Derivation and external validation of a model to predict 2-year mortality risk of patients with advanced schistosomiasis after discharge

Impact of historical disease conditions on mortality and life expectancy in patients with advanced schistosomiasis in Hunan Province, China

BACKGROUND

Although the prognosis of advanced schistosomiasis patients has significantly improved, the impact of historical disease conditions on life expectancy remains unclear.

METHODS

Utilizing data from an advanced schistosomiasis cohort (n=10 362) from 2008 to 2019 in Hunan, China, we examined five historical disease conditions: times of praziquantel treatment, the history of ascites, splenectomy, upper gastrointestinal bleeding (UGIB) and hepatic coma. Using latent class analysis, participants were categorized into three groups: Group 1 (characterized by no risk conditions), Group 2 (had ≤3 times of praziquantel treatment without UGIB history) and Group 3 (had UGIB history). Life expectancies were calculated using the life table method.

RESULTS

At the age of 45 y, patients with ≤3 times of praziquantel treatment, a history of ascites, UGIB, hepatic coma and those without splenectomy exhibited lower life expectancies. Groups 1, 2 and 3 had estimated life expectancies of 32.32, 26.76 and 25.38 y, respectively. Compared with Group 1, women in Group 3 experienced greater life expectancy loss than those in Group 2, with the difference narrowing with age.

CONCLUSIONS

Based on the consideration of overall physical conditions, tailored treatment and healthcare, along with public health interventions targeting diverse populations, could mitigate the prevalence of poor disease conditions and premature deaths.

High-throughput autoantibody profiling of different stages of Schistosomiasis japonica

Infection by the Schistosoma japonicum can result in acute, chronic and late-stage manifestations. The latter often presents with severe organ failures and premature death. Importantly, infection can also produce autoimmune phenomena reflected by the development of autoantibodies. We wished to explore and profile the presence of autoantibodies in sera of patients with different stages of S. japonicum infection with the added aim of providing a reference assisting diagnosis. Blood samples from 55 patients with chronic and 20 patients with late-stage schistosomiasis japonica together, with a control group of 50 healthy people were randomly investigated against a microarray of 121 different autoantigens. In addition, the frequency of antibodies against Schistosoma egg antigen (SEA) was examined. In the sera from patients with chronic schistosomiasis japonica, 14 different highly expressed autoantibodies were detected, while patients with late-stage schistosomiasis were found to express as many as 43 autoantibody specificities together with a significantly higher frequency of antibodies against SEA compared to the control group. The findings presented suggest that autoantibody-based classification of schistosomiasis japonica represents a promising approach for the elucidation of subtypes of the disease. This approach may reflect differential disease mechanisms, which could ultimately lead to better treatment.

Single-cell RNA sequencing reveals a peripheral landscape of immune cells in Schistosomiasis japonica

BACKGROUND

Schistosomiasis, also known as bilharzia, is a devastating parasitic disease. This progressive and debilitating helminth disease is often associated with poverty and can lead to chronic poor health. Despite ongoing research, there is currently no effective vaccine for schistosomiasis, and praziquantel remains the only available treatment option. According to the progression of schistosomiasis, infections caused by schistosomes are classified into three distinct clinical phases: acute, chronic and advanced schistosomiasis. However, the underlying immune mechanism involved in the progression of schistosomiasis remains poorly understood.

METHODS

We employed single-cell RNA sequencing (scRNA-seq) to profile the immune landscape of Schistosomiasis japonica infection based on peripheral blood mononuclear cells (PBMCs) from a healthy control group (n = 4), chronic schistosomiasis group (n = 4) and advanced schistosomiasis group (n = 2).

RESULTS

Of 89,896 cells, 24 major cell clusters were ultimately included in our analysis. Neutrophils and NK/T cells accounted for the major proportion in the chronic group and the healthy group, and monocytes dominated in the advanced group. A preliminary study showed that NKT cells were increased in patients with schistosomiasis and that CXCR2 + NKT cells were proinflammatory cells. Plasma cells also accounted for a large proportion of B cells in the advanced group. MHC molecules in monocytes were notably lower in the advanced group than in the chronic group or the healthy control group. However, monocytes in the advanced group exhibited high expression of FOLR3 and CCR2.

CONCLUSIONS

Overall, this study enhances our understanding of the immune mechanisms involved in schistosomiasis. It provides a transcriptional atlas of peripheral immune cells that may contribute to elimination of the disease. This preliminary study suggests that the increased presence of CCR2 + monocyte and CXCR2 + NKT cells might participate in the progression of schistosomiasis.

Network Analysis and Nomogram in the Novel Classification and Prognosis Prediction of Advanced Schistosomiasis Japonica

Clinical classification of advanced schistosomiasis japonica is important for treatment options and prognosis prediction. Network analysis was used to solve the problem of complexity and co-occurrence complications in classification of advanced schistosomiasis. A total of 4,125 retrospective patients were enrolled and divided randomly into a training cohort (n = 2,888) and a validation cohort (n = 1,237). Network analysis was used to cluster the isolated complications of advanced schistosomiasis. The accuracy of the network was evaluated. Nomograms based on the clustered complications were built to predict 1- to 5-year survival rates in advanced schistosomiasis. The predictive performance of the nomogram was also evaluated and validated. Fifteen isolated complications were identified: metabolic syndromes, minimal hepatic encephalopathy, hepatic encephalopathy, chronic obstructive pulmonary disease, pulmonary hypertension, respiratory failure, right heart failure, gastroesophageal variceal bleeding, gastrointestinal ulcer bleeding, splenomegaly, fibrosis, chronic kidney disease, ascites, colorectal polyp, and colorectal cancer. Through network analysis, three major clustered complications were achieved-namely, schistosomal abnormal metabolic syndromes (related to chronic metabolic abnormalities), schistosomal abnormal hemodynamics syndromes (related to severe portal hypertension and portosystemic shunting), and schistosomal inflammatory granulomatous syndromes (related to granulomatous inflammation). The nomograms showed a good performance in prognosis prediction of advanced schistosomiasis. The novel classification-based nomogram was useful in predicting the survival rate in advanced schistosomiasis japonica.

A Nomogram for Predicting Prognosis of Advanced Schistosomiasis japonica in Dongzhi County-A Case Study

BACKGROUNDS

Advanced schistosomiasis is the late stage of schistosomiasis, seriously jeopardizing the quality of life or lifetime of infected people. This study aimed to develop a nomogram for predicting mortality of patients with advanced schistosomiasis japonica, taking Dongzhi County of China as a case study.

METHOD

Data of patients with advanced schistosomiasis japonica were collected from Dongzhi Schistosomiasis Hospital from January 2019 to July 2022. Data of patients were randomly divided into a training set and validation set with a ratio of 7:3. Candidate variables, including survival outcomes, demographics, clinical features, laboratory examinations, and ultrasound examinations, were analyzed and selected by LASSO logistic regression for the nomogram. The performance of the nomogram was assessed by concordance index (C-index), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). The calibration of the nomogram was evaluated by the calibration plots, while clinical benefit was evaluated by decision curve and clinical impact curve analysis.

RESULTS

A total of 628 patients were included in the final analysis. Atrophy of the right liver, creatinine, ascites level III, N-terminal procollagen III peptide, and high-density lipoprotein were selected as parameters for the nomogram model. The C-index, sensitivity, specificity, PPV, and NPV of the nomogram were 0.97 (95% [CI]: [0.95-0.99]), 0.78 (95% [CI]: [0.64-0.87]), 0.97 (95% [CI]: [0.94-0.98]), 0.78 (95% [CI]: [0.64-0.87]), 0.97 (95% [CI]: [0.94-0.98]) in the training set; and 0.98 (95% [CI]: [0.94-0.99]), 0.86 (95% [CI]: [0.64-0.96]), 0.97 (95% [CI]: [0.93-0.99]), 0.79 (95% [CI]: [0.57-0.92]), 0.98 (95% [CI]: [0.94-0.99]) in the validation set, respectively. The calibration curves showed that the model fitted well between the prediction and actual observation in both the training set and validation set. The decision and the clinical impact curves showed that the nomogram had good clinical use for discriminating patients with high risk of death.

CONCLUSIONS

A nomogram was developed to predict prognosis of advanced schistosomiasis. It could guide clinical staff or policy makers to formulate intervention strategies or efficiently allocate resources against advanced schistosomiasis.

Single-cell RNA sequencing to dissect the immunological network of liver fibrosis in Schistosoma japonicum-infected mice

Introduction

Liver fibrosis is a poor outcome of patients with schistosomiasis, impacting the quality of life and even survival. Eggs deposited in the liver were the main pathogenic factors of hepatic fibrosis in Schistosomiasis japonica. However, the mechanism of hepatic fibrosis in schistosomiasis remains not well defined and there is no effective measure to prevent and treat schistosome-induced hepatic fibrosis.

Methods

In this study, we applied single-cell sequencing to primarily explore the mechanism of hepatic fibrosis in murine schistosomiasis japonica (n=1) and normal mouse was served as control (n=1).

Results

A total of 10,403 cells were included in our analysis and grouped into 18 major cell clusters. Th2 cells and NKT cells were obviously increased and there was a close communication between NKT cells and FASLG signaling pathway. Flow cytometry analysis indicated that the expression of Fasl in NKT cells, CD8+ T cell and NK cell were higher in SJ groups. Arg1, Retnla and Chil3, marker genes of alternatively activated macrophages (M2), were mainly expressed in mononuclear phagocyte(1) (MP(1)), suggesting that Kupffer cells might undergo M2-like polarization in fibrotic liver of schistosomiasis. CXCL and CCL signaling pathway analysis with CellChat showed that Cxcl16-Cxcr6, Ccl6-Ccr2 and Ccl5-Ccr5 were the most dominant L-R and there were close interactions between T cells and MPs.

Conclusion

Our research profiled a preliminary immunological network of hepatic fibrosis in murine schistosomiasis japonica, which might contribute to a better understanding of the mechanisms of liver fibrosis in schistosomiasis. NKT cells and CXCL and CCL signaling pathway such as Cxcl16-Cxcr6, Ccl6-Ccr2 and Ccl5-Ccr5 might be potential targets to alleviate hepatic fibrosis of schistosomiasis.

Potential application of nanotechnology in the treatment, diagnosis, and prevention of schistosomiasis

Schistosomiasis is one of the neglected tropical diseases that affect millions of people worldwide. Globally, it affects economically poor countries, typically due to a lack of proper sanitation systems, and poor hygiene conditions. Currently, no vaccine is available against schistosomiasis, and the preferred treatment is chemotherapy with the use of praziquantel. It is a common anti-schistosomal drug used against all known species of Schistosoma. To date, current treatment primarily the drug praziquantel has not been effective in treating Schistosoma species in their early stages. The drug of choice offers low bioavailability, water solubility, and fast metabolism. Globally drug resistance has been documented due to overuse of praziquantel, Parasite mutations, poor treatment compliance, co-infection with other strains of parasites, and overall parasitic load. The existing diagnostic methods have very little acceptability and are not readily applied for quick diagnosis. This review aims to summarize the use of nanotechnology in the treatment, diagnosis, and prevention. It also explored safe and effective substitute approaches against parasitosis. At this stage, various nanomaterials are being used in drug delivery systems, diagnostic kits, and vaccine production. Nanotechnology is one of the modern and innovative methods to treat and diagnose several human diseases, particularly those caused by parasite infections. Herein we highlight the current advancement and application of nanotechnological approaches regarding the treatment, diagnosis, and prevention of schistosomiasis.

Application of kNN and SVM to predict the prognosis of advanced schistosomiasis

Predictive models for prognosis of small sample advanced schistosomiasis patients have not been well studied. We aimed to construct prognostic predictive models of small sample advanced schistosomiasis patients using two machine learning algorithms, k nearest neighbour (kNN) and support vector machine (SVM) utilising routinely available data under the government medical assistance programme. The predictive models were derived from 229 patients from Xiantao and externally validated by 77 patients of Jiayu, two county-level cities in Hubei province, China. Candidate predictors were selected according to expert opinions and literature reports, including clinical features, sociodemographic characteristics, and medical examinations results. An area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the models' predictive performances. The AUC values were 0.879 for the kNN model and 0.890 for the SVM model in the training set, 0.852 for the kNN model, and 0.785 for the SVM model in the external validation set. The kNN and SVM models can be used to improve the health services provided by healthcare planners, clinicians, and policymakers.

Development of New Technologies for Risk Identification of Schistosomiasis Transmission in China

Schistosomiasis is serious parasitic disease with an estimated global prevalence of active infections of more than 190 million. Accurate methods for the assessment of schistosomiasis risk are crucial for schistosomiasis prevention and control in China. Traditional approaches to the identification of epidemiological risk factors include pathogen biology, immunology, imaging, and molecular biology techniques. Identification of schistosomiasis risk has been revolutionized by the advent of computer network communication technologies, including 3S, mathematical modeling, big data, and artificial intelligence (AI). In this review, we analyze the development of traditional and new technologies for risk identification of schistosomiasis transmission in China. New technologies allow for the integration of environmental and socio-economic factors for accurate prediction of the risk population and regions. The combination of traditional and new techniques provides a foundation for the development of more effective approaches to accelerate the process of schistosomiasis elimination.

Sodium butyrate ameliorates Schistosoma japonicum-induced liver fibrosis by inhibiting HMGB1 expression

Schistosomiasis is a prevalent zoonotic parasitic disease caused by schistosomes. Its main threat to human health is hepatic granuloma and fibrosis due to worm eggs. Praziquantel remains the first choice for the treatment of schistosomiasis but has limited benefit in treating liver fibrosis. Therefore, the need to develop effective drugs for treating schistosomiasis-induced hepatic fibrosis is urgent. High-mobility group box 1 protein (HMGB1) is a potential immune mediator that is highly associated with the development of some fibrotic diseases and may be involved in the liver pathology of schistosomiasis. We speculated that HMGB1 inhibitors could have an anti-fibrotic effect. Sodium butyrate (SB), a potent inhibitor of HMGB1, has shown anti-inflammatory activity in some animal disease models. In this study, we evaluated the effects of SB on a murine schistosomiasis model. Mice were percutaneously infected with 20 ± 2 cercariae of Schistosoma japonicum. SB (500 mg/kg/day) was administered every 3 days for the entire experiment period. The activity of alanine aminotransferase (ALT) and aspartate aminotransferase (AST), liver histopathology, HMGB1 expression, and the levels of interferon gamma (IFN-γ), transforming growth factor-β1 (TGF-β1), and interleukin-6 (IL-6) in serum were analyzed. SB reduced hepatic granuloma and fibrosis of schistosomiasis, reflected by the decreased levels of ALT and AST in serum and the reduced expression of pro-inflammatory and fibrogenic cytokines (IFN-γ, TGF-β1, and IL-6). The protective effect could be attributable to the inhibition of the expression of HMGB1 and release by SB.

Artificial neural network and decision tree models of post-stroke depression at 3 months after stroke in patients with BMI ≥ 24

OBJECTIVE

Previous studies have shown that excess weight (including obesity and overweight) can increase the risk of cardiovascular, cerebrovascular and other diseases, but there is no study on the incidence of post-stroke depression (PSD) and related factors in patients with excessive weight. The main purpose of this study was to find related factors of PSD at 3 months after stroke in patients with excessive weight and construct artificial neural network (ANN) and decision tree (DT) models.

METHODS

This is a prospective multicenter cohort study (Registration number: ChiCTR-ROC-17013993). Five hundred and three stroke patients with Body Mass Index(BMI) ≥ 24 were included in this study. The diagnostic criteria of PSD is according to the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-V) diagnostic criteria for depression due to other medical conditions and the HAMD-17 scores > 7 at 3 months after stroke was used as the primary endpoint. The χ² test, Mann-Whitney U test or t-test were used to check for statistical significance.

RESULTS

Our study found that sleeping time < 5 h, CHD, physical exercise, BI score, N dimension(EPQ) and subjective support(SSRS) were associated with PSD in patients with excessive weight. Physical exercise(odd ratio [OR] = 0.49, p = 0.001, 95%CI [confidence interval]: 0.32-0.75) and BI score(OR = 0.99, p < 0.001, 95%CI: 0.98-0.99) were protective factors; sleeping time < 5 h(OR = 2.86, p < 0.001, 95%CI: 1.62-5.04), CHD(OR = 2.18, p = 0.018, 95%CI: 1.14-4.15), N dimension(OR = 1.08, p = 0.001, 95%CI: 1.03-1.13) and subjective support(OR = 1.04, p = 0.022, 95%CI: 1.01-1.07) were risk factors.

CONCLUSION

This study found several factors related to the occurrence of PSD at 3 months in patients with excessive weight. Meanwhile, ANN and DT models were constructed for clinicians to use.

SMASH-U aetiological classification: A predictor of long-term functional outcome after intracerebral haemorrhage

BACKGROUND

SMASH-U is a systematic aetiological classification system for intracerebral haemorrhage (ICH) proven to be a predictor of post-ICH haematoma expansion and mortality. However, its role in predicting functional outcome remains elusive. Therefore, we aimed to investigate whether SMASH-U is associated with long-term functional outcome after ICH and improves the accuracy of prediction when added to max-ICH score.

METHODS

Consecutive acute ICH patients from 2012 to 2018 from the neurology department of Tongji Hospital were enrolled. ICH aetiology was classified according to the SMASH-U system. The association of SMASH-U with 12-month functional outcome after ICH and the predictive value were evaluated.

RESULTS

Of 1938 ICH patients, the aetiology of 1295 (66.8%) patients were classified as hypertension, followed by amyloid angiopathy (n = 250, 12.9%), undetermined (n = 159, 8.2%), structural lesions (n = 149, 7.7%), systemic disease (n = 74, 3.8%) and medication (n = 11, 0.6%). The baseline characteristics were different among the six aetiologies. In multivariate analysis, SMASH-U was proven to be a predictor of 12-month unfavourable functional outcome. When adding the SMASH-U system, the predictive performance of max-ICH score was improved (area under the receiver operating characteristic curve from 0.802 to 0.812, p = 0.010) and the predictive accuracy was enhanced (integrated discrimination improvement [IDI]: 1.60%, p < 0.001; continuous net reclassification improvement [NRI]: 28.16%, p < 0.001; categorical NRI: 3.34%, p = 0.004).

CONCLUSIONS

SMASH-U predicted long-term unfavourable functional outcomes after acute ICH and improved the accuracy of prediction when added to max-ICH score. Integrating the aetiology to a score model to predict the post-ICH outcome may be meaningful and worthy of further exploration.

Development and Validation of 3-Month Major Post-Stroke Depression Prediction Nomogram After Acute Ischemic Stroke Onset

Purpose

The early detection of major post-stroke depression (PSD) is essential to optimize patient care. A major PSD prediction tool needs to be developed and validated for early screening of major PSD patients.

Patients and Methods

A total of 639 acute ischemic stroke (AIS) patients from three hospitals were consecutively recruited and completed a 3-month follow-up. Sociodemographic, clinical and laboratory test data were collected on admission. With major depression criteria being met in the DSM-V, 17-item Hamilton Rating Scale For Depression (HRSD) score ≥17 at 3 months after stroke onset was regarded as the primary endpoint. Multiple imputation was used to substitute the missing values and multivariable logistic regression model was fitted to determine associated factors with a bootstrap backward selection process. The nomogram was constructed based on the regression coefficients of the associated factors. Performance of the nomogram was assessed by discrimination (C-statistics) and calibration curve.

Results

A total of 7.04% (45/639) of patients were diagnosed with major PSD at 3 months. The final logistic regression model included age, baseline NIHSS and mRS scores, educational level, calcium-phosphorus product, history of hypertension and atrial fibrillation. The model had acceptable discrimination, based on a C-statistic of 0.81 (95% CI, 0.791-0.829), with 71.1% sensitivity and 78.6% specificity. We also transformed the model to a nomogram, an easy-to-use clinical tool which could be used to facilitate the early screening of major PSD patients at 3 months.

Conclusion

We identified several associated factors of major PSD at 3 months and constructed a convenient nomogram to guide follow-up and aid accurate prognostic assessment.

Prevalence and incidence of advanced schistosomiasis and risk factors for case fatality in Hunan Province, China

Advanced schistosomiasis has become a major public health problem in areas with a heavy burden of schistosomiasis infection. Our objective was to determine the incidence and prevalence of advanced schistosomiasis and risk factors associated with case fatality of advanced schistosomiasis. Data were abstracted from hospitalization records of patients with advanced schistosomiasis from Hunan Province, China. The incidence and prevalence of advanced schistosomiasis were determined and the risk factors for death in advanced patients were assessed using logistic regression analysis. A total of 10,362 patients with advanced schistosomiasis were recruited into our study and 65% of them were categorized as the ascites type. There were 1249 deaths between 2005 and 2018 and the case fatality was 12.05%. The incidence of advanced schistosomiasis increased from 2002 to 2010, peaked in 2010 and then leveled off. The prevalence of advanced schistosomiasis increased from 2005 to 2014, and was stable afterwards. HBV was a risk factor for death in advanced patients (adjusted odds ratio (aOR=1.93, 95% confidence interval (CI: 1.55 to 2.41). Patients without splenectomy had a higher risk of death (aOR=1.29, 95%CI: 1.08 to 1.56). Upper gastrointestinal bleeding was positively associated with the risk of death (aOR=1.42, 95% CI: 1.15 to 1.76). Besides, abnormal ALT, ascites and anemia were also significantly associated with the risk of death in advanced patients. Advanced schistosomiasis was effectively controlled in recent years. Splenectomy could reduce the case fatality of advanced patients. HBV infection, abnormal ALT, upper gastrointestinal bleeding and anemia also predicted the risk of death for advanced patients.

Machine learning algorithms to predict the 1 year unfavourable prognosis for advanced schistosomiasis

Short-term prognosis of advanced schistosomiasis has not been well studied. We aimed to construct prognostic models using machine learning algorithms and to identify the most important predictors by utilising routinely available data under the government medical assistance programme. An established database of advanced schistosomiasis in Hunan, China was utilised for analysis. A total of 9541 patients for the period from January 2008 to December 2018 were enrolled in this study. Candidate predictors were selected from demographics, clinical features, medical examinations and test results. We applied five machine learning algorithms to construct 1 year prognostic models: logistic regression (LR), decision tree (DT), random forest (RF), artificial neural network (ANN) and extreme gradient boosting (XGBoost). An area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance. The important predictors of the optimal model for unfavourable prognosis within 1 year were identified and ranked. There were 1249 (13.1%) cases having unfavourable prognoses within 1 year of discharge. The mean age of all participants was 61.94 years, of whom 70.9% were male. In general, XGBoost showed the best predictive performance with the highest AUC (0.846; 95% confidence interval (CI): 0.821, 0.871), compared with LR (0.798; 95% CI: 0.770, 0.827), DT (0.766; 95% CI: 0.733, 0.800), RF (0.823; 95% CI: 0.796, 0.851), and ANN (0.806; 95% CI: 0.778, 0.835). Five most important predictors identified by XGBoost were ascitic fluid volume, haemoglobin (HB), total bilirubin (TB), albumin (ALB), and platelets (PT). We proposed XGBoost as the best algorithm for the evaluation of a 1 year prognosis of advanced schistosomiasis. It is considered to be a simple and useful tool for the short-term prediction of an unfavourable prognosis for advanced schistosomiasis in clinical settings.

A prognostic model for elderly patients with squamous non-small cell lung cancer: a population-based study

Background

Squamous cell carcinoma (SCC) is a main pathological type of non-small cell lung cancer. It is common among elderly patients with poor prognosis. We aimed to establish an accurate nomogram to predict survival for elderly patients (≥ 60 years old) with SCC based on the Surveillance, Epidemiology, and End Results (SEER) database.

Methods

The gerontal patients diagnosed with SCC from 2010 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The independent prognostic factors were identified using multivariate Cox proportional hazards regression analysis, which were utilized to conduct a nomogram for predicting survival. The novel nomogram was evaluated by Concordance index (C-index), calibration curves, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).

Results

32,474 elderly SCC patients were included in the analysis, who were randomly assigned to training cohort (n = 22,732) and validation cohort (n = 9742). The following factors were contained in the final prognostic model: age, sex, race, marital status, tumor site, AJCC stage, surgery, radiation and chemotherapy. Compared to AJCC stage, the novel nomogram exhibited better performance: C-index (training group: 0.789 vs. 0.730, validation group: 0.791 vs. 0.733), the areas under the receiver operating characteristic curve of the training set (1-year AUC: 0.846 vs. 0.791, 3-year AUC: 0.860 vs. 0.801, 5-year AUC: 0.859 vs. 0.794) and the validation set (1-year AUC: 0.846 vs. 0.793, 3-year AUC: 0.863 vs. 0.806, 5-year AUC: 0.866 vs. 0.801), and the 1-, 3- and 5-year calibration plots. Additionally, the NRI and IDI and 1-, 3- and 5-year DCA curves all confirmed that the nomogram was a great prognosis tool.

Conclusions

We constructed a novel nomogram that could be practical and helpful for precise evaluation of elderly SCC patient prognosis, thus helping clinicians in determining the appropriate therapy strategies for individual SCC patients.

In vivo assessment of the antischistosomal activity of curcumin loaded nanoparticles versus praziquantel in the treatment of Schistosoma mansoni

Schistosomiasis is a serious parasitic infection affecting millions worldwide. This study aimed to explore the anti-schistosomal activity of curcumin and curcumin loaded gold-nanoparticles (Cur-GNPs) with or without praziquantel (PZQ). We used six groups of the C57BL/6 mice in which five groups were infected with Schistosoma Mansoni (S. mansoni) cercariae and exhibited, separately, to different treatment regimens of curcumin, curcumin loaded nanoparticle, and PZQ, in addition to one untreated group which acts as a control. Mice were sacrificed at the 8th week where both worms and eggs were counted in the hepatic and porto-mesenteric vessels in the liver and intestine, respectively, in addition to a histopathological examination of the liver granuloma. Curcumin caused a significant reduction in the worms and egg count (45.45%) at the 3rd week. A significant schistosomicidal effect of PZQ was found in all groups. Cur-GNPs combined with PZQ 97.4% reduction of worm burden in the 3rd week and the highest reduction in the intestinal and hepatic egg content, as well, besides 70.1% reduction of the granuloma size. The results suggested the curcumin in combination with PZQ as a strong schistosomicidal regimen against S. mansoni as it alters the hematological, biochemical, and immunological changes induced.

Nomograms to predict 2-year overall survival and advanced schistosomiasis-specific survival after discharge: a competing risk analysis

Background

The prognosis of patients with advanced schistosomiasis is poor. Pre-existing prognosis studies did not differentiate the causes of the deaths. The objectives were to evaluate the 2-year overall survival (OS) and advanced schistosomiasis-specific survival (ASS) in patients with advanced schistosomiasis after discharge through competing risk analysis and to build predictive nomograms.

Methods

Data was extracted from a previously constructed database from Hubei province. Patients were enrolled from September 2014 to January 2015, with follow up to January 2017. OS and ASS were primary outcome measures. Nomograms for estimating 2-year OS and ASS rates after discharge were established based on univariate and multivariate Cox regression model and Fine and Gray’s model. Their predictive performances were evaluated using C-index and validated in both internal and external validation cohorts.

Results

The training cohort included 1487 patients with advanced schistosomiasis. Two-year mortality rate of the training cohort was 8.27% (123/1487). Competing events accounted for 26.83% (33/123). Older age, splemomegaly clinical classification, abnormal serum DBil, AST, ALP and positive HBsAg were significantly associated with 2-year OS. Older age, splemomegaly clinical classification, abnormal serum AST, ALP and positive HBsAg were significantly associated with 2-year ASS. The established nomograms were well calibrated, and had good discriminative ability, with a C-index of 0.813 (95% CI 0.803–0.823) for 2-year OS prediction and 0.834 (95% CI 0.824–0.844) for 2-year ASS prediction. Their predictive performances were well validated in both internal and external validation cohorts.

Conclusion

The effective predictors of 2-year OS and ASS were discovered through competing risk analysis. The nomograms could be used as convenient predictive tools in clinical practice to guide follow-up and aid accurate prognostic assessment.