Clinical Features and Outcomes of Twin Pregnancies with Antiphospholipid Antibodies Positivity: A Retrospective Study.

Objective: This study aimed to evaluate the impact of twin pregnancies with antiphospholipid antibody (aPL) positivity, a rare and complex clinical condition that remains a huge challenge for management. Methods: This study enrolled twin-pregnant women at our hospital between January 2018 and August 2023. Women with and without aPL positivity were selected using propensity score matching (PSM). Clinical features and pregnancy outcomes were compared between the two groups in the PSM cohort. To analyze the effect of aPL positivity on pregnancy outcomes, multivariate logistic models were used to obtain adjusted odds ratios (aOR) with 95% confidence intervals (CI). Results: Among the 773 women with twin pregnancies, aPL positivity was found in 26 women (3.36%). In the PSM cohort, there were 24 twin-pregnant women with positive aPL, and 48 women without aPL were selected as controls. Twin-pregnant women with aPL positivity had a higher proportion of abortion (8.33% vs 0, P = 0.043), preterm birth < 34 weeks (33.33% vs 8.33%, P = 0.007) and very low birthweight (<1500 g) (20.83% vs 4.17%, P = 0.016) than the control group. In addition, stillbirth of one fetus was observed in one twin-pregnant woman with positive aPL. Multivariate logistic regression analysis revealed that twin pregnancy with aPL positivity was associated with preterm birth < 34 weeks (aOR = 2.76, 95% CI: 0.83-4.70, P = 0.005), very low birthweight (<1500 g) (OR = 2.40, 95% CI: 0.18-4.67, P = 0.034) and small for gestational age (SGA) (aOR = 1.66, 95% CI: 0.22-3.10, P =0.024). Conclusion: Twin pregnancies with aPL positivity were correlated with obstetric complications, including abortion, preterm birth < 34 weeks and very low birthweight (<1500 g). The detection of aPL may be of clinical significance for women with twin pregnancies and should be considered in future studies.

Comparison of cytokine expression and disease severity between plasma cell-dominant and eosinophil-dominant patients in chronic rhinosinusitis with nasal polyps.

PURPOSE: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a heterogeneous disease characterized by inflammation of the nasal and sinus mucosa. The inflammatory patterns may differ among patients, leading to different subtypes based on the dominant inflammatory cell type. This study aimed to compare the differences in cytokine expression and disease severity between plasma cell-dominant and eosinophil-dominant subtypes in patients with CRSwNP. METHODS: This study included 53 CRSwNP patients and 19 control subjects who did not have asthma or a history of cigarette smoking. The expression of cytokines and inflammatory cells was assessed via enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry, respectively. RESULTS: Among the cytokines analyzed, only IL-6 was significantly different between the two subtypes. A greater proportion of mast cells and IgE cells was present in plasma cell-dominant CRSwNP patients than in eosinophil-dominant group. For the three disease severity scores (LMK-CT, TPS and SNOT-22), objective scores (LMK-CT and TPS) were greater in the eosinophil-dominant CRSwNP group, while the opposite result was shown for the subjective score (SNOT-22). Additionally, the percentage of plasma cell-dominant cells was significantly positively correlated with disease severity according to the TPS and SNOT-22 scores. CONCLUSIONS: Our data revealed that plasma cell-dominant inflammation, a subtype of type 2 CRS, was significantly correlated with subjective disease severity. The study also highlights the role of IL-6, IgE and mast cells as distinguishing factors between eosinophil-dominant and plasma cell-dominant CRSwNP. This information could be useful for clinical diagnosis and personalized treatment.

TOP2A modulates signaling via the AKT/mTOR pathway to promote ovarian cancer cell proliferation.

Ovarian cancer (OC) is a form of gynecological malignancy that is associated with worse patient outcomes than any other cancer of the female reproductive tract. Topoisomerase II α (TOP2A) is commonly regarded as an oncogene that is associated with malignant disease progression in a variety of cancers, its mechanistic functions in OC have yet to be firmly established. We explored the role of TOP2A in OC through online databases, clinical samples, in vitro and in vivo experiments. And initial analyses of public databases revealed high OC-related TOP2A expression in patient samples that was related to poorer prognosis. This was confirmed by clinical samples in which TOP2A expression was elevated in OC relative to healthy tissue. Kaplan-Meier analyses further suggested that higher TOP2A expression levels were correlated with worse prognosis in OC patients. In vitro, TOP2A knockdown resulted in the inhibition of OC cell proliferation, with cells entering G1 phase arrest and undergoing consequent apoptotic death. In rescue assays, TOP2A was confirmed to regulate cell proliferation and cell cycle through AKT/mTOR pathway activity. Mouse model experiments further affirmed the key role that TOP2A plays as a driver of OC cell proliferation. These data provide strong evidence supporting TOP2A as an oncogenic mediator and prognostic biomarker related to OC progression and poor outcomes. At the mechanistic level, TOP2A can control tumor cell growth via AKT/mTOR pathway modulation. These preliminary results provide a foundation for future research seeking to explore the utility of TOP2A inhibitor-based combination treatment regimens in platinum-resistant recurrent OC patients.

Sleep deprivation changes frequency-specific functional organization of the resting human brain.

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies have widely explored the temporal connection changes in the human brain following long-term sleep deprivation (SD). However, the frequency-specific topological properties of sleep-deprived functional networks remain virtually unclear. In this study, thirty-seven healthy male subjects underwent resting-state fMRI during rested wakefulness (RW) and after 36 hours of SD, and we examined frequency-specific spectral connection changes (0.01-0.08 Hz, interval = 0.01 Hz) caused by SD. First, we conducted a multivariate pattern analysis combining linear SVM classifiers with a robust feature selection algorithm, and the results revealed that accuracies of 74.29%-84.29% could be achieved in the classification between RW and SD states in leave-one-out cross-validation at different frequency bands, moreover, the spectral connection at the lowest and highest frequency bands exhibited higher discriminative power. Connection involving the cingulo-opercular network increased most, while connection involving the default-mode network decreased most following SD. Then we performed a graph-theoretic analysis and observed reduced low-frequency modularity and high-frequency global efficiency in the SD state. Moreover, hub regions, which were primarily situated in the cerebellum and the cingulo-opercular network after SD, exhibited high discriminative power in the aforementioned classification consistently. The findings may indicate the frequency-dependent effects of SD on the functional network topology and its efficiency of information exchange, providing new insights into the impact of SD on the human brain.

Frequency-specific segregation and integration of human cerebral cortex: An intrinsic functional atlas.

The cognitive and behavioral functions of the human brain are supported by its frequency multiplexing mechanism. However, there is limited understanding of the dynamics of the functional network topology. This study aims to investigate the frequency-specific topology of the functional human brain using 7T rs-fMRI data. Frequency-specific parcellations were first performed, revealing frequency-dependent dynamics within the frontoparietal control, parietal memory, and visual networks. An intrinsic functional atlas containing 456 parcels was proposed and validated using stereo-EEG. Graph theory analysis suggested that, in addition to the task-positive vs. task-negative organization observed in static networks, there was a cognitive control system additionally from a frequency perspective. The reproducibility and plausibility of the identified hub sets were confirmed through 3T fMRI analysis, and their artificial removal had distinct effects on network topology. These results indicate a more intricate and subtle dynamics of the functional human brain and emphasize the significance of accurate topography.

Oncological and reproductive outcomes of endometrial atypical hyperplasia and endometrial cancer patients undergoing conservative therapy with hysteroscopic resection: A systematic review and meta-analysis.

INTRODUCTION: Our objective was to conduct a systematic review and meta-analysis of studies evaluating the oncological and reproductive outcomes of patients with endometrial atypical hyperplasia (AH) and endometrioid endometrial cancer (EEC) undergoing conservative therapy with hysteroscopic resection (HR). MATERIAL AND METHODS: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement for systematic reviews and meta-analyses. The study strictly followed the methodological framework proposed by the Cochrane Handbook and was retrospectively registered in PROSPERO (CRD42023469986). Searches were conducted in PubMed, Embase, and the Cochrane Library, from inception to October 10, 2023. A checklist based on items of the Newcastle-Ottawa Scale and the Methodological Index for Non-randomized Studies was used for quality assessment. The primary end points for this meta-analysis were complete response (CR), pregnancy, and live birth rates following HR-based therapy in patients with EEC or AH. The secondary end point was the recurrence rate (RR). RESULTS: Twenty-one articles involving 407 patients with clinical stage IA, low or intermediate grade, EEC, and 444 patients with AH managed with HR-based conservative treatment were included for this systematic review. CR to HR-based conservative therapy was achieved in 88.6% of patients with EEC and 97.0% of patients with AH. Of these, 30.6% and 24.2%, respectively, had live births. The overall pooled disease RR was 18.3% and 10.8% in patients with EEC and AH, respectively. Further subset analyses revealed that EEC patients with body mass index (BMI) ≤28 kg/m2 had higher CR rates as well as higher chances of pregnancy and live birth (91.6% CR, 32.9% pregnancy, 31.1% live birth) compared with patients with BMI >28 kg/m2 (86.4% CR, 28.4% pregnancy, 23.0% live birth). The HR followed by oral progestogen subgroup had higher CR rates and higher chances of pregnancy and live birth (91.8% CR, 36.3% pregnancy, 28.2% live birth) than the HR followed by the levonorgestrel intrauterine system subgroup (82.5% CR, 25.3% pregnancy, 16.3% live birth). CONCLUSIONS: Hysteroscopic resection followed by progestins appears to be a promising choice for fertility-sparing treatment in young patients with AH and EEC, with effective and safe responses. The live birth rate remains to be improved by providing medical guidance and encouragement.

Determinants of the transition towards circular economy in SMEs: a sustainable supply chain management perspective.

Small and medium-sized businesses (SMEs) increasingly know the benefits of improving resource efficiency and closing loops. These benefits include lowering material costs, establishing competitive advantages, and gaining access to new markets. As a consequence of implementing new regulations, manufacturing companies, particularly those in the automobile industry, are compelled to modify and change their business practices related to the circular economy (CE). More stringent the implementation of environmentally responsible policies and strengthening environmental regulations. CE is the most important factor in improving environmental conditions since it reduces waste and boosts output. This facet calls for the attention of fresh academics and policymakers with years of relevant expertise. Recent studies have investigated how green logistics management might improve a company's overall performance in terms of environmental responsibility. However, we believe that the connection between environmentally responsible companies is not a direct one but rather one that is mediated by the practices of circular economies. We investigate the direct and indirect effects of the environmentally responsible impact of proper logistics management on organizations' overall environmental performance via the application of circular economy practices. Our theoretical underpinnings are the resource-based viewpoint and the resource dependence theory. This research also investigates whether or not the traceability of the supply chain has a mitigating influence on the connections. We evaluated the hypotheses using the PLS-SEM method, drawing on the empirical data provided by 245 Chinese factories considered modest or medium size. The results demonstrate that the management of green logistics has a constructive effect on circular economy practices and businesses' sustainability performance. In addition, although it greatly impacts circular economy practice among SMEs, supply chain traceability does not attenuate the connection between eco-friendly supply chain management and environmental impact. Green logistics management in SMEs is linked to improved sustainability performance via the circular economy practice. To further verify the efficacy of the mediation, we also ran the sober test. Our results strengthen knowledge of circular economy, environmentally friendly logistics management practices, and sustainability performance while advancing natural resource-based planning and the resource dependence theory, which are the two approaches. Given the scarcity of information research analyzing the interplay between these factors, our results are very significant.

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.

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.

The Application of CT Radiomics in the Diagnosis of Vein Wall Invasion in Patients With Renal Cell Carcinoma Combined With Tumor Thrombus.

OBJECTIVE: The objective of this study was to explore the application of radiomics combined with machine learning to establish different models to assist in the diagnosis of venous wall invasion in patients with renal cell carcinoma and venous tumor thrombus and to evaluate the diagnostic efficacy. MATERIALS AND METHODS: We retrospectively reviewed the data of 169 patients in Peking University Third Hospital from March 2015 to January 21, who was diagnosed as renal mass with venous invasion. According to the intraoperative findings, 111 patients were classified to the venous wall invasion group and 58 cases in the non-invasion group. ITK-snap was used for tumor segmentation and PyRadiomics 3.0.1 package was used for feature extraction. A total of 1598 features could be extracted from each CT image. The patients were divided into training set and testing set by time. The elastic-net regression with 4-fold cross-validation was used as a dimension-reduction method. After feature selection, a support vector machines (SVM) model, a logistic regression (LR) model, and an extra trees (ET) model were established. Then the sensitivity, specificity, accuracy, and the area under the curve (AUC) were calculated to evaluate the diagnostic performance of each model on the testing set. RESULTS: Patients before September 2019 were divided into the training set, of which 88 patients were in the invasion group and 42 patients were in the non-invasion group. The others were in the testing set, of which 32 patients were in the invasion group and 16 patients were in the non-invasion group. A total of 34 radiomics features were obtained by the elastic-net regression. The SVM model had an AUC value of 0.641 (95% CI, 0.463-0.769), a sensitivity of 1.000, and a specificity of 0.062. The LR model had an AUC value of 0.769 (95% CI, 0.620-0.877), a sensitivity of 0.913, and a specificity of 0.312. The ET model had an AUC value of 0.853 (95% CI, 0.734-0.948), a sensitivity of 0.783, and a specificity of 0.812. Among the 3 models, the ET model had the best diagnostic effect, with a good balance of sensitivity and specificity. And the higher the tumor thrombus grade, the better the diagnostic efficacy of the ET model. In inferior vena cava tumor thrombus, the sensitivity, specificity, accuracy, and AUC of ET model can be improved to 0.889, 0.800, 0.857, 0.878 (95% CI, 0.745-1.000). CONCLUSION: Machine learning combined with radiomics method can effectively identify whether venous wall was invaded by tumor thrombus and has high diagnostic efficacy with an AUC of 0.853 (95% CI, 0.734-0.948).

Defocused mode in depressed mood and its changes in time-frequency attention-related beta.

Depressed mood has been proposed to possibly possess a unique mode of defocused attention. However, this argument needs to be supported by experimental evidence based on attentional performance. The present study used a perceptual load paradigm, combining factors of perceptual load, distractor-target compatibility, and eccentricity, to investigate the degree of attentional distraction in depressed mood. In addition, the mode of attentional distraction associated with depressed mood was explored with the time-frequency features of electroencephalography (EEG). The behavioral results showed that the high depressed mood (HD) group had significantly higher attentional distraction than the low depressed mood (LD) group. EEG results showed that 1) the beta power (especially beta-2, 18-30 Hz) of the two groups differed in the medio-late part of the attentional distraction, with significantly lower power in the HD group than in the LD group; 2) the results of the correlation between beta-2 power and depression scores revealed a significant negative correlation. These results imply that beta-2 is a potential marker that may be sensitive to depressed mood during attentional processing, which was further supported by the classification results of the support vector machine (SVM) with 80.65% accuracy between the HD and LD groups.

Multimodal Vigilance Estimation With Modality-Pairwise Contrastive Loss.

Nowadays, how to estimate vigilance with higher accuracy has become a hot field of research direction. Although the increasing available modalities opens the door for amazing new possibilities to achieve good performance, the uncertain cross-modal interaction still poses a real challenge to the multimodal fusion. In this paper, a cross-modality alignment method has been proposed based on the contrastive learning for extracting shared but not the same information among modalities. The contrastive learning is adopted to minimize the intermodal differences by maximizing the similarity of semantic representation of modalities. Applying our proposed modeling framework, we evaluated our approach on SEED-VIG dataset consisting of EEG and EOG signals. Experiments showed that our study achieved state-of-the-art multimodal vigilance estimation performance both in intra-subject and inter-subject situations, the average of RMSE/CORR were improved to 0.092/0.893 and 0.144/0.887, respectively. In addition, analysis on the frequency bands showed that theta and alpha activities contain valuable information for vigilance estimation, and the correlation between them and PERCLOS can be significantly improved by contrastive learning. We argue that the proposed method in the inter-subject case could offer the possibility of reducing the high-cost of data annotation, and further analysis may provide an idea for the application of multimodal vigilance regression.

EEG based emotion recognition by hierarchical bayesian spectral regression framework.

Spectral regression (SR), a graph-based learning regression model, can be used to extract features from graphs to realize efficient dimensionality reduction. However, due to the SR method remains a regularized least squares problem and being defined in L2-norm space, the effect of artifacts in EEG signals cannot be efficiently resisted. In this work, to further improve the robustness of the graph-based regression models, we propose to utilize the prior distribution estimation in the Bayesian framework and develop a robust hierarchical Bayesian spectral regression framework (named HB-SR), which is designed with the hierarchical Bayesian ensemble strategies. In the proposed HB-SR, the impact of noises can be effectively reduced by the adaptive adjustment approach in model parameters with the data-driven manner. Specifically, in the current work, three different distributions have been elaborately designed to enhance the universality of the proposed HB-SR, i.e., Gaussian distribution, Laplace distribution, and Student-t distribution. To objectively evaluate the performance of the HB-SR framework, we conducted both simulation studies and emotion recognition experiments based on emotional EEG signals. Experimental results have consistently indicated that compared with other existing spectral regression methods, the proposed HB-SR can effectively suppress the influence of noises and achieve robust EEG emotion recognition.

Spontaneous alpha-band oscillations reflect individual differences in audiovisual temporal perception.

Light, and sound are persistently out of sync for subjective temporal perception called point of subjective simultaneity (PSS). It is stable within individuals but variable among individuals. Previous studies found that spontaneous alpha power, functioning in attention-related brain states, predicts individual PSS in the temporal order judgment (TOJ) task. However, the neural mechanisms underlying individual differences in audiovisual PSS have not been elucidated in the simultaneity judgment (SJ) task. A hypothesis that the spontaneous alpha band power might reflect the individual subjective temporal bias was proposed. We designed an SJ task EEG experiment where subjects judged whether the beep-flash stimuli are synchronous to test the above hypothesis. We primarily explored the correlation between the alpha-band power differences (visual- and auditory-leading conditions) with individual PSS. We used the V50A (~50% proportion of synchronous responses) to represent visual-leading conditions while A50V represents auditory-leading ones. We found the higher alpha power difference (V50A - A50V) predicted larger individual PSS. This study extends previous results and found that individual difference effects in the alpha band power also exist in the SJ task. The results suggested that alpha power might be associated with a spontaneous attentional state and reflect individuals' subjective temporal bias.

The increasing instance of negative emotion reduce the performance of emotion recognition.

Introduction: Emotion recognition plays a crucial role in affective computing. Recent studies have demonstrated that the fuzzy boundaries among negative emotions make recognition difficult. However, to the best of our knowledge, no formal study has been conducted thus far to explore the effects of increased negative emotion categories on emotion recognition. Methods: A dataset of three sessions containing consistent non-negative emotions and increased types of negative emotions was designed and built which consisted the electroencephalogram (EEG) and the electrocardiogram (ECG) recording of 45 participants. Results: The results revealed that as negative emotion categories increased, the recognition rates decreased by more than 9%. Further analysis depicted that the discriminative features gradually reduced with an increase in the negative emotion types, particularly in the θ, α, and ß frequency bands. Discussion: This study provided new insight into the balance of emotion-inducing stimuli materials.

[Predictive model of early urinary continence recovery based on prostate gland MRI parameters after laparoscopic radical prostatectomy].

OBJECTIVE: Constructing a predictive model for urinary incontinence after laparoscopic radical prostatectomy (LRP) based on prostatic gland related MRI parameters. METHODS: In this study, 202 cases were included. All the patients were diagnosed with prostate cancer by prostate biopsy and underwent LRP surgery in Peking University Third Hospital. The preoperative MRI examination of all the patients was completed within 1 week before the prostate biopsy. Prostatic gland related parameters included prostate length, width, height, prostatic volume, intravesical prostatic protrusion length (IPPL), prostate apex shape, etc. From the first month after the operation, the recovery of urinary continence was followed up every month, and the recovery of urinary continence was based on the need not to use the urine pad all day long. Logistic multivariate regression analysis was used to analyze the influence of early postoperative recovery of urinary continence. Risk factors were used to draw the receiver operator characteristic (ROC) curves of each model to predict the recovery of postoperative urinary continence, and the difference of the area under the curve (AUC) was compared by DeLong test, and the clinical net benefit of the model was evaluated by decision curve analysis (DCA). RESULTS: The average age of 202 patients was 69.0 (64.0, 75.5) years, the average prostate specific antigen (PSA) before puncture was 12.12 (7.36, 20.06) µg/L, and the Gleason score < 7 points and ≥ 7 points were 73 cases (36.2%) and 129 cases (63.9%) respectively, with 100 cases (49.5%) at T1/T2 clinical stage, and 102 cases (50.5%) at T3 stage. The prostatic volume measured by preoperative MRI was 35.4 (26.2, 51.1) mL, the ratio of the height to the width was 0.91 (0.77, 1.07), the membranous urethral length (MUL) was 15 (11, 16) mm, and the IPPL was 2 (0, 6) mm. The prostatic apex A-D subtypes were 67 cases (33.2%), 80 cases (39.6%), 24 cases (11.9%) and 31 cases (15.3%), respectively. The training set and validation set were 141 cases and 61 cases, respectively. The operations of all the patients were successfully completed, and the urinary continence rate was 59.4% (120/202) in the 3 months follow-up. The results of multivariate analysis of the training set showed that the MUL (P < 0.001), IPPL (P=0.017) and clinical stage (P=0.022) were independent risk factors for urinary incontinence in the early postoperative period (3 months). The nomogram and clinical decision curve were made according to the results of multivariate analysis. The AUC value of the training set was 0.885 (0.826, 0.944), and the AUC value of the validation set was 0.854 (0.757, 0.950). In the verification set, the Hosmer-Lemeshow goodness-of-fit test was performed on the model, and the Chi-square value was 5.426 (P=0.711). CONCLUSION: Preoperative MUL, IPPL, and clinical stage are indepen-dent risk factors for incontinence after LRP. The nomogram developed based on the relevant parameters of MRI glands can effectively predict the recovery of early urinary continence after LRP. The results of this study require further large-scale clinical research to confirm.

[Near-infrared targeted probe designed for intraoperative imaging of prostatic neurovascular bundles].

OBJECTIVE: To investigate the imaging effect of a near-infrared fluorescent targeted probe ICG-NP41 on the neurovascular bundles (NVB) around the prostate in rats. METHODS: A near-infrared fluorescent targeted probe ICG-NP41 was synthesized. An animal model for NVB imaging was established using Sprague-Dawley rats (250-400 g). Experiments were conducted using a custom-built near-infrared windowⅡ(NIR-Ⅱ) small animal in vivo imaging system, and images collected were processed using ImageJ and Origin. The fluorescence signal data were statistically analyzed using GraphPad Prism. The signal-to-background ratio (SBR) for NVB was quantitatively calculated to explore the effective dosage and imaging time points. Finally, paraffin pathology sections and HE staining were performed on the imaging structures. RESULTS: Except for rats in the control group (n=2), right-sided NVB of the rats injected with ICG-NP41 (n=2 per group) were all observed in NIR-Ⅱ fluorescence mode 2 h and 4 h after administration. At 2 h and 4 h, average SBR of cavernous nerve in 2 mg/kg group in fluorescence mode was 1.651±0.142 and 1.619±0.110, respectively, both higher than that in white light mode (1.111±0.036), with no significant difference (P>0.05); average SBR of 4 mg/kg group in fluorescence mode were 1.168±0.066 and 1.219±0.118, respectively, both higher than that in white light mode (1.081±0.040), with no significant difference (P>0.05). At 2 h and 4 h, the average SBR of 2 mg/kg and 4 mg/kg groups in fluorescence mode were higher than that of the control group (SBR=1), the average SBR of the 2 mg/kg group was higher than that of the 4 mg/kg group, and all the above with no significant difference (P>0.05). The average diameter of the nerve measured by full width at half maxima method was about (178±15) µm. HE staining of paraffin sections showed the right major pelvic ganglion. CONCLUSION: The near-infrared fluorescent targeted probe ICG-NP41 can be used for real-time imaging of the NVB around the prostate in rats, providing a potential feasible solution for localizing NVB in real time during nerve-sparing radical prostatectomy.

An Improved Canonical Correlation Analysis for EEG Inter-Band Correlation Extraction.

(1) Background: Emotion recognition based on EEG signals is a rapidly growing and promising research field in affective computing. However, traditional methods have focused on single-channel features that reflect time-domain or frequency-domain information of the EEG, as well as bi-channel features that reveal channel-wise relationships across brain regions. Despite these efforts, the mechanism of mutual interactions between EEG rhythms under different emotional expressions remains largely unexplored. Currently, the primary form of information interaction between EEG rhythms is phase-amplitude coupling (PAC), which results in computational complexity and high computational cost. (2) Methods: To address this issue, we proposed a method of extracting inter-bands correlation (IBC) features via canonical correlation analysis (CCA) based on differential entropy (DE) features. This approach eliminates the need for surrogate testing and reduces computational complexity. (3) Results: Our experiments verified the effectiveness of IBC features through several tests, demonstrating that the more correlated features between EEG frequency bands contribute more to emotion classification accuracy. We then fused IBC features and traditional DE features at the decision level, which significantly improved the accuracy of emotion recognition on the SEED dataset and the local CUMULATE dataset compared to using a single feature alone. (4) Conclusions: These findings suggest that IBC features are a promising approach to promoting emotion recognition accuracy. By exploring the mutual interactions between EEG rhythms under different emotional expressions, our method can provide valuable insights into the underlying mechanisms of emotion processing and improve the performance of emotion recognition systems.

Neural oscillations reflect the individual differences in the temporal perception of audiovisual speech.

Multisensory integration occurs within a limited time interval between multimodal stimuli. Multisensory temporal perception varies widely among individuals and involves perceptual synchrony and temporal sensitivity processes. Previous studies explored the neural mechanisms of individual differences for beep-flash stimuli, whereas there was no study for speech. In this study, 28 subjects (16 male) performed an audiovisual speech/ba/simultaneity judgment task while recording their electroencephalography. We examined the relationship between prestimulus neural oscillations (i.e. the pre-pronunciation movement-related oscillations) and temporal perception. The perceptual synchrony was quantified using the Point of Subjective Simultaneity and temporal sensitivity using the Temporal Binding Window. Our results revealed dissociated neural mechanisms for individual differences in Temporal Binding Window and Point of Subjective Simultaneity. The frontocentral delta power, reflecting top-down attention control, is positively related to the magnitude of individual auditory leading Temporal Binding Windows (auditory Temporal Binding Windows; LTBWs), whereas the parieto-occipital theta power, indexing bottom-up visual temporal attention specific to speech, is negatively associated with the magnitude of individual visual leading Temporal Binding Windows (visual Temporal Binding Windows; RTBWs). In addition, increased left frontal and bilateral temporoparietal occipital alpha power, reflecting general attentional states, is associated with increased Points of Subjective Simultaneity. Strengthening attention abilities might improve the audiovisual temporal perception of speech and further impact speech integration.

Comprehensive analyses of fatty acid metabolism-related lncRNA for ovarian cancer patients.

Ovarian cancer (OC) is a disease with difficult early diagnosis and treatment and poor prognosis. OC data profiles were downloaded from The Cancer Genome Atlas. Eight key fatty acid metabolism-related long non-coding RNAs (lncRNAs) were finally screened for building a risk scoring model by univariate/ multifactor and least absolute shrinkage and selection operator (LASSO) Cox regression. To make this risk scoring model more applicable to clinical work, we established a nomogram containing the clinical characteristics of OC patients after confirming that the model has good reliability and validity and the ability to distinguish patient prognosis. To further explore how these key lncRNAs are involved in OC progression, we explored their relationship with LUAD immune signatures and tumor drug resistance. The structure shows that the risk scoring model established based on these 8 fatty acid metabolism-related lncRNAs has good reliability and validity and can better predict the prognosis of patients with different risks of OC, and LINC00861in these key RNAs may be a hub gene that affects the progression of OC and closely related to the sensitivity of current OC chemotherapy drugs. In addition, combined with immune signature analysis, we found that patients in the high-risk group are in a state of immunosuppression, and Tfh cells may play an important role in it. We innovatively established a prognostic prediction model with excellent reliability and validity from the perspective of OC fatty acid metabolism reprogramming and lncRNA regulation and found new molecular/cellular targets for future OC treatment.