Objective To construct a model for predicting ki-67 expression in hepatocellular carcinoma using the intratumoral and peritumoral radiomic features of contrast enhanced magnetic resonance imaging(CEMRI)in the arterial phase as well as clinical imaging features.Methods A total of 120 patients pathologically diagnosed with hepatocellular carcinoma(HCC)from January 2016 to December 2024 in No.910 Hospital of the Joint Logis-tics Support Force of the Chinese People's Liberation Army were retrospectively enrolled and randomly divided into a training set(84 cases)and a test set(36 cases)in a ratio of 7∶3.ITK-SNAP software was used to delineate the global region of interest(ROI)of HCC on the arterial phase MR images.The ROIs of all patients were automatically expanded outward by 2 mm,and then the intratumoral ROI areas were eliminated to obtain the peritumoral ROI.With the help of PyRadiomics software,1 198 intratumoral and peritumoral radiomic features were extracted.Spearman correlation analysis,maximum relevance-minimum redundancy(mRMR),and least absolute shrinkage and selection operator(LASSO)regression were used to reduce the data dimension and select the best features.Then,a radiomics model of the logistic regression(LR)machine learning algorithm was constructed.A combined model including clinical imaging features and radiomics features was established.The area under the curve(AUC),accuracy,sensitivity,specificity,positive predictive value(PPV),negative predictive value(NPV),calibration curve and decision curve analysis(DCA)were used to evaluate the efficacy of the intratumoral and peritumoral radiomics features combined with clinical imaging features model in predicting ki-67 expression in hepatocellular car-cinoma.Results The intratumor model exhibited an efficacy in predicting the expression of ki-67 in hepatocellular carcinoma with AUC values of 0.817 and 0.787 in the training set and test set,respectively.The peritumoral model showed an efficacy with AUC values of 0.805 and 0.633 in the training set and test set,respectively.The intratumoral and peritumoral model demonstrated AUC values of 0.874 and 0.836 in the training set and test set,respectively.The combined model constructed by integrating the intratumoral and peritumoral model with clinical imaging features yielded AUC values of 0.877 and 0.849 in the training set and test set,respectively,indicating clinical imaging features improved the performance of the model.DCA showed that the combined models all had good clinical benefits,with the intratumoral and peritumoral model performing the best.Conclusion The intratumoral and peritumoral radiomics model based on CEMRI arterial phase combined with clinical imaging data can accurately predict the expression of ki-67 in hepatocellular carcinoma.This combined model yields the best clinical benefit.

Objective To explore its application value of LNM in patients with gastric adenocarcinoma based on the construction of a radiomic nomogram prediction model for preoperative lymph node metastasis in gastric adenocarcinoma using enhanced CT imaging features and parameters.Methods 131 patients with gastric adenocarcinoma who underwent radical gastric cancer surgery(Billroth Ⅱ)+lymph node dissection in our hospital from August 2019 to October 2023 were retrospectively analysed,and the preoperative enhancement CT extracted lesions,histological features of lymph node images,and pathological examination of LNM were recorded respectively,and the statistically significant Enhanced CT image histological features and parameters,multifactorial logistic regression to analyse the independent risk factors of gastric adenocarcinoma LNM(+)and construct a column-line diagram,and evaluate its performance.The performance and clinical value of the prediction model were evaluated using subject work characteristic(ROC)curves,and the column-line diagram was internally validated.Results The results of univariate analysis showed that the maximum diameter of the tumour,the lymph node status,the short-axis length and volume of the largest lymph node in the lymph node group and the sum of the short-axis lengths of all the lymph nodes were associated with the occurrence of LNM,and the difference was statistically significant(P＜0.05);multifactorial logistic regression analysis suggested that the maximum diameter of the tumour was ≥15 mm and the lymph node status was positive in the enhanced CT imaging examination,maximum lymph node short-axis length ≥ 7 mm in the lymph node group,the sum of short-axis lengths of all lymph nodes ≥ 11 mm,and the maximum lymph node short-axis volume ≥ 300 mm3 in the lymph node group were the independent risk factors for gastric adenocarcinoma patients with LNM(+)(P＜0.05);the area under the curve of the model was plotted using a ROC curve with an AUC=0.816(95％ CI 0.810-0.939),with the sensitivity of 0.91 and the specificity of 0.86.Model validation was performed using the BOOTSTRAP method containing 500 resamples,and the results suggested that the differences between the fitted curves and the ideal curves of the constructed column-line graphical model were not significant,and the validity and reliability were good.Conclusion The radiomic nomogram prediction model based on enhanced CT imaging features and parameters can effectively predict preoperative lymph node metastasis in gastric adenocarcinoma,providing strong support for clinical decision-making in gastric adenocarcinoma patients.

Objective To construct a model for predicting ki-67 expression in hepatocellular carcinoma using the intratumoral and peritumoral radiomic features of contrast enhanced magnetic resonance imaging(CEMRI)in the arterial phase as well as clinical imaging features.Methods A total of 120 patients pathologically diagnosed with hepatocellular carcinoma(HCC)from January 2016 to December 2024 in No.910 Hospital of the Joint Logis-tics Support Force of the Chinese People's Liberation Army were retrospectively enrolled and randomly divided into a training set(84 cases)and a test set(36 cases)in a ratio of 7∶3.ITK-SNAP software was used to delineate the global region of interest(ROI)of HCC on the arterial phase MR images.The ROIs of all patients were automatically expanded outward by 2 mm,and then the intratumoral ROI areas were eliminated to obtain the peritumoral ROI.With the help of PyRadiomics software,1 198 intratumoral and peritumoral radiomic features were extracted.Spearman correlation analysis,maximum relevance-minimum redundancy(mRMR),and least absolute shrinkage and selection operator(LASSO)regression were used to reduce the data dimension and select the best features.Then,a radiomics model of the logistic regression(LR)machine learning algorithm was constructed.A combined model including clinical imaging features and radiomics features was established.The area under the curve(AUC),accuracy,sensitivity,specificity,positive predictive value(PPV),negative predictive value(NPV),calibration curve and decision curve analysis(DCA)were used to evaluate the efficacy of the intratumoral and peritumoral radiomics features combined with clinical imaging features model in predicting ki-67 expression in hepatocellular car-cinoma.Results The intratumor model exhibited an efficacy in predicting the expression of ki-67 in hepatocellular carcinoma with AUC values of 0.817 and 0.787 in the training set and test set,respectively.The peritumoral model showed an efficacy with AUC values of 0.805 and 0.633 in the training set and test set,respectively.The intratumoral and peritumoral model demonstrated AUC values of 0.874 and 0.836 in the training set and test set,respectively.The combined model constructed by integrating the intratumoral and peritumoral model with clinical imaging features yielded AUC values of 0.877 and 0.849 in the training set and test set,respectively,indicating clinical imaging features improved the performance of the model.DCA showed that the combined models all had good clinical benefits,with the intratumoral and peritumoral model performing the best.Conclusion The intratumoral and peritumoral radiomics model based on CEMRI arterial phase combined with clinical imaging data can accurately predict the expression of ki-67 in hepatocellular carcinoma.This combined model yields the best clinical benefit.

Objective To explore its application value of LNM in patients with gastric adenocarcinoma based on the construction of a radiomic nomogram prediction model for preoperative lymph node metastasis in gastric adenocarcinoma using enhanced CT imaging features and parameters.Methods 131 patients with gastric adenocarcinoma who underwent radical gastric cancer surgery(Billroth Ⅱ)+lymph node dissection in our hospital from August 2019 to October 2023 were retrospectively analysed,and the preoperative enhancement CT extracted lesions,histological features of lymph node images,and pathological examination of LNM were recorded respectively,and the statistically significant Enhanced CT image histological features and parameters,multifactorial logistic regression to analyse the independent risk factors of gastric adenocarcinoma LNM(+)and construct a column-line diagram,and evaluate its performance.The performance and clinical value of the prediction model were evaluated using subject work characteristic(ROC)curves,and the column-line diagram was internally validated.Results The results of univariate analysis showed that the maximum diameter of the tumour,the lymph node status,the short-axis length and volume of the largest lymph node in the lymph node group and the sum of the short-axis lengths of all the lymph nodes were associated with the occurrence of LNM,and the difference was statistically significant(P＜0.05);multifactorial logistic regression analysis suggested that the maximum diameter of the tumour was ≥15 mm and the lymph node status was positive in the enhanced CT imaging examination,maximum lymph node short-axis length ≥ 7 mm in the lymph node group,the sum of short-axis lengths of all lymph nodes ≥ 11 mm,and the maximum lymph node short-axis volume ≥ 300 mm3 in the lymph node group were the independent risk factors for gastric adenocarcinoma patients with LNM(+)(P＜0.05);the area under the curve of the model was plotted using a ROC curve with an AUC=0.816(95％ CI 0.810-0.939),with the sensitivity of 0.91 and the specificity of 0.86.Model validation was performed using the BOOTSTRAP method containing 500 resamples,and the results suggested that the differences between the fitted curves and the ideal curves of the constructed column-line graphical model were not significant,and the validity and reliability were good.Conclusion The radiomic nomogram prediction model based on enhanced CT imaging features and parameters can effectively predict preoperative lymph node metastasis in gastric adenocarcinoma,providing strong support for clinical decision-making in gastric adenocarcinoma patients.

The emergence of SARS-CoV-2 variants of concern and repeated outbreaks of coronavirus epidemics in the past two decades emphasize the need for next-generation pan-coronaviral therapeutics. Drugging the multi-functional papain-like protease (PLpro) domain of the viral nsp3 holds promise. However, none of the known coronavirus PLpro inhibitors has been shown to be in vivo active. Herein, we screened a structurally diverse library of 50,080 compounds for potential coronavirus PLpro inhibitors and identified a noncovalent lead inhibitor F0213 that has broad-spectrum anti-coronaviral activity, including against the Sarbecoviruses (SARS-CoV-1 and SARS-CoV-2), Merbecovirus (MERS-CoV), as well as the Alphacoronavirus (hCoV-229E and hCoV-OC43). Importantly, F0213 confers protection in both SARS-CoV-2-infected hamsters and MERS-CoV-infected human DPP4-knockin mice. F0213 possesses a dual therapeutic functionality that suppresses coronavirus replication via blocking viral polyprotein cleavage, as well as promoting antiviral immunity by antagonizing the PLpro deubiquitinase activity. Despite the significant difference of substrate recognition, mode of inhibition studies suggest that F0213 is a competitive inhibitor against SARS2-PLpro via binding with the 157K amino acid residue, whereas an allosteric inhibitor of MERS-PLpro interacting with its 271E position. Our proof-of-concept findings demonstrated that PLpro is a valid target for the development of broad-spectrum anti-coronavirus agents. The orally administered F0213 may serve as a promising lead compound for combating the ongoing COVID-19 pandemic and future coronavirus outbreaks.
Animals ; Coronavirus Papain-Like Proteases/antagonists & inhibitors* ; Cricetinae ; Humans ; Mice ; Pandemics ; SARS-CoV-2/enzymology* ; COVID-19 Drug Treatment

Genome-scale metabolomics analysis is increasingly used for pathway and function dis-covery in the post-genomics era.The great potential offered by developed mass spectrometry(MS)-based technologies has been hindered,since only a small portion of detected metabolites were iden-tifiable so far.To address the critical issue of low identification coverage in metabolomics,we adopted a deep metabolomics analysis strategy by integrating advanced algorithms and expanded reference databases.The experimental reference spectra and in silico reference spectra were adopted to facilitate the structural annotation.To further characterize the structure of metabolites,two approaches were incorporated into our strategy,i.e.,structural motif search combined with neutral loss scanning and metabolite association network.Untargeted metabolomics analysis was performed on 150 rice cultivars using ultra-performance liquid chromatography coupled with quadrupole-Orbitrap MS.Consequently,a total of 1939 out of 4491 metabolite features in the MS/MS spectral tag(MS2T)library were annotated,representing an extension of annotation coverage by an order of magnitude in rice.The differential accumulation patterns of flavonoids between indica and japon-ica cultivars were revealed,especially O-sulfated flavonoids.A series of closely-related flavonolignans were characterized,adding further evidence for the crucial role of tricin-oligolignols in lignification.Our study provides an important protocol for exploring phytochemical diversity in other plant species.

BACKGROUND:Our previous studies have shown that the poly(hydroxybutyrate- co-hydroxyvalerate) - sol-gel bioactive glass (PHBV-SGBG) has good biocompatibility and promote bone tissue repair, but its specific role in periodontal tissue regeneration has not been investigated. OBJECTIVE:To investigate the periodontal regenerative effects of a PHBV-SGBG scaffold in beagle dogs. METHODS:Alveolar bone defects (5 mm×5 mm) were surgicaly created bilateraly at the buccal side of the mandibular third and fourth premolars of four beagle dogs. PHBV-SGBG scaffold was randomly filed in the defects as experimental group and nothing was put into the contralateral as control group. Histological and scanning electron microscopy observations, cone-beam CT evaluation and the Ca/P concentration ratio analysis were processed at 2, 4, 8 and 12 weeks after surgery. RESULTS AND CONCLUSION:After surgery, the height of the regenerated tissue increased with time in both groups, and the regenerated tissue height in the experiment group was higher than that in the control group (P < 0.05). At 12 weeks after surgery, the Ca/P concentration ratio of the experiment group was close to that in the normal tissue (P > 0.05), but higher than that of the control group (P < 0.05); the histological observation showed that the regenerated tissue of the experimental group was close to the normal tissue, and the regenerated tissue of the control group tended to be mature, with a smal amount of new blood vessels. Under the scanning electron microscope, no scaffold structure was visible in the experimental group with the presence of bone lacuna at 8 weeks after surgery, while in the control group, there was no bone lacuna and obvious osteoblasts; at 12 weeks after surgery, the structure of the regenerated tissue of experimental group was more regular and close to the normal tissue with no remarkable osteoblasts, and in the control group, the regenerated tissue was disordered, with several cavity. These results show that the PHBV-SGBG scaffold can enhance periodontal bone regeneration effectively.

Adolescent ; Adult ; Aged ; Child ; Female ; Follow-Up Studies ; Gastrointestinal Hemorrhage ; complications ; surgery ; Humans ; Hypertension, Portal ; mortality ; surgery ; Male ; Middle Aged ; Renal Veins ; surgery ; Splenic Vein ; surgery ; Time Factors