Background and Aims:Pancreatic cancer is a highly malignant digestive system tumor characterized by poor prognosis and limited therapeutic response.The tumor microenvironment plays a crucial role in its progression,where oxidative stress and lactate metabolism are two tightly interconnected processes influencing tumor growth,immune escape,and therapeutic resistance.However,their combined prognostic impact remains poorly understood.This study aimed to integrate oxidative stress-and lactate metabolism-related genes to establish and validate a robust prognostic model for pancreatic cancer,and to explore its association with immune microenvironment characteristics.Methods:Transcriptomic and clinical data of 177 pancreatic cancer patients were obtained from TCGA database and an external validation was performed using the GEO dataset(GSE57495).Differentially expressed genes associated with oxidative stress and lactate metabolism were identified using the"limma"package.Univariate Cox regression was used to screen prognostic genes,followed by LASSO regression to construct a multi-gene risk model.Model performance was evaluated by Kaplan-Meier survival analysis,receiver operating characteristic(ROC)curves,concordance index(C-index),nomogram calibration,and decision curve analysis(DCA).The CIBERSORT and ssGSEA algorithms were used to analyze immune cell infiltration and immune functional differences between risk groups.Results:A six-gene signature(MUC1,KRT18,SDC1,AREG,DDC,and ATPAF2)was identified to construct the prognostic model.Based on the calculated risk score,patients were stratified into high-and low-risk groups.Kaplan-Meier analysis revealed significantly worse overall survival in the high-risk group(P<0.01).The model showed good predictive accuracy with 1-,2-,and 3-year AUCs of 0.710,0.674,and 0.649,respectively.The C-index and calibration curves confirmed its reliability,and multivariate Cox regression indicated that the risk score was an independent prognostic factor.External validation using GEO data demonstrated consistent predictive performance.Immune infiltration analysis revealed that M0 macrophages were markedly enriched in the high-risk group,while cytotoxic and effector T-cell populations were reduced,suggesting that an immunosuppressive microenvironment may contribute to poor outcomes.Conclusion:This study developed and validated a novel prognostic model for pancreatic cancer based on oxidative stress and lactate metabolism-related genes.The model accurately predicts patient survival,reflects immune microenvironment heterogeneity,and provides new molecular insights for risk stratification and individualized therapeutic strategies in pancreatic cancer management.

Objective:To develop and evaluate the reliability and validity of the Unmet Needs Scale for Middle-aged and Young Women with Gynecological Cancer after Hysterectomy.Methods:Based on the Existence-Relatedness-Growth (ERG) theory, the initial version of the scale was developed through literature review, Delphi expert consultation, and a pilot survey. A total of 684 middle-aged and young women with gynecological cancer who underwent hysterectomy were selected using convenience sampling from three Class ⅢGrade A hospitals in Hebei Province between April and October 2024 for item analysis and psychometric testing.Results:The item-level content validity index ( I- CVI) of the scale ranged from 0.833 to 1.000, and the scale-level content validity index ( S- CVI) was 0.911. Exploratory factor analysis extracted seven common factors, explaining a cumulative variance of 71.083%. Confirmatory factor analysis showed good model fit. The final scale consisted of 45 items across seven dimensions: life needs, emotional needs, disease information needs, economic and medical care needs, needs for love and respect, self-actualization needs, and inner peace needs. Conclusions:The Unmet Needs Scale for Middle-aged and Young Women with Gynecological Cancer after Hysterectomy constructed based on ERG theory demonstrated good reliability and validity, and can serve as a useful tool for clinical assessment of unmet needs in this patient population.

Background and Aims:Pancreatic cancer is a highly malignant digestive system tumor characterized by poor prognosis and limited therapeutic response.The tumor microenvironment plays a crucial role in its progression,where oxidative stress and lactate metabolism are two tightly interconnected processes influencing tumor growth,immune escape,and therapeutic resistance.However,their combined prognostic impact remains poorly understood.This study aimed to integrate oxidative stress-and lactate metabolism-related genes to establish and validate a robust prognostic model for pancreatic cancer,and to explore its association with immune microenvironment characteristics.Methods:Transcriptomic and clinical data of 177 pancreatic cancer patients were obtained from TCGA database and an external validation was performed using the GEO dataset(GSE57495).Differentially expressed genes associated with oxidative stress and lactate metabolism were identified using the"limma"package.Univariate Cox regression was used to screen prognostic genes,followed by LASSO regression to construct a multi-gene risk model.Model performance was evaluated by Kaplan-Meier survival analysis,receiver operating characteristic(ROC)curves,concordance index(C-index),nomogram calibration,and decision curve analysis(DCA).The CIBERSORT and ssGSEA algorithms were used to analyze immune cell infiltration and immune functional differences between risk groups.Results:A six-gene signature(MUC1,KRT18,SDC1,AREG,DDC,and ATPAF2)was identified to construct the prognostic model.Based on the calculated risk score,patients were stratified into high-and low-risk groups.Kaplan-Meier analysis revealed significantly worse overall survival in the high-risk group(P<0.01).The model showed good predictive accuracy with 1-,2-,and 3-year AUCs of 0.710,0.674,and 0.649,respectively.The C-index and calibration curves confirmed its reliability,and multivariate Cox regression indicated that the risk score was an independent prognostic factor.External validation using GEO data demonstrated consistent predictive performance.Immune infiltration analysis revealed that M0 macrophages were markedly enriched in the high-risk group,while cytotoxic and effector T-cell populations were reduced,suggesting that an immunosuppressive microenvironment may contribute to poor outcomes.Conclusion:This study developed and validated a novel prognostic model for pancreatic cancer based on oxidative stress and lactate metabolism-related genes.The model accurately predicts patient survival,reflects immune microenvironment heterogeneity,and provides new molecular insights for risk stratification and individualized therapeutic strategies in pancreatic cancer management.

Objective:To develop and evaluate the reliability and validity of the Unmet Needs Scale for Middle-aged and Young Women with Gynecological Cancer after Hysterectomy.Methods:Based on the Existence-Relatedness-Growth (ERG) theory, the initial version of the scale was developed through literature review, Delphi expert consultation, and a pilot survey. A total of 684 middle-aged and young women with gynecological cancer who underwent hysterectomy were selected using convenience sampling from three Class ⅢGrade A hospitals in Hebei Province between April and October 2024 for item analysis and psychometric testing.Results:The item-level content validity index ( I- CVI) of the scale ranged from 0.833 to 1.000, and the scale-level content validity index ( S- CVI) was 0.911. Exploratory factor analysis extracted seven common factors, explaining a cumulative variance of 71.083%. Confirmatory factor analysis showed good model fit. The final scale consisted of 45 items across seven dimensions: life needs, emotional needs, disease information needs, economic and medical care needs, needs for love and respect, self-actualization needs, and inner peace needs. Conclusions:The Unmet Needs Scale for Middle-aged and Young Women with Gynecological Cancer after Hysterectomy constructed based on ERG theory demonstrated good reliability and validity, and can serve as a useful tool for clinical assessment of unmet needs in this patient population.

Gelatin microspheres were discussed as a scaffold material for bone tissue engineering, with the advantages of its porosity, biodegradability, biocompatibility, and biosafety highlighted. This review discusses how bone regeneration is aided by the three fundamental components of bone tissue engineering-seed cells, bioactive substances, and scaffold materials-and how gelatin microspheres can be employed for in vitro seed cell cultivation to ensure efficient expansion. This review also points out that gelatin microspheres are advantageous as drug delivery systems because of their multifunctional nature, which slows drug release and improves overall effectiveness. Although gelatin microspheres are useful for bone tissue creation, the scaffolds that take into account their porous structure and mechanical characteristics might be difficult to be created. This review then discusses typical techniques for creating gelatin microspheres, their recent application in bone tissue engineering, as well as possible future research directions.
Tissue Engineering/methods* ; Tissue Scaffolds/chemistry* ; Gelatin/chemistry* ; Microspheres ; Bone and Bones ; Porosity

Objective:To observe the efficacy of Tegafur gimeracil oteracil potassium(Gio) capsules combined with oxaliplatin in the treatment of advanced gastric cancer,and explore its effect on matrix metalloproteinase-9 (MMP-9) expression in cancer tissues. Methods: Totally 120 patients with advanced gastric cancer were randomly divided into the observation group and the control group, and both groups were treated with neoadjuvant chemotherapy for 3 cycles as follows:the observation group was given Gio capsules and oxaliplatin,and the control group was given 5-fluorouracil combined with oxaliplatin. The short term efficacy,adverse reactions and ex-pression of MMP-9 in cancer tissue before and after the chemotherapy were observed in the two groups. Results:After the treatment, the objective response rate in the two groups had no significant difference(P>0.05);the clinical benefit rate of the observation group was significantly higher than that of the control group(P<0.05);the incidence of severe bone marrow suppression and liver and kid-ney dysfunction in the observation group was significantly lower than that in the control group(P<0.05);after the treatment,the pos-itive expression of MMP-9 in the observation group was significantly lower than that in the control group(P<0.05). Conclusion:Gio capsules combined with oxaliplatin can improve the clinical benefit rate of the patients with advanced gastric cancer,and effectively re-duce the expression of MMP-9 in cancer tissue.

Objective To investigate the relationship between K+ concentration in rabbit vitreous humor and postmortem interval (PMI) under different ambient temperature. Methods Rabbit corpses were stored at 5℃ , 15℃ , 25℃ , and 35℃ after execution, and 80~100μL vitreous humor was extracted from each eye of the rabbit in turn every 12 hours. The concentrations of K+ were examined by Modular DPPI automatic biochemistry analyzer. The Interpolation Functions were used to analyze the statistical relationship between PMI and K+ concentration under different temperature. Results In each animal group, K+ concentration increased with PMI. Equation was obtained after interpolation analysis on range of temperature 5℃ ~30℃ . The three-variable quintic surface equation was f(x,y)=-1.998e14+1.345e12x+5.902e13y+0.005585x2-4.509e11xy-3.876e12y2-0.0002868x3+0.003545x2y+4.406e10xy2-1.746e10y3+2.669e-6x4-1.568e-5x3y-0.0001771x2y2-1.64e9xy3+6.669e9y4-8.672e-9x5+4.467e-8x4y+2.354e-7x3y2+2.459e-6x2y3+2.05e7xy4-1.214e8y5(R2=0.9956), x stands for temperature, y stands for K+ concentration, f(x,y) stands for PMI. Conclusion The rule of K+ concentration changes at ambient temperature complied with three-variable quintic surface equation distribution. Measurement of interpolation function may be used for PMI estimation at different ambient temperature.