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Background Reliable identification of protein‑protein interactions (PPIs) is crucial for deciphering cellular functional networks. Current research models still face limitations in aligning heterogeneous features and handling sparse supervision signals in graph learning. To address these issues, this study proposes a prediction framework named DSS‑PPI. This framework aims to enhance prediction performance by integrating multimodal sequence semantics with self‑supervised graph learning, thereby transforming static protein sequence embeddings into dynamic, topology‑aware representations. Results DSS‑PPI employs a dual‑stream architecture that synergistically integrates ProTrek’s cross‑modal aligned embeddings with ProtT5’s deep sequence features. The study innovatively constructs a context encoder that leverages Smith‑Waterman sequence similarity as quantitative edge features to guide graph attention weights, and incorporates Deep Graph Infomax (DGI) for self‑supervised pretraining. Furthermore, a gated fusion mechanism enables the model to adaptively integrate sequence semantics with network topological information. Experimental results indicate that the model achieves competitive performance compared to existing state‑of‑the‑art algorithms on both human and multi‑species benchmark datasets, with an accuracy of 0.73 on the rigorously designed Bernett test set. Conclusions This study demonstrates the synergistic effect of multimodal embeddings and self‑supervised graph learning in PPI prediction. Ablation experiments and SHAP interpretability analysis further confirm that DSS‑PPI can effectively capture genuine physical interaction patterns. The framework provides a reliable computational tool for understanding complex biological networks and holds broad potential for biomedical applications.

The abnormal insular-related static and dynamic functional connectivity (sFC and dFC) in patients with trigeminal neuralgia (TN) is not well understood. Therefore, we aimed to explore alterations in the sFC and dFC of the insular cortex(IC) and the relationships between functional connectivity (FC) alterations and clinical measures in TN. The study included 40 patients with TN and 30 healthy controls (HCs) who underwent resting-state functional magnetic resonance imaging and completed the visual analog scale (VAS) and mood questionnaires. Voxel-based sFC and dFC in the bilateral IC were computed. For the voxel-wise FC differences between TN and HC, a two-sample t-test was performed on the individual maps in a voxel-by-voxel manner. To examine linear relationships with clinical measures, Pearson correlations were calculated between FC alterations and VAS and mood measures. Compared with HCs, patients with TN exhibited significantly decreased sFC between the bilateral IC and right superior temporal gyrus, left Rolandic operculum, left thalamus, left supramarginal gyrus, and left superior parietal gyrus, and decreased dFC between the IC and cerebellar vermis. Correlation analysis revealed negative correlations between sFC of IC-thalamus and VAS and dFC of IC-vermis and duration of pain. Our findings reveal brain regions related to pain sensation and regulation showing abnormal sFC and dFC alterations in TN, suggesting their pivotal roles in the neural mechanisms underlying TN.

Neuroimaging studies have documented brain structural alterations induced by chronic pain, particularly in gray matter volume. However, the effects of trigeminal neuralgia (TN), a severe paroxysmal pain disorder, on cortical morphology are not yet known. In this study, we recruited 30 TN patients and 30 age-, and gender-matched healthy controls (HCs). Using Computational Anatomy Toolbox (CAT12), we calculated and compared group differences in cortical thickness, gyrification, and sulcal depth with two-sample t tests (p < 0.05, multiple comparison corrected). Relationships between altered cortical characteristics and pain intensity were investigated with correlation analysis. Compared to HCs, TN patients exhibited significantly decreased cortical thickness in the left inferior frontal, and left medial orbitofrontal cortex; decreased gyrification in the left superior frontal cortex; and decreased sulcal depth in the bilateral superior frontal (extending to anterior cingulate) cortex. In addition, we found significantly negative correlations between the mean cortical thickness in left medial orbitofrontal cortex and pain intensity, and between the mean gyrification in left superior frontal cortex and pain intensity. Chronic pain may be associated with abnormal cortical thickness, gyrification and sulcal depth in trigeminal neuralgia. These morphological changes might contribute to understand the underlying neurobiological mechanism of trigeminal neuralgia.

Introduction Cardiac arrest (CA) is a life-threatening emergency with a global one-year survival rate of 2%-10%. Brain injury significantly impacts CA outcomes, and neuroinflammation is a key mediator of cerebral damage. Interleukin-17 (IL-17) has been implicated in multiple inflammatory disorders, yet its contribution to CA-induced cerebral damage remains undefined. Objective To elucidate the role of the IL-17 axis in CA-triggered neuroinflammation and to determine whether IL-17 blockade can attenuate hippocampal injury and improve neurologic recovery. Methods Asphyxial CA was induced in adult Sprague-Dawley rats followed by cardiopulmonary resuscitation. Blood–brain barrier (BBB) integrity, Th17 infiltration, astrocyte polarization, and downstream signaling were assessed by flow cytometry, RNA-seq, qRT-PCR, ELISA, immunofluorescence, and western blotting. IL-17 A or IL-17RA was neutralized in vivo with specific antibodies, and human SVGP12 astrocytes were employed for mechanistic validation. Results CA promotes Th17 cell differentiation and enhances blood-brain barrier (BBB) permeability, facilitating the infiltration of Th17 cells and their secreted IL-17 A/F into the hippocampus. IL-17 A/F specifically binds to IL-17RA/RC on astrocytes, activating NF-κB, and MAPK pathways, which drive A1 polarization of astrocytes and exacerbate neuroinflammation. IL-17 A neutralization reverses A1 polarization of astrocytes, reduces neuronal apoptosis, improves 24-hour neurologic deficit scores, and enhances survival in CA rats. In vitro, IL-17 A induced A1 polarization and inflammatory cytokine release in astrocytes, effects abolished by IL-17RA blockade. Conclusion Our study elucidates the mechanisms underlying CA-induced neuroinflammation and identifies the IL-17 A pathway as a potential therapeutic target for mitigating neurological injury following cardiac arrest. Highlights 1. Cardiac arrest drives Th17 cell differentiation and disrupts the blood–brain barrier, enabling IL-17A/F infiltration into the hippocampus. 2. IL-17A/F binding to astrocytic IL-17RA/RC activates NF-κB/MAPK signaling, triggering neurotoxic A1 astrocyte polarization. 3. A1 astrocytes secrete pro-inflammatory cytokines that amplify neuronal apoptosis and worsen neurological outcomes. 4. IL-17RA neutralization reverses A1 polarization, reduces neuroinflammation, and improves survival after cardiac arrest. Graphical abstract CA induces Th17 cell differentiation and disrupts the BBB. Th17 cell and their secreted IL-17A/F infiltrates into hippocampus, activates astrocytic IL-17RA/RC, and propagates neurotoxic A1 astrocyte-mediated neuroinflammation. Targeting the IL-17 pathway represents a promising therapeutic strategy to mitigate hippocampal injury and improve neurologic prognosis after CA.

Human decision-making involves model-free and model-based reinforcement learning (RL) strategies, largely implemented by prefrontal-striatal circuits. Combining human brain imaging with neural network modelling in a probabilistic reversal learning task, we identify a unique role for the mediodorsal thalamus (MD) in arbitrating between RL strategies. While both dorsal PFC and the striatum support rule switching, the former does so when subjects predominantly adopt model-based strategy, and the latter model-free. The lateral and medial subdivisions of MD likewise engage these modes, each with distinct PFC connectivity. Notably, prefrontal transthalamic processing increases during the shift from stable rule use to model-based updating, with model-free updates at intermediate levels. Our CogLinks model shows that model-free strategies emerge when prefrontal-thalamic mechanisms for context inference fail, resulting in a slower overwriting of prefrontal strategy representations - a phenomenon we empirically validate with fMRI decoding analysis. These findings reveal how prefrontal transthalamic pathways implement flexible RL-based cognition. The mediodorsal thalamus (MD) is central to flexible decision-making. Here, the authors identify MD’s unique roles in arbitrating between different reinforcement learning strategies through prefrontal-striatal brain circuits.

The FtsH gene family encodes ATP-dependent zinc metalloproteases essential for protein quality control, organelle homeostasis, and stress response in plants. Nevertheless, research on the FtsH gene family in Chrysanthemum morifolium is limited. This study identified 32 CmFtsH genes through bioinformatics approaches and systematically analyzed their family members. Phylogenetic analysis clarified their evolutionary relationships, while chromosomal localization, sequence alignment, and promoter cis -element prediction were utilized to analyze gene characteristics. Tissue-specific expression profiling identified key genes, and overexpression experiments confirmed the cadmium (Cd) tolerance of the candidate gene CmFtsH-15 . The analysis indicated a close evolutionary relationship with Asteraceae plants such as lettuce and sunflower, demonstrating lineage-specific differentiation. The 32 CmFtsH genes are unevenly distributed across 16 chromosomes, exhibiting significant differences in sequence length and motif composition. Promoter regions are abundant in stress and hormone response elements, indicating potential involvement in abiotic stress adaptation. CmFtsH-15 is significantly expressed in leaves, and its overexpression alleviates oxidative damage by reducing Cd accumulation, enhancing antioxidant activity, and decreasing malondialdehyde content, thereby enhancing Cd tolerance in transgenic lines. Furthermore, CmFtsH-15 interacts with the heat shock protein CmHSP70, suggesting a synergistic regulation of stress response. This study systematically explored the FtsH gene family in Chrysanthemum , highlighting the protective role of CmFtsH-15 under Cd stress, thus providing a promising candidate for developing Cd-resistant germplasm resources.

Little is known about connectivity within the default mode network (DMN) in heroin-dependent individuals (HDIs). In the current study, diffusion-tensor imaging (DTI) and resting-state functional MRI (rs-fMRI) were combined to investigate both structural and functional connectivity within the DMN in HDIs. Fourteen HDIs and 14 controls participated in the study. Structural (path length, tracts count, (fractional anisotropy) FA and (mean diffusivity) MD derived from DTI tractography)and functional (temporal correlation coefficient derived from rs-fMRI) DMN connectivity changes were examined in HDIs. Pearson correlation analysis was performed to compare the structural/functional indices and duration of heroin use/Iowa gambling task(IGT) performance in HDIs. HDIs had lower FA and higher MD in the tract connecting the posterior cingulate cortex/precuneus (PCC/PCUN) to right parahippocampal gyrus (PHG), compared to the controls. HDIs also had decreased FA and track count in the tract connecting the PCC/PCUN and medial prefrontal cortex (MPFC), as well as decreased functional connectivity between the PCC/PCUN and bilateral PHG and MPFC, compared to controls. FA values for the tract connecting PCC/PCUN to the right PHG and connecting PCC/PCUN to the MPFC were negatively correlated to the duration of heroin use. The temporal correlation coefficients between the PCC/PCUN and the MPFC, and the FA values for the tract connecting the PCC/PCUN to the MPFC were positively correlated to IGT performance in HDIs. Structural and functional connectivity within the DMN are both disturbed in HDIs. This disturbance progresses as duration of heroin use increases and is related to deficits in decision making in HDIs.

Antimicrobial peptides (AMPs) are short peptides with diverse biological activities and playing a crucial role in various biological processes. Due to the widespread misuse of traditional antibiotics and the increasing resistance of microorganisms to these drugs, AMPs have emerged as a promising alternative. Consequently, the identification of AMPs has garnered significant research interest. Numerous computational methods based on machine learning algorithms have been developed to facilitate AMP recognition. However, some existing AMPs recognition models only focus on binary classification tasks or only identify the functional activity of a limited number of AMPs categories in multi-class classification tasks. To address this limitation, this study proposes a two-stage AMPs recognition model, iAMP-SeE. The iAMP-SeE model extracts features from protein sequences using ESM2, employs a Convolutional Neural Network (CNN) module to capture local patterns from ESM features and utilizes a Bidirectional Long Short-Term Memory (BiLSTM) network to capture long-term dependencies. Furthermore, it incorporates Squeeze-and-Excitation (SE) and Efficient Channel Attention (ECA) mechanisms, which focus on global and local channel relationships, respectively. These two attention mechanisms are complementary, as they enhance features across various dimensions and granularities while simultaneously suppressing irrelevant or redundant features, thereby boosting the model's performance. Additionally, to address the issue of imbalanced datasets, the Synthetic Minority Over-sampling Technique (SMOTE) is incorporated into the multi-classification task. This method balances the number of AMP categories and ensures that minority classes are not overlooked during model training. Evaluation across a range of classification thresholds demonstrated the stability of the model's performance metrics in both binary and multi-class tasks. Furthermore, comparative experiments with existing AMP recognition models confirmed the superior performance of iAMP-SeE. Rigorous experimental comparisons and ablation studies demonstrate the effectiveness of iAMP-SeE for both binary and multi-class AMP classification tasks. The source code is publicly available at: https://github.com/cqw0715/iAMP-SeE.git.

To investigate the effect of pollination on the fruit quality of ‘Dangshan Su’ pear, ‘Dangshan Su’ was fertilized by the pollen of ‘Wonhwang’ (Pyrus pyrifolia Nakai.) (DW) and ‘Jingbaili’ (Pyrus ussuriensis Maxim.) (DJ). The analysis of primary metabolites was achieved through untargeted metabolomics, and the quantitative analysis of intermediate metabolites of lignin synthesis was undertaken using targeted metabolomics. The untargeted metabolomics analysis was performed via gas chromatography-mass spectrometry (GC-MS). The targeted metabolomics analysis was performed using ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) under the multiple reaction monitoring (MRM) mode. The results showed that the metabolite content was significantly different between DW and DJ. Compared with that in DJ, the sugar and amino acid content in DW was higher and the fatty acid content was lower at 47 days after pollination (DAPs), and the sugar, amino acid, and fatty acid content in DW was lower at 63 DAPs. The intermediate metabolites of lignin synthesis were analyzed using the orthogonal partial least squares discriminant analysis (OPLS-DA) model, and the differential metabolites at 47 DAPs were p-coumaric acid, ferulic acid, sinapaldehyde, coniferyl alcohol, and sinapyl alcohol. The differential significant metabolite at 63 DAPs was p-coumaric acid. At 47 DAPs and 63 DAPs, the p-coumaric acid level was significantly different, and the p-coumaric acid content was positively correlated with lignin synthesis. The pollination pollen affects the quality of ‘Dangshan Su’ pear fruit through regulation of the sugar, amino acid, and fatty acid content; at the same time, regulating the levels of intermediate metabolites of lignin synthesis, especially the p-coumaric acid content, to affect lignin synthesis ultimately affects the stone cell content and improves the quality of the pears.

Background Recurrent tracheoesophageal fistula (RTEF) is one of the most challenging long-term complications following primary repair of esophageal atresia/tracheoesophageal fistula (EA/TEF). Thoracoscopic revision has been increasingly used in patients. Anastomotic leakage (AL) remains an important postoperative complication after RTEF repair. Objective predictors for surgical timing and AL risk are limited. Methods We retrospectively analyzed patients undergoing thoracoscopic RTEF repair at Beijing Children’s Hospital between January 2019 and January 2025. Preoperative, intraoperative, and postoperative variables were evaluated to identify predictors of AL. Based on preoperative features, a transfer learning (TL) model was developed to predict AL risk. Results A total of 92 thoracoscopic repairs were performed in 84 patients, with AL occurring in 23/92 cases (25.0%). Univariate analysis identified significant associations between AL and preoperative CT-determined pulmonary inflammation adjacent to the anastomosis ( P  < 0.001), persistent upper esophageal negative-pressure suction ( P  = 0.008), oral feeding ( P  = 0.037), approach used for primary EA/TEF repair ( P  = 0.021), and esophageal gap > 2 cm ( P  < 0.001). Multivariate analysis confirmed that preoperative inflammation severity (moderate: OR = 17.07, P  = 0.014; severe: OR = 50.62, P  = 0.001) independently increased AL risk, whereas an esophageal gap ≤ 2 cm was protective (OR = 0.18, P  = 0.014). The TL model integrating preoperative features achieved excellent discrimination (training AUROC 0.929 [95%CI: 0.850–0.986]; validation AUROC 0.863 [95%CI: 0.688–1.000]) with high accuracy (0.919). Conclusions Thoracoscopic RTEF repair is safe and effective when guided by preoperative assessment. CT-quantified pulmonary inflammation at the T4 vertebral level is a powerful independent predictor of AL. The proposed TL-based model enables individualized, data-driven optimization of surgical timing for improved outcomes.