Decomposing Task-Relevant Information from Surface Electromyogram for User-Generic Dexterous Finger Force Decoding.

Existing electromyographic (EMG) based motor intent detection algorithms are typically user-specific, and a generic model that can quickly adapt to new users is highly desirable. However, establishing such a model remains a challenge due to high inter-person variability and external interference with EMG signals. In this study, we present a feature disentanglement approach, implemented by an autoencoder-like architecture, designed to decompose user-invariant, motor-task-sensitive high-level representations from user-sensitive, task-irrelevant representations in EMG amplitude features. Our method is usergeneric and can be applied to unseen users for continuous multi-finger force predictions. We evaluated our approach on eight subjects, predicting the force of three fingers (index, middle, and ring-pinky) concurrently. We assessed the decoder's performance through a rigorous leave-onesubject-out validation. Our developed approach consistently outperformed both the conventional EMG amplitude method and a commonly used feature projection approach, principal component analysis (PCA), with a lower force prediction error (RMSE: 6.91 ± 0.45% MVC; R 2 : 0.835 ± 0.026) and a higher finger classification accuracy (83.0 ± 4.5%). The comparison with the state-of-the-art neural networks further demonstrated the superior performance of our method in user-generic force predictions. Overall, our methods provide novel insights into the development of user-generic and accurate neural decoding for myoelectric control of assistive robotic hands.

Unsupervised neural decoding for concurrent and continuous multi-finger force prediction.

Reliable prediction of multi-finger forces is crucial for neural-machine interfaces. Various neural decoding methods have progressed substantially for accurate motor output predictions. However, most neural decoding methods are performed in a supervised manner, i.e., the finger forces are needed for model training, which may not be suitable in certain contexts, especially in scenarios involving individuals with an arm amputation. To address this issue, we developed an unsupervised neural decoding approach to predict multi-finger forces using spinal motoneuron firing information. We acquired high-density surface electromyogram (sEMG) signals of the finger extensor muscle when subjects performed single-finger and multi-finger tasks of isometric extensions. We first extracted motor units (MUs) from sEMG signals of the single-finger tasks. Because of inevitable finger muscle co-activation, MUs controlling the non-targeted fingers can also be recruited. To ensure an accurate finger force prediction, these MUs need to be teased out. To this end, we clustered the decomposed MUs based on inter-MU distances measured by the dynamic time warping technique, and we then labeled the MUs using the mean firing rate or the firing rate phase amplitude. We merged the clustered MUs related to the same target finger and assigned weights based on the consistency of the MUs being retained. As a result, compared with the supervised neural decoding approach and the conventional sEMG amplitude approach, our new approach can achieve a higher R2 (0.77 ± 0.036 vs. 0.71 ± 0.11 vs. 0.61 ± 0.09) and a lower root mean square error (5.16 ± 0.58 %MVC vs. 5.88 ± 1.34 %MVC vs. 7.56 ± 1.60 %MVC). Our findings can pave the way for the development of accurate and robust neural-machine interfaces, which can significantly enhance the experience during human-robotic hand interactions in diverse contexts.

Correlation between serum markers and transjugular intrahepatic portosystemic shunt prognosis in patients with cirrhotic ascites.

BACKGROUND: Individuals with refractory ascites in the context of liver cirrhosis typically face an adverse prognosis. The transjugular intrahepatic portosystemic shunt (TIPS) is an efficacious intervention, but there is a lack of reliable tools for postoperative prognosis assessment. Previously utilized clinical biochemical markers, such as the serum albumin concentration (Alb), sodium (Na+) concentration, and serum creatinine (Scr), have limited predictive value. Therefore, the quest for novel, specific biomarkers to evaluate the post-TIPS prognosis in patients with liver cirrhosis and refractory ascites holds significant practical importance. AIM: To investigate the associations between the Child-Pugh score, model for end-stage liver disease (MELD) score, and serum cystatin C (Cys C) level and post-TIPS prognosis in patients with liver cirrhosis and refractory ascites. METHODS: A retrospective analysis was conducted on 75 patients with liver cirrhosis and refractory ascites who underwent TIPS at our institution from August 2019 to August 2021. These patients were followed up regularly for two years, and the death toll was meticulously documented. The patients were allocated into a survival group (n = 45 patients) or a deceased group (n = 30 patients) based on their prognosis status. The clinical data of the two groups were collected, and Child-Pugh scores and MELD scores were calculated for analysis. Spearman correlation analysis was carried out to evaluate the correlation of prognosis with Child-Pugh grade, MELD score, and Cys C level. Additionally, a multiple-factor analysis utilizing the Cox proportional hazard model was used to identify independent risk factors affecting the post-TIPS prognosis of patients with liver cirrhosis and refractory ascites. The receiver operating characteristic curve (ROC) ascertained the predictive value of the Cys C concentration, Child-Pugh grade, and MELD score for the prognosis of liver cirrhosis with refractory ascites in post-TIPS patients. RESULTS: During a 2-year follow-up period, among 75 patients with liver cirrhosis and refractory ascites who underwent TIPS treatment, 30 patients (40.00%) passed away. The deceased cohort exhibited heightened aspartate aminotransferase, alanine aminotransferase, total bilirubin, Scr, prothrombin time, Cys C, international normalized ratio, Child-Pugh, and MELD scores compared to those of the survival cohort, while Alb and Na+ levels were attenuated in the deceased group (P < 0.05). Spearman analysis revealed moderate to high positive correlations between prognosis and Child-Pugh score, MELD score, and Cys C level (r = 0.709, 0.749, 0.671, P < 0.05). Multivariate analysis using the Cox proportional hazard model demonstrated that the independent risk factors for post-TIPS prognosis in patients with liver cirrhosis and refractory ascites were Cys C (HR = 3.802; 95%CI: 1.313-11.015), Child-Pugh (HR = 3.030; 95%CI: 1.858-4.943), and MELD (HR = 1.222; 95%CI: 1.073-1.393) scores. ROC analysis confirmed that, compared to those of the classic prognostic models for Child-Pugh and MELD scores, the predictive accuracy of Cys C for post-TIPS prognosis in patients with liver cirrhosis and refractory ascites was slightly lower. This analysis yielded sensitivity and specificity values of 83.33% and 82.22%, respectively. The area under the curve value at this juncture was 0.883, with an optimal cutoff value set at 1.95 mg/L. CONCLUSION: Monitoring the serum Cys C concentration is valuable for assessing the post-TIPS prognosis in patients with liver cirrhosis and refractory ascites. Predictive models based on serum Cys C levels, as opposed to Scr levels, are more beneficial for evaluating the condition and prognosis of patients with ascites due to cirrhosis.

Efficacy of transjugular intrahepatic portosystemic shunts in treating cirrhotic esophageal-gastric variceal bleeding.

BACKGROUND: Esophageal-gastric variceal bleeding (EGVB) represents a severe complication among patients with cirrhosis and often culminates in fatal outcomes. Interventional therapy, a rapidly developing treatment modality over the past few years, has found widespread application in clinical practice due to its minimally invasive characteristics. However, whether transjugular intrahepatic portosystemic shunt (TIPS) treatment has an impact on patient prognosis remains controversial. AIM: To probing the efficacy of TIPS for treating cirrhotic EGVB and its influence on the prognosis of patients afflicted by this disease. METHODS: A retrospective study was conducted on ninety-two patients presenting with cirrhotic EGVB who were admitted to our hospital between September 2020 and September 2022. Based on the different modes of treatment, the patients were assigned to the study group (TIPS received, n = 50) or the control group (percutaneous transhepatic varices embolization received, n = 42). Comparative analyses were performed between the two groups preoperatively and one month postoperatively for the following parameters: Varicosity status; hemodynamic parameters [portal vein flow velocity (PVV) and portal vein diameter (PVD); platelet count (PLT); red blood cell count; white blood cell count (WBC); and hepatic function [albumin (ALB), total bilirubin (TBIL), and aspartate transaminase (AST)]. The Generic Quality of Life Inventory-74 was utilized to assess quality of life in the two groups, and the 1-year postoperative rebleeding and survival rates were compared. RESULTS: Following surgical intervention, there was an improvement in the incidence of varicosity compared to the preoperative status in both cohorts. Notably, the study group exhibited more pronounced enhancements than did the control group (P < 0.05). PVV increased, and PVD decreased compared to the preoperative values, with the study cohort achieving better outcomes (P < 0.05). PLT and WBC counts were elevated postoperatively in the two groups, with the study cohort displaying higher PLT and WBC counts (P < 0.05). No differences were detected between the two groups in terms of serum ALB, TBIL, or AST levels either preoperatively or postoperatively (P < 0.05). Postoperative scores across all dimensions of life quality surpassed preoperative scores, with the study cohort achieving higher scores (P < 0.05). At 22.00%, the one-year postoperative rebleeding rate in the study cohort was significantly lower than that in the control group (42.86%; P < 0.05); conversely, no marked difference was observed in the 1-year postoperative survival rate between the two cohorts (P > 0.05). CONCLUSION: TIPS, which has demonstrated robust efficacy in managing cirrhotic EGVB, remarkably alleviates varicosity and improves hemodynamics in patients. This intervention not only results in a safer profile but also contributes significantly to a more favorable prognosis.

Killing Two Birds with One Stone: Shape-Memory Cryogels for Hemostasis and Wound Repair.

The development of shape-memory hemostatic agents is crucial for the treatment of deep incompressible bleeding tissue. However, there are few reports on biomaterials that can monitor bacterial infection at the wound site in real time following hemostasis and effectively promote repair. In this study, we propose a multifunctional QCSG/FLZ cryogel composed of glycidyl methacrylate-functionalized quaternary chitosan (QCSG), fluorescein isothiocyanate (FITC), and a lysozyme (LYZ)-modified zeolitic imidazolate framework (ZIF-8) for incompressible bleeding tissue hemostasis and wound repair. QCSG/FLZ cryogels possess interconnected microporous structure and enhanced mechanical properties, allowing them to be molded into different shapes for effective hemostasis in deep incompressible wounds. Furthermore, the fluorescence quench signal of QCSG/FLZ cryogels enables timely monitoring of bacterial infection when wound triggers infection. Meanwhile, the acidic microenvironment of bacterial infection induces structural lysis of ZIF-8, releasing LYZ and Zn2+, which effectively kill bacteria and accelerate wound repair. In conclusion, our study not only provides potential application of QCSG/FLZ cryogels for hemostasis in deep incompressible wounds but promisingly promotes the development of a tissue repair technique.

Towards Efficient Neural Decoder for Dexterous Finger Force Predictions.

OBJECTIVE: Dexterous control of robot hands requires a robust neural-machine interface capable of accurately decoding multiple finger movements. Existing studies primarily focus on single-finger movement or rely heavily on multi-finger data for decoder training, which requires large datasets and high computation demand. In this study, we investigated the feasibility of using limited single-finger surface electromyogram (sEMG) data to train a neural decoder capable of predicting the forces of unseen multi-finger combinations. METHODS: We developed a deep forest-based neural decoder to concurrently predict the extension and flexion forces of three fingers (index, middle, and ring-pinky). We trained the model using varying amounts of high-density EMG data in a limited condition (i.e., single-finger data). RESULTS: We showed that the deep forest decoder could achieve consistently commendable performance with 7.0% of force prediction errors and R2 value of 0.874, significantly surpassing the conventional EMG amplitude method and convolutional neural network approach. However, the deep forest decoder accuracy degraded when a smaller amount of data was used for training and when the testing data became noisy. CONCLUSION: The deep forest decoder shows accurate performance in multi-finger force prediction tasks. The efficiency aspect of the deep forest lies in the short training time and small volume of training data, which are two critical factors in current neural decoding applications. SIGNIFICANCE: This study offers insights into efficient and accurate neural decoder training for advanced robotic hand control, which has the potential for real-life applications during human-machine interactions.

E2F3/5/8 serve as potential prognostic biomarkers and new therapeutic direction for human bladder cancer.

OBJECTS: Human bladder cancer (BC) is the most common urogenital system malignancy. E2F transcription factors (E2Fs) have been reported to be involved in the growth of various cancers. However, the expression patterns, prognostic value and immune infiltration in the tumor microenvironment of the 8 E2Fs in BC have yet fully to be explored. METHODS AND STRATEGY: We investigated the differential expression of E2Fs in BC patients, the prognostic value and correlation with immune infiltration by analyzing a range of databases. RESULTS: We found that the mRNA expression levels of E2F1/2/3/4/5/7/8 were significantly higher in BC patients than that of control tissues. And the increased mRNA expression levels of all E2Fs were associated with tumor stage of BC. The survival analysis revealed that the elevated mRNA expression levels of E2F3/5/8 were significantly correlated with the overall survival (OS) of BC patients. And the genetic changes of E2Fs in BC patients were associated with shorter overall survival (OS) and progression-free survival (PFS). In addition, we revealed that the E2F3/5/8 expressions were closely correlated with tumor-infiltrating lymphocytes (TILs). CONCLUSIONS: E2F3/5/8 might serve as promising prognostic biomarkers and new therapeutic direction for BC patients.

Case Report: Successful Treatment of Cerebral Phaeohyphomycosis with Voriconazole: A Case Report and Literature Review.

Fonsecaea monophora is a species of Fonsecaea that belongs to Chaetothyriales. It is usually isolated from tropical and subtropical regions, causing reactive inflammation, skin abscesses, and pain. Cerebral infection caused by F. monophora is rare but often fatal. Diagnosing this disease at an early stage is difficult, and appropriate antifungal therapy is often delayed as a result. We report the case of a 53-year-old woman with type 2 diabetes who presented with a headache 2 months ago and progressive right-sided weakness of 1 month's duration. Magnetic resonance imaging revealed a space-occupying lesion in the left frontal lobe and corpus callosum. The cystic mass was removed by surgical intervention, and the identification of the sample based on sequencing of the internal transcribed spaced region in BLAST-N search showed that the sequences producing most significant alignments were F. monophora or similar (query cover 99%, E value 0.0, per ident 99.84). The patient was treated with a 3-month course of twice daily voriconazole, leading to complete recovery.

Modified motor unit properties in residual muscle following transtibial amputation.

Objective.Neural signals in residual muscles of amputated limbs are frequently decoded to control powered prostheses. Yet myoelectric controllers assume muscle activities of residual muscles are similar to that of intact muscles. This study sought to understand potential changes to motor unit (MU) properties after limb amputation.Approach.Six people with unilateral transtibial amputation were recruited. Surface electromyogram (EMG) of residual and intacttibialis anterior(TA) andgastrocnemius(GA) muscles were recorded while subjects traced profiles targeting up to 20% and 35% of maximum activation for each muscle (isometric for intact limbs). EMG was decomposed into groups of MU spike trains. MU recruitment thresholds, action potential amplitudes (MU size), and firing rates were correlated to model Henneman's size principle, the onion-skin phenomenon, and rate-size associations. Organization (correlation) and modulation (rates of change) of relations were compared between intact and residual muscles.Main results.The residual TA exhibited significantly lower correlation and flatter slopes in the size principle and onion-skin, and each outcome covaried between the MU relations. The residual GA was unaffected for most subjects. Subjects trained prior with myoelectric prostheses had minimally affected slopes in the TA. Rate-size association correlations were preserved, but both residual muscles exhibited flatter decay rates.Significance.We showed peripheral neuromuscular damage also leads to spinal-level functional reorganizations. Our findings suggest models of MU recruitment and discharge patterns for residual muscle EMG generation need reparameterization to account for disturbances observed. In the future, tracking MU pool adaptations may also provide a biomarker of neuromuscular control to aid training with myoelectric prostheses.

Concurrent Prediction of Dexterous Finger Flexion and Extension Force via Deep Forest.

Neuromuscular injuries can impair hand function and profoundly impacting the quality of life. This has motivated the development of advanced assistive robotic hands. However, the current neural decoder systems are limited in their ability to provide dexterous control of these robotic hands. In this study, we propose a novel method for predicting the extension and flexion force of three individual fingers concurrently using high-density electromyogram (HD-EMG) signals. Our method employs two deep forest models, the flexor decoder and the extensor decoder, to extract relevant representations from the EMG amplitude features. The outputs of the two decoders are integrated through linear regression to predict the forces of the three fingers. The proposed method was evaluated on data from three subjects and the results showed that it consistently outperforms the conventional EMG amplitude-based approach in terms of prediction error and robustness across both target and non-target fingers. This work presents a promising neural decoding approach for intuitive and dexterous control of the fingertip forces of assistive robotic hands.

Association between glucose-to-lymphocyte ratio and in-hospital mortality in acute myocardial infarction patients.

BACKGROUND: Inflammation is involved in the development and progression of atherosclerosis. Recent studies indicated that glucose-to-lymphocyte ratio (GLR) level were significantly associated with the risk of mortality from inflammatory diseases, and showed a specific prognostic value. Herein, this study intended to explore the association between GLR level and in-hospital mortality in patients with acute myocardial infarction (AMI), and evaluate the predictive value of GLR on AMI prognosis. METHODS: Data of patients with AMI were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database in 2012-2019 in this retrospective cohort study. Univariate COX proportional hazard model was used to screen covariates. The associations between GLR and in-hospital mortality were evaluated using univariate and multivariate COX proportional hazard models. Subgroup analysis of age, gender, vasopressor use, SOFA scores, renal replacement therapy, coronary artery bypass graft, and ß blockers use were performed. The evaluated index was hazard ratios (HRs) and 95% confidence intervals (CIs). In addition, the predictive performance of GLR, glucose, and lymphocytes on in-hospital mortality was assessed respectively. RESULTS: Among eligible patients, 248 (13.74%) died in the hospital. After adjusting for covariates, we found that a higher GLR level was associated with an increased risk of in-hospital mortality [HR = 1.70, 95%CI: (1.24-2.34)]. This relationship was also found in patients who were male, aged ≥65 years old, did not have renal replacement therapy, coronary artery bypass graft, or ß blockers, used vasopressor or not, and whatever the SOFA scores (all P<0.05). Moreover, the predictive performance of GLR on in-hospital mortality seemed superior to that of glucose or lymphocytes. CONCLUSION: GLR may be a potential predictor for AMI prognosis, which provided some references for identifying and managing high-risk populations early in clinical.

Evaluation of brain nerve function in ICU patients with Delirium by deep learning algorithm-based resting state MRI.

The purpose of this study was to explore the value of resting-state magnetic resonance imaging (MRI) based on the brain extraction tool (BET) algorithm in evaluating the cranial nerve function of patients with delirium in intensive care unit (ICU). A total of 100 patients with delirium in hospital were studied, and 20 healthy volunteers were used as control. All the subjects were examined by MRI, and the images were analyzed by the BET algorithm, and the convolution neural network (CNN) algorithm was introduced for comparison. The application effects of the two algorithms were analyzed, and the differences of brain nerve function between delirium patients and normal people were explored. The results showed that the root mean square error, high frequency error norm, and structural similarity of the BET algorithm were 70.4%, 71.5%, and 0.92, respectively, which were significantly higher than those of the CNN algorithm (P < 0.05). Compared with normal people, the ReHo values of pontine, hippocampus (right), cerebellum (left), midbrain, and basal ganglia in delirium patients were significantly higher. ReHo values of frontal gyrus, middle frontal gyrus, left inferior frontal gyrus, parietal lobe, and temporal lobe and anisotropy scores (FA) of cerebellums (left), frontal lobe, temporal lobe (left), corpus callosum, and hippocampus (left) decreased significantly. The average diffusivity (MD) of medial frontal lobe, superior temporal gyrus (right), the first half of cingulate gyrus, bilateral insula, and caudate nucleus (left) increased significantly (P < 0.05). MRI based on the deep learning algorithm can effectively improve the image quality, which is valuable in evaluating the brain nerve function of delirium patients. Abnormal brain structure damage and abnormal function can be used to help diagnose delirium.

Ankle Torque Estimation With Motor Unit Discharges in Residual Muscles Following Lower-Limb Amputation.

There has been increased interest in using residual muscle activity for neural control of powered lower-limb prostheses. However, only surface electromyography (EMG)-based decoders have been investigated. This study aims to investigate the potential of using motor unit (MU)-based decoding methods as an alternative to EMG-based intent recognition for ankle torque estimation. Eight people without amputation (NON) and seven people with amputation (AMP) participated in the experiments. Subjects conducted isometric dorsi- and plantarflexion with their intact limb by tracing desired muscle activity of the tibialis anterior (TA) and gastrocnemius (GA) while ankle torque was recorded. To match phantom limb and intact limb activity, AMP mirrored muscle activation with their residual TA and GA. We compared neuromuscular decoders (linear regression) for ankle joint torque estimation based on 1) EMG amplitude (aEMG), 2) MU firing frequencies representing neural drive (ND), and 3) MU firings convolved with modeled twitch forces (MUDrive). In addition, sensitivity analysis and dimensionality reduction of optimization were performed on the MUDrive method to further improve its practical value. Our results suggest MUDrive significantly outperforms (lower root-mean-square error) EMG and ND methods in muscles of NON, as well as both intact and residual muscles of AMP. Reducing the number of optimized MUDrive parameters degraded performance. Even so, optimization computational time was reduced and MUDrive still outperformed aEMG. Our outcomes indicate integrating MU discharges with modeled biomechanical outputs may provide a more accurate torque control signal than direct EMG control of assistive, lower-limb devices, such as exoskeletons and powered prostheses.

Development of hydroxypropyl cellulose and graphene oxide modified molecularly imprinted polymers for separation and enrichment of podophyllotoxin.

A novel hydroxylpropyl cellulose (HPC) modified graphene oxide (GO)-based molecularly imprinted polymers (HPC-GO-MIP) have been developed as a solid phase extraction (SPE) material for the selective separation and extraction of podophyllotoxin. In this strategy, the cellulose with rich hydroxyl groups was introduced to form bi-functional monomers with methacrylic acid to provide more recognition sites for the improving of extraction efficiency, then GO was added as a two-dimensional substrate for MIP to improve the material morphology and surface area. The extraction performances of obtained HPC-GO-MIP material were tested, and the results prove its high efficiency and selectivity for podophyllotoxin extraction. The saturated adsorption capacity reached 23.1 µg/mg, and high enrichment efiiciency of 463.8 folds was realized under the premise of ensuring the recovery rate. The selective imprinting factor was much higher than those of kaempferol and quercetin, which were the main compounds in podophyllum fruit. Under the optimized SPE conditions, the HPC-GO-MIP based SPE-HPLC method showed the detection limit of 14.2 ng/mL for podophyllotoxin assay. When applied to podophyllum fruit samples, the material showed excellent ability of selective separation and enrichment of podophyllotoxin, and the relative standard deviations (RSD) of intra and inter batches were less than 8.1 % and 5.7 % in real samples detection. The HPC-GO-MIP SPE method broadened the application for high multiple extraction in trace analyte samples and provided a valuable solution to improve the selective separation and detection.

Salicylic acid doped silica nanoparticles as a fluorescent nanosensor for the detection of Fe3+ in aqueous solution.

A novel organic-inorganic hybrid nanosensor (SASP) was prepared by a one-step sol-gel method and characterized by scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectroscopy, thermogravimetric analysis, N2 adsorption-desorption, fluorescence spectroscopy, etc. The nanosensor showed almost 3-fold fluorescence emission quenching upon excitation with a 293 nm wavelength in the presence of 20 µM Fe3+ ions. The presence of 18 other metal ions had no observable effect on the sensitivity and selectivity of the nanosensor. A fluorescence analysis method based on the SASP for the selective detection of Fe3+ was established under optimal conditions. The results showed that there was a linear relationship between the log luminescence value and the concentration of Fe3+ over the range of 2.0 × 10-7-9.0 × 10-5 mol L-1 with a detection limit (3σ) of 2.5 × 10-8 mol L-1. Furthermore, the proposed method was successfully applied for the determination of trace Fe3+ in fetal bovine serum without the interference of other molecules and ions. Good recovery (96.5-104.5%) and a relative standard deviation of less than 8.6% were obtained from serum samples spiked with four levels of Fe3+. Additionally, the nanosensor showed a good reversibility; the fluorescence could be switched "off" and "on" in two ways, by adjusting the pH of the solution and adding metal chelating agent EDTA.

A quantum dot aptamer fluorescent sensor based on magnetic graphene oxide for the detection of zearalenone.

As an estrogenic mycotoxin found in a wide range of agricultural crops, the toxicity of zearalenone (ZEN) poses a serious risk to human health. Accordingly, to achieve rapid detection of zearalenone in complex samples, an aptamer fluorescence sensor based on magnetic graphene oxide was developed. Compared with traditional methods, this technique has the virtues of simple operation, low cost, and reliable performance. Magnetic graphene oxide (MGO) was synthesized as a fluorescent bursting agent, using a chemical precipitation approach by depositing Fe3O4 on the surface of graphene oxide. As a fluorescent probe, an aptamer coupling with CdTe quantum dots and zearalenone was used. Following the specific binding of zearalenone and the aptamer, the affinity interaction between the fluorescent probe and MGO was weakened, resulting in the recovery of fluorescence and making the qualitative and quantitative analysis of zearalenone available via fluorescence intensity determination. The results indicated that the method's linear range was 5-120 µg L-1 and its detection limit was 2.9 µg L-1. In addition, the recoveries varied from 76.4 to 118.8% for crop samples, with relative standard deviations (RSDs) between 0.8 and 9.5%, which suggests an effective method for the separation and detection of mycotoxins in actual environmental samples.

Robust neural decoding for dexterous control of robotic hand kinematics.

BACKGROUND: Manual dexterity is a fundamental motor skill that allows us to perform complex daily tasks. Neuromuscular injuries, however, can lead to the loss of hand dexterity. Although numerous advanced assistive robotic hands have been developed, we still lack dexterous and continuous control of multiple degrees of freedom in real-time. In this study, we developed an efficient and robust neural decoding approach that can continuously decode intended finger dynamic movements for real-time control of a prosthetic hand. METHODS: High-density electromyogram (HD-EMG) signals were obtained from the extrinsic finger flexor and extensor muscles, while participants performed either single-finger or multi-finger flexion-extension movements. We implemented a deep learning-based neural network approach to learn the mapping from HD-EMG features to finger-specific population motoneuron firing frequency (i.e., neural-drive signals). The neural-drive signals reflected motor commands specific to individual fingers. The predicted neural-drive signals were then used to continuously control the fingers (index, middle, and ring) of a prosthetic hand in real-time. RESULTS: Our developed neural-drive decoder could consistently and accurately predict joint angles with significantly lower prediction errors across single-finger and multi-finger tasks, compared with a deep learning model directly trained on finger force signals and the conventional EMG-amplitude estimate. The decoder performance was stable over time and was robust to variations of the EMG signals. The decoder also demonstrated a substantially better finger separation with minimal predicted error of joint angle in the unintended fingers. CONCLUSIONS: This neural decoding technique offers a novel and efficient neural-machine interface that can consistently predict robotic finger kinematics with high accuracy, which can enable dexterous control of assistive robotic hands.

Concurrent and Continuous Prediction of Finger Kinetics and Kinematics via Motoneuron Activities.

OBJECTIVE: Robust neural decoding of intended motor output is crucial to enable intuitive control of assistive devices, such as robotic hands, to perform daily tasks. Few existing neural decoders can predict kinetic and kinematic variables simultaneously. The current study developed a continuous neural decoding approach that can concurrently predict fingertip forces and joint angles of multiple fingers. METHODS: We obtained motoneuron firing activities by decomposing high-density electromyogram (HD EMG) signals of the extrinsic finger muscles. The identified motoneurons were first grouped and then refined specific to each finger (index or middle) and task (finger force and dynamic movement) combination. The refined motoneuron groups (separate matrix) were then applied directly to new EMG data in real-time involving both finger force and dynamic movement tasks produced by both fingers. EMG-amplitude-based prediction was also performed as a comparison. RESULTS: We found that the newly developed decoding approach outperformed the EMG-amplitude method for both finger force and joint angle estimations with a lower prediction error (Force: 3.47±0.43 vs 6.64±0.69% MVC, Joint Angle: 5.40±0.50° vs 12.8±0.65°) and a higher correlation (Force: 0.75±0.02 vs 0.66±0.05, Joint Angle: 0.94±0.01 vs 0.5±0.05) between the estimated and recorded motor output. The performance was also consistent for both fingers. CONCLUSION: The developed neural decoding algorithm allowed us to accurately and concurrently predict finger forces and joint angles of multiple fingers in real-time. SIGNIFICANCE: Our approach can enable intuitive interactions with assistive robotic hands, and allow the performance of dexterous hand skills involving both force control tasks and dynamic movement control tasks.

Asynchronous axonal firing patterns evoked via continuous subthreshold kilohertz stimulation.

Objective.Transcutaneous electrical stimulation of peripheral nerves is a common technique to assist or rehabilitate impaired muscle activation. However, conventional stimulation paradigms activate nerve fibers synchronously with action potentials time-locked with stimulation pulses. Such synchronous activation limits fine control of muscle force due to synchronized force twitches. Accordingly, we developed a subthreshold high-frequency stimulation waveform with the goal of activating axons asynchronously.Approach.We evaluated our waveform experimentally and through model simulations. During the experiment, we delivered continuous subthreshold pulses at frequencies of 16.67, 12.5, or 10 kHz transcutaneously to the median and ulnar nerves. We obtained high-density electromyographic (EMG) signals and fingertip forces to quantify the axonal activation patterns. We used a conventional 30 Hz stimulation waveform and the associated voluntary muscle activation for comparison. We modeled stimulation of biophysically realistic myelinated mammalian axons using a simplified volume conductor model to solve for extracellular electric potentials. We compared the firing properties under kHz and conventional 30 Hz stimulation.Main results.EMG activity evoked by kHz stimulation showed high entropy values similar to voluntary EMG activity, indicating asynchronous axon firing activity. In contrast, we observed low entropy values in EMG evoked by conventional 30 Hz stimulation. The muscle forces evoked by kHz stimulation also showed more stable force profiles across repeated trials compared with 30 Hz stimulation. Our simulation results provide direct evidence of asynchronous firing patterns across a population of axons in response to kHz frequency stimulation, while 30 Hz stimulation elicited synchronized time-locked responses across the population.Significance.We demonstrate that the continuous subthreshold high-frequency stimulation waveform can elicit asynchronous axon firing patterns, which can lead to finer control of muscle forces.

Alterations of the fecal and vaginal microbiomes in patients with systemic lupus erythematosus and their associations with immunological profiles.

Background: Exploring the human microbiome in multiple body niches is beneficial for clinicians to determine which microbial dysbiosis should be targeted first. We aimed to study whether both the fecal and vaginal microbiomes are disrupted in SLE patients and whether they are correlated, as well as their associations with immunological features. Methods: A group of 30 SLE patients and 30 BMI-age-matched healthy controls were recruited. Fecal and vaginal samples were collected, the 16S rRNA gene was sequenced to profile microbiomes, and immunological features were examined. Results: Distinct fecal and vaginal bacterial communities and decreased microbial diversity in feces compared with the vagina were found in SLE patients and controls. Altered bacterial communities were found in the feces and vaginas of patients. Compared with the controls, the SLE group had slightly lower gut bacterial diversity, which was accompanied by significantly higher bacterial diversity in their vaginas. The most predominant bacteria differed between feces and the vagina in all groups. Eleven genera differed in patients' feces; for example, Gardnerella and Lactobacillus increased, whereas Faecalibacterium decreased. Almost all the 13 genera differed in SLE patients' vaginas, showing higher abundances except for Lactobacillus. Three genera in feces and 11 genera in the vagina were biomarkers for SLE patients. The distinct immunological features were only associated with patients' vaginal microbiomes; for example, Escherichia-Shigella was negatively associated with serum C4. Conclusions: Although SLE patients had fecal and vaginal dysbiosis, dysbiosis in the vagina was more obvious than that in feces. Additionally, only the vaginal microbiome interacted with patients' immunological features.