Objective:To investigate the factors influencing early recurrence for patients with initially unresectable hepatocellular carcinoma (HCC) who underwent salvage liver resection (SLR) following transcatheter arterial chemoembolization-based downstaging treatment, and construct a predictive model to evaluate its predicting performance.Methods:The retrospective cohort study was constructed. The clinicopathological data of 305 patients with initially unresectable HCC who were admitted to 4 medical centers in China, including the Third Affiliated Hospital of Naval Medical University (Shanghai Eastern Hepatobiliary Surgery Hospital) et al, from January 2019 to December 2021 were collected. There were 286 males and 19 females, aged (48.7±10.4)years. A total of 133 patients who were admitted from January 2019 to December 2020 were set as the training cohort, and the other 172 patients who were admitted from January to December 2021 were set as the validation cohort. Observation indicators: (1) postoperative recurrence-free survival in HCC patients; (2) analysis of factors influencing postoperative early recurrence in HCC patients; (3) construction and validation of the predictive model. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data was conducted using the rank sum test. Univariate and multivariate analyses were conducted using the Cox regre-ssion model. The Kaplan-Meier method was used to calculate survival. The Log-rank test was used for survival analysis. The predicting performance of the model was evaluated using the concordance index (C-index) and the area under curve (AUC) of time-dependent receiver operating characteristic (ROC) curve, and the accuracy of the model was validated using the calibration curve. The total net gain of the model was evaluated using the decision curve. Results:(1) Postoperative recurrence-free survival in HCC patients. The recurrence-free survival time of 133 HCC patients in the training cohort was 10.0(range, 1.5-24.0)months, with 1-, 2-year recurrence-free survival rate of 47.3% and 36.8%. The recurrence-free survival time of 172 HCC patients in the validation cohort was 11.0(range, 1.0-24.0)months, with 1-, 2-year recurrence-free survival rate of 51.7% and 37.2%. There was no significant difference in recurrence-free survival between patients in the training cohort and the validation cohort ( χ2=0.075, P>0.05). (2) Analysis of factors influencing postoperative early recur-rence in HCC patients. Results of multivariate analysis showed that tumor burden prior to down-staging treatment, grade of albumin-bilirubin (ALBI) score prior to SLR, alpha-fetoprotein (AFP) half-life prior to SLR, and tumor response prior to SLR were independent factors influencing early recurrence in HCC patients after surgery [ hazard ratio=3.212, 2.526, 2.304, 1.575, 95% confidence interal ( CI) as 1.262-8.175, 1.324-4.818, 1.477-3.595, 1.138-2.180, P<0.05]. (3) Construction and validation of the predictive model. A nomogram predictive model for postoperative early recurrence was constructed base on the results of multivariate analysis. The C-index of predictive model was 0.786 for the training cohort and 0.734 for the validation cohort. The AUC of ROC curve of nomogram predictive model for 12-, 18-, and 24-month recurrence-free survival rate in the training cohort were 0.890 (95% CI as 0.836-0.944), 0.895 (95% CI as 0.842-0.947), and 0.887 (95% CI as 0.831-0.942), respectively. The AUC of ROC curve of nomogram predictive model for 12-, 18-, and 24-month recurrence-free survival rate in the validation cohort were 0.845 (95% CI as 0.781-0.909], 0.888 (95% CI as 0.826-0.950), and 0.919 (95% CI as 0.870-0.968), respectively. Results of calibration curve showed high consistency between the predicted results of nomogram predictive model and actual outcomes. Results of decision curve showed the nomogram predictive model with a good total net gain at a threshold of 0.10-0.50. Conclusions:Tumor burden prior to downstaging treatment, grade of ALBI score prior to SLR, AFP half-life prior to SLR, and tumor response prior to SLR are independent factors influencing early recurrence in initially unresectable HCC patients undergoing SLR following downstaging treatment. The nomogram predictive model based on these factors can effectively evaluate the prognosis of this patient population.

The study investigated the specific mechanism by which oxocrebanine, the anti-hepatic cancer active ingredient in Stephania hainanensis, inhibits the proliferation of hepatic cancer cells. Firstly, methyl thiazolyl tetrazolium(MTT) assay, 5-bromodeoxyuridine(BrdU) labeling, and colony formation assay were employed to investigate whether oxocrebanine inhibited the proliferation of HepG2 and Hep3B2.1-7 cells. Propidium iodide(PI) staining was used to observe the oxocrebanine-induced apoptosis of HepG2 and Hep3B2.1-7 cells. Western blot was employed to verify whether apoptotic effector proteins, such as cleaved cysteinyl aspartate-specific protease 3(c-caspase-3), poly(ADP-ribose) polymerase 1(PARP1), B-cell lymphoma-2(Bcl-2), Bcl-2-associated X protein(Bax), Bcl-2 homologous killer(Bak), and myeloid cell leukemia-1(Mcl-1) were involved in apoptosis. Secondly, HepG2 cells were simultaneously treated with oxocrebanine and the autophagy inhibitor 3-methyladenine(3-MA), and the changes in the autophagy marker LC3 and autophagy-related proteins [eukaryotic translation initiation factor 4E-binding protein 1(4EBP1), phosphorylated 4EBP1(p-4EBP1), 70-kDa ribosomal protein S6 kinase(P70S6K), and phosphorylated P70S6K(p-P70S6K)] were determined. The results of MTT assay, BrdU labeling, and colony formation assay showed that oxocrebanine inhibited the proliferation of HepG2 and Hep3B2.1-7 cells in a dose-dependent manner. The results of flow cytometry suggested that the apoptosis rate of HepG2 and Hep3B2.1-7 cells increased after treatment with oxocrebanine. Western blot results showed that the protein levels of c-caspase-3, Bax, and Bak were up-regulated and those of PARP1, Bcl-2, and Mcl-1 were down-regulated in the HepG2 cells treated with oxocrebanine. The results indicated that oxocrebanine induced apoptosis, thereby inhibiting the proliferation of hepatic cancer cells. The inhibition of HepG2 cell proliferation by oxocrebanine may be related to the induction of protective autophagy in hepatocellular carcinoma cells. Oxocrebanine still promoted the conversion of LC3-Ⅰ to LC3-Ⅱ, reduced the phosphorylation levels of 4EBP1 and P70S6K, which can be reversed by the autophagy inhibitor 3-MA. It is prompted that oxocrebanine can inhibit the proliferation of hepatic cancer cells by inducing autophagy. In conclusion, oxocrebanine inhibits the proliferation of hepatic cancer cells by inducing apoptosis and autophagy.
Humans ; Apoptosis/drug effects* ; Autophagy/drug effects* ; Cell Proliferation/drug effects* ; Hep G2 Cells ; Liver Neoplasms/genetics* ; Carcinoma, Hepatocellular/genetics* ; Caspase 3/genetics*

Objective:To investigate the factors influencing early recurrence for patients with initially unresectable hepatocellular carcinoma (HCC) who underwent salvage liver resection (SLR) following transcatheter arterial chemoembolization-based downstaging treatment, and construct a predictive model to evaluate its predicting performance.Methods:The retrospective cohort study was constructed. The clinicopathological data of 305 patients with initially unresectable HCC who were admitted to 4 medical centers in China, including the Third Affiliated Hospital of Naval Medical University (Shanghai Eastern Hepatobiliary Surgery Hospital) et al, from January 2019 to December 2021 were collected. There were 286 males and 19 females, aged (48.7±10.4)years. A total of 133 patients who were admitted from January 2019 to December 2020 were set as the training cohort, and the other 172 patients who were admitted from January to December 2021 were set as the validation cohort. Observation indicators: (1) postoperative recurrence-free survival in HCC patients; (2) analysis of factors influencing postoperative early recurrence in HCC patients; (3) construction and validation of the predictive model. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data was conducted using the rank sum test. Univariate and multivariate analyses were conducted using the Cox regre-ssion model. The Kaplan-Meier method was used to calculate survival. The Log-rank test was used for survival analysis. The predicting performance of the model was evaluated using the concordance index (C-index) and the area under curve (AUC) of time-dependent receiver operating characteristic (ROC) curve, and the accuracy of the model was validated using the calibration curve. The total net gain of the model was evaluated using the decision curve. Results:(1) Postoperative recurrence-free survival in HCC patients. The recurrence-free survival time of 133 HCC patients in the training cohort was 10.0(range, 1.5-24.0)months, with 1-, 2-year recurrence-free survival rate of 47.3% and 36.8%. The recurrence-free survival time of 172 HCC patients in the validation cohort was 11.0(range, 1.0-24.0)months, with 1-, 2-year recurrence-free survival rate of 51.7% and 37.2%. There was no significant difference in recurrence-free survival between patients in the training cohort and the validation cohort ( χ2=0.075, P>0.05). (2) Analysis of factors influencing postoperative early recur-rence in HCC patients. Results of multivariate analysis showed that tumor burden prior to down-staging treatment, grade of albumin-bilirubin (ALBI) score prior to SLR, alpha-fetoprotein (AFP) half-life prior to SLR, and tumor response prior to SLR were independent factors influencing early recurrence in HCC patients after surgery [ hazard ratio=3.212, 2.526, 2.304, 1.575, 95% confidence interal ( CI) as 1.262-8.175, 1.324-4.818, 1.477-3.595, 1.138-2.180, P<0.05]. (3) Construction and validation of the predictive model. A nomogram predictive model for postoperative early recurrence was constructed base on the results of multivariate analysis. The C-index of predictive model was 0.786 for the training cohort and 0.734 for the validation cohort. The AUC of ROC curve of nomogram predictive model for 12-, 18-, and 24-month recurrence-free survival rate in the training cohort were 0.890 (95% CI as 0.836-0.944), 0.895 (95% CI as 0.842-0.947), and 0.887 (95% CI as 0.831-0.942), respectively. The AUC of ROC curve of nomogram predictive model for 12-, 18-, and 24-month recurrence-free survival rate in the validation cohort were 0.845 (95% CI as 0.781-0.909], 0.888 (95% CI as 0.826-0.950), and 0.919 (95% CI as 0.870-0.968), respectively. Results of calibration curve showed high consistency between the predicted results of nomogram predictive model and actual outcomes. Results of decision curve showed the nomogram predictive model with a good total net gain at a threshold of 0.10-0.50. Conclusions:Tumor burden prior to downstaging treatment, grade of ALBI score prior to SLR, AFP half-life prior to SLR, and tumor response prior to SLR are independent factors influencing early recurrence in initially unresectable HCC patients undergoing SLR following downstaging treatment. The nomogram predictive model based on these factors can effectively evaluate the prognosis of this patient population.

OBJECTIVES: To investigate the clinicopathological characteristics and prognostic factors of pediatric Hodgkin lymphoma (HL). METHODS: A retrospective analysis was conducted on the clinical data of children with newly diagnosed HL from January 2011 to December 2023 at four hospitals: Fujian Medical University Union Hospital, Fujian Medical University Zhangzhou Hospital, First Affiliated Hospital of Xiamen University, and Fujian Children's Hospital. Patients were categorized into low-risk (R1), intermediate-risk (R2), and high-risk (R3) groups based on HL staging and pre-treatment risk factors. The patients received ABVD regimen or Chinese Pediatric HL-2013 regimen chemotherapy. Early treatment response and long-term efficacy were assessed, and prognostic factors were analyzed using the Cox proportional hazards regression model. RESULTS: The overall complete response (CR) rates after 2 and 4 cycles of chemotherapy were 42% and 68%, respectively. Compared with the ABVD regimen group, patients treated with the HL-2013 regimen in the R1 group showed significantly higher CR rates after both 2 and 4 cycles (P<0.05). However, no statistically significant differences in CR rates were observed between the two regimens in the R2 and R3 groups (P>0.05). The 5-year event-free survival (EFS) rate, overall survival rate, and freedom from treatment failure rate were 83%±4%, 97%±2%, and 88%±4%, respectively. Cox analysis indicated that the presence of a large tumor mass at diagnosis and failure to achieve CR after 4 cycles of chemotherapy were independent risk factors for lower EFS rates (P<0.05). CONCLUSIONS Pediatric HL generally has a favorable prognosis. The presence of a large tumor mass at diagnosis and failure to achieve CR after 4 cycles of chemotherapy indicate poor prognosis.
Humans ; Hodgkin Disease/pathology* ; Male ; Child ; Female ; Adolescent ; Retrospective Studies ; Child, Preschool ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Prognosis ; Proportional Hazards Models ; Survival Analysis ; Infant

Background::Clinical opportunistic screening is a cost-effective cancer screening modality. This study aimed to establish an easy-to-use diagnostic model serving as a risk stratification tool for identification of individuals with malignant gastric lesions for opportunistic screening.Methods::We developed a questionnaire-based diagnostic model using a joint dataset including two clinical cohorts from northern and southern China. The cohorts consisted of 17,360 outpatients who had undergone upper gastrointestinal endoscopic examination in endoscopic clinics. The final model was derived based on unconditional logistic regression, and predictors were selected according to the Akaike information criterion. External validation was carried out with 32,614 participants from a community-based randomized controlled trial.Results::This questionnaire-based diagnostic model for malignant gastric lesions had eight predictors, including advanced age, male gender, family history of gastric cancer, low body mass index, unexplained weight loss, consumption of leftover food, consumption of preserved food, and epigastric pain. This model showed high discriminative power in the development set with an area under the receiver operating characteristic curve (AUC) of 0.791 (95% confidence interval [CI]: 0.750–0.831). External validation of the model in the general population generated an AUC of 0.696 (95% CI: 0.570–0.822). This model showed an ideal ability for enriching prevalent malignant gastric lesions when applied to various scenarios.Conclusion::This easy-to-use questionnaire-based model for diagnosis of prevalent malignant gastric lesions may serve as an effective prescreening tool in clinical opportunistic screening for gastric cancer.

Near infrared spectroscopy(NIRS)technology combined with chemometrics algorithms has been widely used in quantitative and qualitative analysis of food and medicine.However,traditional chemometrics methods,especially linear classification methods,often yield unsatisfactory results when addressing multi-class classification problems.Convolutional neural network(CNN)is adept at extracting deep-level features from data and suitable for handling non-linear relationships.The modeling performance of CNN depends on the size and diversity of sample,while the collection and preprocessing of NIRS sample data is often time-consuming and labor-intensive.This study proposed a NIRS qualitative analysis method based on data augmentation strategies and CNN.The data augmentation strategy included two steps.Firstly,applying Bootstrap resampling and generative adversarial network(GAN)methods to augment three NIRS datasets(Medicine,coffee and grape).Secondly,combining the original samples(Y)with the Bootstrap augmented samples(B)and GAN augmented samples(G)to obtain three augmented datasets(Y-B,Y-G and Y-B-G).Based on this,a CNN model structure suitable for these datasets was designed,consisting of 2 one-dimensional convolutional layers,1 max-pooling layer,and 1 fully connected layer.The results showed that compared to the optimal models of partial least squares discriminant analysis(PLS-DA),support vector machine(SVM),and back propagation neural network(BP),the CNN model based on Y-B dataset achieved average accuracy improvements of 3.998%,9.364%,and 4.689%for medicine(Binary classification);the CNN model based on the Y-B-G dataset achieved average accuracy improvements of 6.001%,2.004%,and 7.523%for coffee(7-class classification);and the CNN model based on the Y-B dataset achieved average accuracy improvements of 33.408%,51.994%,and 34.378%for grapes(20-class classification).It was evident that the models established based on data augmentation strategies and CNN demonstrated better classification accuracy and generalization performance with different datasets and classification categories.

Hip fracture in the elderly is characterized by high incidence, high disability rate, and high mortality and has been recognized as a public health issue threatening their health. Surgery is the preferred choice for the treatment of elderly patients with hip fracture. However, lower extremity deep venous thrombosis (DVT) has an extremely high incidence rate during the perioperative period, and may significantly increase the risk of patients′ death once it progresses to pulmonary embolism. In response to this issue, the clinical guidelines and expert consensuses all emphasize active application of comprehensive preventive measures, including basic prevention, physical prevention, and pharmacological prevention. In this prevention system, basic prevention is the basis of physical and pharmacological prevention. However,there is a lack of unified and definite recommendations for basic preventive measures in clinical practice. To this end, the Orthopedic Nursing Professional Committee of the Chinese Nursing Association and Nursing Department of the Orthopedic Branch of the China International Exchange and Promotive Association for Medical and Health Care organized relevant nursing experts to formulate Expert consensus on perioperative basic prevention for lower extremity deep venous thrombosis in elderly patients with hip fracture ( version 2024) . A total of 10 recommendations were proposed, aiming to standardize the basic preventive measures for lower extremity DVT in elderly patients with hip fractures during the perioperative period and promote their subsequent rehabilitation.

The aim of our research was to obtain Listeria bacteriophages from food and related environments,and conduc-ted the analysis of the electron microscopic morphology,host range specificity,and biological characteristics of the purified phages.The double-layer agar method and the spot test were employed for the isolation and identification of a virulent Listeria phage named LMLPA5,with the isolated strain Listeria in-nocua Lin08 as the host.Phage morphology was observed by transmission electron microscope.The biological characteris-tics of the phage were assessed by determining their host range,optimal multiplicity of infection(MOI),one-step growth curve,and physicochemical stability.Additionally,the preservation efficacy of the phage at 4 ℃,-20 ℃,and-80 ℃ was explored.The phage LMLPA5 belongs to the family Myoviridae based on morphology,exhibiting clear and transparent plaques without halo surrounded.Strains of sever-al Listeria species and different serotypes strains of Listeria monocytogenes were susceptible to lysis by LMLPA5,indica-ting its broad-spectrum activity against Listeria monocytogenes.Optimal MOIs and single-step growth curve analyses revealed optimal MOIs of 0.1 and latent period of 10 minutes for LMLPA5,with average burst size at 95.2 PFU/cell.LMLPA5 was sensitive to high temperatures,and completely inactivated after exposure to 70 ℃ for 1 h,while the phage remained stable for over 32 hours ranging from 4 ℃ to 40 ℃.Within the pH range of 4 to 10,phage titer remained stable and completely inactiva-ted until 60 minutes of ultraviolet exposure.LMLPA5 displayed insensitivity to chloroform,confirming its non-enveloped phage morphology.The phages remained stable for over 8 months when store at 4 ℃ and-80 ℃.The biological characteristics and lysis capacity of phage LMLPA5 were elucidated in this study,which provide the basis for further application.

The objective of this study was to determine the frequency and resistance patterns of ESBL-producing E.coli in lambs with diarrhea in the Kashi area,Xinjiang.The findings may provide guidance for the prevention and control of clinical E.coli disease.We collected 385 samples of perianal feces from lambs with diarrhea in the Kashgar area.From these samples,we isolated 371 strains of E.coli.We then used the double-paper-sheet synergistic method to screen for ESBL-producing E.coli.Additionally,we conducted analyses to identify drug-resistance genes,analyze drug resistance,and study the phylo-typing of the screened strains.Of 371 E.coli strains,204 were identified as ESBL-producing strains.The prevalence rates of blaCTX-M,blaCTX-M-1G,blaCTX-M-9G,and bla TEM resistance genes was 67.65％,69.12％,30.39％,and 63.73％,respectively.All ESBL-pro-ducing strains were resistant to multiple drugs,with resistance rates ranging from 90.69％to 100％for eight specific drugs:ampicillin,cefotaxime,gentamicin,enrofloxacin,azithromy-cin,tetracycline,chloramphenicol,methotrexate,and amitrazine.The phylogenetic subgroups of the strains were distributed primarily in groups A and D.Among group A strains,41.11％exhibited resistance to ten drugs,whereas among group D strains,40％exhibited resistance to 11 drugs.ESBL-pro-ducing strains of Escherichia coli are the main pathogens cau-sing diarrhea in lambs in the Kashgar region;group A is the main group,and all groups are multi-drug resistant.

Sustained and controlled release preparation is ideal for reducing the side effects of drugs, improving patient compliance and enhancing efficacy, among which oral sustained-release tablets are the most widely used. The in vitro release of the preparation is closely related to the in vivo absorption of the drug. However, current in vitro release experiments are labor-intensive and destructive, and the lack of big data also makes it difficult to establish good in vivo and in vitro correlations. Computer modeling, as a technical means that can transform objective principles into mathematical models, has great prospects in data prediction. This review explores the existing computer modeling methods that can be used to predict in vitro release profiles of oral sustained-release tablets, and further discusses auxiliary technologies that can improve the accuracy of the models, providing new ideas for drug development.