ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

BACKGROUND:Stress-induced damage to hippocampal neurons may underlie abnormalities in neuronal structure and function,ultimately leading to mood disorders.G protein-coupled receptors in brain tissue play an important role in mood regulation.OBJECTIVE:To analyze the mechanism of depression-like behavior in chronic social defeat stress mice based on high-throughput sequencing and bioinformatics analysis.METHODS:C57BL/6J mice were randomly divided into control group and model group.There was no special treatment in the control group,while a mouse model of chronic social defeat stress was established in the model group.Depression-like behavior was assessed through the sucrose preference test,tail suspension test,and forced swim test.Anxiety behavior was evaluated using the elevated plus-maze,while social behavior was measured through the social interaction test.Cognitive function was assessed with the Y-maze spontaneous alternation test.Immunofluorescence staining was performed to quantify microglia markers in the mouse hippocampus,and Nissl staining was used to examine neuronal damage in mice.High-throughput sequencing was used to identify differentially expressed genes and gene enrichment in the mouse hippocampus,and qPCR was used to measure the expression of G protein-coupled receptors in the mouse hippocampus.RESULTS AND CONCLUSION:(1)Compared with the control group,chronic social defeat stress mice showed significant behavioral impairments,including increased anxiety,depression,and cognitive deficits.(2)Additionally,the Nissl body light density in hippocampal neurons was significantly reduced in chronic social defeat stress mice.(3)Sequencing results revealed synaptic damage in the neurons after chronic social defeat stress.Microglia activation was also markedly increased in the hippocampus of CSDS mice.Furthermore,the expression of G protein-coupled receptors in the hippocampus was significantly higher in chronic social defeat stress mice compared with the control group.These findings suggest that chronic social defeat stress induces anxiety,depression,and cognitive deficits in mice,accompanied by neuropathological changes in the hippocampus,and that altered G protein-coupled receptors expression may play a key role in these behavioral and neuropathological changes.

Objective To explore the prevalence of Helicobacter pylori (Hp) infection and its association with dietary and lifestyle habits among the adult physical examination population in Xuzhou area. Methods Retrospectively selected the physical examination population who underwent HP testing at our hospital's physical examination center from May 2021 to December 2023 as the research object. The prevalence of Hp infection in the population was analyzed based on the physical examination results. A questionnaire survey was used to collect information on the eating and living habits of all study subjects. Logistic regression was used to analyze the relationship between eating and living habits and Hp infection. Results A total of 1 354 physical examination people were included in the study, and the Hp infection rate was 37.30% (505/1354). The difference in Hp infection rates among people of different age groups is statistically significant (P<0.05), with the middle-aged population (41-59 years old) having the highest Hp positive infection rate (45.38%).High salt (41.11%), hot diet (40.56%), history of smoking (45.23%) and drinking (43.80%), less consumption of fruits and vegetables (43.73%), irregular exercise (41.29%), irregular diet People who frequently eat out (43.56%) and eat out frequently (42.57%) have a higher Hp infection rate (P<0.05).After adjusting for demographic factors such as gender, age, place of residence and education level, multivariate Logistic regression results showed that high-salt diet (OR=3.975, 95%CI: 2.670-5.917) and hot diet (OR=3.357, 95%CI: 2.291-4.919), smoking (OR=1.458, 95%CI: 1.082-1.964), drinking alcohol (OR=1.654, 95%CI: 1.279-2.138), eating fruits and vegetables (OR=1.759, 95%CI: 1.345-2.301), regular exercise (OR=1.822, 95%CI: 1.371-2.421), regular diet (OR=1.893, 95%CI: 1.391-2.575), eating out (OR=1.690, 95%CI: 1.277-2.237) were associated with the risk of Hp infection (P<0.05). Conclusion The positive infection rate of Hp among the physical examination population in Xuzhou is slightly lower than the average epidemic level in China. Cultivating healthy eating and living habits can effectively reduce the risk of Hp infection.

Ethical review represents the core of the scientific and technological ethical governance， and its quality depends on the participation of ethics. The absence of ethics and ethical experts will compromise the quality of the review. According to the spirit of the Guidelines on Strengthening the Governance over Ethics in Science and Technology， this paper analyzed the process of separating scientific and technological ethics from the field of scientific research morality， clarified the ethical attributes of ethical review， and argued that scientific research and technological innovation activities originated from ethics. On this basis， the fundamental principle of “ethics first” was proposed， aiming to proactively embed ethical considerations throughout the entire process of scientific and technological activities. This principle was the primary requirement for ensuring governance effectiveness and can also eliminate the risk of ethics being obscured in ethical governance. In practice， “ethics first” manifested specifically in dimensions such as prioritizing academic systems， prioritizing publicity， education， and training， as well as further advancing ethical considerations.

ObjectiveTo investigate the mechanisms by which Liuwei Buqi prescription （LWBQ） regulates alveolar macrophage efferocytosis and improves inflammatory responses in rats with chronic obstructive pulmonary disease （COPD） characterized by lung-kidney Qi deficiency based on the nuclear factor erythroid 2-related factor 2 （Nrf2）/macrophage receptor with collagenous structure （MARCO） pathway. MethodsSuccessfully modeled rats were randomly divided into a model group， low-dose LWBQ group （LWBQ-L， 2.25 g·kg^-1·d^-1）， medium-dose LWBQ group （LWBQ-M， 4.5 g·kg^-1·d^-1）， high-dose LWBQ group （LWBQ-H， 9 g·kg^-1·d^-1）， and aminophylline group （AMIN， 50 mg·kg^-1·d^-1）， with 8 rats in each group. Another 8 healthy rats were included as the blank group. Except for the blank group， rats in the remaining groups were subjected to smoke exposure combined with forced swimming， intratracheal lipopolysaccharide （LPS） instillation， and subcutaneous hydrocortisone injection to establish a COPD model with lung-kidney Qi deficiency. After successful modeling， rats were administered different doses of LWBQ or AMIN by gavage. Body weight， fur condition， and oral secretions were observed. Pulmonary function was measured using an animal lung function analyzer. Enzyme-linked immunosorbent assay （ELISA） was used to detect the expression levels of interferon-γ （IFN-γ）， interleukin-6 （IL-6）， interleukin-1 （IL-1）， and tumor necrosis factor-α （TNF-α） in bronchoalveolar lavage fluid （BALF） and serum （SER）. Hematoxylin-eosin （HE） staining was used to examine pathological changes in lung tissue. Giemsa staining was performed to detect eosinophils， basophils， and neutrophils in BALF. Terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling （TUNEL） was used to detect apoptosis in lung tissue. Western blot and real-time polymerase chain reaction （Real-time PCR） were employed to determine the protein and mRNA expression levels of efferocytosis-related proteins growth arrest-specific gene 6 （GAS6）， milk fat globule-epidermal growth factor 8 （MFG-E8）， and pathway-related proteins Nrf2 and MARCO in lung tissue. ResultsCompared with the blank group， the model group showed reduced food intake， nasal and oral secretions with sputum， and decreased body weight （P<0.01）， decreased peak expiratory flow （PEF） （P<0.01）， increased forced vital capacity （FVC） （P<0.01）， and decreased forced expiratory volume in 0.3 s/forced vital capacity ［FEV0.3/FVC （%）］ （P<0.01）. The expression levels of IFN-γ， IL-6， IL-1， and TNF-α in BALF and SER were increased （P<0.01）. Lung tissue exhibited structural destruction， hyperplasia， inflammatory exudation， increased apoptotic cells， and increased mean optical density （P<0.01）. The protein and mRNA expression levels of GAS6， MFG-E8， and MARCO， as well as Nrf2 mRNA expression， were increased （P<0.01）. Compared with the model group， the LWBQ groups showed increased food intake， reduced nasal and oral secretions with sputum， and increased body weight （P<0.05， P<0.01）. PEF was increased （P<0.01）. FVC was increased in rats treated with low- and medium-dose LWBQ （P<0.01）， and FEV0.3/FVC （%） was increased in rats treated with medium- and high-dose LWBQ （P<0.05， P<0.01）. The expression levels of IFN-γ， IL-6， IL-1， and TNF-α in BALF and SER were decreased （P<0.01）. Lung tissue structure was relatively intact， with improvement in hyperplasia and inflammatory exudation. The number of apoptotic cells in lung tissue was reduced， and mean optical density was decreased （P<0.05， P<0.01）. The protein and mRNA expression levels of efferocytosis-related proteins GAS6 and MFG-E8 and pathway-related proteins Nrf2 and MARCO were increased （P<0.01）. ConclusionLWBQ can alleviate pulmonary and systemic inflammation， improve lung function， and reduce lung tissue damage in rats with COPD characterized by lung-kidney Qi deficiency. The mechanism may be related to enhancement of alveolar macrophage efferocytosis through regulation of the Nrf2/MARCO pathway.

BACKGROUND:Stress-induced damage to hippocampal neurons may underlie abnormalities in neuronal structure and function,ultimately leading to mood disorders.G protein-coupled receptors in brain tissue play an important role in mood regulation.OBJECTIVE:To analyze the mechanism of depression-like behavior in chronic social defeat stress mice based on high-throughput sequencing and bioinformatics analysis.METHODS:C57BL/6J mice were randomly divided into control group and model group.There was no special treatment in the control group,while a mouse model of chronic social defeat stress was established in the model group.Depression-like behavior was assessed through the sucrose preference test,tail suspension test,and forced swim test.Anxiety behavior was evaluated using the elevated plus-maze,while social behavior was measured through the social interaction test.Cognitive function was assessed with the Y-maze spontaneous alternation test.Immunofluorescence staining was performed to quantify microglia markers in the mouse hippocampus,and Nissl staining was used to examine neuronal damage in mice.High-throughput sequencing was used to identify differentially expressed genes and gene enrichment in the mouse hippocampus,and qPCR was used to measure the expression of G protein-coupled receptors in the mouse hippocampus.RESULTS AND CONCLUSION:(1)Compared with the control group,chronic social defeat stress mice showed significant behavioral impairments,including increased anxiety,depression,and cognitive deficits.(2)Additionally,the Nissl body light density in hippocampal neurons was significantly reduced in chronic social defeat stress mice.(3)Sequencing results revealed synaptic damage in the neurons after chronic social defeat stress.Microglia activation was also markedly increased in the hippocampus of CSDS mice.Furthermore,the expression of G protein-coupled receptors in the hippocampus was significantly higher in chronic social defeat stress mice compared with the control group.These findings suggest that chronic social defeat stress induces anxiety,depression,and cognitive deficits in mice,accompanied by neuropathological changes in the hippocampus,and that altered G protein-coupled receptors expression may play a key role in these behavioral and neuropathological changes.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveTo establish a castrated male mouse model and to preliminarily investigate the effects of testosterone replacement therapy （TRT） on behavior， serum indices， and histopathological changes in castrated mice， as well as to explore the role of androgens in cognitive function. MethodsForty 6-month-old male C57/BL6J mice were randomly divided into sham operation group， castration group， testosterone propionate （0.5，1.0 mg/kg） treated group， with 10 mice in each group. Following castration and subcutaneous administration of testosterone propionate at different doses （0.5 and 1.0 mg/kg） for TRT， learning and memory abilities were assessed using the Morris water maze （MWM） test and the passive avoidance test. Serum testosterone and serum brain-derived neurotrophic factor （BDNF） levels were measured by ELISA， and histopathological changes in the hippocampus were examined using hematoxylin-eosin （HE） staining. ResultsRoutine observations： there were no statistically significant differences in body weight among groups at any time point. MWM test： compared with castration group， sham operation group and testosterone propionate-treated groups （0.5， 1.0 mg/kg） showed significantly reduced escape latency on days 4 and 5 （P0.05）， while the number of platform crossings and the time spent in the target quadrant significantly increased （P0.05）. Passive avoidance test： the number of passive avoidance errors significantly decreased in sham operation group and testosterone propionate （1.0 mg/kg）-treated group （P0.05）， and the passive avoidance latency was significantly prolonged in sham-operated group and testosterone propionate-treated groups （0.5， 1.0 mg/kg） （P0.05）. Serum testosterone and serum BDNF assays： serum testosterone levels and serum BDNF concentrations significantly increased in sham operation group and testosterone propionate-treated groups （0.5， 1.0 mg/kg） （P0.01）. HE staining： compared with sham operation group， neuronal density in all hippocampal subregions was slightly reduced in castration group； in the testosterone propionate （0.5 mg/kg）-treated group， neuronal arrangement in the CA1 and CA3 regions was improved and apoptotic cells were reduced compared with castration group； in testosterone propionate （1.0 mg/kg）-treated group， the pyramidal cell layer in the CA3 region was more compactly arranged， with fewer apoptotic cells than in castration group. ConclusionTRT improves learning and memory performance in castration male mice， potentially through modulation of hippocampal BDNF signaling pathways.