Objective To study the effect of Compound Jinguanlan preparation on acne vulgaris through the p38MAPK signaling pathway.Methods New Zealand White Rabbits were randomly divided into high,medium,and low concentration groups of Jinguanlan,a positive control group,a model control group,and a blank control group.A rabbit ear acne model was constructed and each group was given the corresponding drug intervention.The gross changes in the skin tissue of the rabbit ears were observed.HE staining was used to observe pathological changes.ELISA was used to detect the levels of IL-6 and TNF-α in the serum.Western blot was used to detec the levels of p38MAPK protein expression.Results Jinguanlan significantly inhibited the inflammation of acne vulgaris,reducing swelling,erythema,hyperkeratosis,and inflammatory cell infiltration in the rabbit ears.ELISA results showed that compared with the model control group,the levels of IL-6 and TNF-α in the serum of each concentration of Jinguanlan group were significantly reduced,with the high concentration group showing a more pronounced decrease(P＜0.01).Western blot results indicated that compared with the model group,the relative expression of p38 MAPK protein in the Jinguanlan group was reduced.Conclusion Jinguanlan can inhibit the inflammatory response of acne vulgaris,and its effect may be achieved by regulating the p38MAPK signaling pathway and reducing the expression levels of IL-6 and TNF-α in the serum.

Objective To observe the value of machine learning(ML)models based on ultrasound radiomics for preoperatively distinguishing atypical parathyroid tumor(APT)/parathyroid carcinoma(PC)and parathyroid adenoma(PA).Methods Totally 330 primary hyperparathyroidism patients who underwent surgical treatments were retrospectively enrolled and categorized into APT/PC group(n=78)and PA group(n=252)according to surgical pathology and clinical follow-up results,also divided into training set(n=231)and test set(n=99)at the ratio of 7∶3.Based on preoperative ultrasound,545 radiomics features were extracted,and recursive feature elimination(RFE),Kruskal-Wallis or analysis of variance methods were used to screen the features,respectively.Support vector machine(SVM),linear discriminant analysis(LDA),least absolute shrinkage and selection operator logistic regression(LRLASSO),also random forest(RF)and decision tree(DT)algorithms were adopted to construct ML models for differentiating APT/PC and PA,respectively.Then the models were trained in training set,their performance were verified in test set,and a 5-fold cross-validation was adopted to screen out the better combinations.Results Compared with Kruskal-Wallis and analysis of variance methods,the distinguishing efficacy of SVM,LDA,LRLASSO,RF and DT models constructed based on features screened out using RFE method in training set(area under the curve[AUC]=0.870,0.878,0.850,0.847,1.000)and test set(AUC=0.856,0.842,0.827,0.847 and 0.704)were all relatively higher.In test set,the AUC of SVM,LDA,LRLASSO and RF models constructed based on the features screened out using RFE method(included 25,23,17 and 23 features)were all higher than that of DT model(8 features)(all P＜0.001).No significant difference of AUC was found between SVM,LRLASSO or RF models and LDA model(all P＞0.05).The AUC of SVM and RF models were higher than that of LRLASSO model(both P＜0.05),while of SVM and RF models were not significantly different(P＞0.05),indicating that SVM,LDA and RF models were better ones.Conclusion SVM,LDA,LRLASSO,RF and DT models based on ultrasound radiomics could effectively distinguish APT/PC and PA preoperatively,among which SVM,LDA and RF models had better diagnostic efficacy.

Objective To study the effect of Compound Jinguanlan preparation on acne vulgaris through the p38MAPK signaling pathway.Methods New Zealand White Rabbits were randomly divided into high,medium,and low concentration groups of Jinguanlan,a positive control group,a model control group,and a blank control group.A rabbit ear acne model was constructed and each group was given the corresponding drug intervention.The gross changes in the skin tissue of the rabbit ears were observed.HE staining was used to observe pathological changes.ELISA was used to detect the levels of IL-6 and TNF-α in the serum.Western blot was used to detec the levels of p38MAPK protein expression.Results Jinguanlan significantly inhibited the inflammation of acne vulgaris,reducing swelling,erythema,hyperkeratosis,and inflammatory cell infiltration in the rabbit ears.ELISA results showed that compared with the model control group,the levels of IL-6 and TNF-α in the serum of each concentration of Jinguanlan group were significantly reduced,with the high concentration group showing a more pronounced decrease(P＜0.01).Western blot results indicated that compared with the model group,the relative expression of p38 MAPK protein in the Jinguanlan group was reduced.Conclusion Jinguanlan can inhibit the inflammatory response of acne vulgaris,and its effect may be achieved by regulating the p38MAPK signaling pathway and reducing the expression levels of IL-6 and TNF-α in the serum.

Objective　To conduct an in-depth study of the molecular biological characteristics and evolutionary trends of H9N2 avian influenza virus (AIV) in live poultry markets in Yunnan Province in 2023, and to provide scientific evidence for the development of control strategies for H9N2 avian influenza in the region. Methods　Environmental samples were collected from live poultry markets in Yunnan Province in 2023 for H9N2 subtype nucleic acid detection. Positive samples were subjected to virus isolation using chicken embryos, and the genome of the 8 isolated strains was amplified, sequenced, and analyzed for genetic characteristics. Results　The eight avian influenza virus (AIV) isolates had the hemagglutinin (HA) cleavage site sequence PSRSSRGLF, which is a non-continuous basic amino acid sequence, consistent with the genetic characteristics of typical low-pathogenicity avian influenza viruses. Mutations Q234L and H191N were observed in the left arm of the HA protein, which enhanced the affinity for α-2,6 sialic acid receptors, suggesting that these viruses may have the potential to infect humans. The neuraminidase (NA) protein exhibited a deletion of three amino acids (TEI) at positions 62–64 in the stalk region, displaying characteristics of high pathogenicity at the molecular level. The increase or absence in potential glycosylation sites were observed in both HA and NA genes. The non-structural protein 1 (NS1) showed no D92E mutation, and had a C-terminal truncation of 13 amino acids, indicating that this virus is of low pathogenicity and poses a lower risk of human transmission. Mutations T37A, R95K, S224N, and K242N in the M1 protein of some isolates increased the risk of infection, while one isolate carried the V27A or S31N mutation in the M2 protein, conferring resistance to M2 ion channel inhibitors. Mutations M317I and S678N were identified in the PB1 protein, which may enhance pathogenicity in mice and increase the potential for mammalian infection. The PB2 protein carried the I292V mutation, which exhibited a stronger infectivity to mammals. Phylogenetic analysis revealed that the HA, NA, and PB2 gene segments belonged to the Y280 lineage, NP and PB1 gene segments were classified under the F98 lineage, the M gene segment of the NH013 isolate belonged to the F98 lineage, while M genes, as well as the NS and PA genes of other isolates belonged to the G1 lineage. Conclusions　These eight AIV isolates exhibited characteristics of low pathogenicity, but simultaneously carry the potential risk of infecting humans. Despite the HA cleavage site and NS1 protein mutations indicating low pathogenicity, the Q234L and H191N mutations in the HA protein enhanced its affinity for human receptors, suggesting the potential for human infection. The TEI deletion in the NA protein, mutations in the M1 protein, and resistance mutations in the M2 protein further increase the risk of human infection. Mutations in the PB1 and PB2 proteins increase the potential for these eight AIV strains to infect humans or mammals.

Objective To observe the value of machine learning(ML)models based on ultrasound radiomics for preoperatively distinguishing atypical parathyroid tumor(APT)/parathyroid carcinoma(PC)and parathyroid adenoma(PA).Methods Totally 330 primary hyperparathyroidism patients who underwent surgical treatments were retrospectively enrolled and categorized into APT/PC group(n=78)and PA group(n=252)according to surgical pathology and clinical follow-up results,also divided into training set(n=231)and test set(n=99)at the ratio of 7∶3.Based on preoperative ultrasound,545 radiomics features were extracted,and recursive feature elimination(RFE),Kruskal-Wallis or analysis of variance methods were used to screen the features,respectively.Support vector machine(SVM),linear discriminant analysis(LDA),least absolute shrinkage and selection operator logistic regression(LRLASSO),also random forest(RF)and decision tree(DT)algorithms were adopted to construct ML models for differentiating APT/PC and PA,respectively.Then the models were trained in training set,their performance were verified in test set,and a 5-fold cross-validation was adopted to screen out the better combinations.Results Compared with Kruskal-Wallis and analysis of variance methods,the distinguishing efficacy of SVM,LDA,LRLASSO,RF and DT models constructed based on features screened out using RFE method in training set(area under the curve[AUC]=0.870,0.878,0.850,0.847,1.000)and test set(AUC=0.856,0.842,0.827,0.847 and 0.704)were all relatively higher.In test set,the AUC of SVM,LDA,LRLASSO and RF models constructed based on the features screened out using RFE method(included 25,23,17 and 23 features)were all higher than that of DT model(8 features)(all P＜0.001).No significant difference of AUC was found between SVM,LRLASSO or RF models and LDA model(all P＞0.05).The AUC of SVM and RF models were higher than that of LRLASSO model(both P＜0.05),while of SVM and RF models were not significantly different(P＞0.05),indicating that SVM,LDA and RF models were better ones.Conclusion SVM,LDA,LRLASSO,RF and DT models based on ultrasound radiomics could effectively distinguish APT/PC and PA preoperatively,among which SVM,LDA and RF models had better diagnostic efficacy.

Background::Limited information exists regarding the impact of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection on psoriasis patients. The objective of this study was to identify clinical factors associated with the prognosis of psoriasis following SARS-CoV-2 infection.Methods::A retrospective, multicenter study was conducted between March and May 2023. Univariable and multivariable logistic regression analyses were employed to identify factors associated with coronavirus disease 2019 (COVID-19)-related psoriasis outcomes. The study included 2371 psoriasis patients from 12 clinical centers, with 2049 of them having been infected with SARS-CoV-2.Results::Among the infected groups, lower exacerbation rates were observed in individuals treated with biologics compared to those receiving traditional systemic or nonsystemic treatments (22.3% [236/1058] vs. 39.8% [92/231] vs. 37.5% [140/373], P <0.001). Psoriasis progression with lesions (adjusted odds ratio [OR] = 8.197, 95% confidence interval [95% CI] = 5.685–11.820, compared to no lesions), hypertension (adjusted OR = 1.582, 95% CI = 1.068–2.343), traditional systemic (adjusted OR = 1.887, 95% CI= 1.263–2.818), and nonsystemic treatment (adjusted OR= 1.602, 95% CI= 1.117–2.297) were found to be associated with exacerbation of psoriasis after SARS-CoV-2 infection, but not biologics (adjusted OR = 0.931, 95% CI = 0.680–1.274, compared to no treatment), according to multivariable logistic regression analysis. Conclusions::A reduced risk of psoriasis exacerbation after SARS-CoV-2 infection was observed with biologics compared to traditional systemic and nonsystemic treatments. Significant risk factors for exacerbation after infection were identified as existing psoriatic lesions and hypertension.

Objective:To establish and evaluate a clinical and ultrasound parameters-based nomogram for the preoperative differentiating diagnosis of intrahepatic cholangiocarcinoma (ICC).Methods:A total of 723 patients undergoing hepatectomy in Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University from January 2016 to August 2022 were retrospectively screened. A total of 399 patients with hepatocellular carcinoma (HCC, 198 cases) or ICC (201 cases) were enrolled in this study, including 284 males and 115 females, aged (60.5±10.5) years. Through random sampling using computer-generated random numbers, patients were divided into training ( n=279) and validation groups ( n=120) in a ratio of 7∶3. Univariate and multivariate logistic regression were performed to identify factors differentiating ICC, and a nomogram was established using R software based on independent risk factors for ICC. The accuracy of the nomogram was evaluated by receiver operating characteristic curve and calibration curves. Decision curve analysis was performed to assess the net benefit of the model. Results:Multivariate logistic regression analysis showed that irregular shape, cholangiectasis, female, cirrhosis, carbohydrate antigen 242 >10 U/ml, carbohydrate antigen 125 >30 U/ml and alpha-fetoprotein >10 μg/L were independent differentiating factors for ICC (all P<0.05). A nomogram was constructed based on those factors. The nomogram showed a better discrimination between ICC and HCC. The area under the curve of the training group and the validation group were 0.966 and 0.956, respectively. The calibration curve showed that the prediction effect of the model is in good agreement with the actual situation. Decision curve analysis showed that the nomogram was more effective than diagnosing all patients as either HCC or ICC, which yielded a net benefit at the most reasonable threshold probabilities. Conclusion:The nomogram for the preoperative diagnosis of ICC based on clinical and ultrasound features showed a good diagnostic performance.

Objective:To establish a nomogram for preoperative differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC) based on clinical, ultrasound, and contrast-enhanced ultrasound (CEUS) data.Methods:A retrospective analysis was conducted on ultrasound and CEUS data of 462 patients who underwent hepatectomy in Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School from January 2016 to December 2023, including 262 cases of HCC (56.7%) and 200 cases of ICC (43.3%). The data were randomly divided into training set ( n=324) and validation set ( n=138) in a 7∶3 ratio. Univariate analysis was used to initially screen for variables with statistically significant differences between HCC and ICC groups in the training set, and LASSO regression was performed to select the variables with higher coefficients. Logistic regression analyses were then used to predict independent risk factors for ICC. A nomogram was drawn using R software. The performance of the nomogram was then validated using ROC curve, calibration curve, and decision curve analysis (DCA). Results:Univariate analysis showed that there were significant differences in age, gender, liver cirrhosis, HBsAg (+ ), ALP >185 U/L, CA19-9 >27 kU/L, CA242>10 kU/L, irregular shape, border, cholangiectasis, portal vein tumor thrombus, enhanced pattern in arterial phase, clearance time <60 s, intra-tumoral vein between ICC and HCC groups (all P<0.05). The top 10 features were selected for LASSO regression analysis. Logistic regression analysis revealed that gender, cirrhosis, CA19-9>27 kU/L, CA242>10 kU/L, cholangiectasis, clearance time <60 s, intra-tumoral vein and enhanced pattern in arterial phase were risk factors for ICC (all P<0.05). The area under the ROC curve in the training and validation groups were 0.963 and 0.914, respectively. In the training group, the specificity and sensitivity of the nomogram were 0.926 and 0.917, respectively, and in the validation group, they were 0.875 and 0.871, respectively. The calibration curve showed that the prediction effect of the model was in good agreement with the actual situation. DCA showed that the nomogram could increase the net benefit to the different diagnosis of ICC in patients. Conclusions:The nomogram based on clinical, ultrasound and CEUS features has a good predictive value for preoperative identification of ICC and provides reliable evidence for clinical practice.

Objective: To investigate the mechanism of action of Wuzi Yanzong pill (WYP) in rats with oligoasthenozoospermia (OAZ) via metabolomics and to provide a possible basis for improving this WYP-based treatment. Methods: A rat model of OAZ was established by treating male Sprague–Dawley rats with glucosides from Tripterygium wilfordii Hook. F. Seventy-two rats were randomly divided into six groups: control, L-carnitine (positive control), model, and low-, medium-, and high-dose WYP groups. Rats in the experimental groups were treated with WYP for 4 weeks. At the end of the treatment period, sperm cell quality (density, motility, and viability) was assessed using a semen analysis system, mitochondrial membrane potential (MMP) was assessed using flow cytometry, and testicular injury was assessed using hematoxylin and eosin staining to validate the therapeutic effect of WYP in OAZ. Further, serum metabolomics-based analysis was performed using high-performance liquid chromatography-mass spectrometry to identify differential metabolic pathways and possible mechanisms of action of WYP in OAZ treatment. Results: A rat model of OAZ was considered successfully-established after comparing the quality of spermatozoa in the model group to that in the control group. WYP-M and WYP-H treatments significantly improved sperm cell density, motility, and viability compared with those in the model group (all P < .05). Compared with the model group, both WYP-M and WYP-H treatments increased MMP values (P = .006 and P = .021 respectively), while there was no significant difference in the L-carnitine group. L-carnitine and WYP administration reversed damage to the testes to varying degrees compared with that in the model group. Further, 44 differential metabolites and four metabolic pathways, especially autophagy pathway, related to OAZ were identified via metabolomics. Conclusions WYP improves sperm cell quality and MMP in OAZ primarily via autophagy regulation. These findings can be employed to improve the efficacy of WYP in humans.

Objective:To investigate the relationship between body mass index(BMI) and osteoporosis using Mendelian randomization analysis.Methods:The genetic variation strongly related to BMI was selected as the instrumental variables in the collection data set of the genome-wide association study(GWAS). The MR-Egger regression, weighted median estimator(WME), inverse variance weighted(IVW), simple mode and weighted mode were used for Mendelian randomization(MR) analysis. The causal association between BMI and osteoporosis was evaluated by odds ratio and 95% confidence interval. The MR-APSS method was applied to make the causal inference results based on MR more reliable. The Linkage disequilibrium score regression was applied to evaluate the genetic correlation, and the horizontal pleiotropy test, heterogeneity test, and leave-one-out method were used to evaluate whether the results were reliable, The influence of heterogeneity and horizontal pleiotropy were reduced by the MR-PRESSO outlier test.Results:A total of 421 SNPs were included, with inverse variance-weighted method as the main analysis approach. The calculated OR value and 95% CI were 0.994(95% CI 0.992-0.997), indicating a protective effect of BMI on osteoporosis. The MR-APSS method showed that the effect of BMI on osteoporosis was statistically significant. Linkage disequilibrium score regression demonstrated a genetic correlation between BMI and osteoporosis. MR-Egger regression intercept showed no horizontal pleiotropy of instrumental variables, and the funnel plot showed no bias in instrumental variables. Leave-one-out analysis confirmed robust results. Conclusion:There may be a negative causal relationship between BMI and osteoporosis and BMI is a protective factor for osteoporosis.