Objective: To analyze the structural changes in whole blood and apheresis platelet donations in Chinese Society of Blood Transfusion (CSBT) sentinel units from 2021 to 2025, and to provide evidence for donor recruitment and blood service management. Methods: Aggregated data reported by sentinel sites were collected and stratified according to six indicators: age, sex, donation history, donation volume, donation site, and organization mode. Pearson χ test, Cramer′V coefficient, Cochran-Armitage trend test and simple linear regression were adopted for analysis. Results: A total of 16 256 973 whole blood donations and 1 576 546 apheresis platelet donations were included. Among whole-blood donations, the proportion of donors aged 18-22 years decreased from 27.30% to 10.60%. The proportion of male donors increased from 58.81% to 63.13%; the proportion of people aged 30 and above increased from 58.40% to 75.30%; the proportion of fixed locations rose from 28.37% to 36.57%; the proportion of social groups increased from 21.69% to 39.69%. The 400-mL donations decreased from 62.12% to 53.46%, while individually initiated voluntary donations remained above 50%. Apheresis platelet donations were predominantly from males, individuals aged 30-39 years, and repeat donors; 2-treatment-unit apheresis platelet donations increased from 67.19% to 74.93%, and individually initiated donations accounted for 92.97%-96.02%. Statistically significant overall annual compositional differences were observed for all six indicators (all P<0.001), whereas linear trends for some sub-categories were not statistically significant. Conclusion: In the participating CSBT sentinel units, among whole blood donations, the proportions of donors aged 18-22 years, university-organized group donations, and donations at blood mobiles decreased, whereas the proportions of organizational group donations and donations at fixed venues increased. The apheresis platelet donation cohort is relatively mature and is evolving toward a high-efficiency donation model. Individual voluntary donation remains the primary organization mode for both donation types. Strategies such as youth donor recruitment, optimized layout of fixed donation sites, and retention of repeat donors should be strengthened to improve the resilience of blood supply.

Intervention in chronically activated microglia-mediated neuroinflammation is a novel approach to treat Alzheimer's disease (AD). The low permeability of the blood‒brain barrier (BBB) and non-selective distribution in the brain severely restrict AD drugs' disease-modifying efficacy. Here, an immunosuppressant TREM2-lowing antisense oligonucleotides (ASOs) and resveratrol co-loaded cationic liposome is developed as an immune reprogramming nanomodulator modified by acid-cleavable BBB-targeting peptide and microglia-targeting peptide (Res@TcMNP/ASO) for AD management. Res@TcMNP/ASO can enter brain endothelial cells via D-T7 peptides. Then D-T7 undergoes an acid-responsive cleavage, facilitating the escape of Res@MNP/ASO from endo/lysosomes to cross the BBB. The detached Res@MNP/ASO specifically targets M1-phenotype microglia via exposed MG1 peptides to prompt the simultaneous delivery of two drugs into activated microglia. This nanomodulator can not only restore the immune function of microglia through TREM2-lowing ASO but also mitigate the immune stimulation to microglia caused by reactive oxygen species (ROS) through resveratrol, thereby synergistically inhibiting the chronic activation of microglia to alleviate neuroinflammation in AD. Our results indicate that this combination treatment can achieve significant behavioral and cognitive improvements in late APP/PS1 mice.

Alzheimer's disease (AD), characterized by β-amyloid (Aβ) aggregation and neuroinflammation, remains a formidable clinical challenge. Herein, we present an innovative nose-to-brain delivery platform utilizing lactoferrin (Lf)-functionalized lipid nanoparticles (LNPs) co-encapsulating α-mangostin (α-M) and β-site APP cleaving enzyme 1 (BACE1) siRNA (siB). This dual-modal therapeutic system synergistically combines the neuroprotective and microglia-reprogramming capabilities of α-M with the transcriptional silencing of BACE1 via siB, thereby simultaneously inhibiting Aβ production and enhancing its clearance. Fabricated via a microfluidic approach, the LNPs exhibited uniform particle size distribution, great encapsulation efficiency, and robust colloidal stability. Upon intranasal administration, Lf-functionalization enabled superior brain-targeting efficacy through receptor-mediated transcytosis. In vitro studies demonstrated that α-M reversed Aβ-induced low-density lipoprotein receptor downregulation, promoting microglial phagocytosis and autophagic degradation of Aβ, while siB effectively suppressed BACE1 expression, abrogating Aβ synthesis. In vivo investigations in APP/PS1 transgenic mice revealed remarkable cognitive recovery, substantial Aβ plaque reduction, and alleviation of neuroinflammation and oxidative stress. This intricately designed LNP system, exploiting a non-invasive and efficient nose-to-brain delivery route, provides a biocompatible, synergistic, and transformative therapeutic strategy for the multifaceted management of AD.

The activation of the sirtuin1 (SIRT1)/nuclear factor erythroid 2-related factor 2 (Nrf2)/heme oxygenase 1 (HO-1) pathway has been shown to mitigate oxidative stress-induced apoptosis and mitochondrial damage by reducing reactive oxygen species (ROS) levels. Clinical trials have demonstrated that Zhongfeng Xingnao Liquid (ZFXN) ameliorates post-stroke cognitive impairment (PSCI). However, the underlying mechanism, particularly whether it involves protecting mitochondria and inhibiting apoptosis through the SIRT1/Nrf2/HO-1 pathway, remains unclear. This study employed an oxygen-glucose deprivation (OGD) cell model using SH-SY5Y cells and induced PSCI in rats through modified bilateral carotid artery ligation (2VO). The effects of ZFXN on learning and memory, neuroprotective activity, mitochondrial function, oxidative stress, and the SIRT1/Nrf2/HO-1 pathway were evaluated both in vivo and in vitro. Results indicated that ZFXN significantly increased the B-cell lymphoma 2 (Bcl2)/Bcl2-associated X (Bax) ratio, reduced terminal deoxynucleotidyl transferase-mediated dUTP nick-end-labeling (TUNEL)⁺ cells, and markedly improved cognition, synaptic plasticity, and neuronal function in the hippocampus and cortex. Furthermore, ZFXN exhibited potent antioxidant activity, evidenced by decreased ROS and malondialdehyde (MDA) content and increased superoxide dismutase (SOD), catalase (CAT), and glutathione (GSH) levels. ZFXN also demonstrated considerable enhancement of mitochondrial membrane potential (MMP), Tom20 fluorescence intensity, adenosine triphosphate (ATP) and energy charge (EC) levels, and mitochondrial complex I and III activity, thereby inhibiting mitochondrial damage. Additionally, ZFXN significantly increased SIRT1 activity and elevated SIRT1, nuclear Nrf2, and HO-1 levels. Notably, these effects were substantially counteracted when SIRT1 was suppressed by the inhibitor EX-527 in vitro. In conclusion, ZFXN alleviates PSCI by activating the SIRT1/Nrf2/HO-1 pathway and preventing mitochondrial damage.
Sirtuin 1/genetics* ; Animals ; NF-E2-Related Factor 2/genetics* ; Cognitive Dysfunction/genetics* ; Male ; Rats, Sprague-Dawley ; Rats ; Humans ; Signal Transduction/drug effects* ; Drugs, Chinese Herbal/administration & dosage* ; Heme Oxygenase-1/genetics* ; Stroke/complications* ; Oxidative Stress/drug effects* ; Apoptosis/drug effects* ; Mitochondria/metabolism* ; Reactive Oxygen Species/metabolism* ; Neuroprotective Agents

Objective:To investigate the diagnostic value of different conventional ultrasound-based radiomics models and their combination with clinical ultrasound features in the pathological subtyping of renal cell carcinoma.Methods:Retrospective data from 286 patients diagnosed with renal cell carcinoma by pathology at the Tenth Affiliated Hospital of Southern Medical University between May 1，2017 and June 7，2024 were collected. Among the 286 patients，203 were clear cell carcinoma，44 were papillary renal cell carcinoma，and 39 were chromophobe renal cell carcinoma. The patients were randomly divided into a training group（201 cases）and a validation group（85 cases）in a ratio of 7 to 3. Regions of interest（ROI）were delineated on conventional ultrasound images，and the radiomics features were extracted. Feature selection was performed using Student's t-test，Pearson correlation，and the least absolute shrinkage and selection operator（LASSO）. Six different machine learning methods included category gradient boosting（CatBoost），light gradient boosting machine（LightGBM），Logistic regression（LR），random forest（RF），support vector machine（SVM）and extreme gradient boosting（XGBoost）were used to establish radiomics models. Weight balancing was applied to correct for sample imbalance，and an imaging genomics model was constructed after balancing the samples. Independent predictors of renal cell carcinoma subtyping were selected from clinical ultrasound features using univariate and multivariate logistic regression analyses，and a clinical imaging model was constructed. The best-performing radiomics model was combined with the clinical independent predictors to construct a combined model. Receiver operating characteristic curves and the obuchowski index were plotted to evaluate model performance. Results:Among the radiomics models，the model constructed using Random Forest（RS RF）after balancing the samples exhibited the best predictive performance，with area under the curve（AUCs）of 0.918（micro-average ROC）and 0.903（macro-average ROC），and the obuchowski index was 0.885 in the validation group. The long and short axes of ultrasound image tumor masses were used as imaging independent predictors to construct a clinical imaging model. In the validation group，the AUCs of the clinical model were 0.886（micro-average ROC）and 0.606（macro-average ROC），and the obuchowski index was 0.569. The combined model achieved AUCs of 0.888（micro-average ROC）and 0.967（macro-average ROC），with an obuchowski index of 0.933，outperforming any single model. Conclusions:The combination of conventional ultrasound-based radiomics models with clinical ultrasound features demonstrates high diagnostic value in differentiating clear cell carcinoma，papillary renal cell carcinoma，and chromophobe renal cell carcinoma. It may serve as an auxiliary tool for providing timely and effective clinical guidance.

Objective:To construct a triple pre-rehabilitation intervention program for patients undergoing radical prostatectomy, to provide a basis for promoting pre-rehabilitation in patients undergoing radical prostatectomy.Methods:Literature analysis was used to search domestic and foreign databases such as China National Knowledge Infrastructure, Wanfang, PubMed, etc, on triple pre-rehabilitation and rapid rehabilitation of patients undergoing radical prostatectomy, with a search time frame from January 1, 2013 to December 31, 2023. The Delphi expert letter consultation was conducted later, and the final draft of the triple pre-rehabilitation intervention program for patients with radical prostatectomy was finally formed.Results:A total of 20 experts completed 2 rounds of inquiries, all were female, with an age of (40.85 ± 5.40) years old. The response rates for the 2 rounds of expert inquiries were both 100%(20/20). The authority coefficients of the experts were 0.87 and 0.90, respectively. Kendall coordination coefficients were 0.11 and 0.21, respectively. The coefficient of variation for each item in the second round of inquiries ranged from 0 to 0.15. The triple pre-rehabilitation intervention program for patients with radical resection of prostate cancer was constructed, including 4 primary indexes, 8 secondary indexes and 25 tertiary indexes.Conclusions:It is scientific, targeted and feasible to construct a triple pre-rehabilitation intervention program for patients undergoing radical prostatectomy based on Delphi method, which can provide clinical basis for pre-rehabilitation of elderly patients undergoing prostate cancer surgery.

The conventional identification and prediction for failures of medical equipment mainly depend on experience and knowledge of manager for equipment,which are not able to be quantified and have lower efficiency. Therefore,it is obvious that the prediction for the failure of medical equipment is not accurate. With the technical development of computer and machine learning,the conventional identification and prediction that depend on experiences can deal with characteristics of failures through machine learning method to improve efficiency,which are hopeful in filling the gap of discipline about the identification and prediction for failures of medical equipment. This article summarized the application situation of machine learning in identifying and predicting failures of the medical equipment and the similarly electric equipment at home and abroad. Based on the key technique of identification and prediction,this article proposed suggestion about corresponding design architecture. According to the characteristics of the failure of medical equipment,this article summarized a series of information about algorithms of various machine learning in scene and accurate rate of identification and prediction,so as to provide references for relevant research of this field.

The conventional identification and prediction for failures of medical equipment mainly depend on experience and knowledge of manager for equipment,which are not able to be quantified and have lower efficiency. Therefore,it is obvious that the prediction for the failure of medical equipment is not accurate. With the technical development of computer and machine learning,the conventional identification and prediction that depend on experiences can deal with characteristics of failures through machine learning method to improve efficiency,which are hopeful in filling the gap of discipline about the identification and prediction for failures of medical equipment. This article summarized the application situation of machine learning in identifying and predicting failures of the medical equipment and the similarly electric equipment at home and abroad. Based on the key technique of identification and prediction,this article proposed suggestion about corresponding design architecture. According to the characteristics of the failure of medical equipment,this article summarized a series of information about algorithms of various machine learning in scene and accurate rate of identification and prediction,so as to provide references for relevant research of this field.

Objective:To construct a triple pre-rehabilitation intervention program for patients undergoing radical prostatectomy, to provide a basis for promoting pre-rehabilitation in patients undergoing radical prostatectomy.Methods:Literature analysis was used to search domestic and foreign databases such as China National Knowledge Infrastructure, Wanfang, PubMed, etc, on triple pre-rehabilitation and rapid rehabilitation of patients undergoing radical prostatectomy, with a search time frame from January 1, 2013 to December 31, 2023. The Delphi expert letter consultation was conducted later, and the final draft of the triple pre-rehabilitation intervention program for patients with radical prostatectomy was finally formed.Results:A total of 20 experts completed 2 rounds of inquiries, all were female, with an age of (40.85 ± 5.40) years old. The response rates for the 2 rounds of expert inquiries were both 100%(20/20). The authority coefficients of the experts were 0.87 and 0.90, respectively. Kendall coordination coefficients were 0.11 and 0.21, respectively. The coefficient of variation for each item in the second round of inquiries ranged from 0 to 0.15. The triple pre-rehabilitation intervention program for patients with radical resection of prostate cancer was constructed, including 4 primary indexes, 8 secondary indexes and 25 tertiary indexes.Conclusions:It is scientific, targeted and feasible to construct a triple pre-rehabilitation intervention program for patients undergoing radical prostatectomy based on Delphi method, which can provide clinical basis for pre-rehabilitation of elderly patients undergoing prostate cancer surgery.

Objective:To investigate the diagnostic value of different conventional ultrasound-based radiomics models and their combination with clinical ultrasound features in the pathological subtyping of renal cell carcinoma.Methods:Retrospective data from 286 patients diagnosed with renal cell carcinoma by pathology at the Tenth Affiliated Hospital of Southern Medical University between May 1，2017 and June 7，2024 were collected. Among the 286 patients，203 were clear cell carcinoma，44 were papillary renal cell carcinoma，and 39 were chromophobe renal cell carcinoma. The patients were randomly divided into a training group（201 cases）and a validation group（85 cases）in a ratio of 7 to 3. Regions of interest（ROI）were delineated on conventional ultrasound images，and the radiomics features were extracted. Feature selection was performed using Student's t-test，Pearson correlation，and the least absolute shrinkage and selection operator（LASSO）. Six different machine learning methods included category gradient boosting（CatBoost），light gradient boosting machine（LightGBM），Logistic regression（LR），random forest（RF），support vector machine（SVM）and extreme gradient boosting（XGBoost）were used to establish radiomics models. Weight balancing was applied to correct for sample imbalance，and an imaging genomics model was constructed after balancing the samples. Independent predictors of renal cell carcinoma subtyping were selected from clinical ultrasound features using univariate and multivariate logistic regression analyses，and a clinical imaging model was constructed. The best-performing radiomics model was combined with the clinical independent predictors to construct a combined model. Receiver operating characteristic curves and the obuchowski index were plotted to evaluate model performance. Results:Among the radiomics models，the model constructed using Random Forest（RS RF）after balancing the samples exhibited the best predictive performance，with area under the curve（AUCs）of 0.918（micro-average ROC）and 0.903（macro-average ROC），and the obuchowski index was 0.885 in the validation group. The long and short axes of ultrasound image tumor masses were used as imaging independent predictors to construct a clinical imaging model. In the validation group，the AUCs of the clinical model were 0.886（micro-average ROC）and 0.606（macro-average ROC），and the obuchowski index was 0.569. The combined model achieved AUCs of 0.888（micro-average ROC）and 0.967（macro-average ROC），with an obuchowski index of 0.933，outperforming any single model. Conclusions:The combination of conventional ultrasound-based radiomics models with clinical ultrasound features demonstrates high diagnostic value in differentiating clear cell carcinoma，papillary renal cell carcinoma，and chromophobe renal cell carcinoma. It may serve as an auxiliary tool for providing timely and effective clinical guidance.