Objective: To analyze characteristics of hepatitis B infection in HBsAg-/HBV DNA+ blood donors in Taiyuan, so as to provide evidence for adjusting blood screening strategies. Methods: Blood samples of HBsAg-/HBV DNA+ donors were tested using enzyme-linked immunosorbent assay (ELISA), chemiluminescence assay, nucleic acid qualitative test, and nucleic acid quantitative test. Data on HBsAg-/HBV DNA+ donors in Taiyuan region from January 1, 2016 to December 31, 2024 were statistically analyzed to evaluate the detection rate, demographic characteristics, influencing factors of detection rate, nucleic acid quantitative results, and serological patterns of HBsAg-/HBV DNA+ donors. Results: From January 1, 2016 to December 31, 2024, 991 565 donor samples underwent nucleic acid testing in Taiyuan. A total of 309 HBsAg-/HBV DNA+ samples were detected, resulting in an HBsAg-/HBV DNA+ detection rate of 3.12 per 10 000. The detection rate varied significantly across different years (P<0.05). Males had a significantly higher HBsAg-/HBV DNA+ detection rate than females, first-time donors had a higher rate than repeat donors, and whole blood donors had a higher rate than apheresis donors (P<0.05). The detection rate also differed significantly among age groups (P<0.05). Logistic regression analysis showed that gender, age, donation frequency, and donation type were all influencing factors for HBsAg-/HBV DNA+ detection (all P<0.05). The predominant serological patterns among HBsAg-/HBV DNA+ donors were HBsAb+/HBcAb+ (43.69%, 135/309) or HBcAb+ alone (24.27%, 75/309). Viral load was detectable in 53.40% (165/309) of the HBsAg-/HBV DNA+ donors. Among these, 61.21% (101/165) donors had a viral load<20 IU/mL, and 94.55% (156/165) had a viral load<200 IU/mL. Donors with viral load<200 IU/mL primarily exhibited HBsAb+/HBcAb+ (41.67%, 65/156) or HBcAb+alone (36.54%, 57/156) serological patterns. Conclusion: The prevalence of HBsAg-/HBV DNA+ is low among blood donors in Taiyuan. Higher detection rates were observed in the 46-55 years age group, males, first-time donors, and whole blood donors. HBsAg-/HBV DNA+ donors exhibit specific serological patterns and generally have low viral loads, indicating a potential residual transfusion risk. It is recommended to add HBcAb testing, together with high-sensitivity nucleic acid testing technologies and donor follow-up, to ensure blood safety and guide donor reentry.

OBJECTIVE To prepare the β -cyclodextrin （ β -CD） inclusion complex with volatile oil from Qianghuo qushi qingwen granules， and to characterize and quantitatively analyze the inclusion complex. METHODS The comprehensive scores calculated by inclusion rate and inclusion compound yield were used as indicators for screening the inclusion method. The single-factor experiments and Box-Behnken response surface experiments were used to op timize the inclusion conditions， with the above comprehensive score as response value， and taking the ratio of β -CD to volatile oil， inclusion temperature and inclusion time as indexes. The volatile oil inclusion complex of Qianghuo qushi qingwen granules was prepared according to the determined optimal process， followed by validation. Ultraviolet （UV）-visible spectroscopy， thin-layer chromatography （TLC）， and microscopic imaging were also performed. Ultra-high performance liquid chromatography was used to determine the contents of perillaldehyde， pogostone and atractylodin. RESULTS The saturation aqueous solution method was adopted. The optimal inclusion process conditions were as follows： the ratio of β -CD to volatile oil was 7.5∶1， the inclusion temperature was 40 ℃， and the inclusion time was 2.2 h. In three verification experiments， the average inclusion rate was 72.32%， the average yield of inclusion compound was 74.45%， the average comprehensive score was 72.96 points， and the relative error with the predicted value （74.15 points） of the model was 1.61%. UV-visible spectroscopy， TLC and microscopic imaging showed that β -CD and volatile oil successfully formed a new inclusion complex. The average contents of perillaldehyde， pogostone and atractylodin were 4.498 2， 0.814 9， 0.905 7 mg/g， respectively， with RSDs of 0.31%， 0.56% and 0.63% （ n ＝3）. CONCLUSIONS A stable and feasible preparation process of the volatile oil inclusion complex of Qianghuo qushi qingwen granules is successfully established.

AIM: To investigate the expression of Ficolin-3 and secreted frizzled-related protein 5(SFRP5)in the serum of patients with type 2 diabetes mellitus(T2DM)combined with diabetic retinopathy(DR)and their diagnostic value.METHODS: Prospectively selected patients with T2DM combined with DR admitted to the hospital from May 2023 to May 2025 were divided into non-proliferative and proliferative groups according to the severity of DR. Another patients with T2DM alone during the same period were selected as the T2DM group. ELISA was used to detect Ficolin-3 and SFRP5 levels; Correlation of serum Ficolin-3, SFRP5 levels, and inflammatory markers in T2DM patients with DR were analyzed using Pearson method; Logistic regression was used to analyze related influencing factors; ROC curve analysis was used to evaluate the diagnostic value of serum Ficolin-3 and SFRP5 for DR in T2DM patients.RESULTS: This study included a total of 108 patients with T2DM combined with DR(57 cases in the non-proliferative group, 51 cases in the proliferative group)and 108 cases in the T2DM group. The non-proliferative group had an average age of 59.01±6.28 y, with 34 males and 23 females. The proliferative group had an average age of 59.09±6.35 y, with 30 males and 21 females. The T2DM group had an average age of 58.96±6.18 y, with 62 males and 46 females.The serum levels of Ficolin-3, TNF-α, and IL-6 in the non-proliferative and proliferative groups were higher than those in the T2DM group(all P<0.05), while the level of SFRP5 was lower than that in the T2DM group(all P<0.05). The serum levels of Ficolin-3, TNF-α, and IL-6 in the proliferative group were higher than those in the non-proliferative group(all P<0.05), and the level of SFRP5 was lower than that in the non-proliferative group(P<0.05).Complying with Pearson correlation analysis showed that serum Ficolin-3 was negatively correlated with SFRP5(P<0.05), and both were related to TNF-α and IL-6(all P<0.001). Logistic analysis showed that the course of diabetes, SUA, HbA1c, Ficolin-3, TNF-α, and IL-6 were the risk factors for T2DM patients with DR(all P<0.05), and SFRP5 was a protective factor(P<0.05). Complying with the ROC curve, the AUC values of serum Ficolin-3 and SFRP5 alone and their combination for diagnosing T2DM patients with DR were 0.774, 0.793, and 0.864, respectively. The AUC of combined diagnosis was better than that of single diagnosis(Z=2.694, Z=2.708, both P<0.05).CONCLUSION: In patients with T2DM complicated by DR, serum levels of Ficolin-3 and SFRP5 are abnormally expressed. Both are influencing factors for T2DM with DR, and the combined detection can improve the diagnostic value in these patients.

OBJECTIVE To analyze the incidence of cyclosporine （CsA）-associated acute kidney injury （AKI） in hospitalized patients， identify influencing factors， and construct a risk prediction model. METHODS A single-center retrospective study was conducted， enrolling clinical data from hospitalized patients treated with CsA at the First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital from January 2018 to July 2024. The patients were classified into AKI group and non-AKI group based on the occurrence of CsA-related AKI. Univariate analysis and multivariate Logistic regression analysis were used to identify independent risk factors for CsA-related AKI， and a risk prediction model was constructed and its performance was evaluated. RESULTS A total of 439 patients were included， of whom 54 developed CsA-related AKI， with an incidence rate of 12.30%. The occurrence of CsA-associated AKI was positively correlated with concurrent bacterial pulmonary infection， cytomegalovirus viremia， respiratory failure， renal insufficiency， gastrointestinal bleeding， and peripheral central venous catheterization （odds ratios of 763.750， 16.944， 41.933， 236.806， 17.537 and 212.789， respectively； P ＜0.05）； while uric acid， prealbumin， and calcium levels were negatively associated with it （odds ratios of 0.983， 0.967 and 0.058， respectively； P ＜0.05）. The prediction model constructed based on the above factors yielded a χ 2 value of 10.254 （ P ＞0.05） in the Hosmer-Lemeshow test. The average area under the curve （AUC） from 10-fold cross-validation was 0.885. The AUC of the receiver operating characteristic curve was 0.883， with a sensitivity of 84.3% and a specificity of 80.4%， respectively， at the optimal cutoff value of 0.1. CONCLUSIONS Six factors， including concurrent bacterial pulmonary infection and cytomegalovirus viremia， are positively associated with the occurrence of CsA-related AKI； while uric acid， prealbumin， and calcium levels are negatively associated. The Logistic regression model constructed based on these factors demonstrates good predictive performance and can assist clinic in conducting early risk assessment and personalized interventions.

Objective: To optimize the technology and process for recovering immune cells from discarded leukocyte filter through reverse flushing, and to investigate the effects of different reverse flushing solutions and physical conditions (temperature, soaking, and ultrasound) on the cell recovery rate. Additionally, to evaluate the feasibility and quality of isolating and culturing NK cells from the recovered cells. Methods: Normal saline, PBS, and PBS-EDTA were used as reverse flushing solutions to backwash the filter, and the white blood cell recovery rates were compared. The reverse flushing efficiency was optimized by adjusting the temperature of the reverse flushing solution, filter soaking treatment and ultrasonic treatment. The Ficoll separation method was employed to isolate peripheral blood mononuclear cells (PBMCs) from the elution solution and conduct in vitro expansion and culture. Lymphocyte subsets were identified and quantified using flow cytometry. Results: The physiological saline served as the backflush fluid was the most stable. The reverse temperature had a significant impact on the recovery rate of white blood cell subgroups. The recovery rates of lymphocytes and monocytes at 37℃ were significantly higher than those at 4℃ [(35.56±5.55)% vs (22.88±0.31)%, (32.42±4.82)% vs (8.00±1.6)%, t=5.581 and 5.758, respectively, both P<0.001)], and at 25℃ [(35.56±5.55)% vs (22.42±6.13)%, t=5.904, P<0.001; (32.4±4.82)% vs (21.11±1.23)%, t=2.629, P=0.030)]. The recovery rates of white blood cell subgroups after 37℃ immersion were higher than those after direct reverse flow at 37℃, but the difference was not statistically significant (P>0.05). The recovery rate of cells after ultrasonic immersion at 25℃ was further increased compared to that after immersion at 25℃, but the difference was not statistically significant (P>0.05). The purity of the NK cells obtained through sorting reached (94.2±4.5)%, and the viability was (94.8±0.9)%. After 15 days of culture, the cell count was expanded approximately 450 times, yielding more than 10 /mL highly active NK cells. Conclusion: By optimizing the backflushing process, immune cells can be efficiently recovered from discarded filters. The 37℃ backflow liquid has the best backflow effect; soaking and ultrasonic treatment can be used as auxiliary methods to improve recovery rates. The NK cells from this source have high purity, good activity, and strong expansion ability, and can be used as a reliable cell source for immunotherapy research, and in line with the circular economy concept of resource reuse.

ObjectiveTo investigate the differential expression of LEFTY1 in peritoneal dialysis （PD）-associated peritoneal fibrosis （PF） and its clinical value. MethodsLevels of LEFTY1， transforming growth factor-β （TGF-β）， and fibronectin （FN） in peritoneal dialysis effluent were measured in 72 PD patients， and clinical data were collected. Differences in these indicators were compared among groups with different dialysis durations， and the correlation between LEFTY1 and clinical parameters was analyzed to evaluate its predictive value for ultrafiltration insufficiency. ResultsLEFTY1 levels decreased progressively with longer dialysis duration： short-term group ［（2 262.49±270.47） ng/mL］ > mid-term group ［（1 853.52±226.06） ng/mL］ > long-term group ［（1 767.24±264.57） ng/mL］， with significant differences among groups （P<0.001）. TGF-β and FN levels increased with prolonged dialysis duration （P<0.05）. Correlation analysis revealed that LEFTY1 was significantly negatively correlated with FN （r=-0.327， P=0.004）， dialysis duration （r=-0.496，P<0.001）， peritoneal Kt/V （r=-0.333， P=0.004）， peritoneal creatinine clearance （r=-0.239， P=0.043）， and serum calcium （r=-0.410， P<0.001）， while positively correlated with total creatinine clearance （r=0.283， P=0.016）. The area under the ROC curve for LEFTY1 combined with peritoneal Kt/V and peritoneal Ccr in predicting ultrafiltration insufficiency was 0.739. Multivariate regression analysis identified dialysis duration as an independent influencing factor for LEFTY1 expression （P=0.001）. ConclusionThe expression of LEFTY1 in the peritoneal dialysis effluent of PD patients decreases with prolonged dialysis duration and is negatively correlated with pro-fibrotic indicators， suggesting that it may play a potential anti-fibrotic role in the progression of PF. The combined detection of LEFTY1 and solute removal indicators holds value for the early identification of ultrafiltration dysfunction.

ObjectiveTo investigate the risk factors for rebleeding after endoscopic therapy for esophageal and gastric varices in liver cirrhosis patients， to construct a clinical predictive model， and to provide a reference for predicting rebleeding in the early stage， reducing the incidence rate of rebleeding， and improving the clinical outcome of patients. MethodsA retrospective analysis was performed for the clinical data of 194 liver cirrhosis patients with gastroesophageal variceal bleeding who received initial endoscopic therapy at Department of Gastroenterology， The Second People’s Hospital of Lanzhou， from January 1， 2021 to May 31， 2025. According to whether rebleeding occurred within 1 year after endoscopic therapy， the patients were divided into rebleeding group and non-rebleeding group. The independent-samples t test or the Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. The patients enrolled were randomly divided into a training set and a validation set at a ratio of 7∶3. In the training set， the Lasso regression analysis was used to obtain optimal predictive variables， and the factors that might affect prognosis were included in the univariate and multivariate Logistic regression analyses to identify independent predictive factors for rebleeding after endoscopic therapy for esophageal and gastric varices in liver cirrhosis patients， which were used to construct a nomogram model. In both the training set and the validation set， the receiver operating characteristic （ROC） curve and the calibration curve were used to assess the discriminatory ability and calibration of the model， and decision curve analysis and the clinical impact curve were used to assess the clinical practicability of the model. ResultsAmong the 194 liver cirrhosis patients with esophageal and gastric varices， 116 （59.79%） experienced rebleeding within 1 year after endoscopic therapy， with 76 patients in the training set and 40 patients in the validation set. In the training set， the Lasso regression analysis and the univariate and multivariate Logistic regression analyses showed that etiology of liver cirrhosis （odds ratio ［OR］=3.540， 95% confidence interval ［CI］： 1.520 — 7.150， P<0.001）， Child-Pugh class （OR=3.560， 95%CI： 1.380 — 9.500， P=0.019）， severity of esophageal and gastric varices （OR=8.190， 95%CI： 3.568 — 17.850， P=0.026）， and main portal vein diameter （OR=2.954， 95%CI： 1.349 — 15.030， P=0.044） were independent predictive factors for rebleeding of esophageal and gastric varices in liver cirrhosis patients. A nomogram model was constructed based on the above independent predictive factors. The ROC curve analysis showed that this model had an area under the ROC curve of 0.845 （95%CI： 0.778 — 0.912） in the training set and 0.801 （95%CI： 0.798 — 0.868） in the validation set. The model had an index of concordance of 0.832 in the training set and 0.820 in the validation set， suggesting that the model had a good discriminatory ability. The Hosmer-Lemeshow test showed P values of 0.320 and 0.550 in the training set and validation set， respectively， the calibration curve indicated that the predicted probabilities of the nomogram model were in good concordance with the actual observed probabilities， suggesting that the model had good calibration. The decision curve analysis and the clinical impact curve showed that the model had good clinical utility. ConclusionThe nomogram model based on etiology of liver cirrhosis， Child-Pugh class， severity of esophageal and gastric varices， and main portal vein diameter has a certain clinical value in predicting the risk of rebleeding from esophageal and gastric varices in liver cirrhosis.

Objective: To evaluate the effect of repeated testing combined with cross-validation using different principles of NAT systems on improving the detection rate of occult HBV infection (OBI) in HBsAg-negative/NDR samples. Methods: A total of 89 NDR samples from the Taiyuan Blood Center between August 2024 and May 2025 were enrolled. All samples were tested for HBsAg by ELISA and subjected to NAT screening using the Roche system (PCR method). For all HBsAgnegative, MPNAT+/IDNAT (NDR) samples, further crossvalidation was performed using repeat IDNAT with the Roche system and the Grifols system (TMAbased) dHBV discriminatory assay; in addition, HBV serological markers were detected, and the results were confirmed by realtime quantitative PCR and nested PCR with sequencing genotyping. The NAT yield/window period ratio model was used to estimate the residual risk of HBV transfusion for the Roche single system in actual use, and the theoretical residual risk was calculated by assuming that the Grifols system was applied to individual screening of all blood donors. Results: The detection rate of HBsAg-negative/NDR was 0.99‰ (89/90 115), consistent with the national average. Among the 89 NDR samples, 4 were confirmed as HBV DNA-positive by nested PCR (viral load 15 - 20 IU/mL), including 3 of genotype C1 and 1 of genotype B2. Serological analysis showed that the anti-HBc positivity rate in NDR samples was 24.7% (22/89); all 4 OBI samples were antiHBcpositive (18.2%) and none were antiHB-spositive. In the single-NAT system, the residual risk for first-time donors (RR_FD) was 48.98/10 person-years for the Roche system and 21.01/10 person-years for the Grifols system, both higher than the corresponding residual risk for repeat donors (RR_RD: 15.23/10 and 9.80/10 person-years, respectively), but the differences showed no statistical significance (P>0.05). Conclusion: Implementing repeated testing combined with cross-validation using different principles of NAT systems can effectively increase the detection rate of low-viral-load OBI and reduce the risk of missed detection.

BACKGROUND:Rapid and accurate segmentation of brain tissue in medical images is of great significance for three-dimensional biomechanical modeling and diagnosis of craniocerebral injuries.Currently,artificial intelligence(AI)-based baseline models exhibit excellent generalization capabilities on large-scale datasets.However,due to the specificity and complexity of craniocerebral tissues,these models have certain limitations in their application to craniocerebral tissue segmentation.Additionally,the scarcity of craniocerebral tissue samples makes it difficult for baseline models to achieve precise segmentation results through fine-tuning.OBJECTIVE:To construct a craniocerebral tissue segmentation model based on texture feature retrieval enhancement to improve segmentation accuracy under a small number of samples.METHODS:Segment Anything in Medical Images(MedSAM)model was selected as the basic framework,and texture features were combined with deep learning to build a brain tissue segmentation model based on texture feature retrieval enhancement(DP-MedSAM).Dice Coefficient and mean intersection over union(MIoU)were selected to evaluate the efficiency of image segmentation results.In comparison with the original MedSAM model,the ablation experiment systematically evaluated the influence of key components on the model performance.The sensitivities of MedSAM,the Segment Anything Model(SAM)for medical image segmentation(SAM-Med2D)and DP-MedSAM in the mandible,left optic nerve,and left parotid gland were compared.RESULTS AND CONCLUSION:(1)By verifying the impact of the number of point prompts on segmentation results on the HaN-Seg dataset,the experimental results indicated that the optimal Dice score was achieved with the addition of three points.(2)DP-MedSAM demonstrated performance improvements compared with MedSAM and SAM-Med2D on two datasets(HaN and Public Domain Database for Computational Anatomy).Especially on the Public Domain Database for Computational Anatomy dataset,in terms of the MIoU metric,DP-MedSAM outperformed MedSAM by 6.59％and SAM-Med2D by 37.35％;in terms of the Dice metric,DP-MedSAM outperformed MedSAM and SAM-Med2D by 4.34％and 25.32％,respectively.(3)The ablation experiment results showed that removing the texture feature extraction module in the DP-MedSAM model,relying solely on original image features,led to a significant decrease in results on the test set.Furthermore,removing the vector cache database and its retrieval enhancement function from the model,which deprived the ability of the model to perform similarity retrieval using an external knowledge base,further reduced model performance.(4)Under conditions of limited data resources,the DP-MedSAM model outperformed the other two models in all evaluation metrics.The DP-MedSAM model performed excellently when processing simple and moderately difficult samples,demonstrating a clear advantage over the other two models and indicating good generalization ability.Processing the fine structures of difficult samples placed higher demands on the model's segmentation capabilities.Although the performance of the DP-MedSAM model declined slightly,it still outperformed the other two models.(5)This study proposes an innovative craniocerebral tissue segmentation model,DP-MedSAM,which improves the baseline model's performance in capturing local details and global structural information in medical images by introducing target region texture feature extraction.Through vector similarity retrieval technology,DP-MedSAM can retrieve the feature vector most similar to the current target region from a pre-constructed vector database,providing more precise guiding information for the segmentation process.

BACKGROUND:Rapid and accurate segmentation of brain tissue in medical images is of great significance for three-dimensional biomechanical modeling and diagnosis of craniocerebral injuries.Currently,artificial intelligence(AI)-based baseline models exhibit excellent generalization capabilities on large-scale datasets.However,due to the specificity and complexity of craniocerebral tissues,these models have certain limitations in their application to craniocerebral tissue segmentation.Additionally,the scarcity of craniocerebral tissue samples makes it difficult for baseline models to achieve precise segmentation results through fine-tuning.OBJECTIVE:To construct a craniocerebral tissue segmentation model based on texture feature retrieval enhancement to improve segmentation accuracy under a small number of samples.METHODS:Segment Anything in Medical Images(MedSAM)model was selected as the basic framework,and texture features were combined with deep learning to build a brain tissue segmentation model based on texture feature retrieval enhancement(DP-MedSAM).Dice Coefficient and mean intersection over union(MIoU)were selected to evaluate the efficiency of image segmentation results.In comparison with the original MedSAM model,the ablation experiment systematically evaluated the influence of key components on the model performance.The sensitivities of MedSAM,the Segment Anything Model(SAM)for medical image segmentation(SAM-Med2D)and DP-MedSAM in the mandible,left optic nerve,and left parotid gland were compared.RESULTS AND CONCLUSION:(1)By verifying the impact of the number of point prompts on segmentation results on the HaN-Seg dataset,the experimental results indicated that the optimal Dice score was achieved with the addition of three points.(2)DP-MedSAM demonstrated performance improvements compared with MedSAM and SAM-Med2D on two datasets(HaN and Public Domain Database for Computational Anatomy).Especially on the Public Domain Database for Computational Anatomy dataset,in terms of the MIoU metric,DP-MedSAM outperformed MedSAM by 6.59％and SAM-Med2D by 37.35％;in terms of the Dice metric,DP-MedSAM outperformed MedSAM and SAM-Med2D by 4.34％and 25.32％,respectively.(3)The ablation experiment results showed that removing the texture feature extraction module in the DP-MedSAM model,relying solely on original image features,led to a significant decrease in results on the test set.Furthermore,removing the vector cache database and its retrieval enhancement function from the model,which deprived the ability of the model to perform similarity retrieval using an external knowledge base,further reduced model performance.(4)Under conditions of limited data resources,the DP-MedSAM model outperformed the other two models in all evaluation metrics.The DP-MedSAM model performed excellently when processing simple and moderately difficult samples,demonstrating a clear advantage over the other two models and indicating good generalization ability.Processing the fine structures of difficult samples placed higher demands on the model's segmentation capabilities.Although the performance of the DP-MedSAM model declined slightly,it still outperformed the other two models.(5)This study proposes an innovative craniocerebral tissue segmentation model,DP-MedSAM,which improves the baseline model's performance in capturing local details and global structural information in medical images by introducing target region texture feature extraction.Through vector similarity retrieval technology,DP-MedSAM can retrieve the feature vector most similar to the current target region from a pre-constructed vector database,providing more precise guiding information for the segmentation process.