Audiovisual integration and sensory dominance effects in older adults with subjective cognitive decline: Enhanced redundant effects and stronger fusion illusion susceptibility.

INTRODUCTION: Subjective cognitive decline (SCD) refers to individuals' perceived decline in memory and/or other cognitive abilities relative to their previous level of performance. Sensory decline is one of the main manifestations of decline in older adults with SCD. The efficient integration of visual and auditory information, known as audiovisual integration, is a crucial perceptual process. This study aims to evaluate audiovisual integration in older adults with SCD. METHODS: We adopted the audiovisual detection task, the Colavita task, and the Sound-Induced Flash Illusion (SIFI) task to evaluate the audiovisual integration by examining both redundant and illusory effects. Older adults diagnosed with SCD (N = 50, mean age = 67.8 years) and a control group of non-SCD older adults (N = 51, mean age = 66.5 years) were recruited. All participants took part in the three aforementioned experiments. RESULTS: The outcomes showed that a redundant effect occurred in both SCD and non-SCD older adults, with SCD older adults gaining more benefits in audiovisual detection task. Moreover, an equivalent amount of the visual dominance effect was observed among both SCD and non-SCD older adults in Colavita task. In addition, older adults with SCD perceived an equal fission illusion but a bigger fusion illusion compared with non-SCD older adults in SIFI task. CONCLUSIONS: Overall, older adults with SCD exhibit increased audiovisual redundant effects and stronger fusion illusion susceptibility compared to non-SCD older adults. Besides, visual dominance was observed in both groups via the Colavita task, with no significant difference between non-SCD and SCD older adults. These findings implied that audiovisual integration might offer a potential way for the identification of SCD.

Single-cell RNA sequencing reveals heterogeneity and differential expression of the maternal-fetal interface during ICP and normal pregnancy.

OBJECTIVE: Intrahepatic cholestasis of pregnancy (ICP) can cause adverse perinatal outcomes. Previous studies have demonstrated that the placenta of an ICP pregnancy differs in morphology and gene expression from the placenta of a normal pregnancy. To date, however, the genetic mechanism by which ICP affects the placenta is poorly understood. Therefore, the aim of this study was to investigate the differences in main cell types, gene signatures, cell ratio, and functional changes in the placenta between ICP and normal pregnancy. METHODS: Single-cell RNA sequencing (scRNA-seq) technology was used to detect the gene expression of all cells at the placental maternal-fetal interface. Two individuals were analyzed - one with ICP and one without ICP. The classification of cell types was determined by a graph-based clustering algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the R software phyper () function and DAVID website. The differentially expressed genes (DEGs) encoding transcription factors (TFs) were identified using getorf and DIAMOND software. RESULTS: We identified 14 cell types and 22 distinct cell subtypes that showed unique functional properties. Additionally, we found differences in the proportions of fibroblasts 1, helper T (Th) cells, extravillous trophoblasts, and villous cytotrophoblasts, and we observed heterogeneity of gene expression between ICP and control placentas. Furthermore, we identified 263 DEGs that belonged to TF families, including zf-C2H2, HMGI/HMGY, and Homeobox. In addition, 28 imprinted genes were preferentially expressed in specific cell types, such as PEG3 and PEG10 in trophoblasts as well as DLK1 and DIO3 in fibroblasts. CONCLUSIONS: Our results revealed the differences in cell-type ratios, gene expression, and functional changes between ICP and normal placentas, and heterogeneity was found among cell subgroups. Hence, the imbalance of various cell types affects placental activity to varying degrees, indicating the complexity of the cell networks that form the placental tissue system, and this alteration of placental function is associated with adverse events in the perinatal period.

Local anesthetic delivery systems for the management of postoperative pain.

Postoperative pain (POP) is a major clinical challenge. Local anesthetics (LAs), including amide-type LAs, ester-type LAs, and other potential ion-channel blockers, are emerging as drugs for POP management because of their effectiveness and affordability. However, LAs typically exhibit short durations of action and prolonging the duration by increasing their dosage or concentration may increase the risk of motor block or systemic local anesthetic toxicity. In addition, techniques using LAs, such as intrathecal infusion, require professional operation and are prone to catheter displacement, dislodgement, infection, and nerve damage. With the development of materials science and nanotechnology, various LAs delivery systems have been developed to compensate for these disadvantages. Numerous delivery systems have been designed to continuously release a safe dose in a single administration to ensure minimal systemic toxicity and prolong pain relief. LAs delivery systems can also be designed to control the duration and intensity of analgesia according to changes in the external trigger conditions, achieve on-demand analgesia, and significantly improve pain relief and patient satisfaction. In this review, we summarize POP pathways, animal models and methods for POP testing, and highlight LAs delivery systems for POP management. STATEMENT OF SIGNIFICANCE: Postoperative pain (POP) is a major clinical challenge. Local anesthetics (LAs) are emerging as drugs for POP management because of their effectiveness and affordability. However, they exhibit short durations and toxicity. Various LAs delivery systems have been developed to compensate for these disadvantages. They have been designed to continuously release a safe dose in a single administration to ensure minimal toxicity and prolong pain relief. LAs delivery systems can also be designed to control the duration and intensity of analgesia to achieve on-demand analgesia, and significantly improve pain relief and patient satisfaction. In this paper, we summarize POP pathways, animal models, and methods for POP testing and highlight LAs delivery systems for POP management.

Seizure Pathways Changes at the Subject-Specific Level via Dynamic Step Effective Network Analysis.

The variability in the propagation pathway in epilepsy is a main factor contributing to surgical treatment failure. Ways to accurately capture the brain propagation network and quantitatively assess its evolution remain poorly described. This work aims to develop a dynamic step effective network (dSTE) to obtain the propagation path network of multiple seizures in the same patient and explore the degree of dissimilarity. Multichannel stereo-electroencephalography (sEEG) signals were acquired with ictal processes involving continuous changes in information propagation. We utilized high-order dynamic brain networks to obtain propagation networks through different levels of linking steps. We proposed a dissimilarity index based on singular value decomposition to quantitatively compare seizure pathways. Simulated data were generated through The Virtual Brain, and the reliability of this method was verified through ablation experiments. By applying the proposed method to two datasets consisting of 29 patients total, the evolution processes of each patient's seizure networks was obtained, and the within-patient dissimilarities were quantitatively compared. Finally, three types of brain network connectivity patterns were found. Type I patients have a good prognosis, while type III patients are prone to postoperative recurrence. This method captures the evolution of seizure propagation networks and assesses their dissimilarity more reliably than existing methods, demonstrating good robustness for studying the propagation path differences for multiple seizures in epilepsy patients. The three different patterns will be important considerations when planning epilepsy surgery under sEEG guidance.

Ropivacaine-loaded hydrogels for prolonged relief of chemotherapy-induced peripheral neuropathic pain and potentiated chemotherapy.

Chemotherapy can cause severe pain for patients, but there are currently no satisfactory methods of pain relief. Enhancing the efficacy of chemotherapy to reduce the side effects of high-dose chemotherapeutic drugs remains a major challenge. Moreover, the treatment of chemotherapy-induced peripheral neuropathic pain (CIPNP) is separate from chemotherapy in the clinical setting, causing inconvenience to cancer patients. In view of the many obstacles mentioned above, we developed a strategy to incorporate local anesthetic (LA) into a cisplatin-loaded PF127 hydrogel for painless potentiated chemotherapy. We found that multiple administrations of cisplatin-loaded PF127 hydrogels (PFC) evoked severe CIPNP, which correlated with increased pERK-positive neurons in the dorsal root ganglion (DRG). However, incorporating ropivacaine into the PFC relieved PFC-induced CIPNP for more than ten hours and decreased the number of pERK-positive neurons in the DRG. Moreover, incorporating ropivacaine into the PFC for chemotherapy is found to upregulate major histocompatibility complex class I (MHC-I) expression in tumor cells and promote the infiltration of cytotoxic T lymphocytes (CD8+ T cells) in tumors, thereby potentiating chemotherapy efficacy. This study proposes that LA can be used as an immunemodulator to enhance the effectiveness of chemotherapy, providing new ideas for painless cancer treatment.

Analgesic and potentiated photothermal therapy with ropivacaine-loaded hydrogels.

Rationale: Tumor ablation can cause severe pain to patients, but there is no satisfactory means of analgesia available. In addition, recurrence of residual tumors due to incomplete ablation threatens patient safety. Photothermal therapy (PTT), a promising approach for tumor ablation, also faces the aforementioned problems. Therefore, developing novel photothermal agents that can efficiently relieve PTT-associated pain and potentiate the PTT efficacy are urgently needed. Methods: The Pluronic F127 hydrogel doped with indocyanine green (ICG) was served as photothermal agent for PTT. Mouse model that inoculation of tumor near the sciatic nerve was constructed to assess the PTT-evoked pain. Subcutaneous and sciatic nerve vicinal tumor-bearing mice were used to test the efficacy of PTT. Results: PTT-evoked pain depends on an increase in tumor temperature and is accompanied by the activation of TRPV1. A simple introduction of local anesthetic (LA) ropivacaine into ICG-loaded hydrogels relieves PTT-induced pain and exerts long-lasting analgesia compared with opioid analgesia. More interestingly, ropivacaine upregulates major histocompatibility complex class I (MHC-I) in tumor cells by impairing autophagy. Therefore, a hydrogel co-doped with ropivacaine, TLR7 agonist imiquimod and ICG was rationally designed. In the hydrogel system, imiquimod primes tumor-specific CD8+ T cells through promoting DCs maturation, and ropivacaine facilitates tumor cells recognition by primed CD8+ T cells through upregulating MHC-I. Consequently, the hydrogel maximumly increases CD8+ T cells infiltration into tumor and potentiates PTT efficacy. Conclusion: This study for the first time provides an LA-dopped photothermal agents for painless PTT and innovatively proposes that a LA can be used as an immunomodulator to potentiate the PTT efficacy.

Spatial frequencies affect cuteness perception of infant faces.

Cuteness perception is a basic function in social interactions. Most studies focus on the impact of facial elemental features on cuteness ratings, but there are many factors that affect cuteness perception. Spatial frequency (SF) is one of the most important parameters in studies on faces. However, few studies have investigated the impact of SFs on cuteness perception. In this study, 16 images of infant faces with four cuteness levels were selected by a prerating experiment. Using a 7-point Likert scale paradigm, participants were asked to rate the cuteness of infant faces, including one version of broad unfiltered faces and four versions of filtered faces. The results showed that filtered SFs reduced cuteness ratings and that the impact of SFs was related to the cuteness levels of faces. Specifically, faces with low SFs got the lowest cuteness ratings. The ratings of faces with low SFs in neutral cuteness had a greater reduction than that in positive cuteness. In comparison, faces with medium and high SFs obtained relatively high cuteness ratings. However, the ratings in medium SFs were higher than that in high SFs if the cuteness of faces exceeded a certain level. Interestingly, their ratings reduction size increased with the improvement of cuteness levels. These results extend our understanding of the cuteness mechanism from an SF processing perspective. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Global surface features contribute to human haptic roughness estimations.

Previous studies have paid special attention to the relationship between local features (e.g., raised dots) and human roughness perception. However, the relationship between global features (e.g., curved surface) and haptic roughness perception is still unclear. In the present study, a series of roughness estimation experiments was performed to investigate how global features affect human roughness perception. In each experiment, participants were asked to estimate the roughness of a series of haptic stimuli that combined local features (raised dots) and global features (sinusoidal-like curves). Experiments were designed to reveal whether global features changed their haptic roughness estimation. Furthermore, the present study tested whether the exploration method (direct, indirect, and static) changed haptic roughness estimations and examined the contribution of global features to roughness estimations. The results showed that sinusoidal-like curved surfaces with small periods were perceived to be rougher than those with large periods, while the direction of finger movement and indirect exploration did not change this phenomenon. Furthermore, the influence of global features on roughness was modulated by local features, regardless of whether raised-dot surfaces or smooth surfaces were used. Taken together, these findings suggested that an object's global features contribute to haptic roughness perceptions, while local features change the weight of the contribution that global features make to haptic roughness perceptions.

Analysis of CYP27A1 mutations in Han Chinese women with intrahepatic cholestasis of pregnancy.

OBJECTIVE: To explore the cytochrome P450 family 27 subfamily A member 1 (CYP27A1) gene mutations in Chinese women with intrahepatic cholestasis of pregnancy (ICP) and the correlation between CYP27A gene mutations and BA (bile acid) level changes. METHODS: In this study, the entire coding region of the CYP27A1 gene was sequenced in 151 Han Chinese women with ICP and 1029 matched samples, and the pathogenicity of identified CYP27A1 gene mutations was judged through evolutionary conservation analysis, computational analysis and protein structure modeling. Finally, we verified the relationship between gene mutations and total serum bile acid (TBA) and cholesterol (CHOL) levels through experiments in cell culture. RESULTS: We identified five heterozygous CYP27A1 missense mutations in five ICP samples. Three online tools, Polyphen-2, MutationTaster and SIFT, predicted that the five CYP27A1 mutations were pathogenic. Furthermore, all five mutations caused marked protein structural changes. Experiments in cells showed that the intracellular and medium levels of TBA in the mutant groups were lower than those in the wild-type group, while the CHOL levels were higher in all mutants except for the R158H mutant. CONCLUSIONS: CYP27A1 mutations are associated with the levels of TBA and CHOL, suggesting that CYP27A1 mutations contribute to abnormal total cholesterol and BA levels, which leads to ICP.

A hybrid deep neural network for classification of schizophrenia using EEG Data.

Schizophrenia is a serious mental illness that causes great harm to patients, so timely and accurate detection is essential. This study aimed to identify a better feature to represent electroencephalography (EEG) signals and improve the classification accuracy of patients with schizophrenia and healthy controls by using EEG signals. Our research method involves two steps. First, the EEG time series is preprocessed, and the extracted time-domain and frequency-domain features are transformed into a sequence of red-green-blue (RGB) images that carry spatial information. Second, we construct hybrid deep neural networks (DNNs) that combine convolution neural networks and long short-term memory to address RGB images to classify schizophrenic patients and healthy controls. The results show that the fuzzy entropy (FuzzyEn) feature is more significant than the fast Fourier transform (FFT) feature in brain topography. The deep learning (DL) method that we propose achieves an average accuracy of 99.22% with FuzzyEn and an average accuracy of 96.34% with FFT. These results show that the best effect is to extract fuzzy features as input features from EEG time series and then use a hybrid DNN for classification. Compared with the most advanced methods in this field, significant improvements have been achieved.

Eye Size Affects Cuteness in Different Facial Expressions and Ages.

Researchers have suggested that infants exhibiting baby schema are considered cute. These similar studies have mainly focused on changes in overall baby schema facial features. However, whether a change in only eye size affects the perception of cuteness across different facial expressions and ages has not been explicitly evaluated until now. In the present study, a paired comparison method and 7-point scale were used to investigate the effects of eye size on perceived cuteness across facial expressions (positive, neutral, and negative) and ages (adults and infants). The results show that stimuli with large eyes were perceived to be cuter than both unmanipulated eyes and small eyes across all facial expressions and age groups. This suggests not only that the effect of baby schema on cuteness is based on changes in a set of features but also that eye size as an individual feature can affect the perception of cuteness.

Dynamic Complexity of Spontaneous BOLD Activity in Alzheimer's Disease and Mild Cognitive Impairment Using Multiscale Entropy Analysis.

Alzheimer's disease (AD) is characterized by progressive deterioration of brain function among elderly people. Studies revealed aberrant correlations in spontaneous blood oxygen level-dependent (BOLD) signals in resting-state functional magnetic resonance imaging (rs-fMRI) over a wide range of temporal scales. However, the study of the temporal dynamics of BOLD signals in subjects with AD and mild cognitive impairment (MCI) remains largely unexplored. Multiscale entropy (MSE) analysis is a method for estimating the complexity of finite time series over multiple time scales. In this research, we applied MSE analysis to investigate the abnormal complexity of BOLD signals using the rs-fMRI data from the Alzheimer's disease neuroimaging initiative (ADNI) database. There were 30 normal controls (NCs), 33 early MCI (EMCI), 32 late MCI (LMCI), and 29 AD patients. Following preprocessing of the BOLD signals, whole-brain MSE maps across six time scales were generated using the Complexity Toolbox. One-way analysis of variance (ANOVA) analysis on the MSE maps of four groups revealed significant differences in the thalamus, insula, lingual gyrus and inferior occipital gyrus, superior frontal gyrus and olfactory cortex, supramarginal gyrus, superior temporal gyrus, and middle temporal gyrus on multiple time scales. Compared with the NC group, MCI and AD patients had significant reductions in the complexity of BOLD signals and AD patients demonstrated lower complexity than that of the MCI subjects. Additionally, the complexity of BOLD signals from the regions of interest (ROIs) was found to be significantly associated with cognitive decline in patient groups on multiple time scales. Consequently, the complexity or MSE of BOLD signals may provide an imaging biomarker of cognitive impairments in MCI and AD.

The Abnormality of Topological Asymmetry in Hemispheric Brain Anatomical Networks in Bipolar Disorder.

Convergent evidences have demonstrated a variety of regional abnormalities of asymmetry in bipolar disorder (BD). However, little is known about the alterations in hemispheric topological asymmetries. In this study, we used diffusion tensor imaging to construct the hemispheric brain anatomical network of 49 patients with BD and 61 matched normal controls. Graph theory was then applied to quantify topological properties of the hemispheric networks. Although small-world properties were preserved in the hemispheric networks of BD, the degrees of the asymmetry in global efficiency, characteristic path length, and small-world property were significantly decreased. More changes in topological properties of the right hemisphere than those of left hemisphere were found in patients compared with normal controls. Consistent with such changes, the nodal efficiency in patients with BD also showed less rightward asymmetry mainly in the frontal, occipital, parietal, and temporal lobes. In contrast to leftward asymmetry, significant rightward asymmetry was found in supplementary motor area of BD, and attributed to more deficits in nodal efficiency of the left hemisphere. Finally, these asymmetry score of nodal efficiency in the inferior parietal lobule and rolandic operculum were significantly associated with symptom severity of BD. Our results suggested that abnormal hemispheric asymmetries in brain anatomical networks were associated with aberrant neurodevelopment, and providing insights into the potential neural biomarkers of BD by measuring the topological asymmetry in hemispheric brain anatomical networks.

Epileptic Seizure Prediction Based on Permutation Entropy.

Epilepsy is a chronic non-communicable disorder of the brain that affects individuals of all ages. It is caused by a sudden abnormal discharge of brain neurons leading to temporary dysfunction. In this regard, if seizures could be predicted a reasonable period of time before their occurrence, epilepsy patients could take precautions against them and improve their safety and quality of life. However, the potential that permutation entropy(PE) can be applied in human epilepsy prediction from intracranial electroencephalogram (iEEG) recordings remains unclear. Here, we described the novel application of PE to track the dynamical changes of human brain activity from iEEG recordings for the epileptic seizure prediction. The iEEG signals of 19 patients were obtained from the Epilepsy Centre at the University Hospital of Freiburg. After preprocessing, PE was extracted in a sliding time window, and a support vector machine (SVM) was employed to discriminate cerebral state. Then a two-step post-processing method was applied for the purpose of prediction. The results showed that we obtained an average sensitivity (SS) of 94% and false prediction rates (FPR) with 0.111 h-1. The best results with SS of 100% and FPR of 0 h-1 were achieved for some patients. The average prediction horizon was 61.93 min, leaving sufficient treatment time before a seizure. These results indicated that applying PE as a feature to extract information and SVM for classification could predict seizures, and the presented method shows great potential in clinical seizure prediction for human.

Epileptic Seizure Detection Based on EEG Signals and CNN.

Epilepsy is a neurological disorder that affects approximately fifty million people according to the World Health Organization. While electroencephalography (EEG) plays important roles in monitoring the brain activity of patients with epilepsy and diagnosing epilepsy, an expert is needed to analyze all EEG recordings to detect epileptic activity. This method is obviously time-consuming and tedious, and a timely and accurate diagnosis of epilepsy is essential to initiate antiepileptic drug therapy and subsequently reduce the risk of future seizures and seizure-related complications. In this study, a convolutional neural network (CNN) based on raw EEG signals instead of manual feature extraction was used to distinguish ictal, preictal, and interictal segments for epileptic seizure detection. We compared the performances of time and frequency domain signals in the detection of epileptic signals based on the intracranial Freiburg and scalp CHB-MIT databases to explore the potential of these parameters. Three types of experiments involving two binary classification problems (interictal vs. preictal and interictal vs. ictal) and one three-class problem (interictal vs. preictal vs. ictal) were conducted to explore the feasibility of this method. Using frequency domain signals in the Freiburg database, average accuracies of 96.7, 95.4, and 92.3% were obtained for the three experiments, while the average accuracies for detection in the CHB-MIT database were 95.6, 97.5, and 93% in the three experiments. Using time domain signals in the Freiburg database, the average accuracies were 91.1, 83.8, and 85.1% in the three experiments, while the signal detection accuracies in the CHB-MIT database were only 59.5, 62.3, and 47.9% in the three experiments. Based on these results, the three cases are effectively detected using frequency domain signals. However, the effective identification of the three cases using time domain signals as input samples is achieved for only some patients. Overall, the classification accuracies of frequency domain signals are significantly increased compared to time domain signals. In addition, frequency domain signals have greater potential than time domain signals for CNN applications.