reviewed and proposals regarding future research directions are discussed. We hope that this survey can provide a detailed introduction to TRL and motivate future research in this rapidly developing field.

Effect of Sacubitril/Valsartan on Cognitive Function in Patients With Heart Failure With Preserved Ejection Fraction: A Prespecified Analysis of PARAGON-HF.

BACKGROUND: A hypothetical concern has been raised that sacubitril/valsartan might cause cognitive impairment because neprilysin is one of several enzymes degrading amyloid-ß peptides in the brain, some of which are neurotoxic and linked to Alzheimer-type dementia. To address this, we examined the effect of sacubitril/valsartan compared with valsartan on cognitive function in patients with heart failure with preserved ejection fraction in a prespecified substudy of PARAGON-HF (Prospective Comparison of Angiotensin Receptor Neprilysin Inhibitor With Angiotensin Receptor Blocker Global Outcomes in Heart Failure With Preserved Ejection Fraction). METHODS: In PARAGON-HF, serial assessment of cognitive function was conducted in a subset of patients with the Mini-Mental State Examination (MMSE; score range, 0-30, with lower scores reflecting worse cognitive function). The prespecified primary analysis of this substudy was the change from baseline in MMSE score at 96 weeks. Other post hoc analyses included cognitive decline (fall in MMSEs score of ≥3 points), cognitive impairment (MMSE score <24), or the occurrence of dementia-related adverse events. RESULTS: Among 2895 patients included in the MMSE substudy with baseline MMSE score measured, 1453 patients were assigned to sacubitril/valsartan and 1442 to valsartan. Their mean age was 73 years, and the median follow-up was 32 months. The mean±SD MMSE score at randomization was 27.4±3.0 in the sacubitril/valsartan group, with 10% having an MMSE score <24; the corresponding numbers were nearly identical in the valsartan group. The mean change from baseline to 96 weeks in the sacubitril/valsartan group was -0.05 (SE, 0.07); the corresponding change in the valsartan group was -0.04 (0.07). The mean between-treatment difference at week 96 was -0.01 (95% CI, -0.20 to 0.19; P=0.95). Analyses of a ≥3-point decline in MMSE, decrease to a score <24, dementia-related adverse events, and combinations of these showed no difference between sacubitril/valsartan and valsartan. No difference was found in the subgroup of patients tested for apolipoprotein E ε4 allele genotype. CONCLUSIONS: Patients with heart failure with preserved ejection fraction in PARAGON-HF had relatively low baseline MMSE scores. Cognitive change, measured by MMSE, did not differ between treatment with sacubitril/valsartan and treatment with valsartan in patients with heart failure with preserved ejection fraction. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01920711.

Learning Compact Hyperbolic Representations of Latent Space for Old Photo Restoration.

Recent restoration methods for handling real old photos have achieved significant improvements using generative networks. However, the restoration quality under the usual generative architectures is greatly affected by the encoded properties of latent space, which reflect pivotal semantic information in the recovery process. Therefore, how to find the suitable latent space and identify its semantic factors is an important issue in this challenging task. To this end, we propose a novel generative network with hyperbolic embeddings to restore old photos that suffer from multiple degradations. Specifically, we transform high-dimensional Euclidean features into a compact latent space via the hyperbolic operations. In order to enhance the hierarchical representative capability, we perform the channel mixing and group convolutions for the intermediate hyperbolic features. By using attention-based aggregation mechanism in a hyperbolic space, we can further obtain the resulting latent vectors, which are more effective in encoding the important semantic factors and improving the restoration quality. Besides, we design a diversity loss to guide each latent vector to disentangle different semantics. Extensive experiments have shown that our method is able to generate visually pleasing photos and outperforms state-of-the-art restoration methods.

Automated Cytometric Gating with Human-Level Performance Using Bivariate Segmentation.

Recent advances in cytometry technology have enabled high-throughput data collection with multiple single-cell protein expression measurements. The significant biological and technical variance between samples in cytometry has long posed a formidable challenge during the gating process, especially for the initial gates which deal with unpredictable events, such as debris and technical artifacts. Even with the same experimental machine and protocol, the target population, as well as the cell population that needs to be excluded, may vary across different measurements. To address this challenge and mitigate the labor-intensive manual gating process, we propose a deep learning framework UNITO to rigorously identify the hierarchical cytometric subpopulations. The UNITO framework transformed a cell-level classification task into an image-based semantic segmentation problem. For reproducibility purposes, the framework was applied to three independent cohorts and successfully detected initial gates that were required to identify single cellular events as well as subsequent cell gates. We validated the UNITO framework by comparing its results with previous automated methods and the consensus of at least four experienced immunologists. UNITO outperformed existing automated methods and differed from human consensus by no more than each individual human. Most critically, UNITO framework functions as a fully automated pipeline after training and does not require human hints or prior knowledge. Unlike existing multi-channel classification or clustering pipelines, UNITO can reproduce a similar contour compared to manual gating for each intermediate gating to achieve better interpretability and provide post hoc visual inspection. Beyond acting as a pioneering framework that uses image segmentation to do auto-gating, UNITO gives a fast and interpretable way to assign the cell subtype membership, and the speed of UNITO will not be impacted by the number of cells from each sample. The pre-gating and gating inference takes approximately 2 minutes for each sample using our pre-defined 9 gates system, and it can also adapt to any sequential prediction with different configurations.

Serum Magnesium Levels Are Negatively Associated with Obesity and Abdominal Obesity in Type 2 Diabetes Mellitus: A Real-World Study.

Background: There remains controversy over the relationship between serum magnesium levels and obesity in type 2 diabetes mellitus (T2DM). Therefore, the aim of this study was to assess whether there is any association of serum magnesium levels with obesity and abdominal obesity in T2DM. Methods: This cross-sectional, real-world study was conducted in 8,010 patients with T2DM, which were stratified into quintiles according to serum magnesium levels. The clinical characteristics and the prevalence of obesity and abdominal obesity were compared across serum magnesium quintiles in T2DM. Regression analyses were used to evaluate the relationship of serum magnesium with obesity and abdominal obesity in T2DM (clinical trial registration number: ChiCTR1800015893). Results: After adjustment for age, sex, and duration of diabetes, the prevalence of obesity and abdominal obesity was significantly declined across magnesium quintiles (obesity: 51.3%, 50.8%, 48.9%, 45.3%, and 43.8%, respectively, P<0.001 for trend; abdominal obesity: 71.5%, 70.5%, 68.2%, 66.4%, and 64.5%, respectively, P=0.001 for trend). After controlling for confounders, there were clearly negative associations of serum magnesium levels and quintiles with obesity and abdominal obesity in T2DM. Moreover, C-reactive protein partly mediates the effect of serum magnesium on obesity and abdominal obesity (P=0.016 and P=0.004, respectively). Conclusion: The significantly negative relationship between serum magnesium and the risk of obesity and abdominal obesity was observed in T2DM. Furthermore, the independently negative association of serum magnesium with obesity may be explained by its anti-inflammatory functions. Serum magnesium levels may be applied to assess the risk of obesity and abdominal obesity in T2DM.

Association of composite dietary antioxidant index with mortality in adults with hypertension: evidence from NHANES.

Objective: The objective of this study is to assess the correlation between composite dietary antioxidant index (CDAI) with all-cause mortality and cause-specific mortality in adults with hypertension. Methods: The cohort study comprised adult participants with hypertension from the NHANES database, spanning 9 cycles from 2001 to 2018. Follow-up was conducted until December 31, 2019. Multi-variable Cox regression analysis was utilized to ascertain hazard ratios (HR) and their corresponding 95% confidence intervals, evaluating the relationship between CDAI and the risks of all-cause and cause-specific mortality. To further investigate the association between CDAI and mortality rates in adults with hypertension, Kaplan-Meier survival curves, restricted cubic splines (RCS), subgroup analyses and sensitivity analyses were employed. Results: The analysis included 16,713 adults with hypertension (mean age 56.93 ± 0.23 years, 8,327 [49.61%] male). During the mean follow-up time 102.11 ± 1.22 months, with 3,908 (18.08%) all-cause mortality occurred, 1,082 (4.84%) cardiovascular mortality and 833 (3.80%) cancer mortality. Compared to the lowest quartile of CDAI, the weighted multivariate hazard ratios of participants in the highest quartile was 0.77 (95% CI, 0.68-0.87) for all-cause mortality, 0.83 (95% CI, 0.67-1.04) for cardiovascular mortality, and 0.64 (95% CI, 0.50-0.82) for cancer mortality. RCS analysis demonstrated a nonlinear association of CDAI with all-cause and cancer mortality, and a linear association between CDAI and cardiovascular mortality. The results were robust in subgroup analyses and sensitivity analyses. Conclusion: Higher CDAI is associated with reduced all-cause mortality, cardiovascular mortality, and cancer mortality in hypertensive adults. Our findings highlight the importance of an antioxidant diet in improving outcomes in adults with hypertension.

Federated Learning With Only Positive Labels by Exploring Label Correlations.

Federated learning (FL) aims to collaboratively learn a model by using the data from multiple users under privacy constraints. In this article, we study the multilabel classification (MLC) problem under the FL setting, where trivial solution and extremely poor performance may be obtained, especially when only positive data with respect to a single class label is provided for each client. This issue can be addressed by adding a specially designed regularizer on the server side. Although effective sometimes, the label correlations are simply ignored and thus suboptimal performance may be obtained. Besides, it is expensive and unsafe to exchange user's private embeddings between server and clients frequently, especially when training model in the contrastive way. To remedy these drawbacks, we propose a novel and generic method termed federated averaging (FedAvg) by exploring label correlations (FedALCs). Specifically, FedALC estimates the label correlations in the class embedding learning for different label pairs and utilizes it to improve the model training. To further improve the safety and also reduce the communication overhead, we propose a variant to learn fixed class embedding for each client, so that the server and clients only need to exchange class embeddings once. Extensive experiments on multiple popular datasets demonstrate that our FedALC can significantly outperform the existing counterparts.

Effect of anterior segment structure on vault and position after implantable collamer lens implantation.

PURPOSE: To evaluate the effect of anterior-segment structure on vault and position after implantable collamer lens (ICL) implantation using ultrasound biomicroscopy. METHODS: The retrospective case-control study included insufficient vault eyes (<250 µm), ideal vault eyes (250-750 µm), and excessive vault eyes (>750 µm). The preoperative biometric parameters of the anterior-segment structure and basic data between the three groups were analyzed using one-way analysis of variance. RESULTS: There were significant differences ( P < 0.05) between the three groups in maximum ciliary body thickness (CBT max ), iris-zonule distance (IZD), and trabecular-ciliary angle (TCA). The vault gradually decreased as CBT max decreased and TCA increased. In the pairwise comparison, the CBT max comparison between the insufficient vault (<250 µm) group and the excessive vault (>750 µm) group was statistically significant ( P = 0.024, 95% CI: -0.17-0.017 µm); the TCA comparison between the insufficient vault (<250 µm) group and the excessive vault (>750 µm) group was statistically significant ( P = 0.005, 95% CI: 1.78°-12.15°); The IZD comparison between the insufficient vault (<250 µm) group and the excessive vault (>750 µm) group was statistically significant ( P = 0.037, 95% CI: 0.0027-0.1119 µm). The analysis of 284 ICL haptics locations showed that there were 16.67%, 32.69%, and 70.83% haptics located in the ciliary sulcus in three groups, respectively. CONCLUSION: The vault and ICL haptics position are related to anterior-segment structure. A thinner and posteriorly positioned ciliary body would increase the risk of low vault and fewer ICL haptics located in the ciliary sulcus after ICL implantation. This provides guidance for the selection of the ICL size and placement position before surgery.

Multiple instance learning for eosinophil quantification of sinonasal histopathology images: A hierarchical determination on whole slide images.

KEY POINTS: We proposed a hierarchical framework including an unsupervised candidate image selection and a weakly supervised patch image detection based on multiple instance learning (MIL) to effectively estimate eosinophil quantities in tissue samples from whole slide images. MIL is an innovative approach that can help deal with the variability in cell distribution detection and enable automated eosinophil quantification from sinonasal histopathological images with a high degree of accuracy. The study lays the foundation for further research and development in the field of automated histopathological image analysis, and validation on more extensive and diverse datasets will contribute to real-world application.

General Fabrication of Robust Alloyed Metal Anodes for High-Performance Metal Batteries.

Revitalizing metal anodes for rechargeable batteries confronts challenges such as dendrite formation, limited cyclicity, and suboptimal energy density. Despite various efforts, a practical fabrication method for dendrite-free metal anodes remains unavailable. Herein, focusing on Li as exemplar, a general strategy is reported to enhance reversibility of the metal anodes by forming alloyed metals, which is achieved by induction heating of 3D substrate, lithiophilic metals, and Li within tens of seconds. It is demonstrated that preferred alloying interactions between substrates and lithiophilic metals created a lithiophilic metal-rich region adjacent to the substrate, serving as ultrastable lithiophilic host to guide dendrite-free deposition, particularly during prolonged high-capacity cycling. Simultaneously, an alloying between lithiophilic metals and Li creates a Li-rich region adjacent to electrolyte that reduces nucleation overpotential and constitutes favorable electrolyte-Li interface. The resultant composite Li anodes paired with high areal loading LiNi0.8Co0.1Mn0.1O2 cathodes achieve superior cycling stability and remarkable energy density above 1200 Wh L-1 (excluding packaging). Furthermore, this approach shows broader applicability to other metal anodes plagued by dendrite-related challenges, such as Na and Zn. Overall, this work paves the way for development of commercially viable metal-based batteries that offer a combination of safety, high energy density, and durability.

A novel ESIPT fluorescent probe for early detection and assessment of ferroptosis-mediated acute kidney injury via peroxynitrite fluctuation.

BACKGROUND: Acute kidney injury (AKI) poses a severe risk to public health, mostly manifested by damage and death of renal tubular epithelial cells. However, routine blood examination, a conventional approach for clinical detection of AKI, is not available for identifying early-stage AKI. Plenty of reported methods were lack of early biomarkers and real time evaluation tools, which resulted in a vital challenge for early diagnosis of AKI. Therefore, developing novel probes for early detection and assessment of AKI is exceedingly crucial. RESULTS: Based on ESIPT mechanism, a new fluorescent probe (MEO-NO) with 2-(2'-hydroxyphenyl) benzothiazole (HBT) derivatives as fluorophore has been synthesized for dynamic imaging peroxynitrite (ONOO-) levels in ferroptosis-mediated AKI. Upon the addition of ONOO-, MEO-NO exhibited obvious fluorescence changes, a significant Stokes shift (130 nm) and rapid response (approximately 45 s), and featured exceptional sensitivity (LOD = 7.28 nM) as well as high selectivity from the competitive species at physiological pH. In addition, MEO-NO was conducive to the biological depth imaging ONOO- in cells, zebrafish, and mice. Importantly, MEO-NO could monitor ONOO- levels during sorafenib-induced ferroptosis and CP-induced AKI. With the assistance of MEO-NO, we successfully visualized and tracked ONOO- variations for early detection and assessment of ferroptosis-mediated AKI in cells, zebrafish and mice models. SIGNIFICANCE AND NOVELTY: Benefiting from the superior performance of MEO-NO, experimental results further demonstrated that the levels of ONOO- was overexpressed during ferroptosis-mediated AKI in cells, zebrafish, and mice models. The developed novel probe MEO-NO provided a strong visualization tool for imagining ONOO-, which might be a potential method for the prevention, diagnosis, and treatment of ferroptosis-mediated AKI.

Mineralocorticoid Receptor Antagonists in Patients With Heart Failure and Impaired Renal Function.

BACKGROUND: Kidney dysfunction often leads to reluctance to start or continue life-saving heart failure (HF) therapy. OBJECTIVES: This study sought to examine the efficacy and safety of mineralocorticoid receptor antagonists (MRAs) in patients with HF with reduced ejection fraction experiencing significant kidney dysfunction. METHODS: We pooled individual patient data from the RALES (Randomized Aldactone Evaluation Study) and EMPHASIS-HF (Eplerenone in Mild Patients Hospitalization and Survival Study in Heart Failure) trials. The association between MRA treatment and outcomes was assessed according to whether the estimated glomerular filtration rate (eGFR) declined to <30 mL/min/1.73 m2 or not. The primary outcome was cardiovascular death or HF hospitalization. RESULTS: Among 4,355 patients included, 295 (6.8%) experienced a deterioration of eGFR after randomization to <30 mL/min/1.73 m2. These patients had more impaired baseline cardiac and kidney function (eGFR 47.3 ± 13.4 mL/min/1.73 m2 vs 70.5 ± 21.8 mL/min/1.73 m2) and had a higher risk of the primary outcome than patients without eGFR deterioration (HR: 2.49; 95% CI: 2.01-3.08; P < 0.001). However, the risk reduction in the primary outcome with MRA therapy was similar in those who experienced a decrease in eGFR to <30 mL/min/1.73 m2 (HR: 0.65; 95% CI: 0.43-0.99) compared with those who did not (HR: 0.63; 95% CI: 0.56-0.71) (Pinteraction = 0.87). In patients with a decrease in eGFR to <30 mL/min/1.73 m2, 21 fewer individuals (per 100 person-years) experienced the primary outcome with MRA treatment, vs placebo, compared with an excess of 3 more patients with severe hyperkalemia (>6.0 mmol/L). CONCLUSIONS: Because patients experiencing a decrease in eGFR to <30 mL/min/1.73 m2 are at very high risk, the absolute risk reduction with an MRA in these patients is large and this decline in eGFR should not automatically lead to treatment discontinuation.

[Transcriptomic characteristics analysis of bone from chronic osteomyelitis].

OBJECTIVE: To explore the molecular mechanism of chronic osteomyelitis and to clarify the role of MAPK signal pathway in the pathogenesis of chronic osteomyelitis, by collecting and analyzing the transcriptional information of bone tissue in patients with chronic osteomyelitis. METHODS: Four cases of traumatic osteomyelitis in limbs from June 2019 to June 2020 were selected, and the samples of necrotic osteonecrosis from chronic osteomyelitis (necrotic group), and normal bone tissue (control group) were collected. Transcriptome information was collected by Illumina Hiseq Xten high throughput sequencing platform, and the gene expression in bone tissue was calculated by FPKM. The differentially expressed genes were screened by comparing the transcripts of the Necrotic group and control group. Genes were enriched by GO and KEGG. MAP3K7 and NFATC1 were selected as differential targets in the verification experiments, by using rat osteomyelitis animal model and immunohistochemical analysis. RESULTS: A total of 5548 differentially expressed genes were obtained by high throughput sequencing by comparing the necrotic group and control group, including 2701 up-regulated and 2847 down-regulated genes. The genes enriched in MAPK pathway and osteoclast differentiation pathway were screened, the common genes expressed in both MAPK and osteoclast differentiation pathway were (inhibitor of nuclear factor κ subunit Beta, IκBKß), (mitogen-activated protein kinase 7, MAP3K7), (nuclear factor of activated t cells 1, NFATC1) and (nuclear factor Kappa B subunit 2, NFκB2). In rat osteomyelitis model, MAP3K7 and NFATC1 were highly expressed in bone marrow and injured bone tissue. CONCLUSION: Based on the transcriptome analysis, the MAPK signaling and osteoclast differentiation pathways were closely related to chronic osteomyelitis, and the key genes IκBKß, MAP3K7, NFATC1, NFκB2 might be new targets for clinical diagnosis and therapy of chronic osteomyelitis.