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Background: Malignant epithelial neoplasms of the lacrimal apparatus are rare and are typically treated with surgery and occasionally adjuvant radiation therapy (RT). The purpose of this study was to assess treatment outcomes by type of surgery (orbital exenteration vs eye-sparing surgery) and clarify the role of adjuvant RT for this rare disease.Methods: Forty-six patients with malignant epithelial neoplasms of the lacrimal apparatus were treated at a single institution from 1945 through 2008. Twenty-seven patients (59%) were treated with orbital exenteration and 19 (41%) with eye-sparing surgery; 64% of the orbital exenteration group and 83% of the eye-sparing surgery group also received adjuvant RT (median dose, 60 grays). Median follow-up time for all patients was 38 months (range, 3-460 months).Results: For the orbital exenteration and eye-sparing surgery groups, the 5-year overall survival (OS) rates were 59% and 62%, and the 5-year disease-free survival (DFS) rates were 49% and 39%, respectively (P = .56, P = .35). Tumor status (T1-2 vs T3-4) was associated with OS (P = .02), and tumor size (<3.5 vs >3.5 cm) with DFS (P = .015). Median time to locoregional recurrence was 85 months for orbital exenteration, and 123 months for eye-sparing surgery. All patients who did not receive RT experienced local recurrence, and RT extended time to locoregional recurrence (median 460 vs 30 months, P = .009). Seven grade ≥3 complications were experienced after adjuvant RT.Conclusions: For appropriately selected patients, an eye-sparing surgery for lacrimal apparatus tumors can achieve similar survival outcomes to those in patients treated with an orbital exenteration. Adjuvant RT should be considered for all patients presenting with these rare tumors. [ABSTRACT FROM AUTHOR]

Goals Of Work: We report the first analysis of demographic, socioeconomic, and toxicity data from the Longitudinal Oncology Registry of Head and Neck Carcinoma (LORHAN).Materials and Methods: Eligible patients include newly diagnosed Head and Neck Cancer (HNC) patients, scheduled to receive radiotherapy or drug therapy, > or =18 years of age, and able to provide informed consent. Assessments are completed at baseline, at the completion of therapy, and yearly thereafter. Patient data are entered in the registry electronically and transferred via Secure HTTP protocols.Results: Reported use of supportive care differed by treatment setting. When compared to community sites, patients at academic centers received more supportive interventions: feeding tube (59% vs. 48%; p = 0.001), tracheotomy tube (16% vs. 9%; p = 0.002), opioid analgesics (89% vs. 59%; p < 0.0001), anti-emetics (83% vs. 68%; p < 0.0001), and amifostine (17% vs. 12%; p = 0.02). Reported grades 3-4 mucositis/stomatitis was also higher in patients treated at academic centers (38% vs. 28%; p = 0.001).Conclusion: There was a marked decrease in the documented use of supportive care measures in the community setting. This may be due to (1) lower rates of toxicity requiring less supportive care, (2) less stringent documentation, or (3) less aggressive use of supportive care measures. The documented rate of mucositis was less than expected. This is likely due to inadequate assessment or documentation. Further exploration of these findings is warranted as they may indicate an under appreciation and undertreatment of clinically significant acute tumor and treatment-related toxicities. [ABSTRACT FROM AUTHOR]

A greater understanding of radiobiology led to the development of two classes of radiation fractionation schedules for the treatment of head and neck cancers. The aim of accelerated fractionation is to reduce tumour proliferation, which is a major cause of relapse, by shortening the total duration of radiotherapy. By contrast, hyperfractionation exploits the differential sensitivity of tumour cells and normal tissues to radiation, to increase the therapeutic gain. The results of clinical trials of various types of altered fractionation schedules in head and neck carcinomas are examined in this review. Acceleration of radiation by 1 week without dose reduction and hyperfractionation are consistently better than standard fractionation for locoregional control of intermediate to advanced carcinomas without an increase in late toxic effects. However, improvement in survival of patients has not been consistent. Clinical investigations show that improvement in locoregional disease control and consistent gain in survival have been achieved with combinations of radiotherapy and concurrent chemotherapy in patients with mostly stage IV carcinomas. However, these benefits have been at the expense of increased late morbidity. Consequently, concurrent radiochemotherapy is now preferred for non-surgical treatment of patients with locally advanced carcinomas, whereas altered fractionation is generally selected for patients with intermediate-stage tumours or who are medically unfit to receive chemotherapy. Further data is needed before the combination of altered fractionation with chemotherapy can be recommended outside of a study setting. [ABSTRACT FROM AUTHOR]

Purpose: Previous analysis of this Intergroup trial demonstrated that with a median follow-up among surviving patients of 45.9 months, the concurrent postoperative administration of cisplatin and radiation therapy improved local-regional control and disease-free survival of patients who had high-risk resectable head-and-neck carcinomas. With a minimum of 10 years of follow-up potentially now available for all patients, these results are updated here to examine long-term outcomes.Methods and Materials: A total of 410 analyzable patients who had high-risk resected head-and-neck cancers were prospectively randomized to receive either radiation therapy (RT: 60 Gy in 6 weeks) or identical RT plus cisplatin, 100 mg/m(2)i.v. on days 1, 22, and 43 (RT + CT).Results: At 10 years, the local-regional failure rates were 28.8% vs 22.3% (P=.10), disease-free survival was 19.1% vs 20.1% (P=.25), and overall survival was 27.0% vs 29.1% (P=.31) for patients treated by RT vs RT + CT, respectively. In the unplanned subset analysis limited to patients who had microscopically involved resection margins and/or extracapsular spread of disease, local-regional failure occurred in 33.1% vs 21.0% (P=.02), disease-free survival was 12.3% vs 18.4% (P=.05), and overall survival was 19.6% vs 27.1% (P=.07), respectively.Conclusion: At a median follow-up of 9.4 years for surviving patients, no significant differences in outcome were observed in the analysis of all randomized eligible patients. However, analysis of the subgroup of patients who had either microscopically involved resection margins and/or extracapsular spread of disease showed improved local-regional control and disease-free survival with concurrent administration of chemotherapy. The remaining subgroup of patients who were enrolled only because they had tumor in 2 or more lymph nodes did not benefit from the addition of CT to RT. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the dosimetry and feasibility of carotid-sparing intensity-modulated radiotherapy (IMRT) for early glottic cancer and to report preliminary clinical experience.Methods and Materials: Digital Imaging and Communications in Medicine radiotherapy (DICOM-RT) datasets from 6 T1-2 conventionally treated glottic cancer patients were used to create both conventional IMRT plans. We developed a simplified IMRT planning algorithm with three fields and limited segments. Conventional and IMRT plans were compared using generalized equivalent uniform dose and dose-volume parameters for in-field carotid arteries, target volumes, and organs at risk. We have treated 11 patients with this simplified IMRT technique.Results: Intensity-modulated radiotherapy consistently reduced radiation dose to the carotid arteries (p < 0.05) while maintaining the clinical target volume coverage. With conventional planning, median carotid V35, V50, and V63 were 100%, 100%, and 69.0%, respectively. With IMRT planning these decreased to 2%, 0%, and 0%, respectively (p < 0.01). Radiation planning and treatment times were similar for conventional radiotherapy and IMRT. Treatment results have been excellent thus far.Conclusions: Intensity-modulated radiotherapy significantly reduced unnecessary radiation dose to the carotid arteries compared with conventional lateral fields while maintaining clinical target volume coverage. Further experience and longer follow-up will be required to demonstrate outcomes for cancer control and carotid artery effects. [ABSTRACT FROM AUTHOR]

BACKGROUND: We aimed to examine deficiencies in established methods of summarising adverse events, and to create a new reporting system (TAME) for summarising the toxicity burden of cancer treatment. TAME consolidates traditional adverse-event data into three risk domains: short-term (acute) Toxicity (T), Adverse long-term (late) effects (A), and Mortality risk (M) generated by a treatment programme (E=End results); and assigns treatments to risk classes for each risk domain. METHODS: We examined formally an established method for summarising adverse events (the max-grade method) in five trials of patients with head and neck cancer done between September, 1991, and August, 2000, by the Radiation Therapy Oncology Group (RTOG) that involved 13 treatment groups (2304 patients). We calculated TAME summary metrics that included time and multiplicity factors in the same patient groups. We compared relative T values with relative values for toxic effects from the max-grade approach. We also calculated the range of individual patient T scores in two groups from one of the trials (the laryngeal-preservation trial). RESULTS: The max-grade method systematically excluded 29-70% of total reported high-grade (grade 3-4) acute adverse events, contained progressive bias, and favoured higher toxicity programmes. Relative T values in the 13 treatment programmes tested showed an increase of almost 500% in acute toxicity burden (100-590) between treatment groups compared with a 170% increase (100-270) between treatment groups by use of the max-grade method. The difference between these two summary systems was statistically significant (mean difference -102 [95% CI -167 to -37], p=0.005, t test for paired differences). Four risk classes were designated for acute and relative late effects: low (100-140), moderate (150-390), high (400-490), and extreme (>/=500). The distribution of individual patient T scores showed that 82 (60%) patients who received concurrent platinum-radiotherapy for larynx preservation reported two or more high-grade events, and 34 (20%) reported four or more high-grade events; these findings differed significantly from the distribution of individual patient T scores for patients who received radiotherapy alone, in which 32 (19%) reported two or more high-grade events and 3 (3%) reported four or more high-grade events (p<0.0001). The max-grade method also systematically excluded 26-48% of high-grade (grade 3-4) late adverse events. However, less variation was noted in the relative risk of late events (100-270) by the TAME method for late effects.. INTERPRETATION: Traditional methods for summarising adverse events systematically exclude important data, giving an inaccurate impression of the toxicity burden in complex multimodality trials. By contrast, T values use data on all high-grade adverse events. T values are proportional to the intensity of treatment, showing a 500% increase between treatment groups in acute toxicity burden in RTOG trials of head and neck cancer done during this study interval. TAME reporting provides a concise and uniform method to compare relative risk among treatment options. Future studies should include testing the performance of the TAME system in additional datasets (from different research organisations and disease sites), prospective correlation of TAME endpoints with predefined outcome measures, and assessment of its usefulness in clinical decision making. [ABSTRACT FROM AUTHOR]

Background: Several trials have studied the role of unconventional fractionated radiotherapy in head and neck squamous cell carcinoma, but the effect of such treatment on survival is not clear. The aim of this meta-analysis was to assess whether this type of radiotherapy could improve survival.Methods: Randomised trials comparing conventional radiotherapy with hyperfractionated or accelerated radiotherapy, or both, in patients with non-metastatic HNSCC were identified and updated individual patient data were obtained. Overall survival was the main endpoint. Trials were grouped in three pre-specified categories: hyperfractionated, accelerated, and accelerated with total dose reduction.Findings: 15 trials with 6515 patients were included. The median follow-up was 6 years. Tumours sites were mostly oropharynx and larynx; 5221 (74%) patients had stage III-IV disease (International Union Against Cancer, 1987). There was a significant survival benefit with altered fractionated radiotherapy, corresponding to an absolute benefit of 3.4% at 5 years (hazard ratio 0.92, 95% CI 0.86-0.97; p=0.003). The benefit was significantly higher with hyperfractionated radiotherapy (8% at 5 years) than with accelerated radiotherapy (2% with accelerated fractionation without total dose reduction and 1.7% with total dose reduction at 5 years, p=0.02). There was a benefit on locoregional control in favour of altered fractionation versus conventional radiotherapy (6.4% at 5 years; p<0.0001), which was particularly efficient in reducing local failure, whereas the benefit on nodal control was less pronounced. The benefit was significantly higher in the youngest patients (hazard ratio 0.78 [0.65-0.94] for under 50 year olds, 0.95 [0.83-1.09] for 51-60 year olds, 0.92 [0.81-1.06] for 61-70 year olds, and 1.08 [0.89-1.30] for over 70 year olds; test for trends p=0.007).Interpretation: Altered fractionated radiotherapy improves survival in patients with head and neck squamous cell carcinoma. Comparison of the different types of altered radiotherapy suggests that hyperfractionation has the greatest benefit. [ABSTRACT FROM AUTHOR]

Multimodal brain-computer interfaces (BCIs) that combine electrical features from electroencephalography (EEG) and hemodynamic features from functional near-infrared spectroscopy (fNIRS) have the potential to improve performance. In this paper, we propose a multimodal EEG- and fNIRS-based BCI system with soft robotic (BCI-SR) components for personalized stroke rehabilitation. We propose a novel method of personalizing rehabilitation by aligning each patient's specific abilities with the treatment options available. We collected 160 single trials of motor imagery using the multimodal BCI from 10 healthy participants. We identified a confounding effect of respiration in the fNIRS signal data collected. Hence, we propose to incorporate a breathing sensor to synchronize motor imagery (MI) cues with the participant's respiratory cycle. We found that implementing this respiration synchronization (RS) resulted in less dispersed readings of oxyhemoglobin (HbO). We then conducted a clinical trial on the personalized multimodal BCI-SR for stroke rehabilitation. Four chronic stroke patients were recruited to undergo 6 weeks of rehabilitation, three times per week, whereby the primary outcome was measured using upper-extremity Fugl-Meyer Motor Assessment (FMA) and Action Research Arm Test (ARAT) scores on weeks 0, 6, and 12. The results showed a striking coherence in the activation patterns in EEG and fNIRS across all patients. In addition, FMA and ARAT scores were significantly improved on week 12 relative to the pre-trial baseline, with mean gains of 8.75 ± 1.84 and 5.25 ± 2.17, respectively (mean ± SEM). These improvements were all better than the Standard Arm Therapy and BCI-SR group when retrospectively compared to previous clinical trials. These results suggest that personalizing the rehabilitation treatment leads to improved BCI performance compared to standard BCI-SR, and synchronizing motor imagery cues to respiration increased the consistency of HbO levels, leading to better motor imagery performance. These results showed that the proposed multimodal BCI-SR holds promise to better engage stroke patients and promote neuroplasticity for better motor improvements.

Detecting stress is important for improving human health and potential, because moderate levels of stress may motivate people towards better performance at cognitive tasks, while chronic stress exposure causes impaired performance and health risks. We propose a Brain-Computer Interface (BCI) system to detect stress in the context of high-pressure work environments. The BCI system includes an electroencephalogram (EEG) headband with dry electrodes and an electrocardiogram (ECG) chest belt. We collected EEG and ECG data from 40 participants during two stressful cognitive tasks: the Cognitive Vigilance Task (CVT), and the Multi-Modal Integration Task (MMIT) we designed. We also recorded self-reported stress levels using the Dundee Stress State Questionnaire (DSSQ). The DSSQ results indicated that performing the MMIT led to significant increases in stress, while performing the CVT did not. Subsequently, we trained two different models to classify stress from non-stress states, one using EEG features, and the other using heart rate variability (HRV) features extracted from the ECG. Our EEG-based model achieved an overall accuracy of 81.0% for MMIT and 77.2% for CVT. However, our HRV-based model only achieved 62.1% accuracy for CVT and 56.0% for MMIT. We conclude that EEG is an effective predictor of stress in the context of stressful cognitive tasks. Our proposed BCI system shows promise in evaluating mental stress in high-pressure work environments, particularly when utilizing an EEG-based BCI., Competing Interests: Conflicts of InterestThe authors declare no conflicts of interest., (© 2024 by the authors.)

Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are crucial to prevent the development of ulcers and bedsores. This study presents a novel sparse sensor-based spatiotemporal convolutional neural network (S 3 CNN) for detecting sleep posture. This S 3 CNN holistically incorporates a pair of spatial convolution neural networks to capture cardiorespiratory activity maps and a pair of temporal convolution neural networks to capture the heart rate and respiratory rate. Sleep data were collected in actual sleep conditions from 22 subjects using a sparse sensor array. The S 3 CNN was then trained to capture the spatial pressure distribution from the cardiorespiratory activity and temporal cardiopulmonary variability from the heart and respiratory data. Its performance was evaluated using three rounds of 10 fold cross-validation on the 8583 data samples collected from the subjects. The results yielded 91.96% recall, 92.65% precision, and 93.02% accuracy, which are comparable to the state-of-the-art methods that use significantly more sensors for marginally enhanced accuracy. Hence, the proposed S 3 CNN shows promise for sleep posture monitoring using sparse sensors, demonstrating potential for a more cost-effective approach.

Emotions are a series of subconscious, fleeting, and sometimes elusive manifestations of the human innate system. They play crucial roles in everyday life-influencing the way we evaluate ourselves, our surroundings, and how we interact with our world. To date, there has been an abundance of research on the domains of neuroscience and affective computing, with experimental evidence and neural network models, respectively, to elucidate the neural circuitry involved in and neural correlates for emotion recognition. Recent advances in affective computing neural network models often relate closely to evidence and perspectives gathered from neuroscience to explain the models. Specifically, there has been growing interest in the area of EEG-based emotion recognition to adopt models based on the neural underpinnings of the processing, generation, and subsequent collection of EEG data. In this respect, our review focuses on providing neuroscientific evidence and perspectives to discuss how emotions potentially come forth as the product of neural activities occurring at the level of subcortical structures within the brain's emotional circuitry and the association with current affective computing models in recognizing emotions. Furthermore, we discuss whether such biologically inspired modeling is the solution to advance the field in EEG-based emotion recognition and beyond.

The appearance of senescent cells in age-related diseases has spurred the search for compounds that can target senescent cells in tissues, termed senolytics. However, a major caveat with current senolytic screens is the use of cell lines as targets where senescence is induced in vitro, which does not necessarily reflect the identity and function of pathogenic senescent cells in vivo. Here, we developed a new pipeline leveraging a fluorescent murine reporter that allows for isolation and quantification of p16Ink4a+ cells in diseased tissues. By high-throughput screening in vitro, precision-cut lung slice (PCLS) screening ex vivo, and phenotypic screening in vivo, we identified a HSP90 inhibitor, XL888, as a potent senolytic in tissue fibrosis. XL888 treatment eliminated pathogenic p16Ink4a+ fibroblasts in a murine model of lung fibrosis and reduced fibrotic burden. Finally, XL888 preferentially targeted p16INK4a-hi human lung fibroblasts isolated from patients with idiopathic pulmonary fibrosis (IPF), and reduced p16INK4a+ fibroblasts from IPF PCLS ex vivo. This study provides proof of concept for a platform where p16INK4a+ cells are directly isolated from diseased tissues to identify compounds with in vivo and ex vivo efficacy in mice and humans, respectively, and provides a senolytic screening platform for other age-related diseases.

Objective. Motor imagery (MI) brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have been developed primarily for stroke rehabilitation, however, due to limited stroke data, current deep learning methods for cross-subject classification rely on healthy data. This study aims to assess the feasibility of applying MI-BCI models pre-trained using data from healthy individuals to detect MI in stroke patients. Approach. We introduce a new transfer learning approach where features from two-class MI data of healthy individuals are used to detect MI in stroke patients. We compare the results of the proposed method with those obtained from analyses within stroke data. Experiments were conducted using Deep ConvNet and state-of-the-art subject-specific machine learning MI classifiers, evaluated on OpenBMI two-class MI-EEG data from healthy subjects and two-class MI versus rest data from stroke patients. Main results. Results of our study indicate that through domain adaptation of a model pre-trained using healthy subjects' data, an average MI detection accuracy of 71.15% (±12.46%) can be achieved across 71 stroke patients. We demonstrate that the accuracy of the pre-trained model increased by 18.15% after transfer learning ( p <0.001). Additionally, the proposed transfer learning method outperforms the subject-specific results achieved by Deep ConvNet and FBCSP, with significant enhancements of 7.64% ( p <0.001) and 5.55% ( p <0.001) in performance, respectively. Notably, the healthy-to-stroke transfer learning approach achieved similar performance to stroke-to-stroke transfer learning, with no significant difference ( p >0.05). Explainable AI analyses using transfer models determined channel relevance patterns that indicate contributions from the bilateral motor, frontal, and parietal regions of the cortex towards MI detection in stroke patients. Significance. Transfer learning from healthy to stroke can enhance the clinical use of BCI algorithms by overcoming the challenge of insufficient clinical data for optimal training., (© 2024 IOP Publishing Ltd.)

Multiple myeloma (MM) is more common among Black/African American (AA) patients than White patients, but survival rate improvements are less pronounced for AA patients. This study evaluated treatment patterns and survival among 1810 AA and 5904 White adults in the United States with ≥1 MM treatment and ≥3 months of follow-up. Median time from diagnosis to systemic treatment was longer (37 [0-3053] vs. 35 [0-3664] days) and median time to stem cell transplant (SCT) was longer for AA than White patients (255 [1-2352] vs. 225 [1-3094] days), and AA patients were less likely to receive SCT (odds ratio [OR]: 0.66; 95% confidence interval [CI]: 0.58-0.76). Despite disparities in treatment between AA and White patients, AA patients demonstrated lower risk of death (OR: 0.89; 95% CI: 0.81-0.96). These data highlight the value of equal access to care for the improvement of health outcomes in underserved populations.

Research has shown the effectiveness of motor imagery in patient motor rehabilitation. Transcranial electrical stimulation has also demonstrated to improve patient motor and non-motor performance. However, mixed findings from motor imagery studies that involved transcranial electrical stimulation suggest that current experimental protocols can be further improved towards a unified design for consistent and effective results. This paper aims to review, with some clinical and neuroscientific findings from literature as support, studies of motor imagery coupled with different types of transcranial electrical stimulation and their experiments onhealthy and patient subjects. This review also includes the cognitive domains of working memory, attention, and fatigue, which are important for designing consistent and effective therapy protocols. Finally, we propose a theoretical all-inclusive framework that synergizes the three cognitive domains with motor imagery and transcranial electrical stimulation for patient rehabilitation, which holds promise of benefiting patients suffering from neuromuscular and cognitive disorders.

Background: Metabolic syndrome score in children assesses the risk of developing cardiovascular disease in future. We aim to probe the role of the caudate in relation to the metabolic syndrome score. Furthermore, using both functional and structural neuroimaging, we aim to examine the interplay between functional and structural measures., Methods: A longitudinal birth cohort study with functional and structural neuroimaging data obtained at 4.5, 6.0 and 7.5 years and metabolic syndrome scores at 8.0 years was used. Pearson correlation and linear regression was used to test for correlation fractional anisotropy (FA) and fractional amplitude of low frequency fluctuations (fALFF) of the caudate with metabolic syndrome scores. Mediation analysis was used to test if later brain measures mediated the relation between earlier brain measures and metabolic syndrome scores. Inhibitory control was also tested as a mediator of the relation between caudate brain measures and metabolic syndrome scores., Results: FA at 4.5 years and fALFF at 7.5 years of the left caudate was significantly correlated with metabolic syndrome scores. Post-hoc mediation analysis showed that fALFF at 7.5 years fully mediated the relation between FA at 4.5 years and metabolic syndrome scores. Inhibitory control was significantly correlated with fALFF at 7.5 years, but did not mediate the relation between fALFF at 7.5 years and metabolic syndrome scores., Conclusions: We found that variations in caudate microstructure at 4.5 years predict later variation in functional activity at 7.5 years. This later variation in functional activity fully mediates the relation between microstructural changes in early childhood and metabolic syndrome scores at 8.0 years., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Yap-Seng Chong is part of an academic consortium that has received research funding from Abbott Nutrition, Nestec, and Danone. All other authors report no financial relationships with commercial interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging method that measures oxygenated hemoglobin (HbO) levels in the brain to infer neural activity using near-infrared light. Measured HbO levels are directly affected by a person's respiration. Hence, respiration cycles tend to confound fNIRS readings in motor imagery-based fNIRS Brain-Computer Interfaces (BCI). To reduce this confounding effect, we propose a method of synchronizing the motor imagery cue timing with the subject's respiration cycle using a breathing sensor. We conducted an experiment to collect 160 single trials from 10 subjects performing motor imagery using an fNIRS-based BCI and the breathing sensor. We then compared the HbO levels in trials with and without respiration synchronization. The results showed that respiration synchronization yielded HbO levels that were less dispersed across trials, and a negative correlation between the dispersion index of HbO levels with MI decoding accuracies was found across the 10 subjects. This showed that synchronizing motor imagery cues to respiration can yield increased HbO level consistency leading to better MI performance. Hence, the proposed method holds promise to improve the decoding performance of fNIRS-BCI by reducing the confounding effects of respiration.

The brain criticality hypothesis suggests that neural networks and multiple aspects of brain activity self-organize into a critical state, and criticality marks the transition between ordered and disordered states. This hypothesis is appealing from computer science perspective because neural networks at criticality exhibit optimal processing and computing properties while having implications in clinical applications to neurological disorders. In this paper, we introduced brain criticality analysis to track neurodevelopment from childhood to adolescence using the electroencephalogram (EEG) data of 662 subjects aged 5 to 16 years from the Child Mind Institute. We computed brain criticality from long-range temporal correlation (LRTC) using detrended fluctuation analysis (DFA). We also compared the brain criticality analysis with standard EEG power analysis. The results showed a statistically significant increase in brain criticality from childhood to adolescence in the alpha band. A decreasing trend was observed in theta band from EEG power analysis, but a much higher variance was observed compared to the brain criticality analysis. However, the significant results were only observed in some EEG channels, and not observed if the analysis were performed separately with eyes-open and eyes-close condition. Nonetheless, the results suggest that brain criticality may serve as a biomarker of brain development and maturation, but further research is needed to improve brain criticality algorithms and EEG analysis methods.Clinical Relevance- The brain criticality analysis may be used to characterize and predict neurodevelopment in early childhood.

Hypodiploid acute lymphoblastic leukemia (ALL) is an aggressive blood cancer with a poor prognosis despite intensive chemotherapy or stem cell transplant. Children and adolescents with positive end-of-induction minimal residual disease have an overall survival lower than 30%. However, data regarding therapeutic alternatives for this disease is nearly nonexistent, emphasizing the critical need for new or adjunctive therapies that can improve outcomes. We previously reported on the therapeutic efficacy of venetoclax (ABT-199) in hypodiploid B-lineage ALL but with limitations as monotherapy. In this study, we set out to identify drugs enhancing the anti-leukemic effect of venetoclax in hypodiploid ALL. Using a highthroughput drug screen, we identified dinaciclib, a cyclin-dependent kinase inhibitor that worked synergistically with venetoclax to induce cell death in hypodiploid cell lines. This combination eradicated leukemic blasts within hypodiploid ALL patient-derived xenografts mice with low off-target toxicity. Our findings suggest that dual inhibition of BCL-2 (venetoclax) and CDK9/MCL-1 (dinaciclib) is a promising therapeutic approach in hypodiploid ALL, warranting further investigation to inform clinical trials in this high-risk patient population.

Decoding human action intention prior to motion onset with surface electromyograms (sEMG) is an emerging neuroengineering topic with interesting clinical applications such as intelligent control of powered prosthesis/exoskeleton devices. Despite extensive prior works in the related fields, it remains a technical challenge due to considerable variability of complex multi-muscle activation patterns in terms of volatile spatio-temporal characteristics. To address this issue, we first hypothesize that the inherent variability of the idle state immediately preceding the motion initiation needs to be addressed explicitly. We therefore design a hierarchical dynamic Bayesian learning network model that integrates an array of Gaussian mixture model - hidden Markov models (GMM-HMMs), where each GMM-HMM learns the multi-sEMG processes either during the idle state, or during the motion initiation phase of a particular motion task. To test the hypothesis and evaluate the new learning network, we design and build a upper-limb sEMG-joystick motion study system, and collect data from 11 healthy volunteers. The data collection protocol adapted from the psychomotor vigilance task includes repeated and randomized binary hand motion tasks (push or pull) starting from either of two designated idle states: relaxed (with minimal muscle tones), or prepared (with muscle tones). We run a series of cross-validation tests to examine the performance of the method in comparison with the conventional techniques. The results suggest that the idle state recognition favors the dynamic Bayesian model over a static classification model. The results also show a statistically significant improvement in motion prediction accuracy by the proposed method (93.83±6.41%) in comparison with the conventional GMM-HMM method (89.71±8.98%) that does not explicitly account for the idle state. Moreover, we examine the progress of prediction accuracy over the course of motion initiation and identify the important hidden states that warrant future research., (© 2023. The Author(s).)

Brain-computer interfaces (BCIs) have recently been shown to be clinically effective as a novel method of stroke rehabilitation. In many BCI-based studies, the activation of the ipsilesional hemisphere was considered a key factor required for motor recovery after stroke. However, emerging evidence suggests that the contralesional hemisphere also plays a role in motor function rehabilitation. The objective of this study is to investigate the effectiveness of the BCI in detecting motor imagery of the affected hand from contralesional hemisphere. We analyzed a large EEG dataset from 136 stroke patients who performed motor imagery of their stroke-impaired hand. BCI features were extracted from channels covering either the ipsilesional, contralesional or bilateral hemisphere, and the offline BCI accuracy was computed using 10 [Formula: see text] 10-fold cross-validations. Our results showed that most stroke patients can operate the BCI using either their contralesional or ipsilesional hemisphere. Those with the ipsilesional BCI accuracy of less than 60% had significantly higher motor impairments than those with the ipsilesional BCI accuracy above 80%. Interestingly, those with the ipsilesional BCI accuracy of less than 60% achieved a significantly higher contralesional BCI accuracy, whereas those with the ipsilesional BCI accuracy more than 80% had significantly poorer contralesional BCI accuracy. This study suggests that contralesional BCI may be a useful approach for those with a high motor impairment who cannot accurately generate signals from ipsilesional hemisphere to effectively operate BCI., (© 2022. The Author(s).)

Implanted microelectrode arrays can directly pick up electrode signals from the primary motor cortex (M1) during movement, and brain-machine interfaces (BMIs) can decode these signals to predict the directions of contemporaneous movements. However, it is not well known how much each individual input is responsible for the overall performance of a BMI decoder. In this paper, we seek to quantify how much each channel contributes to an artificial neural network (ANN)-based decoder, by measuring how much the removal of each individual channel degrades the accuracy of the output. If information on movement direction was equally distributed among channels, then the removal of one would have a minimal effect on decoder accuracy. On the other hand, if that information was distributed sparsely, then the removal of specific information-rich channels would significantly lower decoder accuracy. We found that for most channels, their removal did not significantly affect decoder performance. However, for a subset of channels (16 out of 61), removing them significantly reduced the decoder accuracy. This suggests that information is not uniformly distributed among the recording channels. We propose examining these channels further to optimize BMIs more effectively, as well as understand how M1 functions at the neuronal level.

This article reviews the current evidence on traumatic fractures in soccer, and assesses how this can guide practice. The incidence of traumatic soccer-related fractures was found to be 0.64 to 0.71/1000 in the general population. Demographics vary between the general population and professional soccer players, with 68% of traumatic soccer fractures occurring in the upper extremity in the general population, and only 23% of traumatic soccer fractures occurring in the upper extremity in professional players. Within the general population, around 80% of traumatic soccer-related fractures are managed non-operatively, with 20% managed operatively. The optimal treatment method is determined by fracture location and configuration. There is an increasing role for primary operative treatment in unstable, non-displaced fracture types, to facilitate an accelerated return to soccer. Around 86% of soccer players return to sport post-fracture. Return times vary by fracture locations and playing level, with elite players having quicker return times than the general population. Regarding injury prevention, shin guards appear to confer substantial benefit against tibial diaphyseal fractures. However, further research is required to determine the optimal preventative measures against fractures in soccer., Competing Interests: None. (Please see ICMJE Disclosure Form for Declarations)., (© 2022 Professor P K Surendran Memorial Education Foundation. Published by Elsevier B.V. All rights reserved.)

Current motor imagery-based brain-computer interface (BCI) systems require a long calibration time at the beginning of each session before they can be used with adequate levels of classification accuracy. In particular, this issue can be a significant burden for long term BCI users. This article proposes a novel transfer learning algorithm, called r-KLwDSA, to reduce the BCI calibration time for long-term users. The proposed r-KLwDSA algorithm aligns the user's EEG data collected in previous sessions to the few EEG trials collected in the current session, using a novel linear alignment method. Thereafter, the aligned EEG trials from the previous sessions and the few EEG trials from the current sessions are fused through a weighting mechanism before they are used for calibrating the BCI model. To validate the proposed algorithm, a large dataset containing the EEG data from 11 stroke patients, each performing 18 BCI sessions, was used. The proposed framework demonstrated a significant improvement in the classification accuracy, of over 4% compared to the session-specific algorithm, when there were as few as two trials per class available from the current session. The proposed algorithm was particularly successful in improving the BCI accuracy of the sessions that had initial session-specific accuracy below 60%, with an average improvement of around 10% in the accuracy, leading to more stroke patients having meaningful BCI rehabilitation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Giles, Ang, Phua and Arvaneh.)

Although brain-computer interface (BCI) shows promising prospects to help post-stroke patients recover their motor function, its decoding accuracy is still highly dependent on feature extraction methods. Most current feature extractors in BCI are classification-based methods, yet very few works from literature use metric learning based methods to learn representations for BCI. To circumvent this shortage, we propose a deep metric learning based method, Weighted Convolutional Siamese Network (WCSN) to learn representations from electroencephalogram (EEG) signal. This approach can enhance the decoding accuracy by learning a low dimensional embedding to extract distance-based representations from pair-wise EEG data. To enhance training efficiency and algorithm performance, a temporal-spectral distance weighted sampling method is proposed to select more informative input samples. In addition, an adaptive training strategy is adopted to address the session-to-session non-stationarity by progressively updating the subject-specific model. The proposed method is applied on both upper limb and lower limb neurorehabilitation datasets acquired from 33 stroke patients, with a total of 358 sessions. Results indicate that using k-Nearest Neighbor as the classification algorithm, the proposed method yielded 72.8% and 66.0% accuracies for the two datasets respectively, significantly better than the other state-of-the-arts ( ). Without losing generality, we also evaluated the proposed method on two publicly available datasets acquired from healthy subjects, wherein the proposed algorithm demonstrated superior performance at most cases as well. Our results support, for the first time, the use of a metric learning based feature extractor to learn representations from non-stationary EEG signals for BCI-assisted post-stroke rehabilitation.

Background . A number of recent randomized controlled trials reported the efficacy of brain-computer interface (BCI) for upper-limb stroke rehabilitation compared with other therapies. Despite the encouraging results reported, there is a significant variance in the reported outcomes. This paper aims to investigate the effectiveness of different BCI designs on poststroke upper-limb rehabilitation. Methods. The effect sizes of pooled and individual studies were assessed by computing Hedge's g values with a 95% confidence interval. Subgroup analyses were also performed to examine the impact of different BCI designs on the treatment effect. Results. The study included 12 clinical trials involving 298 patients. The analysis showed that the BCI yielded significant superior short-term and long-term efficacy in improving the upper-limb motor function compared to the control therapies (Hedge's g  = 0.73 and 0.33, respectively). Based on our subgroup analyses, the BCI studies that used the intention of movement had a higher effect size compared to those used motor imagery (Hedge's g  = 1.21 and 0.55, respectively). The BCI studies using band power features had a significantly higher effect size than those using filter bank common spatial patterns features (Hedge's g  = 1.25 and - 0.23, respectively). Finally, the studies that used functional electrical stimulation as the BCI feedback had the highest effect size compared to other devices (Hedge's g  = 1.2). Conclusion. This meta-analysis confirmed the effectiveness of BCI for upper-limb rehabilitation. Our findings support the use of band power features, the intention of movement, and the functional electrical stimulation in future BCI designs for poststroke upper-limb rehabilitation.

Affective Computing is a multidisciplinary area of research that allows computers to perform human emotion recognition, with potential applications in areas such as healthcare, gaming and intuitive human computer interface design. Hence, this paper proposes an affective interaction system using dry EEG-based Brain-Computer Interface and Virtual Reality (BCI-VR). The proposed BCI-VR system integrates existing low-cost consumer devices such as an EEG headband with frontal and temporal dry electrodes for brain signal acquisition, and a low-cost VR headset that houses an Android handphone. The handphone executes an in-house developed software that connects wirelessly to the headband, processes the acquired EEG signals, and displays VR content to elicit emotional responses. The proposed BCI-VR system was used to collect EEG data from 13 subjects while they watched VR content that elicits positive or negative emotional responses. EEG bandpower features were extracted to train Linear Discriminant and Support Vector Machine classifiers. The classification performances of these classifiers on this dataset and the results of a public dataset (SEED-IV) are then evaluated. The results in classifying positive vs negative emotions in both datasets (~66% for 2-class) show promise that positive and negative emotions can be detected by the proposed low cost BCIVR system, yielding nearly the same performance on the public dataset that used wet EEG electrodes. Hence the results show promise of the proposed BCI-VR system for real-time affective interaction applications in future.

There is a strong demand for acquisition, processing and understanding of a variety of physiological and behavioral signals from the measurements in human-robot interface (HRI). However, multiple data streams from these measurements bring considerable challenges for their synchronizations, either for offline analysis or for online HRI applications, especially when the sensors are wirelessly connected, without synchronization mechanisms, such as a network-time-protocol. In this paper, we presented a full wireless multi-modality sensor system comprising biopotential measurements such as EEG, EMG and inertial parameter data of articulated body-limb motions. In the paper, we propose two methods to synchronize and calibrate the transmission latencies from different wireless channels. The first method employs the traditional artificial electrical timing signal. The other one employs the force-acceleration relationship governed by Newton's Second Law to facilitate reconstruction of the sample-to-sample alignment between the two wireless sensors. The measured latencies are investigated and the result show that they could be determined consistently and accurately by the devised techniques.

Background and aim Contact burn injuries to the hand are common in the paediatric population, with the most common aetiology involving touching hot surfaces in the household. The hand is also often involved in paediatric scald injuries. The aim of this study was to determine the different presentations of hand burn injuries and analyse the outcomes in the paediatric population at Royal Aberdeen Children's Hospital (RACH). Methods Anonymised clinic data for paediatric patients with hand burns presenting to our burn centre from 2017 to 2020 were retrospectively reviewed. A total of 52 patients (65 affected hands) were included in the study. Clinic letters stored on NHS Grampian's electronic patient record system were reviewed for burn surface area, time to healing, management measures including medications prescribed and sequelae of the burn injury. Results The average patient age was three years and four months old. There were 31 male patients and 21 female patients. Paediatric hand burns were most commonly confined to the palm only, followed by the fingers only. Contact with a hob was the most common aetiology, followed by scald burns. The average time to healing was 10 days (range 2-28 days). No correlation was found between length of stay on initial hospital admission and time to complete healing. A total of 86.5% (n=45) of patients were managed with dressings and 13.5% (n=7) of patients underwent surgical management. Of these seven patients, four had surgical debridement of burn tissue, washout, and dressing, and the remaining three had an excision and grafting with thick split-thickness skin grafts. Of these three patients, one patient had to undergo secondary reconstruction with a full-thickness skin graft. Conclusion It has been found that most patients in this study completely healed with primarily conservative measures of dressing care and regular check-ups. Isolated hand burns in the paediatric population present a low rate of sequelae and palms are the most common area of burn injury in this demographic., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2021, Bhatti et al.)

The performance of a classifier in a brain-computer interface (BCI) system is highly dependent on the quality and quantity of training data. Typically, the training data are collected in a laboratory where the users perform tasks in a controlled environment. However, users' attention may be diverted in real-life BCI applications and this may decrease the performance of the classifier. To improve the robustness of the classifier, additional data can be acquired in such conditions, but it is not practical to record electroencephalogram (EEG) data over several long calibration sessions. A potentially time- and cost-efficient solution is artificial data generation. Hence, in this study, we proposed a framework based on the deep convolutional generative adversarial networks (DCGANs) for generating artificial EEG to augment the training set in order to improve the performance of a BCI classifier. To make a comparative investigation, we designed a motor task experiment with diverted and focused attention conditions. We used an end-to-end deep convolutional neural network for classification between movement intention and rest using the data from 14 subjects. The results from the leave-one subject-out (LOO) classification yielded baseline accuracies of 73.04% for diverted attention and 80.09% for focused attention without data augmentation. Using the proposed DCGANs-based framework for augmentation, the results yielded a significant improvement of 7.32% for diverted attention ( ) and 5.45% for focused attention ( ). In addition, we implemented the method on the data set IVa from BCI competition III to distinguish different motor imagery tasks. The proposed method increased the accuracy by 3.57% ( ). This study shows that using GANs for EEG augmentation can significantly improve BCI performance, especially in real-life applications, whereby users' attention may be diverted.

Brain-computer interface-assisted motor imagery (MI-BCI) or transcranial direct current stimulation (tDCS) has been proven effective in post-stroke motor function enhancement, yet whether the combination of MI-BCI and tDCS may further benefit the rehabilitation of motor functions remains unknown. This study investigated brain functional activity and connectivity changes after a 2 week MI-BCI and tDCS combined intervention in 19 chronic subcortical stroke patients. Patients were randomized into MI-BCI with tDCS group and MI-BCI only group who underwent 10 sessions of 20 min real or sham tDCS followed by 1 h MI-BCI training with robotic feedback. We derived amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) from resting-state functional magnetic resonance imaging (fMRI) data pre- and post-intervention. At baseline, stroke patients had lower ALFF in the ipsilesional somatomotor network (SMN), lower ReHo in the contralesional insula, and higher ALFF/Reho in the bilateral posterior default mode network (DMN) compared to age-matched healthy controls. After the intervention, the MI-BCI only group showed increased ALFF in contralesional SMN and decreased ALFF/Reho in the posterior DMN. In contrast, no post-intervention changes were detected in the MI-BCI + tDCS group. Furthermore, higher increases in ALFF/ReHo/FC measures were related to better motor function recovery (measured by the Fugl-Meyer Assessment scores) in the MI-BCI group while the opposite association was detected in the MI-BCI + tDCS group. Taken together, our findings suggest that brain functional re-normalization and network-specific compensation were found in the MI-BCI only group but not in the MI-BCI + tDCS group although both groups gained significant motor function improvement post-intervention with no group difference. MI-BCI and tDCS may exert differential or even opposing impact on brain functional reorganization during post-stroke motor rehabilitation; therefore, the integration of the two strategies requires further refinement to improve efficacy and effectiveness., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Hu, Cheng, Ji, Chong, Lu, Huang, Ang, Phua, Chuang, Jiang, Chew, Guan and Zhou.)