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Background Symptom networks can provide empirical evidence for the development of personalized and precise symptom management strategies. However, few studies have explored the symptom networks of multidimensional symptom experiences in cancer survivors. The objectives of this study were to generate symptom networks of multidimensional symptom experiences in cancer survivors and explore the centrality indices and density in these symptom networks Methods Data from 1065 cancer survivors were obtained from the Shanghai CANcer Survivor (SCANS) Report. The MD Anderson Symptom Inventory was used to assess the prevalence and severity of 13 cancer‐related symptoms. We constructed contemporaneous networks with all 13 symptoms after controlling for covariates. Results Distress (rs = 9.18, rc = 0.06), sadness (rs = 9.05, rc = 0.06), and lack of appetite (rs = 9.04, rc = 0.06) had the largest values for strength and closeness. The density of the “less than 5 years” network was significantly different from that of the “5–10 years” and “over 10 years” networks (p < 0.001). We found that while fatigue was the most severe symptom in cancer survivorship, the centrality of fatigue was lower than that of the majority of other symptoms. Conclusion Our study demonstrates the need for the assessment of centrality indices and network density as an essential component of cancer care, especially for survivors with <5 years of survivorship. Future studies are warranted to develop dynamic symptom networks and trajectories of centrality indices in longitudinal data to explore causality among symptoms and markers of interventions. This study use data from 1065 cancer survivors from the Shanghai CANcer Survivor (SCANS) Report. We constructed contemporaneous networks with all 13 symptoms after controlling for covariates. We found that while fatigue was the most severe symptom in cancer survivorship, the centrality of fatigue was lower than that of the majority of other symptoms.

Cyclic GMP-AMP synthase (cGAS) plays a major role in detecting pathogenic DNA. It produces cyclic dinucleotide cGAMP, which subsequently binds to the adaptor protein STING and further triggers antiviral innate immune responses. However, the molecular mechanisms regulating cGAS enzyme activity remain largely unknown. Here, we characterize the cGAS-interacting protein Poly(rC)-binding protein 2 (PCBP2), which plays an important role in controlling cGAS enzyme activity, thereby mediating appropriate cGAS-STING signaling transduction. We find that PCBP2 overexpression reduces cGAS-STING antiviral signaling, whereas loss of PCBP2 significantly increases cGAS activity. Mechanistically, we show that PCBP2 negatively regulates anti-DNA viral signaling by specifically interacting with cGAS but not other components. Moreover, PCBP2 decreases cGAS enzyme activity by antagonizing cGAS condensation, thus ensuring the appropriate production of cGAMP and balancing cGAS-STING signal transduction. Collectively, our findings provide insight into how the cGAS-mediated antiviral signaling is regulated. cGAS senses viral DNA and forms cytosolic cGAS-DNA granules to mediate anti-DNA viral signaling pathway. Here the authors show that PCBP2 interacts with cGAS and antagonizes condensation of cGAS-DNA granules, thus maintaining host immune homeostasis.

Apples are one of the most productive fruits in the world, in addition to their nutritional and health advantages for humans. Even with the continuous development of AI in agriculture in general and apples in particular, automated systems continue to encounter challenges identifying rotten fruit and variations within the same apple category, as well as similarity in type, color, and shape of different fruit varieties. These issues, in addition to apple diseases, substantially impact the economy, productivity, and marketing quality. In this paper, we first provide a novel comprehensive collection named Apple Fruit Varieties Collection (AFVC) with 29,750 images through 85 classes. Second, we distinguish fresh and rotten apples with Apple Fruit Quality Categorization (AFQC), which has 2,320 photos. Third, an Apple Diseases Extensive Collection (ADEC), comprised of 2,976 images with seven classes, was offered. Fourth, following the state of the art, we develop an Optimized Apple Orchard Model (OAOM) with a new loss function named measured focal cross-entropy (MFCE), which assists in improving the proposed model’s efficiency. The proposed OAOM gives the highest performance for apple varieties identification with AFVC; accuracy was 93.85%. For the apples rotten recognition with AFQC, accuracy was 98.28%. For the identification of the diseases via ADEC, it was 99.66%. OAOM works with high efficiency and outperforms the baselines. The suggested technique boosts apple system automation with numerous duties and outstanding effectiveness. This research benefits the growth of apple’s robotic vision, development policies, automatic sorting systems, and decision-making enhancement.

Jinlida granule (JLD) is a Traditional Chinese Medicine (TCM) formula used for the treatment of type 2 diabetes mellitus (T2DM). However, the mechanism of JLD treatment for T2DM is not fully revealed. In this study, we explored the mechanism of JLD against T2DM by an integrative pharmacology strategy. Active components and corresponding targets were retrieved from Traditional Chinese Medicine System Pharmacology (TCMSP), SwissADME and Bioinformatics Analysis Tool for Molecular Mechanisms of Traditional Chinese Medicine Database (BATMAN-TCM) database. T2DM-related targets were obtained from Drugbank and Genecards databases. The protein–protein interaction (PPI) network was constructed and analyzed with STRING (Search Toll for the Retrieval of Interacting Genes/proteins) and Cytoscape to get the key targets. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) enrichment analyses were performed with the Database for Annotation, Visualization and Integrated Discovery (DAVID). Lastly, the binding capacities and reliability between potential active components and the targets were verified with molecular docking and molecular dynamics simulation. In total, 185 active components and 337 targets of JLD were obtained. 317 targets overlapped with T2DM-related targets. RAC-alpha serine/threonine-protein kinase (AKT1), tumor necrosis factor (TNF), interleukin-6 (IL-6), cellular tumor antigen p53 (TP53), prostaglandin G/H synthase 2 (PTGS2), Caspase-3 (CASP3) and signal transducer and activator of transcription 3 (STAT3) were identified as seven key targets by the topological analysis of the PPI network. GO and KEGG enrichment analyses showed that the effects were primarily associated with gene expression, signal transduction, apoptosis and inflammation. The pathways were mainly enriched in PI3K-AKT signaling pathway and AGE-RAGE signaling pathway in diabetic complications. Molecular docking and molecular dynamics simulation verified the good binding affinity between the key components and targets. The predicted results may provide a theoretical basis for drug screening of JLD and a new insight for the therapeutic effect of JLD on T2DM.

Mycoplasma pneumoniae (MP) is a major pathogen of community-acquired pneumonia in children. However, the specific pathogenesis of the progression of Mycoplasma pneumoniae pneumonia (MPP) is unclear. We aimed to reveal the landscape of microbiota and the host immune response in MPP. This self-controlled study analyzed the microbiome and transcriptome of bronchoalveolar lavage fluid (BALF) from the severe side (SD) and opposite side (OD) of 41 children with MPP from January to December 2021 and revealed the differences of the peripheral blood neutrophil function among children with mild MPP, severe MPP, and healthy children through transcriptome sequencing. The MP load or the pulmonary microbiota had no significant difference between the SD group and OD group, and the deterioration of MPP was related to the immune response, especially the intrinsic immune response. The immune response plays a role in MPP, which may inform treatment strategies for MPP.

pneumonia (MPP) may lead to various significant outcomes, such as necrotizing pneumonia(NP) and refractory MPP (RMPP). We investigated the potential of the peripheral blood neutrophil-to-lymphocyte ratio (NLR) to predict outcomes in patients with MPP. This was a prospective study of patients with MPP who were admitted to our hospital from 2019 to 2021. Demographic and clinical data were collected from patient records and associated with the development of NP and RMPP and other outcome measures. Of the 1,401 patients with MPP included in the study, 30 (2.1%) developed NP. The NLR was an independent predictor of NP (odds ratio 1.153, 95% confidence interval 1.022-1.300, =0.021). The probability of NP was greater in patients with a high NLR (≥1.9) than in those with a low NLR (<1.9) ( <0.001). The NLR was also an independent predictor of RMPP (odds ratio 1.246, 95% confidence interval 1.102-1.408, <0.005). Patients with a high NLR were more likely to develop NP and RMPP and require intensive care, and had longer total fever duration, longer hospital stays, and higher hospitalization expenses than those with a low NLR (all <0.005). The NLR can serve as a predictor of poor prognosis in patients with MPP. It can predict the occurrence of NP, RMPP, and other poor outcomes. The use of this indicator would allow the simple and rapid prediction of prognosis in the early stages of MPP, enabling the implementation of appropriate treatment strategies.

Background Many gaps remain in our understanding of the immune and molecular characteristics that underlie activated phosphoinositide 3‐kinase delta syndrome (APDS). Methods We performed RNA sequencing of peripheral blood leukocytes obtained from a child with APDS and his healthy parents and deconvoluted bulk transcriptional data to assess immune cell status. Results Pathway enrichment analysis suggested signaling pathways enriched in virus infection as well as the PI3K, mitogen‐activated protein kinase (MAPK), natural killer cell‐mediated cytotoxicity, and nucleotide‐binding oligomerization domain (NOD)‐like receptor signaling pathways. The proportion of B cells memory, T cells CD4 memory resting and dendritic cells activated were reduced, whereas B cells naïve, T cells CD8, NK cells resting, monocytes and macrophages M2 were increased in the child. Top 10 hub genes were screened and showed moderate to strong relatedness with immune cell proportions. Conclusion Deconvolution of bulk RNA sequencing to assess immune cells status can provide further insight into the alterations in immunological features underlying APDS and other rare diseases.

Anaplastic thyroid carcinoma (ATC) is an aggressive malignant tumor composed of undifferentiated thyroid follicular cells. Pathological diagnosis of ATC can be challenging as the tumor may show morphological overlap with other neoplasms with anaplastic morphology. Immunohistochemical demonstration of thyroid origin facilitates the diagnosis of ATC. Previous studies using the polyclonal anti-PAX8 antibody 10336-1-AP suggested that PAX8 was the most sensitive marker, expressed in up to 80% of ATC. According to a 2018 NordiQC report, the monoclonal anti-PAX8 antibody MRQ-50 has become the most commonly used anti-PAX8 antibody worldwide. However, validation of this antibody in ATC is lacking. In this study, we recruited 182 ATC cases from seven institutions. Pathology slides were subjected to histology review. PAX8 immunohistochemistry using the MRQ-50 antibody was performed in whole tissue slides (n = 147) or tissue microarray sections (n = 35). We found PAX8 expression in 54.4% of the cases, which was significantly lower than those reported in prior studies with the polyclonal antibody. PAX8 expression was positively correlated with the presence of an epithelial pattern (63.6% vs 37.5%, p = 0.0008) and a coexisting differentiated thyroid carcinoma component (71.6% vs 44.3%, p = 0.0004), but was not associated with age, gender, specimen type, or presence of giant cell and sarcomatoid patterns. In conclusion, we demonstrated PAX8 expression using the monoclonal antibody MRQ-50 in only half of the cases in a large ATC series. Pathologists should be aware that PAX8 expression in ATC is less than those reported in early studies to avoid misdiagnosis.

City Information Modelling (CIM) is becoming an important base model of Smart City and Digital Twin City, which can realise intelligent city design and management. Lately, CIM has become the focuses of urban planning and design studies. Under the influence of building information model (BIM), smart city and three-dimensional city simulation, city-level information modelling, CIM connects different BIM levels integrates the spatial expression effect of GIS. This review introduces CIM development from using generating procedures, such as rules and typological processes, to analyse urban scenarios to form the city full information scene through the integration of BIM, GIS, and IoT. The paper also overviews the technical path of construction with CIM implementation, problems existing in the current practice of CIM technology, including all information of digital and lightweight data, scene fast calls and data standard uniformity, etc. Following the latest CIM progress, the paper puts forward some ways to realise the effective use of CIM in urban planning and design. The further review focuses on big data security, publicity, urban design element and CIM platform practical use in China.

Accurate prediction of the latent period and disease progression in tomato fungal infections is critical for enabling timely interventions and effective disease management. Unlike existing AI-based approaches that primarily classify diseases after symptom emergence, this study innovates by predicting infection stages from the asymptomatic (latent) phase through complete symptom development, integrating biologically grounded feature extraction with explainable artificial intelligence (XAI). This study presents a novel, XAI framework capable of day-wise prediction of infection stages, including the latent period, for four major fungal pathogens in tomatoes: Alternaria alternata, Alternaria solani, Botrytis cinerea, and Fusarium oxysporum. A high-resolution (Red-Green-Blue) RGB image dataset was collected under controlled inoculation conditions, capturing daily changes in infected and healthy tomato leaves over six days post-infection. The pipeline included image preprocessing, lesion segmentation, and extraction of biologically meaningful features (texture, color, and shape) reflecting underlying physiological changes in the plant. Feature relevance across infection stages was dynamically assessed using the Relief algorithm, providing interpretability by linking visual changes to disease biology. Machine learning classifiers, Support Vector Machine (SVM) and Random Forest (RF), were optimized using Particle Swarm Optimization (PSO), achieving significant improvements in infection day prediction accuracy across all four pathogens. For example, RF accuracy increased from 76.14% to 94.17% for A. alternata (with 97.96% sensitivity and 99.48% specificity on day 6 post-inoculation) and from 80.01% to 97.08% for B. cinerea. Critically, the model accurately identified the latent period for each pathogen, detecting microscopic texture changes on day 1 post-inoculation when no visible symptoms were present. By bridging the gap between AI and plant pathology, this framework enables early diagnosis of fungal diseases with explainable outputs. The approach offers a scalable, non-destructive, and biologically grounded tool for integrated disease management, with potential applications across diverse crops in precision agriculture.