Identification of common mechanisms and biomarkers of atrial fibrillation and heart failure based on machine learning

AIMS

Atrial fibrillation (AF) is the most common arrhythmia. Heart failure (HF) is a disease caused by heart dysfunction. The prevalence of AF and HF were progressively increasing over time. The co-existence of AF and HF presents a significant therapeutic challenge. In order to provide new ideas for the diagnosis of AF and HF, it is necessary to carry out biomarker related studies.

METHODS AND RESULTS

The training set and validation set data of AF and HF patient samples were downloaded from the GEO database, 'limma' was used to compare the differences in gene expression levels between the disease group and the normal group to screen for differentially expressed genes (DEGs). Weighted correlation network analysis (WGCNA) identified the modules with the highest positive correlation with AF and HF. Functional enrichment and PPI network construction of key genes were carried out. Biomarkers were screened by machine learning. The infiltration of immune cells in AF and HF groups was evaluated by R-packet 'CIBERSORT'. The miRNA network was constructed and potential therapeutic agents for biomarker genes were predicted through the drugbank database. Through WGCNA analysis, it was found that the modules most positively correlated with AF and HF were MEturquoise (r = 0.21, P value = 0.09) and MEbrown (r = 0.62, P value = 8e-12), respectively. We screened 25 genes that were highly correlated with both AF and HF. Lasso regression analysis results showed 7 and 20 core genes in AF and HF groups, respectively. The top 20 important genes in AF and HF groups were obtained as core genes by RF model analysis. Four biomarkers were obtained after the intersection of core genes in four groups, namely, GLUL, NCF2, S100A12, and SRGN. The diagnostic efficacy of four genes in AF validation sets was good (AUC: GLUL 0.76, NCF2 0.64, S100A12 0.68, and SRGN 0.76), as well as in the HF validation set (AUC: GLUL 0.76, NCF2 0.84, S100A12 0.92, and SRGN 0.68). The highest correlation with neutrophils was observed for GLUL, NCF2, and S100A12, while SRGN exhibited the strongest correlation with T cells CD4 memory resting in the AF group. GLUL, NCF2, S100A12, and SRGN were most associated with neutrophils in the HF group. A total of 101 miRNAs were predicted by four genes, and GLUL, NCF2, and S100A12 predicted a total of 10 potential therapeutic agents.

CONCLUSIONS

We identified four biological markers that are highly correlated with AF and HF, namely, GLUL, NCF2, S100A12, and SRGN. Our findings provide theoretical basis for the clinical diagnosis and treatment of AF and HF.

KCNH2A561V Heterozygous Mutation Inhibits KCNH2 Protein Expression via The Activation of UPR Mediated by ATF6

The potassium channel protein KCNH2 is encoded by KCNH2 gene, and there are more than 300 mutations of KCNH2. Unfolded protein response (UPR) is typically initiated in response to an accumulation of unfolded and/or misfolded proteins in the endoplasmic reticulum (ER). The present study aimed to explore the UPR process and the role of activating transcription factor 6 (ATF6) in the abnormal expression of potassium voltage-gated channel subfamily H member 2 (KCNH2)A561V. The wild-type (wt) KCNH2 and A561V mutant KCNH2 was constructed with his-tag. The 293 cells were used and divided into KCNH2wt+KCNH2A561V, KCNH2wt and KCNH2A561V groups. The expression levels of ATF6 and KCNH2 in different groups were detected by Western blotting, reverse transcription-quantitative PCR, immunofluorescence and immuno-coprecipitation assays. The protein types and abundance of immuno-coprecipitation samples were analyzed by mass spectrometry. The proteomic analysis of the mass spectrometry results was carried out by using the reactome database and GO (Gene Ontology) tool. The mRNA expression levels of KCNH2 and ATF6 in the KCNH2wt+KCNH2A561V group were higher compared with the KCNH2A561V group. However, the full-length protein expression of ATF6 was inhibited, indicating that ATF6 was highly activated and a substantial number of ATF6 was sheared in KCNH2wt+KCNH2A561V group compared with control group. Furthermore, A561V-KCNH2 mutation leading to the accumulation of the immature form of KCNH2 (135 kDa bands) in ER, resulting in the reduction of the ratio of 155 kDa/135 kDa. In addition, the abundance of UPR-related proteins in the KCNH2A561V group was higher compared with the KCNH2wt+KCNH2A561V group. The 'cysteine biosynthetic activity' of GO:0019344 process and the 'positive regulation of cytoplasmic translation activity' of GO:2000767 process in the KCNH2A561V group were higher compared with the KCNH2wt+KCNH2A561V group. Hence, co-expression of wild-type and A561V mutant KCNH2 in 293 cells activated the UPR process, which led to the inhibition of protein translation and synthesis, in turn inhibiting the expression of KCNH2. These results provided a theoretical basis for clinical treatment of Long QT syndrome.

Organ-aware CBCT enhancement via dual path learning for prostate cancer treatment

BACKGROUND

Cone-beam computed tomography (CBCT) plays a crucial role in the intensity modulated radiotherapy (IMRT) of prostate cancer. However, poor image contrast and fuzzy organ boundaries pose challenges to precise targeting for dose delivery and plan reoptimization for adaptive therapy.

PURPOSE

In this work, we aim to enhance pelvic CBCT images by translating them to high-quality CT images with a particular focus on the anatomical structures important for radiotherapy.

METHODS

We develop a novel dual-path learning framework, covering both global and local information, for organ-aware enhancement of the prostate, bladder and rectum. The global path learns coarse inter-modality translation at the image level. The local path learns organ-aware translation at the regional level. This dual-path learning architecture can serve as a plug-and-play module adaptable to other medical image-to-image translation frameworks.

RESULTS

We evaluated the performance of the proposed method both quantitatively and qualitatively. The training dataset consists of unpaired 40 CBCT and 40 CT scans, the validation dataset consists of 5 paired CBCT-CT scans, and the testing dataset consists of 10 paired CBCT-CT scans. The peak signal-to-noise ratio (PSNR) between enhanced CBCT and reference CT images is 27.22 ± 1.79, and the structural similarity (SSIM) between enhanced CBCT and the reference CT images is 0.71 ± 0.03. We also compared our method with state-of-the-art image-to-image translation methods, where our method achieves the best performance. Moreover, the statistical analysis confirms that the improvements achieved by our method are statistically significant.

CONCLUSIONS

The proposed method demonstrates its superiority in enhancing pelvic CBCT images, especially at the organ level, compared to relevant methods.

An Automated Brain Metastasis Detection and Segmentation System from MRI with a Large Multi-Institutional Dataset

PURPOSE/OBJECTIVE(S)

Developments of automated systems for brain metastasis (BM) detection and segmentation from MRI for assisting early detection and stereotactic radiosurgery (SRS) have been reported but most based upon relatively small datasets from single institutes. This work aims to develop and evaluate a system using a large multi-institutional dataset, and to improve both identification of small/subtle BMs and segmentation accuracy of large BMs.

MATERIALS/METHODS

A 3D U-Net system was trained and evaluated to detect and segment intraparenchymal BMs with a size > 2mm using 1856 MRI volumes from 1791 patients treated with SRS from seven institutions (1539 volumes for training, 183 for validation, and 134 for testing). All patients had 3D post-Gd T1w MRI scans pre-SRS. Gross tumor volumes (GTVs) of BMs for SRS were curated by each institute first. Then, additional efforts were spent to create GTVs for the untreated and/or uncontoured BMs, including central reviews by two radiologists, to improve accuracy of ground truth. The training dataset was augmented with synthetic BMs of 3773 MRIs using a 3D generative pipeline. Our system consists of two U-Nets with one using small 3D patches dedicated for detecting small BMs and another using large 3D patches for segmenting large BMs, and a random-forest based fusion module for combining the two network outputs. The first U-Net was trained with 3D patches containing at least one BM < 0.1 cm3. For detection performance, we measured BM-level sensitivity and case-level false-positive (FP) rate. For segmentation performance, we measured BM-level Dice similarity coefficient (DSC) and 95-percentile Hausdorff distance (HD95). We also stratified performances based upon BM sizes.

RESULTS

For 739 BMs in the 134 testing cases, the overall lesion-level sensitivity was 0.870 with an average case-level FP of 1.34±1.92 (95% CI: 1.02-1.67). The sensitivity was >0.969 for the BMs >0.1 cm3, but dropped to 0.755 for the BMs < 0.1 cm3 (Table 1). The average DSC and HD95 for all detected BMs were 0.786 and 1.35mm. The worse performance for BMs > 20 cm3 was caused by a case with 83 cm3 GTV and artifacts in the MRI volume.

CONCLUSION

We achieved excellent detection sensitivity and segmentation accuracy for BMs > 0.1 cm3, and promising performance for small BMs (<0.1cm3) with a controlled FP rate using a large multi-institutional dataset. Clinical utility for assisting early detection and SRS planning will be investigated. Table 1: Per-lesion detection and segmentation performance stratified by individual BM size. N is the number of BMs in each category.

Dynamic Cross-Task Representation Adaptation for Clinical Targets Co-Segmentation in CT Image-Guided Post-Prostatectomy Radiotherapy

Adjuvant and salvage radiotherapy after radical prostatectomy requires precise delineations of prostate bed (PB), i.e., the clinical target volume, and surrounding organs at risk (OARs) to optimize radiotherapy planning. Segmenting PB is particularly challenging even for clinicians, e.g., from the planning computed tomography (CT) images, as it is an invisible/virtual target after the operative removal of the cancerous prostate gland. Very recently, a few deep learning-based methods have been proposed to automatically contour non-contrast PB by leveraging its spatial reliance on adjacent OARs (i.e., the bladder and rectum) with much more clear boundaries, mimicking the clinical workflow of experienced clinicians. Although achieving state-of-the-art results from both the clinical and technical aspects, these existing methods improperly ignore the gap between the hierarchical feature representations needed for segmenting those fundamentally different clinical targets (i.e., PB and OARs), which in turn limits their delineation accuracy. This paper proposes an asymmetric multi-task network integrating dynamic cross-task representation adaptation (i.e., DyAdapt) for accurate and efficient co-segmentation of PB and OARs in one-pass from CT images. In the learning-to-learn framework, the DyAdapt modules adaptively transfer the hierarchical feature representations from the source task of OARs segmentation to match up with the target (and more challenging) task of PB segmentation, conditioned on the dynamic inter-task associations learned from the learning states of the feed-forward path. On a real-patient dataset, our method led to state-of-the-art results of PB and OARs co-segmentation. Code is available at https://github.com/ladderlab-xjtu/DyAdapt.

Focused liquid ultrasonography in dropsy protocol for quantitative assessment of subcutaneous edema

BACKGROUND

Although subcutaneous edema is a common symptom of critically ill patients, it is still underreported due to the lack of a systematic method for evaluating it. The present study aims to describe the occurrence and distribution of subcutaneous edema, as well as the risk factors associated with it, in critically ill patients using the focused liquid ultrasonography in dropsy (FLUID) protocol, and to assess their impact on ICU mortality.

METHODS

The FLUID protocol and the pitting test were performed on general ICU patients in China. Cohen's Kappa coefficient and Bland-Altman plots were used to evaluate the agreement between the two methods at each measurement site and between the whole-body subcutaneous edema scores, respectively, while a repeated measures ANOVA was performed to compare the differences between the two methods in whole-body and body-part measurements. A generalized linear model was used to evaluate the risk factors for subcutaneous edema development and the relationship between subcutaneous edema severity and ICU mortality.

RESULTS

A total of 145 critically ill patients were evaluated using both approaches, of whom 40 (27.6%) experienced subcutaneous edema. Over 1440 measurements, it was found that ultrasound discovered more subcutaneous edema than the pitting test (ultrasound: 522[36.3%], pitting test: 444[30.8%], χ² = 9.477, p = 0.002). The FLUID protocol scored edema severity significantly higher than the pitting test in the whole body and specific body parts, including the abdominal wall, thighs, chest wall, and hands. Subcutaneous edema exhibited gravity-dependent distribution patterns, particularly in the abdominal wall. The APACHE II, NT-proBNP, serum creatinine, and sepsis were independent risk factors for subcutaneous edema development. The score of ultrasonic subcutaneous edema was related to ICU mortality.

CONCLUSIONS

The FLUID protocol provides a comprehensive strategy for the semi-quantitative assessment of subcutaneous edema in critically ill patients. In detecting the onset and severity of edema, ultrasound was found to outperform the pitting test. Subcutaneous edema showed a gravity-dependent distribution pattern, and its severity was associated with mortality.

Sequential monoscopic image-guided motion compensation in tomotherapy stereotactic body radiotherapy (SBRT) for prostate cancer

PURPOSE

To manage intra-fractional motions, recent developments in tomotherapy enable a unique capability of adjusting MLC/jaw to track the moving target based on the intra-fractional motions detected by sequential monoscopic imaging. In this study, we evaluated the effectiveness of motion compensation with a realistic imaging rate for prostate stereotactic body radiotherapy (SBRT). The obtained results will guide optimizing treatment parameters and image-guided radiation therapy (IGRT) in tomotherapy using this approach.

METHODS

Ten retrospective prostate cases with actual prostate motion curves previously recorded through the Calypso system were used in this study. Based on the recorded peak-to-peak motion, these cases represented either large (> 5 mm) or median (≤ 5 mm) intra-fractional prostate motions. All the cases were re-planned on tomotherapy using 35 Gy/5 fractions SBRT regimen and three different jaw settings of 1 cm static, 2.5 cm static, and 2.5 cm dynamic jaw. Two motion compensation methods were evaluated: a complete compensation that adjusted the jaw and MLC every 0.1 s (the same rate as the Calypso motion trace), and a realistic compensation that adjusted the jaw and MLC at an average imaging interval of 6 s from sequential monoscopic images. An in-house 4D dose calculation software was then applied to calculate the dosimetric outcomes from the original motion-free plan, the motion-contaminated plan, and the two abovementioned motion-compensated plans. During the process, various imaging rates were also simulated in one case with unusually large motions to quantify the impact of the KV-imaging rate on the effectiveness of motion compensation.

RESULTS

The effectiveness of motion compensation was evaluated based on the PTV coverage and OAR sparing. Without any motion-compensation, the PTV coverage (PTV V100%) of patients with large prostate motions decreased remarkably to 55%-82% when planning with the 1 cm jaw but to a less level of 67-94% with the 2.5 cm jaw. In contrast, motion compensation improved the PTV coverage (>92%) when combined with the 2.5 cm jaw, but less effective, around 75%-94%, with the 1 cm jaw. For OAR sparing, the bladder D1cc, bladder D10cc, and rectum D1cc all increased in the motion-contaminated plans. Motion compensation improved OAR sparing to the equivalent level of the original motion-free plans. For patients with median prostate motion, motion-induced degradation in PTV coverage was only observed when planning with the 1 cm jaw. After motion compensation, the PTV coverage improved to better than 94% for all three jaw settings. Additionally, the effectiveness of motion compensation depends on the imaging rate. Motion compensation with a typical rate of two KV images per gantry rotation effectively reduces motion-induced dosimetric uncertainties. However, a higher imaging rate is recommended when planning with a 1 cm jaw for patients with large motions.

CONCLUSION

Our results demonstrated that the performance of sequential monoscopic imaging-guided motion compensation on tomotherapy depends on the amplitude of intra-fractional prostate motion, the plan parameter settings, especially jaw setting, gantry rotation, and the imaging rate for motion compensation. Creating a patient-specific imaging guidance protocol is essential to balance the effectiveness of motion compensation and achievable imaging rate for intra-fractional motion tracking.

OpenKBP-Opt: an international and reproducible evaluation of 76 knowledge-based planning pipelines

Objective.To establish an open framework for developing plan optimization models for knowledge-based planning (KBP).Approach.Our framework includes radiotherapy treatment data (i.e. reference plans) for 100 patients with head-and-neck cancer who were treated with intensity-modulated radiotherapy. That data also includes high-quality dose predictions from 19 KBP models that were developed by different research groups using out-of-sample data during the OpenKBP Grand Challenge. The dose predictions were input to four fluence-based dose mimicking models to form 76 unique KBP pipelines that generated 7600 plans (76 pipelines × 100 patients). The predictions and KBP-generated plans were compared to the reference plans via: the dose score, which is the average mean absolute voxel-by-voxel difference in dose; the deviation in dose-volume histogram (DVH) points; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models.Main results.The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50-0.62, which indicates that the quality of the predictions was generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better (P< 0.05; one-sided Wilcoxon test) on 18 of 23 DVH points. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans, which satisfied 3.5% more criteria than the set of all dose predictions. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for an inverse planning model.Significance.This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. We found that the best performing models significantly outperformed the reference dose and dose predictions. In the interest of reproducibility, our data and code is freely available.

HIV testing and related factors among 50 years and older MSM in China: results from a cross-sectional study

ABSTRACTHIV prevalence has increased continuously, and the age groups most afflicted by the epidemic have also shifted to people aged above 50 years. Informed by the theory of HBM, we aimed to investigate related factors associated with regular HIV testing behavior. Cross-sectional data were collected using online questionnaire from geosocial networking (GSN) mobile application (apps) for MSM during May 2020 (N = 1259). Data were analyzed by univariate and multivariate logistic regression. Around 62.0% (n = 781) had received HIV testing before. Participants being divorced/widowed (AOR = 1.5,95%CI:1.1-2.0), being aware of HIV/AIDS-related knowledge (AOR = 1.8,95%CI:1.4-2.3), having disclosed sexual orientation (AOR = 1.9,95%CI:1.5-2.5), ever had sexually transmitted infections symptoms (STIs)before (AOR = 2.4,95%CI:1.8-3.2), having had≥2 sexual partners (AOR = 1.8,95%CI:1.4-2.3) and with high self-efficacy (AOR = 1.1,95%CI:1.0-1.1) were more likely to receive HIV testing. Findings suggest that many Chinses MSM aged 50 and above have not been tested for HIV. Interventions for promoting HIV testing should focus on expanding scales of HIV/STIs screening, providing HIV/AIDS-related knowledge, creating a more supportive social environment and improving self-efficacy of HIV testing.

[Receiving Human Immunodeficiency Virus Serostatus Disclosure from Male Sexual Partners and Related Factors among Men Who Have Sex with Men Aged 50 and Above]

Objective To investigate the rate and correlates of receiving human immunodeficiency virus(HIV) serostatus disclosure from their most recent male sexual partners among men who have sex with men(MSM) aged 50 and above. Methods With a geosocial networking application,we recruited participants through online convenience sampling to collect the demographic variables,behavioral information,receiving HIV serostatus disclosure,etc.Univariate and multivariate analyses were performed to interpret the associated factors of receiving HIV serostatus disclosure. Results Overall,38.4%(398/1037) of participants received HIV serostatus disclosure from their most recent male sexual partners.The multivariable analysis demonstrated that the following populations were less likely to receive HIV serostatus disclosure from their most recent male sexual partners:participants with junior high school degree or below(OR=0.660,95%CI=0.473-0.922, P=0.015) compared to those with senior high school degree or above;participants unemployed(OR=0.537,95%CI=0.322-0.896, P=0.017) and employed(OR=0.663,95%CI=0.466-0.944, P=0.022) compared to those retired;participants without knowledge about HIV or acquired immune deficiency syndrome(AIDS) compared to those with knowledge about HIV/AIDS(OR=0.636,95%CI=0.466-0.868, P=0.004);participants having ≥2 male sexual partners in the last year(OR=0.433,95%CI=0.320-0.586, P<0.001) compared to those having none or one male sexual partner;participants never been tested for HIV(OR=0.544,95%CI=0.403-0.734, P<0.001) compared to those ever been tested for HIV;participants ever been diagnosed to have sexually transmitted infection(STI)(OR=0.472,95%CI=0.349-0.637, P<0.001) compared to those never diagnosed to have STI;and participants with higher level of HIV stigma(OR=0.742,95%CI=0.604-0.912, P=0.005). Conclusions Our findings indicated that the MSM aged 50 and above had low possibility of receiving HIV serostatus disclosure from the most recent male sexual partners.Education,employment status,number of sexual partners,HIV/AIDS-related knowledge,HIV testing behaviors,STI infection history,and HIV stigma contributed to this result.

Synthetic digital reconstructed radiographs for MR-only robotic stereotactic radiation therapy: A proof of concept

PURPOSE

To create synthetic CTs and digital reconstructed radiographs (DRRs) from MR images that allow for fiducial visualization and accurate dose calculation for MR-only radiosurgery.

METHODS

We developed a machine learning model to create synthetic CTs from pelvic MRs for prostate treatments. This model has been previously proven to generate synthetic CTs with accuracy on par or better than alternate methods, such as atlas-based registration. Our dataset consisted of 11 paired CT and conventional MR (T2) images used for previous CyberKnife (Accuray, Inc) radiotherapy treatments. The MR images were pre-processed to mimic the appearance of fiducial-enhancing images. Two models were trained for each parameter case, using a sub-set of the available image pairs, with the remaining images set aside for testing and validation of the model to identify the optimal patch size and number of image pairs used for training. Four models were then trained using the identified parameters and used to generate synthetic CTs, which in turn were used to generate DRRs at angles 45° and 315°, as would be used for a CyberKnife treatment. The synthetic CTs and DRRs were compared visually and using the mean squared error and peak signal-to-noise ratio against the ground-truth images to evaluate their similarity.

RESULTS

The synthetic CTs, as well as the DRRs generated from them, gave similar visualization of the fiducial markers in the prostate as the true counterparts. There was no significant difference found for the fiducial localization for the CTs and DRRs. Across the 8 DRRs analyzed, the mean MSE between the normalized true and synthetic DRRs was 0.66 ± 0.42% and the mean PSNR for this region was 22.9 ± 3.7 dB. For the full CTs, the mean MAE was 72.9 ± 88.1 HU and the mean PSNR was 31.2 ± 2.2 dB.

CONCLUSIONS

Our machine learning-based method provides a proof of concept of a way to generate synthetic CTs and DRRs for accurate dose calculation and fiducial localization for use in radiation treatment of the prostate.

[Analysis of the effect of preventive intervention on occupational exposure of nurses after tumor particle implantation in thoracic surgery]

Objective: To study the effect of preventive intervention on occupational exposure of nurses after tumor particle implantation in thoracic surgery. Methods: In March 2020, 99 nurses who were engaged in postoperative nursing of tumor particle implantation in thoracic surgery department of our hospital from February 2019 to February 2020 were selected as the research objects. According to different preventive interventions, they were divided into observation group (51 cases) and control group (48 cases) . The observation group received preventive intervention, while the control group received routine intervention. The differences of radiation dose, psychological state and abnormal rate of important organ function between the two groups were analyzed. Results: Compared with the control group, the radiation dose of the observation group was significantly less, and the scores of anxiety and depression were lower after the intervention, the difference were statistically significant (P<0.05) . There was no significant difference of the abnormal rate of important organ function between the two groups (P>0.05) . Conclusion: Preventive intervention can reduce the risk of occupational exposure and improve the psychological status of nurses after tumor particle implantation in thoracic surgery.

LINC00641 induces the malignant progression of colorectal carcinoma through the miRNA-424-5p/PLSCR4 feedback loop

OBJECTIVE

To illustrate the role of LINC00641 in inducing the malignant progression of colorectal cancer (CRC) through the miRNA-424-5p/PLSCR4 feedback loop.

PATIENTS AND METHODS

LINC00641 levels in paired CRC and non-tumoral tissues were detected by quantitative real-time polymerase chain reaction (qRT-PCR). Its prognostic potential in CRC was assessed by Kaplan-Meier method. Changes in proliferative and migratory abilities of HCT116 and SW620 cells transfected with si-LINC00641 were evaluated by 5-Ethynyl-2'- deoxyuridine (EdU), cell counting kit-8 (CCK-8) and transwell assay. The feedback loop LINC00641/miRNA-424-5p/PLSCR4 was identified through Dual-Luciferase reporter assay and its involvement in CRC progression was finally explored by rescue experiments.

RESULTS

LINC00641 was upregulated in CRC tissues, which was an unfavorable factor to the overall survival of CRC. Proliferative and migratory abilities of HCT116 and SW620 cells were inhibited by knockdown of LINC00641. LINC00641 could competitively bind miRNA-424-5p, thereby abolishing its inhibitory effect on PLSCR4 expression. Knockdown of PLSCR4 could inhibit proliferative and migratory abilities of HCT116 and SW620 cells.

CONCLUSIONS

LINC00641 stimulates proliferative and migratory abilities of CRC through the miRNA-424-5p/PLSCR4 feedback loop.

Asymmetric multi-task attention network for prostate bed segmentation in computed tomography images

Post-prostatectomy radiotherapy requires accurate annotation of the prostate bed (PB), i.e., the residual tissue after the operative removal of the prostate gland, to minimize side effects on surrounding organs-at-risk (OARs). However, PB segmentation in computed tomography (CT) images is a challenging task, even for experienced physicians. This is because PB is almost a "virtual" target with non-contrast boundaries and highly variable shapes depending on neighboring OARs. In this work, we propose an asymmetric multi-task attention network (AMTA-Net) for the concurrent segmentation of PB and surrounding OARs. Our AMTA-Net mimics experts in delineating the non-contrast PB by explicitly leveraging its critical dependency on the neighboring OARs (i.e., the bladder and rectum), which are relatively easy to distinguish in CT images. Specifically, we first adopt a U-Net as the backbone network for the low-level (or prerequisite) task of the OAR segmentation. Then, we build an attention sub-network upon the backbone U-Net with a series of cascaded attention modules, which can hierarchically transfer the OAR features and adaptively learn discriminative representations for the high-level (or primary) task of the PB segmentation. We comprehensively evaluate the proposed AMTA-Net on a clinical dataset composed of 186 CT images. According to the experimental results, our AMTA-Net significantly outperforms current clinical state-of-the-arts (i.e., atlas-based segmentation methods), indicating the value of our method in reducing time and labor in the clinical workflow. Our AMTA-Net also presents better performance than the technical state-of-the-arts (i.e., the deep learning-based segmentation methods), especially for the most indistinguishable and clinically critical part of the PB boundaries. Source code is released at https://github.com/superxuang/amta-net.

Mindfulness-based online intervention on mental health and quality of life among COVID-19 patients in China: an intervention design

BACKGROUND

COVID-19 can lead to increased psychological symptoms such as post-traumatic stress disorder (PTSD), depression, and anxiety among patients with COVID-19. Based on the previous mindfulness-based interventions proved to be effective, this protocol reports a design of a randomized controlled trial aiming to explore the efficacy and possible mechanism of a mindful living with challenge (MLWC) intervention developed for COVID-19 survivors in alleviating their psychological problems caused by both the disease and the pandemic.

METHODS

In April 2021, more than 1600 eligible participants from Hubei Province of China will be assigned 1:1 to an online MLWC intervention group or a waitlist control group. All participants will be asked to complete online questionnaires at baseline, post-program, and 3-month follow-up. The differences of mental health status (e.g. PTSD) and physical symptoms including fatigue and sleeplessness between the COVID-19 survivors who receiving the online MLWC intervention and the control group will be assessed. In addition, the possible mediators and moderators of the link between the MLWC intervention and target outcomes will be evaluated by related verified scales, such as the Five Facets Mindfulness Questionnaire. Data will be analyzed based on an intention-to-treat approach, and SPSS software will be used to perform statistical analysis.

DISCUSSION

The efficacy and potential mechanism of MLWC intervention in improving the quality of life and psychological status of COVID-19 survivors in China are expected to be reported. Findings from this study will shed light on a novel and feasible model in improving the psychological well-being of people during such public health emergencies. Trial registration Chinese Clinical Trial Registry (ChiCTR), ChiCTR2000037524; Registered on August 29, 2020, http://www.chictr.org.cn/showproj.aspx?proj=60034 .

[HIV infection and related factors among men who have sex with men aged 50 and above]

Objectives: To explore the HIV prevalence and related factors among MSM aged 50 and above and provide evidence on the prevention and control of HIV/AIDS. Methods: Based on an MSM social application software Blued 7.1.6, we recruited participants through online convenience sampling to collect demographic variables, behavioral and self-reported HIV infection status, etc. Univariate χ2 test and multivariate logistic regression were used to analyze the related factors of self-reported HIV infection. Results: Self-reported HIV infection rate was 17.6%(126/714) among the participants. In multivariable analysis, participants who got divorced or widowed had a 2.07(95%CI: 1.34-3.21) times greater risk of self-reported HIV-positive than those who were married. Participants unaware of HIV-related knowledge showed a 1.92(95%CI:1.21-3.04) times greater risk of self-reported HIV-positive than those with better HIV-related knowledge. Participants who have ever been diagnosed with sexually transmitted disease (STD) showed a 3.17(95%CI:2.09-4.83) times greater risk of self-reported HIV-positive than those without STD infection history. Conclusion: Our findings indicated that the self-reported HIV infection rate was high among MSM aged 50 and above. Being divorced or widowed, being unaware of HIV-related knowledge and STD infection history was proved related with self-reported HIV infection.

[Role of Orai 1-mediated store-operated calcium entry in the immune function of CD4+ T cells in septic mice]

Objective: To investigate the role of Orai1-mediated store-operated calcium entry in the immune damage of CD4⁺ T cells in septic mice. Methods: Sepsis mouse model was established by cecal ligation and puncture(CLP). Balb/c mice of clean grade were sacrificed 1, 3, and 5 days after operation. Spleen samples were harvested at given intervals. Splenic CD4⁺ T cells were selected by immunomagnetic beads and the expression of Orai1 protein was detected by western blotting, the storage operated calcium entry (SOCE) was detected by flow cytometry, the apoptosis of CD4⁺ T cells was detected by flow cytometry, the proliferation of CD4⁺ T cells was detected by CCK-8, and the IFN-γ and IL-4 were detected by enzyme-linked immunosorbent assay (ELISA). Then the expression of Orai1 protein was regulated to further detect the SOCE and immune function of splenic CD4⁺ T cells in mice. The experiment was divided into 4 groups, sham group, CLP3 group, Orai1 down group (Orai1-down group) and Orai1 up regulation group (Orai1-up group). Results: The relative expression of Orai1 protein in splenic CD4⁺ T cells in sham group was 1.03±0.16. Compared with sham group, Orai1 protein levels in CLP Group were all significantly lower (F=19.64, P=0.000 5). The increased value of splenic CD4⁺ T cells fluorescence intensity in sham group was 494±41. Compared with sham group, the levels of SOCE in CLP Group were all lower (F=30.01, P=0.001). The ratio of early and late apoptosis of CD4⁺ T cells in sham group was 8.7%±1.5%. Compared with sham group, the early and late apoptosis rates of CLP Group were significantly higher (F=32.29, P=0.000 1). The OD of sham group was 0.81±0.10 at 450 nm. Compared with sham group, the proliferation ability of splenic CD4⁺ T cells in CLP Group were significantly decreased (F=7.26, P=0.001 8). Compared with sham group, the secretion of IFN-γ and IL-4 by CD4⁺ T cells and the ratio of IFN-γ/IL-4 in CLP Group were all significantly decreased (F=19.690, 6.183, 11.230, all P<0.05). Compared with CLP3 group, the increased value of fluorescence intensity of CD4⁺ T cells was significantly decreased, the early and late apoptosis ratio of CD4⁺ T cells was significantly increased, the OD450 nm value of CD4⁺ T cells was decreased, the multiplication capacity of splenic CD4⁺ T cells were decreased, the level of IFN-γ and IL-4 secreted by T cells were decreased, and the value of IFN-γ/IL-4 in orai1-down group was decreased (t=4.819, 7.952, 2.988, 28.760, 3.140, 7.670, all P<0.05). However, Orail-up group showed the opposite trend. Conclusion: Orai1-mediated store-operated calcium entry can alleviate the immune dysfunction of CD4⁺ T cells in septic mice.

Boundary Coding Representation for Organ Segmentation in Prostate Cancer Radiotherapy

Accurate segmentation of the prostate and organs at risk (OARs, e.g., bladder and rectum) in male pelvic CT images is a critical step for prostate cancer radiotherapy. Unfortunately, the unclear organ boundary and large shape variation make the segmentation task very challenging. Previous studies usually used representations defined directly on unclear boundaries as context information to guide segmentation. Those boundary representations may not be so discriminative, resulting in limited performance improvement. To this end, we propose a novel boundary coding network (BCnet) to learn a discriminative representation for organ boundary and use it as the context information to guide the segmentation. Specifically, we design a two-stage learning strategy in the proposed BCnet: 1) Boundary coding representation learning. Two sub-networks under the supervision of the dilation and erosion masks transformed from the manually delineated organ mask are first separately trained to learn the spatial-semantic context near the organ boundary. Then we encode the organ boundary based on the predictions of these two sub-networks and design a multi-atlas based refinement strategy by transferring the knowledge from training data to inference. 2) Organ segmentation. The boundary coding representation as context information, in addition to the image patches, are used to train the final segmentation network. Experimental results on a large and diverse male pelvic CT dataset show that our method achieves superior performance compared with several state-of-the-art methods.

Machine learning and statistical prediction of patient quality-of-life after prostate radiation therapy

Thanks to advancements in diagnosis and treatment, prostate cancer patients have high long-term survival rates. Currently, an important goal is to preserve quality of life during and after treatment. The relationship between the radiation a patient receives and the subsequent side effects he experiences is complex and difficult to model or predict. Here, we use machine learning algorithms and statistical models to explore the connection between radiation treatment and post-treatment gastro-urinary function. Since only a limited number of patient datasets are currently available, we used image flipping and curvature-based interpolation methods to generate more data to leverage transfer learning. Using interpolated and augmented data, we trained a convolutional autoencoder network to obtain near-optimal starting points for the weights. A convolutional neural network then analyzed the relationship between patient-reported quality-of-life and radiation doses to the bladder and rectum. We also used analysis of variance and logistic regression to explore organ sensitivity to radiation and to develop dosage thresholds for each organ region. Our findings show no statistically significant association between the bladder and quality-of-life scores. However, we found a statistically significant association between the radiation applied to posterior and anterior rectal regions and changes in quality of life. Finally, we estimated radiation therapy dose thresholds for each organ. Our analysis connects machine learning methods with organ sensitivity, thus providing a framework for informing cancer patient care using patient reported quality-of-life metrics.

Asymmetrical Multi-task Attention U-Net for the Segmentation of Prostate Bed in CT Image

Segmentation of the prostate bed, the residual tissue after the removal of the prostate gland, is an essential prerequisite for post-prostatectomy radiotherapy but also a challenging task due to its non-contrast boundaries and highly variable shapes relying on neighboring organs. In this work, we propose a novel deep learning-based method to automatically segment this "invisible target". As the main idea of our design, we expect to get reference from the surrounding normal structures (bladder&rectum) and take advantage of this information to facilitate the prostate bed segmentation. To achieve this goal, we first use a U-Net as the backbone network to perform the bladder&rectum segmentation, which serves as a low-level task that can provide references to the high-level task of the prostate bed segmentation. Based on the backbone network, we build a novel attention network with a series of cascaded attention modules to further extract discriminative features for the high-level prostate bed segmentation task. Since the attention network has one-sided dependency on the backbone network, simulating the clinical workflow to use normal structures to guide the segmentation of radiotherapy target, we name the final composition model asymmetrical multi-task attention U-Net. Extensive experiments on a clinical dataset consisting of 186 CT images demonstrate the effectiveness of this new design and the superior performance of the model in comparison to the conventional atlas-based methods for prostate bed segmentation. The source code is publicly available at https://github.com/superxuang/amta-net.

CT Male Pelvic Organ Segmentation via Hybrid Loss Network With Incomplete Annotation

Sufficient data with complete annotation is essential for training deep models to perform automatic and accurate segmentation of CT male pelvic organs, especially when such data is with great challenges such as low contrast and large shape variation. However, manual annotation is expensive in terms of both finance and human effort, which usually results in insufficient completely annotated data in real applications. To this end, we propose a novel deep framework to segment male pelvic organs in CT images with incomplete annotation delineated in a very user-friendly manner. Specifically, we design a hybrid loss network derived from both voxel classification and boundary regression, to jointly improve the organ segmentation performance in an iterative way. Moreover, we introduce a label completion strategy to complete the labels of the rich unannotated voxels and then embed them into the training data to enhance the model capability. To reduce the computation complexity and improve segmentation performance, we locate the pelvic region based on salient bone structures to focus on the candidate segmentation organs. Experimental results on a large planning CT pelvic organ dataset show that our proposed method with incomplete annotation achieves comparable segmentation performance to the state-of-the-art methods with complete annotation. Moreover, our proposed method requires much less effort of manual contouring from medical professionals such that an institutional specific model can be more easily established.

Iterative Label Denoising Network: Segmenting Male Pelvic Organs in CT From 3D Bounding Box Annotations

Obtaining accurate segmentation of the prostate and nearby organs at risk (e.g., bladder and rectum) in CT images is critical for radiotherapy of prostate cancer. Currently, the leading automatic segmentation algorithms are based on Fully Convolutional Networks (FCNs), which achieve remarkable performance but usually need large-scale datasets with high-quality voxel-wise annotations for full supervision of the training. Unfortunately, such annotations are difficult to acquire, which becomes a bottleneck to build accurate segmentation models in real clinical applications. In this paper, we propose a novel weakly supervised segmentation approach that only needs 3D bounding box annotations covering the organs of interest to start the training. Obviously, the bounding box includes many non-organ voxels that carry noisy labels to mislead the segmentation model. To this end, we propose the label denoising module and embed it into the iterative training scheme of the label denoising network (LDnet) for segmentation. The labels of the training voxels are predicted by the tentative LDnet, while the label denoising module identifies the voxels with unreliable labels. As only the good training voxels are preserved, the iteratively re-trained LDnet can refine its segmentation capability gradually. Our results are remarkable, i.e., reaching  ∼ 94% (prostate),  ∼ 91% (bladder), and  ∼ 86% (rectum) of the Dice Similarity Coefficients (DSCs), compared to the case of fully supervised learning upon high-quality voxel-wise annotations and also superior to several state-of-the-art approaches. To our best knowledge, this is the first work to achieve voxel-wise segmentation in CT images from simple 3D bounding box annotations, which can greatly reduce many labeling efforts and meet the demands of the practical clinical applications.

Initial assessment of 3D magnetic resonance fingerprinting (MRF) towards quantitative brain imaging for radiation therapy

PURPOSE

Magnetic resonance fingerprinting (MRF) provides quantitative T1/T2 maps, enabling applications in clinical radiotherapy such as large-scale, multi-center clinical trials for longitudinal assessment of therapy response. We evaluated the feasibility of a quantitative three-dimensional-MRF (3D-MRF) towards its radiotherapy applications of primary brain tumors.

METHODS

A fast whole-brain 3D-MRF sequence initially developed for diagnostic radiology was optimized using flexible body coils, which is the typical MR imaging setup for radiotherapy treatment planning and for MR imaging (MRI)-guided treatment delivery. Optimization criteria included the accuracy and the precision of T1/T2 quantifications of polyvinylpyrrolidone (PVP) solutions, compared to those from the 3D-MRF using a 32-channel head coil. The accuracy of T1/T2 quantifications from the optimized MRF was first examined in healthy volunteers with two different coil setups. The intra- and inter-scanner variations of image intensity from the optimized sequence were quantified by longitudinal scans of the PVP solutions on two 3T scanners. Using a 3D-printed MRI geometry phantom, susceptibility-induced distortion with the optimized 3D-MRF was quantified as the Dice coefficient of phantom contours, compared to those from CT images. By introducing intentional head motion during 10% of the scan, the robustness of the optimized 3D-MRF towards motion was evaluated through visual inspection of motion artifacts and through quantitative analysis of image sharpness in brain MRF maps.

RESULTS

The optimized sequence acquired whole-brain T1, T2 and proton density maps and with a resolution of 1.2 × 1.2 × 3 mm³ in 10 min, similar to the total acquisition time of 3D T1- and T2-weighted images of the same resolution. In vivo T1 and T2 values of the white and gray matter were consistent with literature. The intra- and inter-scanner variability of the intensity-normalized MRF T1 was 1.0% ± 0.7% and 2.3% ± 1.0% respectively, in contrast to 5.3% ± 3.8% and 3.2% ± 1.6% from the normalized T1-weighted MRI. Repeatability and reproducibility of MRF T1 were independent of intensity normalization. Both phantom and human data demonstrated that the optimized 3D-MRF is more robust to subject motion and artifacts from subject-specific susceptibility difference. Compared to CT contours, the Dice coefficient of phantom contours from 3D-MRF was 0.93, improved from 0.87 from the T1-weighted MRI.

CONCLUSION

Compared to conventional MRI, the optimized 3D-MRF demonstrated improved repeatability across time points and reproducibility across scanners for better tissue quantification, as well as improved robustness to subject-specific susceptibility and motion artifacts under a typical MR imaging setup for radiotherapy. More importantly, quantitative MRF T1/T2 measurements lead to promising potentials towards longitudinal quantitative assessment of treatment response for better adaptive therapy and for large-scale, multi-center clinical trials.

[Baicalin regulates STIM1-mediated calcium overload and reduces apoptosis of cardiomyocytes induced by lipopolysaccharide]

Objective: To investigate the protective effect of Baicalin on apoptosis induced by lipopolysaccharide in H9C2 cardiomyocytes and its possible mechanism. Methods: In order to establish apoptosis model of H9C2 cardiomyocytes, H9C2 cardiomyocytes were cultured and divided into four groups: the control group; the baicalin group was treated with baicalin at the final concentration of 10μmol/L for 12 hours; the LPS group was stimulated with LPS at the final concentration of 1 μg/ml for 6 hours; The LPS+baicalin group was stimulated with LPS at the final concentration of 1 μg/ml for 6 hours within treated with baicalin at the final concentration of 10μmol/L for 12 hours. Collecting cell samples, CCK-8 (The Cell Counting Kit-8) was used to detect cell activity, and Terminal-deoxynucleoitidyl Transferase Mediated Nick End Labeling (TUNEL) was used to detect the expression levels of apoptosis. Laser Scanning Confocal Microscopy was used to detect the expression levels of store-operated calcium entry in H9C2 cardiomyocytes. Western blot was used to detect the protein expression levels of STIM1, cleaved-caspase3, Bax and Bcl-2. Fluorogenic quantitative PCR was used to detect the mRNA expression level of STIM1. Results: Compared with the control group, LPS-induced H9C2 cardiomyocyte survival rate decreased (P<0.05), the expression level of apoptosis increased (P<0.05), the internal flow of calcium increased (P<0.05), the expression levels of cleaved-caspase3, Bax protein levels increased (P<0.05), Bcl-2 protein level decreased (P<0.05), the expression of STIM1 mRNA and protein level increased (P<0.05). Compared with LPS group, the survival rate of H9C2 cardiomyocytes in baicalin intervention group increased (P<0.05), the expression level of apoptosis decreased (P<0.05), the internal flow of calcium decreased (P<0.05), the expression levels of cleaved-caspase3, Bax protein decreased (P<0.05), and the level of Bcl-2 protein increased (P<0.05), the expression of STIM1 mRNA and protein level decreased (P<0.05). Conclusion: Baicalin may alleviate LPS-induced cardiomyocyte apoptosis by alleviating calcium overload, and improve cell survival.

High-Resolution Encoder-Decoder Networks for Low-Contrast Medical Image Segmentation

Automatic image segmentation is an essential step for many medical image analysis applications, include computer-aided radiation therapy, disease diagnosis, and treatment effect evaluation. One of the major challenges for this task is the blurry nature of medical images (e.g., CT, MR and, microscopic images), which can often result in low-contrast and vanishing boundaries. With the recent advances in convolutional neural networks, vast improvements have been made for image segmentation, mainly based on the skip-connection-linked encoder-decoder deep architectures. However, in many applications (with adjacent targets in blurry images), these models often fail to accurately locate complex boundaries and properly segment tiny isolated parts. In this paper, we aim to provide a method for blurry medical image segmentation and argue that skip connections are not enough to help accurately locate indistinct boundaries. Accordingly, we propose a novel high-resolution multi-scale encoder-decoder network (HMEDN), in which multi-scale dense connections are introduced for the encoder-decoder structure to finely exploit comprehensive semantic information. Besides skip connections, extra deeply-supervised high-resolution pathways (comprised of densely connected dilated convolutions) are integrated to collect high-resolution semantic information for accurate boundary localization. These pathways are paired with a difficulty-guided cross-entropy loss function and a contour regression task to enhance the quality of boundary detection. Extensive experiments on a pelvic CT image dataset, a multi-modal brain tumor dataset, and a cell segmentation dataset show the effectiveness of our method for 2D/3D semantic segmentation and 2D instance segmentation, respectively. Our experimental results also show that besides increasing the network complexity, raising the resolution of semantic feature maps can largely affect the overall model performance. For different tasks, finding a balance between these two factors can further improve the performance of the corresponding network.

STRAINet: Spatially Varying sTochastic Residual AdversarIal Networks for MRI Pelvic Organ Segmentation

Accurate segmentation of pelvic organs is important for prostate radiation therapy. Modern radiation therapy starts to use a magnetic resonance image (MRI) as an alternative to computed tomography image because of its superior soft tissue contrast and also free of risk from radiation exposure. However, segmentation of pelvic organs from MRI is a challenging problem due to inconsistent organ appearance across patients and also large intrapatient anatomical variations across treatment days. To address such challenges, we propose a novel deep network architecture, called "Spatially varying sTochastic Residual AdversarIal Network" (STRAINet), to delineate pelvic organs from MRI in an end-to-end fashion. Compared to the traditional fully convolutional networks (FCN), the proposed architecture has two main contributions: 1) inspired by the recent success of residual learning, we propose an evolutionary version of the residual unit, i.e., stochastic residual unit, and use it to the plain convolutional layers in the FCN. We further propose long-range stochastic residual connections to pass features from shallow layers to deep layers; and 2) we propose to integrate three previously proposed network strategies to form a new network for better medical image segmentation: a) we apply dilated convolution in the smallest resolution feature maps, so that we can gain a larger receptive field without overly losing spatial information; b) we propose a spatially varying convolutional layer that adapts convolutional filters to different regions of interest; and c) an adversarial network is proposed to further correct the segmented organ structures. Finally, STRAINet is used to iteratively refine the segmentation probability maps in an autocontext manner. Experimental results show that our STRAINet achieved the state-of-the-art segmentation accuracy. Further analysis also indicates that our proposed network components contribute most to the performance.

An Adiponectin Receptor Agonist Reduces Type 2 Diabetic Periodontitis

Periodontitis is twice as prevalent in diabetics as in nondiabetics, and type 2 diabetes (T2D)-associated periodontitis is severe in many cases due to the altered and aberrant functions of bone cells in hyperglycemic conditions. Therefore, developing an effective method to halt the disease process, as well as restore and regenerate lost alveolar bone to reserve the natural teeth in diabetics, is critically important. In the current study, we applied a newly discovered adiponectin receptor agonist AdipoRon (APR) in experimental periodontitis in diabetic animal models and demonstrated the underlying molecular mechanisms. We found that when APR systemically quenched the blood sugar level in diet-induced obesity (DIO) diabetic mice, it reduced osteoclast numbers and alveolar bone loss significantly due to APR's inhibition on osteoclast differentiation shown in our in vitro studies. APR also decreased the production of proinflammatory molecules CC chemokine ligand 2 and interleukin 6 in diseased gingival tissues. On the other hand, APR promoted alveolar bone regeneration through enhancing osteogenic differentiation and decreasing stromal cell-derived factor 1 in the bone marrow that facilitates stem cell migration. Same results were achieved by APR treatment of periodontitis induced in adiponectin (APN) knockout mice, indicating the ability of APR to activate the endogenous APN receptors to exert osteoanabolic effects. In summary, our study supports the notion that APR could be used as an effective multipronged approach to target T2D-associated periodontitis.

Medical Image Synthesis with Deep Convolutional Adversarial Networks

Medical imaging plays a critical role in various clinical applications. However, due to multiple considerations such as cost and radiation dose, the acquisition of certain image modalities may be limited. Thus, medical image synthesis can be of great benefit by estimating a desired imaging modality without incurring an actual scan. In this paper, we propose a generative adversarial approach to address this challenging problem. Specifically, we train a fully convolutional network (FCN) to generate a target image given a source image. To better model a nonlinear mapping from source to target and to produce more realistic target images, we propose to use the adversarial learning strategy to better model the FCN. Moreover, the FCN is designed to incorporate an image-gradient-difference-based loss function to avoid generating blurry target images. Long-term residual unit is also explored to help the training of the network. We further apply Auto-Context Model to implement a context-aware deep convolutional adversarial network. Experimental results show that our method is accurate and robust for synthesizing target images from the corresponding source images. In particular, we evaluate our method on three datasets, to address the tasks of generating CT from MRI and generating 7T MRI from 3T MRI images. Our method outperforms the state-of-the-art methods under comparison in all datasets and tasks.

IL6 derived from cancer-associated fibroblasts promotes chemoresistance via CXCR7 in esophageal squamous cell carcinoma

Various factors and cellular components in the tumor microenvironment are key drivers associated with drug resistance in many cancers. Here, we analyzed the factors and molecular mechanisms involved in chemoresistance in patients with esophageal squamous cell carcinoma (ESCC). We found that interleukin 6 (IL6) derived mainly from cancer-associated fibroblasts played the most important role in chemoresistance by upregulating C-X-C motif chemokine receptor 7 (CXCR7) expression through signal transducer and activator of transcription 3/nuclear factor-κB pathway. CXCR7 knockdown resulted in the inhibition of IL6-induced proliferation and chemoresistance. In addition, CXCR7 silencing significantly decreased gene expression associated with stemness, chemoresistance and epithelial-mesenchymal transition and suppressed the proliferation ability of ESCC cells in three-dimensional culture systems and angiogenesis assay. In clinical samples, ESCC patients with high expression of CXCR7 and IL6 presented a significantly worse overall survival and progression-free survival upon receiving cisplatin after operation. These results suggest that the IL6-CXCR7 axis may provide a promising target for the treatment of ESCC.

Cortical Potential Imaging of Brain Electrical Activity by Means of Parametric Projection Filter

Objectives: The objective of this study was to explore suitable spatial filters for inverse estimation of cortical potentials from the scalp electroencephalogram. The effect of incorporating noise covariance into inverse procedures was examined by computer simulations and tested in human experiment.

Methods: The parametric projection filter, which allows inverse estimation with the presence of information on the noise, was applied to an inhomogeneous three-concentric-sphere model under various noise conditions in order to estimate the cortical potentials from the scalp potentials. The method for determining the optimum regularization parameter, which can be applied for parametric inverse techniques, is also discussed.

Results: Human visual evoked potential experiment was carried out to examine the performance of the proposed restoration method. The parametric projection filter gave more localized inverse solution of cortical potential distribution than the truncated SVD and Tikhonov regularization.

Conclusion: The present simulation results suggest that incorporation of information on the noise covariance allows better estimation of cortical potentials, than inverse solutions without knowledge about the noise covariance, when the correlation between the signal and noise is low.

Preparation of nitrogen-doped porous carbons for high-performance supercapacitor using biomass of waste lotus stems

In this study, advanced nitrogen-doped porous carbon materials for supercapacitor was prepared using low-cost and environmentally friendly waste lotus stems (denoted as LS-NCs). Nitrogen in the surface functionalities of LS-NCs was investigated using X-ray photoelectron spectroscopy analysis. The sum of pyridine nitrogen (N-6) and pyrrolic/pyridinic (N-5) contents accounted for 94.7% of the total nitrogen and significantly contributed to conductivity. Pore structure and surface area of activated carbons were measured using the Brunauer–Emmett–Teller method. A maximum specific surface area of 1322 m² g⁻¹ was achieved for LS-NCs. The porous carbons exhibited excellent electrochemical properties with a specific capacitance of 360.5 F g⁻¹ at a current density of 0.5 A g⁻¹ and excellent cycling stability (96% specific capacitance retention after 5000 cycles). The above findings indicate that taking advantage of the unique structure of abundant waste lotus stem provides a low-cost and feasible design for high-performance supercapacitors.

In this study, advanced nitrogen-doped porous carbon materials for supercapacitor was prepared using low-cost and environmentally friendly waste lotus stems (denoted as LS-NCs).

Mechanism of iodine release from iodoapatite in aqueous solution

Incongruent release of iodine from iodoapatite (Pb₅(VO₄)₃I) for immobilization of¹²⁹iodine, controlled by exchange of iodide and hydroxide in solution.

Medical Image Synthesis with Context-Aware Generative Adversarial Networks

Computed tomography (CT) is critical for various clinical applications, e.g., radiation treatment planning and also PET attenuation correction in MRI/PET scanner. However, CT exposes radiation during acquisition, which may cause side effects to patients. Compared to CT, magnetic resonance imaging (MRI) is much safer and does not involve radiations. Therefore, recently researchers are greatly motivated to estimate CT image from its corresponding MR image of the same subject for the case of radiation planning. In this paper, we propose a data-driven approach to address this challenging problem. Specifically, we train a fully convolutional network (FCN) to generate CT given the MR image. To better model the nonlinear mapping from MRI to CT and produce more realistic images, we propose to use the adversarial training strategy to train the FCN. Moreover, we propose an image-gradient-difference based loss function to alleviate the blurriness of the generated CT. We further apply Auto-Context Model (ACM) to implement a context-aware generative adversarial network. Experimental results show that our method is accurate and robust for predicting CT images from MR images, and also outperforms three state-of-the-art methods under comparison.

Are we making good use of our public resources? The false-positive rate of screening by fundus photography for diabetic macular oedema

INTRODUCTION

A large proportion of patients diagnosed with diabetic maculopathy using fundus photography and hence referred to specialist clinics following the current screening guidelines adopted in Hong Kong and United Kingdom are found to be false-positive, implying that they did not have macular oedema. This study aimed to evaluate the false-positive rate of diabetic maculopathy screening using the objective optical coherence tomography scan.

METHODS

This was a cross-sectional observational study. Consecutive diabetic patients from the Hong Kong West Cluster Diabetic Retinopathy Screening Programme with fundus photographs graded R1M1 were recruited between October 2011 and June 2013. Spectral-domain optical coherence tomography imaging was performed. Central macular thickness of ≥300 μm and/or the presence of optical coherence tomography signs of diabetic macular oedema were used to define the presence of diabetic macular oedema. Patients with conditions other than diabetes that might affect macular thickness were excluded. The mean central macular thickness in various subgroups of R1M1 patients was calculated and the proportion of subjects with central macular thickness of ≥300 μm was used to assess the false-positive rate of this screening strategy.

RESULTS

A total of 491 patients were recruited during the study period. Of the 352 who were eligible for analysis, 44.0%, 17.0%, and 38.9% were graded as M1 due to the presence of foveal 'haemorrhages', 'exudates', or 'haemorrhages and exudates', respectively. The mean (±standard deviation) central macular thickness was 265.1±55.4 μm. Only 13.4% (95% confidence interval, 9.8%-17.0%) of eyes had a central macular thickness of ≥300 μm, and 42.9% (95% confidence interval, 37.7%-48.1%) of eyes had at least one optical coherence tomography sign of diabetic macular oedema. For patients with retinal haemorrhages only, 9.0% (95% confidence interval, 4.5%-13.5%) had a central macular thickness of ≥300 μm; 23.2% (95% confidence interval, 16.6%-29.9%) had at least one optical coherence tomography sign of diabetic macular oedema. The false-positive rate of the current screening strategy for diabetic macular oedema was 86.6%.

CONCLUSION

The high false-positive rate of the current diabetic macular oedema screening adopted by the United Kingdom and Hong Kong may lead to unnecessary psychological stress for patients and place a financial burden on the health care system. A better way of screening is urgently needed. Performing additional spectral-domain optical coherence tomography scans on selected patients fulfils this need.