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.

OBJECTIVES: To explore the measures to improve the follow-up rate of preterm infants after discharge, and to evaluate the effectiveness of these measures using quality improvement methodology. METHODS: The follow-up status of preterm infants discharged from March to May 2017 was used as the baseline before quality improvement, and a specific quality improvement goal for the follow-up rate was proposed. The Pareto chart was used to analyze the causes of follow-up failure, and a key driver diagram was constructed based on the links involved in improving follow-up rate. The causes of failure were analyzed to determine the key links and intervention measures for quality improvement, and the follow-up rate was monitored weekly using a control chart until the quality improvement goal was achieved. RESULTS: The follow-up rate of preterm infants after discharge was 57.92% (117/202) at baseline before quality improvement, and the quality improvement goal was set to increase the follow-up rate of preterm infants from baseline to more than 80% within 12 months. The Pareto chart analysis showed that the main causes of follow-up failure were deficiencies in follow-up file management and irregular follow-up times (33.70%, 31/92), insufficient follow-up education and poor communication (25.00%, 23/92), and the inability to meet the diverse needs of parents (18.48%, 17/92). Based on the key links for quality improvement and the main causes of follow-up failure, the following intervention measures were adopted: (1) strengthen follow-up publicity and education; (2) build a follow-up team; and (3) establish a follow-up platform and system. The control chart indicated that with the implementation of the above intervention measures, the weekly follow-up rate increased to 74.09% (306/413) in July 2017 and 83.09% (511/615) in December 2017, finally achieving the quality improvement goal. During the COVID-19 pandemic, the follow-up rate of preterm infants fluctuated between 23.54% (460/1 954) and 70.97% (1 931/2 721), and subsequently, it returned to pre-pandemic levels starting in February 2023. CONCLUSIONS The application of quality improvement methodology can help to formulate intervention measures based on the main causes of follow-up failure, thereby improving the follow-up rate of preterm infants after discharge. This quality improvement method is feasible and practical and thus holds promise for clinical application.
Humans ; Quality Improvement ; Infant, Premature ; Infant, Newborn ; Patient Discharge ; Follow-Up Studies ; Female ; Male

OBJECTIVE: To investigate the molecular alterations of splenocytes associated with anti-factor Ⅷ (FⅧ) immune response and the underlying mechanisms based on hemophilia A (HA) murine model via RNA sequencing (RNA-seq) technology. METHODS: Severe HA mice were immunized with recombinant human factor Ⅷ (rhF8) weekly for 4 weeks to establish an FⅧ inhibitor model. High quality raw data were obtained by using bulk RNA-seq and CASAVA base identification technology, and the differentially expressed genes (DEGs) were identified. The DEGs were statistically classified by gene ontology (GO) annotation to obtain information on the major signaling pathways and biological processes involved in anti-FⅧ immune response in HA mouse splenocytes. The cell clusters, genes, and signaling pathway datasets were comprehensively analyzed by GO, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and single cell RNA-seq (ScRNA-seq) analysis, respectively. Flow cytometry analysis was used to verify the changes in T follicular helper cells (Tfh) and regulatory T cells (Treg). RESULTS: A total of 3731 DEGs was identified, including 2275 genes with up-regulated expression and 1456 genes with down-regulated expression. The DEGs were enriched in helper T cell differentiation, cytokine receptor, T cell receptor signaling pathway, ferroptosis, etc. Uniform Manifold Approximation and Project (UMAP) downscaling and visualization analysis yielded a total number of 11 T/NK cell subsets, visualizing the overall expression distribution of C-X-C chemokine-specific receptor gene cxcr5 among these T/NK cell subsets. Higher expression of cxcr5 was found in activated Tfh from FⅧ inhibitor mice, in comparison to the control group. The visualization using Upset plot R language showed a close interaction between Tfh and Treg. Moreover, the increased frequencies of Tfh and the decreased frequencies of Treg in inhibitor mouse splenocytes were further verified by flow cytometry analysis. CONCLUSION Multiple immune cell subsets, signaling pathways, and characteristic genes may be involved in the process of anti-FⅧ immune response in HA mouse splenocytes. The molecules involved in the regulation of Tfh/Treg may play key roles, which provide potential biological targets and therapeutic strategies for HA patients with inhibitors in the future.
Animals ; Hemophilia A/genetics* ; Mice ; Sequence Analysis, RNA ; Disease Models, Animal ; Spleen/cytology* ; T-Lymphocytes, Regulatory/immunology* ; Humans ; Signal Transduction ; Factor VIII/immunology* ; T-Lymphocytes, Helper-Inducer/immunology*

OBJECTIVE: As an age-related neurodegenerative disease, the prevalence of mild cognitive impairment (MCI) increases with age. Within the framework of traditional Chinese medicine, spleen-kidney deficiency syndrome (SKDS) is recognized as the most frequent MCI subtype. Due to the covert and gradual onset of MCI, in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes. There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS (MCI-SKDS). METHODS: This investigation enrolled 312 elderly individuals diagnosed with MCI, who were randomly distributed into training and test datasets at a 3:1 ratio. Five machine learning methods, including logistic regression (LR), decision tree (DT), naive Bayes (NB), support vector machine (SVM), and gradient boosting (GB), were used to build a diagnostic prediction model for MCI-SKDS. Accuracy, sensitivity, specificity, precision, F1 score, and area under the curve were used to evaluate model performance. Furthermore, the clinical applicability of the model was evaluated through decision curve analysis (DCA). RESULTS: The accuracy, precision, specificity and F1 score of the DT model performed best in the training set (test set), with scores of 0.904 (0.845), 0.875 (0.795), 0.973 (0.875) and 0.973 (0.875). The sensitivity of the training set (test set) of the SVM model performed best among the five models with a score of 0.865 (0.821). The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset. The DCA of all models showed good clinical application value. The study identified ten indicators that were significant predictors of MCI-SKDS. CONCLUSION The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical; the model demonstrates good predictive value and clinical applicability, and the DT model had the best performance. Please cite this article as: Ai YT, Zhou S, Wang M, Zheng TY, Hu H, Wang YC, Li YC, Wang XT, Zhou PJ. Development of a machine learning-based risk prediction model for mild cognitive impairment with spleen-kidney deficiency syndrome in the elderly. J Integr Med. 2025; 23(4): 390-397.
Humans ; Cognitive Dysfunction/diagnosis* ; Aged ; Male ; Female ; Machine Learning ; Spleen ; Aged, 80 and over ; Kidney ; Medicine, Chinese Traditional

OBJECTIVE: This study aimed to reexplore minimum iodine excretion and to build a dietary iodine recommendation for Chinese adults using the obligatory iodine loss hypothesis. METHODS: Data from 171 Chinese adults (19-21 years old) were collected and analyzed based on three balance studies in Shenzhen, Yinchuan, and Changzhi. The single exponential equation was accordingly used to simulate the trajectory of 24 h urinary iodine excretion as the low iodine experimental diets offered (iodine intake: 11-26 μg/day) and to further deduce the dietary reference intakes (DRIs) for iodine, including estimated average requirement (EAR) and recommended nutrient intake (RNI). RESULTS: The minimum iodine excretion was estimated as 57, 58, and 51 μg/day in three balance studies, respectively. Moreover, it was further suggested as 57, 58, and 51 μg/day for iodine EAR, and 80, 81, and 71 μg/day for iodine RNI or expressed as 1.42, 1.41, and 1.20 μg/(day·kg) of body weight. CONCLUSION The iodine DRIs for Chinese adults were established based on the obligatory iodine loss hypothesis, which provides scientific support for the amendment of nutrient requirements.
Humans ; Iodine/administration & dosage* ; Male ; Female ; China ; Young Adult ; Diet ; Adult ; Nutritional Requirements ; East Asian People

OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index

Nodal marginal zone B-cell lymphoma(NMZL),the least common subtype of marginal zone lymphoma,represents a low-grade malignancy arising from the marginal zone of lymph node follicles,composed of small B-cells with an inert non-Hodgkin lymphoma nature.It accounts for 1.5% to 1.8% of all non-Hodgkin lymphomas and 10% of all marginal zone lymphomas.The low incidence and lack of typical clinical and pathological features pose a challenge to the diagnosis and clinical management of NMZL.In this article,we reported the diagnosis and treatment of a case of NMZL located in the parapharyngeal space of the left neck and reviewed the relevant literature from both domestic and international sources.We summarized the clinical manifestations,histopathological features,immunohistochemical characteristics,imaging features,diagnosis and treatment modalities,and prognosis of NMZL.
Humans ; Lymphoma, B-Cell, Marginal Zone/pathology* ; Lymph Nodes/pathology* ; Neck/pathology* ; Male

A centrifugation-free,single-reaction colorimetric method for detection of glucose,utilizing a dual nanozyme cascade system based on gold nanoparticles(AuNPs)and iron-doped carbon dots(FeCDs),was developed in this work.The AuNPs exhibited glucose oxidase-like activity to catalyze glucose oxidation for generation of H2O2,while the FeCDs demonstrated peroxidase-like activity to subsequently catalyze the H2O2-mediated oxidation of 3,3',5,5'-tetramethylbenzidine(TMB).To prevent interference from the blue signal generated by self-aggregation of AuNPs in subsequent quantitative detection,the reaction system was terminated with HCl,converting oxTMB into a stable yellow product.Based on changes in the absorbance at 450 nm of this yellow solution,a quantitative relationship was established between glucose concentration and absorbance at 450 nm(A450).Experimental results demonstrated that this sensor achieved a linear detection range of 44 μmol/L to 11.11 mmol/L(R2=0.993)with a detection limit of 30.68 μmol/L and spiked recoveries of 97.9%-104.7%.By integrating smartphone-based color recognition capabilities,a rapid visual detection platform was established for quantification of glucose through RGB analysis.The validation experimental results using commercial glucose injection samples further confirmed the practical application potential of this methodology.

AIM To investigate the effects of Changpu Yujin Decoction(CPYJD)on striatal mitophagy and PINK1/Parkin signaling pathway in a rat model of Tourette syndrome(TS).METHODS Thirty-six SPF male SD rats were randomly assigned to the control group(n=9)and the TS modeling group(n=27).Rats in the modeling group received daily intraperitoneal injections of 3,3'-iminodipropionitrile(IDPN)(300 mg/kg)for 7 consecutive days to establish the TS model.Post-modeling,successfully induced TS rats were re-randomized into model group(no treatment),tiapride group(47.91 mg/kg)and CPYJD group(77.28 g/kg).All groups received their respective interventions via intragastric administration daily for 28 days.Following drug administration,behavioral scores were assessed in each group.Pathological alterations in the striatum were examined using HE staining,while ultrastructural changes were evaluated by transmission electron microscopy(TEM).Neuronal apoptosis was quantified via TUNEL staining,and ROS levels in striatum were measured by ELISA.Co-localization of PINK1 and LC3B was assessed using immunofluorescence(IF).Finally,mRNA and protein expressions of PINK1,Parkin,Beclin-1,P62 and LC3B(LC3B-Ⅱ/Ⅰ ratio)were analyzed by RT-qPCR and Western blot.RESULTS Compared to the control group,the model group demonstrated significantly increased behavioral scores(P＜0.01),elevated neuronal apoptosis rate and higher ROS levels in the striatum(P＜0.01);severe neuronal and mitochondrial damage in the striatum;significantly reduced mRNA and protein expressions of PINK1,Parkin,Beclin-1 and LC3B(LC3B-Ⅱ/Ⅰ ratio)in the striatum(P＜0.01);markedly upregulated P62 mRNA and protein expressions(P＜0.01).Compared to the model group,both the tiapride and CPYJD intervention groups exhibited significantly reduced behavioral scores(P＜0.01);decreased neuronal apoptosis rate and lower ROS levels(P＜0.01);improved pathological alterations in the striatal neurons and mitochondria;increased mRNA and protein expressions of PINK1,Parkin and Beclin-1 in the striatum(P＜0.05,P＜0.01);and decreased P62 mRNA and protein expressions(P＜0.01).Furthermore,the rats in the CPYJD group specifically showed elevated LC3B mRNA level and LC3B-Ⅱ/Ⅰ protein ratio in striatum(P＜0.05,P＜0.01).CONCLUSION The effect of CPYJD intervention in TS rats may involve activation of mitophagy through regulation of the PINK1/Parkin signaling pathway,improving mitochondrial function,reducing ROS levels,and thereby protecting neurons.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.