Chronic diabetic wounds, severely complicated by multidrug-resistant (MDR) bacterial infections, represent a profound and escalating global health crisis. The intrinsically hostile microenvironment of diabetic wounds, characterized by localized hypoxia, persistent oxidative stress, and poor vascularization, creates an ideal niche for opportunistic pathogens such as Staphylococcus aureus and Pseudomonas aeruginosa. These bacteria readily construct dense extracellular polymeric substance (EPS) biofilms, which not only physically shield the microbes from host immune responses but also actively trap the wound in a state of chronic, unresolved inflammation. Consequently, conventional systemic and topical antibiotic therapies are becoming increasingly futile, as poor perfusion at the wound site restricts drug bioavailability, while the rapid genetic evolution of bacteria and the impenetrable nature of biofilms lead to catastrophic treatment failures, often culminating in severe tissue necrosis and lower-extremity amputations. To circumvent the limitations of traditional antimicrobials, therapeutic gas delivery has emerged as a highly promising, paradigm-shifting strategy. Gaseous signaling molecules, particularly nitric oxide (NO), carbon monoxide (CO), hydrogen sulfide (H₂S), and hydrogen (H₂), possess unique physicochemical properties that allow them to seamlessly penetrate dense biofilm matrices and cellular membranes. Once inside, these gases operate via multi-targeted mechanisms that are incredibly difficult for bacteria to develop resistance against; for instance, NO induces severe lipid peroxidation and DNA cleavage in bacteria, CO downregulates pro-inflammatory cytokines, H₂S significantly accelerates endothelial cell migration for neovascularization, and H₂ acts as a powerful selective antioxidant to neutralize tissue-damaging reactive oxygen species (ROS). Together, these therapeutic gases not only exert broad-spectrum bactericidal effects but also actively reprogram the wound bed by promoting the critical M1-to-M2 macrophage polarization and stimulating angiogenesis. Despite their immense biological potential, the direct clinical translation of gas therapies is severely hindered by inherent physicochemical drawbacks, including extreme volatility, short physiological half-lives, poor aqueous solubility, and the high risk of off-target systemic toxicity, if applied indiscriminately. To conquer these immense pharmacokinetic barriers, cutting-edge advancements in materials science have driven the development of gas-releasing micro- and nanoplatforms. Utilizing sophisticated carriers such as metal-organic frameworks (MOFs), mesoporous silica, polymeric nanoparticles, liposomes, and injectable hydrogels, researchers can now encapsulate gas-donor molecules to achieve sustained, localized delivery. More importantly, these advanced nanoplatforms are ingeniously engineered to be stimuli-responsive. By exploiting the pathological hallmarks of the diabetic wound environment, such as elevated glucose concentrations, acidic pH, and overexpressed ROS, or by utilizing external triggers like near-infrared (NIR) light irradiation and ultrasound, these intelligent platforms ensure on-demand, precise spatio-temporal gas release. This often allows for powerful synergistic combinations, such as photothermal or photodynamic therapy coupled with gas release, thereby obliterating biofilms while sparing healthy tissue. While the therapeutic outcomes of these smart delivery systems in eradicating MDR infections and accelerating tissue repair are unprecedented, several critical challenges remain before widespread clinical adoption, as long-term biosafety profiles of the carrier nanomaterials, complexities in large-scale good manufacturing practice (GMP) production, and stringent regulatory hurdles must be rigorously addressed. Looking forward, the next frontier lies in the realm of precision medicine and theranostics, where future research must focus on the seamless integration of these gas-releasing platforms with flexible, wearable biosensors capable of continuously monitoring wound biomarkers (e.g., pH, temperature, uric acid) in real-time. Coupled with artificial intelligence algorithms to govern automated, closed-loop adaptive dosing, these next-generation smart dressings hold the ultimate potential to comprehensively transform the clinical management of complex, infected diabetic wounds.

Chronic diabetic wounds, severely complicated by multidrug-resistant (MDR) bacterial infections, represent a profound and escalating global health crisis. The intrinsically hostile microenvironment of diabetic wounds, characterized by localized hypoxia, persistent oxidative stress, and poor vascularization, creates an ideal niche for opportunistic pathogens such as Staphylococcus aureus and Pseudomonas aeruginosa. These bacteria readily construct dense extracellular polymeric substance (EPS) biofilms, which not only physically shield the microbes from host immune responses but also actively trap the wound in a state of chronic, unresolved inflammation. Consequently, conventional systemic and topical antibiotic therapies are becoming increasingly futile, as poor perfusion at the wound site restricts drug bioavailability, while the rapid genetic evolution of bacteria and the impenetrable nature of biofilms lead to catastrophic treatment failures, often culminating in severe tissue necrosis and lower-extremity amputations. To circumvent the limitations of traditional antimicrobials, therapeutic gas delivery has emerged as a highly promising, paradigm-shifting strategy. Gaseous signaling molecules, particularly nitric oxide (NO), carbon monoxide (CO), hydrogen sulfide (H₂S), and hydrogen (H₂), possess unique physicochemical properties that allow them to seamlessly penetrate dense biofilm matrices and cellular membranes. Once inside, these gases operate via multi-targeted mechanisms that are incredibly difficult for bacteria to develop resistance against; for instance, NO induces severe lipid peroxidation and DNA cleavage in bacteria, CO downregulates pro-inflammatory cytokines, H₂S significantly accelerates endothelial cell migration for neovascularization, and H₂ acts as a powerful selective antioxidant to neutralize tissue-damaging reactive oxygen species (ROS). Together, these therapeutic gases not only exert broad-spectrum bactericidal effects but also actively reprogram the wound bed by promoting the critical M1-to-M2 macrophage polarization and stimulating angiogenesis. Despite their immense biological potential, the direct clinical translation of gas therapies is severely hindered by inherent physicochemical drawbacks, including extreme volatility, short physiological half-lives, poor aqueous solubility, and the high risk of off-target systemic toxicity, if applied indiscriminately. To conquer these immense pharmacokinetic barriers, cutting-edge advancements in materials science have driven the development of gas-releasing micro- and nanoplatforms. Utilizing sophisticated carriers such as metal-organic frameworks (MOFs), mesoporous silica, polymeric nanoparticles, liposomes, and injectable hydrogels, researchers can now encapsulate gas-donor molecules to achieve sustained, localized delivery. More importantly, these advanced nanoplatforms are ingeniously engineered to be stimuli-responsive. By exploiting the pathological hallmarks of the diabetic wound environment, such as elevated glucose concentrations, acidic pH, and overexpressed ROS, or by utilizing external triggers like near-infrared (NIR) light irradiation and ultrasound, these intelligent platforms ensure on-demand, precise spatio-temporal gas release. This often allows for powerful synergistic combinations, such as photothermal or photodynamic therapy coupled with gas release, thereby obliterating biofilms while sparing healthy tissue. While the therapeutic outcomes of these smart delivery systems in eradicating MDR infections and accelerating tissue repair are unprecedented, several critical challenges remain before widespread clinical adoption, as long-term biosafety profiles of the carrier nanomaterials, complexities in large-scale good manufacturing practice (GMP) production, and stringent regulatory hurdles must be rigorously addressed. Looking forward, the next frontier lies in the realm of precision medicine and theranostics, where future research must focus on the seamless integration of these gas-releasing platforms with flexible, wearable biosensors capable of continuously monitoring wound biomarkers (e.g., pH, temperature, uric acid) in real-time. Coupled with artificial intelligence algorithms to govern automated, closed-loop adaptive dosing, these next-generation smart dressings hold the ultimate potential to comprehensively transform the clinical management of complex, infected diabetic wounds.

Aim To investigate the impact of G pro-tein-coupled estrogen receptor 1(GPER1),also known as GPR30 playing a significant role in the nerv-ous system,on neuronal damage and cognitive dysfunc-tion following epileptic seizures.Methods The pro-tein expression levels of GPER1 and the DNA damage marker γ-H2AX in epileptic rats were assessed using Western blot.The hippocampal neuronal damage and apoptosis in pilocarpine-induced epilepsy models were evaluated using Nissl and TUNEL staining techniques,compared with GPER1 knockdown(GPER1-KD)rats with wild-type(WT)controls.The behavioral activi-ties,including memory and spatial learning,were mo-nitored during the chronic phase of epilepsy using the IntelliCage system.Results Compared to the control group,GPER1 protein expression in the cerebral cortex and hippocampus significantly increased 24 hours post-epilepsy onset.In the GPER1-KD+EP group,hipp-ocampal neuronal damage was more severe,with a sig-nificant increase in apoptotic neurons compared to the WT+EP group.The IntelliCage data revealed that during free exploration,nose contact,position learn-ing,and reverse position learning stages in the GPER1-KD+EP group exhibited fewer visits and a higher error rate than in the WT+EP group.Conclu-sions Deficiency in GPER1 impairs memory and spa-tial learning abilities following epilepsy,potentially due to exacerbated neuronal injury,apoptosis,and inflam-mation.GPER1 represents a promising therapeutic tar-get for mitigating post-epileptic nerve damage and cog-nitive impairment.

Objective To construct radiomics-semantic models to predict the treatment resistance of chemoradiotherapy in brain gliomas based on MRI and clinical data of multicenter patients.Methods Among 2 108 brain gliomas patients from five medical institutions,132 patients had residual gliomas after surgery.The clinical risk factors and multimodal MRI were collected.All patients were divided into training set(n=95)and validation set(n=37).The treatment response of gliomas after standardized chemoradiotherapy were divided into resistant and non-resistant types.The semantic features of MRI were evaluated by two radiologists.Three different segmentation regions of interest(ROI)were delineated to extract radiomics features.And that three groups of radiomics models were con-structed based on different sequence MRIs.The radiomics model with the best predictive efficacy in each group was selected and combined with MRI semantic features,three radiomics-semantic models(combined models)were established.Finally,a MRI semantic model,three groups of radiomics models and three combined models were developed.Results Comparisons between the different models showed that the radiomics-semantic model based on pre-operative T2-fluid attenuated inversion recovery(FLAIR)sequence,had the best predictive efficacy,the area under the curve(AUC)in the training and validation sets were 0.866[95％confidence interval(CI)0.790-0.942]and 0.810(95％CI 0.667-0.952),respectively.The radiomics-semantic model based on postoperative T1 WI sequence performed the second best,with the AUC of the training and validation sets being 0.812(95％CI 0.726-0.898)and 0.711(95％CI 0.541-0.881),respectively.Conclusion The combined models based on MRI radiomics and semantic features are able to predict the treatment resistance of chemoradiotherapy in brain gliomas patients,and may be used as an important basis for optimizing treatment.

AIM To study the chemical constituents from the buds of Aralia chinensis L.var.nuda Nakai and their in vitro anti-inflammatory activities.METHODS The 70％ethanol extract from the buds of A.chinensis var.nuda was isolated and purified by silica gel,Sephadex LH-20,ODS and semi-preparative HPLC,then the structures of compounds were identified by physicochemical properties and spectral data.Their anti-inflammatory activities in vitro were evaluated by RAW264.7 model.RESULTS Sixteen compounds were isolated and identified as 4-(2,2-dibutoxyethyl)phenol(1),trans-linalool-3,7-oxide-6-O-β-D-glucopyranoside(2),2'-O-(9Z,12Z,15Z-octadecatrienoyl)glyceryl β-D-galactopyranoside(3),quercetin-3-O-β-D-glucopyranoside(3'→ O-3''')quercetin-3-O-β-D-galactopyranoside(4),syringaresinol-4'-O-β-D-glucopyranoside(5),p-hydroxybenzaldehyde(6),7α-hydroxystigmasterol 3-O-β-D-glucopyranoside(7),trans-p-hydroxy cinnamic acid methyl ester(8),funingensin A(9),3,4-dihydroxy-acetophenone(10),N-acetyltyramine(11),3,4-di-O-caffeoyl quinic acid(12),chlorogenic acid(13),aralia cerebroside(14),caffeic acid methyl ester(15),tetradecanoic acid(16).The IC50values of compounds 8,10,12 and 13 were(22.19±1.59),(35.25±1.30),(13.38±0.72),(15.73±1.16)μmol/L,respectively.CONCLUSION Compound 1 is a new compound,2-13 are isolated from genus Aralia for the first time.Compounds 8,10,12,13 exhibit significant in vitro anti-inflammatory activities.

AIM To study the chemical constituents from the buds of Aralia chinensis L.var.nuda Nakai and their in vitro anti-inflammatory activities.METHODS The 70％ethanol extract from the buds of A.chinensis var.nuda was isolated and purified by silica gel,Sephadex LH-20,ODS and semi-preparative HPLC,then the structures of compounds were identified by physicochemical properties and spectral data.Their anti-inflammatory activities in vitro were evaluated by RAW264.7 model.RESULTS Sixteen compounds were isolated and identified as 4-(2,2-dibutoxyethyl)phenol(1),trans-linalool-3,7-oxide-6-O-β-D-glucopyranoside(2),2'-O-(9Z,12Z,15Z-octadecatrienoyl)glyceryl β-D-galactopyranoside(3),quercetin-3-O-β-D-glucopyranoside(3'→ O-3''')quercetin-3-O-β-D-galactopyranoside(4),syringaresinol-4'-O-β-D-glucopyranoside(5),p-hydroxybenzaldehyde(6),7α-hydroxystigmasterol 3-O-β-D-glucopyranoside(7),trans-p-hydroxy cinnamic acid methyl ester(8),funingensin A(9),3,4-dihydroxy-acetophenone(10),N-acetyltyramine(11),3,4-di-O-caffeoyl quinic acid(12),chlorogenic acid(13),aralia cerebroside(14),caffeic acid methyl ester(15),tetradecanoic acid(16).The IC50values of compounds 8,10,12 and 13 were(22.19±1.59),(35.25±1.30),(13.38±0.72),(15.73±1.16)μmol/L,respectively.CONCLUSION Compound 1 is a new compound,2-13 are isolated from genus Aralia for the first time.Compounds 8,10,12,13 exhibit significant in vitro anti-inflammatory activities.

Objective:To investigate the characteristics of elastic program of excited shear wave of plantar soft tissue of diabetic patients and its correlation with levels of glucose metabolism.Methods:A total of 92 patients with type 2 diabetes who admitted to Cangzhou Hospital of Integrated Traditional Chinese Medicine and Western Medicine during January 2022 and January 2023 were selected as diabetes group,and another 92 patients in the prediabetes stage were included in the prediabetes group.Both groups of patients used excited shear wave with different frequencies(400,450,500,550,and 600 Hz)under elastic technique of shear wave.The velocities of shear waves at the 1st metatarsal head,the 3rd metatarsal head and calcaneus of plantar soft tissue,and the thickness of soft tissue of these patients were assessed.The fasting plasma glucose(FPG),2-hour postprandial glucose(2hPG),and glycosylated hemoglobin(HbA1c)levels of sugar metabolism level of two groups were measured,and the correlations between velocity of shear wave,thickness of soft tissue,and level of glucose metabolism were further analyzed.The occurrence of diabetic foot ulcer in the diabetes group was investigated by using follow-up.The receiver operating characteristic(ROC)curve was adopted to assess the predictive value of the velocity of shear wave,and the thickness of soft tissue for diabetic foot ulcer.Results:The FPG,2hPG and HbA1c of glucose metabolism level in the diabetes group were(6.48±0.48)mmol/L,(9.64±1.31)mmol/L and(6.21±0.15)%,respectively,all of which were higher than those[(8.03±0.62)mmol/L,(12.26±1.25)mmol/L and(7.66±0.42)%]in the prediabetes group,and the differences were statistically significant(t=18.961,18.961,31.185,P<0.05).The thickness level of soft tissue at the first metatarsal head was(10.42±3.11)mm in the diabetes group,which was less than(11.76±3.24)mm in the prediabetes group,with a statistically significant difference(t=2.862,P<0.05).Spearman correlation analysis showed that the velocity of shear wave at 500 Hz frequency had a strongest correlation with FPG and HbA1c(r=0.320,0.256,P<0.05),respectively.In the follow-up for patients of diabetes group,14 cases occurred ulcers and 78 cases did not occur ulcers.The velocities of shear wave of the first metatarsal head of 14 patients with ulcers were higher than those of patients without ulcers at all tested frequencies(400,450,500,550,and 600 Hz).The area under curve(AUC)value of ROC curve,specificity and sensitivity of 500 Hz were respectively 0.775,88.46%,and 78.57%in predicting plantar ulcer of patients with diabetes.Conclusion:The velocity of shear wave of soft tissue at the first metatarsal head is significantly correlated with abnormal glucose metabolism in patients with type 2 diabetes.The velocity of shear wave appeared strongest correlation with FPG and HbA1c at500 Hz frequency,which has higher predictive value for diabetic foot ulcer.

Objective:This study was aimed to explore the preference and heterogeneity in community health management service model selection among older patients with multiple chronic diseases,and to provide scientific evidence for optimizing the model.Methods:A multi-stage stratified and convenience sampling approach was adopted.A discrete choice experiment was conducted with 360 elderly patients with multiple chronic diseases from six regions in Jiangsu Province.The Mixed Logit Model was used to analyze service selection preferences and willingness to pay,while the Latent Class Logit Model was applied to explore heterogeneity among patient groups.Results:Patients showed a stronger preference for a service model featuring"twice-monthly visits,medication guidance+lifestyle counseling,and face-to-face consultations,"with willingness to pay values of 170.18 CNY,162.90 CNY,and 112.70 CNY,respectively.Willingness to pay decreased as out-of-pocket costs increased.Heterogeneity analysis identified three distinct preference groups,with statistically significant differences in urban-rural distribution,income levels,and health insurance types.Conclusions and suggestions:Medication and lifestyle guidance are the most valued components of community health management services among older patients with multiple chronic diseases.Patients'demographic and socioeconomic characteristics have a structural influence on their service preferences,highlighting the need to tailor service provision to different population groups.It is recommended to enhance medication guidance capacity at the primary care level,especially in rural areas;promote physical medicine integration to improve the accuracy and effectiveness of lifestyle guidance provided by primary healthcare personnel;strengthen digital infrastructure and streamline service processes to moderately increase the frequency of face-to-face consultations;and improve the integration of medical insurance and public health funding mechanisms to enhance service accessibility and equity.

Aim To investigate the key targets and mechanisms of diabetic gastroparesis(DGP)by in-tegrating network pharmacology and molecular docking technology with animal experiments,and to specifically focus on exploring the effects of hedysarum polybotrys polysaccharide(HPS)on DGP through animal experi-mentation to validate its potential as a treatment for di-abetic gastroparesis.Methods The chemical constit-uents of HPS were analyzed,and the active chemical components of Radix Astragali were identified using the TCMSP database.The Swisstarget database was utilized to screen for HPS active ingredient targets,while DGP-related targets were identified from disease databases such as TTD,GeneCards,Drugbank,and DisGeNET.The STRING database was used to construct the PPI network,and Cytoscape 3.10.1 software was employed for network topology analysis and selection of key tar-gets.Subsequently,a compound-target-pathway net-work diagram was constructed.Key targets underwent GO function(biological function,molecular function,and cellular function)and KEGG pathway enrichment analysis using the Metascape database.Molecular doc-king was performed using Pymol 2.5 and AutoDock software.DGP rat model was established to observe the histopathological changes in small intestine after eight weeks of HPS intervention through HE staining.Addi-tionally,Western blot was conducted to detect the ex-pression of AGEs,RAGE,and NF-κB in eggs.The re-sults revealed a total of 302 key targets.Results A total of 302 key targets which were further analyzed for gene GO function and KEGG pathway enrichment.CUL3,YWHAZ,and NTRK1 were predicted as the key targets with critical pathways including the AGE-RAGE signaling pathway in diabetic complications,viral carci-nogenesis,hepatitis B,and alcoholism signaling path-way among others.Furthermore,in vivo experiments confirmed that HPS could improve small intestine histo-pathology in DGP rats,resulting in significant protective effects on this organ.It also reduced the expression of AGEs,RAGE,and NF-κB protein,hence achieving its purpose of treating DGP.Conclusion HPS has the characteristics of multi-component,multi-target and multi-pathway action,which may affect the regulatory role of AGE-RAGE signaling pathway on DGP,and provide new ideas for the subsequent clinical improve-ment of DGP.

OBJECTIVE To establish the HPLC fingerprint of Xintong granules and the quantitative analysis of multi-components by single-marker method(QAMS)to determine the contents of 7 components,so as to provide a scientific basis for their quality control.METHODS HPLC method was used to establish the fingerprints for 10 batches of Xintong granules(No.S1-S10),and similarity evaluation,cluster analysis(CA)and partial least squares-discriminant analysis(PLS-DA)were performed.At the same time,the contents of seven components,including puerarin,daidzin,calycosin-7-O-β-D-glucoside,stilbene glycoside,naringin,icariin and tanshinone ⅡA,were determined by QAMS method,and were compared with the results of external standard method.RESULTS A total of 18 common peaks were marked and 7 peaks were identified in the HPLC fingerprints for 10 batches of Xintong granules,namely puerarin(peak 4),daidzin(peak 7),calycosin-7-O-β-D-glucoside(peak 9),stilbene glycoside(peak 10),naringin(peak 12),icariin(peak 17),and tanshinone ⅡA(peak 18);the similarities among them were more than 0.990,and CA and PLS-DA results showed that S4-S5,S8-S10,S1-S3 and S6-S7 were clustered into three categories,respectively.Using naringin as the internal standard,the contents of puerarin,daidzin,calycosin-7-O-β-D-glucoside,stilbene glycoside,icariin and tanshinone ⅡA were determined to be 7.868 1-10.181 2,1.709 2-2.374 1,0.285 2-0.326 3,1.024 1-1.523 9,0.140 2-0.290 4,and 0.077 1-0.219 4 mg/g,respectively,by the QAMS.These results showed no significant differences compared to those obtained by the external standard method.CONCLUSIONS Established HPLC fingerprint and QAMS method are convenient,stable and accurate,which can provide a basis for the quality evaluation of Xintong granules.