ObjectiveEarly blight is a common destructive disease in the growth process of Solanaceae crops, which can lead to crop failure and serious losses. Traditional crop disease detection methods are difficult to detect disease characteristics in a timely manner during the incubation period of disease, and thus take scientific and effective prevention and control measures. This study obtained hyperspectral images of early blight of peppers at different infection stages through continuous monitoring with a hyperspectral imager. The earliest identifiable time during the incubation period of early blight in peppers (the earliest identifiable time during the incubation period in this experiment was 24 h after inoculation) was determined using the spectral angle cosine-correlation coefficient and Chebyshev distance. MethodsTaking the symptoms of the latent period of early blight in peppers as the research object, 13 characteristic wavelengths were selected using a genetic algorithm. An identification model of crop disease latent period symptoms based on spectral features was established through optimized combinations of characteristic wavelengths combined with a logistic regression model. Simultaneously, a recognition model of the latent period of early blight in peppers based on image texture features was established using local binary patterns. ResultsThe experiment was tested with 120 samples. The accuracy of the identification model of crop disease latent period symptoms based on spectral features reached over 93% in both the training set and the test set. The accuracy of the identification model of crop disease latent period symptoms based on texture features reached 98.96% and 100% in the training set and test set, respectively. ConclusionBoth spectral features and texture features can be used to detect and identify crop disease latent period symptoms. Texture features more significantly revealed the characteristics of the latent period of the disease compared to spectral features, effectively improving the detection performance of the model. The research results in this article can provide theoretical references for monitoring and identifying other crop disease latent period symptoms.

G-protein coupled receptors (GPCRs) are an essential family of proteins on the cell membrane, widely distributed in various types of tissues and cells. Typical GPCRs are composed of characteristic 7 transmembraneα-helix domains, extracellular domain and intracellular domain. They play a key role in transmitting information inside and outside cells. These receptors can sense and respond to a variety of external signals, including odor molecules, hormones, neurotransmitters, chemokines, and so on, thereby regulating the physiological functions and metabolic activities of cells. When external signal molecules bind, these receptors undergo conformational changes, thereby activating signal transduction pathways inside cells. The most common downstream signal pathway is the activation of G proteins, but it may also activate the β-arrestin signaling pathway. This series of signal transduction processes ultimately regulates physiological processes such as cell metabolism, proliferation, and differentiation, and also plays an important role in the occurrence and development of diseases. Due to its importance in regulating cell functions and participating in the development of diseases, GPCRs have become important targets in the field of drug research and development. The mechanism of action of many drugs is achieved by intervening in the GPCR signaling pathway. As important form of function regulating, dimerization has attracted widespread attention in the research of GPCR field. In the early days, the formation of GPCR dimerization and its effect on receptor function were mainly studied by immunoprecipitation, immunofluorescence and radioligand binding experiments in overexpression systems. Nowadays, with the continuous development of biochemical and biophysical methods, more and more GPCR dimers have been identified. GPCR dimer refers to the process in which two GPCR subunits bind to each other to form a complex. The same GPCR subunits form homodimers, and different GPCR subunits form heterodimers through direct interaction. Dimerization changes the activity, affinity, internalization, localization and transport, and signal transduction characteristics of GPCR, thereby producing more complex and delicate regulation of cellular physiological processes. In recent years, the research on GPCR dimers has been continuously deepened, revealing its important role in a variety of physiological and pathological processes. In general, the structure of GPCR dimers is complex and diverse, and its formation and stability are affected by many factors, including the specificity of receptor interaction interface, the conformational changes of receptor, and the regulation of intracellular and extracellular environment. By understanding the mechanism of GPCR dimerization, we can better understand the behavior of these receptors in signal transduction and provide new ideas and opportunities for the development of novel drug targets. More and more studies have reported the dimerization of GPCR and its structure and function regulation mechanism. This article reviews the research progress on the structure and function of GPCR dimers, and summarizes some research methods and technologies, which provide a basis for understanding the discovery of GPCR dimers, dimerization methods, structure and function regulation mechanisms, and further targeting GPCR dimers. It provides a research basis for the development of polymer drugs.

Objective To evaluate the quality of prediction model on healthcare-associated infections in China,so as to standardize research process and reporting methods.Methods It performed a literature search for healthcare-associated infections prediction model studies published using the following databases by the end of 2022.After independently screening the literature and cross-checking the extracted data according to the inclusion and exclusion criteria,the research team applied the prediction model risk of bias assessment tool(PROBAST)to evaluate the methodological quality,and the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD)statement to evaluate the quality of study reporting.Results A total of 81 healthcare-associated infections prediction studies were identified.Their median PROBAST overall adherence were 58.11%±13.88%,median TRIPOD adherence were 56.11%±16.35%.The main methodological flaws involved participants defined,ignored complexities in data,and omitted missing data.The reporting flaws lay in the items of risk groups,sample size,and supplementary information.Conclusion There are methodological deficiencies and incomplete reporting of domestic hospital infection prediction modelling studies,which limit the reliability and applicability of the results and leave much room for improvement.

Objective To evaluate the quality of prediction model on healthcare-associated infections in China,so as to standardize research process and reporting methods.Methods It performed a literature search for healthcare-associated infections prediction model studies published using the following databases by the end of 2022.After independently screening the literature and cross-checking the extracted data according to the inclusion and exclusion criteria,the research team applied the prediction model risk of bias assessment tool(PROBAST)to evaluate the methodological quality,and the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD)statement to evaluate the quality of study reporting.Results A total of 81 healthcare-associated infections prediction studies were identified.Their median PROBAST overall adherence were 58.11%±13.88%,median TRIPOD adherence were 56.11%±16.35%.The main methodological flaws involved participants defined,ignored complexities in data,and omitted missing data.The reporting flaws lay in the items of risk groups,sample size,and supplementary information.Conclusion There are methodological deficiencies and incomplete reporting of domestic hospital infection prediction modelling studies,which limit the reliability and applicability of the results and leave much room for improvement.

Objective To evaluate the quality of prediction model on healthcare-associated infections in China,so as to standardize research process and reporting methods.Methods It performed a literature search for healthcare-associated infections prediction model studies published using the following databases by the end of 2022.After independently screening the literature and cross-checking the extracted data according to the inclusion and exclusion criteria,the research team applied the prediction model risk of bias assessment tool(PROBAST)to evaluate the methodological quality,and the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD)statement to evaluate the quality of study reporting.Results A total of 81 healthcare-associated infections prediction studies were identified.Their median PROBAST overall adherence were 58.11%±13.88%,median TRIPOD adherence were 56.11%±16.35%.The main methodological flaws involved participants defined,ignored complexities in data,and omitted missing data.The reporting flaws lay in the items of risk groups,sample size,and supplementary information.Conclusion There are methodological deficiencies and incomplete reporting of domestic hospital infection prediction modelling studies,which limit the reliability and applicability of the results and leave much room for improvement.

Objective To evaluate the quality of prediction model on healthcare-associated infections in China,so as to standardize research process and reporting methods.Methods It performed a literature search for healthcare-associated infections prediction model studies published using the following databases by the end of 2022.After independently screening the literature and cross-checking the extracted data according to the inclusion and exclusion criteria,the research team applied the prediction model risk of bias assessment tool(PROBAST)to evaluate the methodological quality,and the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD)statement to evaluate the quality of study reporting.Results A total of 81 healthcare-associated infections prediction studies were identified.Their median PROBAST overall adherence were 58.11%±13.88%,median TRIPOD adherence were 56.11%±16.35%.The main methodological flaws involved participants defined,ignored complexities in data,and omitted missing data.The reporting flaws lay in the items of risk groups,sample size,and supplementary information.Conclusion There are methodological deficiencies and incomplete reporting of domestic hospital infection prediction modelling studies,which limit the reliability and applicability of the results and leave much room for improvement.

Objective To evaluate the quality of prediction model on healthcare-associated infections in China,so as to standardize research process and reporting methods.Methods It performed a literature search for healthcare-associated infections prediction model studies published using the following databases by the end of 2022.After independently screening the literature and cross-checking the extracted data according to the inclusion and exclusion criteria,the research team applied the prediction model risk of bias assessment tool(PROBAST)to evaluate the methodological quality,and the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD)statement to evaluate the quality of study reporting.Results A total of 81 healthcare-associated infections prediction studies were identified.Their median PROBAST overall adherence were 58.11%±13.88%,median TRIPOD adherence were 56.11%±16.35%.The main methodological flaws involved participants defined,ignored complexities in data,and omitted missing data.The reporting flaws lay in the items of risk groups,sample size,and supplementary information.Conclusion There are methodological deficiencies and incomplete reporting of domestic hospital infection prediction modelling studies,which limit the reliability and applicability of the results and leave much room for improvement.

Objective To evaluate the quality of prediction model on healthcare-associated infections in China,so as to standardize research process and reporting methods.Methods It performed a literature search for healthcare-associated infections prediction model studies published using the following databases by the end of 2022.After independently screening the literature and cross-checking the extracted data according to the inclusion and exclusion criteria,the research team applied the prediction model risk of bias assessment tool(PROBAST)to evaluate the methodological quality,and the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD)statement to evaluate the quality of study reporting.Results A total of 81 healthcare-associated infections prediction studies were identified.Their median PROBAST overall adherence were 58.11%±13.88%,median TRIPOD adherence were 56.11%±16.35%.The main methodological flaws involved participants defined,ignored complexities in data,and omitted missing data.The reporting flaws lay in the items of risk groups,sample size,and supplementary information.Conclusion There are methodological deficiencies and incomplete reporting of domestic hospital infection prediction modelling studies,which limit the reliability and applicability of the results and leave much room for improvement.

Atypical teratoid/rhabdoid tumor (AT/RT) is a rare malignancy located primarily in infratentorial or subcortical areas with a poor prognosis, and rarely in the lateral ventricle with a very poor prognosis. So far, only 6 cases of AT/RT in lateral ventricle have been reported in China. This article reports the diagnosis and treatment of four children with AT/RT in the lateral ventricle, and discusses the clinical manifestations, differentiation and diagnosis, treatment and prognosis of the disease through literature review, in order to improve clinicians' understanding of the disease and reduce missed diagnosis and misdiagnosis.

Objective To evaluate the quality of prediction model on healthcare-associated infections in China,so as to standardize research process and reporting methods.Methods It performed a literature search for healthcare-associated infections prediction model studies published using the following databases by the end of 2022.After independently screening the literature and cross-checking the extracted data according to the inclusion and exclusion criteria,the research team applied the prediction model risk of bias assessment tool(PROBAST)to evaluate the methodological quality,and the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis(TRIPOD)statement to evaluate the quality of study reporting.Results A total of 81 healthcare-associated infections prediction studies were identified.Their median PROBAST overall adherence were 58.11%±13.88%,median TRIPOD adherence were 56.11%±16.35%.The main methodological flaws involved participants defined,ignored complexities in data,and omitted missing data.The reporting flaws lay in the items of risk groups,sample size,and supplementary information.Conclusion There are methodological deficiencies and incomplete reporting of domestic hospital infection prediction modelling studies,which limit the reliability and applicability of the results and leave much room for improvement.