Comparative Proteomics Identified EXOSC1 as a Target Protein of Anticancer Peptide LVTX-8 in Nasopharyngeal Carcinoma Cells.

Nasopharyngeal carcinoma (NPC) is a prevalent malignancy that usually occurs among the nose and throat. Due to mild initial symptoms, most patients are diagnosed in the late stage, and the recurrence rate of tumors is high, resulting in many deaths every year. Traditional radiotherapy and chemotherapy are prone to causing drug resistance and significant side effects. Therefore, searching for new bioactive drugs including anticancer peptides is necessary and urgent. LVTX-8 is a peptide toxin synthesized from the cDNA library of the spider Lycosa vittata, which is consisting of 25 amino acids. In this study, a series of in vitro cell experiments such as cell toxicity, colony formation, and cell migration assays were performed to exam the anticancer activity of LVTX-8 in NPC cells (5-8F and CNE-2). The results suggested that LVTX-8 significantly inhibited cell proliferation and migration of NPC cells. To find the potential molecular targets for the anticancer capability of LVTX-8, high-throughput proteomic and bioinformatics analysis were conducted on NPC cells. The results identified EXOSC1 as a potential target protein with significantly differential expression levels under LVTX-8+/LVTX-8- conditions. The results in this research indicate that spider peptide toxin LVTX-8 exhibits significant anticancer activity in NPC, and EXOSC1 may serve as a target protein for its anticancer activity. These findings provide a reference for the development of new therapeutic drugs for NPC and offer new ideas for the discovery of biomarkers related to NPC diagnosis. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (https://proteomecentral.proteomexchange.org) via the iProX partner repository with the data set identifier PXD050542.

[Artificial intelligence in wearable electrocardiogram monitoring].

Electrocardiogram (ECG) monitoring owns important clinical value in diagnosis, prevention and rehabilitation of cardiovascular disease (CVD). With the rapid development of Internet of Things (IoT), big data, cloud computing, artificial intelligence (AI) and other advanced technologies, wearable ECG is playing an increasingly important role. With the aging process of the population, it is more and more urgent to upgrade the diagnostic mode of CVD. Using AI technology to assist the clinical analysis of long-term ECGs, and thus to improve the ability of early detection and prediction of CVD has become an important direction. Intelligent wearable ECG monitoring needs the collaboration between edge and cloud computing. Meanwhile, the clarity of medical scene is conducive for the precise implementation of wearable ECG monitoring. This paper first summarized the progress of AI-related ECG studies and the current technical orientation. Then three cases were depicted to illustrate how the AI in wearable ECG cooperate with the clinic. Finally, we demonstrated the two core issues-the reliability and worth of AI-related ECG technology and prospected the future opportunities and challenges.

Trace Sample Proteome Quantification by Data-Dependent Acquisition without Dynamic Exclusion.

Despite continuous technological improvements in sample preparation, mass-spectrometry-based proteomics for trace samples faces the challenges of sensitivity, quantification accuracy, and reproducibility. Herein, we explored the applicability of turboDDA (a method that uses data-dependent acquisition without dynamic exclusion) for quantitative proteomics of trace samples. After systematic optimization of acquisition parameters, we compared the performance of turboDDA with that of data-dependent acquisition with dynamic exclusion (DEDDA). By benchmarking the analysis of trace unlabeled human cell digests, turboDDA showed substantially better sensitivity in comparison with DEDDA, whether for unfractionated or high pH fractionated samples. Furthermore, through designing an iTRAQ-labeled three-proteome model (i.e., tryptic digest of protein lysates from yeast, human, and E. coli) to document the interference effect, we evaluated the quantification interference, accuracy, reproducibility of iTRAQ labeled trace samples, and the impact of PIF (precursor intensity fraction) cutoff for different approaches (turboDDA and DEDDA). The results showed that improved quantification accuracy and reproducibility could be achieved by turboDDA, while a more stringent PIF cutoff resulted in more accurate quantification but less peptide identification for both approaches. Finally, the turboDDA strategy was applied to the differential analysis of limited amounts of human lung cancer cell samples, showing great promise in trace proteomics sample analysis.

ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning.

The value of Electrocardiogram (ECG) monitoring in early cardiovascular disease (CVD) detection is undeniable, especially with the aid of intelligent wearable devices. Despite this, the requirement for expert interpretation significantly limits public accessibility, underscoring the need for advanced diagnosis algorithms. Deep learning-based methods represent a leap beyond traditional rule-based algorithms, but they are not without challenges such as small databases, inefficient use of local and global ECG information, high memory requirements for deploying multiple models, and the absence of task-to-task knowledge transfer. In response to these challenges, we propose a multi-resolution model adept at integrating local morphological characteristics and global rhythm patterns seamlessly. We also introduce an innovative ECG continual learning (ECG-CL) approach based on parameter isolation, designed to enhance data usage effectiveness and facilitate inter-task knowledge transfer. Our experiments, conducted on four publicly available databases, provide evidence of our proposed continual learning method's ability to perform incremental learning across domains, classes, and tasks. The outcome showcases our method's capability in extracting pertinent morphological and rhythmic features from ECG segmentation, resulting in a substantial enhancement of classification accuracy. This research not only confirms the potential for developing comprehensive ECG interpretation algorithms based on single-lead ECGs but also fosters progress in intelligent wearable applications. By leveraging advanced diagnosis algorithms, we aspire to increase the accessibility of ECG monitoring, thereby contributing to early CVD detection and ultimately improving healthcare outcomes.

A Causal Intervention Scheme for Semantic Segmentation of Quasi-Periodic Cardiovascular Signals.

Precise segmentation is a vital first step to analyze semantic information of cardiac cycle and capture anomaly with cardiovascular signals. However, in the field of deep semantic segmentation, inference is often unilaterally confounded by the individual attribute of data. Towards cardiovascular signals, quasi-periodicity is the essential characteristic to be learned, regarded as the synthesize of the attributes of morphology ( Am) and rhythm ( Ar). Our key insight is to suppress the over-dependence on Am or Ar while the generation process of deep representations. To address this issue, we establish a structural causal model as the foundation to customize the intervention approaches on Am and Ar, respectively. In this article, we propose contrastive causal intervention (CCI) to form a novel training paradigm under a frame-level contrastive framework. The intervention can eliminate the implicit statistical bias brought by the single attribute and lead to more objective representations. We conduct comprehensive experiments with the controlled condition for QRS location and heart sound segmentation. The final results indicate that our approach can evidently improve the performance by up to 0.41% for QRS location and 2.73% for heart sound segmentation. The efficiency of the proposed method is generalized to multiple databases and noisy signals.

Diagnostic Yield of Computed Tomography for Cancer Detection in a Tertiary Referral Population of Idiopathic Inflammatory Myositis Patients.

OBJECTIVE: To inform guidance for cancer detection in patients with idiopathic inflammatory myopathy (IIM), we evaluated the diagnostic yield of computed tomography (CT) imaging for cancer screening/surveillance within distinct IIM subtypes and myositis-specific autoantibody strata. METHODS: We conducted a single-center, retrospective cohort study in IIM patients. Overall diagnostic yield (number of cancers diagnosed/number of tests performed), percentage of false positives (number of biopsies performed not leading to cancer diagnosis/number of tests performed), and test characteristics were determined on CT of the chest and abdomen/pelvis. RESULTS: Within the first 3 years since IIM symptom onset, a total of 9 of 1,011 (0.9%) chest CT scans and 12 of 657 (1.8%) abdomen/pelvis CT scans detected cancer. Diagnostic yields for both CT of the chest and CT of the abdomen/pelvis were highest in dermatomyositis, specifically anti-transcription intermediary factor 1γ (2.9% and 2.4% for CT of the chest and abdomen/pelvis, respectively). The highest percentage of false positives was in patients with antisynthetase syndrome (ASyS) (4.4%) and immune-mediated necrotizing myopathy (4.4%) on CT of the chest, and ASyS (3.8%) on CT of the abdomen/pelvis. Patients ages <40 years old at IIM onset had both low diagnostic yields (0% and 0.5%) and high false-positive rates (1.9% and 4.4%) for CT of the chest and abdomen/pelvis, respectively. CONCLUSION: In a tertiary referral cohort of IIM patients, CT imaging has a wide range of diagnostic yield and frequency of false positives for contemporaneous cancer. These findings suggest that cancer detection strategies targeted according to IIM subtype, autoantibody positivity, and age may maximize cancer detection while minimizing the harms and costs of over-screening.

Subsets of Idiopathic Inflammatory Myositis Enriched for Contemporaneous Cancer Relative to the General Population.

OBJECTIVE: This study investigates cancer risk in idiopathic inflammatory myopathy (IIM) relative to the general population. METHODS: We conducted a single-center, retrospective cohort study of IIM patients and malignancy. Myositis-specific and -associated autoantibodies were determined by Euroimmun line blot, enzyme-linked immunosorbent assay, and immunoprecipitation. We calculated standardized prevalence ratios (SPRs) and adjusted for calendar year, age, sex, race, and ethnicity by comparing observed cancers in IIM patients versus expected cancers in the general population using the Surveillance, Epidemiology, and End Results registry. RESULTS: Of 1,172 IIM patients, 203 (17%) patients with a cancer history were studied. Over a median follow-up of 5.2 years, the observed number of IIM patients diagnosed with cancer was increased 1.43-fold (SPR 1.43 [95% confidence interval (95% CI) 1.15-1.77]; P = 0.002). Within 3 years of IIM symptom onset, an increased SPR was observed for anti-transcription intermediary factor 1γ (anti-TIF1γ)-positive patients for ovarian and breast cancer (ovarian SPR 18.39 [95% CI 5.01-47.08], P < 0.001; breast SPR 3.84 [95% CI 1.99-6.71], P < 0.001). As expected, anti-TIF1γ positivity was associated with a significantly elevated SPR; however, only 55% (36 of 66) of all cancers within 3 years of dermatomyositis onset were observed in anti-TIF1γ-positive patients. Other myositis-specific autoantibodies, including anti-Mi-2, anti-small ubiquitin-like modifier activating enzyme (SAE), and anti-nuclear matrix protein 2 (NXP-2), accounted for 26% (17 of 66) of cancers diagnosed within 3 years of dermatomyositis onset. No cancer association, positive or negative, was observed for patients with antisynthetase, anti-melanoma differentiation-associated protein 5 (anti-MDA-5), or anti-hydroxymethylglutaryl-coenzyme A reductase (anti-HMGCR) antibodies. CONCLUSION: In a tertiary referral center population, anti-TIF1γ was most strongly associated with breast and ovarian cancer. Patients with antisynthetase, anti-MDA-5, or anti-HMGCR antibodies had the same cancer risk as the general population.

SSA-ICL: Multi-domain adaptive attention with intra-dataset continual learning for Facial expression recognition.

Facial expression recognition (FER) is a kind of affective computing that identifies the emotional state represented in facial photographs. Various methods have been developed for completing this critical task. In spite of this progress, three significant obstacles, the interaction between spatial action units, the inadequacy of semantic information about spectral expressions and the unbalanced data distribution, are not well addressed. In this work, we propose SSA-ICL, a novel approach for FER, and solve these three difficulties inside a coherent framework. To address the first two challenges, we develop a Spectral and Spatial Attention (SSA) module that integrates spectral semantics with spatial locations to improve the performance of the model. We provide an Intra-dataset Continual Learning (ICL) module to combat the issue of long-tail distribution in FER datasets. By subdividing a single long-tail dataset into multiple sub-datasets, ICL repeatedly trains well-balanced representations from each subset and finally develop a independent classifier. We performed extensive experiments on two publicly available datasets, AffectNet and RAFDB. In comparison to existing attention modules, our SSA achieves an accuracy improvement of 3.8%∼6.7%, as evidenced by testing results. In the meanwhile, our proposed SSA-ICL can achieve superior or comparable performance to state-of-the-art FER methods (65.78% on AffectNet and 89.44% on RAFDB).

On the Distinctive Hardness, Anti-Corrosion Properties and Mechanisms of Flame-Deposited Carbon Coating with a Hierarchical Structure in Contrast to a Graphene Layer via Chemical Vapor Deposition.

Two carbonaceous (amorphous carbon and graphene) coatings were catalytically grown on bulk Ni plates. It was found that the flame-deposited carbon (FDC) layers exhibited a unique hierarchical structure with the formation of FDC/Ni nano-interlocking interface. The effect of the flame coating time on its corrosion protective efficiency (PE) was studied and compared with that of graphene coating produced via chemical vapor deposition. The FDC grown for 10 min exhibited a PE of 92.7%, which was much greater than that of the graphene coating (75.6%). The anti-corrosive mechanisms of both coatings were revealed and compared. For graphene coatings, the higher reaction temperature than that for FDC resulted in large grain boundaries inherent in the coating. Such boundaries were weak points and easily initiated grain boundary corrosion. In contrast, corrosion started at only certain local defects in FDC layers, whose unique interface structure likely promoted its PE as well. Moreover, after the coating process, the hardness of FDC-coated Ni remained almost unchanged, in contrast to that of graphene-coated samples (reduced by ~30%). This is suggested to be related to the crystal structure evolution of the Ni substrate caused by the heat treatment accompanying the coating process.

Unsupervised machine learning reveals epicardial adipose tissue subtypes with distinct atrial fibrosis profiles in patients with persistent atrial fibrillation: A prospective 2-center cohort study.

BACKGROUND: Epicardial adipose tissue (EAT) accumulation is associated with the progression of atrial fibrillation. However, the histological features of EATs are poorly defined and their correlation with atrial fibrosis is unclear. OBJECTIVE: The purpose of this study was to identify and characterize EAT subgroups in the persistent atrial fibrillation (PeAF) cohorts. METHODS: EATs and the corresponding left atrial appendage samples were obtained from patients with PeAF via surgical intervention. Adipocyte markers, that is, Uncoupling Protein 1, Transcription Factor 21, and CD137, were examined. On the basis of expression of adipocyte markers, patients with PeAF were categorized into subgroups by using unsupervised clustering analysis. Clinical characteristics, histological analyses, and outcomes were subsequently compared across the clusters. External validation was performed in a validation cohort. RESULTS: The ranking of feature importance revealed that the 3 adipocyte markers were the most relevant factors for atrial fibrosis compared with other clinical indicators. On the k-medoids analysis, patients with PeAF could be categorized into 3 clusters in the discovery cohort. The histological studies revealed that patients in cluster 1 exhibited statistically larger size of adipocytes in EATs and severe atrial fibrosis in left atrial appendages. Findings were replicated in the validation cohort, where severe atrial fibrosis was noted in cluster 1. Moreover, in the validation cohort, there was a high degree of overlap between the supervised classification results and the unsupervised cluster results from the k-medoids method. CONCLUSION: Machine learning-based cluster analysis could identify subtypes of patients with PeAF having distinct atrial fibrosis profiles. Additionally, EAT whitening (increased proportion of white adipocytes) may be involved in the process of atrial fibrosis.

Decreased sample entropy during sleep-to-wake transition in sleep apnea patients.

Objective. This study aimed to prove that there is a sudden change in the human physiology system when switching from one sleep stage to another and physical threshold-based sample entropy (SampEn) is able to capture this transition in an RR interval time series from patients with disorders such as sleep apnea.Approach. Physical threshold-based SampEn was used to analyze different sleep-stage RR segments from sleep apnea subjects in the St. Vincents University Hospital/University College Dublin Sleep Apnea Database, and SampEn differences were compared between two consecutive sleep stages. Additionally, other standard heart rate variability (HRV) measures were also analyzed to make comparisons.Main results. The findings suggested that the sleep-to-wake transitions presented a SampEn decrease significantly larger than intra-sleep ones (P < 0.01), which outperformed other standard HRV measures. Moreover, significant entropy differences between sleep and subsequent wakefulness appeared when the previous sleep stage was either S1 (P < 0.05), S2 (P < 0.01) or S4 (P < 0.05).Significance. The results demonstrated that physical threshold-based SampEn has the capability of depicting physiological changes in the cardiovascular system during the sleep-to-wake transition in sleep apnea patients and it is more reliable than the other analyzed HRV measures. This noninvasive HRV measure is a potential tool for further evaluation of sleep physiological time series.

Temporal-Framing Adaptive Network for Heart Sound Segmentation Without Prior Knowledge of State Duration.

OBJECTIVE: This paper presents a novel heart sound segmentation algorithm based on Temporal-Framing Adaptive Network (TFAN), including state transition loss and dynamic inference. METHODS: In contrast to previous state-of-the-art approaches, TFAN does not require any prior knowledge of the state duration of heart sounds and is therefore likely to generalize to non sinus rhythm. TFAN was trained on 50 recordings randomly chosen from Training set A of the 2016 PhysioNet/Computer in Cardiology Challenge and tested on the other 12 independent databases (2,099 recordings and 52,180 beats). And further testing of performance was conducted on databases with three levels of increasing difficulty (LEVEL-I, -II and -III). RESULTS: TFAN achieved a superior F1 score for all 12 databases except for 'Test-B,' with an average of 96.72%, compared to 94.56% for logistic regression hidden semi-Markov model (LR-HSMM) and 94.18% for bidirectional gated recurrent neural network (BiGRNN). Moreover, TFAN achieved an overall F1 score of 99.21%, 94.17%, 91.31% on LEVEL-I, -II and -III databases respectively, compared to 98.37%, 87.56%, 78.46% for LR-HSMM and 99.01%, 92.63%, 88.45% for BiGRNN. CONCLUSION: TFAN therefore provides a substantial improvement on heart sound segmentation while using less parameters compared to BiGRNN. SIGNIFICANCE: The proposed method is highly flexible and likely to apply to other non-stationary time series. Further work is required to understand to what extent this approach will provide improved diagnostic performance, although it is logical to assume superior segmentation will lead to improved diagnostics.

An Explainable Artificial Intelligence Predictor for Early Detection of Sepsis.

OBJECTIVES: Early detection of sepsis is critical in clinical practice since each hour of delayed treatment has been associated with an increase in mortality due to irreversible organ damage. This study aimed to develop an explainable artificial intelligence model for early predicting sepsis by analyzing the electronic health record data from ICU provided by the PhysioNet/Computing in Cardiology Challenge 2019. DESIGN: Retrospective observational study. SETTING: We developed our model on the shared ICUs publicly data and verified on the full hidden populations for challenge scoring. PATIENTS: Public database included 40,336 patients' electronic health records sourced from Beth Israel Deaconess Medical Center (hospital system A) and Emory University Hospital (hospital system B). A total of 24,819 patients from hospital systems A, B, and C (an unidentified hospital system) were sequestered as full hidden test sets. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 168 features were extracted on hourly basis. Explainable artificial intelligence sepsis predictor model was trained to predict sepsis in real time. Impact of each feature on hourly sepsis prediction was explored in-depth to show the interpretability. The algorithm demonstrated the final clinical utility score of 0.364 in this challenge when tested on the full hidden test sets, and the scores on three separate test sets were 0.430, 0.422, and -0.048, respectively. CONCLUSIONS: Explainable artificial intelligence sepsis predictor model achieves superior performance for predicting sepsis risk in a real-time way and provides interpretable information for understanding sepsis risk in ICU.

A wearable real-time telemonitoring electrocardiogram device compared with traditional Holter monitoring.

Arrhythmias are very common in the healthy populations as well as patients with cardiovascular diseases. Among them, atrial fibrillation (AF) and malignant ventricular arrhythmias are usually associated with some clinical events. Early diagnosis of arrhythmias, particularly AF and ventricular arrhythmias, is very important for the treatment and prognosis of patients. Holter is a gold standard commonly recommended for noninvasive detection of paroxysmal arrhythmia. However, it has some shortcomings such as fixed detection timings, delayed report and inability of remote real-time detection. To deal with such problems, we designed and applied a new wearable 72-hour triple-lead H3-electrocardiogram (ECG) device with a remote cloud-based ECG platform and an expert-supporting system. In this study, 31 patients were recruited and 24-hour synchronous ECG data by H3-ECG and Holter were recorded. In the H3-ECG group, ECG signals were transmitted using remote real-time modes, and confirmed reports were made by doctors in the remote expert-supporting system, while the traditional modes and detection systems were used in the Holter group. The results showed no significant differences between the two groups in 24-hour total heart rate (HR), averaged HR, maximum HR, minimum HR, premature atrial complexes (PACs) and premature ventricular complexes (PVCs) ( P>0.05). The sensitivity and specificity of capture and remote automatic cardiac events detection of PACs, PVCs, and AF by H3-ECG were 93% and 99%, 98% and 99%, 94% and 98%, respectively. Therefore, the long-term limb triple-lead H3-ECG device can be utilized for domiciliary ECG self-monitoring and remote management of patients with common arrhythmia under medical supervision.

Analysis and imaging of boron distribution in maize by quantitative neutron capture radiography.

Quantitative neutron capture radiography (QNCR) of 10B found in pre-dried maize samples has been conducted. Calibration standards constructed from filter paper mimicked plant tissues to reduce confounding matrix effects. A mathematical track elimination method improves the LOD as well as the visual contrast image at low boron concentration levels. The LOD for total boron is 1.7 µg/g in a 4 mm2 region of interest (ROI). The w(B) in five individual maize tassel meristems has been determined to be 14.9 µg/g - 21.2 µg/g.