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Abstract Intracranial lesions in children often have good prognoses, allowing long-term survival. Cognitive functions, crucial for life quality, need more attention. Previous research has focused on adults, with pediatric studies limited by varied lesions and complex treatments. This study aims to evaluate cognitive and brain network changes in children with frontal lobe lesions, which significantly impact cognitive function, using a before-and-after comparison. The study enrolled 20 children with frontal lesions who underwent fMRI and cognitive tests before and after surgery, with only surgical treatment initially. Brain network changes were evaluated using functional metrics, and cognitive shifts were measured through test scores. Correlations were analyzed to explore brain mechanisms behind cognitive changes. Additionally, 20 healthy children underwent the same assessments for baseline data. Preliminary evidence of cognitive recovery, notably in social cognition, was observed about three months post-surgery, potentially linked to increased functional connectivity between the right lingual gyrus and right middle temporal gyrus. Children with frontal lobe lesions have demonstrated short-term postoperative cognitive improvement and associated reorganization and repair of brain networks, though this capacity for repair may diminish over time. This underscores the importance of timely rehabilitation interventions. This study offers unique insights into cognitive neuroscience and potential rehabilitation targets.

BackgroundTemporal lobe mass lesions are the most common intracranial space-occupying lesions in children, among various brain lobes. The temporal lobe is critically involved in higher cognitive functions, and surgical interventions often risk causing damage to these functions. If necessary interventions and prehabilitation can be conducted preoperatively, it might be possible to achieve a larger extent of lesion resection with minimal cognitive impairment. However, research in this area has been relatively limited in the past. Our study aims to fill this gap.MethodsWe enrolled 15 children with temporal lobe mass lesions and 15 age- and gender-matched healthy children as controls. All participants underwent cognitive assessments and functional MRI scans. The cognitive testing data and functional MRI data were then analyzed and compared between the two groups.ResultsOur findings suggest that children with temporal lobe mass lesions primarily exhibit impairments in working memory and sustained attention. Multiple brain network indices were altered in the affected children, with the most prominent change being hyperactivation of the default mode network (DMN). This hyperactivation was correlated with cognitive impairments, indicating that the overactivation of the DMN might represent an inefficient compensatory mechanism within the brain’s networks.ConclusionCompared to healthy children, those with temporal lobe mass lesions experience deficits in working memory and sustained attention, and the hyperactivation of the DMN may be the underlying network mechanism driving these cognitive impairments. Our research offers a unique and clinically valuable reference for future studies on preoperative interventions and prehabilitation in this population.

The complex interaction between the immune system and autoinflammatory disorders highlights the centrality of autoimmune mechanisms in the pathogenesis of autoinflammatory diseases. With the exploration of PSTPIP2, it has been discovered to play an inhibitory role in immune diseases, suggesting its potential utility in the research and treatment of rheumatic diseases. This review outlines the mechanisms of PSTPIP2 in chronic multifocal osteomyelitis (CMO), rheumatoid arthritis (RA), synovitis-acne-pustulosis-hyperostosis-osteitis (SAPHO) syndrome, liver diseases, renal diseases, pressure ulcer sepsis and diabetic obesity. The mechanisms include inhibiting the IL-1β inflammatory responses, NF-κB, ERK phosphorylation etc., promoting Erβ, and modulating the polarization of macrophage to prevent the inflammatory diseases. This review summarized current findings and offered perspectives on future research directions, laying a foundation for applying of PSTPIP2 in inflammatory diseases.

BackgroundGliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.MethodsThis study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.ResultsThe TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (P

Abstract As the importance of cell heterogeneity has begun to be emphasized, single-cell sequencing approaches are rapidly adopted to study cell heterogeneity and cellular evolutionary relationships of various cells, including stem cell populations. The hematopoietic stem and progenitor cell (HSPC) compartment contains HSC hematopoietic stem cells (HSCs) and distinct hematopoietic cells with different abilities to self-renew. These cells perform their own functions to maintain different hematopoietic lineages. Undeniably, single-cell sequencing approaches, including single-cell RNA sequencing (scRNA-seq) technologies, empower more opportunities to study the heterogeneity of normal and pathological HSCs. In this review, we discuss how these scRNA-seq technologies contribute to tracing origin and lineage commitment of HSCs, profiling the bone marrow microenvironment and providing high-resolution dissection of malignant hematopoiesis, leading to exciting new findings in HSC biology.

Abstract Purpose Investigate the clinical characteristics of splenomegaly secondary to acute pancreatitis (SSAP) and construct a nomogram prediction model based on Lasso-Logistic regression. Methods A retrospective case-control study was conducted to analyze the laboratory parameters and computed tomography (CT) imaging of acute pancreatitis (AP) patients recruited at Xuanwu Hospital from December 2014 to December 2021. Lasso regression was used to identify risk factors, and a novel nomogram was developed. The performance of the nomogram in discrimination, calibration, and clinical usefulness was evaluated through internal validation. Results The prevalence of SSAP was 9.2% (88/950), with the first detection occurring 65(30, 125) days after AP onset. Compared with the control group, the SSAP group exhibited a higher frequency of persistent respiratory failure, persistent renal failure, infected pancreatic necrosis, and severe AP, along with an increased need for surgery and longer hospital stay (P

Abstract Background Metabolic dysfunction-associated steatotic liver disease(MASLD) is the most common cause of chronic liver disease. Clinical evidences have demonstrated the link between MASLD and the increased risk of cardiovascular disease (CVD) development. We aimed to investigate the relationship between Life’s Essential 8 (LE8), an enhanced approach to assessing cardiovascular health(CVH), and MASLD. Methods Data were extracted from the National Health and Nutrition Examination Survey (NHANES) in 2017–2020 cycles. MASLD was assessed by the latest diagnostic criteria. LE8 scores (range 0–100) were obtained from measurements based on American Heart Association definitions, divided into health factor and health behavior scores. Multivariable logistic and restricted cubic spline models were used to assess the associations. Results 5646 participants were included based on the inclusion and exclusion criteria, 2616 (46.33%) participants were diagnosed with MASLD. After adjusting for confounding variables, higher LE8 scores were associated with a lower risk of MASLD (OR = 0.19, 95%CI 0.17–0.21; P

The nondestructive detection of kiwifruit texture has become an important necessity that influences kiwifruit economic efficiency and consumer recognition of the kiwifruit. This study investigated the feasibility of using hyperspectral imaging technology to identify textural characteristics of kiwifruit, and to establish the model with best performance. Firstly, a near-infrared hyperspectral online grading system (1000–2500 nm) was constructed to acquire spectral images. Textural characteristics were measured via three different textural analysis tests: textural profile analysis, puncture test, and shear test. Then, samples were divided into a calibration set and a prediction set based on rank sampling at a ratio of 3:1. Mean centralization, standard normal variate, and multiplicative scatter correction methods were applied to pre-process obtained spectra. Finally, the partial least squares method was used to establish prediction models of kiwifruit textural characteristics. The models of hardness1, chewiness, resilience, peel hardness, average hardness, and corrected hardness achieved good performance. Both the correlation coefficients of calibration (rc) and prediction (rp) values exceeded 0.9. The difference between the root mean square error (RMSE) of calibration and prediction was small, and the ratio of prediction to deviation (RPD) value exceeded 2, which was used to predict kiwifruit quality. However, the model of average shear force had a low accuracy with an RPD of 1.680 and the model of shear force also had an RPD below 1.5 and a low RMSE. The results showed that near-infrared hyperspectral imaging technique can be used as a nondestructive method for the textural characteristics kiwifruit.

Climate change significantly impacts agricultural production, ecosystem stability, and socioeconomic development. Global climate models (GCMs) serve as the primary tool for simulating historical and future precipitation patterns. However, due to issues such as coarse resolution, boundary condition, and parameterization, model outputs require bias correction (BC). With the evolution of deep learning techniques, supervised convolutional neural network (CNN) frameworks have gained popularity in the area of climate model BC but face limitations in spatial correlation assumptions and data sparsity, particularly for extreme precipitation This study proposed an unsupervised learning approach using cycle generative adversarial network (CycleGAN) to correct the ensemble mean bias of models and compare its performance with CNN and Quantile Mapping methods. The results demonstrate that the proposed CycleGAN approach outperforms both CNN and Quantile Mapping in ensemble mean BC. It effectively learns the overall distribution of precipitation through an adversarial process and yields better extreme precipitation predictions.