Parkinson's disease （PD） is a neurodegenerative disorder primarily characterized by motor dysfunction. Traditionally， its main clinical features include resting tremor， muscular rigidity， bradykinesia， and postural balance disorders. However， an increasing number of studies have shown that its non-motor symptoms （NMS） exert an even greater impact on patients' quality of life than motor symptoms， severely affecting daily functioning and increasing the burden on families and society. Among these， depression is one of the most common and most debilitating NMS， with statistics indicating that the incidence of depression among PD patients reaches as high as 40%-50%. The pathological mechanisms are complex， involving the interplay between degenerative changes in dopaminergic neurons and disruptions in emotional regulatory circuits， which poses a substantial challenge to clinical treatment. Traditional Chinese medicine （TCM）， characterized by holistic regulation and multi-target intervention， has demonstrated significant advantages in the treatment of PD and depression， offering new insights for managing PD-depression comorbidity. This study integrates research extracted from multiple databases， including PubMed， Web of Science， Google Scholar， and China National Knowledge Infrastructure （CNKI）， that investigates the potential mechanisms of PD and depression as well as TCM-based treatments for these conditions. The aim is to elucidate the shared pathological mechanisms underlying PD and depression and to explore the therapeutic potential of TCM in effectively combating PD-depression comorbidity through these shared mechanisms， thereby providing valuable insights for the development of targeted therapies.

ObjectiveThis study aims to explore the neurotoxicity mechanism of Dictamni Cortex by integrating network toxicology and metabolomics techniques. MethodsThe neurotoxicity targets induced by Dictamni Cortex were screened by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP）， Traditional Chinese Medicine Information Database （TCM-ID）， and Comparative Toxicogenomics Database （CTD）. The target predictions of the components were performed by the Swiss Target Prediction tool. Neurotoxicity-related targets were collected from the Pharmacophore Mapping and Potential Target Identification Platform （PharmMapper）， GeneCards Human Gene Database （GeneCards）， DisGeNET Disease Gene Network （DisGeNET）， and Online Mendelian Inheritance in Man （OMIM）， and the intersection targets were identified. Protein-protein interaction （PPI） analysis， Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway analysis， and Gene Ontology （GO） enrichment analysis were conducted. A "drug-compound-toxicity target-pathway" network was constructed via Cytoscape software to display the core regulatory network. Based on the prediction results， the neurotoxicity mechanism of Dictamni Cortex in mice was verified by using hematoxylin-eosin （HE） staining， Nissl staining， enzyme-linked immunosorbent assay （ELISA）， quantitative real-time fluorescence polymerase chain reaction （Real-time PCR）， and Western blot. The effects of Dictamni Cortex on the metabolic profile of mouse brain tissue were further explored by non-targeted metabolomics. ResultsNetwork toxicology screening identified 13 compounds and 175 targets in Dictamni Cortex that were related to neurotoxicity. PPI network analysis revealed that serine/threonine-protein kinase （Akt1） and tumor protein 53 （TP53） were the core targets. Additionally， GO/KEGG enrichment analysis indicated that Dictamni Cortex may regulate the phosphatidylinositol 3-kinase （PI3K）/Akt pathway and affect oxidative stress and cell apoptosis， thereby inducing neural damage. The "Dictamni Cortex-compound-toxicity target-pathway-neural damage" network showed that dictamnine， phellodendrine， and fraxinellone may be the toxic compounds. Animal experiments showed that compared with those in the blank group， the hippocampal neurons in the brain tissue of mice treated with Dictamni Cortex were damaged. The level of superoxide dismutase （SOD） and acetylcholine （ACh） in the brain tissue was significantly reduced， while the content of malondialdehyde （MDA） was significantly increased. The level of Akt1 and p-Akt1 mRNAs and proteins in the brain tissue was significantly decreased， while the level of TP53 was significantly increased. Non-targeted metabolomics results showed that Dictamni Cortex could disrupt the level of 40 metabolites in mouse brain tissue， thereby regulating the homeostasis of 13 metabolism pathways， including phenylalanine， glycerophospholipid， and retinol. Combined analysis revealed that Akt1， p-Akt1， and TP53 were significantly correlated with phenylalanine， glycerophospholipid， and retinol metabolites. This suggested that Dictamni Cortex induced neurotoxicity in mice by regulating Akt1， p-Akt1， and TP53 and further modulating the phenylalanine， glycerophospholipid， and retinol metabolism pathways. ConclusionDictamni Cortex can induce neurotoxicity in mice， and its potential mechanism may be closely related to the activation of oxidative stress， inhibition of the PI3K/Akt signaling pathway， and regulation of phenylalanine， glycerophospholipid， and retinol metabolism pathways.

ObjectiveThis study aims to investigate the effect of Dictamni Cortex on intestinal barrier damage in rats and its mechanism by untargeted metabolomics and targeted metabolomics for short-chain fatty acids （SCFAs）. MethodsRats were randomly divided into a control group， a high-dose group of Dictamni Cortex （8.1 g·kg^-1）， a medium-dose group （2.7 g·kg^-1）， and a low-dose group （0.9 g·kg^-1）. Except for the control group， the other groups were administered different doses of Dictamni Cortex by gavage for eight consecutive weeks. Hematoxylin-eosin （HE） staining was used to observe the pathological changes in the ileal tissue. Enzyme-linked immunosorbent assay （ELISA） was employed to detect the level of cytokines， including tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， and interleukin-1β （IL-1β）， in the ileal tissue of rats. Quantitative real-time fluorescence polymerase chain reaction （Real-time PCR） technology was used to detect the expression level of tight junction proteins， including zonula occludens-1 （ZO-1）， Occludin， and Claudin-1 mRNAs， in the ileal tissue of rats to preliminarily explore the effects of Dictamni Cortex on intestinal damage. The dose with the most significant toxic phenotype was selected to further reveal the effects of Dictamni Cortex on the metabolic profile of ileal tissue in rats by non-targeted metabolomics combined with targeted metabolomics for SCFAs. ResultsCompared with the control group， all doses of Dictamni Cortex induced varying degrees of pathological damage in the ileum， increased TNF-α （P<0.01）， IL-6 （P<0.01）， and IL-1β （P<0.01） levels in the ileal tissue， and decreased the expression level of ZO-1 （P<0.05， P<0.01）， Occludin （P<0.01）， and Claudin-1 （P<0.05） in the ileal tissue， with the high-dose group showing the most significant toxic phenotypes. The damage mechanisms of the high-dose group of Dictamni Cortex on the ileal tissue were further explored by integrating non-targeted metabolomics and targeted metabolomics for SCFAs. The non-targeted metabolomics results showed that 21 differential metabolites were identified in the control group and the high-dose group. Compared with that in the control group， after Dictamni Cortex intervention， the level of 14 metabolites was significantly increased （P<0.05， P<0.01）， and the level of seven metabolites was significantly decreased （P<0.05， P<0.01） in the ileal contents. These metabolites collectively acted on 10 related metabolic pathways， including glycerophospholipids and primary bile acid biosynthesis. The quantitative data of targeted metabolomics for SCFAs showed that Dictamni Cortex intervention disrupted the level of propionic acid， butyric acid， acetic acid， caproic acid， isobutyric acid， isovaleric acid， valeric acid， and isocaproic acid in the ileal contents of rats. Compared with those in the control group， the level of isobutyric acid， isovaleric acid， and valeric acid were significantly increased， while the level of propionic acid， butyric acid， and acetic acid were significantly decreased in the ileal contents of rats after Dictamni Cortex intervention （P<0.05， P<0.01）. ConclusionDictamni Cortex can induce intestinal damage by regulating glycerophospholipid metabolism， primary bile acid biosynthesis， and metabolic pathways for SCFAs.

Mitochondrial dysfunction is a critical factor in the pathogenesis of Alzheimer's disease (AD). The mitochondrial contact site and cristae organizing system (MICOS) plays a pivotal role in shaping the inner mitochondrial membrane, forming cristae junctions and establishing interaction sites between the inner and outer mitochondrial membranes and thereby serving as a cornerstone of mitochondrial structure and function. In the past decade, MICOS abnormalities have been extensively linked to AD pathogenesis. In particular, dysregulated expression of MICOS subunits and mutations in MICOS-related genes have been identified in AD, often in association with hallmark pathological features such as amyloid-β plaque accumulation, neurofibrillary tangle formation, and neuronal apoptosis. Furthermore, MICOS subunits interact with several etiologically relevant proteins, significantly influencing AD progression. The intricate crosstalk between these proteins and MICOS subunits underscores the relevance of MICOS dysfunction in AD. Therapeutic strategies targeting MICOS subunits or their interacting proteins may offer novel approaches for AD treatment. In the present review, we introduce current understanding of MICOS structures and functions, highlight MICOS pathogenesis in AD, and summarize the available MICOS-targeting drugs potentially useful for AD.

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent inflammation and joint damage, accompanied by the accumulation of plasma cells, which contributes to its pathogenesis. Understanding the genetic alterations occurring during plasma cell differentiation in RA can deepen our comprehension of its pathogenesis and guide the development of targeted therapeutic interventions. Here, our study elucidates the intricate molecular mechanisms underlying plasma cell differentiation by demonstrating that PRDX1 interacts with DOK3 and modulates its degradation by the autophagy-lysosome pathway. This interaction results in the inhibition of plasma cell differentiation, thereby alleviating the progression of collagen-induced arthritis. Additionally, our investigation identifies Salvianolic acid B (SAB) as a potent small molecular glue-like compound that enhances the interaction between PRDX1 and DOK3, consequently impeding the progression of collagen-induced arthritis by inhibiting plasma cell differentiation. Collectively, these findings underscore the therapeutic potential of developing chemical stabilizers for the PRDX1-DOK3 complex in suppressing plasma cell differentiation for RA treatment and establish a theoretical basis for targeting PRDX1-protein interactions as specific therapeutic targets in various diseases.

Probiotics play a crucial role in colon cancer treatment by metabolizing prebiotics to generate short-chain fatty acids (SCFAs). Colon cancer patients are frequently propositioned to supplement with probiotics to enhance the conversion and utilization of prebiotics. Nevertheless, the delivery and colonization of probiotics is hindered by the harsh conditions of gastrointestinal tract (GIT). Here, we devised a straightforward yet potent modified prebiotic-based "shield" (Gelatin-Inulin, GI), employing dietary inulin and natural polymer gelatin crosslinked via hydrogen bonding for enveloping Lactobacillus reuteri (Lr) to formulate synbiotic hydrogel capsules (Lr@Gl). The GI "shield" serves as a dynamic barrier, augmenting the resistance of Lr to gastric acid and facilitating its bioactivity and adherence in the GIT, synergizing with Lr to elicit an anti-tumor effect. Simultaneously, Lr@GI demonstrates anti-tumor effects by depleting glutathione to release reactive oxygen species, accompanied by the activation of NLRP3 (NOD-like receptor family pyrin domain containing 3), and the induction M1 macrophage polarization. Furthermore, Lr@GI can not only promote the recovery of intestinal barrier but also regulate intestinal flora, promoting the production of SCFAs and further exerting anti-tumor effect. Crucially, Lr@GI also potentiates the anti-tumor effect of 5-Fluorouracil. The construction and synergistic anti-tumor mechanism of synbiotic hydrogel capsules system provide valuable insights for gut microbial tumor therapy.

Electrical impedance tomography (EIT) is a new non-invasive functional imaging technology, which has the advantages of non-invasion, non-radiation, low cost, fast response, portability and visualization. In recent years, more and more studies have shown that EIT has great potential in the detection of lung diseases and has been applied to early diagnosis and treatment of some diseases. This paper introduced the basic principle of EIT, discussed the research and clinical application of EIT in the detection of acute respiratory distress syndrome, chronic obstructive pulmonary disease, pneumothorax and pulmonary embolism, and focused on the summary and introduction of indicators and functional images of EIT related to the detection of lung diseases. This review will help medical workers understand and use EIT, and promote the further development of EIT in lung diseases as well as other fields.
Humans ; Electric Impedance ; Tomography/methods* ; Lung Diseases/diagnosis* ; Pulmonary Disease, Chronic Obstructive/diagnosis* ; Pulmonary Embolism/diagnosis* ; Respiratory Distress Syndrome/diagnosis*

Objective:To investigate the effect of nuclide distribution with time from the in vivo metabolism based on measurement of radioactive contamination using lung counting method. Methods:The distribution of nuclides in the body with time was calculated on a basis of a single inhalation of aerosols containing 235U and the International Commission of Radiological Protection(ICRP) nuclide metabolism compartment model. A passive efficiency calibration of the lung counter, was performed using the simulation and calculation software Geant4 to obtain the contribution of the tissue or organs of interest to the lung counter, and to investigate the effect of the nuclide distribution on the lung counting method. Results:The time elapsed after inhalation of radionuclides, as well as their physicochemical state, has the effect on their distribution in the body and on the detection efficiency of the lung counter. Radionuclides with smaller particulate sizes have a higher initial retention in the lungs, and those with an activity median aerodynamic diameter (ADAM) of 1 μm contributed more fraction to the lung counter than those with an ADAM of 5 μm. F-type compounds were metabolized more rapidly by the respiratory system, and after 8 h of ingestion, nuclides were distributed in the lungs. F-type compounds were metabolized in the respiratory system at a relatively fast rate, and 8 h after inhalation, the fraction of nuclides retained in the lung contributed no more than 30% to the lung counter. Within 3 d after ingestion of M-type and S-type compounds, radioactive particulats largely deposites in the nasopharyngeal region. With biological metabolization and clearance, the fraction contributed by lung to counter is in rising, and the fraction to the lung counter typically remained larger than 80% after 3 d.Conclusions:Radionuclide metabolization in the body varies with their physicochemical properties and measurement time and site. For estimating internal contamination, consideration should be given to the distribution of nuclides, in order to avoid the overestimation.

Objective To construct a quality control model for cervical liquid-based thin cell smears using two different artificial intelligence(AI)techniques and to compare the total use of the two methods to improve the level of quality control of cervical liquid-based thin cell smears through the assistance of hybrid AI.Methods In this study,105 cervical liquid-based thin cell smear samples were used.Convolutional neural network(CNN)algorithm and Transformer network algorithm were used as specific AI algorithms in the AI model.The labeled features included the number of cells in the slice,excessive red blood cells,excessive inflammatory cells,and air bubbles.The smear samples were pre-processed and digitized by smear,followed by image segmentation and feature extraction.Using the labeled feature data,machine learning models were trained and optimized.Statistical AI and physician QC results were analyzed by calculating KAPPA index,sensitivity,specificity,area under the curve(AUC),and other indexes for AI QC results.Results CNN algorithm QC results in normal smear,inflammatory background and bloody background were significantly different from the expert review QC results(P＜0.001).Transformer algorithm QC results were similar to the expert review results,with no statistical difference(P＞0.05).General practitioner QC results were statistically different from the expert review QC results in normal smear detection rate and bloody background(P＜0.001).CNN algorithm Kappa value was 0.567,which had medium consistency with expert review results.Transformer algorithm Kappa value was 0.890,with the best consistency with expert review results.General practitioner Kappa value was 0.675,which had better consistency with expert review results.Using the expert review results as a reference standard,the predictive efficacy of the Transformer algorithm and the general practitioners' QC results was evaluated,and the predictive efficacy of the Transformer algorithm was higher than that of the general practitioners in detecting hemorrhagic backgrounds and normal smears(inflammatory backgrounds:AUC=1.000;normal smears:AUC=0.768)(hemorrhagic backgrounds:AUC=0.849;normal smears:AUC=0.849;normal smear:AUC=0.500).Conclusion In this study,we found that the Transformer algorithm was effective in improving the quality control of cervical liquid-based thin-layer cell smears by assisting doctors to perform smear quality control scoring and improving the efficiency and accuracy of smear sample quality control.It can be used as a new quality control method for cervical cancer cytological screening and has potential clinical applications.

Due to its high sensitivity and non-destructive nature, Raman spectroscopy has become an essential analytical tool in biopharmaceutical analysis and drug development. Despite of the computational demands, data requirements, or ethical considerations, artificial intelligence (AI) and particularly deep learning algorithms has further advanced Raman spectroscopy by enhancing data processing, feature extraction, and model optimization, which not only improves the accuracy and efficiency of Raman spectroscopy detection, but also greatly expands its range of application. AI-guided Raman spectroscopy has numerous applications in biomedicine, including characterizing drug structures, analyzing drug forms, controlling drug quality, identifying components, and studying drug-biomolecule interactions. AI-guided Raman spectroscopy has also revolutionized biomedical research and clinical diagnostics, particularly in disease early diagnosis and treatment optimization. Therefore, AI methods are crucial to advancing Raman spectroscopy in biopharmaceutical research and clinical diagnostics, offering new perspectives and tools for disease treatment and pharmaceutical process control. In summary, integrating AI and Raman spectroscopy in biomedicine has significantly improved analytical capabilities, offering innovative approaches for research and clinical applications.