Objective To explore the number of peripheral blood regulatory T cells (Treg) in patients with chronic kidney disease (CKD) and its correlation with vascular calcification. Methods This was a single-center, cross-sectional, and observational study. Non-dialysis patients with CKD treated at Zhongshan Hospital, Fudan University from March 2021 to March 2022 were enrolled. Abdominal aortic calcification (AAC) was assessed using lateral abdominal X-ray. Number of Treg and cytokine levels were measured by flow cytometry. Logistic regression analysis was performed to evaluate the related factors for AAC in CKD patients. Results A total of 83 patients were included, aged 17–86 years, with 57 males (68.7%). The distribution of CKD stages was as follows: stage G1 in 7 patients (8.4%), stage G2 in 17 patients (20.5%), stage G3 in 21 patients (25.3%), stage G4 in 19 patients (22.9%), and stage G5 in 19 patients (22.9%). No AAC was observed in patients with stages G1 and G2, while the prevalence of AAC in patients with stages G3, G4, and G5 was 23.8%, 21.1%, and 26.3%, respectively. Compared with stage G1 patients, those with stages G3–5 showed decreased number of peripheral blood Treg and elevated levels of interleukin (IL)-6 and IL-17F (P＜0.05). The area under the receiver operating characteristic curve for number of peripheral blood Treg in predicting AAC in CKD patients was 0.766 (95%CI 0.652–0.879, P＝0.002). Logistic regression analysis showed that decreased number of Treg was related factor for AAC in CKD patients (OR＝0.957, 95%CI 0.922–0.992, P＝0.018). Conclusion As CKD progresses, number of peripheral blood Treg significantly decreases, which is correlated with AAC in CKD patients.

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.

Objective To explore the mediating role of mindful attention and awareness in depressive symptoms and insomnia severity among patients with comorbid depression and insomnia.Methods A cross-sectional study was conducted,enrolling 267 patients with comorbid depression and insomnia who were treated in the outpatient Department of Medical Psychology of Second Affiliated Hospital of Army Medical University,from March to May 2024.Basic demographic and clinical data were collected using a general information questionnaire.Depressive symptom severity was measured via the Patient Health Questionnaire-9(PHQ-9),insomnia severity via the Insomnia Severity Index(ISI),perceived stress via the Perceived Stress Scale-10(PSS-10),and mindful attention and awareness via the Mindful Attention Awareness Scale(MAAS).Pearson correlation analysis was used to examine the correlations between depressive severity,insomnia severity,perceived stress,and mindful attention and awareness.Mediation analysis was performed using Process 4.1.Results The PHQ-9 score was(13.80±5.98)and the ISI score was(17.10±5.56)in the 267 patients.Pearson correlation analysis showed that depressive severity and insomnia severity were positively correlated with perceived stress(r=0.531,0.351,P<0.001)and negatively correlated with mindful attention and awareness(r=-0.373,-0.350,P<0.001).Mediation analysis using Process 4.1 indicated that the combined mediating effect of mindful attention and awareness and insomnia between perceived stress level and depressive level was 0.157,with a 95%confidence interval(CI)of 0.102～0.217,and the total mediating effect was significant(P<0.001).Conclusion Perceived stress directly positively affects depression and indirectly exacerbates depression through insomnia as a mediator,and mindful attention and awareness can weaken the promoting effect of perceived stress on insomnia.

Nitrofurantoin(NFT)is a nitrofuran antibiotic commonly used as a veterinary drug to treat bacterial infections in animals.However,due to the low solubility and bioaccumulation properties,NFT is prone to leave excessive residues in animal-derived foods and water systems,posing serious threats to human health and ecosystems.Therefore,there is an urgent need to develop an efficient and rapid detection method for NFT.In this work,nitrogen-doped carbon nanomaterials with unique bowl-like structures(N-CNBs)were synthesized via a hydrothermal-carbonization method.The morphology,surface structure,and specific surface area of N-CNBs were characterized using transmission electron microscopy(TEM),scanning electron microscopy(SEM),and X-ray photoelectron spectroscopy(XPS).The N-CNB modified glassy carbon electrode(N-CNB/GCE)was prepared,and the electrochemical test revealed that the N-CNB/GCE exhibited higher conductivity and larger electrochemical active surface area compared to bare GCE and nitrogen-doped hollow carbon nanosphere-modified electrode(N-HCNS/GCE).Additionally,the N-CNB/GCE demonstrated superior electrocatalytic activity toward NFT.An NFT electrochemical sensor was constructed based on N-CNB/GCE.The detection conditions of the sensor were optimized,and differential pulse voltammetry(DPV)was employed for NFT detection under optimal experimental conditions.The established NFT electrochemical sensor had a wide linear range of 0.4-500 μmol/L,a low detection limit(S/N=3)of 0.015 μmol/L and high selectivity,with excellent stability and reproducibility.The practical feasibility of this sensor was confirmed by analysis of NFT in milk and tap water samples,with spiked recoveries ranging from 94.2%to 108.9%.

Objective To investigate the prevalence of food intolerance among children in Zhuzhou area and its relationship with age,gender,systemic diseases,and food allergies,so as to provide a basis for the scientific adjustment of children's dietary structure.Methods A retrospective analysis was conducted on totally 1 592 children who underwent food intolerance and food allergen testing in the hospital,the positive rate and distri-bution of 14 kinds of food intolerance were assessed,and their correlation with various factors was analyzed.Results Among 14 kinds of food tested,milk and eggs had the highest positive rates of intolerance,at 82.22％and 55.78％,respectively.The majority of children were intolerant to 1 to 2 kinds of food,with a de-creasing trend in the number of children intolerant to multiple kinds of food.Among the 14 types of food,ex-cept for mushrooms and pork,there were statistically significant differences in the distribution of negative,mild,moderate,and severe intolerance in other foods(P＜0.05).Children tended to have moderate or even se-vere intolerance to milk and eggs,while they tended to have mild intolerance to other foods.There was no sta-tistically significant difference in the overall food intolerance rate between boys and girls(P=0.654),but the positive rate of tomato intolerance in girls was slightly higher than that in boys(P=0.043).Except for pork,there were statistically significant differences in the positive rates of intolerance to 14 different foods among different age groups(P＜0.05).The positive rates of intolerance to cod,mushrooms,and crabs increased with age,while the positive rates of intolerance to beef decreased with age.There was a statistically significant difference in the positive rate of milk intolerance between healthy children and children with skin allergies(P＜0.05).There was a statistically significant difference in the proportion of individuals who were tolerant and not allergic to milk compared to hose who were intolerant and allergic to milk(P＜0.05).There was a statistically significant difference in the proportion of individuals who were tolerant and not allergic to eggs compared to those who were intolerant and allergic to eggs(P＜0.05).Conclusion The positive rate of food intolerance among children in Zhuzhou area is relatively high,with milk and eggs being the main intolerant foods.There are differences in the positive rate of intolerance among different gender and age groups,and in-tolerance to milk and eggs is associated with food allergies to some extent.

The development and validation of clinical prediction models based on artificial intelligence (AI) and machine learning methods have become increasingly widespread. However, the prediction model bias risk and applicability evaluation tool developed in 2019 (i.e., PROBAST-2019) has shown significant limitations. Therefore, an expanded and updated version of the PROBAST-2019 tool was released in 2025, known as the PROBAST+AI tool. The tool is divided into two parts including model development and model evaluation. It aims to comprehensively and systematically evaluate potential methodological quality issues in model development, bias risks in model evaluation, and the applicability of models, regardless of the modeling method used. This paper provides a systematic interpretation of the PROBAST+AI tool's items and case analyses, with the aim of guiding and assisting researchers engaged in related studies and promoting the high-quality development of clinical predictive model research.

Diabetes is a very complex endocrine disease whose common feature is the increase in blood glucose concentration. Persistent hyperglycemia can lead to blindness, kidney and heart disease, neurodegeneration, and many other serious complications that have a significant impact on human health and quality of life. The number of people with diabetes is increasing yearly. The global diabetes prevalence in 20-79 year olds in 2021 was estimated to be 10.5% (536.6 million), and it will rise to 12.2% (783.2 million) in 2045. The main modes of intervention for diabetes include medication, dietary management, and exercise conditioning. Medication is the mainstay of treatment. Marketed diabetes drugs such as metformin and insulin, as well as GLP-1 receptor agonists, are effective in controlling blood sugar levels to some extent, but the preventive and therapeutic effects are still unsatisfactory. Peptide drugs have many advantages such as low toxicity, high target specificity, and good biocompatibility, which opens up new avenues for the treatment of diabetes and other diseases. Currently, insulin and its analogs are by far the main life-saving drugs in clinical diabetes treatment, enabling effective control of blood glucose levels, but the risk of hypoglycemia is relatively high and treatment is limited by the route of delivery. New and oral anti-diabetic drugs have always been a market demand and research hotspot. Inhibitor cystine knot (ICK) peptides are a class of multifunctional cyclic peptides. In structure, they contain three conserved disulfide bonds (C3-C20, C7-C22, and C15-C32) form a compact “knot” structure, which can resist degradation of digestive protease. Recent studies have shown that ICK peptides derived from legume, such as PA1b, Aglycin, Vglycin, Iglycin, Dglycin, and aM₁, exhibit excellent regulatory activities on glucose and lipid metabolism at the cellular and animal levels. Mechanistically, ICK peptides promote glucose utilization by muscle and liver through activation of IR/AKT signaling pathway, which also improves insulin resistance. They can repair the damaged pancrease through activation of PI3K/AKT/Erk signaling pathway, thus lowering blood glucose. The biostability and hypoglycemic efficacy of the ICK peptides meet the requirements for commercialization of oral drugs, and in theory, they can be developed into natural oral anti-diabetes peptide drugs. In this review, the structural properties, activity and mechanism of ICK pattern peptides in regulating glucose and lipid metabolism were summaried, which provided a reference for the development of new oral peptides for diabetes.

ObjectiveMagnetoencephalography (MEG), a non-invasive neuroimaging technique, meticulously captures the magnetic fields emanating from brain electrical activity. Compared with MEG based on superconducting quantum interference devices (SQUID), MEG based on optically pump magnetometer (OPM) has the advantages of higher sensitivity, better spatial resolution and lower cost. However, most of the current studies are clinical studies, and there is a lack of animal studies on MEG based on OPM technology. Pain, a multifaceted sensory and emotional phenomenon, induces intricate alterations in brain activity, exhibiting notable sex differences. Despite clinical revelations of pain-related neuronal activity through MEG, specific properties remain elusive, and comprehensive laboratory studies on pain-associated brain activity alterations are lacking. The aim of this study was to investigate the effects of inflammatory pain (induced by Complete Freund’s Adjuvant (CFA)) on brain activity in a rat model using the MEG technique, to analysis changes in brain activity during pain perception, and to explore sex differences in pain-related MEG signaling. MethodsThis study utilized adult male and female Sprague-Dawley rats. Inflammatory pain was induced via intraplantar injection of CFA (100 μl, 50% in saline) in the left hind paw, with control groups receiving saline. Pain behavior was assessed using von Frey filaments at baseline and 1 h post-injection. For MEG recording, anesthetized rats had an OPM positioned on their head within a magnetic shield, undergoing two 15-minute sessions: a 5-minute baseline followed by a 10-minute mechanical stimulation phase. Data analysis included artifact removal and time-frequency analysis of spontaneous brain activity using accumulated spectrograms, generating spectrograms focused on the 4-30 Hz frequency range. ResultsMEG recordings in anesthetized rats during resting states and hind paw mechanical stimulation were compared, before and after saline/CFA injections. Mechanical stimulation elevated alpha activity in both male and female rats pre- and post-saline/CFA injections. Saline/CFA injections augmented average power in both sexes compared to pre-injection states. Remarkably, female rats exhibited higher average spectral power 1 h after CFA injection than after saline injection during resting states. Furthermore, despite comparable pain thresholds measured by classical pain behavioral tests post-CFA treatment, female rats displayed higher average power than males in the resting state after CFA injection. ConclusionThese results imply an enhanced perception of inflammatory pain in female rats compared to their male counterparts. Our study exhibits sex differences in alpha activities following CFA injection, highlighting heightened brain alpha activity in female rats during acute inflammatory pain in the resting state. Our study provides a method for OPM-based MEG recordings to be used to study brain activity in anaesthetized animals. In addition, the findings of this study contribute to a deeper understanding of pain-related neural activity and pain sex differences.

Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.