Based on commonly used acupoints in the clinical acupuncture treatment of functional dyspepsia （FD）， this study systematically analyzes the therapeutic differences and synergistic effects between local and distal point selection. It also examines the suitability of primary acupoint selection for different FD subtypes， postprandial distress syndrome （PDS） and epigastric pain syndrome （EPS）. The findings suggest that a combination of local and distal acupoints may be more appropriate as primary points for PDS， whereas local acupoints alone may be more suitable for EPS. Additionally， the study explores the impact of various factors， such as stimulation techniques， needling order， intensity or stimulation parameters， and depth， on the efficacy of acupuncture. It concludes that the intrinsic properties of acupoints are the primary determinants of therapeutic direction. Other factors mainly influence the magnitude rather than the direction of the effect. Future research may further investigate how different acupoint combinations， local versus distal， affect the treatment outcomes of FD subtypes， providing new insights for clinical acupuncture prescriptions.

Immunotherapy has become a pivotal modality in clinical cancer treatment. However, its effectiveness is limited to a small subset of patients due to the low antigenicity, impaired innate response, and various adaptive immune resistance mechanisms of the tumor microenvironment (TME). Accumulating evidence reveals the critical roles of metal elements in shaping immunity against tumor progression and metastasis. The marriage of metalloimmunotherapy and nanotechnology further presents new opportunities to optimize the physicochemical and pharmacokinetic properties of metal ions in a precise spatiotemporal control manner. Several metallodrugs have demonstrated encouraging immunotherapeutic potential in preliminary studies and are currently undergoing clinical trials at different stages, yet challenges persist in scaling up production and addressing long-term biosafety concerns. This review delineates how metal materials modulate biological activities across diverse cell types to orchestrate antitumor immunity. Moreover, it summarizes recent progress in smart drug delivery-release systems integrating metal elements, either as cargo or vehicles, to enhance antitumor immune responses. Finally, the review introduces current clinical applications of nanomedicines in metalloimmunotherapy and discusses potential challenges that impede its widespread translation into clinical practice.

Objective:In carbon ion treatment planning of water phantom, establish a conversion factor calculation system and conversion factor curves for organs at risk (OAR) for microdosimetric kinetic models (MKM) and local effect models (LEM), and validate them in clinical patient planning.Methods:Using a uniform spherical water phantom as the research object, relative biological effectiveness-weighted doses (RWD) for the LEM were re-calculated based on the physical dose of RayStation-MKM. The median dose within the planning target volume (PTV) of LEM and MKM was regarded as the conversion factor. The impacts of single-fraction target prescription dose, spread-out Bragg peak (SOBP) width and depth, shape, and irradiation mode on the conversion factor were assessed, and a conversion factor calculation system was established. Additionally, the accuracy of the conversion factor calculation system was validated using both water phantoms and clinical patient cases. The conversion factor curves for OAR were computed based on clinical patient treatment plans.Results:The primary influencing factors for the conversion factors were the single-fraction prescription dose, target SOBP width and depth. The conversion factors were increased with the increase of SOBP width and target depth, whereas decreased with the increase of the single-fraction prescription dose. Under single-field irradiation, a conversion factor calculation system was established based on above 3 parameters. For the plans of 9 patients, the average difference between the calculated results and the conversion factor calculation system was 0.340% ± 0.203%, and the average difference in the conversion curves for OAR was 2.650% ± 2.399%.Conclusion:A dose conversion factor calculation system and conversion factor curves for OAR for carbon ion radiotherapy are established for MKM and LEM, and their accuracy meets the requirements for use in clinical patient treatment plans.

Objective:To explore the mediating effect of sleep quality on the relationship between serum vitamin D3 level and depressive symptoms in community-dwelling elderly people with mild cognitive impairment(MCI).Methods:A total of 480 elderly people with MCI meeting inclusion criteria in a community health service center from Jan 2023 to Dec 2023 were selected as the research objects.The general information questionnaire,mini-mental state examination(MMSE),activity of daily living scale(ADL),Center for Epidemiological Studies Depression Scale(CES-D)and Pittsburgh sleep quality index(PSQI)were used to investigate the subjects.Serum vitamin D3 level was detected within 1 week.Spearson correlation analysis was used to explore the correlation between sleep quality,serum vitamin D3 level and depressive symptoms.The mediating effect model was tested by Model 4 in the Process plug-in.Results:Among the 480 MCI elderly,the average score of depression was(12.28±4.75),the average serum vitamin D3 level was(25.81±7.09)ng/ml,and the average score of sleep quality was(7.17±2.71).Depression severity was positively correlated with sleep quality score(r=0.294,P＜0.01),and negatively correlated with serum vitamin D3 level(r=-0.237,P＜0.01).Serum vitamin D3 level was negatively correlated with sleep quality score(r=-0.225,P＜0.01).Bootstrap analysis showed that the mediating effect of sleep quality between serum vitamin D3 level and depression level was-0.064(95%CI:-0.115--0.023),accounting for 26.57%of the total effect.Conclusion:Sleep quality partially mediates the relationship between serum vitamin D3 level and depressive symptoms in the elderly with MCI in the community.

Objective:To explore the mediating effect of sleep quality on the relationship between serum vitamin D3 level and depressive symptoms in community-dwelling elderly people with mild cognitive impairment(MCI).Methods:A total of 480 elderly people with MCI meeting inclusion criteria in a community health service center from Jan 2023 to Dec 2023 were selected as the research objects.The general information questionnaire,mini-mental state examination(MMSE),activity of daily living scale(ADL),Center for Epidemiological Studies Depression Scale(CES-D)and Pittsburgh sleep quality index(PSQI)were used to investigate the subjects.Serum vitamin D3 level was detected within 1 week.Spearson correlation analysis was used to explore the correlation between sleep quality,serum vitamin D3 level and depressive symptoms.The mediating effect model was tested by Model 4 in the Process plug-in.Results:Among the 480 MCI elderly,the average score of depression was(12.28±4.75),the average serum vitamin D3 level was(25.81±7.09)ng/ml,and the average score of sleep quality was(7.17±2.71).Depression severity was positively correlated with sleep quality score(r=0.294,P＜0.01),and negatively correlated with serum vitamin D3 level(r=-0.237,P＜0.01).Serum vitamin D3 level was negatively correlated with sleep quality score(r=-0.225,P＜0.01).Bootstrap analysis showed that the mediating effect of sleep quality between serum vitamin D3 level and depression level was-0.064(95%CI:-0.115--0.023),accounting for 26.57%of the total effect.Conclusion:Sleep quality partially mediates the relationship between serum vitamin D3 level and depressive symptoms in the elderly with MCI in the community.

Objective:In carbon ion treatment planning of water phantom, establish a conversion factor calculation system and conversion factor curves for organs at risk (OAR) for microdosimetric kinetic models (MKM) and local effect models (LEM), and validate them in clinical patient planning.Methods:Using a uniform spherical water phantom as the research object, relative biological effectiveness-weighted doses (RWD) for the LEM were re-calculated based on the physical dose of RayStation-MKM. The median dose within the planning target volume (PTV) of LEM and MKM was regarded as the conversion factor. The impacts of single-fraction target prescription dose, spread-out Bragg peak (SOBP) width and depth, shape, and irradiation mode on the conversion factor were assessed, and a conversion factor calculation system was established. Additionally, the accuracy of the conversion factor calculation system was validated using both water phantoms and clinical patient cases. The conversion factor curves for OAR were computed based on clinical patient treatment plans.Results:The primary influencing factors for the conversion factors were the single-fraction prescription dose, target SOBP width and depth. The conversion factors were increased with the increase of SOBP width and target depth, whereas decreased with the increase of the single-fraction prescription dose. Under single-field irradiation, a conversion factor calculation system was established based on above 3 parameters. For the plans of 9 patients, the average difference between the calculated results and the conversion factor calculation system was 0.340% ± 0.203%, and the average difference in the conversion curves for OAR was 2.650% ± 2.399%.Conclusion:A dose conversion factor calculation system and conversion factor curves for OAR for carbon ion radiotherapy are established for MKM and LEM, and their accuracy meets the requirements for use in clinical patient treatment plans.

TCP family as plant specific transcription factor, plays an important role in different aspects of plant development. In order to screen TCP family members in tobacco, the homologous sequences of tobacco and Arabidopsis TCP family were identified by genome-wide homologous alignment. The physicochemical properties, phylogenetic relationships and cis-acting elements were analyzed by bioinformatics. The homologous genes of AtTCP3/AtTCP4 were screened, and RT-qPCR was used to detect the changes of gene expression upon 20% PEG6000 treatment. The results show that tobacco contains 63 TCP family members. Their amino acid sequence length ranged from 89 aa to 596 aa, and their protein hydropathicity grand average of hydropathicity (GRAVY) ranged from -1.147 to 0.125. The isoelectric point (pI) ranges from 4.42 to 9.94, the number of introns is 0 to 3, and the subcellular location is all located in the nucleus. The results of conserved domain and phylogenetic relationship analysis showed that the tobacco TCP family can be divided into PCF, CIN and CYC/TB1 subfamilies, and each subfamily has a stable sequence. The results of cis-acting elements in gene promoter region showed that TCP family genes contain low docile acting elements (LTR) and a variety of stress and metabolic regulation related elements (MYB, MYC). Analysis of gene expression patterns showed that AtTCP3/AtTCP4 homologous genes (NtTCP6, NtTCP28, NtTCP30, NtTCP33, NtTCP42, NtTCP57, NtTCP63) accounted for 20% PEG6000 treatment significantly up-regulated/down-regulated expression, and NtTCP30 and NtTCP57 genes were selected as candidate genes in response to drought. The results of this study analyzed the TCP family in the tobacco genome and provided candidate genes for the study of drought-resistance gene function and variety breeding in tobacco.
Nicotiana/genetics* ; Phylogeny ; Plant Breeding ; Amino Acid Sequence ; Arabidopsis ; Polyethylene Glycols

Objective:To screen the aging genes closely associated with pelvic organ prolapse(POP)by bioinformatics techniques,and to clarify the potential clinical significance and value of key genes.Methods:Gene Expression Omnibus(GEO)Database was used to download the datasets GSE53868 and GSE151188 for POP-related genes with the keyword"pelvic organ prolapse".The aging-related genes were obtained from Aging Atlas,CellAge,and the Human Ageing Genomic Resources(HAGR)Databases;the intersection of genes related with POP in two groups provided a list of differentially expressed genes(DEGs)associated with aging in POP;gene Set Enrichment Analysis(GSEA)was conducted with R software version 4.2.1;Gene Ontology(GO)functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analysis of DEGs were conducted by the Database for Annotation,Visualization and Integrated Discovery(DAVID);the protein-protein interaction(PPI)network was constructed with Cytoscape 3.9.1 software;the top 10 Hub genes were selected by cytoHubba plugin;the infiltration of 22 types of immune cells in the patients in POP group and control group was analyzed by CIBERSORT deconvolution method using R software;the key genes were further screened by LASSO regression algorithm;the correlation and diagnostic efficacy between key genes and immune cell infiltration were analyzed.Results:From the Aging Atlas,CellAge,and HAGR Databases,724 aging-related genes were identified.Intersection with the POP expression profile yielded an aging gene expression matrix related to POP containing 624 genes,and 29 POP-related DEGs were identified after differential analysis,including 2 upregulated genes and 27 downregulated genes.The GSEA results showed that the upregulated pathways were mainly related to diabetes and cellular senescence,whereas the downregulated pathways included Alzheimer's disease and hypoxia-inducible factor-1(HIF-1)signaling pathways.The GO functional enrichment analysis mainly enriched in the biological processes such as the response of the cells to lipopolysaccharide,inflammatory response,and negative regulation of cell proliferation.The KEGG signaling pathway enrichment analysis mainly enriched in interleukin-17(IL-17),tumor necrosis factor(TNF),and nuclear factor-kappa B(NF-κB)signaling pathways.The PPI network analysis got 10 Hub genes including interleukin-6(IL-6),interleukin-1B(IL-1B),prostaglandin-endoperoxide synthase 2(PTGS2),and NF-kappa-B inhibitor alpha(NFKBIA).The CIBERSORT deconvolution method results showed a relatively higher infiltration proportion of neutrophils and activated mast cells in the patients in POP group,the activated mast cells had a positive correlation with most of the DEGs(r>0.5)and the macrophages had a significant positive correlation with IL-1B(r>0.6).The key genes Jun D proto-oncogene(JUND),Snail homolog 1(SNAI1),amphiregulin(AREG),Lamin A/C(LMNA),and superoxide dismutase 2(SOD2)selected by LASSO regression analysis had high diagnostic efficacies,and the area under receiver operating characteristic curve(ROC)(AUC)were all greater than 0.75.Conclusion:During the aging process,the genes such as JUND,SNAI1,AREG,LMNA,and SOD2 may participate in the pathophysiology of POP through various pathways,including inflammation-related pathways,transcription regulation,and affecting collagen secretion and metabolism,thereby influence the connective tissue support function and promote the occurrence and development of POP.

Objective To observe the value of artificial intelligence(AI)knowledge graph and image classification for quality control(QC)of chest posterior-anterior position X-ray radiograph(abbreviated as chest film).Methods Totally 9 236 chest films from 595 medical institutions in Anhui province imaging cloud platform were retrospectively enrolled.QC knowledge graph containing 21 classification labels were constructed.Firstly,QC of chest films based on the above knowledge graph were performed by 10 technicians for 2 rounds of single person and 1 round of multi person,and the results were recorded as A,B and C,respectively.Then AI algorithms were used to classify and evaluate based on knowledge graph,and the result was recorded as D.Finally,a QC expert reviewed results C and D to determine the final QC results and taken those as references to analyze the efficiency of the above 4 QC.Results The area under the curve(AUC)of AI algorithm for QC of chest films were all ≥0.780,with an average value of 0.939.The average precision of QC for chest films of A,B,C and D was 81.15％,85.47％,91.65％and 92.21％,respectively.Conclusion AI knowledge graph and image classification technology could be effectively used for QC of chest posterior-anterior position X-ray radiograph.

Purpose Develop deep learning models to assess the clarity of chest X-ray images and validate the model's effectiveness by comparing it with the subjective evaluations of radiologists.Materials and Methods A retrospective collection of 9 135 chest X-ray images from 590 hospitals in Anhui Province,spanning from June 2015 to August 2022,was organized involving multiple radiologists who repeatedly evaluated the clarity of the images using a five-level scoring system.Individual assessments were designated as A and B,whereas the collective result of multiple assessments was designated as C.By constructing a deep learning model based on ResNet-50,image clarity evaluations of chest X-ray images were performed,we used the result C as the training and testing data for the model.The model's evaluation results were denoted as D.A radiology quality control expert conducted an audit assessment of the model's evaluation results and the multi-person assessments of physicians,serving as the reference standard for image clarity.Their assessment results were labeled as E.Statistical analysis,including Spearman's rank correlation coefficient,root mean square error(RMSE)and accuracy was employed to evaluate the effectiveness of the model.Results Compared with the reference standard E,D achieved an average accuracy of 0.85,exceeding the accuracy of C,which stood at 0.84.The ρ values for A,B,C,D and E were 0.58(0.54,0.62),0.59(0.55,0.63),0.74(0.71,0.77)and 0.80(0.78,0.82),respectively.The model exhibited the highest correlation with E.The ρ between A and B was 0.45(0.41,0.49),indicating a lower correlation between two individual subjective assessments of image clarity.The RMSE values for A,B,C,D and E were 0.99,0.94,0.72,and 0.71,respectively.The model's RMSE was lower than that of manual assessments.Conclusion This research model is capable of accurately assessing the clarity of chest X-ray images,and reducing the subjective interference of manual evaluation through deep learning methods,thereby providing an effective and objective evaluation tool for the assessment of clarity in clinical radiographic images.