Periodontal bone defects, primarily caused by periodontitis, are highly prevalent in clinical settings and manifest as bone fenestration, dehiscence, or attachment loss, presenting a significant challenge to oral health. In regenerative medicine, harnessing developmental principles for tissue repair offers promising therapeutic potential. Of particular interest is the condensation of progenitor cells, an essential event in organogenesis that has inspired clinically effective cell aggregation approaches in dental regeneration. However, the precise cellular coordination mechanisms during condensation and regeneration remain elusive. Here, taking the tooth as a model organ, we employed single-cell RNA sequencing to dissect the cellular composition and heterogeneity of human dental follicle and dental papilla, revealing a distinct Platelet-derived growth factor receptor alpha (PDGFRA) mesenchymal stem/stromal cell (MSC) population with remarkable odontogenic potential. Interestingly, a reciprocal paracrine interaction between PDGFRA⁺ dental follicle stem cells (DFSCs) and CD31⁺ Endomucin⁺ endothelial cells (ECs) was mediated by Vascular endothelial growth factor A (VEGFA) and Platelet-derived growth factor subunit BB (PDGFBB). This crosstalk not only maintains the functionality of PDGFRA⁺ DFSCs but also drives specialized angiogenesis. In vivo periodontal bone regeneration experiments further reveal that communication between PDGFRA⁺ DFSC aggregates and recipient ECs is essential for effective angiogenic-osteogenic coupling and rapid tissue repair. Collectively, our results unravel the importance of MSC-EC crosstalk mediated by the VEGFA and PDGFBB-PDGFRA reciprocal signaling in orchestrating angiogenesis and osteogenesis. These findings not only establish a framework for deciphering and promoting periodontal bone regeneration in potential clinical applications but also offer insights for future therapeutic strategies in dental or broader regenerative medicine.
Receptor, Platelet-Derived Growth Factor alpha/metabolism* ; Humans ; Neovascularization, Physiologic/physiology* ; Dental Sac/cytology* ; Single-Cell Analysis ; Transcriptome ; Mesenchymal Stem Cells/metabolism* ; Bone Regeneration ; Animals ; Dental Papilla/cytology* ; Periodontium/physiology* ; Stem Cells/metabolism* ; Regeneration ; Angiogenesis

ObjectiveTo study the effects of applying different microbial fertilizers on the growth and rhizosphere soil environment of Codonopsis pilosula and provide a theoretical basis for ecological cultivation of this medicinal plant. MethodsSeven groups were designed， including CK （no application of microbial fertilizer）， T1 （Trichoderma longibrachiatum fertilizer）， T2 （Bacillus subtilis fertilizer）， T3 （Trichoderma viride fertilizer）， T4 （compound microbial fertilizer）， T5 （C. pilosula stems and leaves fermented with compound microbial fertilizer）， and T6 （Scutellaria baicalensis stems and leaves fermented with T. viride fertilizer）. The physiological indicators， yield， and quality of C. pilosula and the physicochemical properties， enzyme activities， and microbial diversity in the rhizosphere soil of different fertilizer treatments were measured. ResultsGroup T1 showed slight decreases in soluble protein content （SPC） and superoxide dismutase （SOD）. Groups T2-T6 showed increases in physiological indicators such as proline （Pro）， soluble solids content （SSC）， SPC， catalase （CAT）， and peroxidase （POD） and a decrease in malondialdehyde （MDA） in C. pilosula leaves. All the fertilizer treatments increased the yield of C. pilosula and the total polysaccharide content in the roots. T1， T2， T3， T4， and T5 increased the total flavonoid content in the roots. Meanwhile， T4 increased the total saponin content in the roots. All the fertilizer treatments reduced the pH and increased the electric conductivity （EC）， soil organic matter （SOM）， and alkaline nitrogen （AN） in the soil. T2 and T5 increased the available phosphorus （AP）， and T3， T4， T5， and T6 increased the available potassium （AK） in the soil. All the fertilizer treatments increased the activities of urease， sucrase， and CAT in the soil. Except that T1 decreased the bacterial diversity in the soil， other fertilizer treatments significantly increased bacterial and fungal diversity in the soil. Different fertilizer treatments significantly affected the composition of bacterial and fungal communities in the soil. At the phylum level， the dominant bacterial phyla included Proteobacteria， Acidobacteriota， and Bacteroideta， and the dominant fungal phyla were Ascomycota， Mortierellomycota， and unclassified_fungi in the rhizosphere soil of C. pilosula after bacterial fertilizer treatment. At the genus level， unclassified Gemmatimonadaceae， Sphingomonas， and unclassified Vicinamibacteraceae were the dominant bacterial genera， while unidentified， unclassified Fungi， and unclassified Sordariomycetes were the dominant fungal genera in the rhizosphere soil. The results of redundancy analysis indicated that the main physicochemical factors affecting changes of microbial communities in the rhizosphere soil of C. pilosula were pH， EC， AK， AN， AP， and soil organic matter （SOM） in the soil. The correlation heatmap showed that Bryobacter had significantly positive correlations with EC， AK， and AN. There was a significantly negative correlation between Fusarium and SOM. In summary， applying an appropriate amount of microbial fertilizer can promote the growth and improve the rhizosphere soil environment of C. pilosula. ConclusionThe compound microbial fertilizer and the C. pilosula stems and leaves fermented with compound microbial fertilizer can improve the soil nutrients， growth， development， yield， and quality of C. pilosula， and thus they can be applied to the artificial cultivation of C. pilosula.

ObjectiveTo study the effects of applying different microbial fertilizers on the growth and rhizosphere soil environment of Codonopsis pilosula and provide a theoretical basis for ecological cultivation of this medicinal plant. MethodsSeven groups were designed， including CK （no application of microbial fertilizer）， T1 （Trichoderma longibrachiatum fertilizer）， T2 （Bacillus subtilis fertilizer）， T3 （Trichoderma viride fertilizer）， T4 （compound microbial fertilizer）， T5 （C. pilosula stems and leaves fermented with compound microbial fertilizer）， and T6 （Scutellaria baicalensis stems and leaves fermented with T. viride fertilizer）. The physiological indicators， yield， and quality of C. pilosula and the physicochemical properties， enzyme activities， and microbial diversity in the rhizosphere soil of different fertilizer treatments were measured. ResultsGroup T1 showed slight decreases in soluble protein content （SPC） and superoxide dismutase （SOD）. Groups T2-T6 showed increases in physiological indicators such as proline （Pro）， soluble solids content （SSC）， SPC， catalase （CAT）， and peroxidase （POD） and a decrease in malondialdehyde （MDA） in C. pilosula leaves. All the fertilizer treatments increased the yield of C. pilosula and the total polysaccharide content in the roots. T1， T2， T3， T4， and T5 increased the total flavonoid content in the roots. Meanwhile， T4 increased the total saponin content in the roots. All the fertilizer treatments reduced the pH and increased the electric conductivity （EC）， soil organic matter （SOM）， and alkaline nitrogen （AN） in the soil. T2 and T5 increased the available phosphorus （AP）， and T3， T4， T5， and T6 increased the available potassium （AK） in the soil. All the fertilizer treatments increased the activities of urease， sucrase， and CAT in the soil. Except that T1 decreased the bacterial diversity in the soil， other fertilizer treatments significantly increased bacterial and fungal diversity in the soil. Different fertilizer treatments significantly affected the composition of bacterial and fungal communities in the soil. At the phylum level， the dominant bacterial phyla included Proteobacteria， Acidobacteriota， and Bacteroideta， and the dominant fungal phyla were Ascomycota， Mortierellomycota， and unclassified_fungi in the rhizosphere soil of C. pilosula after bacterial fertilizer treatment. At the genus level， unclassified Gemmatimonadaceae， Sphingomonas， and unclassified Vicinamibacteraceae were the dominant bacterial genera， while unidentified， unclassified Fungi， and unclassified Sordariomycetes were the dominant fungal genera in the rhizosphere soil. The results of redundancy analysis indicated that the main physicochemical factors affecting changes of microbial communities in the rhizosphere soil of C. pilosula were pH， EC， AK， AN， AP， and soil organic matter （SOM） in the soil. The correlation heatmap showed that Bryobacter had significantly positive correlations with EC， AK， and AN. There was a significantly negative correlation between Fusarium and SOM. In summary， applying an appropriate amount of microbial fertilizer can promote the growth and improve the rhizosphere soil environment of C. pilosula. ConclusionThe compound microbial fertilizer and the C. pilosula stems and leaves fermented with compound microbial fertilizer can improve the soil nutrients， growth， development， yield， and quality of C. pilosula， and thus they can be applied to the artificial cultivation of C. pilosula.

Background and Aims:Pancreatic ductal adenocarcinoma(PDAC)is a highly malignant digestive system tumor with an inferior prognosis,and its early diagnosis and treatment remain significant challenges.In recent years,RNA methylation modifications(such as m6A and m5C)have attracted considerable attention for their roles in tumor development;however,their regulatory mechanisms and clinical significance in PDAC remain unclear.This study was conducted to identify prognosis-related long noncoding RNAs(lncRNAs)associated with m6A and m5C in PDAC,construct a reliable prognostic prediction model,and explore their relationship with the tumor immune microenvironment.Methods:Based on RNA-seq data from the TCGA-PDAC cohort,differentially expressed lncRNAs(DElncRNAs)related to m6A and m5C were identified through differential expression analysis and Pearson correlation analysis.The samples were randomly divided into a training set(n=89)and a validation set(n=89).Key DElncRNAs were selected using LASSO-Cox regression to construct a prognostic model,and patients were categorized into high-and low-risk groups based on risk scores.Kaplan-Meier survival analysis,ROC curves,and multivariate Cox regression were used to evaluate the model's predictive performance.Furthermore,CIBERSORT and ESTIMATE scores were used to analyze immune cell infiltration characteristics and tumor microenvironment(TME)differences between the high-and low-risk groups.Results:To construct the prognostic model,four m6A-and m5C-related DElncRNAs(LINC00857,LINC02038,TSPOAP1-AS1,and TRPC7-AS1)were identified.Patients in the high-risk group had significantly lower overall survival than those in the low-risk group(P<0.05),and the risk score was an independent prognostic factor for PDAC(HR=1.551,95%CI=1.297-1.854,P<0.001).ROC curve analysis showed that the risk score model exhibited high predictive efficiency in both the training and validation sets(AUC values for 1,3,and 5 years:0.766,0.875,0.879;0.685,0.711,0.792,respectively).Immune analysis revealed increased infiltration of M0 macrophages with lower TME scores in the high-risk group(all P<0.05),suggesting an immunosuppressive microenvironment.Conclusion:This study successfully established a PDAC prognostic model based on m6A-and m5C-related DElncRNAs and confirmed its independent predictive value.High-risk patients exhibited M0 macrophage enrichment and immunosuppressive microenvironment characteristics,possibly contributing to poor prognosis.

Background and Aims:Pancreatic ductal adenocarcinoma(PDAC)is a highly malignant digestive system tumor with an inferior prognosis,and its early diagnosis and treatment remain significant challenges.In recent years,RNA methylation modifications(such as m6A and m5C)have attracted considerable attention for their roles in tumor development;however,their regulatory mechanisms and clinical significance in PDAC remain unclear.This study was conducted to identify prognosis-related long noncoding RNAs(lncRNAs)associated with m6A and m5C in PDAC,construct a reliable prognostic prediction model,and explore their relationship with the tumor immune microenvironment.Methods:Based on RNA-seq data from the TCGA-PDAC cohort,differentially expressed lncRNAs(DElncRNAs)related to m6A and m5C were identified through differential expression analysis and Pearson correlation analysis.The samples were randomly divided into a training set(n=89)and a validation set(n=89).Key DElncRNAs were selected using LASSO-Cox regression to construct a prognostic model,and patients were categorized into high-and low-risk groups based on risk scores.Kaplan-Meier survival analysis,ROC curves,and multivariate Cox regression were used to evaluate the model's predictive performance.Furthermore,CIBERSORT and ESTIMATE scores were used to analyze immune cell infiltration characteristics and tumor microenvironment(TME)differences between the high-and low-risk groups.Results:To construct the prognostic model,four m6A-and m5C-related DElncRNAs(LINC00857,LINC02038,TSPOAP1-AS1,and TRPC7-AS1)were identified.Patients in the high-risk group had significantly lower overall survival than those in the low-risk group(P<0.05),and the risk score was an independent prognostic factor for PDAC(HR=1.551,95%CI=1.297-1.854,P<0.001).ROC curve analysis showed that the risk score model exhibited high predictive efficiency in both the training and validation sets(AUC values for 1,3,and 5 years:0.766,0.875,0.879;0.685,0.711,0.792,respectively).Immune analysis revealed increased infiltration of M0 macrophages with lower TME scores in the high-risk group(all P<0.05),suggesting an immunosuppressive microenvironment.Conclusion:This study successfully established a PDAC prognostic model based on m6A-and m5C-related DElncRNAs and confirmed its independent predictive value.High-risk patients exhibited M0 macrophage enrichment and immunosuppressive microenvironment characteristics,possibly contributing to poor prognosis.

[摘 要] 目的：采用基于中国人群单核苷酸多态性位点开发的同源重组缺陷（HRD）检测工具评估云南地区卵巢癌患者的HRD状态和BRCA1/2基因突变频率并探讨其临床意义。方法：共纳入2021年1月至2023年5月间在云南省肿瘤医院收治的卵巢癌患者248例，HRD状态采用基因组瘢痕评分法（GSS）（主要依据拷贝数的长度、类型、位置及基因组断片）或HRD评分法（杂合性缺失、端粒等位基因失衡及大片段移位等基因组不稳定事件的总和）进行评估，当组织样本的GSS≥50分或HRD评分≥42分者或检测到有害的BRCA1/2基因突变时HRD被定义为阳性。分析患者HRD状态与临床病理特征的关系。结果：248名卵巢癌患者中70.97%的患者HRD呈阳性，其中BRCA1/2基因突变率为30.65%。Ⅲ~Ⅵ期、高级别浆液腺癌的卵巢癌患者具有更高的HRD阳性率（均P<0.01），HRD评分更高的患者其合并其他基因突变的频率也越高（P<0.05）。HRD状态与卵巢癌的病理类型、临床分期和其他基因突变均有关联（均P<0.01）。结论：云南地区卵巢癌患者HRD阳性率较高，HRD阳性的卵巢癌患者可以从聚ADP核糖聚合酶（PARP）抑制剂治疗中获得更大的收益。

ObjectiveTo investigate the mechanism of Gegen Qinliantang（GQT） on the intestinal flora of antibiotic-associated diarrhea（AAD） by 16S rRNA sequencing and network pharmacology. MethodSixty SD rats were randomly divided into six groups（n=10）， including blank group， model group， GQT high-， medium- and low-dose groups（10.08， 5.04， 2.52 g·kg^-1） as well as Lizhu Changle group（0.15 g·kg^-1）， except for the blank group， each group was given clindamycin（250 mg·kg^-1） by gavage once a day for 7 consecutive days. After successful modeling， the blank group and the model group were given equal volumes of normal saline by gavage. The other groups were given corresponding doses of drugs by gavage for 14 days. Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform（TCMSP） was used to screen the active components and targets of GQT， GeneCards， Online Mendelian Inheritance in Man（OMIM） database， Pharmacogenetics and Pharmacogenomics Knowledge Base（PharmGKB）， DrugBank and DisGeNET were used to search for AAD disease targets. The drug-disease common targets were obtained by R software. STRING was applied to analyze the target protein-protein interaction， and Kyoto Encyclopedia of Genes and Genomes（KEGG） pathway enrichment analysis was performed. Then hematoxylin-eosin（HE） staining was used to observe the pathological changes of the colon， and 16S rRNA sequencing of AAD colon content flora structure further verified the results of network pharmacology. ResultThrough network pharmacology， it was found that 238 active components were screened from GQT and acted on 276 component targets， among which quercetin， puerarin， wogonin and apigenin were the main core components of GQT， 1 097 AAD disease targets and 127 drug-disease intersection targets. The protein-protein interaction network mainly included core targets such as protein kinase B1（Akt1）， interleukin（IL）-6 and IL-1β， which were mainly enriched in the IL-17 signaling pathway. It was verified through animal experiments that compared with the blank group， the colon structure of the model group was seriously abnormal， the intestinal epithelial columnar cells were damaged， the goblet cells were reduced， and a large number of inflammatory cells were infiltrated. Compared with the model group， the colon structure of the GQT high-dose group improved， but there were still abnormalities， the colon structure of GQT medium- and low- dose groups and Lizhu Changle group improved significantly and reached the normal level. GQT could improve the structural diversity of AAD intestinal flora. At the phylum level， the abundance of Firmicutes was increased and the abundance of Bacteroidetes was decreased. At the genus level， the abundance of Lactobacillus was increased， and the abundances of Prevotella and Bacteroides were decreased. Among them， Lactococcus could be used as a biomarker for AAD treatment with GQT， and the prediction of functional metabolism of intestinal flora revealed that GQT could promote acetate and lactate metabolic pathways in the intestine. ConclusionGQT may activate IL-17 signaling pathway by acting on the targets of Akt1 and IL-6 through key components such as quercetin and wogonin， and improve the abundance of Lactococcus in the intestinal tract as well as acetate and lactate metabolic pathways， so as to play a role in repairing the intestinal barrier for the treatment of AAD.

Objective:To retrieve and summarize evidence on non-pharmacological sleep management in adult critically ill patients, providing reference for sleep intervention by ICU medical and nursing staff.Methods:The literature on non-pharmacological sleep management in adult critically ill patients was systematically searched in domestic and foreign databases and guideline websites. The search period was from the establishment of the database to February 2023. After screening the literature based on inclusion and exclusion criteria, two evidence-based trained researchers independently evaluated the quality of the included literature, extracted and summarized evidence.Results:A total of 13 articles were included, including 3 clinical guidelines, 9 systematic reviews, and 1 randomized controlled trial. A total of 21 best pieces of evidence were summarized, covering 5 aspects, including strengthening nurse education and training, improving ward environment, reducing sleep interruption, selecting mechanical ventilation modes reasonably, and promoting physical and mental relaxation.Conclusions:The evidence summarized can provide reference for the development of non-pharmacological sleep management plans for adult critically ill patients. ICU medical and nursing staff should carefully apply evidence in clinical practice based on evidence, fully consider the human resources and actual situation of the department, and combine it with the needs of patients.

Objective:To explore the improvement of diaphragm function after early off-bed mobility intervention in intensive care unit (ICU) patients undergoing mechanical ventilation.Methods:A randomized controlled trial was conducted. A total of 147 adult patients undergoing mechanical ventilation admitted to ICU of Affiliated Hospital of Zunyi Medical University from October 2019 to March 2022 were enrolled. The patients were divided into control group and observation group by convenient sampling. Except for the different intervention programs of early mobility, other treatment and nursing of the patients in the two groups were carried out according to ICU routine. Progressive early activities were performed in the control group, while early off-bed mobility was performed in the observation group. The changes of diaphragm thickness at the end of inspiratory (DTei), diaphragm thickness at the end of expiratory (DTee) and diaphragm thickening fraction (DTF) before and 24, 48, 72 and 96 hours of intervention, and the duration of mechanical ventilation, length of ICU stay and 24-hour re-intubation rate after intervention were compared between the two groups.Results:Among the 147 patients, there were 4 cases of detachment in the control group and 5 cases of detachment in the observation group. Finally, 138 patients were enrolled, 69 cases in the control group and 69 cases in the observation group. There was no significant difference in gender, age, diagnosis of ICU, sedatives, muscle strength, ventilator model, acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) score and DTei, DTee, DTF before intervention between the two groups. The DTei, DTee and DTF in both groups were increased gradually with the extension of intervention time, especially in the observation group [DTei (cm) at 24, 48, 72 and 96 hours of intervention in the observation group were 0.247±0.014, 0.275±0.016, 0.300±0.013 and 0.329±0.013, while in the control group were 0.242±0.015, 0.258±0.013, 0.269±0.014, and 0.290±0.017, effect of time: F = 993.825, P = 0.000, effect of intervention: F = 82.304, P = 0.000, interaction effect between intervention and time: F = 84.457, P = 0.000; DTee (cm) of the observation group were 0.213±0.014, 0.227±0.013, 0.243±0.016, 0.264±0.010, while in the control group were 0.213±0.016, 0.218±0.013, 0.224±0.013, 0.234±0.014, effect of time: F = 385.552, P = 0.000, effect of intervention: F = 28.161, P = 0.000, interaction effect between intervention and time: F = 45.012, P = 0.000; DTF of the observation group were (15.98±4.23)%, (21.35±4.67)%, (24.09±4.44)% and (25.24±3.74)%, while in the control group were (14.17±4.66)%, (18.11±3.92)%, (20.22±4.19)% and (20.98±4.12)%, effect of time: F = 161.552, P = 0.000, effect of intervention: F = 49.224, P = 0.000, interaction effect between intervention and time: F = -4.507, P = 0.000]. The duration of mechanical ventilation and length of ICU stay in the observation group were significantly shorter than those in the control group [duration of mechanical ventilation (hours): 112.68±12.25 vs. 135.32±22.10, length of ICU stay (days): 7.84±1.78 vs. 10.23±2.43, both P < 0.01]. However, there was no significant difference in 24-hour re-intubation rate between the observation group and the control group (0% vs. 2.90%, P > 0.05). Conclusions:Both early off-bed mobility and progressive early activities can prevent diaphragm weakness in ICU patients undergoing mechanical ventilation, and the effect of early off-bed mobility is better. Early off-bed mobility can significantly shorten the duration of mechanical ventilation and length of ICU stay, and it is safe and feasible.

Objective    To explore the application value of machine learning models in predicting postoperative survival of patients with thoracic squamous esophageal cancer. Methods    The clinical data of 369 patients with thoracic esophageal squamous carcinoma who underwent radical esophageal cancer surgery at the Department of Thoracic Surgery of Northern Jiangsu People's Hospital from January 2014 to September 2015 were retrospectively analyzed. There were 279 (75.6%) males and 90 (24.4%) females aged 41-78 years. The patients were randomly divided into a training set (259 patients) and a test set (110 patients) with a ratio of 7 : 3. Variable screening was performed by selecting the best subset of features. Six machine learning models were constructed on this basis and validated in an independent test set. The  performance of the models' predictions was evaluated by area under the curve (AUC), accuracy and logarithmic loss, and the fit of the models was reflected by calibration curves. The best model was selected as the final model. Risk stratification was performed using X-tile, and survival analysis was performed using the Kaplan-Meier method with log-rank test. Results    The 5-year postoperative survival rate of the patients was 67.5%. All clinicopathological characteristics of patients between the two groups in the training and test sets were not statistically different (P>0.05). A total of seven variables, including hypertension, history of smoking, history of alcohol consumption, degree of tissue differentiation, pN stage, vascular invasion and nerve invasion, were included for modelling. The AUC values for each model in the independent test set were: decision tree (AUC=0.796), support vector machine (AUC=0.829), random forest (AUC=0.831), logistic regression (AUC=0.838), gradient boosting machine (AUC=0.846), and XGBoost (AUC=0.853). The XGBoost model was finally selected as the best model, and risk stratification was performed on the training and test sets. Patients in the training and test sets were divided into a low risk group, an intermediate risk group and a high risk group, respectively. In both data sets, the differences in surgical prognosis among three groups were statistically significant (P<0.001). Conclusion    Machine learning models have high value in predicting postoperative prognosis of thoracic squamous esophageal cancer. The XGBoost model outperforms common machine learning methods in predicting 5-year survival of patients with thoracic squamous esophageal cancer, and it has high utility and reliability.