Search

This study aims to develop a cost-effective and reliable motion monitoring device capable of comprehensive fatigue analysis. It achieves this objective by integrating surface electromyography (sEMG) and accelerometer (ACC) signals through a feature fusion strategy. The study introduces a multi-level attention mechanism for classification, leveraging convolutional neural networks (CNNs). The preprocessing phase involves a local feature attention mechanism that enhances local waveform features using the amplitude envelope. A dual-scale attention mechanism, operating at both channel and neuron levels, is employed to enhance the model's learning from high-dimensional fused data, improving feature extraction and generalization. The local feature attention mechanism significantly improves the model's classification accuracy and convergence, as demonstrated in ablation experiments. The model, optimized with multi-level attention mechanisms, excels in accuracy and generalization, particularly in handling data with pseudo-artifacts. Computational analysis indicates that the proposed optimization algorithm has minimal impact on CNN's training and testing times. The study achieves recognition accuracies of 92.52%, 92.38%, and 92.30%, as well as F1-scores of 91.92%, 92.13%, and 92.29% for the three fatigue states, affirming its reliability. This research provides technical support for the development of affordable and dependable wearable motion monitoring devices. [ABSTRACT FROM AUTHOR]

PROteolysis TArgeting Chimeras (PROTACs) has recently emerged as a promising technology. However, the design of rational PROTACs, especially the linker component, remains challenging due to the absence of structure–activity relationships and experimental data. Leveraging the structural characteristics of PROTACs, fragment-based drug design (FBDD) provides a feasible approach for PROTAC research. Concurrently, artificial intelligence–generated content has attracted considerable attention, with diffusion models and Transformers emerging as indispensable tools in this field. In response, we present a new diffusion model, DiffPROTACs, harnessing the power of Transformers to learn and generate new PROTAC linkers based on given ligands. To introduce the essential inductive biases required for molecular generation, we propose the O(3) equivariant graph Transformer module, which augments Transformers with graph neural networks (GNNs), using Transformers to update nodes and GNNs to update the coordinates of PROTAC atoms. DiffPROTACs effectively competes with existing models and achieves comparable performance on two traditional FBDD datasets, ZINC and GEOM. To differentiate the molecular characteristics between PROTACs and traditional small molecules, we fine-tuned the model on our self-built PROTACs dataset, achieving a 93.86% validity rate for generated PROTACs. Additionally, we provide a generated PROTAC database for further research, which can be accessed at https://bailab.siais.shanghaitech.edu.cn/service/DiffPROTACs-generated.tgz. The corresponding code is available at https://github.com/Fenglei104/DiffPROTACs and the server is at https://bailab.siais.shanghaitech.edu.cn/services/diffprotacs. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to evaluate the impact of Lugol‐unstained lesion (LUL) location on the detection yield, which may help the endoscopist select targets for biopsy. Methods: We enrolled 1064 subjects who had LULs at the baseline screening of a population‐based randomized controlled trial. There were 1166 LULs with recorded location and pathologic diagnosis, and these were used for analysis. The detection rate of severe dysplasia and above (SDA) was calculated as the number of LULs identified as SDA divided by the number of LULs biopsied. Logistic regression with a generalized estimating equation was applied to evaluate the association between the location of a given LUL and the risk of the LUL being SDA. Results: The detection rate of SDA for LULs located in the lower, middle, and upper esophagus increased from 5.9% and 10.9% to 16.7%. LUL location was significantly associated with having SDA (adjusted odds ratio (OR)upper vs. lower = 2.88, 95% confidential interval (CI) = 1.48–5.60; adjusted ORmiddle vs. lower = 1.63, 95% CI = 0.96–2.76), and the association was stronger in subgroups with a family history of esophageal squamous cell carcinoma (ESCC) (adjusted ORupper vs. lower = 9.72, 95% CI = 2.57–36.69; adjusted ORmiddle vs. lower = 3.76, 95% CI = 0.93–15.21). Conclusions: Our results suggest that more attention should be paid by endoscopists to LULs in the upper and middle esophagus, particularly for individuals with a family history of ESCC. [ABSTRACT FROM AUTHOR]

Background: Current guidelines recommend only severe dysplasia and above (SDA) lesions of the esophageal squamous epithelium for clinical intervention. However, the histopathologic diagnosis derived from tissue biopsies may be subject to underestimation of severity. Methods: 1073 participants from whom biopsies were taken at baseline chromoendoscopic examination in a population‐based screening trial were enrolled in this study. The size of the Lugol‐unstained lesions (LULs) was mainly analyzed. The outcome was defined as SDA lesions either identified at baseline screening, or during follow‐up, collectively referred to as the cumulative risk of SDA. Multivariable logistic regression models were used to evaluate the cumulative risk of SDA. Results: One hundred and forty‐six SDA cases were identified in the study period. Participants with large LULs had a high cumulative incidence of SDA (cumulative incidence16–20mm: 55.88%; cumulative incidence>20mm: 76.92%) in the median of 7‐year duration. LULs of large size were significantly associated with a higher cumulative risk of SDA, regardless of the pathologic diagnosis (adjusted OR16–20mmvs.≤5mm = 21.02, 95% CI: 7.56–58.47; adjusted OR>20mmvs.≤5mm = 33.62, 95% CI: 11.79–95.87). Conclusions: Results from this study suggest physician–patient shared decision‐making regarding clinical treatment or intensive surveillance should be carried out for LULs >20 mm in the esophagus, regardless of the histologic diagnosis. For those with LULs of 16–20 mm, intensive surveillance would also best be considered. [ABSTRACT FROM AUTHOR]

Background and Aim: The impact of the presence of multiple Lugol‐unstained lesions (LULs) in the esophagus on the risk of having severe dysplasia and above (SDA) lesions among asymptomatic individuals is unknown. Methods: We collected demographic factors, behavioral variables, and features of LULs from 1073 participants who were biopsied at baseline endoscopic screening in a population‐based screening trial, and these individuals were followed over a median time of 7 years. Outcome events were defined as SDA identified at screening, at reexamination, or during follow‐up. "Multiple LULs" were defined as ≥ 2 LULs found in the entirety of the esophagus. Multivariable logistic regression models were fitted to assess the effect of "multiple LULs" on the cumulative risk of SDA. Results: There were 147 SDA cases in the current study. After adjustment for potential risk factors and endoscopic features of LULs, the presence of "multiple LULs" slightly increased the cumulative risk of having SDA with no statistical significance (adjusted odds ratio [OR] = 1.26; 95% confidence interval [CI] [0.85, 1.88]). Further stratified analysis showed that this association was strong among subjects with small LULs (≤ 5 mm) (adjusted OR = 3.29; 95% CI [1.39, 7.79]). However, no such association was observed in subjects with larger LULs (adjusted OR = 0.99; 95% CI [0.63, 1.55], Pinteraction = 0.022). Conclusions: The presence of "multiple small LULs (≤ 5 mm)" in chromoendoscopy indicates a higher cumulative risk of having SDA in the esophagus. We recommend biopsies be taken and surveillance be maintained at a more active level in individuals with relatively small but multiple LULs. [ABSTRACT FROM AUTHOR]

Background: Muscle fatigue is a crucial indicator to determine whether training is in place and to protect trainers. Purpose: To make full use of morphological information of surface EMG and ECG signals in the time domain, a new idea and method for the fatigue assessment of exercise muscles based on data fusion is proposed in this paper. Methods: sEMG and ECG time series with the same length were obtained by signal preprocessing and sequence normalization, feature extraction of sequence tenses was realized by a deep learning network based on sequential convolution and signal fusion model of muscle fatigue evaluation was established by D-S evidence theory. Experiment: Thirty volunteers were recruited and divided into three groups. ECG signals and sEMG signals at the biceps brachii of the right upper limb were monitored in a 20-minute exercise cycle. Results: The prediction result of TCN based on time domain signal is better than the commonly used KNN and SVM recognition algorithm, and the recognition accuracy of relaxed, excessive and fatigue by D-S fusion was 89%, 86%, 88.5%. The accuracy was 0.9055, 0.9494 and 0.9269, respectively. The recall rates of the three conditions were 0.9303, 0.9570 and 0.9435. The F-score of the three conditions was 0.8911, 0.8764 and 0.8837, respectively. Conclusion: Based on time series and time series convolutional network, sEMG and ECG fusion of motor muscle recognition method can better distinguish different state information and has certain practical value in the fields of muscle evaluation, clinical diagnosis, wearable devices and so on. [ABSTRACT FROM AUTHOR]

Simple Summary: Simple and effective risk stratification tools which allow prediction of the risk of malignant esophageal lesions are needed for the practice of opportunistic screening. The aim of the current study was to develop an improved version of the diagnostic model based on a large-scale outpatient cohort and assess the robustness and generalizability of the model through external validations. The improved diagnostic model had seven predictors and generated an area under the receiver operating characteristic curve of 0.860 in the development set. Validation of the model in two external populations also showed high discrimination power and was able to increase the detection rate of malignant esophageal lesions. This questionnaire-based diagnostic model provides an easy-to-use tool to identify high-risk individuals and will be useful for the promotion of the opportunistic screening of esophageal cancer. We aimed to develop an improved version of the diagnostic model predicting the risk of malignant esophageal lesions in opportunistic screening and validate it in external populations. The development set involved 10,595 outpatients receiving endoscopy from a hospital in Hua County, a high-risk region for esophageal squamous cell carcinoma in northern China. Validation set A enrolled 9453 outpatients receiving endoscopy in a non-high-risk region in southern China. Validation set B involved 17,511 residents in Hua County. The improved diagnostic model consisted of seven predictors including age, gender, family history of esophageal squamous cell carcinoma, smoking, body mass index, dysphagia, and retrosternal pain, with an area under the receiver operating characteristic curve (AUC) of 0.860 (95% confidence interval: 0.835–0.886) in the development set. Ideal discrimination ability was achieved in external validations (AUC validation set A: 0.892, 95% confidence interval: 0.858–0.926; AUC validation set B: 0.799, 95% confidence interval: 0.705–0.894). This improved model also markedly increased the detection rate of malignant esophageal lesions compared with universal screening, demonstrating great potential for use in opportunistic screening of malignant esophageal lesions in heterogeneous populations. [ABSTRACT FROM AUTHOR]

Currently, surveillance for esophageal squamous cell carcinoma (ESCC) runs a risk of underestimation of early lesions which show absence of iodine staining, with no or only mild histologic changes. The development of molecular markers that indicate risk of progression is thus warranted. We performed whole‐exome sequencing on biopsies from two sequential endoscopies of a single esophageal lesion and matching blood samples. There were 27 pairs of age‐, gender‐, pathologic stage‐, and sampling interval‐matched progressors and non‐progressors identified in a prospective community‐based ESCC screening trial. Putative molecular progression markers for ESCC were first evaluated by comparing somatic mutation, copy number alteration (CNA), and mutational signature information among progressors and non‐progressors. These markers were then validated with another 24 pairs of matched progressors and non‐progressors from the same population using gene alteration status identified by target sequencing and quantitative PCR. Progressors had more somatic mutation and CNA burden, as well as apolipoprotein B mRNA editing catalytic polypeptide‐like and age‐related signature weights compared with non‐progressors. A gene score consisting of somatic NOTCH1 mutation and CDKN2A deletion is predictive of risk of progression in lesions which show absence of iodine staining under endoscopy but have no or only mild dysplasia. This gene score was also validated in an external cohort of matched progressors and non‐progressors. Absence of NOTCH1 mutation and presence of CDKN2A deletion are markers of progression in squamous lesions of the esophagus. This gene score would be an ideal indicator for assisting the pathologist in the identification of high‐risk individuals who could be potentially 'missed' or subject to a risk underestimation by histologic analysis, and might improve the performance of ESCC surveillance. © 2022 The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]