Time trends in the mortality of testicular cancer across the BRICS: an age-period-cohort analysis for the GBD 2019.

Testicular cancer (TCa) is a rare but impactful malignancy that primarily affects young men. Understanding the mortality rate of TCa is crucial for improving prevention and treatment strategies to reduce the risk of death among patients. We obtained TCa mortality data by place (5 countries), age (20-79 years), and year (1990-2019) from the Global Burden of Disease Study 2019. Age-period-cohort model was used to estimate the net drift, local drift, age effects, period and cohort effects. In 2019, the global mortality of TCa increased to 10842 (95% UI 9961, 11902), with an increase of 50.08% compared to 1990.The all-age mortality rate for TCa in 2019 increased from 0.17/100,000 (95% UI 0.13, 0.20) in China to 0.48/100,000 (95% UI 0.38, 0.59) in Russian Federation, whereas the age-standardized mortality rate in 2019 was highest in the South Africa 0.47/100,000 (95% UI 0.42, 0.53) and lowest in the China 0.16/100,000 (95% UI 0.13, 0.19). China's aging population shifts mortality patterns towards the elderly, while in Russian Federation, young individuals are primarily affected by the distribution of deaths. To address divergent TCa mortality advancements in BRICS countries, we propose a contextually adaptive and resource-conscious approach to prioritize TCa prevention. Tailoring strategies to contextual diversity, including policy frameworks, human resources, and financial capacities, will enhance targeted interventions and effectiveness in reducing TCa mortality.

Using blood routine indicators to establish a machine learning model for predicting liver fibrosis in patients with Schistosoma japonicum.

This study intends to use the basic information and blood routine of schistosomiasis patients to establish a machine learning model for predicting liver fibrosis. We collected medical records of Schistosoma japonicum patients admitted to a hospital in China from June 2019 to June 2022. The method was to screen out the key variables and six different machine learning algorithms were used to establish prediction models. Finally, the optimal model was compared based on AUC, specificity, sensitivity and other indicators for further modeling. The interpretation of the model was shown by using the SHAP package. A total of 1049 patients' medical records were collected, and 10 key variables were screened for modeling using lasso method, including red cell distribution width-standard deviation (RDW-SD), Mean corpuscular hemoglobin concentration (MCHC), Mean corpuscular volume (MCV), hematocrit (HCT), Red blood cells, Eosinophils, Monocytes, Lymphocytes, Neutrophils, Age. Among the 6 different machine learning algorithms, LightGBM performed the best, and its AUCs in the training set and validation set were 1 and 0.818, respectively. This study established a machine learning model for predicting liver fibrosis in patients with Schistosoma japonicum. The model could help improve the early diagnosis and provide early intervention for schistosomiasis patients with liver fibrosis.

IgG persistence showed weak clinical aspects in chronic schistosomiasis patients.

Schistosomiasis is a chronic parasitic disease, which affects the quality of daily life of patients and imposes a huge burden on society. Hepatic fibrosis in response to continuous insult of eggs to the liver is a significant cause of morbidity and mortality. However, the mechanisms of hepatic fibrosis in schistosomiasis are largely undefined. The purpose of our study is to detect the indicator to hepatic fibrosis in schistosomiasis. A total of 488 patients with chronic schistosomiasis japonica were enrolled in our study. The patients were divided into two groups according to liver ultrasound examination, which could indicate liver fibrosis of schistosomiasis with unique reticular changes. Logistic regression analysis showed that globulin, albumin/globulin, GGT levels and anti-Schistosoma IgG were independently associated with liver fibrosis in patients with schistosomiasis and IgG was the largest association of liver fibrosis (OR 2.039, 95% CI 1.293-3.213). We further compared IgG+ patients with IgG- patients. IgG+ patients (ALT 25 U/L, GGT 31 U/L) slightly higher than IgG- patients (ALT 22 U/L, GGT 26 U/L) in ALT and GGT. However, the fibrosis of liver in IgG+ patients (Grade II(19.7%), Grade III(7.3%)) were more severe than that in IgG- patients(Grade II(12.5%), Grade III(2.9%)) according to the grade of liver ultrasonography. Our results showed anti-Schistosoma IgG was independently associated with liver fibrosis in patients with chronic schistosomiasis japonica and patients with persistent anti-Schistosoma IgG might have more liver fibrosis than negative patients despite no obvious clinical signs or symptoms.

Flip-avoiding interpolating surface registration for skull reconstruction.

Skull reconstruction is an important and challenging task in craniofacial surgery planning, forensic investigation and anthropological studies. Existing methods typically reconstruct approximating surfaces that regard corresponding points on the target skull as soft constraints, thus incurring non-zero error even for non-defective parts and high overall reconstruction error. This paper proposes a novel geometric reconstruction method that non-rigidly registers an interpolating reference surface that regards corresponding target points as hard constraints, thus achieving low reconstruction error. To overcome the shortcoming of interpolating a surface, a flip-avoiding method is used to detect and exclude conflicting hard constraints that would otherwise cause surface patches to flip and self-intersect. Comprehensive test results show that our method is more accurate and robust than existing skull reconstruction methods. By incorporating symmetry constraints, it can produce more symmetric and normal results than other methods in reconstructing defective skulls with a large number of defects. It is robust against severe outliers such as radiation artifacts in computed tomography due to dental implants. In addition, test results also show that our method outperforms thin-plate spline for model resampling, which enables the active shape model to yield more accurate reconstruction results. As the reconstruction accuracy of defective parts varies with the use of different reference models, we also study the implication of reference model selection for skull reconstruction.