Predicting Continuous Locomotion Modes via Multidimensional Feature Learning from sEMG

Walking-assistive devices require adaptive control methods to ensure smooth transitions between various modes of locomotion. For this purpose, detecting human locomotion modes (e.g., level walking or stair ascent) in advance is crucial for improving the intelligence and transparency of such robotic systems. This study proposes Deep-STF, a unified end-to-end deep learning model designed for integrated feature extraction in spatial, temporal, and frequency dimensions from surface electromyography (sEMG) signals. Our model enables accurate and robust continuous prediction of nine locomotion modes and 15 transitions at varying prediction time intervals, ranging from 100 to 500 ms. In addition, we introduced the concept of 'stable prediction time' as a distinct metric to quantify prediction efficiency. This term refers to the duration during which consistent and accurate predictions of mode transitions are made, measured from the time of the fifth correct prediction to the occurrence of the critical event leading to the task transition. This distinction between stable prediction time and prediction time is vital as it underscores our focus on the precision and reliability of mode transition predictions. Experimental results showcased Deep-STP's cutting-edge prediction performance across diverse locomotion modes and transitions, relying solely on sEMG data. When forecasting 100 ms ahead, Deep-STF surpassed CNN and other machine learning techniques, achieving an outstanding average prediction accuracy of 96.48%. Even with an extended 500 ms prediction horizon, accuracy only marginally decreased to 93.00%. The averaged stable prediction times for detecting next upcoming transitions spanned from 28.15 to 372.21 ms across the 100-500 ms time advances.Comment: 10 pages,7 figure

Dynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastases

Background and objectiveFor patients with advanced colorectal liver metastases (CRLMs) receiving first-line anti-angiogenic therapy, an accurate, rapid and noninvasive indicator is urgently needed to predict its efficacy. In previous studies, dynamic radiomics predicted more accurately than conventional radiomics. Therefore, it is necessary to establish a dynamic radiomics efficacy prediction model for antiangiogenic therapy to provide more accurate guidance for clinical diagnosis and treatment decisions.MethodsIn this study, we use dynamic radiomics feature extraction method that extracts static features using tomographic images of different sequences of the same patient and then quantifies them into new dynamic features for the prediction of treatmentefficacy. In this retrospective study, we collected 76 patients who were diagnosed with unresectable CRLM between June 2016 and June 2021 in the First Hospital of China Medical University. All patients received standard treatment regimen of bevacizumab combined with chemotherapy in the first-line treatment, and contrast-enhanced abdominal CT (CECT) scans were performed before treatment. Patients with multiple primary lesions as well as missing clinical or imaging information were excluded. Area Under Curve (AUC) and accuracy were used to evaluate model performance. Regions of interest (ROIs) were independently delineated by two radiologists to extract radiomics features. Three machine learning algorithms were used to construct two scores based on the best response and progression-free survival (PFS).ResultsFor the task that predict the best response patients will achieve after treatment, by using ROC curve analysis, it can be seen that the relative change rate (RCR) feature performed best among all features and best in linear discriminantanalysis (AUC: 0.945 and accuracy: 0.855). In terms of predicting PFS, the Kaplan–Meier plots suggested that the score constructed using the RCR features could significantly distinguish patients with good response from those with poor response (Two-sided P<0.0001 for survival analysis).ConclusionsThis study demonstrates that the application of dynamic radiomics features can better predict the efficacy of CRLM patients receiving antiangiogenic therapy compared with conventional radiomics features. It allows patients to have a more accurate assessment of the effect of medical treatment before receiving treatment, and this assessment method is noninvasive, rapid, and less expensive. Dynamic radiomics model provides stronger guidance for the selection of treatment options and precision medicine

Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee Joint Trajectory from sEMG

Predicting lower limb motion intent is vital for controlling exoskeleton robots and prosthetic limbs. Surface electromyography (sEMG) attracts increasing attention in recent years as it enables ahead-of-time prediction of motion intentions before actual movement. However, the estimation performance of human joint trajectory remains a challenging problem due to the inter- and intra-subject variations. The former is related to physiological differences (such as height and weight) and preferred walking patterns of individuals, while the latter is mainly caused by irregular and gait-irrelevant muscle activity. This paper proposes a model integrating two gait cycle-inspired learning strategies to mitigate the challenge for predicting human knee joint trajectory. The first strategy is to decouple knee joint angles into motion patterns and amplitudes former exhibit low variability while latter show high variability among individuals. By learning through separate network entities, the model manages to capture both the common and personalized gait features. In the second, muscle principal activation masks are extracted from gait cycles in a prolonged walk. These masks are used to filter out components unrelated to walking from raw sEMG and provide auxiliary guidance to capture more gait-related features. Experimental results indicate that our model could predict knee angles with the average root mean square error (RMSE) of 3.03(0.49) degrees and 50ms ahead of time. To our knowledge this is the best performance in relevant literatures that has been reported, with reduced RMSE by at least 9.5%

Chlorpromazine Protects Against Apoptosis Induced by Exogenous Stimuli in the Developing Rat Brain

Background: Chlorpromazine (CPZ), a commonly used antipsychotic drug, was found to play a neuroprotective role in various models of toxicity. However, whether CPZ has the potential to affect brain apoptosis in vivo is still unknown. The purpose of this study was to investigate the potential effect of CPZ on the apoptosis induced by exogenous stimuli. Methodology: The ethanol treated infant rat was utilized as a valid apoptotic model, which is commonly used and could trigger robust apoptosis in brain tissue. Prior to the induction of apoptosis by subcutaneous injection of ethanol, 7-day-old rats were treated with CPZ at several doses (5 mg/kg, 10 mg/kg and 20 mg/kg) by intraperitoneal injection. Apoptotic cells in the brain were measured using TUNEL analysis, and the levels of cleaved caspase-3, cytochrome c, the pro-apoptotic factor Bax and the anti-apoptotic factor Bcl-2 were assessed by immunostaining or western blot. Findings: Compared to the group injected with ethanol only, the brains of the CPZ-pretreated rats had fewer apoptotic cells, lower expression of cleaved caspase-3, cytochrome c and Bax, and higher expression of Bcl-2. These results demonstrate that CPZ could prevent apoptosis in the brain by regulating the mitochondrial pathway. Conclusions: CPZ exerts an inhibitory effect on apoptosis induced by ethanol in the rat brain, intimating that it may offer

Research progress of CTC, ctDNA, and EVs in cancer liquid biopsy

Circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and extracellular vehicles (EVs) have received significant attention in recent times as emerging biomarkers and subjects of transformational studies. The three main branches of liquid biopsy have evolved from the three primary tumor liquid biopsy detection targets—CTC, ctDNA, and EVs—each with distinct benefits. CTCs are derived from circulating cancer cells from the original tumor or metastases and may display global features of the tumor. ctDNA has been extensively analyzed and has been used to aid in the diagnosis, treatment, and prognosis of neoplastic diseases. EVs contain tumor-derived material such as DNA, RNA, proteins, lipids, sugar structures, and metabolites. The three provide different detection contents but have strong complementarity to a certain extent. Even though they have already been employed in several clinical trials, the clinical utility of three biomarkers is still being studied, with promising initial findings. This review thoroughly overviews established and emerging technologies for the isolation, characterization, and content detection of CTC, ctDNA, and EVs. Also discussed were the most recent developments in the study of potential liquid biopsy biomarkers for cancer diagnosis, therapeutic monitoring, and prognosis prediction. These included CTC, ctDNA, and EVs. Finally, the potential and challenges of employing liquid biopsy based on CTC, ctDNA, and EVs for precision medicine were evaluated

Prognostic Value of MicroRNA-20b in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a highly heterogeneous disease that requires fine-grained risk stratification for the best prognosis of patients. As a class of small non-coding RNAs with important biological functions, microRNAs play a crucial role in the pathogenesis of AML. To assess the prognostic impact of miR-20b on AML in the presence of other clinical and molecular factors, we screened 90 AML patients receiving chemotherapy only and 74 also undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT) from the Cancer Genome Atlas (TCGA) database. In the chemotherapy-only group, high miR-20b expression subgroup had shorter event-free survival (EFS) and overall survival (OS, both P < 0.001); whereas, there were no significant differences in EFS and OS between high and low expression subgroups in the allo-HSCT group. Then we divided all patients into high and low expression groups based on median miR-20b expression level. In the high expression group, patients treated with allo-HSCT had longer EFS and OS than those with chemotherapy alone (both P < 0.01); however, there were no significant differences in EFS and OS between different treatment subgroups in the low expression group. Further analysis showed that miR-20b was negatively correlated with genes in "ribosome," "myeloid leukocyte mediated immunity," and "DNA replication" signaling pathways. ORAI2, the gene with the strongest correlation with miR-20b, also had significant prognostic value in patients undergoing chemotherapy but not in the allo-HSCT group. In conclusion, our findings suggest that high miR-20b expression is a poor prognostic indicator for AML, but allo-HSCT may override its prognostic impact

Prognostic role of Wnt and Fzd gene families in acute myeloid leukaemia

Wnt-Fzd signalling pathway plays a critical role in acute myeloid leukaemia (AML) progression and oncogenicity. There is no study to investigate the prognostic value of Wnt and Fzd gene families in AML. Our study screened 84 AML patients receiving chemotherapy only and 71 also undergoing allogeneic haematopoietic stem cell transplantation (allo-HSCT) from the Cancer Genome Atlas (TCGA) database. We found that some Wnt and Fzd genes had significant positive correlations. The expression levels of Fzd gene family were independent of survival in AML patients. In the chemotherapy group, AML patients with high Wnt2B or Wnt11 expression had significantly shorter event-free survival (EFS) and overall survival (OS); high Wnt10A expressers had significantly longer OS than the low expressers (all P < .05), whereas, in the allo-HSCT group, the expression levels of Wnt gene family were independent of survival. We further found that high expression of Wnt10A and Wnt11 had independent prognostic value, and the patients with high Wnt10A and low Wnt11 expression had the longest EFS and OS in the chemotherapy group. Pathway enrichment analysis showed that genes related to Wnt10A, Wnt11 and Wnt 2B were mainly enriched in 'cell morphogenesis involved in differentiation', 'haematopoietic cell lineage', 'platelet activation, signalling and aggregation' and 'mitochondrial RNA metabolic process' signalling pathways. Our results indicate that high Wnt2B and Wnt11 expression predict poor prognosis, and high Wnt10A expression predicts favourable prognosis in AML, but their prognostic effects could be neutralized by allo-HSCT. Combined Wnt10A and Wnt11 may be a novel prognostic marker in AML

Overexpression of DHX32 contributes to the growth and metastasis of colorectal cancer

Our previous work demonstrates that DHX32 is upregulated in colorectal cancer (CRC) compared to its adjacent normal tissues. However, how overexpressed DHX32 contributes to CRC remains largely unknown. In this study, we reported that DHX32 was overexpressed in human colon cancer cells. Overexpressed DHX32 promoted SW480 cancer cells proliferation, migration, and invasion, as well as decreased the susceptibility to chemotherapy agent 5-Fluorouracil. Furthermore, PCR array analyses revealed that depleting DHX32 in SW480 colon cancer cells suppressed expression of WISP1, MMP7 and VEGFA in the Wnt pathway, and anti-apoptotic gene BCL2 and CA9, however, elevated expression of pro-apoptotic gene ACSL5. The findings suggested that overexpressed DHX32 played an important role in CRC progression and metastasis and that DHX32 has the potential to serve as a biomarker and a novel therapeutic target for CRC

BVT.2733, a Selective 11β-Hydroxysteroid Dehydrogenase Type 1 Inhibitor, Attenuates Obesity and Inflammation in Diet-Induced Obese Mice

BACKGROUND: Inhibition of 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) is being pursued as a new therapeutic approach for the treatment of obesity and metabolic syndrome. Therefore, there is an urgent need to determine the effect of 11β-HSD1 inhibitor, which suppresses glucocorticoid action, on adipose tissue inflammation. The purpose of the present study was to examine the effect of BVT.2733, a selective 11β-HSD1 inhibitor, on expression of pro-inflammatory mediators and macrophage infiltration in adipose tissue in C57BL/6J mice. METHODOLOGY/PRINCIPAL FINDINGS: C57BL/6J mice were fed with a normal chow diet (NC) or high fat diet (HFD). HFD treated mice were then administrated with BVT.2733 (HFD+BVT) or vehicle (HFD) for four weeks. Mice receiving BVT.2733 treatment exhibited decreased body weight and enhanced glucose tolerance and insulin sensitivity compared to control mice. BVT.2733 also down-regulated the expression of inflammation-related genes including monocyte chemoattractant protein 1 (MCP-1), tumor necrosis factor alpha (TNF-α) and the number of infiltrated macrophages within the adipose tissue in vivo. Pharmacological inhibition of 11β-HSD1 and RNA interference against 11β-HSD1 reduced the mRNA levels of MCP-1 and interleukin-6 (IL-6) in cultured J774A.1 macrophages and 3T3-L1 preadipocyte in vitro. CONCLUSIONS/SIGNIFICANCE: These results suggest that BVT.2733 treatment could not only decrease body weight and improve metabolic homeostasis, but also suppress the inflammation of adipose tissue in diet-induced obese mice. 11β-HSD1 may be a very promising therapeutic target for obesity and associated disease