ObjectiveTo investigate the effects of different fermentation methods and times on the fungal flora and chemical composition of Aurantii Fructus， in order to obtain the optimal fermentation conditions and flora structure， and to ensure the stability and controllability of the fermented varieties. MethodsScanning electron microscopy was used to observe and analyze the colony characteristics on the surface of Aurantii Fructus under different fermentation conditions. Internal transcribed spacer 2（ITS2） high-throughput sequencing， combined with fungal community diversity analysis and fungal community structure analysis， were used to obtain the fungal flora microbial categories of Aurantii Fructus under the conditions of traditional pressure-shelf fermentation and non-pressure-shelf natural fermentation for 7， 14， 21 d（numbered Y1-Y3 for the former， and numbered F1-F3 for the latter）， respectively. At the same time， the chemical components in the fermentation process were detected by ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS/MS）， combined with principal component analysis（PCA）， partial least squares-discriminant analysis（PLS-DA） and compound retention time， parent ions， characteristic fragment ions and other information， the differential compounds between the different fermentation samples were screened and identified. ResultsThe analysis of fungal community diversity showed that the dominant flora did not change at different fermentation time points in the traditional pressure-shelf fermentation method， while in the non-pressure-shelf natural fermentation method， there was a significant difference with the fermentation process， and at the genus level， the dominant genus of samples Y1， Y2， Y3 and F2 was Aspergillus， while the dominant genera of samples F1 and F3 were both Rhizopus. This indicated that the microbial growth environment provided by the traditional fermentation method was more stable， and the microbial community structure was more stable， which was more conducive to the stable and controllable fermentation process and fermented products. A total of 155 compounds were identified by compositional analysis， including 70 flavonoids， 38 coumarins， 10 alkaloids， 34 organic acids and 3 other compounds. After fermentation， two new components of ribalinine and pranferin were produced. Different fermentation conditions also brought about differences in chemical composition， multivariate statistical analysis obtained 26 differential compounds under two different fermentation methods， mainly including flavonoids， organic acids and coumarins. Comprehensively， the microbial community structure of samples fermented by the traditional pressure-shelf method of Aurantii Fructus for 14 d was stable， the species richness was high and the overall content of differential compounds was high， which was the optimal processing condition. ConclusionCompared with non-pressure-shelf natural fermentation， the traditional method has obvious advantages in terms of the stability of the microbial community structure and the content of chemical compounds， and the optimal condition is 14 days of fermentation. This study is helpful to promote the quality stability and fermentation bioavailability of fermented products of Aurantii Fructus， as well as to provide an experimental basis for the further improvement of the quality control methods of this variety.

ObjectiveTo investigate the effects of different fermentation methods and times on the fungal flora and chemical composition of Aurantii Fructus， in order to obtain the optimal fermentation conditions and flora structure， and to ensure the stability and controllability of the fermented varieties. MethodsScanning electron microscopy was used to observe and analyze the colony characteristics on the surface of Aurantii Fructus under different fermentation conditions. Internal transcribed spacer 2（ITS2） high-throughput sequencing， combined with fungal community diversity analysis and fungal community structure analysis， were used to obtain the fungal flora microbial categories of Aurantii Fructus under the conditions of traditional pressure-shelf fermentation and non-pressure-shelf natural fermentation for 7， 14， 21 d（numbered Y1-Y3 for the former， and numbered F1-F3 for the latter）， respectively. At the same time， the chemical components in the fermentation process were detected by ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS/MS）， combined with principal component analysis（PCA）， partial least squares-discriminant analysis（PLS-DA） and compound retention time， parent ions， characteristic fragment ions and other information， the differential compounds between the different fermentation samples were screened and identified. ResultsThe analysis of fungal community diversity showed that the dominant flora did not change at different fermentation time points in the traditional pressure-shelf fermentation method， while in the non-pressure-shelf natural fermentation method， there was a significant difference with the fermentation process， and at the genus level， the dominant genus of samples Y1， Y2， Y3 and F2 was Aspergillus， while the dominant genera of samples F1 and F3 were both Rhizopus. This indicated that the microbial growth environment provided by the traditional fermentation method was more stable， and the microbial community structure was more stable， which was more conducive to the stable and controllable fermentation process and fermented products. A total of 155 compounds were identified by compositional analysis， including 70 flavonoids， 38 coumarins， 10 alkaloids， 34 organic acids and 3 other compounds. After fermentation， two new components of ribalinine and pranferin were produced. Different fermentation conditions also brought about differences in chemical composition， multivariate statistical analysis obtained 26 differential compounds under two different fermentation methods， mainly including flavonoids， organic acids and coumarins. Comprehensively， the microbial community structure of samples fermented by the traditional pressure-shelf method of Aurantii Fructus for 14 d was stable， the species richness was high and the overall content of differential compounds was high， which was the optimal processing condition. ConclusionCompared with non-pressure-shelf natural fermentation， the traditional method has obvious advantages in terms of the stability of the microbial community structure and the content of chemical compounds， and the optimal condition is 14 days of fermentation. This study is helpful to promote the quality stability and fermentation bioavailability of fermented products of Aurantii Fructus， as well as to provide an experimental basis for the further improvement of the quality control methods of this variety.

Skin modeling of transdermal drug delivery system refers to experimental models that mimic the structure and function of human skin to explore and evaluate absorption,penetration,and efficacy of medicines in transdermal drug delivery.It provides an alternative to traditional human skin experiments and reduces the use of human skin in medical research,which is convenient,controllable,and cost effective.For skin models of transdermal drug delivery systems,this article introduces commonly used animal skin models,artificial skin models,and recombinant human skin models from the perspective of the transdermal absorption pathway of medicines,and analyzes their advantages,disadvantages,and applications so provide references the research and development of transdermal formulations and topical therapies.

OBJECTIVE To investigate the potential of low-frequency, low-power ultrasound to enhance the transdermal absorption and efficacy of ketoprofen gel. METHODS Ketoprofen gel was used as a model drug to compare the in vitro transdermal permeation of ultrasound treated group and untreated group. Additionally, a rat model of collagen-induced inflammation provided a basis for evaluating pharmacodynamic differences. Pharmacokinetic studies further elucidated the effects of ultrasound on ketoprofen gel's penetration process. RESULTS Ultrasound treatment enhanced the cumulative transdermal permeation of ketoprofen gel by 3.5-fold over 24 hours compared to untreated. Significant pharmacokinetic improvements in AUC0-t from (4289.02±763.58)ng·h·mL−1 to (11301.10±3386.30)ng·h·mL−1 and a reduction in Tmax from (6.0±1.4)h to (3.0±2.0)h. Ultrasound notably improved the gel's anti-inflammatory effects in the rat model, effectively and rapidly reducing inflammation-induced swelling. CONCLUSION Low-frequency, low-power ultrasound can significantly improve the amount and rate of transdermal absorption of ketoprofen gel and enhance its pharmacological potency, from the aspects of skin permeation tests, pharmacodynamic evaluation, and pharmacokinetic studies, which is an effective penetration enhancer for transdermal administration of ketoprofen gel.

Objective:To evaluate the diagnostic performance of radiomics model based on contrast-enhanced ultrasound(CEUS) in predicting pathological complete response(pCR) after neoadjuvant chemoradiotherapy(nCRT) in patients with locally advanced rectal cancer(LARC).Methods:One hundred and six patients with LARC who underwent total mesorectal excision after nCRT between April 2018 and April 2023 in the First Affiliated Hospital of Guangxi Medical University were retrospectively included, the patients were randomly divided into a training set of 63(14 pCR patients) and a validation set of 43(12 pCR patients) in a 6∶4 ratios. Radiomics features were extracted from the tumors′ region of interest of CEUS images based on PyRadiomics. Intra-class correlation coefficient(ICC), Mann-Whitney U test, and least absolute shrinkage and selection operator(LASSO) algorithms were used to reduce features dimension. Finally, 7 radiomics features relevanted to pCR were selected to construct an ultrasomics model using elastic network regression, based on the R language. A combined model was constructed by jointing clinical feature. The performance of the models was assessed with the area under the ROC curve(AUC). Results:The AUC of the ultrasomics model and the combined model was 0.695(95% CI=0.532-0.859) and 0.726(95% CI=0.584-0.868) respectively in the training set. The AUC of the ultrasomics model and the combined model was 0.763(95% CI=0.625-0.902) and 0.790(95% CI=0.653-0.928) respectively in the validation set. Both univariate and multivariate Logistic regression analyses showed that CA199( P<0.05) and ultrasomics score( P<0.001) could be an independent predictor of pCR after nCRT in patients with LARC. Conclusions:The CEUS-based radiomics scores has certain predictive value for whether LARC patients achieve pCR after nCRT, and may provide a non-invasive imaging biomarker for predicting LARC patients achieve pCR after nCRT.

Objective:To investigate the influence of varied oxygen (O 2) concentration environments on the phenotypic transformation of pulmonary artery smooth muscle cells (PASMC) and the mechanism of pulmonary hypertension. Methods:Primary rat PASMC were isolated and cultured through the process of enzymatic digestion. Following identification, the stable passaged PASMC were subjected to a 6-hour incubation in sealed containers with normal O 2 content (group C) and relative O 2 content comprising 55% (group H55), 75% (group H75), and 95% (group H95). mRNA and protein expression of α-Actin (α-SMA), smooth muscle 22α (SM22α), osteopontin (OPN), and matrix metalloproteinase-2 (MMP-2) were measured using real-time quantitative PCR and western blot analysis. Results:The H55 group displayed no significant difference from the C group in terms of mRNA and relative protein expression levels for α-SMA, SM22α, OPN, and MMP-2 (all P>0.05). On the other hand, groups H75 and H95 exhibited a reduction in mRNA and relative protein expression of α-SMA and SM22α, along with an increase in mRNA and relative protein expression of OPN and MMP-2 when compared with both the C and H55 groups (all P<0.05). The H95 group showed a higher relative mRNA expression of MMP-2 as compared to the H75 group ( P<0.05). Conclusions:Oxygen concentration environments of 75% or higher can serve as the foundation for the pathogenesis of pulmonary hypertension, essentially by inducing a phenotypic transformation in PASMC towards adopting a robust secretory function. This induction is contingent upon the concentration of oxygen present.

Objective To explore the comparative study of constructing prediction models for muscle invasive of bladder cancer based on different machine learning algorithms combined with MRI radiomic.Methods A total of 187 bladder cancer patients who underwent MRI examination and were confirmed by pathology were retrospectively selected.Patients were randomly divided into a training set and a test set in a 7∶3 ratio.The patients were divided into muscle invasive bladder cancer(MIBC)group and non-muscle invasive bladder cancer(NMIBC)group according to the surgical pathology results.Tumor volume of interest(VOI)was outlined on the images of T2 WI,diffusion weighted imaging(DWI),and apparent diffusion coefficient(ADC),and the radiomic features were extracted by A.K software,and dimensionality reduction was performed using the maximum relevance minimum redundancy(mRMR)algorithm combined with least absolute shrinkage and selection operator(LASSO).Six machine learning algorithms,including K-nearest neighbor(KNN),decision tree(DT),support vector machine(SVM),logistic regression(LR),random forest(RF),and explainable boosting machine(EBM)were used to construct the radiomic model and calculate the corresponding area under the curve(AUC),accuracy,sensitivity,and specificity,respectively.Results Six machine learning algorithms,including KNN,DT,SVM,LR,RF,and EBM were used to construct the radiomic model,and the AUC values for predicting MIBC in the training set were 0.863,0.838,0.853,0.866,0.977,0.997,and in the test set were 0.748,0.833,0.860,0.868,0.870,0.900.Among them,the MRI radiomic model constructed based on EBM had the highest predictive efficacy for MIBC,with AUC values,accuracy,sensitivity and specificity of 0.997,0.977,0.957 and 0.981 in the training set,and 0.900,0.877,0.800,and 0.894 in the test set,respectively.Conclusion Multiple machine learning algorithms combined with MRI radiomic to construct models have good predictive efficacy for MIBC,and the model constructed based on EBM shows the highest predictive value.

Oral and maxillofacial space infection(OMSI)as a mostly mixed infection,often requires the combination of anti-aerobe and an-ti-anaerobe antibiotics.The use of third-generation quinolones,such as levofloxacin,is limited in OMSI due to the tendon rupture and other serious adverse drug reactions(ADR).In the treatment of a case with OMSI and anaphilactic shock induced by levofloxacin,the association evaluation method of ADR and literature review method participated by clinical pharmacists were used.Suggestions on rational drug use and ADR treatment were gained,the patient was timely rescued from anaphylactic and cured of OMSI.

Identifying the compound formulae-related xenobiotics in bio-samples is full of challenges.Conventional strategies always exhibit the insufficiencies in overall coverage,analytical efficiency,and degree of automation,and the results highly rely on the personal knowledge and experience.The goal of this work was to establish a software-aided approach,by integrating ultra-high performance liquid chromatography/ion-mobility quadrupole time-of-flight mass spectrometry(UHPLC/IM-QTOF-MS)and in-house high-definition MS2 library,to enhance the identification of prototypes and metabolites of the compound formulae in vivo,taking Sishen formula(SSF)as a template.Seven different MS2 acquisition methods were compared,which demonstrated the potency of a hybrid scan approach(namely high-definition data-independent/data-dependent acquisition(HDDIDDA))in the identification precision,MS1 coverage,and MS2 spectra quality.The HDDIDDA data for 55 reference compounds,four component drugs,and SSF,together with the rat bio-samples(e.g.,plasma,urine,feces,liver,and kidney),were acquired.Based on the UNIFI? platform(Waters),the efficient data processing workflows were estab-lished by combining mass defect filtering(MDF)-induced classification,diagnostic product ions(DPIs),and neutral loss filtering(NLF)-dominated structural confirmation.The high-definition MS2 spectral li-braries,dubbed in vitro-SSF and in vivo-SSF,were elaborated,enabling the efficient and automatic identification of SSF-associated xenobiotics in diverse rat bio-samples.Consequently,118 prototypes and 206 metabolites of SSF were identified,with the identification rate reaching 80.51％and 79.61％,respectively.The metabolic pathways mainly involved the oxidation,reduction,hydrolysis,sulfation,methylation,demethylation,acetylation,glucuronidation,and the combined reactions.Conclusively,the proposed strategy can drive the identification of compound formulae-related xenobiotics in vivo in an intelligent manner.

To analyze the trends in disease burden and risk factors of depression among the elderly population in China from 1990 to 2021, and to provide a theoretical basis for the prevention, treatment, and policy-making of geriatric depression in China.