A Transformer-Based Knowledge Distillation Network for Cortical Cataract Grading

Cortical cataract, a common type of cataract, is particularly difficult to be diagnosed automatically due to the complex features of the lesions. Recently, many methods based on edge detection or deep learning were proposed for automatic cataract grading. However, these methods suffer a large performance drop in cortical cataract grading due to the more complex cortical opacities and uncertain data. In this paper, we propose a novel Transformer-based Knowledge Distillation Network, called TKD-Net, for cortical cataract grading. To tackle the complex opacity problem, we first devise a zone decomposition strategy to extract more refined features and introduce special sub-scores to consider critical factors of clinical cortical opacity assessment (location, area, density) for comprehensive quantification. Next, we develop a multi-modal mix-attention Transformer to efficiently fuse sub-scores and image modality for complex feature learning. However, obtaining the sub-score modality is a challenge in the clinic, which could cause the modality missing problem instead. To simultaneously alleviate the issues of modality missing and uncertain data, we further design a Transformer-based knowledge distillation method, which uses a teacher model with perfect data to guide a student model with modality-missing and uncertain data. We conduct extensive experiments on a dataset of commonly-used slit-lamp images annotated by the LOCS III grading system to demonstrate that our TKD-Net outperforms state-of-the-art methods, as well as the effectiveness of its key components. Codes are available at https://github.com/wjh892521292/Cataract_TKD-Net.

Radiotherapy Combined with PD-L1 Antibody Exerts a Synergistic Anti-tumor Effect in a Mouse Model of Esophageal Squamous Cell Carcinoma

PURPOSE/OBJECTIVE(S)

The combination of radiotherapy and immune checkpoint inhibitors (ICIs) has been shown to exert synergistic anti-tumor effects in various tumors. In esophageal squamous cell carcinoma (ESCC), several clinical trials of radiotherapy combined with ICIs are undergoing or have been finished. However, the efficacy and action mechanisms of radiotherapy combined with ICIs is still not clear in ESCC. Our study aimed to investigate whether radiotherapy improved the anti-cancer effect of PD-L1 antibody and the effect of this combination therapy on tumor-immune microenvironment.

MATERIALS/METHODS

The mouse esophageal cancer cell line mEC25 was implanted into the armpits of female C57 mice. When the tumor volume grew to 150-250 mm3, the mice were randomly divided into different groups including control group (administration of IgG antibody), radiotherapy group (12 Gy at 4 Gy per fraction), PD-L1 antibody group (200 mg/kg i.p. for three times) and combination group (fractionated radiation combined with PD-L1 antibody was delivered on day 1, day 4 and day 7, the day when fractionated radiation began was recorded as day 1).The mice were sacrificed on the 1st, 3rd and 7th day after the treatment was ended. The tumor, spleen and draining lymph nodes of the mice were collected and analyzed using flow cytometry (FCM), and the tumor volume and survival time of mice were calculated.

RESULTS

Compared with radiotherapy or PD-L1 antibody alone, the combination therapy significantly prolonged the survival time of mice and decreased the growth rate of xenograft tumors FCM analysis showed that the combination therapy significantly increased the infiltration and cytotoxicity of effector T cells and reduced the proportion of M2 macrophages in tumor, spleen and lymph nodes. In addition, in tumor and spleen, the proportion of regulatory T cells (Tregs) decreased in the group of combination therapy. In tumor, spleen, and lymph nodes, the proportion of central memory T cell (TCM) was increased in the group of combination therapy.

CONCLUSION

The anti-tumor effect of radiotherapy combined with PD-L1 antibody is superior to single treatment in the mouse model of esophageal cancer. Radiotherapy can shape tumor-immune microenvironment, allowing the infiltration of effector T cells and exclusion of immune-suppressive cells such as M2 macrophages and Tregs.

The Role of Esophagography in the Diagnosis of Orthotopic Esophageal Squamous Cell Carcinoma in a Mouse Mode

PURPOSE/OBJECTIVE(S)

Animal models play an irreplaceable role in pre-clinical studies of various tumors, mainly including xenogeneic/homograft subcutaneous tumor models. However, the subcutaneous transplanted tumor model is separated from the microenvironment of the primary tissue and fail to accurately simulate the growth of tumor cells in vivo. Therefore, orthotopic tumor models play an increasingly important role. For esophageal squamous cell carcinoma, chemotherapy-induced methods have successfully established mouse models of esophageal squamous cell carcinoma, but past experiments have found that it is still a challenge to confirm the success of the mouse model of esophageal cancer. The pathological diagnosis of the esophageal cancer tissue obtained from the killed mouse model remains the gold standard, but it also represents a lost opportunity to evaluate the efficacy of subsequent treatment. Notedly, esophagography is the primary examination for clinical patients with esophageal cancer. Therefore, the aim of this study is to evaluate whether esophagography can be used to establish a successful in situ mouse model of esophageal cancer.

MATERIALS/METHODS

Referring to previous literature, we used the chemical drug 4-Nitroquinoline N-oxide (4-NQO) to induce orthotopic tumor model: A certain amount of 4-NQO was dissolved in propylene glycol to make a 2% stock solution, and then dissolved in 200ml of sterile water for the daily drinking water of mice. The water was kept in the dark and replaced once a week. After 16 weeks of induction, the drinking water was replaced by the same volume of sterile water and continued to be fed for 12 weeks. The mice were subsequently subjected to esophagography: First, the mice were fixed on a plastic plate, and the gavage needle was slowly inserted into the stomach, then slowly withdrawn out and gradually injected with meglumine diphosphate, with a total amount of 0.1-0.2ml. Then the radiolucent film was taken immediately, and three positions were selected, including the plain film, the standing film and the lateral film. After that, the mice were sacrificed and the esophagus of the mice was isolated and embedded in sections for pathological and immunohistochemical diagnosis. Finally, the results were compared with the imaging results.

RESULTS

Based on the comparison of imaging and pathological results, we found that most of the esophageal segments with filling defects on esophagography were pathologically diagnosed as esophageal squamous cell carcinoma, and another part were diagnosed as esophageal papilloma. Compared with CT and MRI, esophagography has the advantages of simple operation and less consumable materials.

CONCLUSION

We found that esophagography is a new non-invasive auxiliary diagnostic method for the successful establishment of an orthotopic mouse model of esophageal squamous cell carcinoma.

Nonalcoholic fatty liver disease (NAFLD) detection and deep learning in a Chinese community-based population

OBJECTIVES

We aimed to develop and validate a deep learning system (DLS) by using an auxiliary section that extracts and outputs specific ultrasound diagnostic features to improve the explainable, clinical relevant utility of using DLS for detecting NAFLD.

METHODS

In a community-based study of 4144 participants with abdominal ultrasound scan in Hangzhou, China, we sampled 928 (617 [66.5%] females, mean age: 56 years ± 13 [standard deviation]) participants (2 images per participant) to develop and validate DLS, a two-section neural network (2S-NNet). Radiologists' consensus diagnosis classified hepatic steatosis as none steatosis, mild, moderate, and severe. We also explored the NAFLD detection performance of six one-section neural network models and five fatty liver indices on our data set. We further evaluated the influence of participants' characteristics on the correctness of 2S-NNet by logistic regression.

RESULTS

Area under the curve (AUROC) of 2S-NNet for hepatic steatosis was 0.90 for ≥ mild, 0.85 for ≥ moderate, and 0.93 for severe steatosis, and was 0.90 for NAFLD presence, 0.84 for moderate to severe NAFLD, and 0.93 for severe NAFLD. The AUROC of NAFLD severity was 0.88 for 2S-NNet, and 0.79-0.86 for one-section models. The AUROC of NAFLD presence was 0.90 for 2S-NNet, and 0.54-0.82 for fatty liver indices. Age, sex, body mass index, diabetes, fibrosis-4 index, android fat ratio, and skeletal muscle via dual-energy X-ray absorptiometry had no significant impact on the correctness of 2S-NNet (p > 0.05).

CONCLUSIONS

By using two-section design, 2S-NNet had improved the performance for detecting NAFLD with more explainable, clinical relevant utility than using one-section design.

KEY POINTS

• Based on the consensus review derived from radiologists, our DLS (2S-NNet) had an AUROC of 0.88 by using two-section design and yielded better performance for detecting NAFLD than using one-section design with more explainable, clinical relevant utility. • The 2S-NNet outperformed five fatty liver indices with the highest AUROCs (0.84-0.93 vs. 0.54-0.82) for different NAFLD severity screening, indicating screening utility of deep learning-based radiology may perform better than blood biomarker panels in epidemiology. • The correctness of 2S-NNet was not significantly influenced by individual's characteristics, including age, sex, body mass index, diabetes, fibrosis-4 index, android fat ratio, and skeletal muscle via dual-energy X-ray absorptiometry.

FraudAuditor: A Visual Analytics Approach for Collusive Fraud in Health Insurance

Collusive fraud, in which multiple fraudsters collude to defraud health insurance funds, threatens the operation of the healthcare system. However, existing statistical and machine learning-based methods have limited ability to detect fraud in the scenario of health insurance due to the high similarity of fraudulent behaviors to normal medical visits and the lack of labeled data. To ensure the accuracy of the detection results, expert knowledge needs to be integrated with the fraud detection process. By working closely with health insurance audit experts, we propose FraudAuditor, a three-stage visual analytics approach to collusive fraud detection in health insurance. Specifically, we first allow users to interactively construct a co-visit network to holistically model the visit relationships of different patients. Second, an improved community detection algorithm that considers the strength of fraud likelihood is designed to detect suspicious fraudulent groups. Finally, through our visual interface, users can compare, investigate, and verify suspicious patient behavior with tailored visualizations that support different time scales. We conducted case studies in a real-world healthcare scenario, i.e., to help locate the actual fraud group and exclude the false positive group. The results and expert feedback proved the effectiveness and usability of the approach.

Prediction of postoperative visual acuity in patients with age-related cataracts using macular optical coherence tomography-based deep learning method

Background

To predict postoperative visual acuity (VA) in patients with age-related cataracts using macular optical coherence tomography-based deep learning method.

Methods

A total of 2,051 eyes from 2,051 patients with age-related cataracts were included. Preoperative optical coherence tomography (OCT) images and best-corrected visual acuity (BCVA) were collected. Five novel models (I, II, III, IV, and V) were proposed to predict postoperative BCVA. The dataset was randomly divided into a training (n = 1,231), validation (n = 410), and test set (n = 410). The performance of the models in predicting exact postoperative BCVA was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The performance of the models in predicting whether postoperative BCVA was improved by at least two lines in the visual chart (0.2LogMAR) was evaluated using precision, sensitivity, accuracy, F1 and area under curve (AUC).

Results

Model V containing preoperative OCT images with horizontal and vertical B-scans, macular morphological feature indices, and preoperative BCVA had a better performance in predicting postoperative VA, with the lowest MAE (0.1250 and 0.1194LogMAR) and RMSE (0.2284 and 0.2362LogMAR), and the highest precision (90.7% and 91.7%), sensitivity (93.4% and 93.8%), accuracy (88% and 89%), F1 (92% and 92.7%) and AUCs (0.856 and 0.854) in the validation and test datasets, respectively.

Conclusion

The model had a good performance in predicting postoperative VA, when the input information contained preoperative OCT scans, macular morphological feature indices, and preoperative BCVA. The preoperative BCVA and macular OCT indices were of great significance in predicting postoperative VA in patients with age-related cataracts.

Mendelian randomization and clinical trial evidence supports TYK2 inhibition as a therapeutic target for autoimmune diseases

BACKGROUND

To explore the associations of genetically proxied TYK2 inhibition with a wide range of disease outcomes and biomarkers to identify therapeutic repurposing opportunities, adverse effects, and biomarkers of efficacy.

METHODS

The loss-of-function missense variant rs34536443 in TYK2 gene was used as a genetic instrument to proxy the effect of TYK2 inhibition. A phenome-wide Mendelian randomization (MR) study was conducted to explore the associations of genetically-proxied TYK2 inhibition with 1473 disease outcomes in UK Biobank (N = 339,197). Identified associations were examined for replication in FinnGen (N = 260,405). We further performed tissue-specific gene expression MR, colocalization analyses, and MR with 247 blood biomarkers. A systematic review of randomized controlled trials (RCTs) on TYK2 inhibitor was performed to complement the genetic evidence.

FINDINGS

PheWAS-MR found that genetically-proxied TYK2 inhibition was associated with lower risk of a wide range of autoimmune diseases. The associations with hypothyroidism and psoriasis were confirmed in MR analysis of tissue-specific TYK2 gene expression and the associations with systemic lupus erythematosus, psoriasis, and rheumatoid arthritis were observed in colocalization analysis. There were nominal associations of genetically-proxied TYK2 inhibition with increased risk of prostate and breast cancer but not in tissue-specific expression MR or colocalization analyses. Thirty-seven blood biomarkers were associated with the TYK2 loss-of-function mutation. Evidence from RCTs confirmed the effectiveness of TYK2 inhibitors on plaque psoriasis and reported several adverse effects.

INTERPRETATION

This study supports TYK2 inhibitor as a potential treatment for psoriasis and several other autoimmune diseases. Increased pharmacovigilance is warranted in relation to the potential adverse effects.

FUNDING

None.

DPVisCreator: Incorporating Pattern Constraints to Privacy-preserving Visualizations via Differential Privacy

Data privacy is an essential issue in publishing data visualizations. However, it is challenging to represent multiple data patterns in privacy-preserving visualizations. The prior approaches target specific chart types or perform an anonymization model uniformly without considering the importance of data patterns in visualizations. In this paper, we propose a visual analytics approach that facilitates data custodians to generate multiple private charts while maintaining user-preferred patterns. To this end, we introduce pattern constraints to model users' preferences over data patterns in the dataset and incorporate them into the proposed Bayesian network-based Differential Privacy (DP) model PriVis. A prototype system, DPVisCreator, is developed to assist data custodians in implementing our approach. The effectiveness of our approach is demonstrated with quantitative evaluation of pattern utility under the different levels of privacy protection, case studies, and semi-structured expert interviews.

A Task Decomposing and Cell Comparing Method for Cervical Lesion Cell Detection

Automatic detection of cervical lesion cells or cell clumps using cervical cytology images is critical to computer-aided diagnosis (CAD) for accurate, objective, and efficient cervical cancer screening. Recently, many methods based on modern object detectors were proposed and showed great potential for automatic cervical lesion detection. Although effective, several issues still hinder further performance improvement of such known methods, such as large appearance variances between single-cell and multi-cell lesion regions, neglecting normal cells, and visual similarity among abnormal cells. To tackle these issues, we propose a new task decomposing and cell comparing network, called TDCC-Net, for cervical lesion cell detection. Specifically, our task decomposing scheme decomposes the original detection task into two subtasks and models them separately, which aims to learn more efficient and useful feature representations for specific cell structures and then improve the detection performance of the original task. Our cell comparing scheme imitates clinical diagnosis of experts and performs cell comparison with a dynamic comparing module (normal-abnormal cells comparing) and an instance contrastive loss (abnormal-abnormal cells comparing). Comprehensive experiments on a large cervical cytology image dataset confirm the superiority of our method over state-of-the-art methods.

Toxic Effects of Thioacetamide-Induced Femoral Damage in New Zealand White Rabbits by Activating the p38/ERK Signaling Pathway

Thioacetamide (TAA) is widely used in the production of drugs, pesticides and dyeing auxiliaries. Moreover, it is a chemical that can cause liver damage and cancer. TAA has recently been identified to cause bone damage in animal models. However, the type of bone damage that TAA causes and its potential pathogenic mechanisms remain unclear. The toxic effects of TAA on the femurs of New Zealand white rabbits and the underlying toxicity mechanism were investigated in this study. Serum samples, the heart, liver, kidney and femurs were collected from rabbits after intraperitoneal injection of TAA for 5 months (100 and 200 mg/kg). The New Zealand white rabbits treated with TAA showed significant weight loss and femoral shortening. The activities of total bilirubin, total bile acid and gamma-glutamyl transpeptidase in the serum were increased following treatment with TAA. In addition, the cortical bone became thinner, and the trabecular thickness decreased significantly in TAA-treated rabbits, which was accompanied by significantly decreased mineral density of the cortical and trabecular bone. Moreover, there was a significant decrease in modulus of elasticity and maximum load on bone stress in TAA-treated rabbits. The western blotting results showed that the expression of phosphorylated (p)-p38 and p-ERK in femur tissues of rabbits were increased after TAA administration. Collectively, these results suggested that TAA may lead to femoral damage in rabbits by activating the p38/ERK signaling pathway.

Full Scale Attention for Automated COVID-19 Diagnosis from CT Images

The wide spread of coronavirus pneumonia (COVID-19) has been a severe threat to global health since 2019. Apart from the nucleic acid detection, medical imaging examination is a vital diagnostic modality to confirm and treat the disease. Thus, implementing the automatic diagnosis of the COVID-19 bears particular significance. However, the limitations of data quality and size strongly hinder the clas-sification and segmentation performance and it also result in high misdiagnosis rate. To this end, we propose a novel full scale attention mechanism (FUSA) to capture more contextual dependencies of features, which enables the model easier to classify positive cases and improve the sensitivity. Specifically, FUSA parallelly extracts the information of channel domain and spatial domain, and fuses them together. The experimental study shows FUSA can significantly improve the COVID-19 automated diagnosis performance and eliminate false negative cases compared with other state-of-the-art ones.

Discriminative Cervical Lesion Detection in Colposcopic Images with Global Class Activation and Local Bin Excitation

Accurate cervical lesion detection (CLD) methods using colposcopic images are highly demanded in computer-aided diagnosis (CAD) for automatic diagnosis of High-grade Squamous Intraepithelial Lesions (HSIL). However, compared to natural scene images, the specific characteristics of colposcopic images, such as low contrast, visual similarity, and ambiguous lesion boundaries, pose difficulties to accurately locating HSIL regions and also significantly impede the performance improvement of existing CLD approaches. To tackle these difficulties and better capture cervical lesions, we develop novel feature enhancing mechanisms from both global and local perspectives, and propose a new discriminative CLD framework, called CervixNet, with a Global Class Activation (GCA) module and a Local Bin Excitation (LBE) module. Specifically, the GCA module learns discriminative features by introducing an auxiliary classifier, and guides our model to focus on HSIL regions while ignoring noisy regions. It globally facilitates the feature extraction process and helps boost feature discriminability. Further, our LBE module excites lesion features in a local manner, and allows the lesion regions to be more fine-grained enhanced by explicitly modelling the inter-dependencies among bins of proposal feature. Extensive experiments on a number of 9888 clinical colposcopic images verify the superiority of our method (AP _.75=20.45) over state-of-the-art models on four widely used metrics.

AmAMP1 from Acropora millepora and damicornin define a family of coral-specific antimicrobial peptides related to the Shk toxins of sea anemones

A candidate antimicrobial peptide (AmAMP1) was identified by searching the whole genome sequence of Acropora millepora for short (<125AA) cysteine-rich predicted proteins with an N-terminal signal peptide but lacking clear homologs in the SwissProt database. It resembled but was not closely related to damicornin, the only other known AMP from a coral, and was shown to be active against both Gram-negative and Gram-positive bacteria. These proteins define a family of AMPs present in corals and their close relatives, the Corallimorpharia, and are synthesised as preproproteins in which the C-terminal mature peptide contains a conserved arrangement of six cysteine residues. Consistent with the idea of a common origin for AMPs and toxins, this Cys motif is shared between the coral AMPs and the Shk neurotoxins of sea anemones. AmAMP1 is expressed at late stages of coral development, in ectodermal cells that resemble the "ganglion neurons" of Hydra, in which it has recently been demonstrated that a distinct AMP known as NDA-1 is expressed.

A Transfer Learning Based Super-Resolution Microscopy for Biopsy Slice Images: The Joint Methods Perspective

Higher-resolution biopsy slice images reveal many details, which are widely used in medical practice. However, taking high-resolution slice images is more costly than taking low-resolution ones. In this paper, we propose a joint framework containing a novel transfer learning strategy and a deep super-resolution framework to generate high-resolution slice images from low-resolution ones. The super-resolution framework called SRFBN+ is proposed by modifying a state-of-the-art framework SRFBN. Specifically, the structure of the feedback block of SRFBN was modified to be more flexible. Besides, it is challenging to use typical transfer learning strategies directly for the tasks on slice images, as the patterns on different types of biopsy slice images are varying. To this end, we propose a novel transfer learning strategy, called Channel Fusion Transfer Learning (CF-Trans). CF-Trans builds a middle domain by fusing the data manifolds of the source domain and the target domain, serving as a springboard for knowledge transfer. Thus, in the transfer learning setting, SRFBN+ can be trained on the source domain and then the middle domain and finally the target domain. Experiments on biopsy slice images validate SRFBN+ works well in generating super-resolution slice images, and CF-Trans is an efficient transfer learning strategy.

The relationship of platelet to lymphocyte ratio and neutrophil to monocyte ratio to radiographic grades of knee osteoarthritis

OBJECTIVE

Accumulating data show that platelet to lymphocyte ratio (PLR) and neutrophil to monocyte ratio (NMR) undergo changes during inflammation in various diseases; however, the clinical features remain unclear in knee osteoarthritis (OA) patients. The purpose of our study was to evaluate PLR and NMR in knee OA patients, and assess their relationship to knee OA's radiographic grades.

METHODS

A retrospective study on 132 adult knee OA patients and 162 healthy controls (HC) was performed. All clinical characteristics of the knee OA patients were obtained from their medical records. PLR and NMR were compared between knee OA patients and HC by non-parametric tests. Correlations of PLR and NMR with Kellgren-Lawrence (KL) classification (KL grade 2, KL grade 3, and KL grade 4) were also analyzed through a Spearman correlation test. Ordinal polytomous logistic regression was used to determine independent factors influencing radiographic grades of knee OA patients.

RESULTS

PLR was increased significantly in knee OA patients, while a statistical difference in NMR was not observed. However, PLR was not relevant to KL grades, while NMR was negatively correlated with these (r = -0.330, P < 0.01) and was independently associated with KL grades of knee OA.

CONCLUSION

PLR could reflect the inflammation response of knee OA. NMR emerged as an independent factor and could be used as a potential marker indicating the severity of knee OA.

Characteristics and Follow-Up of 13 pedigrees with Gitelman syndrome

CONTEXT

Gitelman syndrome (GS) is clinically heterogeneous. The genotype and phenotype correlation has not been well established. Though the long-term prognosis is considered to be favorable, hypokalemia is difficult to cure.

OBJECTIVE

To analyze the clinical and genetic characteristics and treatment of all members of 13 GS pedigrees.

METHODS

Thirteen pedigrees (86 members, 17 GS patients) were enrolled. Symptoms and management, laboratory findings, and genotype-phenotype associations among all the members were analyzed.

RESULTS

The average ages at onset and diagnosis were 27.6 ± 10.2 years and 37.9 ± 11.6 years, respectively. Males were an average of 10 years younger and exhibited more profound hypokalemia than females. Eighteen mutations were detected. Two novel mutations (p.W939X, p.G212S) were predicted to be pathogenic by bioinformatic analysis. GS patients exhibited the lowest blood pressure, serum K⁺, Mg²⁺, and 24-h urinary Ca²⁺ levels. Although blood pressure, serum K⁺ and Mg²⁺ levels were normal in heterozygous carriers, 24-h urinary Na⁺ excretion was significantly increased. During follow-up, only 41.2% of patients reached a normal serum K⁺ level. Over 80% of patients achieved a normal Mg²⁺ level. Patients were taking 2-3 medications at higher doses than usual prescription to stabilize their K⁺ levels. Six patients were taking spironolactone simultaneously, but no significant elevation in the serum K⁺ level was observed.

CONCLUSION

The phenotypic variability of GS and therapeutic strategies deserve further research to improve GS diagnosis and prognosis. Even heterozygous carriers exhibited increased 24-h Na⁺ urine excretion, which may make them more susceptible to diuretic-induced hypokalemia.

Effect of nucleoside analogues in the treatment of hepatitis B cirrhosis and its effect on Th17 cell

OBJECTIVE

We conducted this study to analyze the effects of nucleoside analogues in the treatment of hepatitis B cirrhosis and its effect on Th17 cells.

PATIENTS AND METHODS

120 patients were randomly divided into lamivudine combined with adefovir dipivoxil group (combined group) and entecavir group. There were 59 cases in the combined group and 59 cases in entecavir group. The combined group was administered lamivudine 100 mg/d + adefovir dipivoxil 10 mg/d and entecavir group was administered entecavir 0.5 mg/d. The treatment was continued until there was virus negativity and it maintains for at least 3 months.

RESULTS

The treatment effects were compared. We compared the average rate of viral clearance time and virus clearance of two groups of patients; the difference was not statistically significant (p>0.05). The relapse rate after a negative test of entecavir group was lower than that of the combined group (p<0.05). Before and after treatment, the levels of TBIL, ALT and ALB in the two groups were compared; the differences were not statistically significant (p>0.05). The Th17 cell proportion and the level of IL-17 after treatment of the entecavir group were lower than those before treatment. The combined group exhibited no change, and the entecavir group was lower than combined group; the differences were statistically significant (p<0.05).

CONCLUSIONS

Therefore, the effects of the combination of lamivudine and adefovir dipivoxil is the same as single entecavir treatment of hepatitis B cirrhosis suppression of viral replication. It does not increase liver injury and the antiviral effects of entecavir may be related to inhibition of the expression of Th17 cells and effector molecules IL-17.

Frailty and Health-Related Quality of Life in End Stage Renal Disease Patients of All Ages

BACKGROUND: Frailty is associated with worse health-related quality of life (HRQOL) in older adults and worse clinical outcomes in adults of all ages with end stage renal disease (ESRD). It is unclear whether frail adults of all ages with ESRD are more likely to experience worse HRQOL. OBJECTIVE: The goal of this study was to identify factors associated with worsening HRQOL in this population. DESIGN, SETTING AND MEASUREMENTS: We studied 233 adults of all ages with ESRD enrolled (11/2009-11/2013) in a longitudinal cohort study. Frailty status was measured at enrollment and HRQOL was reported (Excellent, Very Good, Good, Fair or Poor) at the initial assessment and follow-up (median follow-up 9.4 months). We studied factors associated with Fair/Poor HRQOL at follow-up using logistic regression and factors associated with HRQOL change using multinomial regression. All models were adjusted for age, sex, race, education, BMI, diabetes status, history of a previous transplant, type of dialysis and time between assessments. RESULTS: Fair/Poor HRQOL was reported by 28% at initial assessment and 33% at follow-up. 47.2% of participants had stable HRQOL, 22.8% better HRQOL, and 30.0% worse HRQOL at follow-up (P<0.001). In adjusted models, only frailty was associated with Fair/Poor HRQOL at follow-up (OR: 2.79, 95% CI: 1.32-5.90) and worsening HRQOL at follow-up (RR: 2.91, 95%CI: 1.08-7.80). CONCLUSIONS: Frail adults of all ages with ESRD are more likely to experience fair/poor HRQOL and worsening HRQOL over time. Frailty represents a state of decreased physiologic reserve that impacts not only clinical outcomes but also the patient-centered outcome of HRQOL.