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.

Apical microsurgery is accurate and minimally invasive, produces few complications, and has a success rate of more than 90%. However, due to the lack of awareness and understanding of apical microsurgery by dental general practitioners and even endodontists, many clinical problems remain to be overcome. The consensus has gathered well-known domestic experts to hold a series of special discussions and reached the consensus. This document specifies the indications, contraindications, preoperative preparations, operational procedures, complication prevention measures, and efficacy evaluation of apical microsurgery and is applicable to dentists who perform apical microsurgery after systematic training.
Microsurgery/standards* ; Humans ; Apicoectomy ; Contraindications, Procedure ; Tooth Apex/diagnostic imaging* ; Postoperative Complications/prevention & control* ; Consensus ; Treatment Outcome

Objective To explore and identify the nutritional/inflammatory indicator with the highest predictive potential for overall survival(OS)in postoperative tumor patients so as to provide guidance for postoperative rehabilitation of tumor patients.Methods Data from 3 191 surgical patients were collected,including 15 nutritional/inflammatory indicators.The maximum selection rank statistic method was used to calculate the optimal cut-off values for continuous indicators.The Kaplan-Meier method was used to assess OS,and Cox proportional hazards models were used to analyze the association between the aforementioned 15 indicators and survival.The predictive value of these 15 indicators was evaluated with receiver operating characteristic(ROC)curves and C-index.Results Multivariate analysis showed that all 15 indicators were significantly associated with poorer OS in surgical patients(P＜0.05 for all).Time-dependent area under the curve(AUC)and C-index analysis indicated that 3 indicators with the highest predictive potential in OS in postoperative tumor patients were the nutritional risk index(NRI)(C-index:0.597),C-reactive protein-to-albumin ratio(CAR)(C-index:0.587),and C-reactive protein-to-lymphocyte ratio(CLR)(C-index:0.587).The optimal cut-off value for NRI was determined to be 104.31(i.e.,NRI＜104.31 suggests malnutrition)with the maximum selection rank statistic method,the optimal cut-off value for CAR to be 0.05(i.e.,CAR≥0.05 suggests a strong inflammatory response,often accompanied by malnutrition),and the optimal cut-off value for CLR to be 1.18(i.e.,CLR≥1.18 suggests a strong inflammatory response).Subgroup analysis indicated that NRI,CAR,and CLR had good correlation with tumor staging,and there were significant differences between tumor node metastasis(TNM)Ⅲ/Ⅳ stage patients and TNM Ⅰ/Ⅱ stage patients when there was a strong inflammatory response or malnutrition.Conclusion In postoperative tumor patients,NRI,CLR,and CAR have high prognostic value.Combining these with the patient's clinical stage,it enables more precise guidance for clinical diagnosis and treatment strategies.

As a serine protease,thrombin can convert soluble fibrinogen into insoluble fibrin and plays a pivotal role in the coagulation cascade.Therefore,the accurate quantitative assay of thrombin levels is of great value in the evaluation of coagulation function,clinical screening and prognostic monitoring of coagulation-related diseases,and screening of drugs for targeted therapy.Existing methods for thrombin detection can be divided into two categories,e.g.,the assay of concentration levels using nucleic acid aptamers as the affinity elements and the assay of activity levels based on the hydrolytic cleavage of substrate peptides.In recent years,electrochemical biosensors have attracted much attention in thrombin detection due to high sensitivity,high selectivity,simple instrument,fast response,and good portability.In this review,the latest research progress in methods for electrochemical detection of thrombin was summarized,focusing on the detection principles and the applied signal amplification strategies of related electrochemical biosensors.In addition,the challenges with respect to the practical use of electrochemical thrombin biosensors and the prospects were discussed.

Background and purpose:High-throughput detection of plasma cell-free DNA(cfDNA)is widely used for multi-cancer targeted therapy drug screening,and this study investigated the relationship between the type and number of plasma cfDNA class Ⅰ and Ⅱ targeted therapy-related gene variants and cancer survival in patients with non-small cell lung cancer(NSCLC).Methods:The sequencing results and clinical data of NSCLC patients who underwent tumor plasma cfDNA high-throughput sequencing projects in Sun Yat-sen University Cancer Center from 2021 to 2023 were collected.The survival follow-up of enrolled patients was carried out from the day of plasma collection on June 1,2021 to May 27,2024,and GraphPad Prism 8.0 and SPSS Statistics 25.0 were used.Univariate and multivariate statistical analyses were conducted on the types and numbers of class Ⅰ and class Ⅱ targeted therapy-related genes in the survival and clinical data of patients and sequencing results(Ethical approval:B2024-359-01).Results:A total of 313 patients included in this study with NSCLC were categorized into stage Ⅰ 25 patients(7.98%),stageⅡ 20 patients(6.39%),stage Ⅲ 38patients(12.14%),and stage Ⅳ 230 patients(73.48%).Pathological diagnosis results showed that adenocarcinoma accounted for 90.10%,squamous cell carcinoma accounted for 5.11%,large cell carcinoma accounted for 2.87%and other classifications accounted for 1.92%.The number and the percentage of class Ⅰ and class Ⅱ targeted therapy drug-related genes in the plasma cfDNA NSCLC patients were 0(25.24%),1(17.57%),2(19.17%),3(14.38%),4(8.31%),and 5 or more(15.34%).The results of statistical analysis showed that 3 genes with the highest mutation frequencies were EGFR,TP53 and ERBB2,and the mutation frequency of EGFR gene was 36.04%.The mutation frequency of TP53 gene was 30.63%.The mutation frequency of ERBB2 gene was 4.95%.The survival time of patients is related to not only the expression of hotspot targeted genes,but also the number of class Ⅰ and Ⅱ target-related gene variants detected by plasma cfDNA high-throughput sequencing.The survival time of the patients with no targeted therapy-related locus variants after treatment was longer compares with targeted therapy-related locus variants,which can reduce the risk of death by 63.2%.However,patients with a single gene locus variant had longer survival time and lower risk of death than those with multiple driver locus variants,and the measured class Ⅰ and Ⅱ targeted therapy drugs were within 3 genes.Overall,the smaller the number of genes,the longer the survival.Conclusions:The number of class Ⅰ and class Ⅱtargeted therapy-related gene variants in plasma cfDNA high-throughput sequencing also has an effect on the survival of patients after treatment.Plasma cfDNA level detected by high-throughput sequencing could be a prognostic factor for the NSCLC patients.

Background and purpose:High-throughput detection of plasma cell-free DNA(cfDNA)is widely used for multi-cancer targeted therapy drug screening,and this study investigated the relationship between the type and number of plasma cfDNA class Ⅰ and Ⅱ targeted therapy-related gene variants and cancer survival in patients with non-small cell lung cancer(NSCLC).Methods:The sequencing results and clinical data of NSCLC patients who underwent tumor plasma cfDNA high-throughput sequencing projects in Sun Yat-sen University Cancer Center from 2021 to 2023 were collected.The survival follow-up of enrolled patients was carried out from the day of plasma collection on June 1,2021 to May 27,2024,and GraphPad Prism 8.0 and SPSS Statistics 25.0 were used.Univariate and multivariate statistical analyses were conducted on the types and numbers of class Ⅰ and class Ⅱ targeted therapy-related genes in the survival and clinical data of patients and sequencing results(Ethical approval:B2024-359-01).Results:A total of 313 patients included in this study with NSCLC were categorized into stage Ⅰ 25 patients(7.98%),stageⅡ 20 patients(6.39%),stage Ⅲ 38patients(12.14%),and stage Ⅳ 230 patients(73.48%).Pathological diagnosis results showed that adenocarcinoma accounted for 90.10%,squamous cell carcinoma accounted for 5.11%,large cell carcinoma accounted for 2.87%and other classifications accounted for 1.92%.The number and the percentage of class Ⅰ and class Ⅱ targeted therapy drug-related genes in the plasma cfDNA NSCLC patients were 0(25.24%),1(17.57%),2(19.17%),3(14.38%),4(8.31%),and 5 or more(15.34%).The results of statistical analysis showed that 3 genes with the highest mutation frequencies were EGFR,TP53 and ERBB2,and the mutation frequency of EGFR gene was 36.04%.The mutation frequency of TP53 gene was 30.63%.The mutation frequency of ERBB2 gene was 4.95%.The survival time of patients is related to not only the expression of hotspot targeted genes,but also the number of class Ⅰ and Ⅱ target-related gene variants detected by plasma cfDNA high-throughput sequencing.The survival time of the patients with no targeted therapy-related locus variants after treatment was longer compares with targeted therapy-related locus variants,which can reduce the risk of death by 63.2%.However,patients with a single gene locus variant had longer survival time and lower risk of death than those with multiple driver locus variants,and the measured class Ⅰ and Ⅱ targeted therapy drugs were within 3 genes.Overall,the smaller the number of genes,the longer the survival.Conclusions:The number of class Ⅰ and class Ⅱtargeted therapy-related gene variants in plasma cfDNA high-throughput sequencing also has an effect on the survival of patients after treatment.Plasma cfDNA level detected by high-throughput sequencing could be a prognostic factor for the NSCLC patients.

Objective To explore and identify the nutritional/inflammatory indicator with the highest predictive potential for overall survival(OS)in postoperative tumor patients so as to provide guidance for postoperative rehabilitation of tumor patients.Methods Data from 3 191 surgical patients were collected,including 15 nutritional/inflammatory indicators.The maximum selection rank statistic method was used to calculate the optimal cut-off values for continuous indicators.The Kaplan-Meier method was used to assess OS,and Cox proportional hazards models were used to analyze the association between the aforementioned 15 indicators and survival.The predictive value of these 15 indicators was evaluated with receiver operating characteristic(ROC)curves and C-index.Results Multivariate analysis showed that all 15 indicators were significantly associated with poorer OS in surgical patients(P＜0.05 for all).Time-dependent area under the curve(AUC)and C-index analysis indicated that 3 indicators with the highest predictive potential in OS in postoperative tumor patients were the nutritional risk index(NRI)(C-index:0.597),C-reactive protein-to-albumin ratio(CAR)(C-index:0.587),and C-reactive protein-to-lymphocyte ratio(CLR)(C-index:0.587).The optimal cut-off value for NRI was determined to be 104.31(i.e.,NRI＜104.31 suggests malnutrition)with the maximum selection rank statistic method,the optimal cut-off value for CAR to be 0.05(i.e.,CAR≥0.05 suggests a strong inflammatory response,often accompanied by malnutrition),and the optimal cut-off value for CLR to be 1.18(i.e.,CLR≥1.18 suggests a strong inflammatory response).Subgroup analysis indicated that NRI,CAR,and CLR had good correlation with tumor staging,and there were significant differences between tumor node metastasis(TNM)Ⅲ/Ⅳ stage patients and TNM Ⅰ/Ⅱ stage patients when there was a strong inflammatory response or malnutrition.Conclusion In postoperative tumor patients,NRI,CLR,and CAR have high prognostic value.Combining these with the patient's clinical stage,it enables more precise guidance for clinical diagnosis and treatment strategies.

A domestically produced self-expanding transcatheter aortic valve controllable bending delivery system(VitaFlow? Ⅲcontrollable bending retrievable delivery system)was first used to perform transcatheter aortic valve replacement(TAVR)in a symptomatic severe aortic valve stenosis patient with severe heart failure and high risk of surgery in China on September 22,2023.The patient successfully completed TAVR under general anesthesia,with good valve position and function after the operation.Before discharge and at one month of follow-up,the patient's symptoms and degree of heart failure were significantly improved.The follow-up results of this case showed that the VitaFlow? Ⅲ controllable bending retrievable delivery system for TAVR is safe and feasible,and future prospective,multicenter clinical trials are expected to evaluate its efficacy.

Purpose::Cerebral edema (CE) is the main secondary injury following traumatic brain injury (TBI) caused by road traffic accidents (RTAs). It is challenging to be predicted timely. In this study, we aimed to develop a prediction model for CE by identifying its risk factors and comparing the timing of edema occurrence in TBI patients with varying levels of injuries.Methods::This case-control study included 218 patients with TBI caused by RTAs. The cohort was divided into CE and non-CE groups, according to CT results within 7 days. Demographic data, imaging data, and clinical data were collected and analyzed. Quantitative variables that follow normal distribution were presented as mean ± standard deviation, those that do not follow normal distribution were presented as median (Q 1, Q 3). Categorical variables were expressed as percentages. The Chi-square test and logistic regression analysis were used to identify risk factors for CE. Logistic curve fitting was performed to predict the time to secondary CE in TBI patients with different levels of injuries. The efficacy of the model was evaluated using the receiver operator characteristic curve. Results::According to the study, almost half (47.3%) of the patients were found to have CE. The risk factors associated with CE were bilateral frontal lobe contusion, unilateral frontal lobe contusion, cerebral contusion, subarachnoid hemorrhage, and abbreviated injury scale (AIS). The odds ratio values for these factors were 7.27 (95% confidence interval ( CI): 2.08 -25.42, p = 0.002), 2.85 (95% CI: 1.11 -7.31, p = 0.030), 2.62 (95% CI: 1.12 -6.13, p = 0.027), 2.44 (95% CI: 1.25 -4.76, p = 0.009), and 1.5 (95% CI: 1.10 -2.04, p = 0.009), respectively. We also observed that patients with mild/moderate TBI (AIS ≤ 3) had a 50% probability of developing CE 19.7 h after injury (χ 2= 13.82, adjusted R2 = 0.51), while patients with severe TBI (AIS > 3) developed CE after 12.5 h (χ 2= 18.48, adjusted R2 = 0.54). Finally, we conducted a receiver operator characteristic curve analysis of CE time, which showed an area under the curve of 0.744 and 0.672 for severe and mild/moderate TBI, respectively. Conclusion::Our study found that the onset of CE in individuals with TBI resulting from RTAs was correlated with the severity of the injury. Specifically, those with more severe injuries experienced an earlier onset of CE. These findings suggest that there is a critical time window for clinical intervention in cases of CE secondary to TBI.