Comparison efficacy and safety of total laparoscopic gastrectomy and laparoscopically assisted total gastrectomy in treatment of gastric cancer.

BACKGROUND: The development of laparoscopic technology has provided a new choice for surgery of gastric cancer (GC), but the advantages and disadvantages of laparoscopic total gastrectomy (LTG) and laparoscopic-assisted total gastrectomy (LATG) in treatment effect and safety are still controversial. The purpose of this study is to compare the efficacy and safety of the two methods in the treatment of GC, and to provide a basis for clinical decision-making. AIM: To compare the efficacy of totally LTG (TLTG) and LATG in the context of radical gastrectomy for GC. Additionally, we investigated the safety and feasibility of the total laparoscopic esophagojejunostomy technique. METHODS: Literature on comparative studies of the above two surgical methods for GC (TLTG group and LATG group) published before September 2022 were searched in the PubMed, Web of Science, Wanfang Database, CNKI, and other Chinese and English databases. In addition, the following search keywords were used: Gastric cancer, total gastrectomy, total laparoscopy, laparoscopy-assisted, esophagojejunal anastomosis, gastric/stomach cancer, total gastrectomy, totally/completely laparoscopic, laparoscopic assisted/laparoscopy assisted/laparoscopically assisted, and esophagojejunostomy/esophagojejunal anastomosis. Review Manager 5.3 software was used for the meta-analysis after two researchers independently screened the literature, extracted the data, and evaluated the risk of bias in the included studies. RESULTS: After layer-by-layer screening, 258 pieces of literature were recovered, and 11 of those pieces were eventually included. This resulted in a sample size of 2421 instances, with 1115 cases falling into the TLTG group and 1306 cases into the LATG group. Age or sex differences between the two groups were not statistically significant, according to the meta-analysis, however the average body mass index of the TLTG group was considerably higher than that of the LATG group (P = 0.01). Compared with those in the LATG group, the incision length in the TLTG group was significantly shorter (P < 0.001), the amount of intraoperative blood loss was significantly lower (P = 0.003), the number of lymph nodes removed was significantly greater (P = 0.04), and the time of first postoperative feeding and postoperative hospitalization were also significantly shorter (P = 0.03 and 0.02, respectively). There were no significant differences in tumor size, length of proximal incisal margin, total operation time, anastomotic time, postoperative pain score, postoperative anal exhaust time, postoperative anastomosis-related complications (including anastomotic fistula, anastomotic stenosis, and anastomotic hemorrhage), or overall postoperative complication rate (P > 0.05). CONCLUSION: TLTG and esophagojejunostomy are safe and feasible. Compared with LATG, TLTG has the advantages of less trauma, less bleeding, easier access to lymph nodes, and faster postoperative recovery, and TLTG is also suitable for obese patients.

Analysis of lymph node metastasis and survival prognosis in early gastric cancer patients: A retrospective study.

BACKGROUND: Early gastric cancer (EGC) is a common malignant tumor of the digestive system, and its lymph node metastasis and survival prognosis have been concerning. By retrospectively analyzing the clinical data of EGC patients, we can better understand the status of lymph node metastasis and its impact on survival and prognosis. AIM: To evaluate the prognosis of EGC patients and the factors that affect lymph node metastasis. METHODS: The clinicopathological data of 1011 patients with EGC admitted to our hospital between January 2015 and December 2023 were collected in a retrospective cohort study. There were 561 males and 450 females. The mean age was 58 ± 11 years. The patient underwent radical gastrectomy. The status of lymph node metastasis in each group was determined according to the pathological examination results of surgical specimens. The outcomes were as follows: (1) Lymph node metastasis in EGC patients; (2) Analysis of influencing factors of lymph node metastasis in EGC; and (3) Analysis of prognostic factors in patients with EGC. Normally distributed measurement data are expressed as mean ± SD, and a t test was used for comparisons between groups. The data are expressed as absolute numbers or percentages, and the chi-square test was used for comparisons between groups. Rank data were compared using a nonparametric rank sum test. A log-rank test and a logistic regression model were used for univariate analysis. A logistic stepwise regression model and a Cox stepwise regression model were used for multivariate analysis. The Kaplan-Meier method was used to calculate the survival rate and construct survival curves. A log-rank test was used for survival analysis. RESULTS: Analysis of influencing factors of lymph node metastasis in EGC. The results of the multifactor analysis showed that tumor length and diameter, tumor site, tumor invasion depth, vascular thrombus, and tumor differentiation degree were independent influencing factors for lymph node metastasis in patients with EGC (odds ratios = 1.80, 1.49, 2.65, 5.76, and 0.60; 95%CI: 1.29-2.50, 1.11-2.00, 1.81-3.88, 3.87-8.59, and 0.48-0.76, respectively; P < 0.05). Analysis of prognostic factors in patients with EGC. All 1011 patients with EGC were followed up for 43 (0-13) months. The 3-year overall survival rate was 97.32%. Multivariate analysis revealed that age > 60 years and lymph node metastasis were independent risk factors for prognosis in patients with EGC (hazard ratio = 9.50, 2.20; 95%CI: 3.31-27.29, 1.00-4.87; P < 0.05). Further analysis revealed that the 3-year overall survival rates of gastric cancer patients aged > 60 years and ≤ 60 years were 99.37% and 94.66%, respectively, and the difference was statistically significant (P < 0.05). The 3-year overall survival rates of patients with and without lymph node metastasis were 95.42% and 97.92%, respectively, and the difference was statistically significant (P < 0.05). CONCLUSION: The lymph node metastasis rate of EGC patients was 23.64%. Tumor length, tumor site, tumor infiltration depth, vascular cancer thrombin, and tumor differentiation degree were found to be independent factors affecting lymph node metastasis in EGC patients. Age > 60 years and lymph node metastasis are independent risk factors for EGC prognosis.

Computationally-efficient linear scheme for overlap time-gating spatial frequency domain diffuse optical tomography using an analytical diffusion model.

Time-domain (TD) spatial frequency domain (SFD) diffuse optical tomography (DOT) potentially enables laminar tomography of both the absorption and scattering coefficients. Its full time-resolved-data scheme is expected to enhance performances of the image reconstruction but poses heavy computational costs and also susceptible signal-to-noise ratio (SNR) limits, as compared to the featured-data one. We herein propose a computationally-efficient linear scheme of TD-SFD-DOT, where an analytical solution to the TD phasor diffusion equation for semi-infinite geometry is derived and used to formulate the Jacobian matrices with regard to overlap time-gating data of the time-resolved measurement for improved SNR and reduced redundancy. For better contrasting the absorption and scattering and widely adapted to practically-available resources, we develop an algebraic-reconstruction-technique-based two-step linear inversion procedure with support of a balanced memory-speed strategy and multi-core parallel computation. Both simulations and phantom experiments are performed to validate the effectiveness of the proposed TD-SFD-DOT method and show an achieved tomographic reconstruction at a relative depth resolution of ∼4 mm.

Deep-learning approach to stratified reconstructions of tissue absorption and scattering in time-domain spatial frequency domain imaging.

Significance: The conventional optical properties (OPs) reconstruction in spatial frequency domain (SFD) imaging, like the lookup table (LUT) method, causes OPs aliasing and yields only average OPs without depth resolution. Integrating SFD imaging with time-resolved (TR) measurements enhances space-TR information, enabling improved reconstruction of absorption (µa) and reduced scattering (µs') coefficients at various depths. Aim: To achieve the stratified reconstruction of OPs and the separation between µa and µs', using deep learning workflow based on the temporal and spatial information provided by time-domain SFD imaging technique, while enhancing the reconstruction accuracy. Approach: Two data processing methods are employed for the OPs reconstruction with TR-SFD imaging, one is full TR data, and the other is the featured data extracted from the full TR data (E, continuous-wave component, ⟨t⟩, mean time of flight). We compared their performance using a series of simulation and phantom validations. Results: Compared to the LUT approach, utilizing full TR, E and ⟨t⟩ datasets yield high-resolution OPs reconstruction results. Among the three datasets employed, full TR demonstrates the optimal accuracy. Conclusions: Utilizing the data obtained from SFD and TR measurement techniques allows for achieving high-resolution separation reconstruction of µa and µs' at different depths within 5 mm.

Wide-field illumination diffuse optical tomography within a framework of single-pixel time-domain spatial frequency domain imaging.

We present a wide-field illumination time-domain (TD) diffusion optical tomography (DOT) for three-dimensional (3-D) reconstruction within a shallow region under the illuminated surface of the turbid medium. The methodological foundation is laid on the single-pixel spatial frequency domain (SFD) imaging that facilitates the adoption of the well-established time-correlated single-photon counting (TCSPC)-based TD detection and generalized pulse spectrum techniques (GPST)-based reconstruction. To ameliorate the defects of the conventional diffusion equation (DE) in the forward modeling of TD-SFD-DOT, mainly the low accuracy in the near-field region and in profiling early-photon migration, we propose a modified model employing the time-dependent δ-P1 approximation and verify its improved accuracy in comparison with both the Monte Carlo and DE-based ones. For a simplified inversion process, a modified GPST approach is extended to TD-SFD-DOT that enables the effective separation of the absorption and scattering coefficients using a steady-state equivalent strategy. Furthermore, we set up a single-pixel TD-SFD-DOT system that employs the TCSPC-based TD detection in the SFD imaging framework. For assessments of the reconstruction approach and the system performance, phantom experiments are performed for a series of scenarios. The results show the effectiveness of the proposed methodology for rapid 3-D reconstruction of the absorption and scattering coefficients within a depth range of about 5 mean free pathlengths.

Understanding and controlling the formation of single-atom site from supported Cu10 cluster by tuning CeO2 reducibility: Theoretical insight into the Gd-doping effect on electronic metal-support interaction.

Controlling the formation of single-atom (SA) sites from supported metal clusters is an important and interesting issue to effectively improve the catalytic performance of heterogeneous catalysts. For extensively studied CO oxidation over metal/CeO2 systems, the SA formation and stabilization under reaction conditions is generally attributed to CO adsorption, however, the pivotal role played by the reducible CeO2 support and the underlying electronic metal-support interaction (EMSI) are not yet fully understood. Based on a ceria-supported Cu10 catalyst model, we performed density functional theory calculations to investigate the intrinsic SA formation mechanism and discussed the synergistic effect of Gd-doped CeO2 and CO adsorption on the SA formation. The CeO2 reducibility is tuned with doped Gd content ranging from 12.5 % ∼ 25 %. Based on ab initio thermodynamic and ab initio molecular dynamics, the critical condition for SA formation was identified as 21.875 % Gd-doped CeO2 with CO-saturated adsorption on Cu10. Electronic analysis revealed that the open-shell lattice Oδ- (δ < 2) generated by Gd doping facilitates the charge transfer from the bottom-corner Cu (Cubc) to CeO2. The CO-saturated adsorption further promotes this charge transfer process and enhances the EMSI between Cubc and CeO2, leading to the disintegration of Cubc from Cu10 and subsequent formation of the active SA site.

Density Functional Theory Study of Methanol Steam Reforming on Pt3Sn(111) and the Promotion Effect of a Surface Hydroxy Group.

Methanol steam reforming (MSR) is studied on a Pt3Sn surface using the density functional theory (DFT). An MSR network is mapped out, including several reaction pathways. The main pathway proposed is CH3OH + OH → CH3O → CH2O → CH2O + OH → CH2OOH → CHOOH → COOH → COOH + OH → CO2 + H2O. The adsorption strengths of CH3OH, CH2O, CHOOH, H2O and CO2 are relatively weak, while other intermediates are strongly adsorbed on Pt3Sn(111). H2O decomposition to OH is the rate-determining step on Pt3Sn(111). The promotion effect of the OH group is remarkable on the conversions of CH3OH, CH2O and trans-COOH. In particular, the activation barriers of the O-H bond cleavage (e.g., CH3OH → CH3O and trans-COOH → CO2) decrease substantially by ~1 eV because of the involvement of OH. Compared with the case of MSR on Pt(111), the generation of OH from H2O decomposition is more competitive on Pt3Sn(111), and the presence of abundant OH facilitates the combination of CO with OH to generate COOH, which accounts for the improved CO tolerance of the PtSn alloy over pure Pt.

A CRISPR-Cas12a-based electrochemical biosensor for the detection of microphthalmia-associated transcription factor.

A novel electrochemical biosensor that combines the CRISPR-Cas12a system with a gold electrode is reported for the rapid and sensitive detection of microphthalmia-associated transcription factor (MITF). The biosensor consists of a gold electrode modified with DNA1, which contains the target sequence of MITF and is labeled with ferrocene, an electroactive molecule. The biosensor also includes hairpin DNA, which has a binding site for MITF and can hybridize with helper DNA to form a double-stranded complex that activates CRISPR-Cas12a. When MITF is present, it binds to hairpin DNA and prevents its hybridization with helper DNA, thus inhibiting CRISPR-Cas12a activity and preserving the DPV signal of ferrocene. When MITF is absent, hairpin DNA hybridizes with helper DNA and activates CRISPR-Cas12a, which cleaves DNA1 and releases ferrocene, thus reducing the DPV signal. The biosensor can detect MITF with high sensitivity (with an LOD of 8.14 fM), specificity, and accuracy in various samples, such as cell nuclear extracts and human serum. The biosensor can also diagnose and monitor melanocyte-related diseases and melanin production. This work provides a simple, fast, sensitive, and cost-effective biosensor for MITF detection and a valuable tool for applications in genetic testing, disease diagnosis, and drug screening.

Application of Hospital-Community-Family "Trinity" Remote Rehabilitation Guidance Based on Internet + WeChat Platform in Patients with Stroke After Discharge.

Objective: To investigate the impact of an Internet + WeChat platform-based "trinity" remote rehabilitation model involving the hospital, community, and family on stroke patient rehabilitation nursing. Methods: 159 patients with stroke who were discharged from Beijing Luhe Hospital of Capital Medical University from January 1, 2018, to December 31, 2019, were selected and divided into a control group (79 cases) and an experimental group (80 cases) by the random drawing method. The control group was given routine nursing, and the experimental group was given remote rehabilitation nursing intervention by using the WeChat network platform based on the control group. Limb function [Fugl-Meyer Assessment Scale (FMA)] and activities of daily living [Modified Barthel Index (MBI)] were evaluated at enrollment and at the end of 3 months, 6 months and 12 months in both groups. The compliance and satisfaction surveys in the two groups were evaluated after 6 months and 12 months of intervention. Results: (1) Before the intervention, there was no statistical significance in FMA score between the two groups (t = 0.798, P > .05). After 3 months, 6 months and 12 months of intervention, the FMA score in the two groups was increased compared with that before intervention (t = 2.463, P < .05), and the FMA scores in the experimental group at the above time points were higher than those in the control group (ts = 7.057, 14.285, Ps < .05). (2) There was no statistical difference in MBI scores between the two groups before intervention (t = 0.798, P > .05). After 3 months, 6 months, and 12 months of intervention, the MBI score in the two groups was increased compared with that before intervention (t = 0.232, P < .05), and MBI scores in the experimental group at the above time points were higher compared to the control group (ts = 4.959, 8.842, 8.131, Ps < .05). (3) The compliance scores in the experimental group were higher than those in the control group after 6 months and 12 months of intervention (ts = 4.959, 8.842, 8.131, Ps < .05). (4) The satisfaction survey scores in the experimental group after 6 months and 12 months of intervention were higher than those in the control group (ts = 2.120 ~ 14.554, Ps < .05). Conclusion: The Hospital-community-family "trinity" stroke rehabilitation model on the WeChat network platform holds significant importance. Enhancing limb function and daily living for stroke patients improves their quality of life and lessens reliance on caregivers. This positively impacts both survivors' well-being and healthcare resources. Increased patient satisfaction and compliance suggest a potential revolution in post-stroke care, favoring a more patient-centered approach. Overall, this model has transformative potential for stroke treatment, offering holistic and patient-focused strategies. Its success promises better rehabilitation outcomes, patient satisfaction, and cost reduction, while paving the way for innovative research in stroke treatment and rehabilitation.

CGAN-rIRN: a data-augmented deep learning approach to accurate classification of mental tasks for a fNIRS-based brain-computer interface.

Functional near-infrared spectroscopy (fNIRS) is increasingly used to investigate different mental tasks for brain-computer interface (BCI) control due to its excellent environmental and motion robustness. Feature extraction and classification strategy for fNIRS signal are essential to enhance the classification accuracy of voluntarily controlled BCI systems. The limitation of traditional machine learning classifiers (MLCs) lies in manual feature engineering, which is considered as one of the drawbacks that reduce accuracy. Since the fNIRS signal is a typical multivariate time series with multi-dimensionality and complexity, it makes the deep learning classifier (DLC) ideal for classifying neural activation patterns. However, the inherent bottleneck of DLCs is the requirement of substantial-scale, high-quality labeled training data and expensive computational resources to train deep networks. The existing DLCs for classifying mental tasks do not fully consider the temporal and spatial properties of fNIRS signals. Therefore, a specifically-designed DLC is desired to classify multi-tasks with high accuracy in fNIRS-BCI. To this end, we herein propose a novel data-augmented DLC to accurately classify mental tasks, which employs a convolution-based conditional generative adversarial network (CGAN) for data augmentation and a revised Inception-ResNet (rIRN) based DLC. The CGAN is utilized to generate class-specific synthetic fNIRS signals to augment the training dataset. The network architecture of rIRN is elaborately designed in accordance with the characteristics of the fNIRS signal, with serial multiple spatial and temporal feature extraction modules (FEMs), where each FEM performs deep and multi-scale feature extraction and fusion. The results of the paradigm experiments show that the proposed CGAN-rIRN approach improves the single-trial accuracy for mental arithmetic and mental singing tasks in both the data augmentation and classifier, as compared to the traditional MLCs and the commonly used DLCs. The proposed fully data-driven hybrid deep learning approach paves a promising way to improve the classification performance of volitional control fNIRS-BCI.

A combined autofluorescence and diffuse reflectance spectroscopy for mucosa tissue diagnosis: Dual-distance system and data-driven decision.

Combined autofluorescence (AF) and diffuse reflectance (DR) spectroscopies have been expected to offer enhanced diagnostic accuracies for noninvasive early detection of mucosa lesions, that is, oral cavity carcinoma and cervical carcinoma. This work reports on a hybrid AF and DR spectroscopic system that is developed for quantification and diagnosis of mucosa abnormalities. The system stability and reliability are firstly assessed by phantom experiments, showing a measurement variation lower than 1% within 20 min. In vitro and in vivo validations are then conducted for tissue identification and lesion differentiation. For enhanced decision, a data-driven diagnosis algorithm is explored in pilot under different experimental configurations. The results conclude a promising accuracy of >96% for the in vivo classification as well as an excellent sensitivity of >88% for the in vitro mucosa lesions detection, and demonstrate sound potential of the system in early detection of mucosa lesions.

Atomic coordination structural dynamic evolution of single-atom Mo catalyst for promoting H2 activation in slurry phase hydrocracking.

Development of efficient catalysts with high atomic utilization and turnover frequency (TOF) for H2 activation in slurry phase hydrocracking (SPHC) is crucial for the conversion of vacuum residue (VR). Herein, for the first time, we reported a robust and stable single atoms (SAs) Mo catalyst through a polymerization-pyrolysis-in situ sulfurization strategy for activating H2 in SPHC of VR. An interesting atomic coordination structural dynamic evolution of Mo active sites was discovered. During hydrocracking of VR, the O atoms that coordinated with Mo were gradually replaced by S atoms, which led to the O/S exchange process. The coordination structure of the Mo SAs changed from pre-reaction Mo-O3S1 to post-reaction Mo-O1S3 coordination configurations, promoting the efficient homolytic cleavage activation of H2 into H radical species effectively. The evolved Mo SAs catalyst exhibited robust catalytic hydrogenation activity with the per pass conversion of VR of 65 wt%, product yield of liquid oils of 93 wt%, coke content of only 0.63 wt%, TOF calculated for total metals up to 0.35 s-1, and good cyclic stability. Theoretical calculation reveals that the significant variation of occupied Mo 4d states before and after H2 interaction has a direct bearing on the dynamic evolution of Mo SAs catalyst structure. The lower d-band center of Mo-O1S3 site indicates that atomic H diffusion is easy, which is conducive to catalytic hydrogenation. The finding of this study is of great significance to the development of high atom economy catalysts for the industrial application of heavy oil upgrading technology.

Neural network-based optimization of sub-diffuse reflectance spectroscopy for improved parameter prediction and efficient data collection.

In this study, a general and systematical investigation of sub-diffuse reflectance spectroscopy is implemented. A Gegenbauer-kernel phase function-based Monte Carlo is adopted to describe photon transport more efficiently. To improve the computational efficiency and accuracy, two neural network algorithms, namely, back propagation neural network and radial basis function neural network are utilized to predict the absorption coefficient µ a , reduced scattering coefficient µ s ' and sub-diffusive quantifier γ , simultaneously, at multiple source-detector separations (SDS). The predicted results show that the three parameters can be predicated accurately by selecting five SDSs or above. Based on the simulation results, a four wavelength (520, 650, 785 and 830 nm) measurement system using five SDSs is designed by adopting phase-lock-in technique. Furtherly, the trained neural-network models are utilized to extract optical properties from the phantom and in vivo experimental data. The results verify the feasibility and effectiveness of our proposed system and methods in mucosal disease diagnosis.

Tikhonov regularization-based extended Kalman filter technique for robust and accurate reconstruction in diffuse optical tomography.

Diffuse optical tomography (DOT) is a non-invasive imaging modality that uses near-infrared light to probe the optical properties of tissue. In conventionally used deterministic methods for DOT inversion, the measurement errors were not taken into account, resulting in unsatisfactory noise robustness and, consequently, affecting the DOT image reconstruction quality. In order to overcome this defect, an extended Kalman filter (EKF)-based DOT reconstruction algorithm was introduced first, which improved the reconstruction results by incorporating a priori information and measurement errors to the model. Further, to mitigate the instability caused by the ill-condition of the observation matrix in the tomographic imaging problem, a new, to the best of our knowledge, estimation algorithm was derived by incorporating Tikhonov regularization to the EKF method. To verify the effectiveness of the EKF algorithm and Tikhonov regularization-based EKF algorithm for DOT imaging, a series of numerical simulations and phantom experiments were conducted, and the experimental results were quantitatively evaluated and compared with two conventionally used deterministic methods involving the algebraic reconstruction technique and Levenberg-Marquardt algorithm. The results show that the two EKF-based algorithms can accurately estimate the location and size of the target, and the imaging accuracy and noise robustness are obviously improved. Furthermore, the Tikhonov regularization-based EKF obtained optimal parameter estimations, especially under the circumstance of low absorption contrast (1.2) and high noise level (10%).

Synergy of Substrate Chemical Environments and Single-Atom Catalysts Promotes Catalytic Performance: Nitrogen Reduction on Chiral and Defected Carbon Nanotubes.

The catalytic activities of single-atom catalysts (SACs) are strongly influenced by the local chemical environments of their substrates, by which the electronic structures of the SACs can be effectively tuned. Together with the freedom of available reactive metallic centers, it would be feasible to maximize the catalytic performance by means of a synergetic optimization in the chemical space spanned by the features of both the substrate and the catalytic center. In this work, using first-principles calculations, we systematically assessed the synergetic effect between the substrate geometric/electronic structures and the catalytic centers on the electrocatalytic nitrogen reduction reaction (NRR). Carbon nanotubes with different chirality, defects, and chemical functionalization were used to support 15 transition metal atoms. Three SACs, TiN4CNT(3,3), TiN4CNT(5,5), and VN4CNT(3,3), simultaneously possess high NRR selectivities (w.r.t hydrogen evolution) and low overpotentials of 0.35, 0.35, and 0.37 V, respectively. Electronic structure analysis elucidated that larger metal atoms anchored on CNTs with higher curvature and doped by N atoms facilitate the rupture of the N-N bond in *NH2NH2 to lower the overpotentials. The synergy of substrate chemical environments and single atomic catalysis is a promising strategy to optimize the catalytic performance.

Deep-learning informed Kalman filtering for priori-free and real-time hemodynamics extraction in functional near-infrared spectroscopy.

Separation of the physiological interferences and the neural hemodynamics has been a vitally important task in the realistic implementation of functional near-infrared spectroscopy (fNIRS). Although many efforts have been devoted, the established solutions to this issue additionally rely on priori information on the interferences and activation responses, such as time-frequency characteristics and spatial patterns, etc., also hindering the realization of real-time. To tackle the adversity, we herein propose a novel priori-free scheme for real-time physiological interference suppression. This method combines the robustness of deep-leaning-based interference characterization and adaptivity of Kalman filtering: a long short-term memory (LSTM) network is trained with the time-courses of the absorption perturbation baseline for interferences profiling, and successively, a Kalman filtering process is applied with reference to the noise prediction for real-time activation extraction. The proposed method is validated using both simulated dynamic data and in-vivo experiments, showing the comprehensively improved performance and promisingly appended superiority achieved in the purely data-driven way.

Combining robust level extraction and unsupervised adaptive classification for high-accuracy fNIRS-BCI: An evidence on single-trial differentiation between mentally arithmetic- and singing-tasks.

Functional near-infrared spectroscopy (fNIRS) is a safe and non-invasive optical imaging technique that is being increasingly used in brain-computer interfaces (BCIs) to recognize mental tasks. Unlike electroencephalography (EEG) which directly measures neural activation, fNIRS signals reflect neurovascular-coupling inducing hemodynamic response that can be slow in time and varying in the pattern. The established classifiers extend the EEG-ones by mostly employing the feature based supervised models such as the support vector machine (SVM) and linear discriminant analysis (LDA), and fail to timely characterize the level-sensitive hemodynamic pattern. A dedicated classifier is desired for intentional activity recognition of fNIRS-BCI, including the adaptive acquisition of response relevant features and accurate discrimination of implied ideas. To this end, we herein propose a specifically-designed joint adaptive classification method that combines a Kalman filtering (KF) for robust level extraction and an adaptive Gaussian mixture model (a-GMM) for enhanced pattern recognition. The simulative investigations and paradigm experiments have shown that the proposed KF/a-GMM classification method can effectively track the random variations of task-evoked brain activation patterns, and improve the accuracy of single-trial classification task of mental arithmetic vs. mental singing, as compared to the conventional methods, e.g., those that employ combinations of the band-pass filtering (BPF) based feature extractors (mean, slope, and variance, etc.) and the classical recognizers (GMM, SVM, and LDA). The proposed approach paves a promising way for developing the real-time fNIRS-BCI technique.

Suppressing physiological interferences and physical noises in functional diffuse optical tomography via tandem inversion filtering and LSTM classification.

For performance enhancement of functional diffuse optical tomography (fDOT), we propose a tandem method that takes advantage of the inversion filtering and the long short term memory (LSTM) classification to simultaneously suppress the physiological interferences and physical noises in fDOT. In the former phase, the absorption perturbation maps over the scalp-skull (SS) and cerebral-cortex (CC) layers are firstly pre-reconstructed using a two-layer topography scheme. Then, the recovered SS-map is inversed into measurement space by the forward calculation to estimate the intensity changes associated with the physiological interferences. Finally, the raw measurements are adaptively filtered with reference to the estimated intensity changes for accomplishing the model-based full three-dimension (3D) reconstruction. In the later phase, for further removing the randomly distributed physical noises, mainly instrumental noise, a LSTM network based fusion strategy is applied, where a pixel-wise binary mask of "activated" or "inactive" state is generated by performing LSTM classification and then fused with the 3D reconstruction. The results of the simulative investigation and in-vivo experiment show the proposed tandem scheme achieves improved performance in fDOT using a cost-effective and physically explicable way. Thus, the proposed method can be promisingly applied in real-time neuroimaging to acquire cortical neural activation information with improved reliability, quantification and resolution.

Contrast-induced encephalopathy with significantly elevated levels of cerebrospinal fluid protein.

Contrast-induced encephalopathy (CIE) is a rare complication of angiography. According to our knowledge, the majority of CIE reports is imaging observations and rarely includes results of cerebrospinal fluid (CSF) tests. Furthermore, among the cases reporting the data for CSF testing, most of the results were normal. Here, we report a case of CIE presenting with significantly elevated levels of CSF protein. We found that the course of improvement in brain imaging findings was not consistent with the severity of clinical manifestations. The diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) sequences were normal. Considering the lack of convenient direct indicators to observe blood-brain barrier (BBB) function, changes in the levels of CSF protein may be related to BBB permeability and recovery and may serve as a potential prognostic marker.