Step Width Estimation in Individuals With and Without Neurodegenerative Disease Via a Novel Data-Augmentation Deep Learning Model and Minimal Wearable Inertial Sensors.

Step width is vital for gait stability, postural balance control, and fall risk reduction. However, estimating step width typically requires either fixed cameras or a full kinematic body suit of wearable inertial measurement units (IMUs), both of which are often too expensive and time-consuming for clinical application. We thus propose a novel data-augmented deep learning model for estimating step width in individuals with and without neurodegenerative disease using a minimal set of wearable IMUs. Twelve patients with neurodegenerative, clinically diagnosed Spinocerebellar ataxia type 3 (SCA3) performed over ground walking trials, and seventeen healthy individuals performed treadmill walking trials at various speeds and gait modifications while wearing IMUs on each shank and the pelvis. Results demonstrated step width mean absolute errors of 3.3 ± 0.7cm and 2.9 ± 0.5cm for the neurodegenerative and healthy groups, respectively, which were below the minimal clinically important difference of 6.0cm. Step width variability mean absolute errors were 1.5cm and 0.8cm for neurodegenerative and healthy groups, respectively. Data augmentation significantly improved accuracy performance in the neurodegenerative group, likely because they exhibited larger variations in walking kinematics as compared with healthy subjects. These results could enable clinically meaningful and accurate portable step width monitoring for individuals with and without neurodegenerative disease, potentially enhancing rehabilitative training, assessment, and dynamic balance control in clinical and real-life settings.

Corrigendum: Exercise for reducing chemotherapy-induced peripheral neuropathy: a systematic review and meta-analysis of randomized controlled trials.

[This corrects the article DOI: 10.3389/fneur.2023.1252259.].

A proposed rapid upper limb assessment for pianists (RULA-p)1.

BACKGROUND: Pianists often suffered from postural related problems due to prolonged sitting and awkward postures. Despite that postural related problems are common among pianists, there is only one study found by the authors, which applied RULA to assess pianists' postures, without any modification to the original RULA worksheet. None of the other existing literature has applied this postural assessment tool to assess the pianists' overall posture. There is no existing Rapid Upper Limb Assessment (RULA) checklist that exactly fits into the context of piano playing, at least not without some modifications. OBJECTIVE: To propose a Rapid Upper Limb Assessment for Pianists (RULA-p) for postural assessment, which will allow pianists to identify awkward postures (if any) during piano playing, thus, achieving healthful playing for injury prevention. METHOD: This study modified (i) the muscle use score; and (ii) the force/load score, from the existing RULA as a rapid assessment for pianists' posture. RESULTS: Proposed the modified RULA for pianists (RULA-p) in the worksheet format. CONCLUSION: Overall, this study is intended to further expand the existing literature on the early prevention of pianists' PRMDs.

Implementation of a novel computer assisted telephone follow-up model for older patients after emergency department discharge in an Asian population.

BACKGROUND: While the impact of telephone follow-up (TFU) for older emergency department (ED) patients is controversial, its effects on the Asian population remain uncertain. In this study, we evaluated the effectiveness of a novel computer assisted TFU model specifically for this demographic. METHODS: At a Taiwanese tertiary medical center, we developed a TFU protocol that included a referral and case management system within the ED hospital information system. We provided TFU to older discharged patients between April 1, 2021, and May 31, 2021. We compared this cohort with a non-TFU cohort of older ED patients and analyzed demographic characteristics and post-ED discharge outcomes. RESULTS: The TFU model was successfully implemented, with 395 patients receiving TFU and 191 without TFU. TFU patients (median age: 76 years, male proportion: 48.9%) differed from non-TFU patients (median age: 74 years, male proportion: 43.5%). Compared with the non-TFU cohort, the multivariate logistic regression analysis revealed that the TFU cohort had a lower total medical expenditure < 1 month (adjusted odds ratio [AOR]: 0.32; 95% CI: 0.21 - 0.47 for amounts exceeding 5,000 New Taiwan Dollars), and higher satisfaction (AOR: 2.80; 95% CI: 1.46 - 5.36 for scores > 3 on a five-point Likert Scale). However, the TFU cohort also had a higher risk of hospitalization < 1 month (AOR: 2.50; 95% CI: 1.31 - 4.77) compared to the non-TFU cohort. CONCLUSION: Computer-assisted TFU appears promising. Further research involving a larger number of patients and validation in other hospitals is necessary to bolster the evidence and extend the findings to a broader context.

Alkylation Triggered an Acylation of Arenes with a Highly Electrophilic Acylphosphonium Salt.

Friedel-Crafts acylation is usually promoted by Lewis acids or Brønsted acids. In this work, a novel acylation of arenes with a highly electrophilic acylphosphonium salt was developed. The alkylation of the phosphorus atom in acylphosphines generated a neutral trivalent phosphine as a good leaving group and triggered the high electrophilicity of the acylphosphonium salt. Using acylphosphonium salts, 38 examples of acylations of arenes, alcohols, phenol, amines, thioalcohols, and even polystyrene were achieved. The acylation of arenes was monitored by 31P nuclear magnetic resonance and disclosed the existence of an acylphosphonium intermediate. The electrophilic capability of the acylphosphonium salt was ranked by the following series of controlled reactions: AcPR+ ≈ AcOTf > AcI > AcCl.

Urolithin C suppresses colorectal cancer progression via the AKT/mTOR pathway.

Urolithin families are gut-microbial metabolites of ellagic acid (EA). Although urolithin A (UA) and urolithin B (UB) were reported to have antiproliferative activities in cancer cells, the role and related mechanisms of urolithin C (UC) in colorectal cancer (CRC) have not yet been clarified. In this study, we assess the antitumor activities of UC in vitro and in vivo and further explore the underlying mechanisms in CRC cell lines. We found that UC inhibited the proliferation and migration of CRC cells, induced apoptosis, and arrested the cell cycle at the G2/M phase in vitro, and UC inhibited tumor growth in a subcutaneous transplantation tumor model in vivo. Mechanically, UC blocked the activation of the AKT/mTOR signaling pathway by decreasing the expression of Y-box binding protein 1(YBX1). The AKT agonist SC79 could reverse the suppression of cell proliferation in UC-treated CRC cells. In conclusion, our research revealed that UC could prevent the progression of CRC by blocking AKT/mTOR signaling, suggesting that it may have potential therapeutic values.

Cavitation is the determining mechanism for the atomization of high-viscosity liquid.

Piezoelectric atomization is becoming mainstream in the field of inhalation therapy due to its significant advantages. With the rapid development of high-viscosity gene therapy drugs, the demand for piezoelectric atomization devices is increasing. However, conventional piezoelectric atomizers with a single-dimensional energy supply are unable to provide the energy required to atomize high-viscosity liquids. To address this problem, our team has designed a flow tube internal cavitation atomizer (FTICA). This study focuses on dissecting the atomization mechanism of FTICA. In contrast to the widely supported capillary wave hypothesis, our study provides evidence in favor of the cavitation hypothesis, proving that cavitation is the key to atomizing high-viscosity liquids with FTICA. In order to prove that the cavitation is the key to atomizing in the structure of FTICA, the performance of atomization is experimented after changing the cavitation conditions by heating and stirring of the liquids.

Prediction of drug-target binding affinity based on multi-scale feature fusion.

Accurate prediction of drug-target binding affinity (DTA) plays a pivotal role in drug discovery and repositioning. Although deep learning methods are widely used in DTA prediction, two significant challenges persist: (i) how to effectively represent the complex structural information of proteins and drugs; (ii) how to precisely model the mutual interactions between protein binding sites and key drug substructures. To address these challenges, we propose a MSFFDTA (Multi-scale feature fusion for predicting drug target affinity) model, in which multi-scale encoders effectively capture multi-level structural information of drugs and proteins are designed. And then a Selective Cross Attention (SCA) mechanism is developed to filter out the trivial interactions between drug-protein substructure pairs and retain the important ones, which will make the proposed model better focusing on these key interactions and offering insights into their underlying mechanism. Experimental results on two benchmark datasets demonstrate that MSFFDTA is superior to several state-of-the-art methods across almost all comparison metrics. Finally, we provide the ablation and case studies with visualizations to verify the effectiveness and the interpretability of MSFFDTA. The source code is freely available at https://github.com/whitehat32/MSFF-DTA/.

Integration of MoS2 Memtransistor Devices and Analogue Circuits for Sensor Fusion in Autonomous Vehicle Target Localization.

In contemporary autonomous driving systems relying on sensor fusion, traditional digital processors encounter challenges associated with analogue-to-digital conversion and iterative vector-matrix operations, which are encumbered by limitations in terms of response time and energy consumption. In this study, we present an analogue Kalman filter circuit based on molybdenum disulfide (MoS2) memtransistor, designed to accelerate sensor fusion for precise localization in autonomous vehicle applications. The nonvolatile memory characteristics of the memtransistor allow for the storage of a fixed Kalman gain, which eliminates the data convergence and thus accelerates the processing speeds. Additionally, the modulation of multiple conductance states by the gate terminal enables fast adaptability to diverse autonomous driving scenarios by tuning multiple Kalman filter gains. Our proposed analogue Kalman filter circuit accurately estimates the position coordinates of target vehicles by fusing sensor data from light detection and ranging (LiDAR), millimeter-wave radar (Radar), and camera, and it successfully solves real-word problems in a signal-free crossroad intersection. Notably, our system achieves a 1000-fold improvement in energy efficiency compared to that of digital circuits. This work underscores the viability of a memtransistor for achieving fast, energy-efficient real-time sensing, and continuous signal processing in advanced sensor fusion technology.

Enhancing Methane Gas Sensing through Defect Engineering in Ag-Ru Co-doped ZnO Nanorods.

Detection of leaks of flammable methane (CH4) gas in a timely manner can mitigate health, safety, and environmental risks. Zinc oxide (ZnO), a polar semiconductor with controllable surface defects, is a promising material for gas sensing. In this study, Ag-Ru co-doped into self-assembled ZnO nanorod arrays (ZnO NRs) was prepared by a one-step hydrothermal method. The Ag-Ru co-doped sample shows a good hydrophobic property as a result of its particular microstructure, which results in high humidity resistance. In addition, oxygen vacancy density significantly increased after Ag-Ru co-doping. Density functional theory (DFT) calculations revealed an exceptionally high charge density accumulated at the Ru sites and the formation of a localized strong electric field, which provides additional energy for the CH4 reaction with •O2- at the surface at room temperature. Optimized AgRu0.025-ZnO demonstrated an outstanding CH4 sensing performance, with a limit of detection (LOD) as low as 2.24 ppm under free-heat and free-light conditions. These findings suggest that introducing defects into the ZnO lattice, such as oxygen vacancies and localized ions, offers a promising approach to improving the gas sensing performance.

Corrigendum: Exercise for reducing chemotherapy-induced peripheral neuropathy: a systematic review and meta-analysis of randomized controlled trials.

[This corrects the article DOI: 10.3389/fneur.2023.1252259.].

Efficacy and mechanism of acupuncture in animal models of depressive-like behaviors: a systematic review and meta-analysis.

Background: Many studies have investigated the efficacy of acupuncture in treating depression, but the mechanism of acupuncture for depression is still controversial and there is a lack of meta-analysis of mechanisms. Consequently, we investigated acupuncture's efficacy and mechanism of depression. Methods: We searched the Cochrane Library, PubMed, EMBASE, Web of Science. The SYRCLE Risk of Bias Tool was used to assess bias risk. Meta-analysis was performed using Stata 15.0 for indicators of depression mechanisms, body weight and behavioral tests. Results: A total of 22 studies with 497 animals with depressive-like behaviors were included. Meta-analysis showed that acupuncture significantly increased BDNF [SMD = 2.40, 95% CI (1.33, 3.46); I2 = 86.6%], 5-HT [SMD = 2.28, 95% CI (1.08, 3.47); I2 = 87.7%] compared to the control group (p < 0.05), and significantly reduced IL-1ß [SMD = -2.33, 95% CI (-3.43, -1.23); I2 = 69.6%], CORT [SMD = -2.81, 95% CI (-4.74, -0.87); I2 = 86.8%] (p < 0.05). Acupuncture improved body weight [SMD = 1.35, 95% CI (0.58, 2.11); I2 = 84.5%], forced swimming test [SMD = -1.89, 95% CI (-2.55, -1.24); I2 = 76.3%], open field test (crossing number [SMD = 3.08, 95% CI (1.98, 4.17); I2 = 86.7%], rearing number [SMD = 2.53, 95% CI (1.49, 3.57); I2 = 87.0%]) (p < 0.05) compared to the control group. Conclusion: Acupuncture may treat animals of depressive-like behaviors by regulating neurotrophic factors, neurotransmitters, inflammatory cytokines, neuroendocrine system. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023403318, identifier (CRD42023403318).

Self-Supervised Learning Improves Accuracy and Data Efficiency for IMU-Based Ground Reaction Force Estimation.

OBJECTIVE: Recent deep learning techniques hold promise to enable IMU-driven kinetic assessment; however, they require large extents of ground reaction force (GRF) data to serve as labels for supervised model training. We thus propose using existing self-supervised learning (SSL) techniques to leverage large IMU datasets to pre-train deep learning models, which can improve the accuracy and data efficiency of IMU-based GRF estimation. METHODS: We performed SSL by masking a random portion of the input IMU data and training a transformer model to reconstruct the masked portion. We systematically compared a series of masking ratios across three pre-training datasets that included real IMU data, synthetic IMU data, or a combination of the two. Finally, we built models that used pre-training and labeled data to estimate GRF during three prediction tasks: overground walking, treadmill walking, and drop landing. RESULTS: When using the same amount of labeled data, SSL pre-training significantly improved the accuracy of 3-axis GRF estimation during walking compared to baseline models trained by conventional supervised learning. Fine-tuning SSL model with 1-10% of walking data yielded comparable accuracy to training baseline model with 100% of walking data. The optimal masking ratio for SSL is 6.25-12.5%. CONCLUSION: SSL leveraged large real and synthetic IMU datasets to increase the accuracy and data efficiency of deep-learning-based GRF estimation, reducing the need for labeled data. SIGNIFICANCE: This work, with its open-source code and models, may unlock broader use cases of IMU-driven kinetic assessment by mitigating the scarcity of GRF measurements in practical applications.

A web-based tool for real-time adequacy assessment of kidney biopsies.

The escalating incidence of kidney biopsies providing insufficient tissue for diagnosis poses a dual challenge, straining the healthcare system and jeopardizing patients who may require rebiopsy or face the prospect of an inaccurate diagnosis due to an unsampled disease. Here, we introduce a web-based tool that can provide real-time, quantitative assessment of kidney biopsy adequacy directly from photographs taken with a smartphone camera. The software tool was developed using a deep learning-driven automated segmentation technique, trained on a dataset comprising nephropathologist-confirmed annotations of the kidney cortex on digital biopsy images. Our framework demonstrated favorable performance in segmenting the cortex via 5-fold cross-validation (Dice coefficient: 0.788±0.130) (n=100). Offering a bedside tool for kidney biopsy adequacy assessment has the potential to provide real-time guidance to the physicians performing medical kidney biopsies, reducing the necessity for re-biopsies. Our tool can be accessed through our web-based platform: http://www.biopsyadequacy.org.

Self-Supervised Learning Improves Accuracy and Data Efficiency for IMU-Based Ground Reaction Force Estimation.

Objective: Recent deep learning techniques hold promise to enable IMU-driven kinetic assessment; however, they require large extents of ground reaction force (GRF) data to serve as labels for supervised model training. We thus propose using existing self-supervised learning (SSL) techniques to leverage large IMU datasets to pre-train deep learning models, which can improve the accuracy and data efficiency of IMU-based GRF estimation. Methods: We performed SSL by masking a random portion of the input IMU data and training a transformer model to reconstruct the masked portion. We systematically compared a series of masking ratios across three pre-training datasets that included real IMU data, synthetic IMU data, or a combination of the two. Finally, we built models that used pre-training and labeled data to estimate GRF during three prediction tasks: overground walking, treadmill walking, and drop landing. Results: When using the same amount of labeled data, SSL pre-training significantly improved the accuracy of 3-axis GRF estimation during walking compared to baseline models trained by conventional supervised learning. Fine-tuning SSL model with 1-10% of walking data yielded comparable accuracy to training baseline model with 100% of walking data. The optimal masking ratio for SSL is 6.25-12.5%. Conclusion: SSL leveraged large real and synthetic IMU datasets to increase the accuracy and data efficiency of deep-learning-based GRF estimation, reducing the need for labeled data. Significance: This work, with its open-source code and models, may unlock broader use cases of IMU-driven kinetic assessment by mitigating the scarcity of GRF measurements in practical applications.

PPIP5K2 Facilitates Proliferation and Metastasis of Non-Small Lung Cancer (NSCLC) through AKT Signaling Pathway.

Through facilitating DNA homologous recombination repair, PPIP5K2 has been proven to be essential for improving colorectal cancer survival in our previous research. However, its function in the tumorigenesis of NSCLC, the most common cancer and the primary cause of cancer-related death globally, is still unknown. Here, we initially discovered that PPIP5K2 had significant effects on proliferation of NSCLC cells through loss- and gain-of-function assays in vitro and in vivo. Moreover, PPIP5K2 is capable of regulating NSCLC cells metastasis in an EMT-dependent manner. In terms of mechanism exploration, we found that PPIP5K2 knockdown can significantly inhibit the phosphorylation of AKT/mTOR signaling pathway, whereas the overexpression of PPIP5K2 resulted in converse effects. By employing AKT signaling related agonists or antagonists, we further demonstrated that PPIP5K2 regulates NSCLC tumorigenesis partly via the AKT/mTOR pathway. In conclusion, PPIP5K2 plays a key oncogenic role in NSCLC by the activation of the AKT/mTOR signaling axis. It is anticipated that targeting PPIP5K2 might emerge as a viable therapeutic approach for NSCLC patients.

Synthetic IMU Datasets and Protocols Can Simplify Fall Detection Experiments and Optimize Sensor Configuration.

Falls represent a significant cause of injury among the elderly population. Extensive research has been devoted to the utilization of wearable IMU sensors in conjunction with machine learning techniques for fall detection. To address the challenge of acquiring costly training data, this paper presents a novel method that generates a substantial volume of synthetic IMU data with minimal actual fall experiments. First, unmarked 3D motion capture technology is employed to reconstruct human movements. Subsequently, utilizing the biomechanical simulation platform Opensim and forward kinematic methods, an ample amount of training data from various body segments can be custom generated. Synthetic IMU data was then used to train a machine learning model, achieving testing accuracies of 91.99% and 86.62% on two distinct datasets of actual fall-related IMU data. Building upon the simulation framework, this paper further optimized the single IMU attachment position and multiple IMU combinations on fall detection. The proposed method simplifies fall detection data acquisition experiments, provides novel venue for generating low cost synthetic data in scenario where acquiring data for machine learning is challenging and paves the way for customizing machine learning configurations.

Chemical components and against alzheimer's disease effects of the calyxes of Physalis alkekengi L. var. franchetii (Mast.) Makino.

Physalis alkekengi L. var. franchetii (Mast.) Makino (PA), a traditional Chinese medicine, is utilised for treating dermatitis, sore throat, dysuria, and cough. This research aimed to identify the main constituents in the four extracted portions from the calyces of PA (PAC) utilising ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). The Alzheimer's disease (AD) mice model was induced by D-galactose (D-gal) combined with aluminium chloride (AlCl3). Subsequent investigation into the underlying mechanisms involved behavioural and histopathological observations. The results demonstrated that four extracted portions of PAC (PACE) significantly enhanced memory and learning abilities in the Morris water maze. The concentrations of Aß, tau and p-tau in brain tissue exhibited a significant decrease relative to the model group. Moreover, the four PACE treatment groups increased the glutathione (GSH) and superoxide dismutase (SOD) levels, while concurrently reducing malondialdehyde (MDA), interleukin-1ß (IL-1ß) and interleukin-6 (IL-6), tumour necrosis factor-α (TNF-α) levels. In summary, the current study demonstrates that the four PACE formulations exhibit beneficial anti-AD properties, with the most pronounced efficacy observed in the EA group. Additionally, PAC shows potential in mitigating neuroinflammation and oxidative damage by inhibiting the TLR4/NF-κB signalling pathway. This research lays a theoretical groundwork for the future clinical development and utilisation of PAC in treating AD.

A randomized controlled trial investigating the impact of a single flash cupping session on patients with chronic lower back pain using multichannel surface electromyographic assessment.

BACKGROUND: Chronic lower back pain (CLBP) is one of the most common disorders worldwide. Flash cupping has the ability to relieve CLBP; nevertheless, its impact on CLBP and the likely mechanism of action have not been studied. OBJECTIVE: The goal of this study was to assess the impact of a single, brief cupping session on CLBP and low back muscle activity using multichannel surface electromyography (sEMG). METHODS: In this randomized controlled trial, 24 patients with CLBP were enrolled and randomly assigned to the control group (treated by acupuncture) and cupping group (treated by acupuncture and flash cupping). Acupuncture was applied on the shen shu (BL23), dachang shu (BL25), and wei zhong (BL40) acupoints in both the groups. A brief cupping treatment was applied to the shen shu (BL23), qihai shu (BL24), dachang shu (BL25), guanyuan shu (BL26), and xiaochang shu (BL27) acupoints on both sides of the lower back in the cupping group. The numeric rating scale (NRS) was used to assess therapy efficacy for lower back pain (LBP) before and after treatment. Surface EMG data collected during symmetrical trunk flexion-extension movements were utilized to measure lower back muscle activity and the effectiveness of LBP therapy. RESULTS: There was no statistically significant difference (P= 0.63) in pain intensity between the two groups before and after treatment. There was a statistically significant difference (P= 0.04) between the control group and the cupping group in the sEMG topographic map parameter CoGx-To-Midline. CONCLUSION: This study established a connection between the action mechanism of flash cupping and enhanced horizontal synchronization of lower back muscular activity.