Predictive model for non-malignant portal vein thrombosis associated with cirrhosis based on inflammatory biomarkers.

BACKGROUND: Portal vein thrombosis (PVT), a complication of liver cirrhosis, is a major public health concern. PVT prediction is the most effective method for PVT diagnosis and treatment. AIM: To develop and validate a nomogram and network calculator based on clinical indicators to predict PVT in patients with cirrhosis. METHODS: Patients with cirrhosis hospitalized between January 2016 and December 2021 at the First Hospital of Lanzhou University were screened and 643 patients with cirrhosis who met the eligibility criteria were retrieved. Following a 1:1 propensity score matching 572 patients with cirrhosis were screened, and relevant clinical data were collected. PVT risk factors were identified using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis. Variance inflation factors and correlation matrix plots were used to analyze multicollinearity among the variables. A nomogram was constructed to predict the probability of PVT based on independent risk factors for PVT, and its predictive performance was verified using a receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA). Finally, a network calculator was constructed based on the nomograms. RESULTS: This study enrolled 286 cirrhosis patients with PVT and 286 without PVT. LASSO analysis revealed 13 variables as strongly associated with PVT occurrence. Multivariate logistic regression analysis revealed nine indicators as independent PVT risk factors, including etiology, ascites, gastroesophageal varices, platelet count, D-dimer, portal vein diameter, portal vein velocity, aspartate transaminase to neutrophil ratio index, and platelet-to-lymphocyte ratio. LASSO and correlation matrix plot results revealed no significant multicollinearity or correlation among the variables. A nomogram was constructed based on the screened independent risk factors. The nomogram had excellent predictive performance, with an area under the ROC curve of 0.821 and 0.829 in the training and testing groups, respectively. Calibration curves and DCA revealed its good clinical performance. Finally, the optimal cutoff value for the total nomogram score was 0.513. The sensitivity and specificity of the optimal cutoff values were 0.822 and 0.706, respectively. CONCLUSION: A nomogram for predicting PVT occurrence was successfully developed and validated, and a network calculator was constructed. This can enable clinicians to rapidly and easily identify high PVT risk groups.

[MicroRNA Methylation in the Diagnosis and Treatment of Hepatocellular Carcinoma].

MicroRNAs,a group of short non-coding RNAs that regulate gene expression at post-transcriptional level,play a role in a variety of cell activities.Methylation is an essential topic in the study of transcriptional regulation at the genomic level.It is associated with diverse diseases such as tumor and aging by regulating gene expression and silencing.Studies have demonstrated that the abnormal methylation of miRNA can regulate the expression of miRNA and affect the expression and function of the target genes,which is a key signal for the occurrence and development of hepatocellular carcinoma.This research achievement provides a new idea for deciphering the molecular mechanism of the pathogenesis of hepatocellular carcinoma and exploring the therapeutic targets.

ADRM1 as a therapeutic target in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC), a primary liver tumor, is the third leading cause of cancer-related mortality worldwide. The proteasome system is overactivated in the majority of tumors, including HCC. However, targeting the proteasome system in HCC is not as effective as in other types of cancer. Therefore, a new target of HCC therapy needs to be identified, and the potential mechanism must be studied. Using the The Cancer Gene Genome Atlas and GEO datasets, the present investigation demonstrated for the first time that ADRM1 is overexpressed in HCC, and the high level of its expression predicts poor overall survival in HCC patients. The high expression of ADRM1 in HCC was verified using tumor tissue arrays. By comparing paired tumor and nontumor tissues, it was shown that the majority of HCC patients (76.25%) exhibited higher ADRM1 expression in the tumor than in normal tissues. in vitro experiments demonstrated that targeting ADRM1 with shRNAs significantly suppressed the proliferation of HCC cells. RA190, a specific inhibitor of ADRM1, suppressed cell proliferation and colony formation by HCC cells in a concentration-dependent manner. The study of the mechanism of the effects of RA190 revealed that targeting ADRM1 blocked the G2/M transition in the cell cycle and induced apoptosis of HCC cells. Together, the obtained results indicate that ADRM1 is a promising target for HCC therapy and suggest that ADRM1 inhibitors, such as RA190, have the potential for clinical application in the treatment of HCC.

[Application of the China Infectious Diseases Automated-alert and Response System in Guangxi, 2009-2011].

OBJECTIVE: To analyze and further improvement the application of the China Infectious Diseases Automated-alert and Response System (CIDARS) in Guangxi Zhuang Autonomous Region. METHODS: Results related to the amount of signal, proportion of signal responded, time to signal response, manner of signal verification and on each signal of Guangxi in CIDARS from 2009 to 2011 were described. Performance was compared between the periods of pre/ post the adjustment of parameters in CIDARS on December 10, 2010. RESULTS: A total of 29 788 signals were generated on 16 infectious diseases in the system in Guangxi. 100% signals had been responded with the median time to response as 1.5 hours. The average amount of signal per county per week was 1.7;with 624 signals(2.09%)verified as suspected outbreaks preliminarily and 191 outbreaks of 9 diseases were finally confirmed by further field investigation. The sensitivity of CIDARS was 89.25% , and the timeliness of detection was 2.8 d. After adjusting the parameter of CIDARS, the number of signals reduced, and the sensitivity and timeliness of detection improved for most of the diseases. CONCLUSION: The signals of CIDARS were responded timely, and the performance of CIDARS might be improved by adjusting the parameters of early-warning model, which helped enhance the ability of outbreaks-detection for local public health departments. However the current proportion of false positive signals still seemed to be high, suggesting that both the methods and parameters should be improved, according to the characteristics of different diseases.

[The application of China Infectious Diseases Automated-alert and Response System in Zhejiang province, 2012].

OBJECTIVE: To analyze and evaluate the application of China Infectious Diseases Automated-alert and Response System(CIDARS)in Zhejiang province. METHODS: Data through the monitoring program in 2012 was analyzed descriptively and compared with the incidence data in the same period as well information related to public health emergency events. RESULTS: A total of 14 292 signals were generated on 28 kinds of infectious diseases in the system, in Zhejiang province. 100% of the signals had been responded and the median time to response was 0.81 hours. 123 signals (0.86%)were preliminarily verified as suspected outbreaks and 33 outbreaks were finally confirmed by further field investigation, with a positive ratio of 0.23% . Information related to regional distribution showed significant differences which reflecting a positive correlation between the numbers of diseases and the time of early-warning(r = 0. 97, P < 0.01). Distribution of information related to different types of diseases was also significantly different, showing a positive correlation between the prevalent strength of the disease and the amount of information in a specific area(r = 0.80, P < 0.01). CONCLUSION: CIDARS had a good performance which could be used to assist the local public health institutions on early detection of possible outbreaks at the early stage. However, the effectiveness was different for different regions and diseases.

[The comparison of two different types of baseline data regarding the performance of aberration detection algorithm for infectious disease outbreaks].

OBJECTIVE: To compare the performance of aberration detection algorithm for infectious disease outbreaks, based on two different types of baseline data. METHODS: Cases and outbreaks of hand-foot-and-mouth disease (HFMD) reported by six provinces of China in 2009 were used as the source of data. Two types of baseline data on algorithms of C1, C2 and C3 were tested, by distinguishing the baseline data of weekdays and weekends. Time to detection (TTD) and false alarm rate (FAR) were adopted as two evaluation indices to compare the performance of 3 algorithms based on these two types of baseline data. RESULTS: A total of 405 460 cases of HFMD were reported by 6 provinces in 2009. On average, each county reported 1.78 cases per day during the weekdays and 1.29 cases per day during weekends, with significant difference (P < 0.01) between them. When using the baseline data without distinguish weekdays and weekends, the optimal thresholds for C1, C2 and C3 was 0.2, 0.4 and 0.6 respectively while the TTD of C1, C2 and C3 was all 1 day and the FARs were 5.33%, 4.88% and 4.50% respectively. On the contrast, when using the baseline data to distinguish the weekdays and weekends, the optimal thresholds for C1, C2 and C3 became 0.4, 0.6 and 1.0 while the TTD of C1, C2 and C3 also appeared equally as 1 day. However, the FARs became 4.81%, 4.75% and 4.16% respectively, which were lower than the baseline data from the first type. CONCLUSION: The number of HFMD cases reported in weekdays and weekends were significantly different, suggesting that when using the baseline data to distinguish weekdays and weekends, the FAR of C1, C2 and C3 algorithm could effectively reduce so as to improve the accuracy of outbreak detection.

[Comparison on the performance of both temporal and temporal-spatial models for outbreak detection through China Infectious Diseases Automated-alert and Response System (CIDARS)].

OBJECTIVE: To analyze the pilot results of both temporal and temporal-spatial models in outbreaks detection in China Infectious Diseases Automated-alert and Response System (CIDARS) to further improve the system. METHODS: The amount of signal, sensitivity, false alarm rate and time to detection regarding these two models of CIDARS, were analyzed from December 6, 2009 to December 5, 2010 in 221 pilot counties of 20 provinces. RESULTS: The sensitivity of these two models was equal (both 98.15%). However, when comparing to the temporal model, the temporal-spatial model had a 59.86% reduction on the signals (15 702) while the false alarm rate of the temporal-spatial model (0.73%) was lower than the temporal model (1.79%), and the time to detection of the temporal-spatial model (0 day) was also 1 day shorter than the temporal model. CONCLUSION: Comparing to the temporal model, the temporal-spatial model of CIDARS seemed to be better performed on outbreak detection.

[Preliminary application on China Infectious Diseases Automated-alert and Response System (CIDARS), between 2008 and 2010].

OBJECTIVE: To analyze the results of application on China Infectious Diseases Automated-alert and Response System (CIDARS) and for further improving the system. METHODS: Amount of signal, proportion of signal responded, time to signal response, manner of signal verification and the outcome of each signal in CIDARS were descriptively analyzed from July 1, 2008 to June 30, 2010. RESULTS: A total of 533 829 signals were generated nationwide on 28 kinds of infectious diseases in the system. 97.13% of the signals had been responded and the median time to response was 1.1 hours. Among them, 2472 signals were generated by the fixed-value detection method which involved 9 kinds of diseases after the preliminary verification, field investigation and laboratory tests. 2202 signals were excluded, and finally 246 cholera cases, 15 plague cases and 9 H5N1 cases as well as 39 outbreaks of cholera were confirmed. 531 357 signals were generated by the other method - the 'moving percentile method' which involved 19 kinds of diseases. The average amount of signal per county per week was 1.65, with 6603 signals (1.24%) preliminarily verified as suspected outbreaks and 1594 outbreaks were finally confirmed by further field investigation. For diseases in CIDARS, the proportion of signals related to suspected outbreaks to all triggered signals showed a positive correlation with the proportion of cases related to outbreaks of all the reported cases (r = 0.963, P < 0.01). CONCLUSION: The signals of CIDARS were responded timely, and the signal could act as a clue for potential outbreaks, which helped enhancing the ability on outbreaks detection for local public health departments.

[The effectiveness of China Infectious Disease Automated-alert and Response System (CIDARS) in the local regions].

OBJECTIVE: To understand the effectiveness of China Infectious Disease Automated-alert and Response System (CIDARS) for outbreak detection at the regional level. METHODS: Two counties in Hunan province (Yuelu and Shuangfeng county) and two counties in Yunnan province (Xishan and Gejiu county) were chosen as the study areas. Data from CIDARS were analyzed on the following items: reported cases, warning signals, the time interval of signal response feedback, way of signal verification, outcome of signal verification and field investigation, from July 1, 2008 to June 30, 2010. RESULTS: In total, 12 346 cases from 28 kinds of diseases were reported, and 2096 signals of 19 diseases were generated by the system, with an average of 4.94 signals per county per week. The median of time interval on signal verification feedback was 0.70 hours (P(25)-P(75): 0.06 - 1.29 h) and the main way of signal preliminary verification was through the review of surveillance data (account for 63.07%). Among all the signals, 34 of them (1.62%) were considered to be related to suspected events via the preliminary verification at the local level. Big differences were found to have existed on the proportion of signals related to the suspected events of the total signals among the four counties, with Shuangfeng county as 4.71%, Yuelu county as 1.88%, Gejiu county as 0.95% and Xishan county as 0.58%. After an indepth study on the fields of suspected events, 12 outbreaks were finally confirmed, including 5 on rubella, 4 on mumps, 2 on influenza and 1 on typhoid fever. CONCLUSION: CIDARS could be used to assist the local public health institutions on early detection of possible outbreaks at the early stage. However, the effectiveness was different depending on the regions and diseases.

[Comparison on the different thresholds on the 'moving percentile method' for outbreak detection].

OBJECTIVE: To compare the different thresholds of 'moving percentile method' for outbreak detection in the China Infectious Diseases Automated-alert and Response System (CIDARS). METHODS: The thresholds of P(50), P(60), P(70), P(80) and P(90) were respectively adopted as the candidates of early warning thresholds on the moving percentile method. Aberration was detected through the reported cases of 19 notifiable infectious diseases nationwide from July 1, 2008 to June 30, 2010. Number of outbreaks and time to detection were recorded and the amount of signals acted as the indicators for determining the optimal threshold of moving percentile method in CIDARS. RESULTS: The optimal threshold for bacillary and amebic dysentery was P(50). For non-cholera infectious diarrhea, dysentery, typhoid and paratyphoid, and epidemic mumps, it was P(60). As for hepatitis A, influenza and rubella, the threshold was P(70), but for epidemic encephalitis B it was P(80). For the following diseases as scarlet fever, typhoid and paratyphoid, hepatitis E, acute hemorrhagic conjunctivitis, malaria, epidemic hemorrhagic fever, meningococcal meningitis, leptospirosis, dengue fever, epidemic endemic typhus, hepatitis C and measles, it was P(90). When adopting the adjusted optimal threshold for 19 infectious diseases respectively, 64 840 (12.20%) signals had a decrease, comparing to the adoption of the former defaulted threshold (P(50)) during the 2 years. However, it did not reduce the number of outbreaks being detected as well as the time to detection, in the two year period. CONCLUSION: The optimal thresholds of moving percentile method for different kinds of diseases were different. Adoption of the right optimal threshold for a specific disease could further optimize the performance of outbreak detection for CIDARS.

[Application of cumulative sum control chart algorithm in the detection of infectious disease outbreaks].

In recent years, for improving the ability of early detection on infectious disease outbreak, many researchers study the disease outbreak detection algorithms, based on many disease surveillance data, expecting to detect the abnormal increasing and cluster of disease and symptom at an early stage by adopting appropriate algorithm. This paper introduces a cumulative sum control chart method, one of statistical process control algorithms widely used in foreign countries and describes its basic principle and characteristic, key points of design, typical examples in application of disease outbreak detection of cumulative sum method, with expect to provide reference for its application in studies of disease outbreak early warning in China.