[The hemodynamic study of internal jugular vein in patients with pulsatile tinnitus by color doppler sonography]

Objective:To investigate the hemodynamic changes of internal jugular vein（IJV） in patients with pulsatile tinnitus（PT） detected by color doppler sonography（CDS）. To explore the correlation of PT with IJV drainage dominance and the value of CDS in the diagnosis of PT. Method:Two hundred and twenty-one patients with PT were examined with the CDS and IJV compression test. The hemodynamic data of the upper, middle and lower IJV were compared with both PT side and unaffected side. The correlation of the PT with the IJV drainage dominance was analyzed. Result:A total of 125 cases（56.6%） of 221 patients with PT underwent ultrasonic IJV compression test with positive results, 96 cases（43.3%） with negative results. In the positive compression test group, the tinnitus disappeared after IJV being completely compressed in 109 cases（87.2%）, and in 16 cases（12.8%） after IJV being partial closure. The extent of sectional area reduction was: upper jugular vein（75.41 ±9.39）%, middle jugular vein（80.25±13.16）%, lower jugular vein（86.58± 7.53）%. The blood flow volume on the tinnitus side was obviously higher than that on the unaffected side（P=0.001）. In the negative compression test group, there was no significant difference in blood flow volume between tinnitus side and unaffected side. In the IJV positive compression test group, 54 cases（43.2%） were IJV drainage dominance combined, and 71 cases（56.8%） were non-dominant combined. In the IJV negative compression test group, 24 cases（25%） were IJV drainage dominance combined, and 72 cases（75%） were non-dominant combined. Conclusion:The positive result of IJV compression test using CDS and the flow volume of IJV on the affected side significantly higher than that on the unaffected side contributed to the diagnosis of venous PT. In this study, the IJV non-dominant side was more common in the venous PT.

P4671A well-defined subgroup of chronic thromboembolic pulmonary hypertension patients are positive for antiphospholipid antibodies

Introduction

Recurrent thromboembolism is a major risk factor for chronic thromboembolic pulmonary hypertension (CTEPH). Antiphospholipid antibodies (aPL) are a recognized risk factor for recurrent thromboembolism, however, their contribution to the development of CTEPH is currently unknown.

Purpose

The aim of this study was to evaluate the prevalence of antiphospholipid autoantibodies in a large cohort of consecutive patients with CTEPH.

Methods

Starting May 2013 to December 2018, 297 CTEPHpatients were consecutively enrolled. Diagnosis was confirmed with standardized right heart catheterization, 2D or/and 3D pulmonary angiography in all patients.All patients were also screened for thrombophilia including lupus anticoagulant IgG/IgM anticardiolipin antibodies and IgG/IgM aβ2GPI antibodies. Clinical and demographic characteristics, laboratory profile and hemodynamic data were compared between patients with aPL positive and aPL negative laboratory profiles.

Results

Overall, 297 consecutive patients with CTEPH were assessed for aPLs. Of these, 23 patients (7.7%) resulted positive for laboratory tests exploring aPL. Among patients with positive aPL, profile 17 (74%) were positive for all three test exploring aPL (LA+, aCL+, aB2GPI+). When compared to CTEPH patients without aPL test positivity, those positive for aPL were significantly younger (30.0 vs 55.6 years, p<0.0001), had a positive history of pulmonary embolism (95.6% vs 65.7%, p=0.003), deep venous thrombosis (78.3% vs 41.9%, p=0.0008) and were more frequently affected by other autoimmune diseases (43.5% vs 2.9%, p<0.0001). Moreover, positive aPL patients showed better hemodynamics on right heart catheterization, had more proximal lesions, and had more frequently level I and II surgical classification (82.6% vs 40.5%, p=0.0002) on pulmonary angiography.

Conclusions

These findings suggest that young patients with pulmonary embolism should be assessed for aPLs to guide anticoagulation therapy and prevent recurrences. Additionally, younger age, more proximal lesions and less severe hemodynamic profiles make majority patients with APS good candidates for PEA surgery.

Gene Co-Expression Networks Restructured Gene Fusion in Rhabdomyosarcoma Cancers

Rhabdomyosarcoma is subclassified by the presence or absence of a recurrent chromosome translocation that fuses the FOXO1 and PAX3 or PAX7 genes. The fusion protein (FOXO1-PAX3/7) retains both binding domains and becomes a novel and potent transcriptional regulator in rhabdomyosarcoma subtypes. Many studies have characterized and integrated genomic, transcriptomic, and epigenomic differences among rhabdomyosarcoma subtypes that contain the FOXO1-PAX3/7 gene fusion and those that do not; however, few investigations have investigated how gene co-expression networks are altered by FOXO1-PAX3/7. Although transcriptional data offer insight into one level of functional regulation, gene co-expression networks have the potential to identify biological interactions and pathways that underpin oncogenesis and tumorigenicity. Thus, we examined gene co-expression networks for rhabdomyosarcoma that were FOXO1-PAX3 positive, FOXO1-PAX7 positive, or fusion negative. Gene co-expression networks were mined using local maximum Quasi-Clique Merger (lmQCM) and analyzed for co-expression differences among rhabdomyosarcoma subtypes. This analysis observed 41 co-expression modules that were shared between fusion negative and positive samples, of which 17/41 showed significant up- or down-regulation in respect to fusion status. Fusion positive and negative rhabdomyosarcoma showed differing modularity of co-expression networks with fusion negative (n = 109) having significantly more individual modules than fusion positive (n = 53). Subsequent analysis of gene co-expression networks for PAX3 and PAX7 type fusions observed 17/53 were differentially expressed between the two subtypes. Gene list enrichment analysis found that gene ontology terms were poorly matched with biological processes and molecular function for most co-expression modules identified in this study; however, co-expressed modules were frequently localized to cytobands on chromosomes 8 and 11. Overall, we observed substantial restructuring of co-expression networks relative to fusion status and fusion type in rhabdomyosarcoma and identified previously overlooked genes and pathways that may be targeted in this pernicious disease.

[Analysis of the effect and influencing factors of customized sound on tinnitus]

Objective:To explore the effect and influencing factors of customized sound on tinnitus. Method:303 patients with tinnitus who received customized sound therapy for 6 months were enrolled in this study. Tinnitus handicap inventory (THI) was used to evaluate the treatment effect before treatment, 1 month, 2 months, 3 months, 4 months, 5 months and 6 months after treatment, and the influencing factors of sound treatment effect were analyzed. Result:The THI score decreased at the 1, 2, 3, 4, 5 and 6 months after treatment (P<0.01), compared with that before treatment. Gender, tinnitus type and presence or absence of voice allergy had no effect on THI score before treatment and 6 months after treatment (P>0.05). Conclusion:Customized sound therapy can reduce the score of THI in tinnitus patients, which supports that sound therapy can reduce the negative impact and disturbances produced by tinnitus, and its curative effect is not affected by gender, tinnitus type and presence or absence of voice allergy..

Correlation Analysis of Histopathology and Proteogenomics Data for Breast Cancer

Tumors are heterogeneous tissues with different types of cells such as cancer cells, fibroblasts, and lymphocytes. Although the morphological features of tumors are critical for cancer diagnosis and prognosis, the underlying molecular events and genes for tumor morphology are far from being clear. With the advancement in computational pathology and accumulation of large amount of cancer samples with matched molecular and histopathology data, researchers can carry out integrative analysis to investigate this issue. In this study, we systematically examine the relationships between morphological features and various molecular data in breast cancers. Specifically, we identified 73 breast cancer patients from the TCGA and CPTAC projects matched whole slide images, RNA-seq, and proteomic data. By calculating 100 different morphological features and correlating them with the transcriptomic and proteomic data, we inferred four major biological processes associated with various interpretable morphological features. These processes include metabolism, cell cycle, immune response, and extracellular matrix development, which are all hallmarks of cancers and the associated morphological features are related to area, density, and shapes of epithelial cells, fibroblasts, and lymphocytes. In addition, protein specific biological processes were inferred solely from proteomic data, suggesting the importance of proteomic data in obtaining a holistic understanding of the molecular basis for tumor tissue morphology. Furthermore, survival analysis yielded specific morphological features related to patient prognosis, which have a strong association with important molecular events based on our analysis. Overall, our study demonstrated the power for integrating multiple types of biological data for cancer samples in generating new hypothesis as well as identifying potential biomarkers predicting patient outcome. Future work includes causal analysis to identify key regulators for cancer tissue development and validating the findings using more independent data sets.

A promising form-stable phase change material prepared using cost effective pinecone biochar as the matrix of palmitic acid for thermal energy storage

A promising new form-stable phase change material (PA/PB) was fabricated using pinecone biochar (PB) as the supporting material of palmitic acid (PA). The biochar of PB with large surface area was produced by forest residue of pinecone, and it was cheap, environment friendly and easy to prepare. The PB was firstly utilized as the supporter of PA and the characterizations of PA/PB were analyzed by the BET, SEM, XRD, DSC, TGA, FT-IR and thermal conductivity tester. The results demonstrated that the PA was physically absorbed by the PB and the crystal structure of the PA was not destroyed. The results of DSC showed that the fusing and crystallization points of the form-stable phase change material with the maximum content of PA (PA/PB-4) were 59.25 °C and 59.13 °C, and its fusing and freezing latent heat were 84.74 kJ/kg and 83.81 kJ/kg, respectively. The results of TGA suggested that the thermal stability of the PA/PB-4 composite was excellent, which could be used for the applications of thermal energy storage. Furthermore, the thermal conductivity of PA/PB-4 was 0.3926 W/(m∙K), which was increased by 43.76% compared with that of the pure PA. Thus, the study results indicated that the PA/PB-4 had great potential for thermal energy storage applications.

Electrical stimulation shifts healing/scarring towards regeneration in a rat limb amputation model

Different species respond differently to severe injury, such as limb loss. In species that regenerate, limb loss is met with complete restoration of the limbs’ form and function, whereas in mammals the amputated limb’s stump heals and scars. In in vitro studies, electrical stimulation (EStim) has been shown to promote cell migration, and osteo- and chondrogenesis. In in vivo studies, after limb amputation, EStim causes significant new bone, cartilage and vessel growth. Here, in a rat model, the stumps of amputated rat limbs were exposed to EStim, and we measured extracellular matrix (ECM) deposition, macrophage distribution, cell proliferation and gene expression changes at early (3 and 7 days) and later stages (28 days). We found that EStim caused differences in ECM deposition, with less condensed collagen fibrils, and modified macrophage response by changing M1 to M2 macrophage ratio. The number of proliferating cells was increased in EStim treated stumps 7 days after amputation, and transcriptome data strongly supported our histological findings, with activated gene pathways known to play key roles in embryonic development and regeneration. In conclusion, our findings support the hypothesis that EStim shifts injury response from healing/scarring towards regeneration. A better understanding of if and how EStim controls these changes, could lead to strategies that replace scarring with regeneration.

Gene Co-expression Network and Copy Number Variation Analyses Identify Transcription Factors Associated With Multiple Myeloma Progression

Multiple myeloma (MM) has two clinical precursor stages of disease: monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). However, the mechanism of progression is not well understood. Because gene co-expression network analysis is a well-known method for discovering new gene functions and regulatory relationships, we utilized this framework to conduct differential co-expression analysis to identify interesting transcription factors (TFs) in two publicly available datasets. We then used copy number variation (CNV) data from a third public dataset to validate these TFs. First, we identified co-expressed gene modules in two publicly available datasets each containing three conditions: normal, MGUS, and SMM. These modules were assessed for condition-specific gene expression, and then enrichment analysis was conducted on condition-specific modules to identify their biological function and upstream TFs. TFs were assessed for differential gene expression between normal and MM precursors, then validated with CNV analysis to identify candidate genes. Functional enrichment analysis reaffirmed known functional categories in MM pathology, the main one relating to immune function. Enrichment analysis revealed a handful of differentially expressed TFs between normal and either MGUS or SMM in gene expression and/or CNV. Overall, we identified four genes of interest (MAX, TCF4, ZNF148, and ZNF281) that aid in our understanding of MM initiation and progression.

Single-molecule characterization of extrinsic transcription termination by Sen1 helicase

Extrinsic transcription termination typically involves remodeling of RNA polymerase by an accessory helicase. In yeast this is accomplished by the Sen1 helicase homologous to human senataxin (SETX). To gain insight into these processes we develop a DNA scaffold construct compatible with magnetic-trapping assays and from which S. cerevisiae RNA polymerase II (Pol II), as well as E. coli RNA polymerase (ecRNAP), can efficiently initiate transcription without transcription factors, elongate, and undergo extrinsic termination. By stalling Pol II TECs on the construct we can monitor Sen1-induced termination in real-time, revealing the formation of an intermediate in which the Pol II transcription bubble appears half-rewound. This intermediate requires ~40 sec to form and lasts ~20 sec prior to final dissociation of the stalled Pol II. The experiments enabled by the scaffold construct permit detailed statistical and kinetic analysis of Pol II interactions with a range of cofactors in a multi-round, high-throughput fashion.

Yeast’s Sen1 helicase is involved in the suppression of antisense transcription from bidirectional eukaryotic promoters. Here authors develop and utilize a quantitative single-molecule assay reporting on the kinetics of extrinsic eukaryotic transcription termination by the Sen1 helicase and a reaction intermediate in which the Pol II transcription bubble appears half-rewound.

Key factors determining variations in RNA interference efficacy mediated by different double-stranded RNA lengths in Tribolium castaneum

Double-stranded RNA (dsRNA) length may affect RNA interference (RNAi) efficacy. Herein, variation in RNAi efficacy associated with dsRNA molecular length was confirmed via comparison of knockdown results following dsRNA injection into Tribolium castaneum. Through in vitro experiments with T. castaneum midgut, dsRNA accumulation in the midgut, degradation by midgut homogenates and persistence in haemolymph after injection were tested to determine the causes of RNAi efficacy variation. The comparative efficacies of dsRNAs were 480 bp ≈ 240 bp > 120 bp > 60 bp >> 21 bp. The combined midgut dsRNA accumulation and midgut homogenate-induced degradation analyses suggested cellular uptake to be the key barrier for 21 bp dsRNA functioning, but was likely not the main determinant of the variation in longer dsRNAs' (≥60 bp) bioactivity. In vitro RNAi experiment with T. castaneum midgut showed that long dsRNAs all significantly depleted the expression of corresponding genes, suggesting little variation in intracellular RNAi machinery's affinity for different dsRNA lengths. In vivo haemolymph content dynamics of different dsRNAs following injection indicated higher persistence of longer dsRNAs. In addition, comparison of the in vivo and in vitro RNAi efficacy also indicated the importance of haemolymph degradation. Thus, the varied efficacy of long dsRNAs resulted from their degradation by nucleases, which varied with dsRNA length.

SALMON: Survival Analysis Learning With Multi-Omics Neural Networks on Breast Cancer

Improved cancer prognosis is a central goal for precision health medicine. Though many models can predict differential survival from data, there is a strong need for sophisticated algorithms that can aggregate and filter relevant predictors from increasingly complex data inputs. In turn, these models should provide deeper insight into which types of data are most relevant to improve prognosis. Deep Learning-based neural networks offer a potential solution for both problems because they are highly flexible and account for data complexity in a non-linear fashion. In this study, we implement Deep Learning-based networks to determine how gene expression data predicts Cox regression survival in breast cancer. We accomplish this through an algorithm called SALMON (Survival Analysis Learning with Multi-Omics Neural Networks), which aggregates and simplifies gene expression data and cancer biomarkers to enable prognosis prediction. The results revealed improved performance when more omics data were used in model construction. Rather than use raw gene expression values as model inputs, we innovatively use eigengene modules from the result of gene co-expression network analysis. The corresponding high impact co-expression modules and other omics data are identified by feature selection technique, then examined by conducting enrichment analysis and exploiting biological functions, escalated the interpretation of input feature from gene level to co-expression modules level. Our study shows the feasibility of discovering breast cancer related co-expression modules, sketch a blueprint of future endeavors on Deep Learning-based survival analysis. SALMON source code is available at https://github.com/huangzhii/SALMON/.

Integrative analysis based on survival associated co-expression gene modules for predicting Neuroblastoma patients' survival time

Background

More than 90% of neuroblastoma patients are cured in the low-risk group while only less than 50% for those with high-risk disease can be cured. Since the high-risk patients still have poor outcomes, we need more accurate stratification to establish an individualized precise treatment plan for the patients to improve the long-term survival rate.

Results

We focus on extracting features and providing a workflow to improve survival prediction for neuroblastoma patients. With a workflow for gene co-expression network (GCN) mining in microarray and RNA-Seq datasets, we extracted molecular features from each co-expressed module and summarized them into eigengenes. Then we adopted the lasso-regularized Cox proportional hazards model to select the most informative eigengene features regarding association to the risk of metastasis. Nine eigengenes were selected which show strong association with patient survival prognosis. All of the nine corresponding gene modules also have highly enriched biological functions or cytoband locations. Three of them are unique modules to RNA-Seq data, which complement the modules from microarray data in terms of survival prognosis. We then merged all eigengenes from these unique modules and used an integrative method called Similarity Network Fusion to test the prognostic power of these eigengenes for prognosis. The prognostic accuracies are significantly improved as compared to using all eigengenes, and a subgroup of patients with very poor survival rate was identified.

Conclusions

We first compared GCNs mined from microarray and RNA-seq data. We discovered that each data modality yields unique GCNs, which are enriched with clear biological functions. Then we do module unique analysis and use lasso-cox model to select survival-associated eigengenes. Integration of unique and survival-associated eigengenes from both data types provides complementary information that leads to more accurate survival prognosis.

Reviewers

Reviewed by Susmita Datta, Marco Chierici and Dimitar Vassilev.

Electronic supplementary material

The online version of this article (10.1186/s13062-018-0229-2) contains supplementary material, which is available to authorized users.

Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimer's disease patients

Background

Gene co-expression network (GCN) mining is a systematic approach to efficiently identify novel disease pathways, predict novel gene functions and search for potential disease biomarkers. However, few studies have systematically identified GCNs in multiple brain transcriptomic data of Alzheimer’s disease (AD) patients and looked for their specific functions.

Methods

In this study, we first mined GCN modules from AD and normal brain samples in multiple datasets respectively; then identified gene modules that are specific to AD or normal samples; lastly, condition-specific modules with similar functional enrichments were merged and enriched differentially expressed upstream transcription factors were further examined for the AD/normal-specific modules.

Results

We obtained 30 AD-specific modules which showed gain of correlation in AD samples and 31 normal-specific modules with loss of correlation in AD samples compared to normal ones, using the network mining tool lmQCM. Functional and pathway enrichment analysis not only confirmed known gene functional categories related to AD, but also identified novel regulatory factors and pathways. Remarkably, pathway analysis suggested that a variety of viral, bacteria, and parasitic infection pathways are activated in AD samples. Furthermore, upstream transcription factor analysis identified differentially expressed upstream regulators such as ZFHX3 for several modules, which can be potential driver genes for AD etiology and pathology.

Conclusions

Through our state-of-the-art network-based approach, AD/normal-specific GCN modules were identified using multiple transcriptomic datasets from multiple regions of the brain. Bacterial and viral infectious disease related pathways are the most frequently enriched in modules across datasets. Transcription factor ZFHX3 was identified as a potential driver regulator targeting the infectious diseases pathways in AD-specific modules. Our results provided new direction to the mechanism of AD as well as new candidates for drug targets.

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0431-1) contains supplementary material, which is available to authorized users.

Apple rootstocks of different nitrogen tolerance affect the rhizosphere bacterial community composition

AIMS

To select apple rootstocks that are tolerant to low nitrogen and reveal the relationship between the rhizosphere bacterial communities and the low nitrogen tolerance of the apple rootstock.

METHODS AND RESULTS

In total, 235 lines of hybrids of Malus robusta Rehd. × M.9 with low nitrogen stress were cultivated in pots in a greenhouse equipped with a drip irrigation system, and growth characteristics, photosynthesis traits and mineral elements were monitored. The bacterial community structure of the rhizosphere from different rootstocks was determined via Illumina MiSeq sequencing. This study selected three low nitrogen-tolerant (NT) lines that had higher nitrogen concentration, and higher photosynthesis rate than the three low nitrogen-sensitive (NS) lines. The bacterial community structure significantly differed (P ≤ 0·001) among the rootstocks. The bacterial phyla Proteobacteria and Actinobacteria were the dominant groups in the rhizosphere and presented higher abundance in the NT rhizosphere. The N concentration in the apple rootstocks exhibited highly positive Pearson correlations with the bacterial genera Sphingomonas, Pseudoxanthomonas, Bacillus and Acinetobacter, and negative correlations with the bacterial genera Pseudarthrobacter and Bradyrhizobium.

CONCLUSIONS

This study showed that investigated rootstocks achieved increased nitrogen concentration by enhancing their photosynthetic production capacity and shaping their rhizobacteria community structure.

SIGNIFICANCE AND IMPACT OF THE STUDY

The findings provide a basis for studying the mechanisms of resistance to low nitrogen stress in apple rootstocks. Based on these beneficial bacteria, microbial inoculants can be developed for use in sustainable agricultural and horticultural production.

A Generalized Model for Robust Tensor Factorization With Noise Modeling by Mixture of Gaussians

The low-rank tensor factorization (LRTF) technique has received increasing attention in many computer vision applications. Compared with the traditional matrix factorization technique, it can better preserve the intrinsic structure information and thus has a better low-dimensional subspace recovery performance. Basically, the desired low-rank tensor is recovered by minimizing the least square loss between the input data and its factorized representation. Since the least square loss is most optimal when the noise follows a Gaussian distribution, -norm-based methods are designed to deal with outliers. Unfortunately, they may lose their effectiveness when dealing with real data, which are often contaminated by complex noise. In this paper, we consider integrating the noise modeling technique into a generalized weighted LRTF (GWLRTF) procedure. This procedure treats the original issue as an LRTF problem and models the noise using a mixture of Gaussians (MoG), a procedure called MoG GWLRTF. To extend the applicability of the model, two typical tensor factorization operations, i.e., CANDECOMP/PARAFAC factorization and Tucker factorization, are incorporated into the LRTF procedure. Its parameters are updated under the expectation-maximization framework. Extensive experiments indicate the respective advantages of these two versions of MoG GWLRTF in various applications and also demonstrate their effectiveness compared with other competing methods.

Balanced Activity between Kv3 and Nav Channels Determines Fast-Spiking in Mammalian Central Neurons

Fast-spiking (FS) neurons can fire action potentials (APs) up to 1,000 Hz and play key roles in vital functions such as sound location, motor coordination, and cognition. Here we report that the concerted actions of Kv3 voltage-gated K⁺ (Kv) and Na⁺ (Nav) channels are sufficient and necessary for inducing and maintaining FS. Voltage-clamp analysis revealed a robust correlation between the Kv3/Nav current ratio and FS. Expressing Kv3 channels alone could convert ∼30%-60% slow-spiking (SS) neurons to FS in culture. In contrast, co-expression of either Nav1.2 or Nav1.6 together with Kv3.1 or Kv3.3, but not alone or with Kv1.2, converted SS to FS with 100% efficiency. Furthermore, RNA-sequencing-based genome-wide analysis revealed that the Kv3/Nav ratio and Kv3 expression levels strongly correlated with the maximal AP frequencies. Therefore, FS is established by the properly balanced activities of Kv3 and Nav channels and could be further fine-tuned by channel biophysical features and localization patterns.

Supplementation with CTGF, SDF1, NGF, and HGF promotes ovine in vitro oocyte maturation and early embryo development

The strategies for improving the in vitro maturation (IVM) of domestic animal oocytes focus on promoting nuclear and cytoplasmic maturation. The identification of paracrine factors and their supplementation in the culture medium represent effective approaches for oocyte maturation and embryo development. This study investigated the effects of paracrine factor supplementation including connective tissue growth factor (CTGF), nerve growth factor (NGF), hepatocyte growth factor (HGF), and stromal derived factor 1 (SDF1) on ovine oocytes and early parthenogenetic embryos using an in vitro culture system. First, we identified the optimal concentrations of CTGF (30 ng/mL), SDF1 (10 ng/mL), NGF (3 ng/mL), and HGF (100 ng/mL) for promoting oocyte maturation, which combined, induced nuclear maturation in 94.19% of oocytes. This combination also promoted cumulus cell expansion and inhibited oocyte/cumulus apoptosis, while enabling a larger proportion (33.04%) of embryos to develop into blastocysts than in the controls and prevented embryo apoptosis. These novel findings demonstrate that the paracrine factors CTGF, SDF1, NGF, and HGF facilitate ovine oocyte and early parthenogenetic embryo development in vitro. Thus, supplementation with these factors may help optimize the IVM of ovine oocytes and early parthenogenetic embryo development strategies.

Risk factors of haemoglobinuria after microwave ablation of liver tumours

AIM

To explore the risk factors predicting haemoglobinuria after ultrasound-guided percutaneous microwave ablation (MWA) of liver tumours and discuss the treatments and outcomes.

MATERIALS AND METHODS

The present study comprised 2,829 patients admitted for liver tumours treated with MWA from Jan 2011 to April 2017. Ethics committee approval was waived and informed consent for treatment procedures were obtained from the patients. Haemoglobinuria after MWA was found in 149 patients. The influence of 19 risk factors was assessed. Binary logistic regression and receiver operating characteristic (ROC) curve analysis were used for statistical analysis. The treatments and outcomes of patients with haemoglobinuria were summarised.

RESULTS

By univariate analysis, histopathology, liver cirrhosis, MWA volume, MWA energy, and MWA duration were significant risk factors. By multivariate analysis and ROC curve, MWA energy, duration, and volume were identified as predictors of haemoglobinuria after MWA. Drug treatments including kidney protection, adequate hydration, alkalisation of urine, and diuresis were administrated to the patients with haemoglobinuria. One patient progressed to acute kidney injury (AKI) while others had good clinical outcomes.

CONCLUSION

Haemoglobinuria is a controllable side effect after MWA of liver tumours, which is related to high MWA energy, long MWA duration, and great MWA volume. It usually caused few side effects on renal function with correct treatment.

Prospective Comparison of Equations Based on Creatinine and Cystatin C for the Glomerular Filtration Rate Estimation in Chinese Renal Transplant Recipients

BACKGROUND

Currently, there is no dedicated equation to estimate glomerular filtration rate (GFR) for transplanted kidneys. This study aimed to compare the performance of serum creatinine (Scr)- and cystatin C (CysC)-based equations in Chinese renal transplant recipients.

METHODS

A total of 252 stable renal transplant recipients were enrolled in this study. The plasma clearance of ^99mTc-DTPA (rGFR) was used as a reference standard. The Scr, CysC, and rGFR of the patients were measured on the same day. The bias, precision, accuracy (percentage of estimates within 10%, 30%, and 50% of rGFR), and agreements of 8 Scr and 5 CysC eGFR equations were assessed. The factors affecting the accuracy were also evaluated.

RESULTS

Among the Scr-based equations, the Japanese Society of Nephrology-Chronic Kidney Disease Initiatives (JSN-CKDI) equation had the best overall performance with a bias of -6.2 mL/min/1.73 m², and 96.1% of its estimates were within 30% of the rGFR. For the CysC-based equations, the Filler equation had the best performance with a bias of -3.9 mL/min/1.73 m², and 93.7% of its estimates were within 30% of the rGFR. Overall, the CysC-based equations showed better performance than the Scr-based equations. In addition, significant differences were observed between bias and gender and between bias and rGFR value in some equations, whereas transplantation time and immunosuppressive regimens were not correlated with the bias.

CONCLUSION

The JSN-CKDI equation provides the best estimation of the GFR equations, and the CysC-based equations performed better than the Scr-based equations in this population.