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.

Objective:To assess the application value of one-hour post-load glucose (1hPG) for detecting prediabetes among individuals with high risk of type 2 diabetes mellitus (T2DM).Methods:The study was conducted between August 2023 and January 2024, and individuals with a high risk of T2DM were invited to receive an oral glucose tolerance test (OGTT), structural questionnaires, physical measurements, and other biochemical examinations. The fasting, one-, and two-hour glucose and insulin were tested. According to the 1hPG cut point on hyperglycemia suggested by International Diabetes Federation (IDF), normal glucose tolerance (NGT) and prediabetes were further divided into two subgroups, respectively, i.e., NGT with 1hPG<8.6 mmol/L (NGT-1hPG-normal), NGT with 1hPG≥8.6 mmol/L (NGT-1hPG-high), prediabetes with 1hPG<8.6 mmol/L (PDM-1hPG-normal), and prediabetes with 1hPG≥8.6 mmol/L (PDM-1hPG-high). The insulin release curve was drawn by the groups as above. Insulin resistance was evaluated by homeostasis model assessment for insulin resistance (HOMA-IR), and β-cell secretory function was evaluated by homeostasis model assessment for β cell function (HOMA-β)/HOMA-IR. Spearman rank correlation analysis was used to calculate the correlation coefficients among 1hPG, 2hPG and HOMA indices, and Steiger′s Z test was used to compare the difference between two correlation coefficients. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were used to assess the accuracy of 1hPG for detecting prediabetes. Results:A total of 2 469 subjects consisting of 1 485 men (60.1%) and 984 (39.9%) women, with a mean age of (45.76±6.20) years, of which 1 844 (74.7%) had 1hPG≥8.6 mmol/L. The prevalence of 1hPG≥8.6 mmol/L was 46.8%, 93.0% and 99.8% in individuals with NGT, prediabetes and newly diagnosed T2DM, respectively ( χ 2=763.78, P<0.001). The insulin release curve showed that insulin secretion increased rapidly in subjects with NGT-1hPG-high, and peaked at one hour, then decreased rapidly, with a significantly higher level of one- and two-hour insulin than those with NGT-1hPG-normal ( P<0.001). Compared to individuals with NGT-1hPG-normal, the counterparts with NGT-1hPG-high exhibited higher HOMA-IR and lower adjusted HOMA-β ( P<0.001). Spearman rank correlation analysis showed that the correlation coefficient of 1hPG with HOMA-IR was similar to the correlation coefficient of 2hPG with HOMA-IR (0.493 vs. 0.480, P=0.550), while the correlation of 1hPG with adjusted HOMA-β was significantly stronger than that of 2hPG (-0.692 vs. -0.587, P<0.001). Excluding patients with T2DM, according to the cut point recommended by IDF, the AUC of 1hPG≥8.6 mmol/L for detecting prediabetes was 0.731 (95% CI: 0.714-0.748), and the sensitivity and specificity were 0.930 and 0.532, respectively, with the kappa value of 0.45. Conclusion:1hPG is closely related to insulin resistance and islet function, and there′s substantial value for individuals with a high risk of T2DM to detect prediabetes by using the 1hPG cut points recommended by IDF.

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.

As an indispensable trace element in the human body,copper plays an important role in various physiological and biochemical reactions.The dyshomeostasis of copper leads to the disorder of copper metabolism and the occurrence of related diseases.Cuproptosis,a newly proposed regulatory cell death mode,is different from the known apoptosis,pyroptosis,necroptosis,and ferroptosis.Recent studies have found that the dyshomeostasis of copper has been observed in a variety of cancers.Therefore,targeting copper for disease treatment may become a new strategy and a new idea.This article systematically summarizes the fundamental properties of copper,copper dyshomeostasis-related diseases (Menkes syndrome,Wilson's disease,and cancer) and their treatment,and reviews the research progress in cuproptosis.
Humans ; Copper/metabolism* ; Homeostasis ; Neoplasms/metabolism* ; Hepatolenticular Degeneration/metabolism* ; Menkes Kinky Hair Syndrome/metabolism*

Objective:To assess the application value of one-hour post-load glucose (1hPG) for detecting prediabetes among individuals with high risk of type 2 diabetes mellitus (T2DM).Methods:The study was conducted between August 2023 and January 2024, and individuals with a high risk of T2DM were invited to receive an oral glucose tolerance test (OGTT), structural questionnaires, physical measurements, and other biochemical examinations. The fasting, one-, and two-hour glucose and insulin were tested. According to the 1hPG cut point on hyperglycemia suggested by International Diabetes Federation (IDF), normal glucose tolerance (NGT) and prediabetes were further divided into two subgroups, respectively, i.e., NGT with 1hPG<8.6 mmol/L (NGT-1hPG-normal), NGT with 1hPG≥8.6 mmol/L (NGT-1hPG-high), prediabetes with 1hPG<8.6 mmol/L (PDM-1hPG-normal), and prediabetes with 1hPG≥8.6 mmol/L (PDM-1hPG-high). The insulin release curve was drawn by the groups as above. Insulin resistance was evaluated by homeostasis model assessment for insulin resistance (HOMA-IR), and β-cell secretory function was evaluated by homeostasis model assessment for β cell function (HOMA-β)/HOMA-IR. Spearman rank correlation analysis was used to calculate the correlation coefficients among 1hPG, 2hPG and HOMA indices, and Steiger′s Z test was used to compare the difference between two correlation coefficients. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were used to assess the accuracy of 1hPG for detecting prediabetes. Results:A total of 2 469 subjects consisting of 1 485 men (60.1%) and 984 (39.9%) women, with a mean age of (45.76±6.20) years, of which 1 844 (74.7%) had 1hPG≥8.6 mmol/L. The prevalence of 1hPG≥8.6 mmol/L was 46.8%, 93.0% and 99.8% in individuals with NGT, prediabetes and newly diagnosed T2DM, respectively ( χ 2=763.78, P<0.001). The insulin release curve showed that insulin secretion increased rapidly in subjects with NGT-1hPG-high, and peaked at one hour, then decreased rapidly, with a significantly higher level of one- and two-hour insulin than those with NGT-1hPG-normal ( P<0.001). Compared to individuals with NGT-1hPG-normal, the counterparts with NGT-1hPG-high exhibited higher HOMA-IR and lower adjusted HOMA-β ( P<0.001). Spearman rank correlation analysis showed that the correlation coefficient of 1hPG with HOMA-IR was similar to the correlation coefficient of 2hPG with HOMA-IR (0.493 vs. 0.480, P=0.550), while the correlation of 1hPG with adjusted HOMA-β was significantly stronger than that of 2hPG (-0.692 vs. -0.587, P<0.001). Excluding patients with T2DM, according to the cut point recommended by IDF, the AUC of 1hPG≥8.6 mmol/L for detecting prediabetes was 0.731 (95% CI: 0.714-0.748), and the sensitivity and specificity were 0.930 and 0.532, respectively, with the kappa value of 0.45. Conclusion:1hPG is closely related to insulin resistance and islet function, and there′s substantial value for individuals with a high risk of T2DM to detect prediabetes by using the 1hPG cut points recommended by IDF.

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.

Objective This study aimed to comprehensively analyze and compare the clinicopathological features and prognosis of Chinese patients with human epidermal growth factor receptor 2(HER2)-low early breast cancer(BC)and HER2-IHC0 BC. Methods Patients diagnosed with HER2-negative BC(N=999)at our institution between January 2011 and December 2015 formed our study population.Clinicopathological characteristics,association between estrogen receptor(ER)expression and HER2-low,and evolution of HER2 immunohistochemical(IHC)score were assessed.Kaplan-Meier method and log-rank test were used to compare the long-term survival outcomes(5-year follow-up)between the HER2-IHC0 and HER2-low groups. Results HER2-low BC group tended to demonstrate high expression of ER and more progesterone receptor(PgR)positivity than HER2-IHC0 BC group(P<0.001).The rate of HER2-low status increased with increasing ER expression levels(Mantel-Haenszel χ2 test,P<0.001,Pearson's R=0.159,P<0.001).Survival analysis revealed a significantly longer overall survival(OS)in HER2-low BC group than in HER2-IHC0 group(P=0.007)in the whole cohort and the hormone receptor(HR)-negative group.There were no significant differences between the two groups in terms of disease-free survival(DFS).The discordance rate of HER2 IHC scores between primary and metastatic sites was 36.84%. Conclusion HER2-low BC may not be regarded as a unique BC group in this population-based study due to similar clinicopathological features and prognostic roles.