Depression is a widespread psychiatric disorder with complex pathogenesis and unsatisfactory therapeutic effects. As a native flavonoid, Isorhamnetin (ISO) has been deemed to exert neuroprotective effects by antioxidation and regulation of immunity. However, no reports of anti-depressed effect of ISO have yet been found. The present study was conducted to clarify the mechanism basis of anti-depressed effect of ISO utilizing behavioral, biochemical, molecular approaches in vitro and in vivo and bio-informatics analysis. The effects of ISO on depressed mice was investigated through the SPT and FST, and the lesions were examined by H&E staining. Besides, the inflammatory factor and indicator in kynurenine pathway were assessed through detection kits, and the microbiota were checked by 16sRNA. Molecular docking study was performed to investigate the target of ISO. Additionally, Western blot was used to test the activation of PI3K/AKT signaling pathway. The results indicated that ISO could enhance the sugar water preference of mice in SPT and reduce immobility time in FST. Further more, ISO suppressed peripheral and central inflammation, regulated the changes in kynurenine pathway and gut microbiota, inhibited activation of PI3K/AKT pathway, and presented good binding patterns with target proteins on PI3K/AKT signaling pathway. Collectively, these findings demonstrate that ISO alleviated depression-like behaviour by normalizing inflammation-induced dysregulation of the crosstalk between KP and gut microbiota disorder through regulated PI3K/AKT/NF-κB pathway.

Depression is a widespread psychiatric disorder with complex pathogenesis and unsatisfactory therapeutic effects. As a native flavonoid, Isorhamnetin (ISO) has been deemed to exert neuroprotective effects by antioxidation and regulation of immunity. However, no reports of anti-depressed effect of ISO have yet been found. The present study was conducted to clarify the mechanism basis of anti-depressed effect of ISO utilizing behavioral, biochemical, molecular approaches in vitro and in vivo and bio-informatics analysis. The effects of ISO on depressed mice was investigated through the SPT and FST, and the lesions were examined by H&E staining. Besides, the inflammatory factor and indicator in kynurenine pathway were assessed through detection kits, and the microbiota were checked by 16sRNA. Molecular docking study was performed to investigate the target of ISO. Additionally, Western blot was used to test the activation of PI3K/AKT signaling pathway. The results indicated that ISO could enhance the sugar water preference of mice in SPT and reduce immobility time in FST. Further more, ISO suppressed peripheral and central inflammation, regulated the changes in kynurenine pathway and gut microbiota, inhibited activation of PI3K/AKT pathway, and presented good binding patterns with target proteins on PI3K/AKT signaling pathway. Collectively, these findings demonstrate that ISO alleviated depression-like behaviour by normalizing inflammation-induced dysregulation of the crosstalk between KP and gut microbiota disorder through regulated PI3K/AKT/NF-κB pathway.

Depression is a widespread psychiatric disorder with complex pathogenesis and unsatisfactory therapeutic effects. As a native flavonoid, Isorhamnetin (ISO) has been deemed to exert neuroprotective effects by antioxidation and regulation of immunity. However, no reports of anti-depressed effect of ISO have yet been found. The present study was conducted to clarify the mechanism basis of anti-depressed effect of ISO utilizing behavioral, biochemical, molecular approaches in vitro and in vivo and bio-informatics analysis. The effects of ISO on depressed mice was investigated through the SPT and FST, and the lesions were examined by H&E staining. Besides, the inflammatory factor and indicator in kynurenine pathway were assessed through detection kits, and the microbiota were checked by 16sRNA. Molecular docking study was performed to investigate the target of ISO. Additionally, Western blot was used to test the activation of PI3K/AKT signaling pathway. The results indicated that ISO could enhance the sugar water preference of mice in SPT and reduce immobility time in FST. Further more, ISO suppressed peripheral and central inflammation, regulated the changes in kynurenine pathway and gut microbiota, inhibited activation of PI3K/AKT pathway, and presented good binding patterns with target proteins on PI3K/AKT signaling pathway. Collectively, these findings demonstrate that ISO alleviated depression-like behaviour by normalizing inflammation-induced dysregulation of the crosstalk between KP and gut microbiota disorder through regulated PI3K/AKT/NF-κB pathway.

Objective: To investigate the association between plant-based diet and different types of obesity, so as to provide references for obesity prevention. Methods: Residents aged 35-75 years from 33 counties (cities, districts) in Zhejiang Province were selected as study subjects using a multistage stratified random sampling method between April and December 2024. Demographic information and living behaviors were collected using questionnaire surveys. Height, weight and waist circumference were measured, and body mass index (BMI) was calculated. BMI ≥28.0 kg/m2 was defined as obesity, waist circumference ≥90 cm in males or ≥85 cm in females was defined as central obesity, and individual with obesity who also had central obesity was defined as having compound obesity. Food intake over a 3-day period was collected using the consecutive 3-day 24-hour dietary recall method. The plant diet index (PDI), healthful plant diet index (HPDI), and unhealthful plant diet index (UPDI) were calculated, and categorized into quintiles (Q1-Q5) based on their distribution. Association between the PDI, PDI, UPDI and different types of obesity were analyzed using multivariable logistic regression models. Results: A total of 4 882 individuals were surveyed, including 2 233 males (45.74%) and 2 649 females (54.26%). The average age was (55.42±12.14) years. There were 537 individuals of obesity, 1 718 individuals of central obesity, and 500 individuals of compound obesity, with detection rates of 11.00%, 35.19%, and 10.24%, respectively. Multivariable logistic regression analysis showed that, after adjusting for demographic information and living behaviors, compared with Q1 group, HPDI Q5 group showed a 29.6% lower risk of obesity (OR=0.704, 95%CI: 0.525-0.943) and a 32.1% lower risk of compound obesity (OR=0.679, 95%CI: 0.502-0.918). Conversely, the UPDI Q5 group exhibited a 39.5% higher risk of obesity (OR=1.395, 95%CI: 1.032-1.886) and a 39.8% higher risk of compound obesity (OR=1.398, 95%CI: 1.025-1.907). No statistically significant association was found between PDI and obesity, central obesity, and compound obesity (all P>0.05). As HPDI increased, the risks of obesity and compound obesity showed decreasing trends; as UPDI increased, the risks of obesity and compound obesity showed increasing trends (all Ptrend<0.05). Conclusion A healthful plant-based diet is associated with reduced risks of obesity and compound obesity, while an unhealthful plant-based diet is associated with increased risks of obesity and compound obesity.

OBJECTIVE: To screen the population for acupuncture treatment of neck pain, using functional magnetic resonance imaging (fMRI) technology and based on machine learning algorithms. METHODS: Eighty patients with neck pain were recruited. Using FPX25 handheld pressure algometer, the tender points were detected in the areas with high-frequent onset of neck pain and high degree of acupoint sensitization. Acupuncture was delivered at 4 tender points with the lowest pain threshold, once every two days; and the treatment was given 3 times a week and for 2 consecutive weeks. The amplitude of low-frequency fluctuation (ALFF) of the brain before treatment was taken as a predictive feature to construct support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN) models to predict the responses of neck pain patients to acupuncture treatment. A longitudinal analysis of the ALFF features was performed before and after treatment to reveal the potential biological markers of the reactivity to the acupuncture therapy. RESULTS: The SVM model could successfully distinguish high responders (48 cases) and low responders (32 cases) to acupuncture treatment, and its accuracy rate reached 82.5%. Based on the SVM model, the ALFF values of 4 brain regions were identified as the consistent predictive features, including the right middle temporal gyrus, the right superior occipital gyrus, and the bilateral posterior cingulate gyrus. In the patients with high acupuncture response, the ALFF value in the left posterior cingulate gyrus decreased after treatment (P<0.05), whereas in the patients with low acupuncture response, the ALFF value in the right superior occipital gyrus increased after treatment (P<0.01). The longitudinal functional connectivity (FC) analysis found that compared with those before treatment, the high responders showed the enhanced FC after treatment between the left posterior cingulate gyrus and various regions, including the bilateral Crus1 of the cerebellum, the right insula, the bilateral angular gyrus, the left medial superior frontal gyrus, and the left middle cingulate gyrus (GRF: corrected, voxel level: P<0.05, mass level: P<0.05). In contrast, the low responders exhibited the enhanced FC between the left posterior cingulate gyrus and the left Crus2 of the cerebellum, the left middle temporal gyrus, the right posterior cingulate gyrus, and the left angular gyrus; besides, FC was reduced in low responders between the left posterior cingulate gyrus and the right supramarginal gyrus (GRF: corrected, voxel level: P<0.05, mass level: P<0.05). CONCLUSION This study validates the practicality of pre-treatment ALFF feature prediction for acupuncture efficacy on neck pain. The therapeutic effect of acupuncture on neck pain is potentially associated with its impact on the default mode network, and then, alter the pain perception and emotional regulation.
Humans ; Neck Pain/physiopathology* ; Acupuncture Therapy ; Female ; Male ; Adult ; Middle Aged ; Machine Learning ; Magnetic Resonance Imaging ; Young Adult ; Brain/physiopathology* ; Acupuncture Points ; Aged

Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large language models, large visual models, and multimodal large models, offer unprecedented capabilities in processing and interpreting complex medical data across various imaging modalities. By leveraging self-supervised pretraining on vast unlabeled datasets, cross-modal representation learning, and domain-specific medical knowledge adaptation through fine-tuning, large models can achieve higher diagnostic accuracy and more efficient workflows for key clinical tasks. This review summarizes the concepts, methods, and progress of large models in medical imaging, highlighting their potential in precision medicine. The article first outlines the integration of multimodal data under large model technologies, approaches for training large models with medical datasets, and the need for robust evaluation metrics. It then explores how large models can revolutionize applications in critical tasks such as image segmentation, disease diagnosis, personalized treatment strategies, and real-time interactive systems, thus pushing the boundaries of traditional imaging analysis. Despite their potential, the practical implementation of large models in medical imaging faces notable challenges, including the scarcity of high-quality medical data, the need for optimized perception of imaging phenotypes, safety considerations, and seamless integration with existing clinical workflows and equipment. As research progresses, the development of more efficient, interpretable, and generalizable models will be critical to ensuring their reliable deployment across diverse clinical environments. This review aims to provide insights into the current state of the field and provide directions for future research to facilitate the broader adoption of large models in clinical practice.
Humans ; Diagnostic Imaging/methods* ; Precision Medicine/methods* ; Image Processing, Computer-Assisted/methods*

The PA-PB1 interface of the influenza polymerase is an attractive site for antiviral drug design. In this study, we designed and synthesized a mini-library of indazole-containing compounds based on rational structure-based design to target the PB1-binding interface on PA. Biological evaluation of these compounds through a viral yield reduction assay revealed that compounds 27 and 31 both had a low micromolar range of the half maximal effective concentration (EC₅₀) values against A/WSN/33 (H1N1) (8.03 μmol/L for 27; 14.6 μmol/L for 31), while the most potent candidate 24 had an EC₅₀ value of 690 nM. Compound 24 was effective against different influenza strains including a pandemic H1N1 strain and an influenza B strain. Mechanistic studies confirmed that compound 24 bound PA with a K _d which equals to 1.88 μmol/L and disrupted the binding of PB1 to PA. The compound also decreased the lung viral titre in mice. In summary, we have identified a potent anti-influenza candidate with potency comparable to existing drugs and is effective against different viral strains. The therapeutic options for influenza infection have been limited by the occurrence of antiviral resistance, owing to the high mutation rate of viral proteins targeted by available drugs. To alleviate the public health burden of this issue, novel anti-influenza drugs are desired. In this study, we present our discovery of a novel class of indazole-containing compounds which exhibited favourable potency against both influenza A and B viruses. The EC₅₀ of the most potent compounds were within low micromolar to nanomolar concentrations. Furthermore, we show that the mouse lung viral titre decreased due to treatment with compound 24. Thus our findings identify promising candidates for further development of anti-influenza drugs suitable for clinical use.

Purpose/Significance To develop a secure single-cell RNA sequencing(scRNA-seq)classification method,which can enhance data analysis precision and ensure the security of sensitive information,and to promote the application of scRNA-seq technology in various fields.Method/Process The paper proposes a solution based on trusted execution environment(TEE).The training data is encrypted and transmitted to TEE.It is decrypted in a secure and isolated environment,while training the model to obtain the trained model parameters.Automated cell type i-dentification using neural networks(ACTINN)and support vector machine(SVM)are used for cell classification in both TEE and traditional plain-text environments.The results are compared and analyzed.Result/Conclusion The results show that the F1 score of the two classification models in TEE environment reaches 0.904 and 0.879,respectively,which is comparable to the performance in traditional plaintext environment.The secure ex-ecution environment provided by TEE has extremely limited impact on the accuracy and efficiency of the models.This is of great significance for see-king both secure and efficient data processing solutions in scenarios where sensitive or private data needs to be processed.

Lung cancer is the top cause of cancer deaths globally.Advances in immune checkpoint inhibitors(ICIs)have transformed cancer treatment,but their use in lung cancer has led to more side effects.This study examined if past pulmonary tuberculosis(TB)affects ICIs'effectiveness and safety in lung cancer treatment.We reviewed lung cancer patients treated with ICIs at Beijing Chest Hospital from January 2019 to August 2022.We compared outcomes and side effects between patients with and without prior TB.Of 116 patients(40 with TB history,76 without),prior TB didn't reduce treatment effectiveness but did increase severe side effects.Notably,older patients(≥65 years)faced a higher risk of severe side effects.Detailed cases of two patients with severe side effects underscored TB as a risk factor in lung cancer patients receiving ICIs,stressing the need for careful monitoring and personalized care.

Malignant tumors represent a significant health challenge, critically impacting human well-being. Malignant tumors have become one of the leading causes of death worldwide. According to statistics from the World Health Organization, nearly one-sixth of global deaths in 2020 were caused by malignant tumors. The burden of malignant tumors in our country is also increasing. In recent years, with population aging and changes in lifestyle, the incidence and mortality rates of malignant tumors in China have been steadily rising, malignant tumors have gradually become one of the main causes of death in China. Developing effective diagnostic and treatment methods is of great significance in reducing the burden of malignant tumors in our country. Historically, the focus has been on leveraging the biochemical cues of tumors for both diagnosis and treatment. While valuable, this strategy does not recapitulate the full complexity of tumor diagnosis and management. Recently, the integration of biomechanics and mechanobiology with oncology has highlighted the importance of mechanical cues, which have emerged as new hallmarks of tumors, regulating tumor initiation and development are expected to open potential novel routes for cancer diagnosis and therapeutic interventions. Despite the advances, a thorough literature review suggests a pronounced gap in our understanding of the mechanical properties of tumors. The clinical community has not yet completely recognized the diagnostic and therapeutic relevance of the mechanical cues of tumors. To bridge this knowledge gap, we propose and introduce the paradigm of "Tumor Mechanomedicine". We provide a comprehensive overview of the multi-scale mechanical characteristics of tumors, exploring their influence on tumor biology, from the aspects of tumor biomechanics, tumor mechanobiology, tumor mechanodiagnostics, and tumor mechanotherapeutics. By elucidating the diagnostic and therapeutic potential of these mechanical cues, we aim to furnish the oncology community with fresh insights, paving the way for innovative solutions to persistent clinical conundrums.