P12.09.B Extracellular vesicle derived-miR-146a increases melanoma brain metastasis progression via Notch signalling pathway dysregulation

Background

Melanoma has the highest tropism of any cancer to metastasize to the brain, and 40% of late-stage patients develop brain metastasis. Invasion, survival, and progression of tumors is dependent on the support of the surrounding microenvironment; therefore, modulation of neighboring cells is a key factor in metastasis. Extracellular vesicles (EVs) are important in cell-to-cell signalling, shuttling proteins, RNA and DNA to alter the surroundings into a favorable tumor microenvironment. Our aims were to investigate the role of melanoma brain metastasis (MBM) derived EVs in MBM development to find possible contributing mechanisms to cancer progression for eventual therapeutic targeting.

Material and Methods

MBM-EVs isolated via sequential ultracentrifugation were injected into mice as a pre-treatment prior to intracardial injection of MBM cells. EVs were co-cultured with normal human astrocytes (NHA) to investigate phenotypic changes. MiRNA sequencing was performed on EVs collected from MBM cells and compared to NHA and melanocytes to determine a candidate miRNA for targeting. In situ hybridization was utilized to evaluate the level of miRNA in clinical patient MBM samples. Functional in vivo validation was performed by injecting miRNA knockout MBM cells into mice. Sequencing of NHA in the presence or absence of target miRNA mimic was used to determine downstream targets.

Results

Mice primed with EVs had a significant increase in MBM tumor burden, compared to non-primed mice. Co-culture with MBM-EVs resulted in NHA activation in vitro, with increased proliferation, invasion, cytokine production, and upregulation of GFAP. MiR-146a was highly upregulated in MBM EVs, and miR-146a mimics activated NHA. Patient samples had a significant increase in miR-146a expression, compared to healthy brain controls. MiR-146a knockdown in MBM mice models reduced MBM burden and prolonged animal survival. Sequencing of NHA determined NUMB, an inhibitor of the Notch signalling pathway, as a target of miR-146a. Numb and other downstream Notch proteins expression was significantly altered in NHA in the presence of both MBM-EVs and miR-146a.

Conclusion

In conclusion, EVs are important regulators of MBM and establish tumor-supporting reactive astrocytes by delivery of miR-146a. MiR-146a alters Notch signalling in astrocytes via inhibition of the tumor suppressor gene NUMB. Elevated miR-146a levels in patients suggests a potential clinical intervention is possible via miR-146a targeting.

Isolated posterior inferior cerebellar artery dissection with ischaemic stroke: evaluating the radiological features and diagnostic feasibility of high-resolution vessel wall imaging

AIM

To evaluate the radiological features of isolated posterior inferior cerebellar artery dissection (PICAD) and the feasibility of using high-resolution vessel-wall imaging (HR-VWI) for diagnosing PICAD.

MATERIALS AND METHODS

Three hundred and nine patients with arterial dissection involving the posterior cerebral circulation, who underwent HR-VWI between March 2012 and July 2019 were reviewed retrospectively. Among them, 44 patients (14.2%) were diagnosed with isolated PICAD in consensus among a neuroradiologist, a neurointerventionist, and a neurologist. Two neuroradiologists reviewed the vessel wall images independently for evidence of dissection (dissection flap, outer diameter enlargement on T2-weighted imaging [WI], mural haematoma). Diagnostic confidence was also scored on a five-point scale. Intra- and interobserver agreement for diagnosing PICAD and detecting evidence of dissection were evaluated.

RESULTS

Dissection flaps were seen on T2WI in all cases (100%) and on contrast-enhanced T1WI in 34 patients (79.1%). Outer diameter enlargement of the steno-occlusive lesions on angiography was detected in most cases (97.7%). A mural haematoma was detected on three-dimensional (3D) contrast-enhanced motion-sensitised driven-equilibrium T1WI in 97.1% of the cases. The mean diagnostic confidence score derived by two neuroradiologists was 4.72. The two reviewers showed substantial to almost perfect agreement (weighted kappa coefficient: 0.62-0.97).

CONCLUSION

Use of HR-VWI as a diagnostic tool for PICAD is feasible, and a dissection flap with outer wall enlargement on HR-T2WI allows confident dissection diagnosis. The present data suggest that PICAD might be considered as a stroke aetiology in patients with unexplained ischaemic stroke in the PICA territory, and that PICA evaluation with HR-VWI is both necessary and feasible.

Inference of Upcoming Human Grasp Using EMG During Reach-to-Grasp Movement

Electromyography (EMG) data has been extensively adopted as an intuitive interface for instructing human-robot collaboration. A major challenge to the real-time detection of human grasp intent is the identification of dynamic EMG from hand movements. Previous studies predominantly implemented the steady-state EMG classification with a small number of grasp patterns in dynamic situations, which are insufficient to generate differentiated control regarding the variation of muscular activity in practice. In order to better detect dynamic movements, more EMG variability could be integrated into the model. However, only limited research was conducted on such detection of dynamic grasp motions, and most existing assessments on non-static EMG classification either require supervised ground-truth timestamps of the movement status or only contain limited kinematic variations. In this study, we propose a framework for classifying dynamic EMG signals into gestures and examine the impact of different movement phases, using an unsupervised method to segment and label the action transitions. We collected and utilized data from large gesture vocabularies with multiple dynamic actions to encode the transitions from one grasp intent to another based on natural sequences of human grasp movements. The classifier for identifying the gesture label was constructed afterward based on the dynamic EMG signal, with no supervised annotation of kinematic movements required. Finally, we evaluated the performances of several training strategies using EMG data from different movement phases and explored the information revealed from each phase. All experiments were evaluated in a real-time style with the performance transitions presented over time.

Adherence to antihypertensive medication and cardiovascular disease events in hypertensive patients: a dose-response meta-analysis of 2 769 700 participants in cohort study

BACKGROUND

Recently, many studies have investigated the association between adherence to antihypertensive medication (AHM) and risk of cardiovascular disease (CVD) events for hypertensive patients; however, the results varied by different studies.

AIMS

The purpose of our meta-analysis was to explore the comprehensively summarized association between AHM adherence and risk of CVD events in hypertensive patients from cohort studies.

DESIGN

A dose-response meta-analysis.

METHODS AND RESULTS

We conducted a systematic search in two databases (PubMed and Embase) from 1974 to 15 December 2019 to identify English-language reports that assessed the association of AHM adherence with risk of CVD events in cohort studies. Pooled relative risks (RRs) and 95% confidence intervals (CIs) were estimated by using a fixed- or random-effects model. Restricted cubic splines were used to evaluate the possible linear or non-linear association.

RESULTS

We included 16 cohort studies with 2 769 700 participants in the present meta-analysis. The pooled RR of CVD events was 0.66 (95% CI, 0.56-0.78, I2 = 98.6%) for the highest versus lowest AHM adherence categories. We found a linear dose-response association of AHM adherence and CVD events (Pnonlinearity = 0.887), each 20% increase in AHM adherence was associated with a 13% reduced risk of CVD events (RR 0.87, 95% CI 0.83-0.92, I2 = 98.2%) in hypertensive patients.

CONCLUSION

High AHM adherence has a protective effect on CVD events for hypertensive patients, and improving medication adherence may provide long-term CVD benefits.

Association between Sedentary Time and 6-Year All-Cause Mortality in Adults: The Rural Chinese Cohort Study

OBJECTIVES

This study aims to prospectively explore the association between sedentary time and the risk of all-cause mortality in adults based on a cohort from rural areas of China.

METHODS

The study population included 20,194 adults at baseline (2007-2008) who participated in the Rural Chinese Cohort Study. Cox's proportional hazard regression model was used to analyze the hazard ratios (HRs) and 95% confidence intervals (CIs) of sedentary time and all-cause mortality, and a restricted cubic spline was used to model the dose-response relation. We also carried out a series of sensitivity analyses to verify the robustness of our main results.

RESULTS

The median follow-up duration was 6 years, with a total of 17,265 participants (response rate 85.5%) followed up, and 1,106 deaths observed. Data for 17,048 participants were analyzed, with the mean age of participants being 52.00. Compared with sedentary time <4 h/day group, the risk of all-cause mortality was significantly increased in the 8-11 h/day (HR=1.27, 95%CI:1.03-1.56) and ≥11 h/day groups (HR=1.48, 95%CI:1.20-1.84). With increases in sedentary time, the risk of all-cause mortality increased gradually (Ptrend <0.001). For each 1 h/day increase in sedentary time, the risk of all-cause mortality increased by 3% (HR=1.03, 95%CI: 1.01-1.05). Sensitivity analyses showed our main results were consistent.

CONCLUSIONS

Prolonged sedentary time increases the risk of all-cause mortality in the adult rural Chinese population. Reducing sedentary time may have important implications for reducing mortality risk.

Over 50,000 Metagenomically Assembled Draft Genomes for the Human Oral Microbiome Reveal New Taxa

The oral cavity of each person is home to hundreds of bacterial species. While taxa for oral diseases have been studied using culture-based characterization as well as amplicon sequencing, metagenomic and genomic information remains scarce compared to the fecal microbiome. Here, using metagenomic shotgun data for 3346 oral metagenomic samples together with 808 published samples, we obtain 56,213 metagenome-assembled genomes (MAGs), and more than 64% of the 3589 species-level genome bins (SGBs) contain no publicly available genomes. The resulting genome collection is representative of samples around the world and contains many genomes from candidate phyla radiation (CPR) that lack monoculture. Also, it enables the discovery of new taxa such as a genus Candidatus Bgiplasma within the family Acholeplasmataceae. Large-scale metagenomic data from massive samples also allow the assembly of strains from important oral taxa such as Porphyromonas and Neisseria. The oral microbes encode genes that could potentially metabolize drugs. Apart from these findings, a strongly male-enriched Campylobacter species was identified. Oral samples would be more user-friendly collected than fecal samples and have the potential for disease diagnosis. Thus, these data lay down a genomic framework for future inquiries of the human oral microbiome.

Radiomics Can Distinguish Pediatric Supratentorial Embryonal Tumors, High-Grade Gliomas, and Ependymomas

BACKGROUND AND PURPOSE

Pediatric supratentorial tumors such as embryonal tumors, high-grade gliomas, and ependymomas are difficult to distinguish by histopathology and imaging because of overlapping features. We applied machine learning to uncover MR imaging-based radiomics phenotypes that can differentiate these tumor types.

MATERIALS AND METHODS

Our retrospective cohort of 231 patients from 7 participating institutions had 50 embryonal tumors, 127 high-grade gliomas, and 54 ependymomas. For each tumor volume, we extracted 900 Image Biomarker Standardization Initiative-based PyRadiomics features from T2-weighted and gadolinium-enhanced T1-weighted images. A reduced feature set was obtained by sparse regression analysis and was used as input for 6 candidate classifier models. Training and test sets were randomly allocated from the total cohort in a 75:25 ratio.

RESULTS

The final classifier model for embryonal tumor-versus-high-grade gliomas identified 23 features with an area under the curve of 0.98; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.85, 0.91, 0.79, 0.94, and 0.89, respectively. The classifier for embryonal tumor-versus-ependymomas identified 4 features with an area under the curve of 0.82; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.93, 0.69, 0.76, 0.90, and 0.81, respectively. The classifier for high-grade gliomas-versus-ependymomas identified 35 features with an area under the curve of 0.96; the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 0.82, 0.94, 0.82, 0.94, and 0.91, respectively.

CONCLUSIONS

In this multi-institutional study, we identified distinct radiomic phenotypes that distinguish pediatric supratentorial tumors, high-grade gliomas, and ependymomas with high accuracy. Incorporation of this technique in diagnostic algorithms can improve diagnosis, risk stratification, and treatment planning.

A dose-response meta-analysis to evaluate the relationship between high-density lipoprotein cholesterol and all-cause and cardiovascular disease mortality

PURPOSE

Previous studies have not fully described the relationship between high-density lipoprotein cholesterol (HDL-C) and death risks from all cause and cardiovascular disease (CVD). This study quantitatively evaluates HDL-C-mortality associations.

METHODS

Embase and PubMed databases were searched for relevant articles published up to 1 June 2019. Random-effects models were used to pool relative risks (RRs) and 95% confidence intervals (CIs). We used restricted cubic splines to model the dose-response association.

RESULTS

We identified 32 prospective cohort studies including 369,904 participants and 33,473 total deaths (9426 CVD deaths). Compared to the lowest HDL-C levels, all cause and CVD mortality risks were reduced by 18% (RR 0.82; 95% CI, 0.73-0.93) and 36% (0.64, 0.46-0.89), respectively, for the highest HDL-C levels. All cause and CVD mortality risks were reduced by 15% (0.85, 0.79-0.92) and 23% (0.77, 0.69-0.87), respectively, with each 1 mmol/L increment of HDL-C. We found evidence of nonlinear and negative dose-response associations of HDL-C with all cause and CVD mortality (P_nonlinearity < 0.001), and the lowest death risks from all cause and CVD were observed at approximately 1.34 and 1.55 mmol/L, respectively.

CONCLUSION

HDL-C is inversely associated with all cause and CVD mortality risks under approximately 2.05 and 2.33 mmol/L, respectively. Optimal doses require investigation via clinical practice or high-quality research.

Prevalence and Risk Factors for Fall among Rural Elderly: A County-Based Cross-Sectional Survey

AIM

The aim of the study was to provide evidence for the prevention and reduction of falls in the elderly living in rural areas by analyzing epidemiological data of falls among the rural older people (>65 years old) and identifying the risk and protective factors.

METHODS

This study analyzed the sociodemographic characteristics, living environment, lifestyle, chronic disease condition, mental health, activities of daily living (ADL), and detailed information of falls of 3752 rural elderly. Rank tests, chi-square tests, and binary logistic regression were used for data analysis.

RESULTS

The prevalence of falls was 30.0%, and the 75-84-years age group had the highest fall rate (18.8%). According to the binary logistic regression analysis, six variables, including roughage intake frequency, age, gender, cane use, floor tiles, and IADL, were involved in the fall patterns. Low roughage intake (OR = 2.48, 95% CI 1.24-4.97), female gender (OR = 2.12, 95% CI 1.48-3.05), the use of a cane (OR = 2.11, 95% CI 1.08-4.10), and medium IADL (OR = 2.02, 95% CI 1.89-2.32) were the top four risk factors.

CONCLUSION

The fall in the rural elderly was mainly due to the poor living and working conditions. Routine fall assessment could address several preventable risk factors to reduce the prevalence and mitigate the harm of falls.

Classifications of Dynamic EMG in Hand Gesture and Unsupervised Grasp Motion Segmentation

The electromyography (EMG) signals have been widely utilized in human-robot interaction for extracting user hand/arm motion instructions. A major challenge of the online interaction with robots is the reliable EMG recognition from real-time data. However, previous studies mainly focused on using steady-state EMG signals with a small number of grasp patterns to implement classification algorithms, which is insufficient to generate robust control regarding the dynamic muscular activity variation in practice. Introducing more EMG variability during training and validation could implement a better dynamic-motion detection, but only limited research focused on such grasp-movement identification, and all of those assessments on the non-static EMG classification require supervised ground-truth label of the movement status. In this study, we propose a framework for classifying EMG signals generated from continuous grasp movements with variations on dynamic arm/hand postures, using an unsupervised motion status segmentation method. We collected data from large gesture vocabularies with multiple dynamic motion phases to encode the transitions from one intent to another based on common sequences of the grasp movements. Two classifiers were constructed for identifying the motion-phase label and grasptype label, where the dynamic motion phases were segmented and labeled in an unsupervised manner. The proposed framework was evaluated in real-time with the accuracy variation over time presented, which was shown to be efficient due to the high degree of freedom of the EMG data.

Universal Physiological Representation Learning With Soft-Disentangled Rateless Autoencoders

Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological status of users. However, physiological biosignals often vary across users and recording sessions due to unstable physical/mental conditions and task-irrelevant activities. To deal with this challenge, we propose a method of adversarial feature encoding with the concept of a Rateless Autoencoder (RAE), in order to exploit disentangled, nuisance-robust, and universal representations. We achieve a good trade-off between user-specific and task-relevant features by making use of the stochastic disentanglement of the latent representations by adopting additional adversarial networks. The proposed model is applicable to a wider range of unknown users and tasks as well as different classifiers. Results on cross-subject transfer evaluations show the advantages of the proposed framework, with up to an 11.6% improvement in the average subject-transfer classification accuracy.

Radiomic Phenotypes Distinguish Atypical Teratoid/Rhabdoid Tumors from Medulloblastoma

BACKGROUND AND PURPOSE

Atypical teratoid/rhabdoid tumors and medulloblastomas have similar imaging and histologic features but distinctly different outcomes. We hypothesized that they could be distinguished by MR imaging-based radiomic phenotypes.

MATERIALS AND METHODS

We retrospectively assembled T2-weighted and gadolinium-enhanced T1-weighted images of 48 posterior fossa atypical teratoid/rhabdoid tumors and 96 match-paired medulloblastomas from 7 institutions. Using a holdout test set, we measured the performance of 6 candidate classifier models using 6 imaging features derived by sparse regression of 900 T2WI and 900 T1WI Imaging Biomarker Standardization Initiative-based radiomics features.

RESULTS

From the originally extracted 1800 total Imaging Biomarker Standardization Initiative-based features, sparse regression consistently reduced the feature set to 1 from T1WI and 5 from T2WI. Among classifier models, logistic regression performed with the highest AUC of 0.86, with sensitivity, specificity, accuracy, and F1 scores of 0.80, 0.82, 0.81, and 0.85, respectively. The top 3 important Imaging Biomarker Standardization Initiative features, by decreasing order of relative contribution, included voxel intensity at the 90th percentile, inverse difference moment normalized, and kurtosis-all from T2WI.

CONCLUSIONS

Six quantitative signatures of image intensity, texture, and morphology distinguish atypical teratoid/rhabdoid tumors from medulloblastomas with high prediction performance across different machine learning strategies. Use of this technique for preoperative diagnosis of atypical teratoid/rhabdoid tumors could significantly inform therapeutic strategies and patient care discussions.

Adherence to antihypertensive medications for secondary prevention of cardiovascular disease events: a dose-response meta-analysis

OBJECTIVES

The aim of the study was to explore the association between adherence to antihypertensive medications (AHMs) and the risk of recurrence of cardiovascular disease (CVD) events in patients with a history of CVD events from cohort studies.

STUDY DESIGN

This is a dose-response meta-analysis.

METHODS

PubMed and Embase databases were searched up to March 4, 2021, to identify English-language reports of cohort studies that assessed the association of AHM adherence with risk of recurrence of CVD events. Pooled relative risks (RRs) and 95% confidence intervals (CIs) were estimated by using a fixed- or random-effects model. Restricted cubic splines were used to evaluate the possible linear or non-linear association.

RESULTS

We included nine cohort studies (54,349 patients) in the present meta-analysis. The pooled RR of CVD events was 0.66 (95% CI, 0.54-0.78) for the highest versus lowest AHM adherence category. We did not find any evidence of non-linearity association between AHM adherence and risk of CVD events (P_{non-linearity} = 0.534); for patients with a history of CVD events, the risk of CVD events was reduced by 9% for each 20% increase in AHM adherence (RR, 0.91; 95% CI, 0.85-0.97). The results of sensitivity analysis and subgroup analysis were virtually unchanged.

CONCLUSIONS

The high level of adherence to AHM is an effective strategy for preventing recurrence of CVD events. Patients with a history of CVD events should adhere to AHM.

Bone marrow-derived mesenchymal stem cells inhibit CD8+ T cell immune responses via PD-1/PD-L1 pathway in multiple myeloma

High expression of the inhibitory receptor programmed cell death ligand 1 (PD-L1) on tumor cells and tumor stromal cells have been found to play a key role in tumor immune evasion in several human malignancies. However, the expression of PD-L1 on bone marrow mesenchymal stem cells (BMSCs) and whether the programmed cell death 1 (PD-1)/PD-L1 signal pathway is involved in the BMSCs versus T cell immune response in multiple myeloma (MM) remains poorly defined. In this study, we explored the expression of PD-L1 on BMSCs from newly diagnosed MM (NDMM) patients and the role of PD-1/PD-L1 pathway in BMSC-mediated regulation of CD8⁺ T cells. The data showed that the expression of PD-L1 on BMSCs in NDMM patients was significantly increased compared to that in normal controls (NC) (18·81 ± 1·61 versus 2·78± 0·70%; P < 0·001). Furthermore, the PD-1 expression on CD8⁺ T cells with NDMM patients was significantly higher than that in normal controls (43·22 ± 2·98 versus 20·71 ± 1·08%; P < 0·001). However, there was no significant difference in PD-1 expression of CD4⁺ T cells and natural killer (NK) cells between the NDMM and NC groups. Additionally, the co-culture assays revealed that BMSCs significantly suppressed CD8⁺ T cell function. However, the PD-L1 inhibitor effectively reversed BMSC-mediated suppression in CD8⁺ T cells. We also found that the combination of PD-L1 inhibitor and pomalidomide can further enhance the killing effect of CD8⁺ T cells on MM cells. In summary, our findings demonstrated that BMSCs in patients with MM may induce apoptosis of CD8⁺ T cells through the PD-1/PD-L1 axis and inhibit the release of perforin and granzyme B from CD8⁺ T cells to promote the immune escape of MM.

Efficient and stable metabarcoding sequencing data using a DNBSEQ-G400 sequencer validated by comprehensive community analyses

Metabarcoding is a widely used method for fast characterization of microbial communities in complex environmental samples. However, the selction of sequencing platform can have a noticeable effect on the estimated community composition. Here, we evaluated the metabarcoding performance of a DNBSEQ-G400 sequencer developed by MGI Tech using 16S and internal transcribed spacer (ITS) markers to investigate bacterial and fungal mock communities, as well as the ITS2 marker to investigate the fungal community of 1144 soil samples, with additional technical replicates. We show that highly accurate sequencing of bacterial and fungal communities is achievable using DNBSEQ-G400. Measures of diversity and correlation from soil metabarcoding showed that the results correlated highly with those of different machines of the same model, as well as between different sequencing modes (single-end 400 bp and paired-end 200 bp). Moderate, but significant differences were observed between results produced with different sequencing platforms (DNBSEQ-G400 and MiSeq); however, the highest differences can be caused by selecting different primer pairs for PCR amplification of taxonomic markers. These differences suggested that care is needed while jointly analyzing metabarcoding data from differenet experiments. This study demonstrated the high performance and accuracy of DNBSEQ-G400 for short-read metabarcoding of microbial communities. Our study also produced datasets to allow further investigation of microbial diversity.

Evaluation of exosomal miRNAs as potential diagnostic biomarkers for acute myocardial infarction using next-generation sequencing

Background

Acute myocardial infarction (AMI) is one of the most common global causes of death. Although considerable progress has been made in AMI diagnosis, there remains an urgent need for novel diagnostic biomarkers for its prevention and treatment. Functional exosomal microRNAs (miRNAs) are recognized as potential biomarkers in many diseases. This study’s objective was to identify specific plasma exosomal miRNAs with biomarker potential for early AMI detection.

Methods

Exosomes from the plasma of 26 coronary artery disease (CAD) patients, 55 AMI patients, and 37 healthy controls were isolated and characterized by transmission electron microcopy (TEM), western blotting, and nanoparticle tracking analysis (NTA). The miRNAs were purified from exosomes, and unique molecular identifier (UMI) small RNA sequencing was performed. The random forest (RF) model was trained to predict potential biomarkers.

Results

NTA demonstrated that nanoparticle concentration did not change after AMI, while nanoparticle size distribution significantly decreased. The CAD and AMI groups’ miRNA expression profiles significantly differed from the healthy group’s profile. The RF classifier could be used to distinguish the healthy group from the AMI group, but could not be used to distinguish the CAD group from the other groups, which caused a high classification error rate. Eighteen miRNAs were selected as biomarkers based on their RF classifier significance. The diagnostic accuracy of 18 miRNAs was evaluated using AUC values of 0.93, 0.87, and 0.75 to detect healthy controls, AMI, and CAD, respectively.

Conclusions

Nanoparticle diameter and the 18 miRNAs may serve as simple and accessible fingerprints for early AMI diagnosis.

Computer-Aided Diagnostic System for Thyroid Nodules on Ultrasonography: Diagnostic Performance Based on the Thyroid Imaging Reporting and Data System Classification and Dichotomous Outcomes

BACKGROUND AND PURPOSE

Artificial intelligence-based computer-aided diagnostic systems have been introduced for thyroid cancer diagnosis. Our aim was to compare the diagnostic performance of a commercially available computer-aided diagnostic system and radiologist-based assessment for the detection of thyroid cancer based on the Thyroid Imaging Reporting and Data Systems (TIRADS) and dichotomous outcomes.

MATERIALS AND METHODS

In total, 372 consecutive patients with 454 thyroid nodules were enrolled. The computer-aided diagnostic system was set up to render a possible diagnosis in 2 formats, the Korean Society of Thyroid Radiology (K)-TIRADS and the American Thyroid Association (ATA)-TIRADS-classifications, and dichotomous outcomes (possibly benign or possibly malignant).

RESULTS

The diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the computer-aided diagnostic system for thyroid cancer were, respectively, 97.6%, 21.6%, 42.0%, 93.9%, and 49.6% for K-TIRADS; 94.6%, 29.6%, 43.9%, 90.4%, and 53.5% for ATA-TIRADS; and 81.4%, 81.9%, 72.3%, 88.3%, and 81.7% for dichotomous outcomes. The sensitivities of the computer-aided diagnostic system did not differ significantly from those of the radiologist (all P > .05); the specificities and accuracies were significantly lower than those of the radiologist (all P < .001). Unnecessary fine-needle aspiration rates were lower for the dichotomous outcome characterizations, particularly for those performed by the radiologist. The interobserver agreement for the description of K-TIRADS and ATA-TIRADS classifications was fair-to-moderate, but the dichotomous outcomes were in substantial agreement.

CONCLUSIONS

The diagnostic performance of the computer-aided diagnostic system varies in terms of TIRADS classification and dichotomous outcomes and relative to radiologist-based assessments. Clinicians should know about the strengths and weaknesses associated with the diagnosis of thyroid cancer using computer-aided diagnostic systems.

Exogenous 6-benzyladenine application affects root morphology by altering hormone status and gene expression of developing lateral roots in Malus hupehensis

Malus hupehensis is an extensively used apple rootstock in China. In the current study, M. hupehensis seedlings were treated with exogenous 2.2 µm 6-benzyladenine (6-BA) so as to investigate the mechanism by which 6-BA affects lateral root development. The results indicate that 6-BA treatment promotes elongation and thickening of both root and shoot in M. hupehensis, but reduces the number of lateral roots, as well as reducing the auxin level after 6-BA treatment. Moreover, MhAHK4, MhRR1 and MhRR2 were also significantly up-regulated in response to 6-BA treatment. Expression levels of auxin synthesis- and transport-related genes, such as MhYUCCA6, MhYUCCA10, MhPIN1 and MhPIN2, were down-regulated, which corresponds with lower auxin levels in the 6-BA-treated seedlings. A negative regulator of auxin, MhIAA3, was induced by 6-BA treatment, leading to reduced expression of MhARF7 and MhARF19 in 6-BA-treated seedlings. As a result, expression of MhWOX11, MhWOX5, MhLBD16 and MhLBD29 was blocked, which in turn inhibited lateral root initiation. In addition, a lower auxin level decreased expression of MhRR7 and MhRR15, which repressed expression of key transcription factors associated with root development, thus inhibiting lateral root development. In contrast, 6-BA treatment promoted secondary growth (thickening) of the root by inducing expression of MhCYCD3;1 and MhCYCD3;2. Collectively, the changes in hormone levels and gene expression resulted in a reduced number of lateral roots and thicker roots in 6-BA-treated plants.

Disentangled Adversarial Transfer Learning for Physiological Biosignals

Recent developments in wearable sensors demonstrate promising results for monitoring physiological status in effective and comfortable ways. One major challenge of physiological status assessment is the problem of transfer learning caused by the domain inconsistency of biosignals across users or different recording sessions from the same user. We propose an adversarial inference approach for transfer learning to extract disentangled nuisance-robust representations from physiological biosignal data in stress status level assessment. We exploit the trade-off between task-related features and person-discriminative information by using both an adversary network and a nuisance network to jointly manipulate and disentangle the learned latent representations by the encoder, which are then input to a discriminative classifier. Results on cross-subjects transfer evaluations demonstrate the benefits of the proposed adversarial framework, and thus show its capabilities to adapt to a broader range of subjects. Finally we highlight that our proposed adversarial transfer learning approach is also applicable to other deep feature learning frameworks.