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Background Adherence to dietary guidelines is a fundamental aspect of diabetes management; however, it poses a significant challenge for patients with diabetes. Our research aims to assess the level of dietary compliance among individuals with type 2 diabetes (T2DM) and to identify the factors that influence their adherence to dietary advice. Methods This study was a cross-sectional survey. The patients with T2DM undergoing treatment at our hospital from March, 2023, to June, 2024 were included. Compliance with dietary recommendations was assessed using the validated dietary compliance scale for type 2 diabetes mellitus patients (DCS-T2DM). Spearman correlation and logistic regression analyses were conducted to evaluate the factors influencing dietary compliance in patients with T2DM. Results A total of 308 T2DM patients were included in our study. The results revealed that 46.10% of the participants had suboptimal dietary compliance. There were significant correlations between dietary compliance and several demographic and clinical factors, including age ( r  = 0.501), gender ( r  = 0.447), education level ( r  = 0.610), average monthly household per capita income ( r  = 0.627), and the duration, since T2DM diagnosis ( r  = 0.552), all of which were statistically significant ( p  < 0.05). Logistic regression identified age (O R  = 1.705, 95%CI 1.262 ~ 1.987), gender (O R  = 2.401, 95%CI 1.909 ~ 3.134), education level (O R  = 3.083, 95%CI 2.434 ~ 3.957), average monthly household per capita income (O R  = 3.721, 95%CI 2.553 ~ 4.405), and the time since T2DM diagnosis (O R  = 2.470, 95%CI 1.755 ~ 3.262) as significant predictors of dietary compliance. Conclusions 46.10% of patients with T2DM exhibited suboptimal dietary adherence, with age, gender, education, income, and diabetes duration significantly predicting compliance. It is imperative for healthcare providers to devise individualized intervention strategies that incorporate these pivotal factors to enhance dietary adherence in patients with T2DM.

Speed of Processing (SoP) represents a fundamental limiting step in cognitive performance which may underlie General Intelligence. The measure of SoP is particularly sensitive to aging, neurological or cognitive diseases, and has become a benchmark for diagnosis, cognitive remediation, and enhancement. Neural efficiency of the Dorsolateral Prefrontal Cortex (DLPFC) is proposed to account for individual differences in SoP. However, the mechanisms by which DLPFC efficiency is shaped by training and whether it can be enhanced remain elusive. To address this, we monitored the brain activity of sixteen healthy participants using functional Near Infrared Spectroscopy (fNIRS) while practicing a common SoP task (Symbol Digit Substitution Task) across 4 sessions. Furthermore, in each session, participants received counterbalanced excitatory repetitive transcranial magnetic stimulation (rTMS) during mid-session breaks. Results indicate a significant involvement of the left-DLPFC in SoP, whose neural efficiency is consistently increased through task practice. Active neurostimulation, but not Sham, significantly enhanced the neural efficiency. These findings suggest a common mechanism by which neurostimulation may aid to accelerate learning. •Left-DLPFC activity is associated with Symbol-Digit Performance.•Practice of SDST increases neural efficiency.•Excitatory rTMS to left-DLPFC further increases neural efficiency.•Neuroimaging may help evaluate the effects of neurostimulation paradigms.

Music possesses a remarkable capacity to evoke a broad spectrum of subjective responses and promote healing in humans, with rhythm playing a crucial role among its various elements. Rhythmic entrainment is considered as one of the fundamental mechanisms, yet its subsequent behavioral responses and quantitative relationship remain unclear. In the study presented here, we combined behavioral and electroencephalography experiments to explore the relationship between neural entrainment and emotional responses to rhythmic auditory stimuli, focusing on the emotional dimensions of valence, arousal, and dominance. Our findings reveal that while all sequences across 12 different presenting rates significantly entrain neural oscillations, sequences at different rates elicit distinct impacts on subjective emotional experience. The intensity of neural entrainment is associated with changes in emotional valence and dominance under specific frequency conditions. This insight addresses a gap in the existing literature regarding the dominance dimension and provides potential for developing more targeted and precise clinical interventions to enhance emotional well-being in the future.

The emerging cloud storage technology has significantly improved efficiency and productivity in the traditional electronic healthcare field. However, it has also brought about many security concerns. Ciphertext policy attribute-based encryption (CP-ABE) holds immense potential in achieving fine-grained access control, providing robust security for electronic healthcare data in the cloud. However, current CP-ABE schemes still face issues such as inflexible attribute revocation, relatively lower computational capabilities, and key management. To address these issues, this paper introduces a revocable and traceable undeniable ciphertext policy attribute-based encryption scheme (MA-RUABE). MA-RUABE not only enables fast and accurate data traceability, effectively preventing malicious user key leakage, but also includes a direct revocation feature, significantly enhancing computational efficiency. Furthermore, the introduction of a multi-permission mechanism resolves the issue of centralization of power caused by single-attribute permissions. Furthermore, a security analysis demonstrates that our system ensures resilience against chosen plaintext attacks. Experimental results demonstrate that MA-RUABE incurs lower computational overhead, effectively enhancing system performance and ensuring data-sharing security in cloud-based electronic healthcare systems.

Exploring NPP changes and their corresponding drivers is significant for the achievement of sustainable ecosystem management and in addressing climate change. This study aimed to explore the spatiotemporal variation in NPP and analyze the effects of vegetation and climate change on the global NPP from 2003 to 2020. Methodologically, the Theil–Sen and Mann–Kendall methods were used to study the spatiotemporal characteristics of global NPP change. Moreover, a ridge regression model was built by selecting the vegetation indicators of the leaf area index (LAI) and fraction vegetation coverage (FVC) and the climate factors of CO2, shortwave downward solar radiation (Rsd), precipitation (P), and temperature (T). Then, the relative contributions of each factor were evaluated. The results showed that, over the previous two decades, the global mean NPP reached 503.43 g C m−2 yr−1, with a fluctuating upward trend of 1.52 g C m−2 yr−1. The regions with a significant increase in NPP (9.22 g C m−2 yr−1) were mainly located in Central Africa, while the regions with decreasing NPP (−3.21 g C m−2 yr−1) were primarily in the Amazon Rainforest in northern South America. Additionally, CO2, the LAI, and the FVC exhibited positive contributions to the NPP trend, with the predominant factors being CO2 (relative contribution of 32.22%) and the LAI (relative contribution of 21.96%). In contrast, the contributions of Rsd and precipitation were relatively low (<10%). In addition, the contributions varied at different land cover and climate zone scales. The CO2, LAI, FVC, and temperature were the predominant factors affecting NPP across the vegetation types. At the scale of climate zones, CO2 was the predominant factor influencing changes in vegetation NPP. As the climate gradually transitioned towards temperate and cold regions, the contribution of the LAI to NPP increased. The findings of this study help to clarify the effects of vegetation and climate change on the ecosystem, providing theoretical support for ecological environmental protection and other related initiatives.

The individual heterogeneity is a challenge to the prosperous promises of cutting-edge neuroimaging techniques for better diagnosis and early detection of psychiatric disorders. Individuals with similar clinical manifestations may result from very different pathophysiology. Conventional approaches based on comparing group-averages provide insufficient information to support the individualized diagnosis. Here we present an individualized imaging methodology that combines naturalistic imaging and the normative model. This paradigm adopts video clips with rich cognitive, social, and emotional contents to evoke synchronized brain dynamics of healthy participants and builds a spatiotemporal response norm. By comparing individual brain responses with the response norm, we could recognize patients using machine learning techniques. We applied this methodology to recognize first-episode drug-naïve schizophrenia patients in a dataset containing 72 patients and 54 healthy controls. Some segments of the video evoked more synchronized brain activity in the healthy controls than in the schizophrenia patients. We built a spatiotemporal response norm by averaging the brain responses of the healthy controls in a training set, and trained a classifier to recognize patients based on the differences between individual brain responses and the norm. The performance of the classifier was then evaluated using an independent test set. The mean accuracies from a 5-fold cross-validation were 0.71–0.78 depending on the parameters such as the number of features and the width of the sliding windows. These findings reflected the potential of this methodology towards a clinical tool for individualized diagnosis. •An individualized psychiatric imaging methodology based on naturalistic stimuli.•A normative model that evokes synchronized brain responses in healthy individuals.•Comparing individual responses to the healthy norm avoids averaging across patients.•The performance to recognize schizophrenia patients shows its clinical potential.

The emergence of prodromal symptoms of schizophrenia and their evolution into overt psychosis may stem from an aberrant functional reorganization of the brain during adolescence. To examine whether abnormalities in connectome organization precede psychosis onset, we performed a functional connectome analysis in a large cohort of medication-naive youth at risk for psychosis from the Shanghai At Risk for Psychosis (SHARP) study. The SHARP program is a longitudinal study of adolescents and young adults at Clinical High Risk (CHR) for psychosis, conducted at the Shanghai Mental Health Center in collaboration with neuroimaging laboratories at Harvard and MIT. Our study involved a total of 251 subjects, including 158 CHRs and 93 age-, sex-, and education-matched healthy controls. During 1-year follow-up, 23 CHRs developed psychosis. CHRs who would go on to develop psychosis were found to show abnormal modular connectome organization at baseline, while CHR non-converters did not. In all CHRs, abnormal modular connectome organization at baseline was associated with a threefold conversion rate. A region-specific analysis showed that brain regions implicated in early-course schizophrenia, including superior temporal gyrus and anterior cingulate cortex, were most abnormal in terms of modular assignment. Our results show that functional changes in brain network organization precede the onset of psychosis and may drive psychosis development in at-risk youth.

Cognitive impairments in early psychosis are common, yet prior studies have focused mainly on domain-specific deficits rather than inter-domain relationships. Analyzing cognitive network connectivity may uncover insights into early psychosis mechanisms. Cognitive functions were assessed from 2,518 participants, including 988 first-episode schizophrenia (FES), 767 clinical high-risk (CHR), and 763 healthy controls (HC), using the Chinese version of the Measurement and Treatment Research to Improve Cognition in Schizophrenia Consensus Cognitive Battery (MCCB). Results revealed a stage-dependent “dedifferentiation” pattern: mean inter-domain correlation increased from HC (0.28) to CHR (0.33) to FES (0.40). Confirmatory factor analysis revealed a common “g” factor across groups, with significantly reduced strength in FES compared to CHR and HC. The reduction in the “g” factor was associated with increased connectivity and stronger inter-domain correlations. These findings highlight cognitive network dedifferentiation and “g” factor decline as key features of early psychosis. This study reveals stage-dependent cognitive network dedifferentiation in early psychosis, with increasing inter-domain correlations from healthy controls to clinical high risk to first-episode schizophrenia, linked to reduced general intelligence.

Electroencephalography (EEG)-based emotion recognition is increasingly pivotal in the realm of affective brain–computer interfaces. In this paper, we propose TSANN-TG (temporal–spatial attention neural network with a task-specific graph), a novel neural network architecture tailored for enhancing feature extraction and effectively integrating temporal–spatial features. TSANN-TG comprises three primary components: a node-feature-encoding-and-adjacency-matrices-construction block, a graph-aggregation block, and a graph-feature-fusion-and-classification block. Leveraging the distinct temporal scales of features from EEG signals, TSANN-TG incorporates attention mechanisms for efficient feature extraction. By constructing task-specific adjacency matrices, the graph convolutional network with an attention mechanism captures the dynamic changes in dependency information between EEG channels. Additionally, TSANN-TG emphasizes feature integration at multiple levels, leading to improved performance in emotion-recognition tasks. Our proposed TSANN-TG is applied to both our FTEHD dataset and the publicly available DEAP dataset. Comparative experiments and ablation studies highlight the excellent recognition results achieved. Compared to the baseline algorithms, TSANN-TG demonstrates significant enhancements in accuracy and F1 score on the two benchmark datasets for four types of cognitive tasks. These results underscore the significant potential of the TSANN-TG method to advance EEG-based emotion recognition.

Abstract Cortical thickness reductions are evident in schizophrenia (SZ). Associations between antipsychotic medications (APMs) and cortical morphometry have been explored in SZ patients. This raises the question of whether the reconfiguration of morphological architecture by APM plays potential compensatory roles for abnormalities in the cerebral cortex. Structural magnetic resonance imaging was obtained from 127 medication-naive first-episode SZ patients and 133 matched healthy controls. Patients received 12 weeks of APM and were categorized as responders (n = 75) or nonresponders (NRs, n = 52) at follow-up. Using surface-based morphometry and structural covariance (SC) analysis, this study investigated the short-term effects of antipsychotics on cortical thickness and cortico-cortical covariance. Global efficiency was computed to characterize network integration of the large-scale structural connectome. The relationship between covariance and cortical thinning was examined by SC analysis among the top-n regions with thickness reduction. Widespread cortical thickness reductions were observed in pre-APM patients. Post-APM patients showed more reductions in cortical thickness, even in the frontotemporal regions without baseline reductions. Covariance analysis revealed strong cortico-cortical covariance and higher network integration in responders than in NRs. For the NRs, some of the prefrontal and temporal nodes were not covariant between the top-n regions with cortical thickness reduction. Antipsychotic effects are not restricted to a single brain region but rather exhibit a network-level covariance pattern. Neuroimaging connectomics highlights the positive effects of antipsychotics on the reconfiguration of brain architecture, suggesting that abnormalities in regional morphology may be compensated by increasing interregional covariance when symptoms are controlled by antipsychotics.