bluuebunny/crossref_metadata_2025 huggingface dataset:
Evolutionary Relationships Between Dysregulated Genes in Oral Squamous Cell Carcinoma and Oral Microbiota
Authors: Fang Yang , Yang Yi , Liu Chengcheng
Published: July 2022

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers in the world. Changes in the composition and abundance of oral microbiota are associated with the development and metastasis of OSCC. To elucidate the exact roles of the oral microbiota in OSCC, it is essential to reveal the evolutionary relationships between the dysregulated genes in OSCC progression and the oral microbiota. Thus, we interrogated the microarray and high-throughput sequencing datasets to obtain the transcriptional landscape of OSCC. After identifying differentially expressed genes (DEGs) with three different methods, pathway and functional analyses were also performed. A total of 127 genes were identified as common DEGs, which were enriched in extracellular matrix organization and cytokine related pathways. Furthermore, we established a predictive pipeline for detecting the coevolutionary of dysregulated host genes and microbial proteomes based on the homology method, and this pipeline was employed to analyze the evolutionary relations between the seven most dysregulated genes (MMP13, MMP7, MMP1, CXCL13, CRISPO3, CYP3A4, and CRNN) and microbiota obtained from the eHOMD database. We found that cytochrome P450 3A4 (CYP3A4), a member of the cytochrome P450 family of oxidizing enzymes, was associated with 45 microbes from the eHOMD database and involved in the oral habitat of Comamonas testosteroni and Arachnia rubra. The peptidase M10 family of matrix metalloproteinases (MMP13, MMP7, and MMP1) was associated with Lacticaseibacillus paracasei, Lacticaseibacillus rhamnosus, Streptococcus salivarius, Tannerella sp._HMT_286, and Streptococcus infantis in the oral cavity. Overall, this study revealed the dysregulated genes in OSCC and explored their evolutionary relationship with oral microbiota, which provides new insight for exploring the microbiota–host interactions in diseases.
Monocyte Metabolism and Function in Patients Undergoing Cardiac Surgery
Authors: Mayer Daniel , Altvater Marc , Schenz Judith , Arif Rawa , Karck Matthias , Leuschner Florian , Weigand Markus A. , Uhle Florian , Lichtenstern Christoph
Published: July 2022

Objective

Cardiopulmonary bypass (CPB) can lead to systemic inflammation, which is associated with higher morbidity. Therefore, we investigated the metabolism of isolated blood monocytes before and after CPB compared to healthy controls.

Methods

In this prospective, monocentric, observational study, we included 30 patients undergoing CPB and 20 controls. We isolated monocytes from heparinized blood and investigated their metabolism by using Seahorse technology before (t0), 4 h (t4), and 24 h (t24) after the start of the CPB. We also examined programmed cell death 1 ligand (PD-L1), PD-L2, V-domain Ig suppressor of T cell activation (VISTA), and human leukocyte antigen-DR isotype (HLA-DR) using fluorescence-activated cell sorting analysis. Additionally, we investigated plasma cytokine levels in patients without and after ex vivo stimulation.

Results

CPB-induced inflammatory responses are shown by significantly elevated plasma interleukin-6 levels in the CPB group compared to baseline and controls [t0: 0 ng/ml (95%CI 0-0 ng/ml); t4: 0.16 ng/ml (95%CI 0.1-0.197 ng/ml), p < 0.0001; t24: 0.11 ng/ml (95% CI 0.1-0.16 ng/ml), p < 0.0001, and controls: 0 ng/ml (95% CI 0-0 ng/ml)]. The cytokine release in the ex vivo stimulation is reduced for lipopolysaccharide stimulation at t4 [t0: 35.68 ng/ml (95% CI 22.17-46.57 ng/ml) vs. t4: 15.02 (95% CI 10.25-24.78 ng/ml), p < 0.0001]. Intracellular metabolism of monocytes after CPB showed a protracted shift to aerobic glycolysis [t0: 179.2 pmol/min (95% CI 138.0-205.1 pmol/min) vs. t24: 250.1 pmol/min (95% CI 94.8-300.2 pmol/min), p < 0.0001]. Additionally, we observed an altered metabolism in monocytes in patients undergoing cardiac surgery compared to controls even before any surgical procedure [t0: 179.2 pmol/min (95% CI 138.0-205.1) vs. controls 97.4 (95% CI 59.13-144.6 pmol/min), p = 0.0031].

Conclusion

After CPB, patients' monocytes show a shift in metabolism from oxidative phosphorylation to aerobic glycolysis, which is associated with energy-demanding and proinflammatory processes. This is the first study to show changes in monocyte immunometabolism in cardiac surgery. Monocytes of patients undergoing cardiac surgery were leaning toward aerobic glycolysis even before any surgical procedure was conducted. Leaving the question of the pathophysiological mechanisms for future studies to be investigated and paving the way for potential therapy approaches preventing inflammatory effects of CPB.
Cost-Effectiveness of the Pharmacist-Managed Warfarin Therapy vs. Standard Care for Patients With Mechanical Mitral Valve Prostheses: An Egyptian Healthcare Perspective
Authors: Batran Radwa Ahmed , Sabri Nagwa Ali , Ali Ihab , Fahmy Sarah Farid
Published: July 2022

Background

Despite warfarin therapy had been used for decades for patients with mechanical mitral valve prostheses (MMVPs), serious and life-threatening complications are still reported worldwide with a significant economic burden. This study is aimed at assessing the clinical and the cost-effectiveness of adopting pharmacist-managed warfarin therapy (PMWT) services for optimizing warfarin treatment in Egypt.

Methods

A prospective randomized trial in which 59 patients with MMVPs were randomly assigned to receive the PMWT services or the standard care and followed up for 1 year. The primary outcome was percentage time in the therapeutic range (TTR). For the cost-effectiveness analysis, a Markov cohort process model with nine mutually exclusive health states was developed from a medical provider’s perspective. A lifetime horizon was applied. All costs and outcomes were discounted at 3.5% annually.

Results

The study results revealed a significantly higher median TTR in the intervention group as compared to the control group; 96.8% [interquartile range (IQR) 77.9–100%] vs. 73.1% (52.7–95.1%), respectively, p = 0.008. A significant association between standard care and poor anticoagulation control (p = 0.021) was demonstrated by the multivariate regression analysis. For the cost-effectiveness analysis, the total cumulative quality-adjusted life-years (QALYs) and total costs per patient were 21.53 and 10.43; 436.38 and 1,242.25 United States dollar (USD) in the intervention and the control groups, respectively, with an incremental cost-effectiveness ratio (ICER) of −72.5796 for the intervention group.

Conclusion

The PMWT strategy was proven to provide a significantly better anticoagulation control and to be a cost-saving approach in Egyptian patients with MMVPs. Nevertheless, the dominance of this strategy is sustained by maintaining the therapeutic International Normalized Ratio (INR) control within the recommended range. Our findings will benefit Egyptian policy-makers who may seek novel health strategies for better resource allocation.

Clinical Trial Registration

[ClinicalTrials.gov], identifier [NCT04409613].
Gender Differences in Cardiogenic Shock Patients: Clinical Features, Risk Prediction, and Outcomes in a Hub Center
Authors: Lozano-Jiménez Sara , Iranzo-Valero Reyes , Segovia-Cubero Javier , Gómez-Bueno Manuel , Rivas-Lasarte Mercedes , Mitroi Cristina , Escudier-Villa Juan Manuel , Oteo-Dominguez Juan Francisco , Vieitez-Florez Jose María , Villar-García Susana , Hernández-Pérez Francisco José
Published: July 2022

Introduction

There is scarce knowledge about gender differences in clinical presentation, management, use of risk stratification tools and prognosis in cardiogenic shock (CS) patients.

Purpose

The primary endpoint was to investigate the differences in characteristics, management, and in-hospital mortality according to gender in a cohort of CS patients admitted to a tertiary hub center. The secondary endpoint was to evaluate the prognostic performance of the Society of Cardiovascular Angiography and Interventions (SCAI) classification in predicting in-hospital mortality according to sex.

Methods

This is a retrospective single-Center cohort study of CS patients treated by a multidisciplinary shock team between September 2014 and December 2020. Baseline characteristics and clinical outcomes according to gender were registered. Discrimination of SCAI classification was assessed using the area under the receiver operating characteristic curve (AUC).

Results

Overall, 163 patients were included, 39 of them female (24%). Mean age of the overall cohort was 55 years (44–62), similar between groups. Compared with men, women were less likely to be smokers and the prevalence of COPD and diabetes mellitus was significantly lower in this group (p < 0.05). Postcardiotomy (44 vs. 31%) and fulminant myocarditis (13 vs. 2%) were more frequent etiologies in females than in males (p = 0.01), whereas acute myocardial infarction was less common among females (13 vs. 33%). Regarding management, the use of temporary mechanical circulatory support, mechanical ventilation, or renal replacement therapy was frequent and no different between the groups (88, 87, and 49%, respectively, in females vs. 42, 91, and 41% in males, p > 0.05). In-hospital survival in the overall cohort was 53%, without differences between groups (52% in females vs. 55% in males, p = 0.76). Most of the patients (60.7%) were in SCAIE at presentation without differences between sexes. The SCAI classification showed a moderate ability for predicting in-hospital mortality (overall, AUC: 0.653, 95% CI 0.582–0.725). The AUC was 0.636 for women (95% CI 0.491–0.780) and 0.658 for men (95% CI 0.575–0.740).

Conclusions

Only one in four of patients treated at a dedicated CS team were female. This may reflect differences in prevalence of severe heart disease at young (<65) ages, although a patient-selection bias cannot be ruled out. In this very high-risk CS population of multiple etiologies, overall, in-hospital survival was slightly above 50% and showed no differences between sexes. Treatment approaches, procedures, and SCAI risk stratification performance did not show gender disparities among treated patients.
Epi-Drugs in Heart Failure
Authors: Gorica Era , Mohammed Shafeeq A. , Ambrosini Samuele , Calderone Vincenzo , Costantino Sarah , Paneni Francesco
Published: July 2022

Unveiling the secrets of genome’s flexibility does not only foster new research in the field, but also gives rise to the exploration and development of novel epigenetic-based therapies as an approach to alleviate disease phenotypes. A better understanding of chromatin biology (DNA/histone complexes) and non-coding RNAs (ncRNAs) has enabled the development of epigenetic drugs able to modulate transcriptional programs implicated in cardiovascular diseases. This particularly applies to heart failure, where epigenetic networks have shown to underpin several pathological features, such as left ventricular hypertrophy, fibrosis, cardiomyocyte apoptosis and microvascular dysfunction. Targeting epigenetic signals might represent a promising approach, especially in patients with heart failure with preserved ejection fraction (HFpEF), where prognosis remains poor and breakthrough therapies have yet to be approved. In this setting, epigenetics can be employed for the development of customized therapeutic approaches thus paving the way for personalized medicine. Even though the beneficial effects of epi-drugs are gaining attention, the number of epigenetic compounds used in the clinical practice remains low suggesting that more selective epi-drugs are needed. From DNA-methylation changes to non-coding RNAs, we can establish brand-new regulations for drug targets with the aim of restoring healthy epigenomes and transcriptional programs in the failing heart. In the present review, we bring the timeline of epi-drug discovery and development, thus highlighting the emerging role of epigenetic therapies in heart failure.
Multimodal Approach for the Prediction of Atrial Fibrillation Detected After Stroke: SAFAS Study
Authors: Garnier Lucie , Duloquin Gauthier , Meloux Alexandre , Benali Karim , Sagnard Audrey , Graber Mathilde , Dogon Geoffrey , Didier Romain , Pommier Thibaut , Vergely Catherine , Béjot Yannick , Guenancia Charles
Published: July 2022

Background

Intensive screening for atrial fibrillation (AF) has led to a better recognition of this cause in stroke patients. However, it is currently debated whether AF Detected After Stroke (AFDAS) has the same pathophysiology and embolic risk as prior-to-stroke AF. We thus aimed to systematically approach AFDAS using a multimodal approach combining clinical, imaging, biological and electrocardiographic markers.

Methods

Patients without previously known AF admitted to the Dijon University Hospital (France) stroke unit for acute ischemic stroke were prospectively enrolled. The primary endpoint was the presence of AFDAS at 6 months, diagnosed through admission ECG, continuous electrocardiographic monitoring, long-term external Holter during the hospital stay, or implantable cardiac monitor if clinically indicated after discharge.

Results

Of the 240 included patients, 77 (32%) developed AFDAS. Compared with sinus rhythm patients, those developing AFDAS were older, more often women and less often active smokers. AFDAS patients had higher blood levels of NT-proBNP, osteoprotegerin, galectin-3, GDF-15 and ST2, as well as increased left atrial indexed volume and lower left ventricular ejection fraction. After multivariable analysis, galectin-3 ≧ 9 ng/ml [OR 3.10; 95% CI (1.03–9.254), p = 0.042], NT-proBNP ≧ 290 pg/ml [OR 3.950; 95% CI (1.754–8.892, p = 0.001], OPG ≥ 887 pg/ml [OR 2.338; 95% CI (1.015–5.620), p = 0.046) and LAVI ≥ 33.5 ml/m2 [OR 2.982; 95% CI (1.342–6.625), p = 0.007] were independently associated with AFDAS.

Conclusion

A multimodal approach combining imaging, electrocardiography and original biological markers resulted in good predictive models for AFDAS. These results also suggest that AFDAS is probably related to an underlying atrial cardiopathy.

Clinical Trial Registration

[www.ClinicalTrials.gov], identifier [NCT03570060].
Effects of Oral Glucose-Lowering Agents on Gut Microbiota and Microbial Metabolites
Authors: Wang Dongmei , Liu Jieying , Zhou Liyuan , Zhang Qian , Li Ming , Xiao Xinhua
Published: July 2022

The current research and existing facts indicate that type 2 diabetes mellitus (T2DM) is characterized by gut microbiota dysbiosis and disturbed microbial metabolites. Oral glucose-lowering drugs are reported with pleiotropic beneficial effects, including not only a decrease in glucose level but also weight loss, antihypertension, anti-inflammation, and cardiovascular protection, but the underlying mechanisms are still not clear. Evidence can be found showing that oral glucose-lowering drugs might modify the gut microbiome and thereby alter gastrointestinal metabolites to improve host health. Although the connections among gut microbial communities, microbial metabolites, and T2DM are complex, figuring out how antidiabetic agents shape the gut microbiome is vital for optimizing the treatment, meaningful for the instruction for probiotic therapy and gut microbiota transplantation in T2DM. In this review, we focused on the literatures in gut microbiota and its metabolite profile alterations beneficial from oral antidiabetic drugs, trying to provide implications for future study in the developing field of these drugs, such as combination therapies, pre- and probiotics intervention in T2DM, and subjects with pregestational diabetes and gestational diabetes mellitus.
Sex-Specific Associations Between Low Muscle Mass and Glucose Fluctuations in Patients With Type 2 Diabetes Mellitus
Authors: Shi Xiulin , Liu Wenjuan , Zhang Lulu , Xiao Fangsen , Huang Peiying , Yan Bing , Zhang Yiping , Su Weijuan , Jiang Qiuhui , Lin Mingzhu , Liu Wei , Li Xuejun
Published: July 2022

Objective

Studies have shown that sex differences in lean mass, concentrations of sex hormones, and lifestyles influence cle health and glucose metabolism. We evaluated the sex-specific association between low muscle mass and glucose fluctuations in hospitalized patients with type 2 diabetes mellitus (T2DM) receiving continuous subcutaneous insulin infusion (CSII) therapy.

Methods

A total of 1084 participants were included. Body composition was determined by dual-energy X-ray absorptiometry. Intraday blood glucose fluctuation was estimated by the Largest amplitude of glycemic excursions (LAGE) and standard deviation of blood glucose (SDBG).

Results

The prevalence of low muscle mass was higher in males than in females (p<0.001). There was a significant sex-specific interaction between the status of low muscle mass and glucose fluctuations (LAGE and SDBG) (p for interaction=0.025 and 0.036 for SDBG and LAGE, respectively). Among males, low muscle mass was significantly associated with a higher LAGE and SDBG (difference in LAGE: 2.26 [95% CI: 1.01 to 3.51], p < 0.001; difference in SDBG: 0.45 [95% CI: 0.25 to 0.65], p < 0.001) after adjustment for HbA1c, diabetes duration, hyperlipidemia, diabetic peripheral neuropathy, diabetic nephropathy, and cardiovascular disease. These associations remained significant after further adjustment for age and C-peptide. Among females, low muscle mass was not associated with LAGE or SDBG after adjustment for all covariates.

Conclusion

The prevalence of low muscle mass was higher in males than in females. Low muscle mass was significantly associated with higher LAGE and SDBG among males, but not females.
Optimization for Transformer District Operation Considering Carbon Emission and Differentiated Demand Response
Authors: Jia Dexiang , Zhou Yu , Wang Zhongdong , Ding Yuhao , Gao Hongda , Liu Jianye , Lv Ganyun
Published: July 2022

With the promotion of the “dual carbon” goal, a large number of distributed photovoltaic power are connected to the distribution network. Since the current operation optimization of the low-voltage transformer district is based on single objectives such as the economy and power reliability, the model is relatively simple and difficult to adapt to the large-scale access of photovoltaics. Therefore, this article comprehensively considers carbon emissions, different load characteristics, and differentiated demand response of the district. An optimization method for low-voltage transformer district operation under the dual-carbon background is proposed. First, the typical structure of a low-voltage transformer district is introduced. Second, the load types and characteristics of the low-voltage transformer district are analyzed, and differentiated demand response models are established for different types of loads. Finally, taking the minimum economic cost and carbon emission as the objective, the low-voltage transformer district operation optimization model considering carbon emission and differentiated demand response is established by considering the voltage overrun of the photovoltaic access point, substation capacity constraint, and carbon emission constraint. The simulation results show that the model can effectively reduce the economic cost and carbon emissions of the low-voltage transformer district, achieve more than 95% reasonable utilization rate of new energy in the low-voltage transformer district, improve the lateral time distribution of load in the low-voltage transformer district, and provide an effective means for low-carbon scheduling of distribution networks.
An AGC Dynamic Optimization Method Based on Proximal Policy Optimization
Authors: Liu Zhao , Li Jiateng , Zhang Pei , Ding Zhenhuan , Zhao Yanshun
Published: July 2022

The increasing penetration of renewable energy introduces more uncertainties and creates more fluctuations in power systems than ever before, which brings great challenges for automatic generation control (AGC). It is necessary for grid operators to develop an advanced AGC strategy to handle fluctuations and uncertainties. AGC dynamic optimization is a sequential decision problem that can be formulated as a discrete-time Markov decision process. Therefore, this article proposes a novel framework based on proximal policy optimization (PPO) reinforcement learning algorithm to optimize power regulation among each AGC generator in advance. Then, the detailed modeling process of reward functions and state and action space designing is presented. The application of the proposed PPO-based AGC dynamic optimization framework is simulated on a modified IEEE 39-bus system and compared with the classical proportional−integral (PI) control strategy and other reinforcement learning algorithms. The results of the case study show that the framework proposed in this article can make the frequency characteristic better satisfy the control performance standard (CPS) under the scenario of large fluctuations in power systems.
The Impact of Country Risks on the Dependence Patterns of International Cobalt Trade: A Network Analysis Method
Authors: Shi Shuaiwei , Sun Qingru , Xi Zenglei , Hou Meiyi , Guo Jiaoyun
Published: July 2022

Cobalt is a key resource for the global energy transition, and the differences in the natural endowment of cobalt have led to frequent cobalt trade among countries. This study aims to reveal the dependence patterns of cobalt trade among countries and the impact of country risks (including political and economic risks) on the patterns. First, a cobalt import dependence network (CIDN) and a cobalt export dependence network (CEDN) are established using the network analysis method. Furthermore, this study uses network indicators to reveal the dependence patterns of cobalt trade among countries, and construct diversification indices of trade relations to further analyze the import source risk and the market concentration of cobalt trade. The results indicate that most cobalt importers have a high import source risk, and most cobalt exporters have a high market concentration. Finally, based on the panel regression methods, we reveal an interesting result showing that the dependence patterns of cobalt trade are significantly influenced by country risks. Specifically, on the one hand, for importers, an increase in political risk or economic risk has a negative impact on their dependence patterns of cobalt trade. On the other hand, for exporters, an increase in political risk or economic risk has different effects on their dependence patterns of cobalt trade. This study suggests that countries should pay more attention to the role of country risks in driving the dependence patterns when making cobalt trade policies.
Random Forest Classification Method for Predicting Intertidal Wetland Migration Under Sea Level Rise
Authors: Hughes Michael G. , Glasby Tim M. , Hanslow David J. , West Gregory J. , Wen Li
Published: July 2022

Intertidal wetlands such as mangrove and saltmarsh are increasingly susceptible to areal losses related to sea level rise. This exposure is potentially offset by processes that might enable wetlands to accrete in situ or migrate landward under sea level rise, and planning policies that might open new opportunities for migration. We present and demonstrate a method to predict intertidal wetland distribution in the present-day landscape using random forest classification models, and use these models to predict the intertidal wetland distribution in future landscapes under specified sea level scenarios. The method is demonstrably robust in predicting present-day intertidal wetland distribution, with moderate correlation or better between predicted and mapped wetland distributions occurring in nearly all estuaries and strong correlation or better occurring in more than half of the estuaries. Given the accuracy in predicting present-day wetland distribution the method is assumed to be informative in predicting potential future wetland distribution when combined with best available models of future sea level. The classification method uses a variety of hydro-geomorphological surrogates that are derived from digital elevation models, Quaternary geology or soils mapping and land use mapping, which is then constrained by a representation of the future sea level inside estuaries. It is anticipated that the outputs from applying the method would inform assessments of intertidal wetland vulnerability to sea level rise and guide planning for potential wetland migration pathways.
The Relationship Between Economic Growth and CO2 Emissions in EU Countries: A Cointegration Analysis
Authors: Onofrei Mihaela , Vatamanu Anca Florentina , Cigu Elena
Published: July 2022

This paper explores the dynamics of the relationship between economic growth and CO2 emissions in the 27 EU member states in a panel setting for the period 2000–2017. We use qualitative sequential methodology, involving empiric analysis that provides coherence and viability for our study, but also quantitative methods, including Dynamic Ordinary Least Squares (DOLS), unit root tests and cointegration techniques. The results suggest the existence of a long run cointegrating relationship between growth and CO2 emissions in EU countries and the DOLS method indicates a statistically significant effect of economic growth on CO2 emissions for both versions of estimators, revealing that on average, a 1% change in GDP leads to 0.072 change in CO2 emissions. The study also exhibits that higher income levels lead to increased demand for environmental protection and underline the need for designing environmental policies, capable to reduce emissions during periods of economic growth. Moreover, we find that the status of economic growth does not automatically diminish climate vulnerability in EU countries, only the correct type of growth does, thus being necessary that EU policymakers be aware of the energy cost pressure and to achieve economic growth in relationship with appropriate tools in terms of climate risk management.
Linking Responsible Leadership and Green Innovation: The Role of Knowledge Sharing and Leader-Member Exchange
Authors: Huo Chunhui , Safdar Muhammad Arslan , Akhtar Muhammad Waheed , Ahmed Misbah
Published: July 2022

This study aims to explore how to encourage employees to engage in green innovation (GI). Drawing upon social learning theory, a moderated mediation model was theorized and tested to determine how responsible leadership triggers GI in fostering knowledge sharing. Additionally, two critical processes were distinguished by introducing leader-member exchange (LMX) as a moderator. A multi wave study collected from 228 employees from hospitality sector organizations generally supported the predictions. Specifically, responsible leadership was a significant predictor of GI. Moreover, LMX strengthened knowledge sharing mechanisms and attenuated the relationship between responsible leadership and GI. The findings and the theoretical underpinning of this study shed new light on GI in a responsible way that shapes knowledge sharing among organizational members and provides practical implications for leaders determined to improve environmental sustainability in organizations.
Identification of Vesicle Transport Proteins via Hypergraph Regularized K-Local Hyperplane Distance Nearest Neighbour Model
Authors: Fan Rui , Suo Bing , Ding Yijie
Published: July 2022

The prediction of protein function is a common topic in the field of bioinformatics. In recent years, advances in machine learning have inspired a growing number of algorithms for predicting protein function. A large number of parameters and fairly complex neural networks are often used to improve the prediction performance, an approach that is time-consuming and costly. In this study, we leveraged traditional features and machine learning classifiers to boost the performance of vesicle transport protein identification and make the prediction process faster. We adopt the pseudo position-specific scoring matrix (PsePSSM) feature and our proposed new classifier hypergraph regularized k-local hyperplane distance nearest neighbour (HG-HKNN) to classify vesicular transport proteins. We address dataset imbalances with random undersampling. The results show that our strategy has an area under the receiver operating characteristic curve (AUC) of 0.870 and a Matthews correlation coefficient (MCC) of 0.53 on the benchmark dataset, outperforming all state-of-the-art methods on the same dataset, and other metrics of our model are also comparable to existing methods.
Lactate Metabolism-Associated lncRNA Pairs: A Prognostic Signature to Reveal the Immunological Landscape and Mediate Therapeutic Response in Patients With Colon Adenocarcinoma
Authors: Xiao Junbo , Wang Xiaotong , Liu Yajun , Liu Xiaowei , Yi Jun , Hu Jiuye
Published: July 2022

Background

Lactate metabolism is critically involved in the tumor microenvironment (TME), as well as cancer progression. It is important to note, however, that lactate metabolism-related long non-coding RNAs (laRlncRNAs) remain incredibly understudied in colon adenocarcinoma (COAD).

Methods

A gene expression profile was obtained from the Cancer Genome Atlas (TCGA) database to identify laRlncRNA expression in COAD patients. A risk signature with prognostic value was identified from TCGA and Gene Expression Omnibus (GEO) cohort based on laRlncRNA pairs by the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. Quantitative real-time polymerase chain reaction (qRT-PCR) and functional experiments were carried out to verify the expression of laRlncRNAs in COAD. The relationship of laRlncRNA pairs with immune landscape as well as the sensitivity of different therapies was explored.

Results

In total, 2378 laRlncRNAs were identified, 1,120 pairs of which were studied to determine their prognostic validity, followed by a risk signature established based on the screened 5 laRlncRNA pairs. The laRlncRNA pairs-based signature provided a better overall survival (OS) prediction than other published signatures and functioned as a prognostic marker for COAD patients. According to the calculated optimal cut-off point, patients were divided into high- and low-risk groups. The OS of COAD patients in the high-risk group were significantly shorter than that of those in the low-risk group (P=4.252e-14 in the TCGA cohort and P=2.865-02 in the GEO cohort). Furthermore, it remained an effective predictor of survival in strata of gender, age, TNM stage, and its significance persisted after univariate and multivariate Cox regressions. Additionally, the risk signature was significantly correlated with immune cells infiltration, tumor mutation burden (TMB), microsatellite instability (MSI) as well as immunotherapeutic efficacy and chemotherapy sensitivity. Finally, one of the laRlncRNA, LINC01315, promotes proliferation and migration capacities of colon cancer cells.

Conclusion

The newly identified laRlncRNAs pairs-based signature exhibits potential effects in predicting prognosis, deciphering patients’ immune landscape, and mediating sensitivity to immunotherapy and chemotherapy. Findings in our study may provide evidence for the role of laRlncRNAs pairs as novel prognostic biomarkers and potentially individualized therapy targets for COAD patients.
A Novel Type I Interferon Primed Dendritic Cell Subpopulation in TREX1 Mutant Chilblain Lupus Patients
Authors: Eugster Anne , Müller Denise , Gompf Anne , Reinhardt Susanne , Lindner Annett , Ashton Michelle , Zimmermann Nick , Beissert Stefan , Bonifacio Ezio , Günther Claudia
Published: July 2022

Heterozygous TREX1 mutations are associated with monogenic familial chilblain lupus and represent a risk factor for developing systemic lupus erythematosus. These interferonopathies originate from chronic type I interferon stimulation due to sensing of inadequately accumulating nucleic acids. We here analysed the composition of dendritic cell (DC) subsets, central stimulators of immune responses, in patients with TREX1 deficiency. We performed single-cell RNA-sequencing of peripheral blood DCs and monocytes from two patients with familial chilblain lupus and heterozygous mutations in TREX1 and from controls. Type I interferon pathway genes were strongly upregulated in patients. Cell frequencies of the myeloid and plasmacytoid DC and of monocyte populations in patients and controls were similar, but we describe a novel DC subpopulation highly enriched in patients: a myeloid DC CD1C+ subpopulation characterized by the expression of LMNA, EMP1 and a type I interferon- stimulated gene profile. The presence of this defined subpopulation was confirmed in a second cohort of patients and controls by flow cytometry, also revealing that an increased percentage of patient’s cells in the subcluster express costimulatory molecules. We identified a novel type I interferon responsive myeloid DC subpopulation, that might be important for the perpetuation of TREX1-induced chilblain lupus and other type I interferonopathies.
A Complementary Union of SARS-CoV2 Natural and Vaccine Induced Immune Responses
Authors: Torresi Joseph , Edeling Melissa A. , Nolan Terry , Godfrey Dale I.
Published: July 2022

Our understanding of the immune responses that follow SARS-CoV-2 infection and vaccination has progressed considerably since the COVID-19 pandemic was first declared on the 11th of March in 2020. Recovery from infection is associated with the development of protective immune responses, although over time these become less effective against new emerging SARS-CoV-2 variants. Consequently, reinfection with SARS-CoV-2 variants is not infrequent and has contributed to the ongoing pandemic. COVID-19 vaccines have had a tremendous impact on reducing infection and particularly the number of deaths associated with SARS-CoV-2 infection. However, waning of vaccine induced immunity plus the emergence of new variants has necessitated the use of boosters to maintain the benefits of vaccination in reducing COVID-19 associated deaths. Boosting is also beneficial for individuals who have recovered from COVID-19 and developed natural immunity, also enhancing responses immune responses to SARS-CoV-2 variants. This review summarizes our understanding of the immune responses that follow SARS-CoV-2 infection and vaccination, the risks of reinfection with emerging variants and the very important protective role vaccine boosting plays in both vaccinated and previously infected individuals.
Interleukins 4 and 21 Protect Anti-IgM Induced Cell Death in Ramos B Cells: Implication for Autoimmune Diseases
Authors: Hui Chin Wai , Wu Wai Chung , Leung Shui On
Published: July 2022

Interleukins 4 (IL-4) and 21 (IL-21) belong to the common gamma chain cytokine family which are highly involved in the progression of autoimmune diseases. While IL-4 is well known to be involved in the suppression of apoptosis of autoreactive B cells, the role played by IL-21 remains unclear. In the current study, we activated the human Burkitt’s lymphoma Ramos B cells with anti-IgM to mimic B cell hyperactivation observed in patients of autoimmune diseases. Consistent with other reported findings, anti-IgM led to the downregulation of proteins involved in B cell survival and proliferation, as well as the activation of caspase 3 activity and DNA damage, resulting in apoptotic cell death after 48-hour treatment. Although both IL-4 and IL-21 reversed anti-IgM-induced apoptosis and cell cycle arrest, they did soviadifferent mechanisms: while IL-4 could directly suppress anti-IgM-induced caspase 3 activation and marker indicative of DNA damage, IL-21 could induce B cell proliferation in the presence of anti-IgM. Importantly, IL-21 also suppressed activation induced cell death in human primary B cells. Pre-treatment with clinically validated JAK inhibitors completely reversed the effects of IL-4 and IL-21 to rescue anti-IgM induced cell death and DNA damage. The results indicate the underlying mechanisms of how IL-4 and IL-21 differentially promote survival of hyperactivated B cells and provide hints to treat autoimmune diseases.
The Therapeutic Potential of Targeting NIK in B Cell Malignancies
Authors: Haselager Marco V. , Eldering Eric
Published: July 2022

NF-κB-inducing kinase (NIK) is a key player in non-canonical NF-κB signaling, involved in several fundamental cellular processes, and is crucial for B cell function and development. In response to certain signals and ligands, such as CD40, BAFF and lymphotoxin-β activation, NIK protein stabilization and subsequent NF-κB activation is achieved. Overexpression or overactivation of NIK is associated with several malignancies, including activating mutations in multiple myeloma (MM) and gain-of-function in MALT lymphoma as a result of post-translational modifications. Consequently, drug discovery studies are devoted to pharmacologic modulation of NIK and development of specific novel small molecule inhibitors. However, disease-specific in vitro and in vivo studies investigating NIK inhibition are as of yet lacking, and clinical trials with NIK inhibitors remain to be initiated. In order to bridge the gap between bench and bedside, this review first briefly summarizes our current knowledge on NIK activation, functional activity and stability. Secondly, we compare current inhibitors targeting NIK based on efficacy and specificity, and provide a future perspective on the therapeutic potential of NIK inhibition in B cell malignancies.
Macrophages in Renal Injury, Repair, Fibrosis Following Acute Kidney Injury and Targeted Therapy
Authors: Chen Hui , Liu Na , Zhuang Shougang
Published: July 2022

Acute kidney injury (AKI) is a renal disease with a high incidence and mortality. Currently, there are no targeted therapeutics for preventing and treating AKI. Macrophages, important players in mammalian immune response, are involved in the multiple pathological processes of AKI. They are dynamically activated and exhibit a diverse spectrum of functional phenotypes in the kidney after AKI. Targeting the mechanisms of macrophage activation significantly improves the outcomes of AKI in preclinical studies. In this review, we summarize the role of macrophages and the underlying mechanisms of macrophage activation during kidney injury, repair, regeneration, and fibrosis and provide strategies for macrophage-targeted therapies.
IL-6-Driven pSTAT1 Response Is Linked to T Cell Features Implicated in Early Immune Dysregulation
Authors: Lambert Katharina , Diggins Kirsten E. , Jones Britta E. , Hundhausen Christian , Maerz Megan D. , Hocking Anne M. , Sanda Srinath , Greenbaum Carla J. , Linsley Peter S. , Cerosaletti Karen , Buckner Jane H.
Published: July 2022

Elevated levels and enhanced sensing of the pro-inflammatory cytokine interleukin-6 (IL-6) are key features of many autoimmune and inflammatory diseases. To better understand how IL-6 signaling may influence human T cell fate, we investigated the relationships between levels of components of the IL-6R complex, pSTAT responses, and transcriptomic and translational changes in CD4+ and CD8+ T cell subsets from healthy individuals after exposure to IL-6. Our findings highlight the striking heterogeneity in mbIL-6R and gp130 expression and IL-6-driven pSTAT1/3 responses across T cell subsets. Increased mbIL-6R expression correlated with enhanced signaling via pSTAT1 with less impact on pSTAT3, most strikingly in CD4+ naïve T cells. Additionally, IL-6 rapidly induced expression of transcription factors and surface receptors expressed by T follicular helper cells and altered expression of markers of apoptosis. Importantly, many of the features associated with the level of mbIL-6R expression on T cells were recapitulated both in the setting of tocilizumab therapy and when comparing donor CD4+ T cells harboring the genetic variant, IL6R Asp358Ala (rs2228145), known to alter mbIL-6R expression on T cells. Collectively, these findings should be taken into account as we consider the role of IL-6 in disease pathogenesis and translating IL-6 biology into effective therapies for T cell-mediated autoimmune disease.
Predicting Seabird Foraging Habitat for Conservation Planning in Atlantic Canada: Integrating Telemetry and Survey Data Across Thousands of Colonies
Authors: Ronconi Robert A. , Lieske David J. , McFarlane Tranquilla Laura A. , Abbott Sue , Allard Karel A. , Allen Brad , Black Amie L. , Bolduc François , Davoren Gail K. , Diamond Antony W. , Fifield David A. , Garthe Stefan , Gjerdrum Carina , Hedd April , Mallory Mark L. , Mauck Robert A. , McKnight Julie , Montevecchi William A. , Pollet Ingrid L. , Pratte Isabeau , Rail Jean-François , Regular Paul M. , Robertson Gregory J. , Rock Jennifer C. , Savoy Lucas , Shlepr Katherine R. , Shutler Dave , Symons Stephanie C. , Taylor Philip D. , Wilhelm Sabina I.
Published: July 2022

Conservation of mobile organisms is difficult in the absence of detailed information about movement and habitat use. While the miniaturization of tracking devices has eased the collection of such information, it remains logistically and financially difficult to track a wide range of species across a large geographic scale. Predictive distribution models can be used to fill this gap by integrating both telemetry and census data to construct distribution maps and inform conservation goals and planning. We used tracking data from 520 individuals of 14 seabird species in Atlantic Canada to first compare foraging range and distance to shorelines among species across colonies, and then developed tree-based machine-learning models to predict foraging distributions for more than 5000 breeding sites distributed along more than 5000 km of shoreline. Despite large variability in foraging ranges among species, tracking data revealed clusters of species using similar foraging habitats (e.g., nearshore vs. offshore foragers), and within species, foraging range was highly colony-specific. Even with this variability, distance from the nesting colony was an important predictor of distribution for nearly all species, while distance from coastlines and bathymetry (slope and ruggedness) were additional important predictors for some species. Overall, we demonstrated the utility of tree-based machine-learning approach when modeling tracking data to predict distributions at un-sampled colonies. Although tracking and colony data have some shortcomings (e.g., fewer data for some species), where results need to be interpreted with care in some cases, applying methods for modeling breeding season distributions of seabirds allows for broader-scale conservation assessment. The modeled distributions can be used in decisions about planning for offshore recreation and commercial activities and to inform conservation planning at regional scales.
Identification of Major Risk Factors and Non-linear Effects to the Development of Left Ventricular Hypertrophy in Chronic Kidney Disease by Constructing and Validation of Nomograms
Authors: Wu Zhongcai , Shi Mengxia , Wang Le , Yao Ying
Published: July 2022

Background

Left ventricular hypertrophy (LVH) is a common cardiovascular complication among chronic kidney disease (CKD) patients. The present study aimed to identify major independent risk factors and determine their contribution and relationship to LVH development.

Methods

Clinical and echocardiographic data of 2002 pre-dialytic CKD patients were retrospectively collected. Independent risk factors for LVH were identified using univariable and multivariable logistic regression. Nomograms together with restricted cubic splines method were employed to explore the effect size and possible non-linear relationship with regard to LVH. A simplified predictive model was constructed and its predictive ability was validated to demonstrate to which extent the identified risk factors accounted for LVH risk.

Results

Multivariable logistic regression identified age, body mass index (BMI), systolic blood pressure (SBP), eGFR and hemoglobin as independent influencing factors for LVH. Nomogram revealed BMI, SBP and hemoglobin concentration as the most important risk factors. Impaired renal function only showed obvious risk for LVH when eGFR declined below 30 ml/min/1.73 m2. Significant threshold effects existed for blood pressure and obesity that the risks for LVH doubled when SBP exceeded 160 mmHg or BMI exceeded 30 kg/m2. The predictive model constructed performed well on both the training and validation cohort using calibration curve, ROC curve and AUC value, with AUC above 0.80 for both the training cohort and the validation cohort.

Conclusions

With the help of nomogram model, we identified five independent factors that explain a large proportion of LVH risk in CKD patients. Among them, major contribution to LVH development was resulted from comorbidities and complications of CKD (hypertension, anemia, obesity) rather than eGFR reduction per se. Non-linear relationship and threshold relationship between eGFR, blood pressure, obesity and LVH risk were also identified.
In-Hospital Mortality of Sepsis Differs Depending on the Origin of Infection: An Investigation of Predisposing Factors
Authors: Pieroni Mark , Olier Ivan , Ortega-Martorell Sandra , Johnston Brian W. , Welters Ingeborg D.
Published: July 2022

Sepsis is a heterogeneous syndrome characterized by a variety of clinical features. Analysis of large clinical datasets may serve to define groups of sepsis with different risks of adverse outcomes. Clinical experience supports the concept that prognosis, treatment, severity, and time course of sepsis vary depending on the source of infection. We analyzed a large publicly available database to test this hypothesis. In addition, we developed prognostic models for the three main types of sepsis: pulmonary, urinary, and abdominal sepsis. We used logistic regression using routinely available clinical data for mortality prediction in each of these groups. The data was extracted from the eICU collaborative research database, a multi-center intensive care unit with over 200,000 admissions. Sepsis cohorts were defined using admission diagnosis codes. We used univariate and multivariate analyses to establish factors relevant for outcome prediction in all three cohorts of sepsis (pulmonary, urinary and abdominal). For logistic regression, input variables were automatically selected using a sequential forward search algorithm over 10 dataset instances. Receiver operator characteristics were generated for each model and compared with established prognostication tools (APACHE IV and SOFA). A total of 3,958 sepsis admissions were included in the analysis. Sepsis in-hospital mortality differed depending on the cause of infection: abdominal 18.93%, pulmonary 19.27%, and renal 12.81%. Higher average heart rate was associated with increased mortality risk. Increased average Mean Arterial Pressure (MAP) showed a reduced mortality risk across all sepsis groups. Results from the LR models found significant factors that were relevant for specific sepsis groups. Our models outperformed APACHE IV and SOFA scores with AUC between 0.63 and 0.74. Predictive power decreased over time, with the best results achieved for data extracted for the first 24 h of admission. Mortality varied significantly between the three sepsis groups. We also demonstrate that factors of importance show considerable heterogeneity depending on the source of infection. The factors influencing in-hospital mortality vary depending on the source of sepsis which may explain why most sepsis trials have failed to identify an effective treatment. The source of infection should be considered when considering mortality risk. Planning of sepsis treatment trials may benefit from risk stratification based on the source of infection.