Eight compounds were isolated and purified from the ethyl acetate part of 70% acetone extract of Rehmannia glutinosa by various chromatographic techniques such as silica gel, MCI gel CHP-20, ODS, Toyopearl HW-40C, combined with TLC and semi-preparative HPLC. Their structures were elucidated by modern spectroscopy techniques (NMR, MS, UV, IR), and identified as neomartynoside A (1), osmanthuside B₆ (2), martynoside (3), isomartynoside (4), (E)-p-hydroxycinnamic acid (5), caffeic acid (6), ferulic acid (7), and methyl caffeate (8). Compound 1 is a new phenylethanol glycoside, which was identified as neomartynoside A. Compound 2 was isolated from Rehmannia glutinosa for the first time. In addition, compounds 2, 6 and 7 significantly increased relative glucose consumption, showing potential hypoglycemic activity.

ObjectiveIn the realm of forensic science, dust is a valuable type of trace evidence with immense potential for intricate investigations. With the development of DNA sequencing technologies, there is a heightened interest among researchers in unraveling the complex tapestry of microbial communities found within dust samples. Furthermore, striking disparities in the microbial community composition have been noted among dust samples from diverse geographical regions, heralding new possibilities for geographical inference based on microbial DNA analysis. The pivotal role of microbial community data from dust in geographical inference is significant, underscoring its critical importance within the field of forensic science. This study aims to delve deeply into the nuances of fungal community composition across the urban landscapes of Beijing, Fuzhou, Kunming, and Urumqi in China. It evaluates the accuracy of biogeographic inference facilitated by the internal transcribed spacer 2 (ITS2) fungal sequencing while concurrently laying a robust foundation for the operational integration of environmental DNA into geographical inference mechanisms. MethodsITS2 region of the fungal genomes was amplified using universal primers known as 5.8S-Fun/ITS4-Fun, and the resulting DNA fragments were sequenced on the Illumina MiSeq FGx platform. Non-metric multidimensional scaling analysis (NMDS) was employed to visually represent the differences between samples, while analysis of similarities (ANOSIM) and permutational multivariate analysis of variance (PERMANOVA) were utilized to statistically evaluate the dissimilarities in community composition across samples. Furthermore, using Linear Discriminant Analysis Effect Size (LEfSe) analysis to identify and filter out species that exhibit significant differences between various cities. In addition, we leveraged SourceTracker to predict the geographic origins of the dust samples. ResultsAmong the four cities of Beijing, Fuzhou, Kunming and Urumqi, Beijing has the highest species richness. The results of species annotation showed that there were significant differences in the species composition and relative abundance of fungal communities in the four cities. NMDS analysis revealed distinct clustering patterns of samples based on their biogeographic origins in multidimensional space. Samples from the same city exhibited clear clustering, while samples from different cities showed separation along the first axis. The results from ANOSIM and PERMANOVA confirmed the significant differences in fungal community composition between the four cities, with the most pronounced distinctions observed between Fuzhou and Urumqi. Notably, the biogeographic origins of all known dust samples were successfully predicted. ConclusionSignificant differences are observed in the fungal species composition and relative abundance among the cities of Beijing, Fuzhou, Kunming, and Urumqi. Employing fungal ITS2 sequencing on dust samples from these urban areas enables accurate inference of biogeographical locations. The high feasibility of utilizing fungal community data in dust for biogeographical inferences holds particular promise in the field of forensic science.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective To summarize and analyze the clinical phenotypes,hereditary features and treatment and follow-up strategies of different hereditary pheochromocytoma/paragangliomas(PCC/PGL)and related syndromes.Methods Forty-four clinically diagnosed PCC/PGL patients admitted in our hospital from January 2000 to August 2022 were enrolled,and the clinical data of them and their family members were collected.Second-generation sequencing was performed on 43 patients for genetic detection,and Sanger sequencing was applied to verify the mutation of the probands and family members.Results There were 15 patients diagnosed with hereditary PCC/PGL,including 7 cases of von Hippel-Lindau(VHL)syndrome,3 cases of multiple endocrine neoplasia type 2(MEN2),and 5 cases of familial paraganglioma syndrome.Seven VHL syndrome families were diagnosed as VHL2A(c.500G＞A),VHL2B(c.239G＞T and c.444_457del),and VHL2C(c.293A＞G)according to their clinical manifestations.All probands received surgical treatment,and 2 cases of recurrent PCC and the patients with multiple renal cancer also received targeted therapy with sunitinib.Three MEN2 families carried c.1901G＞C,c.1832G＞A,and c.1901G＞A missense mutations,respectively,and were diagnosed with MEN2A clinically.All of them underwent adrenalectomy and thyroidectomy,including one for preventive thyroidectomy.Among the 5 familial paraganglioma syndrome families,4 patients carried SDHB mutations(SDHB:c.343C＞T,c.541-2A＞G,c.575G＞A,c.268C＞T)and 1 patient carried an SDHD mutation(SDHD:c.337_340del).Sporadic retroperitoneal PGL were most common.Conclusion More than 1/3 of PCC/PGL patients carry germline gene mutations,showing obvious genotype-phenotype correlation.Genetic diagnosis technology plays an important guidance role for clinical precision treatment and follow-up,and genetic counseling.

"MS/MS spectrum to structure"plays a critical role in the confirmative identification of complicated matrices and is currently regarded as an extremely challenging endeavor.MS/MS information provides vital clues to structural identification.In this study,a strategy was proposed to facilitate unambiguous identification through matching MS3 with MS2 spectra.Initially,MS3 spectra of the featured ions(c-and y-type ions)generated by the decomposition of ester functional group in esters and the MS2 spectrum of the structural unit([M-H]-)were all captured on the Qtrap-MS platform equipped with two tandem-in-space collision cells,including the second quadrupole cell(q2)and linear ion trap(LIT)chambers(actually the third quadrupole unit).Subsequently,the MS/MS spectrum matching between MS3 spectra of the ester compound and MS2 spectra of the structural unit(s)were achieved.As a result,the findings corresponding to MS3 and MS2 spectra matching were summarized.Finally,based on HR-MS/MS information of total salvianolic acid derivatives(TSA),36 kinds of compounds were preliminarily identified through matching with literature information and database retrieval.The applicability of MS3 and MS2 spectra matching strategy was further justified by the confirmative identification of phenolic acid compounds(Rosmarinic acid and salvianolic acid B)in TSA.Above all,MS3 and MS2 spectra matching strategy was quite meaningful towards advancing"MS/MS spectrum to structure"analysis through recognizing and identifying featured fragment ions,and also provided inspiration and new insights for the structural characterization.

Objective To investigate the toxicokinetic differences of 3,4-methylenedioxy-N-methylamphetamine(MDMA)and its metabolite 4,5-methylene dioxy amphetamine(MDA)in rats af-ter single and continuous administration of MDMA,providing reference data for the forensic identifica-tion of MDMA.Methods A total of 24 rats in the single administration group were randomly divided into 5,10 and 20 mg/kg experimental groups and the control group,with 6 rats in each group.The ex-perimental group was given intraperitoneal injection of MDMA,and the control group was given intraperi-toneal injection of the same volume of normal saline as the experimental group.The amount of 0.5 mL blood was collected from the medial canthus 5 min,30 min,1 h,1.5 h,2 h,4 h,6 h,8 h,10 h,12 h after administration.In the continuous administration group,24 rats were randomly divided into the experi-mental group(18 rats)and the control group(6 rats).The experimental group was given MDMA 7 d by continuous intraperitoneal injection in increments of 5,7,9,11,13,15,17 mg/kg per day,respectively,while the control group was given the same volume of normal saline as the experimental group by in-traperitoneal injection.On the eighth day,the experimental rats were randomly divided into 5,10 and 20 mg/kg dose groups,with 6 rats in each group.MDMA was injected intraperitoneally,and the con-trol group was injected intraperitoneally with the same volume of normal saline as the experimental group.On the eighth day,0.5 mL of blood was taken from the medial canthus 5 min,30 min,1 h,1.5 h,2 h,4 h,6 h,8 h,10 h,12 h after administration.Liquid chromatography-triple quadrupole tandem mass spectrometry was used to detect MDMA and MDA levels,and statistical software was employed for data analysis.Results In the single-administration group,peak concentrations of MDMA and MDA were reached at 5 min and 1 h after administration,respectively,with the largest detection time limit of 12 h.In the continuous administration group,peak concentrations were reached at 30 min and 1.5 h af-ter administration,respectively,with the largest detection time limit of 10 h.Nonlinear fitting equations for the concentration ratio of MDMA and MDA in plasma and administration time in the single-administration group and continuous administration group were as follows:T=10.362C-1.183,R2=0.974 6;T=7.397 3C-0.694,R2=0.961 5(T:injection time;C:concentration ratio of MDMA to MDA in plasma).Conclusions The toxicokinetic data of MDMA and its metabolite MDA in rats,obtained through single and continuous administration,including peak concentration,peak time,detection time limit,and the relationship between concentration ratio and administration time,provide a theoretical and data foundation for relevant forensic identification.