When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture the multidimensional nature of disease transmission. In recent years, combining different models to improve infectious disease prediction has gradually become a research trend and hotspot. Existing studies have shown that combined models usually have higher prediction performance and better generalization ability. The current combined models mainly combine machine learning and other models, including time-series models, dynamic models, etcetera. In addition, integrated learning that combines diverse machine learning techniques also holds significant importance across various research domains. This paper reviews the progress of applying combined models around machine learning in infectious disease prediction to promote the innovation and practice of combined models for infectious diseases and help to build smarter and more efficient infectious disease early warning and prediction methods and systems.

Methods such as compartmental models, agent-based models, time series models, and machine learning can be used for the prediction of infectious disease incidence. When disease epidemics are complex, it is often difficult to use a single model to comprehensively and accurately capture the multi dimensional nature of the disease. Exploring the combined application of different models has gradually become a research trend and hotspot in recent years, and the prediction performance of combined models is often better than that of single ones. Current research related to combined models mainly focus on machine learning or compartmental models. In this review, we focus on the combination of compartmental models and other models, and summarize their combination principles, application progress, and advantages or disadvantages for the purpose of promoting the innovation and application of combined models for infectious disease incidence prediction, and establishing a more intelligent and efficient early warning and prediction method or systems for the prevention and control of infectious disease.

When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture the multidimensional nature of disease transmission. In recent years, combining different models to improve infectious disease prediction has gradually become a research trend and hotspot. Existing studies have shown that combined models usually have higher prediction performance and better generalization ability. The current combined models mainly combine machine learning and other models, including time-series models, dynamic models, etcetera. In addition, integrated learning that combines diverse machine learning techniques also holds significant importance across various research domains. This paper reviews the progress of applying combined models around machine learning in infectious disease prediction to promote the innovation and practice of combined models for infectious diseases and help to build smarter and more efficient infectious disease early warning and prediction methods and systems.

Methods such as compartmental models, agent-based models, time series models, and machine learning can be used for the prediction of infectious disease incidence. When disease epidemics are complex, it is often difficult to use a single model to comprehensively and accurately capture the multi dimensional nature of the disease. Exploring the combined application of different models has gradually become a research trend and hotspot in recent years, and the prediction performance of combined models is often better than that of single ones. Current research related to combined models mainly focus on machine learning or compartmental models. In this review, we focus on the combination of compartmental models and other models, and summarize their combination principles, application progress, and advantages or disadvantages for the purpose of promoting the innovation and application of combined models for infectious disease incidence prediction, and establishing a more intelligent and efficient early warning and prediction method or systems for the prevention and control of infectious disease.

Dilated cardiomyopathy(DCM)has a concealed onset with left or even whole heart enlargement as the main imaging manifestation.It is a common primary disease of heart failure and arrhythmia.With the continuous deepening of research in recent years,the intrinsic molecular mechanism of regulatory cell death(RCD)has gradually become clear.Researchers have found that the RCD mode plays a very important role in the occurrence and development of DCM.At present,the RCD modes involved in DCM mainly include apoptosis,necrotic apoptosis,pyroptosis,iron death,autophagy,and cuproptosis,and a certain correlation exists among them,which interact and regulate each other.This article provides an overview of the current research status on the mechanisms of the six RCD modes involved in DCM to provide a reference for future basic research and clinical applications.

Objective: To study clinical and pathologic characteristics of leiomyomas of the gastrointestinal tract, and to investigate the distribution characteristics of interstitial cells of Cajal ( ICCs ) in gastrointestinal leiomyomas. Methods: One hundred and forty-seven cases of leiomyomas of gastrointestinal tract were collected at the Second Affiliated Hospital of Zhengzhou University from June 2012 to June 2017. Clinical and pathologic findings were analyzed, combined with immunohistochemistry, Alcian blue-osafranin staining and molecular study. Results: The age of patients ranged from 13-82 years with mean age of 52 years. Male to female ratio was about 1∶2. Histologically, all tumors were composed of ovoid to spindle cells arranged in short intersecting fascicles. All tumors were diffusely and strongly positive for smooth muscle antibodies, desmin and h-caldesmon by immunohistochemical staining. A prominent interspersed subpopulation of elongated/dendritic-like cells with CD117 and DOG1 positivity (accounting for 1% to 30% of all tumor cells) and negative for Alcian blue-osafranin staining was identified in all esophageal leiomyomas, 16 of 20 (80%) gastric leiomyomas and 3 of 12 small bowel leiomyomas, but none in colonic/rectal leiomyomas. Mutational analysis in 16 cases showed absence of mutation in exons 9, 11, 13 or 17 of C-KIT and exons 12 or 18 of PDGFRA. Conclusions ICCs are identified in esophageal and gastric leiomyomas, as well as in small percentage of intestinal leiomyomas. Such findings may bring significant diagnostic pitfalls for misdiagnosis as gastrointestinal stromal tumor. Careful attention to the distribution of CD117 and DOG1 positive cells and molecular mutation analysis of C-KIT and PDGFRA may be necessary to establish the correct diagnosis.

Objective To investigate the basic clinical features in 371 cases of colorectal polyps and its relationship with fecal occult blood and carcinoembryonic antigen(CEA).Methods The retrospective analysis was performed on 371 inpatients with colo-rectal polyps.The relationship among gender,number of polyps and polyps anatomical site in different ages of patients was investi-gated,and the relationship between fecal occult blood and CEA with polyp canceration was analyzed by 1.5?3.0 years follow-up. Results Among 371 cases of colorectal polyps,the female patients were gradually increased and single polyp was gradually de-creased along with the age increase;due to different ages,there was the statistically significant difference in the polyp locations (χ2 =9.759,P=0.045);the distribution difference of the patients with polyp canceration among three age groups was statistically significant(χ2 =5.138,4.107,13.153,P<0.05).The cases of fecal occult blood positive and CEA abnormal increase were gradual-ly increased with age increasing(χ2 =15.544,11.959,P<0.01);with the number of polyps increasing,the cases of fecal occult blood positive showed the increasing trend(χ2 =14.043,P=0.001);the canceration rate in colorectal polyp cases of fecal occult blood positive and CEA abnormal increase was significantly higher than that in the cases of fecal occult blood negative and CEA normal range(χ2 =40.165,43.249,all of P< 0.001).Conclusion The fecal occult blood test and CEA detection results have a certain significance to the follow up for preventing colorectal polyps canceration.