Diabetic nephropathy （DKD）， as a common microvascular complication of diabetes mellitus （DM）， is a major cause of end-stage renal disease （ESRD）. Its clinical manifestations include increased urinary protein excretion， thickening of the glomerular basement membrane， and renal tubulointerstitial fibrosis. The pathogenesis of DKD is complex and involves multiple factors， including disordered glucose metabolism， hemodynamic alterations， and oxidative stress. Although modern medical approaches can alleviate certain symptoms， they still have limitations such as insufficient therapeutic targeting and prominent adverse effects. The transforming growth factor-β/Smad （TGF-β/Smad） signaling pathway is not only a tissue fibrosis pathway that has attracted considerable attention in recent years， but also regulates multiple protein molecules， including the glomerular podocyte slit diaphragm protein Podocin， interleukin-1β （IL-1β）， and superoxide dismutase （SOD）， thereby participating in various pathological processes and ultimately mediating renal injury. Flavonoid compounds， owing to their sustained pharmacological effects， broad spectrum of action， and high safety profile， have become ideal candidates for targeted therapy research in DKD. Existing studies have shown that these compounds can exert inhibitory effects on renal fibrosis， alleviate inflammatory responses， protect podocytes， and reduce oxidative stress by regulating the interactions between the TGF-β/Smad signaling pathway and the aforementioned protein molecules， thereby maintaining renal structure and function， reducing proteinuria， and significantly improving DKD lesions. This review briefly outlines the composition and functions of the TGF-β/Smad signaling pathway， elucidates the mechanisms by which this pathway regulates DKD， and focuses on summarizing major studies from the past decade on flavonoid-based interventions in DKD through targeted inhibition of the TGF-β/Smad signaling pathway. Furthermore， it discusses the considerable therapeutic potential of flavonoids in the treatment of this disease， aiming to provide a scientific basis for future clinical prevention and treatment of DKD and to promote the development of targeted drugs.

Objective To investigate the relationship between symptom burden and nutritional status under Traditional Chinese Medicine (TCM) constitution classification in patients undergoing chemotherapy. Methods Data on the physical constitution, symptom burden, and nutritional status of 640 patients with malignant tumors within the 21st–28th days of chemotherapy treatment were collected and analyzed. Results Symptom burden was found to have a positive correlation with nutritional risk and TCM bias (except for idiosyncrasies), suggesting that constitution bias may be an important internal factor for the aggravation of clinical symptoms and malnutrition in patients with cancer. Conclusion The relationship between symptom burden and nutritional status in chemotherapy under the guidance of TCM constitution can be studied to predict the nutritional status and symptom burden of patients after chemotherapy to guide the symptom and nutritional management of patients with cancer in the chemotherapy stage.

ObjectiveTo explore the efficacy and mechanisms of Sini San in the treatment of depression and liver injury based on gut microbiota. MethodsThirty-two male Sprague-Dawley （SD） rats were randomly divided into a normal group， model group （M）， Sini San group （MS， 2.5 g·kg^-1）， and fluoxetine group （MF， 2 mg·kg^-1）. Except for the normal group， rats in the other three groups were subjected to chronic unpredictable mild stress （CUMS）. After 8 weeks， the open-field test and sucrose preference test were conducted. Enzyme-linked immunosorbent assay （ELISA） was used to detect serum corticosterone （CORT）， adrenocorticotropic hormone （ACTH）， corticotropin-releasing factor （CRF）， lipopolysaccharide （LPS）， Zonulin， interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， γ-aminobutyric acid （GABA） levels in the hippocampus and prefrontal cortex， and brain-derived neurotrophic factor （BDNF） levels in the hippocampus. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to detect hippocampal BDNF mRNA expression. Serum alanine aminotransferase （ALT） and aspartate aminotransferase （AST） levels were measured using the ultraviolet lactate dehydrogenase method. The ultrastructure of the intestinal epithelium was observed by electron microscopy， and gut microbiota in rat feces were analyzed using 16S rDNA high-throughput sequencing. ResultsCompared with the normal group， the sucrose preference of rats in the model group was significantly reduced （P0.01）， whereas it was significantly increased in the Sini San group compared with the model group （P0.05）. Compared with the normal group， hippocampal GABA protein levels and BDNF mRNA expression in the model group were significantly decreased （P0.05）， and compared with the model group， both were significantly increased in the Sini San group （P0.05， P0.01）. Compared with the normal group， serum LPS and Zonulin levels in the model group were significantly increased （P0.05， P0.01）， and compared with the model group， Zonulin levels in the Sini San group were significantly decreased （P0.05）. No obvious changes were observed in the ultrastructure of the jejunal mucosa among groups. Compared with the normal group， widened and blurred tight junctions， sparse and shortened microvilli， and mitochondrial swelling with cristae disruption in epithelial cells were observed in the ileal and colonic mucosa of the model group， which were markedly improved in the Sini San and fluoxetine groups. The results of 16S rDNA high-throughput sequencing showed that Sini San improved CUMS-induced dysbiosis of Bacteroidetes and Proteobacteria. Correlation analysis indicated that Bacteroidetes and Proteobacteria were significantly correlated with depression-related indicators， liver function， and intestinal mucosal permeability. ConclusionSini San exerts antidepressant and hepatoprotective effects by improving Bacteroidetes and Proteobacteria and inhibiting the increase in intestinal mucosal permeability in CUMS rats.

ObjectiveTo explore the efficacy and mechanisms of Sini San in the treatment of depression and liver injury based on gut microbiota. MethodsThirty-two male Sprague-Dawley （SD） rats were randomly divided into a normal group， model group （M）， Sini San group （MS， 2.5 g·kg^-1）， and fluoxetine group （MF， 2 mg·kg^-1）. Except for the normal group， rats in the other three groups were subjected to chronic unpredictable mild stress （CUMS）. After 8 weeks， the open-field test and sucrose preference test were conducted. Enzyme-linked immunosorbent assay （ELISA） was used to detect serum corticosterone （CORT）， adrenocorticotropic hormone （ACTH）， corticotropin-releasing factor （CRF）， lipopolysaccharide （LPS）， Zonulin， interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， γ-aminobutyric acid （GABA） levels in the hippocampus and prefrontal cortex， and brain-derived neurotrophic factor （BDNF） levels in the hippocampus. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to detect hippocampal BDNF mRNA expression. Serum alanine aminotransferase （ALT） and aspartate aminotransferase （AST） levels were measured using the ultraviolet lactate dehydrogenase method. The ultrastructure of the intestinal epithelium was observed by electron microscopy， and gut microbiota in rat feces were analyzed using 16S rDNA high-throughput sequencing. ResultsCompared with the normal group， the sucrose preference of rats in the model group was significantly reduced （P<0.01）， whereas it was significantly increased in the Sini San group compared with the model group （P<0.05）. Compared with the normal group， hippocampal GABA protein levels and BDNF mRNA expression in the model group were significantly decreased （P<0.05）， and compared with the model group， both were significantly increased in the Sini San group （P<0.05， P<0.01）. Compared with the normal group， serum LPS and Zonulin levels in the model group were significantly increased （P<0.05， P<0.01）， and compared with the model group， Zonulin levels in the Sini San group were significantly decreased （P<0.05）. No obvious changes were observed in the ultrastructure of the jejunal mucosa among groups. Compared with the normal group， widened and blurred tight junctions， sparse and shortened microvilli， and mitochondrial swelling with cristae disruption in epithelial cells were observed in the ileal and colonic mucosa of the model group， which were markedly improved in the Sini San and fluoxetine groups. The results of 16S rDNA high-throughput sequencing showed that Sini San improved CUMS-induced dysbiosis of Bacteroidetes and Proteobacteria. Correlation analysis indicated that Bacteroidetes and Proteobacteria were significantly correlated with depression-related indicators， liver function， and intestinal mucosal permeability. ConclusionSini San exerts antidepressant and hepatoprotective effects by improving Bacteroidetes and Proteobacteria and inhibiting the increase in intestinal mucosal permeability in CUMS rats.

BACKGROUND:Curcumin is the main active ingredient of turmeric and has significant medicinal value in anti-tumor,anti-inflammatory,antioxidant and other aspects.However,its poor water solubility,unstable chemical properties and easy decomposition lead to difficulty in extracting curcumin and low extraction yield.Therefore,it is particularly important to optimize the curcumin extraction method.OBJECTIVE:To enhance the extraction yield and utilization value of curcumin and optimize the curcumin extraction process and curcumin nanoparticle preparation process.METHODS:Curcumin was extracted from turmeric by ethanol extraction,ultrasonic extraction,ionic liquid extraction,enzyme extraction,and ionic liquid combined with ultrasonic assisted enzyme extraction.The curcumin extraction yield was detected by high performance liquid chromatography;the best extraction method was determined,and subsequent process optimization experiments were carried out.The curcumin extraction yield was the response value with the type of ionic liquid,reaction temperature,ultrasonic time,liquid-to-solid ratio,ionic liquid concentration,and enzyme-drug mass ratio as parameters.The optimal production process of ionic liquid combined with ultrasonic assisted enzyme extraction was determined by single factor combined response surface experiment.The optimal process for preparing curcumin nanoparticles by ionic crosslinking method was determined by single factor combined response surface experiment with acetic acid concentration,chitosan to sodium tripolyphosphate mass ratio,stirring rate,curcumin mass concentration,sodium tripolyphosphate mass concentration,and chitosan mass concentration as parameters,and drug encapsulation efficiency as response value.Curcumin nanoparticles were prepared under the optimal process,and the particle size,polydispersity index,Zata potential value,drug loading,stability,hemolysis rate,and antioxidant capacity in vivo and in vitro of the nanoparticles were detected.RESULTS AND CONCLUSION:(1)Among the five extraction methods,the curcumin yield of ionic liquid combined with ultrasound-assisted enzyme extraction was the highest,and this method was selected as the curcumin extraction method for subsequent experiments.The results of single factor combined response surface experiment showed that the optimal process for curcumin extraction was:ionic liquid selected 1-hexyl-3-methylimidazolium chloride,reaction temperature 55 ℃,liquid-to-solid ratio 40 mL/g,ultrasound time 57 minutes,ionic liquid concentration 57％,enzyme-drug mass ratio 3.5:10,and the obtained turmeric extraction yield was 3.10％.The optimal preparation process of curcumin nanoparticles was:glacial acetic acid concentration 0.5％,chitosan and sodium tripolyphosphate mass ratio 5.0:1,stirring speed 150 r/min,curcumin mass concentration 2.23 mg/mL,sodium tripolyphosphate mass concentration 1.45 mg/mL,chitosan mass concentration 3.63 mg/mL,and the obtained drug encapsulation efficiency was 90.61％.(2)The drug loading of curcumin nanoparticles was(14.49±0.23)％,the average particle size was(76.95±1.65)nm,the polydispersity coefficient was 0.15±0.02,and the Zata potential value was(32.37±1.46)mV.The curcumin nanoparticles had good stability and blood compatibility,did not induce hemolysis,and had stronger antioxidant capacity in vivo and in vitro than free curcumin.(3)The results show that the process optimization not only solves the problems of low extraction yield,poor solubility,and low bioavailability of curcumin,but also enhances its antioxidant activity in vivo and in vitro.

BACKGROUND:Curcumin is the main active ingredient of turmeric and has significant medicinal value in anti-tumor,anti-inflammatory,antioxidant and other aspects.However,its poor water solubility,unstable chemical properties and easy decomposition lead to difficulty in extracting curcumin and low extraction yield.Therefore,it is particularly important to optimize the curcumin extraction method.OBJECTIVE:To enhance the extraction yield and utilization value of curcumin and optimize the curcumin extraction process and curcumin nanoparticle preparation process.METHODS:Curcumin was extracted from turmeric by ethanol extraction,ultrasonic extraction,ionic liquid extraction,enzyme extraction,and ionic liquid combined with ultrasonic assisted enzyme extraction.The curcumin extraction yield was detected by high performance liquid chromatography;the best extraction method was determined,and subsequent process optimization experiments were carried out.The curcumin extraction yield was the response value with the type of ionic liquid,reaction temperature,ultrasonic time,liquid-to-solid ratio,ionic liquid concentration,and enzyme-drug mass ratio as parameters.The optimal production process of ionic liquid combined with ultrasonic assisted enzyme extraction was determined by single factor combined response surface experiment.The optimal process for preparing curcumin nanoparticles by ionic crosslinking method was determined by single factor combined response surface experiment with acetic acid concentration,chitosan to sodium tripolyphosphate mass ratio,stirring rate,curcumin mass concentration,sodium tripolyphosphate mass concentration,and chitosan mass concentration as parameters,and drug encapsulation efficiency as response value.Curcumin nanoparticles were prepared under the optimal process,and the particle size,polydispersity index,Zata potential value,drug loading,stability,hemolysis rate,and antioxidant capacity in vivo and in vitro of the nanoparticles were detected.RESULTS AND CONCLUSION:(1)Among the five extraction methods,the curcumin yield of ionic liquid combined with ultrasound-assisted enzyme extraction was the highest,and this method was selected as the curcumin extraction method for subsequent experiments.The results of single factor combined response surface experiment showed that the optimal process for curcumin extraction was:ionic liquid selected 1-hexyl-3-methylimidazolium chloride,reaction temperature 55 ℃,liquid-to-solid ratio 40 mL/g,ultrasound time 57 minutes,ionic liquid concentration 57％,enzyme-drug mass ratio 3.5:10,and the obtained turmeric extraction yield was 3.10％.The optimal preparation process of curcumin nanoparticles was:glacial acetic acid concentration 0.5％,chitosan and sodium tripolyphosphate mass ratio 5.0:1,stirring speed 150 r/min,curcumin mass concentration 2.23 mg/mL,sodium tripolyphosphate mass concentration 1.45 mg/mL,chitosan mass concentration 3.63 mg/mL,and the obtained drug encapsulation efficiency was 90.61％.(2)The drug loading of curcumin nanoparticles was(14.49±0.23)％,the average particle size was(76.95±1.65)nm,the polydispersity coefficient was 0.15±0.02,and the Zata potential value was(32.37±1.46)mV.The curcumin nanoparticles had good stability and blood compatibility,did not induce hemolysis,and had stronger antioxidant capacity in vivo and in vitro than free curcumin.(3)The results show that the process optimization not only solves the problems of low extraction yield,poor solubility,and low bioavailability of curcumin,but also enhances its antioxidant activity in vivo and in vitro.

This study aims to develop a fully automated method for tooth segmentation and root canal measurement based on cone beam computed tomography (CBCT) images, providing objective, efficient, and accurate measurement results to guide and assist clinicians in root canal diagnosis grading, instrument selection, and preoperative planning. The method utilized Attention U-Net to recognize tooth descriptors, cropped regions of interest (ROIs) based on the center of mass of these descriptors, and applied an integrated deep learning method for segmentation. The segmentation results were mapped back to the original coordinates and position-corrected, followed by automatic measurement and visualization of root canal lengths and angles. The results indicated that the Dice coefficient for segmentation was 96.42%, the Jaccard coefficient was 93.11%, the Hausdorff Distance was 2.07 mm, and the average surface distance was 0.23 mm, all of which surpassed existing methods. The relative error of the root canal working length measurement was 3.15% (< 5%), the curvature angle error was 2.85 °, and the correct classification rate of the treatment difficulty coefficient was 90.48%. The proposed methods all achieved favorable results, which can provide an important reference for clinical application.
Cone-Beam Computed Tomography/methods* ; Deep Learning ; Humans ; Dental Pulp Cavity/diagnostic imaging* ; Image Processing, Computer-Assisted/methods*

Artificial intelligence-enhanced brain-computer interfaces (BCI) are expected to significantly improve the performance of traditional BCIs in multiple aspects, including usability, user experience, and user satisfaction, particularly in terms of intelligence. However, such AI-integrated or AI-based BCI systems may introduce new ethical issues. This paper first evaluated the potential of AI technology, especially deep learning, in enhancing the performance of BCI systems, including improving decoding accuracy, information transfer rate, real-time performance, and adaptability. Building on this, it was considered that AI-enhanced BCI systems might introduce new or more severe ethical issues compared to traditional BCI systems. These include the possibility of making users' intentions and behaviors more predictable and manipulable, as well as the increased likelihood of technological abuse. The discussion also addressed measures to mitigate the ethical risks associated with these issues. It is hoped that this paper will promote a deeper understanding and reflection on the ethical risks and corresponding regulations of AI-enhanced BCIs.
Brain-Computer Interfaces/ethics* ; Artificial Intelligence/ethics* ; Humans ; Deep Learning ; User-Computer Interface ; Electroencephalography

Background With the progression of industrialization, an increasing number of emerging contaminants are entering aquatic environments, posing significant threats to the safety of drinking water. Therefore, establishing a system for identifying unknown hazardous factors and implementing safety warning mechanisms for drinking water is of paramount importance. Among these efforts, non-target screening plays a critical role, but its effectiveness is largely constrained by the scope of coverage of sample pre-treatment methods. Objective To integrate modern chromatography/mass spectrometry techniques with advanced data mining methods to develop a non-discriminatory sample pre-treatment method for comprehensive enrichment of unknown contaminants in drinking water, laying a technical foundation for the discovery and identification of unknown organic hazardous factors in drinking water. Methods A non-discriminatory pre-treatment method based on supramolecular and solid-phase extraction was developed. The final target compounds including 333 pesticides, 194 pharmaceuticals and personal care products (PPCPs), and 59 per- and polyfluoroalkyl substances (PFASs) were used for optimizing the pre-treatment method, confirming its coverage. The impacts of different eluents on the absolute recovery rates of target compounds were compared to select the conditions with the highest recovery for sample pre-treatment. The effects of different supramolecular solvents and salt concentrations on target compound recovery were also evaluated to determine the most suitable solvent and salt concentration. Results The solid-phase extraction elution solvents, supramolecular extraction solvents, and salt concentrations were optimized based on the target compound recovery rates. The optimal recovery conditions were achieved using 2 mL methanol, 2 mL methanol (containing 1% formic acid), 2 mL ethyl acetate, 2 mL dichloromethane, hexanediol supramolecular solvent, and 426 mg salt. The detection method developed based on these conditions showed a good linear relationship for all target compounds in the range of 0.1-100.0 ng·mL⁻¹, with R² > 0.99. The method’s limit of detection ranged from 0.01 ng⁻¹ to 0.95 ng⁻¹, and 95% of target compounds were recovered in the range of 20%-120%, with relative standard deviation (RSD) less than 30%, indicating good precision. Conclusion The combined pre-treatment method of solid-phase extraction and supramolecular solvent extraction can effectively enrich contaminants in drinking water across low, medium, and high polarities, enabling broad-spectrum enrichment of diverse trace contaminants in drinking water. It provides technical support for broad-spectrum, high-throughput screening and identification of organic pollutants in drinking water, and also serves as a reference for establishing urban drinking water public safety warning systems.

Peptide receptor radionuclide therapy (PRRT) with radiolabeled SSTR2 agonists is a treatment option that is highly effective in controlling metastatic and progressive neuroendocrine tumors (NETs). Previous studies have shown that an SSTR2 agonist combined with albumin binding moiety Evans blue (denoted as ¹⁷⁷Lu-EB-TATE) is characterized by a higher tumor uptake and residence time in preclinical models and in patients with metastatic NETs. This study aimed to enhance the in vivo stability, pharmacokinetics, and pharmacodynamics of ¹⁷⁷Lu-EB-TATE by replacing the maleimide-thiol group with a polyethylene glycol chain, resulting in a novel EB conjugated SSTR2-targeting radiopharmaceutical, ¹⁷⁷Lu-LNC1010, for PRRT. In preclinical studies, ¹⁷⁷Lu-LNC1010 exhibited good stability and SSTR2-binding affinity in AR42J tumor cells and enhanced uptake and prolonged retention in AR42J tumor xenografts. Thereafter, we presented the first-in-human dose escalation study of ¹⁷⁷Lu-LNC1010 in patients with advanced/metastatic NETs. ¹⁷⁷Lu-LNC1010 was well-tolerated by all patients, with minor adverse effects, and exhibited significant uptake and prolonged retention in tumor lesions, with higher tumor radiation doses than those of ¹⁷⁷Lu-EB-TATE. Preliminary PRRT efficacy results showed an 83% disease control rate and a 42% overall response rate after two ¹⁷⁷Lu-LNC1010 treatment cycles. These encouraging findings warrant further investigations through multicenter, prospective, and randomized controlled trials.