Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Objective To explore the application value of different deep learning models in predicting the lung cancer spread through air spaces(STAS).Methods A total of 203 patients with stage Ⅰ—Ⅱ primary lung cancer were included,of which 74 were STAS-positive and 129 were STAS-negative.Patients were randomly divided into training set(142 cases)and test set(61 cases)at a 7∶3 ratio.Region of interest(ROI)was outlined using ITK-SNAP software,facilitating the extraction of tumor and peritumoral images.The Resnet18,Inception_v3,and Vision Transformer(Vit)were employed for model training and feature extraction.Feature selection was performed by the least absolute shrinkage and selection operator(LASSO)algorithm and Spearman correlation coefficient,followed by the establishment of a predictive model using the Naive Bayes machine learning algorithm.The receiver operating characteristic(ROC)curve was drawn to compare the prediction performance of each model.The assessment of calibration was performed using calibration curves,and the evaluation of clinical application value was conducted using decision curve analysis(DCA).Results The area under the curve(AUC)for the training and test sets were as follows:the training set Resnet18 0.849-0.930,Inception_v3 0.848-0.888,Vit 0.747-0.842;and the test set Resnet18 0.796-0.846,Inception_v3 0.783-0.804,Vit 0.690-0.796.In tumor-peritumoral images,Resnet18 had a higher calibration and better clinical net benefit,while Vit showed superior calibration and clinical net benefit when only tumor tissue was considered.Conclusion Deep learning models can effectively predict lung cancer STAS,providing more decision support for the preoperative diagnosis and treatment of stages Ⅰ—Ⅱ lung cancer.

Objective:To investigate the prevalence of sarcopenia and potential influencing factors in middle-aged and elderly populations in Zhejiang Province.Methods:Data were obtained from Zhejiang Provincial Household Economic Status Survey, a cross-sectional survey was condcuted in middle-aged and olde adults selected through multi-stage sampling in three cities in Zhejiang (Huzhou, Jiaxing and Shaoxing) in July 2023. A total of 3 019 study participants, average age 62.3 years old, 53.5% men, were included according to the inclusion and exclusion criteria. Sarcopenia screening was conducted by using the questionnaire with five sarcopenia related-items. Univariable and multivariable logistic regression analysis was used to identify the factors associated with sarcopenia.Results:The prevalence of sarcopenia in the middle-aged and old study participants was 4.47%. Significant differences were observed between the participants with or without sarcopenia in terms of age, educational level, BMI, alcohol consumption status, diet habit, physical activity level, sleep quality, number of chronic diseases, childhood socioeconomic status, adulthood community socioeconomic status, muscle strength, walking assistance, ability to stand from seat, ability to climb stairs, and fall frequency ( P<0.05). Multivariable logistic regression analysis revealed that old age (≥75 years: OR=2.82, 95% CI: 1.60-4.97), low body weight ( OR=1.96, 95% CI: 1.06-3.62), unhealthy diet habit ( OR=1.57, 95% CI: 1.01-2.46), physical inactivity ( OR=5.80, 95% CI: 3.09-10.88), poor or very poor sleep quality ( OR=1.65, 95% CI:1.23-2.41), number of chronic diseases (1 chronic disease: OR=1.84, 95% CI: 1.08-3.14; 2 chronic diseases: OR=3.22, 95% CI: 1.81-5.71; 3 or more chronic diseases: OR=3.74, 95% CI: 2.11-6.65), poor childhood socioeconomic status ( OR=2.98, 95% CI: 1.23-7.20), and poor adulthood community socioeconomic status ( OR=3.87, 95% CI: 1.63-9.17) were significant risk factors for sarcopenia. Conclusions:The prevalence of sarcopenia was relatively low in middle-aged and old population in Zhejiang. Age, BMI, unhealthy diet, physical activity level, sleep quality, number of chronic diseases, childhood socioeconomic status, and adulthood community socioeconomic status were identified as significant influencing factors.

Objective To explore the application value of different deep learning models in predicting the lung cancer spread through air spaces(STAS).Methods A total of 203 patients with stage Ⅰ—Ⅱ primary lung cancer were included,of which 74 were STAS-positive and 129 were STAS-negative.Patients were randomly divided into training set(142 cases)and test set(61 cases)at a 7∶3 ratio.Region of interest(ROI)was outlined using ITK-SNAP software,facilitating the extraction of tumor and peritumoral images.The Resnet18,Inception_v3,and Vision Transformer(Vit)were employed for model training and feature extraction.Feature selection was performed by the least absolute shrinkage and selection operator(LASSO)algorithm and Spearman correlation coefficient,followed by the establishment of a predictive model using the Naive Bayes machine learning algorithm.The receiver operating characteristic(ROC)curve was drawn to compare the prediction performance of each model.The assessment of calibration was performed using calibration curves,and the evaluation of clinical application value was conducted using decision curve analysis(DCA).Results The area under the curve(AUC)for the training and test sets were as follows:the training set Resnet18 0.849-0.930,Inception_v3 0.848-0.888,Vit 0.747-0.842;and the test set Resnet18 0.796-0.846,Inception_v3 0.783-0.804,Vit 0.690-0.796.In tumor-peritumoral images,Resnet18 had a higher calibration and better clinical net benefit,while Vit showed superior calibration and clinical net benefit when only tumor tissue was considered.Conclusion Deep learning models can effectively predict lung cancer STAS,providing more decision support for the preoperative diagnosis and treatment of stages Ⅰ—Ⅱ lung cancer.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

Background: s/Aims: Sarcomatoid hepatocellular carcinoma (HCC) is a rare histological subtype of HCC characterized by extremely poor prognosis; however, its molecular characterization has not been elucidated. Methods: In this study, we conducted an integrated multiomics study of whole-exome sequencing, RNA-seq, spatial transcriptome, and immunohistochemical analyses of 28 paired sarcomatoid tumor components and conventional HCC components from 10 patients with sarcomatoid HCC, in order to identify frequently altered genes, infer the tumor subclonal architectures, track the genomic evolution, and delineate the transcriptional characteristics of sarcomatoid HCCs. Results: Our results showed that the sarcomatoid HCCs had poor prognosis. The sarcomatoid tumor components and the conventional HCC components were derived from common ancestors, mostly accessing similar mutational processes. Clonal phylogenies demonstrated branched tumor evolution during sarcomatoid HCC development and progression. TP53 mutation commonly occurred at tumor initiation, whereas ARID2 mutation often occurred later. Transcriptome analyses revealed the epithelial–mesenchymal transition (EMT) and hypoxic phenotype in sarcomatoid tumor components, which were confirmed by immunohistochemical staining. Moreover, we identified ARID2 mutations in 70% (7/10) of patients with sarcomatoid HCC but only 1–5% of patients with non-sarcomatoid HCC. Biofunctional investigations revealed that inactivating mutation of ARID2 contributes to HCC growth and metastasis and induces EMT in a hypoxic microenvironment. Conclusions We offer a comprehensive description of the molecular basis for sarcomatoid HCC, and identify genomic alteration (ARID2 mutation) together with the tumor microenvironment (hypoxic microenvironment), that may contribute to the formation of the sarcomatoid tumor component through EMT, leading to sarcomatoid HCC development and progression.

AIM To explore the clinical effects of Heiguteng Zhuifeng Huoluo Capsules combined with warm acupuncture at musculotendinous pathological nodes on patients with knee osteoarthritis due to Cold-Dampness Obstruction and Fixed Impediment.METHODS One hundred and sixty patients were randomly assigned into control group(80 cases)for 4-week intervention of both warm acupuncture at musculotendinous pathological nodes and Celecoxib Capsules,and observation group(80 cases)for 4-week intervention of Heiguteng Zhuifeng Huoluo Capsules,warm acupuncture at musculotendinous pathological nodes and Celecoxib Capsules.The changes in clinical effects,TCM syndrome scores,WOMAC scores,Lequesne indices,pain mediators(PGE2,β-EP),growth factors(TGF-β1,IGF-1),bone metabolism indices(OPG,RANKL),TLR4/NF-κB signaling pathway and safety indices were detected.RESULTS The observation group demonstrated higher total effective rate than the control group(P＜0.05).After the treatment,the two groups displayed decreased TCM syndrome scores,WOMAC scores,Lequesne indices,PGE2,RANKLE,TLR4,NF-κB(P＜0.05),and increased β-EP,growth factors,OPG(P＜0.05),especially for the observation group(P＜0.05).No serious adverse events or reactions were observable in the two groups.CONCLUSION For the patients with knee osteoarthritis due to Cold-Dampness Obstruction and Fixed Impediment,Heiguteng Zhuifeng Huoluo Capsules combined with warm acupuncture at musculotendinous pathological nodes can safely and effectively alleviate clinical symptoms,improve knee joint pain,and enhance joint functions,whose action mechanisms may contribute to the inhibition of TLR4/NF-κB signaling pathway expression and regulations of serum TGF-β1,IGF-1,OPG,RANKL levels.

OBJECTIVE: The study aim was to investigate the effects of exposure to multiple environmental organic pollutants on cardiopulmonary health with a focus on the potential mediating role of oxidative stress. METHODS: A repeated-measures randomized crossover study involving healthy college students in Beijing was conducted. Biological samples, including morning urine and venous blood, were collected to measure concentrations of 29 typical organic pollutants, including hydroxy polycyclic aromatic hydrocarbons (OH-PAHs), bisphenol A and its substitutes, phthalates and their metabolites, parabens, and five biomarkers of oxidative stress. Health assessments included blood pressure measurements and lung function indicators. RESULTS: Urinary concentrations of 2-hydroxyphenanthrene (2-OH-PHE) ( β = 4.35% [95% confidence interval ( CI): 0.85%, 7.97%]), 3-hydroxyphenanthrene ( β = 3.44% [95% CI: 0.19%, 6.79%]), and 4-hydroxyphenanthrene (4-OH-PHE) ( β = 5.78% [95% CI: 1.27%, 10.5%]) were significantly and positively associated with systolic blood pressure. Exposures to 1-hydroxypyrene (1-OH-PYR) ( β = 3.05% [95% CI: -4.66%, -1.41%]), 2-OH-PHE ( β = 2.68% [95% CI: -4%, -1.34%]), and 4-OH-PHE ( β = 3% [95% CI: -4.68%, -1.29%]) were negatively associated with the ratio of forced expiratory volume in the first second to forced vital capacity. These findings highlight the adverse effects of exposure to multiple pollutants on cardiopulmonary health. Biomarkers of oxidative stress, including 8-hydroxy-2'-deoxyguanosine and extracellular superoxide dismutase, mediated the effects of multiple OH-PAHs on blood pressure and lung function. CONCLUSION Exposure to multiple organic pollutants can adversely affect cardiopulmonary health. Oxidative stress is a key mediator of the effects of OH-PAHs on blood pressure and lung function.
Humans ; Oxidative Stress/drug effects* ; Male ; Cross-Over Studies ; Female ; Young Adult ; Environmental Pollutants/toxicity* ; Environmental Exposure/adverse effects* ; Biomarkers/blood* ; Adult ; Blood Pressure/drug effects* ; Polycyclic Aromatic Hydrocarbons/urine* ; Beijing

Objective To investigate the alterations of cerebral blood flow(CBF)in type 2 diabetic mellitus(T2DM) patients without cognitive impairment by using arterial spin labeling(ASL)technique.Methods A total of 23 T2DM patients without cognitive impairment and 23 healthy controls(HC)matched by age,sex,and education attainment were recruited.Their clinical data were collected,and neuropsychological tests and cerebral magnetic resonance imaging were performed.Then,the outcomes of clinical features,neuropsychological tests,and global and regional CBF were compared between the two groups.The significant regional zCBF(z-transformed relative CBF)values were extracted and correlated with clinical data and neuropsychological scores in T2DM patients,controlling age,sex,and education.Results No significant difference was found in whole brain CBF between the two groups(P=0.155),while significantly higher CBF was identified in the left superior temporal gyrus and left insula in the T2DM group(Gaussian random field correction,initial threshold P < 0.001,cluster level P < 0.05).No correlation was observed between the significant regional zCBF values and the clinical data or the neuropsychological scores in T2DM patients(all P>0.05).Conclusion Alterations in cerebral hemodynamics may precede cognitive function changes in T2DM,suggesting that the ASL technique is promising for early monitoring of cerebral hemodynamic changes associated with cognitive impairment in patients with T2DM.
Humans ; Diabetes Mellitus, Type 2/physiopathology* ; Cerebrovascular Circulation ; Middle Aged ; Male ; Female ; Magnetic Resonance Imaging ; Case-Control Studies ; Cognitive Dysfunction ; Neuropsychological Tests ; Aged

To guide clinical blood transfusion practices for pediatric patients, the National Health Commission has issued the health standard "Guideline for pediatric transfusion" (WS/T 795-2022). Blood transfusion is one of the most commonly used supportive treatments for children with hematological diseases. This guideline provides guidance and recommendations for blood transfusions in children with aplastic anemia, thalassemia, autoimmune hemolytic anemia, glucose-6-phosphate dehydrogenase deficiency, acute leukemia, myelodysplastic syndromes, immune thrombocytopenic purpura, and thrombotic thrombocytopenic purpura. This article presents the evidence and interpretation of the blood transfusion provisions for children with hematological diseases in the "Guideline for pediatric transfusion", aiming to assist in the understanding and implementing the blood transfusion section of this guideline.
Humans ; Child ; Hematologic Diseases/therapy* ; Blood Transfusion/standards* ; Practice Guidelines as Topic