Deep evolutionary fusion neural network: a new prediction standard for infectious disease incidence rates.

BACKGROUND: Previously, many methods have been used to predict the incidence trends of infectious diseases. There are numerous methods for predicting the incidence trends of infectious diseases, and they have exhibited varying degrees of success. However, there are a lack of prediction benchmarks that integrate linear and nonlinear methods and effectively use internet data. The aim of this paper is to develop a prediction model of the incidence rate of infectious diseases that integrates multiple methods and multisource data, realizing ground-breaking research. RESULTS: The infectious disease dataset is from an official release and includes four national and three regional datasets. The Baidu index platform provides internet data. We choose a single model (seasonal autoregressive integrated moving average (SARIMA), nonlinear autoregressive neural network (NAR), and long short-term memory (LSTM)) and a deep evolutionary fusion neural network (DEFNN). The DEFNN is built using the idea of neural evolution and fusion, and the DEFNN + is built using multisource data. We compare the model accuracy on reference group data and validate the model generalizability on external data. (1) The loss of SA-LSTM in the reference group dataset is 0.4919, which is significantly better than that of other single models. (2) The loss values of SA-LSTM on the national and regional external datasets are 0.9666, 1.2437, 0.2472, 0.7239, 1.4026, and 0.6868. (3) When multisource indices are added to the national dataset, the loss of the DEFNN + increases to 0.4212, 0.8218, 1.0331, and 0.8575. CONCLUSIONS: We propose an SA-LSTM optimization model with good accuracy and generalizability based on the concept of multiple methods and multiple data fusion. DEFNN enriches and supplements infectious disease prediction methodologies, can serve as a new benchmark for future infectious disease predictions and provides a reference for the prediction of the incidence rates of various infectious diseases.

A novel prognostic index for extranodal natural killer/T-cell lymphoma in the era of pegaspargase/L-asparaginase.

Aim: This multicenter retrospective study aimed to develop a novel prognostic system for extranodal natural killer/T-cell lymphoma (ENKTL) patients in the era of pegaspargase/L-asparaginase.Materials & methods: A total of 844 newly diagnosed ENKTL patients were included.Results: Multivariable analysis confirmed that Eastern Cooperative Oncology Group performance status, lactate dehydrogenase, Chinese Southwest Oncology Group and Asia Lymphoma Study Group ENKTL (CA) system, and albumin were independent prognostic factors. By rounding up the hazard ratios from four significant variables, a maximum of 7 points were assigned. The model of Huaihai Lymphoma Working Group-Natural killer/T-cell Lymphoma prognostic index (NPI) was identified with four risk groups and the 5-year overall survival was 88.2, 66.7, 54.3 and 30.5%, respectively.Conclusion: Huaihai Lymphoma Working Group-NPI provides a feasible stratification system for patients with ENKTL in the era of pegaspargase/L-asparaginase.

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Prognostic value of prognostic nutritional index on extranodal natural killer/T-cell lymphoma patients: A multicenter propensity score matched analysis of 1022 cases in Huaihai Lymphoma Working Group.

Prognostic nutritional index (PNI), comprised of serum albumin level and lymphocyte count, is associated with the prognosis of several malignant diseases, while the prognostic value of PNI in extranodal natural killer/T cell lymphoma, nasal type (ENKTL) remains unclear. This retrospective multicenter study aimed to investigate the value of PNI in predicting the prognosis of newly diagnosed ENKTL patients by using propensity score matched analysis (PSM). A total of 1022 newly diagnosed ENKTL patients were retrieved from Huaihai Lymphoma Working Group and clinicopathological variables were collected. MaxStat analysis was used to calculate the optimal cut-off points of PNI and other continuous variables. The median age at diagnosis was 47 years and 69.4% were males, with the 5-year OS of 71.7%. According to the MaxStat analysis, 41 was the optimal cut-off point for PNI. The Pseudo R2 before matching was 0.250, and it decreased to less than 0.019 after matching. Confounding factors of the two groups were well balanced after PSM. Multivariable analysis revealed that PNI, Korean Prognostic Index (KPI), eastern cooperative oncology group performance status (ECOG PS), the prognostic index of natural killer lymphoma (PINK) and hemoglobin were independent prognostic factors for ENKTL. The results of subgroup analysis demonstrated that patients with low PNI could predict worse prognosis and re-stratify patients in ECOG PS ≥ 2, EBER-positive, the International Prognostic Index (IPI) (HIR + HR), and PINK (HR) groups. PNI combined with IPI, PINK and KPI could improve the prediction efficiency. In conclusion, PNI could accurately stratify the prognosis of ENKTL by PSM analysis and patients with low PNI had poorer prognosis.

The prognostic value of controlling nutritional status (CONUT) score-based nomogram on extranodal natural killer/T cell lymphoma patients.

Controlling nutritional status (CONUT) score as an original nutritional assessment tool can be used to assess the prognosis of patients with a variety of malignancies. However, the predictive power of CONUT in extranodal natural killer/T cell lymphoma (ENKTL) patients has never been demonstrated. Our retrospective multicenter study aimed to explore the prognostic value of CONUT in newly diagnosed ENKTL. A total of 1085 newly diagnosed ENKTL patients between 2003 and 2021 were retrospectively retrieved. Cox proportional hazard model was used to explore the prognostic factors of overall survival (OS). The survival rate of ENKTL was evaluated using Kaplan-Meier analysis, and log-rank test was applied to the difference between groups. We investigated the prognostic performance of CONUT, the International Prognostic Index (IPI), the Korean Prognostic Index (KPI), and the Prognostic Index of Natural Killer Cell Lymphoma (PINK) using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). The median age at diagnosis for the whole cohort was 47 years, and the male to female ratio was 2.2:1. The 5-year OS for all patients was 72.2%. Multivariable analysis showed that CONUT, age, bone marrow involvement, ECOG PS score, and Chinese Southwest Oncology Group and Asia Lymphoma Study Group ENKTL stage were identified as independent predictive factors for OS. Based on multivariable results, a prognostic nomogram was developed. Subgroup analysis demonstrated that patients with severe malnutrition had poorest clinical outcome. In addition, ROC curves and DCA analysis proved that compared with IPI, KPI, and PINK models, the CONUT score-based nomogram showed a better prognostic predictive efficiency of ENKTL. CONUT could effectively stratify the prognosis of ENKTL and the proposed nomogram based on CONUT was an effective prognostic model for prediction.

Spatio-temporal pattern, matching level and prediction of ageing and medical resources in China.

OBJECTIVE: Population ageing, as a hot issue in global development, increases the burden of medical resources in society. This study aims to assess the current spatiotemporal evolution and interaction between population ageing and medical resources in mainland China; evaluate the matching level of medical resources to population ageing; and forecast future trends of ageing, medical resources, and the indicator of ageing-resources (IAR). METHODS: Data on ageing (EPR) and medical resources (NHI, NBHI, and NHTP) were obtained from China Health Statistics Yearbook and China Statistical Yearbook (2011-2020). We employed spatial autocorrelation to examine the spatial-temporal distribution trends and analyzed the spatio-temporal interaction using a Bayesian spatio-temporal effect model. The IAR, an improved evaluation indicator, was used to measure the matching level of medical resources to population ageing with kernel density analysis for visualization. Finally, an ETS-DNN model was used to forecast the trends in population ageing, medical resources, and their matching level over the next decade. RESULTS: The study found that China's ageing population and medical resources are growing annually, yet distribution is uneven across districts. There is a spatio-temporal interaction effect between ageing and medical resources, with higher levels of both in Eastern China and lower levels in Western China. The IAR is relatively high in Northwest, North China, and the Yangtze River Delta, but showed a declining trend in North China and the Yangtze River Delta. The hybrid model (ETS-DNN) gained an R2 of 0.9719, and the predicted median IAR for 2030 (0.99) across 31 regions was higher than the median IAR for 2020 (0.93). CONCLUSION: This study analyzes the relationship between population ageing and medical resources, revealing a spatio-temporal interaction between them. The IAR evaluation indicator highlights the need to address ageing population challenges and cultivate a competent health workforce. The ETS-DNN forecasts indicate higher concentrations of both medical resources and ageing populations in eastern China, emphasizing the need for region-specific ageing security systems and health service industries. The findings provide valuable policy insights for addressing a hyper-aged society in the future.

Microcrack behavior in bone: Stress field analysis at osteon cement line tips.

Bone microstructure governs microcrack propagation complexity. Current research, relying on linear elastic fracture mechanics, inadequately considers authentic multi-level structures, like cement lines and osteons, impacting stress intensity at cracks. This study, by constructing models encompassing single or multiple osteons, delves into the influence of factors like crack length, osteon radius, and modulus ratio on the stress intensity factor at the crack tip. Employing a fracture mechanics phase-field approach to simulate crack propagation paths, it particularly explores the role of cement lines as weak interfaces in crack extension. The aim is to comprehensively and systematically elucidate the critical factors of bone microstructure in the context of crack propagation.

Immunogenicity and protective efficacy of the HC009 mRNA vaccine against SARS-CoV-2.

With the rapid global spread of COVID-19 and the continuous emergence of variants, there is an urgent need to develop safe and effective vaccines. Here, we developed a novel mRNA vaccine, HC009, based on new formulation by the QTsome delivery platform. Immunogenicity results showed that the prime-boost immunization strategy with HC009 was able to induce robust and durable humoral immunity, as well as Th1-biased cellular responses in rodents or non-human primates (NHPs). After further challenge with live SARS-CoV-2 virus, HC009 provided adequate protection against virus infection in hACE2 transgenic mice. Therefore, HC009 could provide significant immune protection against SARS-CoV-2.

OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features.

Background: Early stroke prognosis assessments are critical for decision-making regarding therapeutic intervention. We introduced the concepts of data combination, method integration, and algorithm parallelization, aiming to build an integrated deep learning model based on a combination of clinical and radiomics features and analyze its application value in prognosis prediction. Methods: The research steps in this study include data source and feature extraction, data processing and feature fusion, model building and optimization, model training, and so on. Using data from 441 stroke patients, clinical and radiomics features were extracted, and feature selection was performed. Clinical, radiomics, and combined features were included to construct predictive models. We applied the concept of deep integration to the joint analysis of multiple deep learning methods, used a metaheuristic algorithm to improve the parameter search efficiency, and finally, developed an acute ischemic stroke (AIS) prognosis prediction method, namely, the optimized ensemble of deep learning (OEDL) method. Results: Among the clinical features, 17 features passed the correlation check. Among the radiomics features, 19 features were selected. In the comparison of the prediction performance of each method, the OEDL method based on the concept of ensemble optimization had the best classification performance. In the comparison to the predictive performance of each feature, the inclusion of the combined features resulted in better classification performance than that of the clinical and radiomics features. In the comparison to the prediction performance of each balanced method, SMOTEENN, which is based on a hybrid sampling method, achieved the best classification performance than that of the unbalanced, oversampled, and undersampled methods. The OEDL method with combined features and mixed sampling achieved the best classification performance, with 97.89, 95.74, 94.75, 94.03, and 94.35% for Macro-AUC, ACC, Macro-R, Macro-P, and Macro-F1, respectively, and achieved advanced performance in comparison with that of methods in previous studies. Conclusion: The OEDL approach proposed herein could effectively achieve improved stroke prognosis prediction performance, the effect of using combined data modeling was significantly better than that of single clinical or radiomics feature models, and the proposed method had a better intervention guidance value. Our approach is beneficial for optimizing the early clinical intervention process and providing the necessary clinical decision support for personalized treatment.

Discovery of potential biomarkers for lung cancer classification based on human proteome microarrays using Stochastic Gradient Boosting approach.

PURPOSE: Early identification of lung cancer (LC) will considerably facilitate the intervention and prevention of LC. The human proteome micro-arrays approach can be used as a "liquid biopsy" to diagnose LC to complement conventional diagnosis, which needs advanced bioinformatics methods such as feature selection (FS) and refined machine learning models. METHODS: A two-stage FS methodology by infusing Pearson's Correlation (PC) with a univariate filter (SBF) or recursive feature elimination (RFE) was used to reduce the redundancy of the original dataset. The Stochastic Gradient Boosting (SGB), Random Forest (RF), and Support Vector Machine (SVM) techniques were applied to build ensemble classifiers based on four subsets. The synthetic minority oversampling technique (SMOTE) was used in the preprocessing of imbalanced data. RESULTS: FS approach with SBF and RFE extracted 25 and 55 features, respectively, with 14 overlapped ones. All three ensemble models demonstrate superior accuracy (ranging from 0.867 to 0.967) and sensitivity (0.917 to 1.00) in the test datasets with SGB of SBF subset outperforming others. The SMOTE technique has improved the model performance in the training process. Three of the top selected candidate biomarkers (LGR4, CDC34, and GHRHR) were highly suggested to play a role in lung tumorigenesis. CONCLUSION: A novel hybrid FS method with classical ensemble machine learning algorithms was first used in the classification of protein microarray data. The parsimony model constructed by the SGB algorithm with the appropriate FS and SMOTE approach performs well in the classification task with higher sensitivity and specificity. Standardization and innovation of bioinformatics approach for protein microarray analysis need further exploration and validation.

Comparative analysis of clinicopathologic characteristics and prognosis between nasal and nonnasal extranodal NK/T-cell lymphoma.

BACKGROUND: The clinicopathologic characteristics and prognosis of nasal and nonnasal extranodal natural killer T-cell lymphoma (ENKTL) are considered to be different. However, the underlying features responsible for these differences are not well clarified especially in the era of asparaginase therapy. METHODS: In total, 1007 newly diagnosed ENKTL patients from 11 medical centers were included in this study. Clinicopathologic characteristics and survival data were collected. The chi-squared test and Kruskal-Wallis test were utilized for the comparison of different groups. Univariable and multivariable Cox proportional hazards models were used to screen prognostic factors. RESULTS: Overall, 869 (86.3%) patients were nasal forms. Compared to patients with nasal ENKTL, nonnasal patients were at more advanced stages and had poor performance status, bone marrow involvement, elevated serum lactate dehydrogenase (LDH), and CD56-negative status (p < 0.05). The 5-year overall survival (OS) for nasal and nonnasal patients were 65.6% and 45.0%, respectively. The OS of nasal forms patients were superior to nonnasal patients, especially in Eastern Cooperative Oncology Group performance status (ECOG PS) (≥2), advanced stage, KPI (HIR/HR), IPI (HIR/HR), PINK (HR), and high EBV DNA load groups. In patients treated with pegaspargase/L-asparaginase-based regimens, the OS of nasal patients was better than that of nonnasal patients. After adjusting the covariates of age, stage, ECOG PS score, LDH, B symptoms, and BM involvement, results showed that the nonnasal site was associated with poor survival of ENKTL. CONCLUSIONS: The clinicopathologic characteristics and prognosis of nasal and nonnasal ENKTL patients are different. Nasal forms patients had superior OS than nonnasal patients, especially in the era of asparaginase.

E-DU: Deep neural network for multimodal medical image segmentation based on semantic gap compensation.

BACKGROUND: U-Net includes encoder, decoder and skip connection structures. It has become the benchmark network in medical image segmentation. However, the direct fusion of low-level and high-level convolution features with semantic gaps by traditional skip connections may lead to problems such as fuzzy generated feature maps and target region segmentation errors. OBJECTIVE: We use spatial enhancement filtering technology to compensate for the semantic gap and propose an enhanced dense U-Net (E-DU), aiming to apply it to multimodal medical image segmentation to improve the segmentation performance and efficiency. METHODS: Before combining encoder and decoder features, we replace the traditional skip connection with a multiscale denoise enhancement (MDE) module. The encoder features need to be deeply convolved by the spatial enhancement filter and then combined with the decoder features. We propose a simple and efficient deep full convolution network structure E-DU, which can not only fuse semantically various features but also denoise and enhance the feature map. RESULTS: We performed experiments on medical image segmentation datasets with seven image modalities and combined MDE with various baseline networks to perform ablation studies. E-DU achieved the best segmentation results on evaluation indicators such as DSC on the U-Net family, with DSC values of 97.78, 97.64, 95.31, 94.42, 94.93, 98.85, and 98.38 (%), respectively. The addition of the MDE module to the attention mechanism network improves segmentation performance and efficiency, reflecting its generalization performance. In comparison to advanced methods, our method is also competitive. CONCLUSION: Our proposed MDE module has a good segmentation effect and operating efficiency, and it can be easily extended to multiple modal medical segmentation datasets. Our idea and method can achieve clinical multimodal medical image segmentation and make full use of image information to provide clinical decision support. It has great application value and promotion prospects.

COVID-19 epidemic and public health interventions in Shanghai, China: Statistical analysis of transmission, correlation and conversion.

Background: The Shanghai COVID-19 epidemic is an important example of a local outbreak and of the implementation of normalized prevention and disease control strategies. The precise impact of public health interventions on epidemic prevention and control is unknown. Methods: We collected information on COVID-19 patients reported in Shanghai, China, from January 30 to May 31, 2022. These newly added cases were classified as local confirmed cases, local asymptomatic infections, imported confirmed cases and imported asymptomatic infections. We used polynomial fitting correlation analysis and illustrated the time lag plot in the correlation analysis of local and imported cases. Analyzing the conversion of asymptomatic infections to confirmed cases, we proposed a new measure of the conversion rate (C r ). In the evolution of epidemic transmission and the analysis of intervention effects, we calculated the effective reproduction number (R t ). Additionally, we used simulated predictions of public health interventions in transmission, correlation, and conversion analyses. Results: (1) The overall level of R t in the first three stages was higher than the epidemic threshold. After the implementation of public health intervention measures in the third stage, R t decreased rapidly, and the overall R t level in the last three stages was lower than the epidemic threshold. The longer the public health interventions were delayed, the more cases that were expected and the later the epidemic was expected to end. (2) In the correlation analysis, the outbreak in Shanghai was characterized by double peaks. (3) In the conversion analysis, when the incubation period was short (3 or 7 days), the conversion rate fluctuated smoothly and did not reflect the effect of the intervention. When the incubation period was extended (10 and 14 days), the conversion rate fluctuated in each period, being higher in the first five stages and lower in the sixth stage. Conclusion: Effective public health interventions helped slow the spread of COVID-19 in Shanghai, shorten the outbreak duration, and protect the healthcare system from stress. Our research can serve as a positive guideline for addressing infectious disease prevention and control in China and other countries and regions.

Integrating Internet multisource big data to predict the occurrence and development of COVID-19 cryptic transmission.

With the recent prevalence of COVID-19, cryptic transmission is worthy of attention and research. Early perception of the occurrence and development risk of cryptic transmission is an important part of controlling the spread of COVID-19. Previous relevant studies have limited data sources, and no effective analysis has been carried out on the occurrence and development of cryptic transmission. Hence, we collect Internet multisource big data (including retrieval, migration, and media data) and propose comprehensive and relative application strategies to eliminate the impact of national and media data. We use statistical classification and regression to construct an early warning model for occurrence and development. Under the guidance of the improved coronavirus herd immunity optimizer (ICHIO), we construct a "sampling-feature-hyperparameter-weight" synchronous optimization strategy. In occurrence warning, we propose an undersampling synchronous evolutionary ensemble (USEE); in development warning, we propose a bootstrap-sampling synchronous evolutionary ensemble (BSEE). Regarding the internal training data (Heilongjiang Province), the ROC-AUC of USEE3 incorporating multisource data is 0.9553, the PR-AUC is 0.8327, and the R2 of BSEE2 fused by the "nonlinear + linear" method is 0.8698. Regarding the external validation data (Shaanxi Province), the ROC-AUC and PR-AUC values of USEE3 were 0.9680 and 0.9548, respectively, and the R2 of BSEE2 was 0.8255. Our method has good accuracy and generalization and can be flexibly used in the prediction of cryptic transmission in various regions. We propose strategy research that integrates multiple early warning tasks based on multisource Internet big data and combines multiple ensemble models. It is an extension of the research in the field of traditional infectious disease monitoring and has important practical significance and innovative theoretical value.

Construction of a Medical Micro-Object Cascade Network for Automated Segmentation of Cerebral Microbleeds in Susceptibility Weighted Imaging.

Aim: The detection and segmentation of cerebral microbleeds (CMBs) images are the focus of clinical diagnosis and treatment. However, segmentation is difficult in clinical practice, and missed diagnosis may occur. Few related studies on the automated segmentation of CMB images have been performed, and we provide the most effective CMB segmentation to date using an automated segmentation system. Materials and Methods: From a research perspective, we focused on the automated segmentation of CMB targets in susceptibility weighted imaging (SWI) for the first time and then constructed a deep learning network focused on the segmentation of micro-objects. We collected and marked clinical datasets and proposed a new medical micro-object cascade network (MMOC-Net). In the first stage, U-Net was utilized to select the region of interest (ROI). In the second stage, we utilized a full-resolution network (FRN) to complete fine segmentation. We also incorporated residual atrous spatial pyramid pooling (R-ASPP) and a new joint loss function. Results: The most suitable segmentation result was achieved with a ROI size of 32 × 32. To verify the validity of each part of the method, ablation studies were performed, which showed that the best segmentation results were obtained when FRN, R-ASPP and the combined loss function were used simultaneously. Under these conditions, the obtained Dice similarity coefficient (DSC) value was 87.93% and the F2-score (F2) value was 90.69%. We also innovatively developed a visual clinical diagnosis system that can provide effective support for clinical diagnosis and treatment decisions. Conclusions: We created the MMOC-Net method to perform the automated segmentation task of CMBs in an SWI and obtained better segmentation performance; hence, this pioneering method has research significance.