Dermatofibrosarcoma protuberans (DFSP) is a rare, low-grade malignant cutaneous tumor characterized by a high propensity for local recurrence, yet low rates of metastasis and mortality. The pathogenesis of DFSP is primarily associated with a chromosomal translocation t(17; 22)(q22; q13), which leads to the fusion of the platelet-derived growth factor subunit B gene with the collagen type I alpha 1 chain gene in the majority of cases. Diagnosis relies on histopathological examination, supported by imaging studies for comprehensive assessment. Surgical resection remains the cornerstone of treatment; however, it is frequently complicated by a high risk of local recurrence. Recent advances in radiotherapy and targeted therapeutic strategies offer promising avenues for reducing recurrence rates and improving patient survival. Nevertheless, the pathological heterogeneity and diverse clinical manifestations of DFSP pose considerable challenges to the development and implementation of individualized treatment approaches. Thus, further fundamental research and refinement of clinical management are imperative to advance the diagnosis and therapy of DFSP.

Objective:Using resting-state functional magnetic resonance imaging (rs-fMRI), we explored the changes in dynamic functional network connections (dFNC) in the brains of patients with first-episode schizophrenia (SZ) and evaluated the potential clinical value of dFNC changes in combination with a machine learning model.Methods:Clinical data of 50 patients with schizophrenia (schizophrenia group), 29 males and 21 females, aged 18-47 (28.3±7.2) years, who attended the psychiatric department of the Second Affiliated Hospital of Xinxiang Medical College from January 2022 to August 2023, were retrospectively included. In the same period, 50 healthy controls matched for age and education (healthy control group) were recruited, of which 24 were male and 26 were female, aged 18-48 (28.0±6.9) years. The rs-fMRI imaging data were acquired for each subject. The dFNC cluster analysis was performed based on independent component analysis, and the differences between groups with different state FNC matrices were statistically analyzed. The dataset samples were divided into a training set (35 SZ patients and 35 healthy controls) and a validation set (15 SZ patients and 15 healthy controls) in a 7∶3 ratio. A machine learning classification model was constructed based on the dFNC matri. The performance of the model for distinguishing between schizophrenia and healthy controls was assessed by five-fold cross-validation using accuracy (ACC), recall (REC), F1 score, and area under curve (AUC) metrics of the working characteristics of the subjects.Results:Five network functional connectivity states were obtained by dFNC cluster analysis. Patients with first SZ showed a wide range of high connectivity and low connectivity changes on the neural dynamic functional networks, as shown by increased dynamic connectivity within the visual network (VIS) in state 1 (weak connectivity); The dynamic connectivity between executive control network (ECN) and VIS, frontal parietal network (FPN) and VIS decreases at state 3 (strong connectivity); The dynamic connectivity between default mode network (DMN) and FPN, DMN and ventral attention network (VAN) decreases at state 4 (weak connectivity). The machine learning results show that the classification model constructed by the dFNC matrix combined with SVM in state 3 (strongly connected) in the validation set obtains the best classification results (ACC=0.938; REC=0.938; F1=0.937; AUC=0.984), and the overall average classification ACC of the five states reaches 0.751, and AUC reaches 0.784.Conclusion:Patients with first-episode SZ have some brain functional network connectivity abnormalities, and a machine learning model based on dFNC features has high classification performance in distinguishing first-episode SZ from HC.

BACKGROUND:H uman pluripotent stem cell-derived cardiomyocytes offer an ideal cellular resource for studying heart diseases,conducting drug screening,developing in vitro heart models,and exploring potential cell therapies.However,human pluripotent stem cell-derived cardiomyocytes are characterized by immaturity with limited specific gene expression,low Ca2+processing levels,and underdeveloped structural,metabolic,and electrophysiological features.These limitations significantly impede the application of human pluripotent stem cell-derived cardiomyocytes.OBJECTIVE:To review the academic progress and clinical application of promoting the maturation of human pluripotent stem cell-derived cardiomyocytes by in vitro synthetic microenvironment.METHODS:CNKI,WanFang,VIP,PubMed,Web of Science,and Medline databases were searched,with"human pluripotent stem cells,human myocardial cells,hPSC-CMs,mature,OA,human pluripotent stem cell-derived cardiomyocytes,hPSC-CMs"as English search terms and"human pluripotent stem cells,cardiomyocytes,mature,OA,hPSC-CMs"as Chinese search terms.All relevant literature published from January 2002 to July 2024 was retrieved and 82 articles were included in the review.RESULTS AND CONCLUSION:(1)In recent years,in vitro synthetic microenvironments have attracted extensive attention due to their excellent intrinsic properties such as stiffness,plasticity,nanoscale morphology,and chemical functionality.(2)Human pluripotent stem cell-derived cardiomyocytes can be used as an effective platform for the treatment of cardiovascular diseases.(3)Mechanical stimulation,electrical stimulation,addition of biochemical molecules,and three-dimensional culture methods are effective methods to promote the maturation of human pluripotent stem cell-derived cardiomyocytes,which can further promote the clinical application of human pluripotent stem cell-derived cardiomyocytes.

BACKGROUND:H uman pluripotent stem cell-derived cardiomyocytes offer an ideal cellular resource for studying heart diseases,conducting drug screening,developing in vitro heart models,and exploring potential cell therapies.However,human pluripotent stem cell-derived cardiomyocytes are characterized by immaturity with limited specific gene expression,low Ca2+processing levels,and underdeveloped structural,metabolic,and electrophysiological features.These limitations significantly impede the application of human pluripotent stem cell-derived cardiomyocytes.OBJECTIVE:To review the academic progress and clinical application of promoting the maturation of human pluripotent stem cell-derived cardiomyocytes by in vitro synthetic microenvironment.METHODS:CNKI,WanFang,VIP,PubMed,Web of Science,and Medline databases were searched,with"human pluripotent stem cells,human myocardial cells,hPSC-CMs,mature,OA,human pluripotent stem cell-derived cardiomyocytes,hPSC-CMs"as English search terms and"human pluripotent stem cells,cardiomyocytes,mature,OA,hPSC-CMs"as Chinese search terms.All relevant literature published from January 2002 to July 2024 was retrieved and 82 articles were included in the review.RESULTS AND CONCLUSION:(1)In recent years,in vitro synthetic microenvironments have attracted extensive attention due to their excellent intrinsic properties such as stiffness,plasticity,nanoscale morphology,and chemical functionality.(2)Human pluripotent stem cell-derived cardiomyocytes can be used as an effective platform for the treatment of cardiovascular diseases.(3)Mechanical stimulation,electrical stimulation,addition of biochemical molecules,and three-dimensional culture methods are effective methods to promote the maturation of human pluripotent stem cell-derived cardiomyocytes,which can further promote the clinical application of human pluripotent stem cell-derived cardiomyocytes.

Dermatofibrosarcoma protuberans (DFSP) is a rare, low-grade malignant cutaneous tumor characterized by a high propensity for local recurrence, yet low rates of metastasis and mortality. The pathogenesis of DFSP is primarily associated with a chromosomal translocation t(17; 22)(q22; q13), which leads to the fusion of the platelet-derived growth factor subunit B gene with the collagen type I alpha 1 chain gene in the majority of cases. Diagnosis relies on histopathological examination, supported by imaging studies for comprehensive assessment. Surgical resection remains the cornerstone of treatment; however, it is frequently complicated by a high risk of local recurrence. Recent advances in radiotherapy and targeted therapeutic strategies offer promising avenues for reducing recurrence rates and improving patient survival. Nevertheless, the pathological heterogeneity and diverse clinical manifestations of DFSP pose considerable challenges to the development and implementation of individualized treatment approaches. Thus, further fundamental research and refinement of clinical management are imperative to advance the diagnosis and therapy of DFSP.

Objective:Using resting-state functional magnetic resonance imaging (rs-fMRI), we explored the changes in dynamic functional network connections (dFNC) in the brains of patients with first-episode schizophrenia (SZ) and evaluated the potential clinical value of dFNC changes in combination with a machine learning model.Methods:Clinical data of 50 patients with schizophrenia (schizophrenia group), 29 males and 21 females, aged 18-47 (28.3±7.2) years, who attended the psychiatric department of the Second Affiliated Hospital of Xinxiang Medical College from January 2022 to August 2023, were retrospectively included. In the same period, 50 healthy controls matched for age and education (healthy control group) were recruited, of which 24 were male and 26 were female, aged 18-48 (28.0±6.9) years. The rs-fMRI imaging data were acquired for each subject. The dFNC cluster analysis was performed based on independent component analysis, and the differences between groups with different state FNC matrices were statistically analyzed. The dataset samples were divided into a training set (35 SZ patients and 35 healthy controls) and a validation set (15 SZ patients and 15 healthy controls) in a 7∶3 ratio. A machine learning classification model was constructed based on the dFNC matri. The performance of the model for distinguishing between schizophrenia and healthy controls was assessed by five-fold cross-validation using accuracy (ACC), recall (REC), F1 score, and area under curve (AUC) metrics of the working characteristics of the subjects.Results:Five network functional connectivity states were obtained by dFNC cluster analysis. Patients with first SZ showed a wide range of high connectivity and low connectivity changes on the neural dynamic functional networks, as shown by increased dynamic connectivity within the visual network (VIS) in state 1 (weak connectivity); The dynamic connectivity between executive control network (ECN) and VIS, frontal parietal network (FPN) and VIS decreases at state 3 (strong connectivity); The dynamic connectivity between default mode network (DMN) and FPN, DMN and ventral attention network (VAN) decreases at state 4 (weak connectivity). The machine learning results show that the classification model constructed by the dFNC matrix combined with SVM in state 3 (strongly connected) in the validation set obtains the best classification results (ACC=0.938; REC=0.938; F1=0.937; AUC=0.984), and the overall average classification ACC of the five states reaches 0.751, and AUC reaches 0.784.Conclusion:Patients with first-episode SZ have some brain functional network connectivity abnormalities, and a machine learning model based on dFNC features has high classification performance in distinguishing first-episode SZ from HC.

Neurodegenerative diseases are often associated with inflammatory mechanisms, where persistent or excessive inflammatory responses can lead to neuronal damage and subsequent pathological changes. In acute neurological conditions such as stroke or traumatic brain injury, inflammation is a key factor that triggers acute neuronal injury and long-term sequelae. In chronic neurodegenerative diseases, including Alzheimer's disease, cognitive dysfunction, Parkinson's disease, and multiple sclerosis, the chronic activation of inflammation is closely related to gradual degeneration of neurons. Resolvin D1 (RvD1), an endogenous pro-resolving mediator, plays a crucial role in controlling the intensity and duration of inflammation by inhibiting excessive activation of immune cells, modulating the release of pro-inflammatory cytokines, and maintaining the integrity of the blood-brain barrier. This review focuses on the mechanisms of RvD1 in mediating inflammatory damage in major neurological diseases, aiming to provide theoretical support for a deeper understanding of disease mechanism, optimized therapeutic strategies, and enhanced outcome.

The evaluation, diagnosis, real-time monitoring, and guided operation conducted by ultrasound specialist nurses in acute and critical care can provide an objective basis for nursing decision-making, which is of great significance for optimizing nursing procedures and improving the survival rate and rehabilitation rate of patients. This paper summarizes the concept, origin, training status, and practice scope of ultrasound specialist nurses in the field of acute and critical care and puts forward the prospect of development in order to provide a reference for the training of ultrasound specialist nurses in the field of acute and critical care in China.

Objective To explore the intervention effect of nursing program based on contact-introduce-communicate-ask-respond-exit(CICARE)communication model on breast cancer patients undergoing chemotherapy.Methods Sixty patients with breast cancer who received routine intervention in Huai an First People's Hospital from January 2019 to June 2020 were selected as routine intervention group.Sixty patients with breast cancer who underwent chemotherapy nursing program based on CICARE communication model on the basis of routine intervention in Huai'an First People's Hospital from July 2020 to December 2021 were selected as CICARE intervention group.The intervention was carried out until discharge in both groups.The psychological state,self-management ability,level of hope and quality of life were compared between the two groups at admission and discharge.Results Compared with those at admission,the scores of Self-rating Anxiety Scale(SAS)and Self-rating Depression Scale(SDS)at discharge decreased in both groups,and the scores in the CICARE intervention group were lower than those in the routine intervention group at discharge(P<0.05).The scores of resilience rating,exercise,and cognitive symptom management implementation,communication with the physician ratings,reality and future of attitude,positive action of attitude,keeping intimate relationships with others and quality of life at discharge increased in both groups,and these scores in the CICARE intervention group were significantly higher than those in the routine intervention group(P<0.05).Conclusion The nursing program based on CICARE communication model can improve psychological state,self-management ability,hope level and quality of life in breast cancer patients undergoing chemotherapy by enhancing the communication between nurses and patients with good intervention effect.

Background::Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRDs). Accurate prediction and timely intervention play a pivotal role in enhancing survival rates. However, there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with AIIRD.Methods::In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022. Univariable logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) were used to select the clinical features for further training with machine learning (ML) methods, including random forest (RF), support vector machines (SVM), neural network (NN), logistic regression (LR), gradient boosted decision tree (GBDT), classification and regression trees (CART), and C5.0 models. The performances of these models were subsequently validated using a multicenter validation cohort.Results::In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respectively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performances, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.962-1.000 in the training cohort and 0.969-0.999 in the validation cohort. CART and C5.0 models achieved AUCs of 0.850 and 0.932, respectively, in the training cohort. Using D-dimer as a pre-screening index, the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort. These results markedly outperformed the use of D-dimer levels alone.Conclusion::ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE.Trial Registration::Chictr.org.cn: ChiCTR2200059599.