[Objective] To assess the data characteristics of quality monitoring indicators for red blood cell (RBC) preparations, so as to provide reference for continuous improvement of blood quality. [Methods] The quality inspection data of 6 types of RBC preparations from Chongqing blood center from 2019 to 2023 were summarized. For the same indicators, the numerical range of quality indicators was monitored by comparing different types of preparations with the national standard GB18469. The loss and/or damage to RBCs caused by different preparation process were compared, and the impact of different preparation processes on the quality of RBCs was discussed. [Results] The appearance and sterility test compliance rates of the six types of RBC preparations were both 100%, while the compliance rates of other items were all ≥75%. The compliance rate of hematocrit for suspended RBCs was the lowest at 75%, with a median of 0.52, which was close to the lower limit of GB18469, while the medians of hematocrit for the other types were all at the midline level of GB18469. The Hb content for different types of RBCs was significantly higher than the corresponding requirements of GB18469 (P<0.05). The hemolysis rate at the end of storage for different types of RBCs was significantly lower than the requirements of GB18469 (P<0.05). The 1 U leukoreduction process resulted in a hemoglobin content loss of about 5% and had a significant impact on the hemolysis rate at the end of storage (P<0.05). The washing process resulted in a hemoglobin content loss of <3% and had no significant impact on the hemolysis rate at the end of storage (P>0.05). The concentration process resulted in a hemoglobin content loss of <3% and had a significant impact on the hemolysis rate at the end of storage (P<0.05). [Conclusion] The impact of different processes on RBC preparations is within a controllable range and meets the requirements of GB18469. The quality monitoring data can provide a reference for clinical blood selection, effectiveness evaluation and revision of related standards.

Objective: To investigate the clinical characteristics, the types of unexpected antibodies, and their impacts on immunological risks among patients with different frequencies of cross-matching incompatibility, so as to propose corresponding solutions. Methods: Data of cross-matching incompatibility samples from 92 medical institutions during 2022 to 2024 were collected and divided into three groups based on the frequency of cross-matching. Statistical analysis was performed on disease types, distribution of hematologic diseases, alloantibody detection rates, and proportions of alloantibody types. Results: The 858 patients were divided into three groups based on the frequency of blood cross-matching incompatibility: ≥5 times (8.28%, 71/858), 2 to 4 times (28.21%, 242/858); 1 time (63.52%, 545/858). There was a clustered distribution of disease types in the ≥5 cross-matchings group, with 71.83% (51/71) of patients having tumors or hematologic and hematopoietic diseases. In contrast, the disease types in the 2 to 4 cross-matchings and 1 cross-matching groups were more diverse. An analysis of 249 patients with hematologic diseases found that multiple myeloma was the most common disease in all three groups, accounting for 31.43% (11/35), 35.37% (29/82), and 37.88% (50/132) respectively. In the ≥5 cross-matchings group, myelodysplastic syndrome (14.29%, 5/35) and thalassemia (14.29%, 5/35) were the second most common diseases. In contrast, in the 2 to 4 cross-matchings group and 1 cross-matching group, autoimmune hemolytic anemia was the second most common disease, with prevalence rates of 20.73% (17/82) and 24.24% (32/132), respectively. Alloantibodies were detected in 54.66% of the patients, with antibodies against Rh blood group being most frequent (>50%) in all three groups. The detection rates of alloantibodies/alloantibodies with coexisting autoantibodies decreased across groups: the ≥5 cross-matchings group (70.42%, 50/71) > the 2 to 4 cross-matchings group (54.96%, 133/242) > the 1 cross-matching group (52.48%, 286/545). Conclusion: The risk of alloantibody production increases in patients with multiple cross-matching incompatibilities, especially in those with tumors or hematologic diseases. For handling of cross-matching incompatibility cases, it is recommended to optimize the cross-matching process, implement individualized transfusion plans, and enhance the technical capabilities of clinical transfusion departments and blood group reference laboratories to ensure the safety and effectiveness of transfusions.

ObjectiveTraditional Chinese medicine (TCM) constitutes a valuable cultural heritage and an important source of antitumor compounds. Poria (Poria cocos (Schw.) Wolf), the dried sclerotium of a polyporaceae fungus, was first documented in Shennong’s Classic of Materia Medica and has been used therapeutically and dietarily in China for millennia. Traditionally recognized for its diuretic, spleen-tonifying, and sedative properties, modern pharmacological studies confirm that Poria exhibits antioxidant, anti-inflammatory, antibacterial, and antitumor activities. Pachymic acid (PA; a triterpenoid with the chemical structure 3β-acetyloxy-16α-hydroxy-lanosta-8,24(31)-dien-21-oic acid), isolated from Poria, is a principal bioactive constituent. Emerging evidence indicates PA exerts antitumor effects through multiple mechanisms, though these remain incompletely characterized. Neuroblastoma (NB), a highly malignant pediatric extracranial solid tumor accounting for 15% of childhood cancer deaths, urgently requires safer therapeutics due to the limitations of current treatments. Although PA shows multi-mechanistic antitumor potential, its efficacy against NB remains uncharacterized. This study systematically investigated the potential molecular targets and mechanisms underlying the anti-NB effects of PA by integrating network pharmacology-based target prediction with experimental validation of multi-target interactions through molecular docking, dynamic simulations, and in vitro assays, aimed to establish a novel perspective on PA’s antitumor activity and explore its potential clinical implications for NB treatment by integrating computational predictions with biological assays. MethodsThis study employed network pharmacology to identify potential targets of PA in NB, followed by validation using molecular docking, molecular dynamics (MD) simulations, MM/PBSA free energy analysis, RT-qPCR and Western blot experiments. Network pharmacology analysis included target screening via TCMSP, GeneCards, DisGeNET, SwissTargetPrediction, SuperPred, and PharmMapper. Subsequently, potential targets were predicted by intersecting the results from these databases via Venn analysis. Following target prediction, topological analysis was performed to identify key targets using Cytoscape software. Molecular docking was conducted using AutoDock Vina, with the binding pocket defined based on crystal structures. MD simulations were performed for 100 ns using GROMACS, and RMSD, RMSF, SASA, and hydrogen bonding dynamics were analyzed. MM/PBSA calculations were carried out to estimate the binding free energy of each protein-ligand complex. In vitro validation included RT-qPCR and Western blot, with GAPDH used as an internal control. ResultsThe CCK-8 assay demonstrated a concentration-dependent inhibitory effect of PA on NB cell viability. GO analysis suggested that the anti-NB activity of PA might involve cellular response to chemical stress, vesicle lumen, and protein tyrosine kinase activity. KEGG pathway enrichment analysis suggested that the anti-NB activity of PA might involve the PI3K/AKT, MAPK, and Ras signaling pathways. Molecular docking and MD simulations revealed stable binding interactions between PA and the core target proteins AKT1, EGFR, SRC, and HSP90AA1. RT-qPCR and Western blot analyses further confirmed that PA treatment significantly decreased the mRNA and protein expression of AKT1, EGFR, and SRC while increasing the HSP90AA1 mRNA and protein levels. ConclusionIt was suggested that PA may exert its anti-NB effects by inhibiting AKT1, EGFR, and SRC expression, potentially modulating the PI3K/AKT signaling pathway. These findings provide crucial evidence supporting PA’s development as a therapeutic candidate for NB.

Background: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia. Methods: A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split. Results: The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts. Conclusion The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.

Background: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia. Methods: A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split. Results: The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts. Conclusion The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.

Background: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia. Methods: A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split. Results: The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts. Conclusion The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.

Background: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatment. Although the Mini-Mental State Examination (MMSE) is widely used to assess cognitive impairment, its standalone efficacy is debated. This study examined the effectiveness of the MMSE alone versus in combination with other cognitive assessments in predicting dementia diagnosis, with the aim of refining the diagnostic accuracy for dementia. Methods: A total of 2,863 participants with subjective cognitive complaints who underwent comprehensive neuropsychological assessments were included. We developed two random forest models: one using only the MMSE and another incorporating additional cognitive tests.These models were evaluated based on their accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) on a 70:30 training-to-testing split. Results: The MMSE-alone model predicted dementia with an accuracy of 86% and AUC of 0.872. The expanded model demonstrated increased accuracy (88%) and an AUC of 0.934.Notably, 17.46% of the cases were reclassified from dementia to non-dementia category upon including additional tests. Higher educational level and younger age were associated with these shifts. Conclusion The findings suggest that although the MMSE is a valuable screening tool, it should not be used in isolation to determine dementia severity. The addition of diverse cognitive assessments can significantly enhance diagnostic precision, particularly in younger and more educated populations. Future diagnostic protocols should integrate multifaceted cognitive evaluations to reflect the complexity of dementia accurately.

OBJECTIVE: CRISPR-Cas protects bacteria from exogenous DNA invasion and is associated with bacterial biofilm formation and pathogenicity. METHODS: We analyzed the type I-F CRISPR-Cas system of Legionella pneumophila WX48, including Cas1, Cas2-Cas3, Csy1, Csy2, Csy3, and Cas6f, along with downstream CRISPR arrays. We explored the effects of the CRISPR-Cas system on the in vitro growth, biofilm-forming ability, and pathogenicity of L. pneumophila through constructing gene deletion mutants. RESULTS: The type I-F CRISPR-Cas system did not affect the in vitro growth of wild-type or mutant strains. The biofilm formation and intracellular proliferation of the mutant strains were weaker than those of the wild type owing to the regulation of type IV pili and Dot/Icm type IV secretion systems. In particular, Cas6f deletion strongly inhibited these processes. CONCLUSION The type I-F CRISPR-Cas system may reduce biofilm formation and intracellular proliferation in L. pneumophila.
Legionella pneumophila/pathogenicity* ; CRISPR-Cas Systems ; Biofilms/growth & development* ; Phenotype ; Bacterial Proteins/metabolism* ; Gene Deletion

Objective To evaluate the value of pre-ascent 6-minute walking test performed at a high altitude in predicting the incidence of acute mountain sickness(AMS)induced by rapid ascent to a very high altitude.Methods After baseline information was collected,participants completed the 6-minute walking test at a high altitude of 2 900 m.Then,they rapidly ascended to a very high altitude of 5 000 m.The Lake Louise score was recorded to assess AMS.Results The AMS group showed a shorter pre-ascent 6-minute walking distance(6MWD)at the high altitude than the non-AMS group[480.00(450.00,521.75)m vs.546.00(516.50,568.50)m,P=0.006].No difference was observed regarding the pre-ascent heart rate or peripheral oxygen saturation(both P>0.05).The pre-ascent 6MWD at the high altitude was negatively correlated with the Lake Louise score assessed after rapid ascent to the very high altitude(r=-0.497,P=0.012).Logistic regression analysis confirmed that the pre-ascent 6MWD at the high altitude was associated with the risk of AMS induced by rapid ascent to the very high altitude(OR=0.971,95% CI=0.947-0.996,P=0.022).The results indicated that the pre-ascent 6MWD demonstrated ideal prediction performance(area under receiver operating characteristic curve=0.846,P=0.006).Conclusion The pre-ascent 6MWD recorded at the high altitude is a convenient and reliable predictor of the AMS induced by rapid ascent to the very high altitude.
Humans ; Altitude Sickness/diagnosis* ; Male ; Adult ; Female ; Young Adult ; Middle Aged ; Acute Disease ; Walk Test ; Walking ; Altitude ; Exercise Test