Non-O1, Non-O139 Vibrio cholerae Bacteremia in an Autoimmune Pancreatitis Patient.

Over 200 different serogroups of Vibrio cholerae based on O-polysaccharide specificity have been described worldwide, including the two most important serogroups, O1 and O139. Non-O1/non-O139 V. cholerae serogroups generally do not produce the cholera-causing toxin but do sporadically cause gastroenteritis and extra-intestinal infections. Recently, however, bloodstream infections caused by non-O1/non-O139 V. cholerae are being increasingly reported, and these infections are associated with high mortality in immunocompromised hosts. We describe a case of non-O1/non-O139 V. cholerae bacteremia in a patient with autoimmune pancreatitis and stenosis of the intra- and extrahepatic bile ducts. The clinical manifestations of bacteremia were fever and mild digestive symptoms. The blood cultures showed V. cholerae, which was identified as a non-O1, non-O139 serogroup by slide agglutination tests and PCR. The bloodstream infection of the patient was likely caused by the consumption of contaminated seafood at a banquet. The patient recovered after the administration of a third-generation cephalosporin. Non-O1/non-O139 V. cholerae infection presents with or without gastrointestinal manifestations; close attention should be paid to the possibility of disseminated non-O1/non-O139 V. cholerae infection in high-risk patients.

Low-intensity ultrasound ameliorates brain organoid integration and rescues microcephaly deficits.

Human brain organoids represent a remarkable platform for modeling neurological disorders and a promising brain repair approach. However, the effects of physical stimulation on their development and integration remain unclear. Here, we report that low-intensity ultrasound significantly increases neural progenitor cell proliferation and neuronal maturation in cortical organoids. Histological assays and single-cell gene expression analyses reveal that low-intensity ultrasound improves the neural development in cortical organoids. Following organoid grafts transplantation into the injured somatosensory cortices of adult mice, longitudinal electrophysiological recordings and histological assays reveal that ultrasound-treated organoid grafts undergo advanced maturation. They also exhibit enhanced pain-related gamma-band activity and more disseminated projections into the host brain than the untreated groups. Finally, low-intensity ultrasound ameliorates neuropathological deficits in a microcephaly brain organoid model. Hence, low-intensity ultrasound stimulation advances the development and integration of brain organoids, providing a strategy for treating neurodevelopmental disorders and repairing cortical damage.

Polyoxometalate solution passivation enabling dendrite-free and high-performance zinc anodes in aqueous zinc-ion batteries.

Zinc metal anodes in aqueous electrolytes commonly face challenges such as dendrite growth and undesirable side reactions, limiting their application in the field of aqueous zinc-ion batteries (AZIBs) for energy storage. Drawing inspiration from industrial practices involving molybdenum salt solutions for metal modification, a polyoxometalate solution was formulated as a passivation solution for zinc anodes (referred to as MO solution). The formed passivation layer, referred to as the MO layer, exhibited a uniform and protective nature with a thickness of approximately 10 µm. The experimental results demonstrated that this passivation layer effectively suppressed side reactions at the zinc anode interface, as evidenced by lower corrosion current density for MO-Zn anodes. Additionally, the newly plated Zn was uniformly deposited atop the MO layer, ensuring coating integrity and inhibiting dendrite growth. As a result, under more demanding conditions such as a larger current of 8 mA cm-2, the MO-Zn anode displayed an extended cycle life exceeding 420 h in a symmetric battery, with an overpotential as low as 98 mV. This performance significantly outperformed that of commercially available pure Zn foils (with a cycle life of 60 h and an overpotential of 192 mV). Notably, a self-made Na-doped V2O5 served as the cathode (referred to as NaVO), forming the MO-Zn//NaVO full battery. Even under high current test conditions of 2 A/g, the specific capacity of the MO-Zn//NaVO full battery remained substantial at 152.83 mAh/g after 1000 cycles. Furthermore, pouch batteries assembled with NaVO//MO-Zn successfully illuminated small bulbs. This study offers a viable optimization strategy for AZIB anodes and demonstrates the potential of using polyoxometalate solution for etching zinc anodes to inhibit dendrite growth and interfacial corrosion of zinc metal anodes.

Dynamic prediction of goal location by coordinated representation of prefrontal-hippocampal theta sequences.

Prefrontal (PFC) and hippocampal (HPC) sequences of neuronal firing modulated by theta rhythms could represent upcoming choices during spatial memory-guided decision-making. How the PFC-HPC network dynamically coordinates theta sequences to predict specific goal locations and how it is interrupted in memory impairments induced by amyloid beta (Aß) remain unclear. Here, we detected theta sequences of firing activities of PFC neurons and HPC place cells during goal-directed spatial memory tasks. We found that PFC ensembles exhibited predictive representation of the specific goal location since the starting phase of memory retrieval, earlier than the hippocampus. High predictive accuracy of PFC theta sequences existed during successful memory retrieval and positively correlated with memory performance. Coordinated PFC-HPC sequences showed PFC-dominant prediction of goal locations during successful memory retrieval. Furthermore, we found that theta sequences of both regions still existed under Aß accumulation, whereas their predictive representation of goal locations was weakened with disrupted spatial representation of HPC place cells and PFC neurons. These findings highlight the essential role of coordinated PFC-HPC sequences in successful memory retrieval of a precise goal location.

Major depressive disorder recognition by quantifying EEG signal complexity using proposed APLZC and AWPLZC.

BACKGROUND: Seeking objective quantitative indicators is important for accurately recognizing major depressive disorder (MDD). Lempel-Ziv complexity (LZC), employed to characterize neurological disorders, faces limitations in tracking dynamic changes in EEG signals due to defects in the coarse-graining process, hindering its precision for MDD objective quantitative indicators. METHODS: This work proposed Adaptive Permutation Lempel-Ziv Complexity (APLZC) and Adaptive Weighted Permutation Lempel-Ziv Complexity (AWPLZC) algorithms by refining the coarse-graining process and introducing weight factors to effectively improve the precision of LZC in characterizing EEGs and further distinguish MDD patients better. APLZC incorporated the ordinal pattern, while False Nearest Neighbor and Mutual Information algorithms were introduced to determine and adjust key parameters adaptively. Furthermore, we proposed AWPLZC by assigning different weights to each pattern based on APLZC. Thirty MDD patients and 30 healthy controls (HCs) were recruited and their 64-channel resting EEG signals were collected. The complexities of gamma oscillations were then separately computed using LZC, APLZC, and AWPLZC algorithms. Subsequently, a multi-channel adaptive K-nearest neighbor model was constructed for identifying MDD patients and HCs. RESULTS: LZC, APLZC, and AWPLZC algorithms achieved accuracy rates of 78.29 %, 90.32 %, and 95.13 %, respectively. Sensitivities reached 67.96 %, 85.04 %, and 98.86 %, while specificities were 88.62 %, 95.35 %, and 89.92 %, respectively. Notably, AWPLZC achieved the best performance in accuracy and sensitivity, with a specificity limitation. LIMITATION: The sample size is relatively small. CONCLUSION: APLZC and AWPLZC algorithms, particularly AWPLZC, demonstrate superior effectiveness in differentiating MDD patients from HCs compared with LZC. These findings hold significant clinical implications for MDD diagnosis.

Enhancing SSVEP-BCI Performance Under Fatigue State Using Dynamic Stopping Strategy.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have emerged as a prominent technology due to their high information transfer rate, rapid calibration time, and robust signal-to-noise ratio. However, a critical challenge for practical applications is performance degradation caused by user fatigue during prolonged use. This work proposes novel methods to address this challenge by dynamically adjusting data acquisition length and updating detection models based on a fatigue-aware stopping strategy. Two 16-target SSVEP-BCIs were employed, one using low-frequency and the other using high-frequency stimulation. A self-recorded fatigue dataset from 24 subjects was utilized for extensive evaluation. A simulated online experiment demonstrated that the proposed methods outperform the conventional fixed stopping strategy in terms of classification accuracy, information transfer rate, and selection time, irrespective of stimulation frequency. These findings suggest that the proposed approach can significantly improve SSVEP-BCI performance under fatigue conditions, leading to superior performance during extended use.

Ultrasound Stimulation Attenuates CRS-Induced Depressive Behavior by Modulating Dopamine Release in the Prefrontal Cortex.

Depression is one of the most serious mental disorders affecting modern human life and is often caused by chronic stress. Dopamine system dysfunction is proposed to contribute to the pathophysiology of chronic stress, especially the ventral tegmental area (VTA) which mainly consists of dopaminergic neurons. Focused ultrasound stimulation (FUS) is a promising neuromodulation modality and multiple studies have demonstrated effective ultrasonic activation of cortical, subcortical, and related networks. However, the effects of FUS on the dopamine system and the potential link to chronic stress-induced depressive behaviors are relatively unknown. Here, we measured the effects of FUS targeting VTA on the improvement of depression-like behavior and evaluated the dopamine concentration in the downstream region - medial prefrontal cortex (mPFC). We found that targeting VTA FUS treatment alleviated chronic restraint stress (CRS) -induced anhedonia and despair behavior. Using an in vivo photometry approach, we analyzed the dopamine signal of mPFC and revealed a significant increase following the FUS, positively associated with the improvement of anhedonia behavior. FUS also protected the dopaminergic neurons in VTA from the damage caused by CRS exposure. Thus, these results demonstrated that targeting VTA FUS treatment significantly rescued the depressive-like behavior and declined dopamine level of mPFC induced by CRS. These beneficial effects of FUS might be due to protection in the DA neuron of VTA. Our findings suggest that FUS treatment could serve as a new therapeutic strategy for the treatment of stress-related disorders.

The role of early cerebral edema and hematoma assessment in aneurysmal subarachnoid hemorrhage (a-SAH) in predicting structural brain abnormalities in cognitive impairments-- case controlled study.

BACKGROUND: Early assessment and management of cerebral edema and hematoma following aneurysmal subarachnoid hemorrhage (a-SAH) can significantly impact clinical cognitive outcomes. However, current clinical practices lack predictive models to identify early structural brain abnormalities affecting cognition. To address this gap, we propose the development of a predictive model termed the a-SAH Early Brain Edema/Hematoma Compression Neural (Structural Brain) Networks Score System (SEBE-HCNNSS). METHODS: In this study, 202 consecutive patients with spontaneous a-SAH underwent initial computed tomography (CT) or magnetic resonance imaging (MRI) scans within 24 hours of ictus with follow-up 2 months after discharge. Using logistic regression analysis (univariate and multivariate), we evaluated the association of clinically relevant factors and various traditional scale ratings with cognitive impairment (CI). Risk factors with the highest area under the curve (AUC) values were included in the multivariate analysis and least absolute shrinkage and selection operator (LASSO) analysis or Cox regression analysis. RESULTS: A total of 177 patients were enrolled in the study, and 43 patients were classified with a high SEBE-HCNNSS grade (3 to 5). After a mean follow-up of 2 months, 121 individuals (68.36%) with a-SAH and 3 control subjects developed incident CI. The CT inter-observer reliability of the SEBE-HCNNSS scale was high, with a Kappa value of 1. Furthermore, ROC analysis identified the SEBE-HCNNSS scale (OR 3.322, 95% CI 2.312-7.237, P=0.00025) as an independent predictor of edema, CI, and unfavorable prognosis. These results were also replicated in a validation cohort. CONCLUSION: Overall, the SEBE-HCNNSS scale represents a simple assessment tool with promising predictive value for CI and clinical outcomes post-a-SAH. Our findings indicate its practical utility as a prognostic instrument for risk evaluation after a-SAH, potentially facilitating early intervention and treatment.

Sensory Schwann cells set perceptual thresholds for touch and selectively regulate mechanical nociception.

Previous work identified nociceptive Schwann cells that can initiate pain. Consistent with the existence of inherently mechanosensitive sensory Schwann cells, we found that in mice, the mechanosensory function of almost all nociceptors, including those signaling fast pain, were dependent on sensory Schwann cells. In polymodal nociceptors, sensory Schwann cells signal mechanical, but not cold or heat pain. Terminal Schwann cells also surround mechanoreceptor nerve-endings within the Meissner's corpuscle and in hair follicle lanceolate endings that both signal vibrotactile touch. Within Meissner´s corpuscles, two molecularly and functionally distinct sensory Schwann cells positive for Sox10 and Sox2 differentially modulate rapidly adapting mechanoreceptor function. Using optogenetics we show that Meissner's corpuscle Schwann cells are necessary for the perception of low threshold vibrotactile stimuli. These results show that sensory Schwann cells within diverse glio-neural mechanosensory end-organs are sensors for mechanical pain as well as necessary for touch perception.

Detection of dynamic changes of electrodermal activity to predict the classroom performance of college students.

It is emphasized in the Self-regulated learning (SRL) framework that self-monitoring of learning state is vital for students to keep effective in studying. However, it's still challenging to get an accurate and timely understanding of their learning states during classes. In this study, we propose to use electrodermal activity (EDA) signals which are deemed to be associated with physiological arousal state to predict the college student's classroom performance. Twenty college students were recruited to attend eight lectures in the classroom, during which their EDA signals were recorded simultaneously. For each lecture, the students should complete pre- and after-class tests, and a self-reported scale (SRS) on their learning experience. EDA indices were extracted from both time and frequency domains, and they were furtherly mapped to the student's learning efficiency. As a result, the indices relevant to the dynamic changes of EDA had significant positive correlations with the learning efficiency. Furthermore, compared with only using SRS, a combination with EDA indices had significantly higher accuracy in predicting the learning efficiency. In conclusion, our findings demonstrate that the EDA dynamics are sensitive to the changes in learning efficiency, suggesting a promising approach to predicting the classroom performance of college students.

Effect of Phase Clustering Bias on Phase-Amplitude Coupling for Emotional EEG.

BACKGROUND: Emotions are thought to be related to distinct patterns of neural oscillations, but the interactions among multi-frequency neural oscillations during different emotional states lack full exploration. Phase-amplitude coupling is a promising tool for understanding the complexity of the neurophysiological system, thereby playing a crucial role in revealing the physiological mechanisms underlying emotional electroencephalogram (EEG). However, the non-sinusoidal characteristics of EEG lead to the non-uniform distribution of phase angles, which could potentially affect the analysis of phase-amplitude coupling. Removing phase clustering bias (PCB) can uniform the distribution of phase angles, but the effect of this approach is unknown on emotional EEG phase-amplitude coupling. This study aims to explore the effect of PCB on cross-frequency phase-amplitude coupling for emotional EEG. METHODS: The technique of removing PCB was implemented on a publicly accessible emotional EEG dataset to calculate debiased phase-amplitude coupling. Statistical analysis and classification were conducted to compare the difference in emotional EEG phase-amplitude coupling prior to and post the removal of PCB. RESULTS: Emotional EEG phase-amplitude coupling values are overestimated due to PCB. Removing PCB enhances the difference in coupling strength between fear and happy emotions in the frontal lobe. Comparable emotion recognition performance was achieved with fewer features after removing PCB. CONCLUSIONS: These findings suggest that removing PCB enhances the difference in emotional EEG phase-amplitude coupling patterns and generates features that contain more emotional information. Removing PCB may be advantageous for analyzing emotional EEG phase-amplitude coupling and recognizing human emotions.

cTBS over primary motor cortex increased contralateral corticomuscular coupling and interhemispheric functional connection.

Objective.Transcranial magnetic stimulation is a non-invasive brain stimulation technique that changes the activity of the cerebral cortex. Contralesional continuous theta burst stimulation (cTBS) has been proposed and verified beneficial to stroke motor recovery. However, the underlying mechanism is still unclear.Approach.20 healthy right-handed subjects were recruited in this study, receiving real-cTBS over their left primary motor cortex or sham-cTBS. We designed the finger tapping task (FTT) before and after stimulation and recorded the accuracy and reaction time (RT) of the task. The electroencephalogram and surface electromyogram signals were recorded during the left finger pinching task (FPT) before and after stimulation. We calculated cortico-muscular coherence (CMC) in the contralateral hemisphere and cortico-cortical coherence (CCC) in the bilateral hemisphere. The two-way repeated measures analysis of variance was used to analyze the effect of cTBS.Main results.In the FTT, there was a significant main effect of 'time' on RT (F(1, 38) = 24.739,p< 0.001). In the FPT, the results showed that there was a significant interaction effect on the CMC peak and area in the beta band (peak:F(1, 38) = 8.562,p= 0.006; area:F(1, 38) = 5.273,p= 0.027), on the CCC peak in the alpha band (F(1, 38) = 4.815,p= 0.034) and area in the beta band (F(1, 38) = 4.822,p= 0.034). The post hoc tests showed that the CMC peak (W= 20,p= 0.002), the CMC area (W= 13,p= 0.003) and the CCC peak (t= -2.696,p= 0.014) increased significantly after real-cTBS. However, there was no significant decrease or increase after sham-cTBS.Significance.Our study found that cTBS can improve CMC of contralateral hemisphere and CCC of bilateral hemisphere, indicating that cTBS can strengthen cortico-muscular and cortico-cortical coupling.

Temporal hyper-connectivity and frontal hypo-connectivity within gamma band in schizophrenia: A resting state EEG study.

OBJECTIVE: The brain network serves as the physiological foundation for information processing of the brain. Many studies have reported abnormalities of gamma oscillations in Schizophrenia. The aim of this study was to investigate the gamma-band connectivity in Schizophrenia patients. METHODS: We recorded the resting state electroencephalogram (EEG) for 15 schizophrenia patients with refractory auditory hallucinations and 14 healthy controls, with eyes open and closed. The brain network was constructed based on weighted phase lag index for gamma band. Whole scalp metrics (clustering coefficient, global efficiency and local efficiency) and local region metrics (degree and betweenness centrality) in the frontal and temporal lobes were computed. Correlation analyses between network metrics and symptom scales were examined to find associations with symptom severity. RESULTS: Schizophrenia patients had larger global efficiency and local efficiency (p < 0.05) with eyes closed, probably representing greater brain activity and information exchange. For degree and betweenness centrality, schizophrenia patients showed an increase (p < 0.05) in the temporal lobe but a decrease (p < 0.05) in the frontal lobe with eyes closed and open, potentially account for the patients' symptoms such as hallucinations and thought disorders. Local efficiency and frontal lobe degree were positively and negatively correlated with the scales, respectively (both p < 0.05). CONCLUSIONS: Altered connectivity of the resting state brain network has been revealed and may be associated with the core symptoms of schizophrenia. Our study provides promising evidence for the investigation of the pathological basis of Schizophrenia and could aid in objective diagnosis.

Fusion of Multi-domain EEG Signatures Improves Emotion Recognition.

BACKGROUND: Affective computing has gained increasing attention in the area of the human-computer interface where electroencephalography (EEG)-based emotion recognition occupies an important position. Nevertheless, the diversity of emotions and the complexity of EEG signals result in unexplored relationships between emotion and multichannel EEG signal frequency, as well as spatial and temporal information. METHODS: Audio-video stimulus materials were used that elicited four types of emotions (sad, fearful, happy, neutral) in 32 male and female subjects (age 21-42 years) while collecting EEG signals. We developed a multidimensional analysis framework using a fusion of phase-locking value (PLV), microstates, and power spectral densities (PSDs) of EEG features to improve emotion recognition. RESULTS: An increasing trend of PSDs was observed as emotional valence increased, and connections in the prefrontal, temporal, and occipital lobes in high-frequency bands showed more differentiation between emotions. Transition probability between microstates was likely related to emotional valence. The average cross-subject classification accuracy of features fused by Discriminant Correlation Analysis achieved 64.69%, higher than that of single mode and direct-concatenated features, with an increase of more than 7%. CONCLUSIONS: Different types of EEG features have complementary properties in emotion recognition, and combining EEG data from three types of features in a correlated way, improves the performance of emotion classification.

Dual role of Nrf2/HO-1 pathway in Z-ligustilide-induced ferroptosis against AML cells.

BACKGROUND: The scarcity of drugs targeting AML cells poses a significant challenge in AML management. Z-Ligustilide (Z-LIG), a phthalide compound, shows promising pharmacological potential as a candidate for AML therapy. However, its precise selective mechanism remains unclear. PURPOSE: In order to assess the selective inducement effects of Z-LIG on ferroptosis in AML cells and explore the possible involvement of the Nrf2/HO-1 pathway in the regulation of ferroptosis. METHODS: Through in vitro cell proliferation and in vivo tumor growth tests, the evaluation of Z-LIG's anticancer activity was conducted. Ferroptosis was determined by the measurement of ROS and lipid peroxide levels using flow cytometry, as well as the observation of mitochondrial morphology. To analyze the iron-related factors, western blot analysis was employed. The up-regulation of the Nrf2/HO-1 axis was confirmed through various experimental techniques, including CRISPR/Cas9 gene knockout, fluorescent probe staining, and flow cytometry. The efficacy of Z-LIG in inducing ferroptosis was further validated in a xenograft nude mouse model. RESULTS: Our study revealed that Z-LIG specifically triggered lipid peroxidation-driven cell death in AML cells. Z-LIG downregulated the total protein and nuclear entrance levels of IRP2, resulting in upregulation of FTH1 and downregulation of TFR1. Z-LIG significantly increased the susceptibility to ferroptosis by upregulating ACSL4 levels and simultaneously suppressing the activity of GPX4. Notably, the Nrf2/HO-1 pathway displayed a twofold impact in the ferroptosis induced by Z-LIG. Mild activation suppressed ferroptosis, while excessive activation promoted it, mainly driven by ROS-induced labile iron pool (LIP) accumulation in AML cells, which was not observed in normal human cells. Additionally, Nrf2 knockout and HO-1 knockdown reversed iron imbalance and mitochondrial damage induced by Z-LIG in HL-60 cells. Z-LIG effectively inhibited the growth of AML xenografts in mice, and Nrf2 knockout partially weakened its antitumor effect by inhibiting ferroptosis. CONCLUSION: Our study presents biological proof indicating that the selective initiation of ferroptosis in leukemia cells is credited to the excessive activation of the Nrf2/HO-1 pathway triggered by Z-LIG.

In Vivo Transcranial Acoustoelectric Brain Imaging of Different Deep Brain Stimulation Currents.

Deep brain stimulation (DBS) is an effective treatment for neurologic disease and its clinical effect is highly dependent on the DBS leads localization and current stimulating state. However, standard human brain imaging modalities could not provide direct feedback on DBS currents spatial distribution and dynamic changes. Acoustoelectric brain imaging (AEBI) is an emerging neuroimaging method that can directly map current density distribution. Here, we investigate in vivo AEBI of different DBS currents to explore the potential of DBS visualization using AEBI. According to the typical DBS stimulus parameters, four types of DBS currents, including time pattern, waveform, frequency, and amplitude are designed to implement AEBI experiments in living rat brains. Based on acoustoelectric (AE) signals, the AEBI images of each type DBS current are explored and the resolution is quantitatively analyzed for performance evaluation. Furtherly, the AE signals are decoded to characterize DBS currents from multiple perspectives, including time-frequency domain, spatial distribution, and amplitude comparation. The results show that in vivo transcranial AEBI can accurately locate the DBS contact position with a millimeter spatial resolution (< 2 mm) and millisecond temporal resolution (< 10 ms). Besides, the decoded AE signal at DBS contact position is capable of describing the corresponding DBS current characteristics and identifying current pattern changes. This study first validates that AEBI can localize in vivo DBS contact and characterize different DBS currents. AEBI is expected to develop into a noninvasive DBS real-time monitoring technology with high spatiotemporal resolution.

An Efficient Multi-Task Synergetic Network for Polyp Segmentation and Classification.

Colonoscopy is considered the best diagnostic tool for early detection and resection of polyps, which can effectively prevent consequential colorectal cancer. In clinical practice, segmenting and classifying polyps from colonoscopic images have a great significance since they provide precious information for diagnosis and treatment. In this study, we propose an efficient multi-task synergetic network (EMTS-Net) for concurrent polyp segmentation and classification, and we introduce a polyp classification benchmark for exploring the potential correlations of the above-mentioned two tasks. This framework is composed of an enhanced multi-scale network (EMS-Net) for coarse-grained polyp segmentation, an EMTS-Net (Class) for accurate polyp classification, and an EMTS-Net (Seg) for fine-grained polyp segmentation. Specifically, we first obtain coarse segmentation masks by using EMS-Net. Then, we concatenate these rough masks with colonoscopic images to assist EMTS-Net (Class) in locating and classifying polyps precisely. To further enhance the segmentation performance of polyps, we propose a random multi-scale (RMS) training strategy to eliminate the interference caused by redundant information. In addition, we design an offline dynamic class activation mapping (OFLD CAM) generated by the combined effect of EMTS-Net (Class) and RMS strategy, which optimizes bottlenecks between multi-task networks efficiently and elegantly and helps EMTS-Net (Seg) to perform more accurate polyp segmentation. We evaluate the proposed EMTS-Net on the polyp segmentation and classification benchmarks, and it achieves an average mDice of 0.864 in polyp segmentation and an average AUC of 0.913 with an average accuracy of 0.924 in polyp classification. Quantitative and qualitative evaluations on the polyp segmentation and classification benchmarks demonstrate that our EMTS-Net achieves the best performance and outperforms previous state-of-the-art methods in terms of both efficiency and generalization.

MetaBCI: An open-source platform for brain-computer interfaces.

BACKGROUND: Recently, brain-computer interfaces (BCIs) have attracted worldwide attention for their great potential in clinical and real-life applications. To implement a complete BCI system, one must set up several links to translate the brain intent into computer commands. However, there is not an open-source software platform that can cover all links of the BCI chain. METHOD: This study developed a one-stop open-source BCI software, namely MetaBCI, to facilitate the construction of a BCI system. MetaBCI is written in Python, and has the functions of stimulus presentation (Brainstim), data loading and processing (Brainda), and online information flow (Brainflow). This paper introduces the detailed information of MetaBCI and presents four typical application cases. RESULTS: The results showed that MetaBCI was an extensible and feature-rich software platform for BCI research and application, which could effectively encode, decode, and feedback brain activities. CONCLUSIONS: MetaBCI can greatly lower the BCI's technical threshold for BCI beginners and can save time and cost to build up a practical BCI system. The source code is available at https://github.com/TBC-TJU/MetaBCI, expecting new contributions from the BCI community.

Electroencephalogram-based objective assessment of cognitive function level associated with age-related hearing loss.

Age-Related Hearing Loss (ARHL) is a common problem in aging. Numerous longitudinal cohort studies have revealed that ARHL is closely related to cognitive function, leading to a significant risk of cognitive decline and dementia. This risk gradually increases with the severity of hearing loss. We designed dual auditory Oddball and cognitive task paradigms for the ARHL subjects, then obtained the Montreal Cognitive Assessment (MoCA) scale evaluation results for all the subjects. Multi-dimensional EEG characteristics helped explore potential biomarkers to evaluate the cognitive level of the ARHL group, having a significantly lower P300 peak amplitude coupled with a prolonged latency. Moreover, visual memory, auditory memory, and logical calculation were investigated during the cognitive task paradigm. In the ARHL groups, the alpha-to-beta rhythm energy ratio in the visual and auditory memory retention period and the wavelet packet entropy value within the logical calculation period were significantly reduced. Correlation analysis between the above specificity indicators and the subjective scale results of the ARHL group revealed that the auditory P300 component characteristics could assess attention resources and information processing speed. The alpha and beta rhythm energy ratio and wavelet packet entropy can become potential indicators to determine working memory and logical cognitive computation-related cognitive ability.