OBJECTIVETo study the effect of the sphingosine kinase 1 (SphK1) inhibitor N,N-dimethylsphingosine (DMS) in combination with chemotherapeutic drugs (DDP, 5-Fu, MMC) on the proliferation of gastric cancer cells (SGC7901) in vitro, and to evaluate whether SphK1 inhibitors could be used as synergetic agents in chemotherapy.

METHODSSGC7901 cells were incubated in vitro with DMS (1 micromol/L) and 5-Fu, DDP, MMC at different concentrations in combination or separately for 24 h. The effects on the growth and survival of SGC7901 cells were determined by MTT assay. The inhibition rates were assessed by response surface analysis and the interactive relationships between the combined drugs were evaluated on the basis of positive/negative values of the cross product coefficients in the response surface equation.

RESULTSThe growth inhibition rate of the gastric cancer cells by treatment with DMS (1 micromol/L) was (10.23 +/- 0.74)%. The growth inhibition rates of the gastric cancer cells treated with 5-Fu (1, 5 and 25 microg/ml) for 24 h were (9.95 +/- 3.24)%, (21.04 +/- 2.19)%, and (45.49 +/- 3.60)%, respectively. The growth inhibition rates of the gastric cancer cells treated with DDP (0.5, 2.5 and 12.5 microg/ml) for 24 h were (9.38 +/- 0.79)%, (19.61 +/- 0.90)%, and (29.83 +/- 0.54)%, respectively. The growth inhibition rates of the gastric cancer cells treated with MMC (0.1, 0.5 and 2.5 microg/ml) for 24 h were (15.35 +/- 0.77)%, (24.72 +/- 0.83)%, and (30.68 +/- 0.28)%, respectively. There were significant differences among the inhibition rates caused by different concentrations of the drugs (P < 0.05). When 1 micromol/L DMS was used in combination with 5-Fu (1, 5, and 25 microg/ml) for 24 h, the growth inhibition rates of the cancer cells were (16.76 +/- 0.41)%, (27.28 +/- 0.29)% and (52.56 +/- 3.60)%, respectively. When 1 micromol/L DMS was used in combination with DDP (0.5, 2.5, and 12.5 microg/ml) for 24 h, the growth inhibition rates of the cancer cells were (15.35 +/- 0.86)%, (25.57 +/- 0.27)%, (36.37 +/- 0.51)%, respectively. When 1 micromol/L DMS was used in combination with MMC (0.1, 0.5, and 2.5 microg/ml) for 24 h, the growth inhibition rates of the cancer cells were (21.02 +/- 0.28)%, (32.10 +/- 0.27)%, (36.36 +/- 0.28)%, respectively. There were also significant differences among the growth inhibition rates caused by different concentrations of the drugs alone and in combination groups (P < 0.05).

CONCLUSIONSDMS can suppress the proliferation of SGC7901 cells in vitro, and there are evident synergetic effects when it is used in combination with chemotherapeutic drugs. The results of this study indicate that SphK1 inhibitors may become novel and promising chemotherapeutic sensitizers.

Antibiotics, Antineoplastic ; pharmacology ; Antimetabolites, Antineoplastic ; pharmacology ; Antineoplastic Agents ; pharmacology ; Cell Line, Tumor ; Cell Proliferation ; drug effects ; Cisplatin ; pharmacology ; Drug Synergism ; Enzyme Inhibitors ; pharmacology ; Fluorouracil ; pharmacology ; Humans ; Mitomycin ; pharmacology ; Phosphotransferases (Alcohol Group Acceptor) ; antagonists & inhibitors ; Sphingosine ; analogs & derivatives ; pharmacology ; Stomach Neoplasms ; pathology

BACKGROUNDIn vivo quantification of choroidal neovascularization (CNV) based on noninvasive optical coherence tomography (OCT) examination and in vitro choroidal flatmount immunohistochemistry stained of CNV currently were used to evaluate the process and severity of age-related macular degeneration (AMD) both in human and animal studies. This study aimed to investigate the correlation between these two methods in murine CNV models induced by subretinal injection.

METHODSCNV was developed in 20 C57BL6/j mice by subretinal injection of adeno-associated viral delivery of a short hairpin RNA targeting sFLT-1 (AAV.shRNA.sFLT-1), as reported previously. After 4 weeks, CNV was imaged by OCT and fluorescence angiography. The scaling factors for each dimension, x, y, and z (μm/pixel) were recorded, and the corneal curvature standard was adjusted from human (7.7) to mice (1.4). The volume of each OCT image stack was calculated and then normalized by multiplying the number of voxels by the scaling factors for each dimension in Seg3D software (University of Utah Scientific Computing and Imaging Institute, available at http://www.sci.utah.edu/cibc-software/seg3d.html). Eighteen mice were prepared for choroidal flatmounts and stained by CD31. The CNV volumes were calculated using scanning laser confocal microscopy after immunohistochemistry staining. Two mice were stained by Hematoxylin and Eosin for observing the CNV morphology.

RESULTSThe CNV volume calculated using OCT was, on average, 2.6 times larger than the volume calculated using the laser confocal microscopy. The correlation statistical analysis showed OCT measuring of CNV correlated significantly with the in vitro method (R 2 =0.448, P = 0.001, n = 18). The correlation coefficient for CNV quantification using OCT and confocal microscopy was 0.693 (n = 18, P = 0.001).

CONCLUSIONSThere is a fair linear correlation on CNV volumes between in vivo and in vitro methods in CNV models induced by subretinal injection. The result might provide a useful evaluation of CNV both for the studies using CNV models induced by subretinal injection and human AMD studies.

Animals ; Choroidal Neovascularization ; pathology ; physiopathology ; Disease Models, Animal ; Fluorescein Angiography ; Humans ; Mice ; Mice, Inbred C57BL ; Tomography, Optical Coherence

Objective:To investigate the diagnostic accuracy of serological indicators and evaluate the diagnostic value of a new established combined serological model on identifying the minimal hepatic encephalopathy (MHE) in patients with compensated cirrhosis.Methods:This prospective multicenter study enrolled 263 compensated cirrhotic patients from 23 hospitals in 15 provinces, autonomous regions and municipalities of China between October 2021 and August 2022. Clinical data and laboratory test results were collected, and the model for end-stage liver disease (MELD) score was calculated. Ammonia level was corrected to the upper limit of normal (AMM-ULN) by the baseline blood ammonia measurements/upper limit of the normal reference value. MHE was diagnosed by combined abnormal number connection test-A and abnormal digit symbol test as suggested by Guidelines on the management of hepatic encephalopathy in cirrhosis. The patients were randomly divided (7∶3) into training set ( n=185) and validation set ( n=78) based on caret package of R language. Logistic regression was used to establish a combined model of MHE diagnosis. The diagnostic performance was evaluated by the area under the curve (AUC) of receiver operating characteristic curve, Hosmer-Lemeshow test and calibration curve. The internal verification was carried out by the Bootstrap method ( n=200). AUC comparisons were achieved using the Delong test. Results:In the training set, prevalence of MHE was 37.8% (70/185). There were statistically significant differences in AMM-ULN, albumin, platelet, alkaline phosphatase, international normalized ratio, MELD score and education between non-MHE group and MHE group (all P<0.05). Multivariate Logistic regression analysis showed that AMM-ULN [odds ratio ( OR)=1.78, 95% confidence interval ( CI) 1.05-3.14, P=0.038] and MELD score ( OR=1.11, 95% CI 1.04-1.20, P=0.002) were independent risk factors for MHE, and the AUC for predicting MHE were 0.663, 0.625, respectively. Compared with the use of blood AMM-ULN and MELD score alone, the AUC of the combined model of AMM-ULN, MELD score and education exhibited better predictive performance in determining the presence of MHE was 0.755, the specificity and sensitivity was 85.2% and 55.7%, respectively. Hosmer-Lemeshow test and calibration curve showed that the model had good calibration ( P=0.733). The AUC for internal validation of the combined model for diagnosing MHE was 0.752. In the validation set, the AUC of the combined model for diagnosing MHE was 0.794, and Hosmer-Lemeshow test showed good calibration ( P=0.841). Conclusion:Use of the combined model including AMM-ULN, MELD score and education could improve the predictive efficiency of MHE among patients with compensated cirrhosis.

Objective:To investigate the diagnostic value of independent and combined subtests of the psychometric hepatic encephalopathy score (PHES) in mild hepatic encephalopathy(MHE) of patients with liver cirrhosis, so as to optimize the PHES.Methods:This was a prospective, multicenter and real-world study which was sponsored by the National Clinical Research Center of Infectious Diseases and the Portal Hypertension Consortium. Twenty-six hospitals from 13 provinces, autonomous regions and municipalities countrywide participated in this study, induding Tianjin Third Central Hospital, the Fourth People′s Hospital of Qinghai Province, the Second Affiliated Hospital of Baotou Medical College, the Third People′s Hospital of Taiyuan, the Fifth Medical Center of PLA General Hospital and so on. From October 2021 to February 2022, outpatients and hospitalized patients with liver cirrhosis and no obvious hepatic encephalopathy were consecutively enrolled. All patients received 5 PHES subjects in the same order: number connection test(NCT)-A, NCT-B, digit symbol test(DST), line tracing test(LTT) and serial dotting test(SDT), and the scores were calculated. The total score of PHES <-4 was taken as the cut-off value for diagnosing MHE. Compare the differences in each subtest between MHE group and non-MHE group. Receiver operating characteristic curve(ROC) and area under the curve(AUC) was performed to assess the diagnostic value of independent and combined subtests in MHE. Mann-Whitney U test and DeLong test were used for statistical analysis. Results:A total of 581 patients with liver cirrhosis were enrolled, 457 were diagnosed as MHE, and the incidence of MHE was 78.7%. The results of NCT-A, NCT-B, SDT, LTT, DST of MHE group were 60.00 s(47.01 s, 88.00 s), 90.45 s(69.32 s, 125.35 s), 74.00 s(57.65 s, 96.60 s), 74.72(60.00, 98.61) and 27.00(20.00, 36.00), respectively. Compared those of non-MHE group(34.00 s(29.15 s, 44.48 s), 50.00 s(40.98 s, 60.77 s), 50.00 s(41.07 s, 63.03 s), 46.23(38.55, 59.42) and 42.00(34.00, 50.75)), the differences were statistically significant( Z=12.37, 12.98, 9.83, 11.56, 10.66; all P<0.001). The AUC(95% confidence interval(95% CI)) of subtests of PHES NCT-B, NCT-A, LTT, DST and SDT alone in MHE diagnosis were 0.880(0.849 to 0.910), 0.862(0.828 to 0.896), 0.838(0.799 to 0.877), 0.812(0.772 to 0.851) and 0.788(0.743 to 0.832), respectively. The combination of 2 PHES subtests significantly increased the diagnostic efficacy. Among them the diagnostic efficacy of the combination of NCT-B and LTT was the best, the AUC(95% CI) was 0.924(0.902 to 0.947), the specificity was 91.9% and the sensitivity was 79.2%, which was better than a single PHES subtest (NCT-A, NCT-B, SDT, LTT and DST) and the combination of NCT-A and DST(AUC was 0.879, 95% CI0.847 to 0.910) which was recommended by guidelines on the management of hepatic encephalopathy in cirrhosis, the differences were statistically significant ( Z=3.78, 3.83, 5.57, 5.51, 5.38, 2.93; all P<0.01). Furthermore, compared between the combination of NCT-B and LTT and the combination of 3 subests of PHES, only the diagnostic efficacy of combination of NCT-B, LTT and SDT (AUC was 0.936, 95% CI 0.916 to 0.956) was better than that of the combination of NCT-B and LTT, the difference was statistically significant( Z=2.32, P=0.020). Conclusion:Based on the diagnostic efficacy and clinical feasibility of PHES subtests and their combinations, the combination of NCT-B and LTT is recommended for the diagnosis of MHE.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.