Objective:To explore the impact of body constitutional metabolic phenotype on the outcomes of hypertensive intracerebral hemorrhage (HICH) patients one year after onset.Methods:This study retrospectively studied the clinical data of 467 HICH patients admitted to the First People's Hospital of Lianyungang City from May 2021 to May 2023. Based on telephone follow-up after one year, the patients were categorized into two groups: a good outcome group (287 cases) and a poor outcome group (180 cases). According to the patients' body mass index (BMI) and metabolic status, the population was divided into six phenotypes: metabolically healthy with normal weight (MH-NW), metabolically healthy with overweight (MH-OW), metabolically healthy obesity (MHO), metabolically unhealthy normal weight (MU-NW), metabolically unhealthy with overweight (MU-OW), and metabolically unhealthy with obesity (MUO). The baseline data of the two groups were compared between two groups. The influencing factors of adverse outcomes in patients with HICH one year after onset were analyzed. Quantitative data that conforms to normal distribution were represented by xˉ±s, and independent sample t-test was used for comparison between two groups; The measurement data of skewed distribution was represented by M ( Q1, Q3), and Mann Whitney U test was used for comparison between the two groups; Count data was presented as an example (%), and comparison between groups was conducted using the χ2 test. Multivariate logistic regression analysis was used to analyze the influencing factors of poor prognosis in HICH patients one year after onset. Results:BMI, high density lipoprotein cholesterol(HDL-C) levels and baseline Glasgow coma score(GCS) score in the poor outcome group were lower than those in the good outcome group [23.8 (22.4, 26.1) kg/m 2 vs. 25.0 (22.5, 27.4) kg/m 2, Z=-2.31, P=0.021; 1.1 (1.0,1.4) mmol/L vs. 1.3 (1.0,1.6) mmol/L, Z=-4.18, P<0.001; 14 (13,15) score vs. 10 (7,13) score, Z=-10.20, P<0.001]. The incidence of hemorrhage into the ventricle, cerebral hernia, pulmonary infection and hydrocephalus [43.3%(78/180) vs. 23.7% (68/287). 5.6%(10/180) vs. 0.7% (2/287), 48.9%(88/180) vs. 6.6% (19/287), 5.0%(9/180) vs. 1.4% (4/287), χ2=19.86, P<0.001, χ2=10.43, P<0.001, χ2=111.90, P<0.001, χ2=5.32, P=0.021], proportion of surgical removal of hematoma [41.1%(74/180) vs. 19.5% (56/287), χ2=25.69, P<0.001], systolic blood pressure [158 (141,173) mmHg vs. 152 (138,169) mmHg, Z=-2.18, P=0.029] and fasting blood glucose [6.9 (5.7,8.2) mmol/L vs. 6.3 (5.4,7.8) mmol/L, Z=-2.08, P=0.038] were higher than those in good outcome group. The metabolic phenotypes in the poor conversion group were as follows: 41 cases (22.8%) of MH-NW, 23 cases (12.8%) of MH-OW, 9 cases (5.0%) of MHO, 54 cases (30.0%) of MU-NW, 33 cases (18.3%) of MU-OW, and 20 cases (11.1%) of MUO. Conversely, the metabolic phenotypes in the good conversion group were as follows: 67 cases (23.3%) of MH-NW, 77 cases (26.8%) of MH-OW, 31 cases (10.8%) of MHO, 40 cases (13.9%) of MU-NW, 46 cases (16.0%) of MU-OW, and 26 cases (9.1%) of MUO. Regarding metabolic types, the poor conversion group comprised 73 healthy cases (40.6%) and 107 unhealthy cases (59.4%), whereas the good conversion group had 177 healthy cases (61.7%) and 110 unhealthy cases (38.3%). In terms of body mass, the poor conversion group included 94 cases (52.2%) of normal weight, 57 cases (31.7%) of overweight, and 29 cases (16.1%) of obesity. Conversely, the good conversion group had 108 cases (37.6%) of normal weight, 122 cases (42.5%) of overweight, and 57 cases (19.9%) of obesity.There were statistically significant differences in the composition ratios of physical metabolic phenotype, metabolic type, and xBMI type between the two groups of patients ( χ2=29.56, P<0.001, χ2=19.83, P<0.001, χ2=9.68, P=0.008). Multivariate Logistic regression analysis showed that after adjusting for other risk factors related to the prognosis of HICH, HDL-C ( OR=0.30, 95% CI: 0.12-0.75, P=0.010), admission GCS score ( OR=0.71, 95% CI:0.64-0.79, P<0.001), MH-OW ( OR=0.38, 95% CI: 0.17-0.82, P=0.013) and MHO ( OR=0.30, 95% CI:0.09-0.99, P=0.048) were all protective factors for adverse outcomes in patients with HICH 1 year after the onset of the disease, and hemorrhage into the ventricle ( OR=2.46, 95% CI:1.41-4.32, P=0.002) and pulmonary infection ( OR=9.13, 95% CI: 4.78- 17.44, P<0.001) were risk factors for adverse outcomes. Conclusions:MH-OW and MHO are beneficial to the prognosis of HICH patients 1 year after the onset of HICH. The secondary prevention of HICH patients should pay attention to the BMI level and comprehensive metabolic status of the patients.

Objective:To analyze the differentially expressed messenger RNA(mRNA), microRNA(miRNA), and long non-coding RNA(lncRNA)during the browning of mouse subcutaneous adipose tissue, construct a competitive endogenous RNA(ceRNA)network, and provide a theoretical basis for investigating the regulatory mechanisms of white adipose tissue browning.Methods:A cold-stimulated mouse model was established for transcriptome sequencing.Bioinformatics tools were employed to screen for differentially expressed mRNAs, miRNAs, and lncRNAs.An integrated mRNA-miRNA-lncRNA analysis was performed to construct a ceRNA network.Gene Ontology(GO), Kyoto Encyclopedia of Genes and Genomes(KEGG), and Gene Set Enrichment Analysis(GSEA)were conducted on the differentially expressed mRNAs and ceRNA networks to explore transcriptional regulation during the cold-induced browning of subcutaneous adipose tissue.Results:Transcriptomic analysis of the cold-stimulated model identified 4, 256 differentially expressed RNAs, which include 3, 600 mRNAs, 588 lncRNAs, and 68 miRNAs.GO and KEGG analyses revealed that the browning of white adipose tissue involves immune-related processes, such as immune system processes, immune responses, adaptive and innate immune responses, and the positive regulation of T-cell activation.A ceRNA network associated with browning regulation was constructed, comprising 233 nodes(188 mRNAs, 34 miRNAs, and 11 lncRNAs)and 351 edges.Protein-protein interaction(PPI)analysis of the mRNAs within the ceRNA network highlighted pathways including apoptosis, intracellular signaling transduction, hypoxia-inducible factor-1(HIF-1), AMP-activated protein kinase(AMPK), Janus kinase-signal transducer and activators of transcription(JAK-STAT)signaling, carbon metabolism, glycolysis, and thyroid hormone pathways, all of which regulate lipid metabolism, hypoxia, and glycolysis.Cytohubba analysis identified the top 10 hub genes: Bcl2, Src, Cebpb, Creb1, Runx1, Foxo3, Ets1, Socs3, Slc2 a4, and Pkm. Conclusions:The ceRNA network that regulates the browning of white adipose tissue is involved in various pathways, including carbon metabolism, glycolysis, thyroid hormone signaling, growth hormone signaling, prolactin signaling, as well as the HIF-1, AMPK, and JAK-STAT pathways.Key regulatory miRNAs in this context include miR-30e-5p, miR-182-5p, miR-20b-5p, miR-144-3p, miR-363-3p, miR-141-3p, miR-203-3p, and miR-107-3p.These miRNAs may serve as critical targets for inducing browning in response to cold exposure.

Moyamoya disease (MMD) is a chronic progressive cerebrovascular disease, and extracranial-intracranial revascularization is the most commonly treatment for MMD. With the continuous development of imaging techniques, the perioperative monitoring methods for MMD patients have gradually become more diverse. However, Doppler ultrasound still holds an important position due to its convenience and non-invasiveness. This article reviews the role of Doppler ultrasound in the perioperative assessment of patients with MMD.

Hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH) is often associated with neurological deterioration and poor prognosis. Early and accurate identification of patients at high risk for HE is crucial for guiding clinical practice. This article summarizes the imaging predictors for HE in patients with sICH, primarily focusing on non-enhanced CT and CT angiography, and briefly introduces various prediction models based on imaging features, in order to provide references for the clinical diagnosis and treatment of sICH.

Objective:To explore the impact of body constitutional metabolic phenotype on the outcomes of hypertensive intracerebral hemorrhage (HICH) patients one year after onset.Methods:This study retrospectively studied the clinical data of 467 HICH patients admitted to the First People's Hospital of Lianyungang City from May 2021 to May 2023. Based on telephone follow-up after one year, the patients were categorized into two groups: a good outcome group (287 cases) and a poor outcome group (180 cases). According to the patients' body mass index (BMI) and metabolic status, the population was divided into six phenotypes: metabolically healthy with normal weight (MH-NW), metabolically healthy with overweight (MH-OW), metabolically healthy obesity (MHO), metabolically unhealthy normal weight (MU-NW), metabolically unhealthy with overweight (MU-OW), and metabolically unhealthy with obesity (MUO). The baseline data of the two groups were compared between two groups. The influencing factors of adverse outcomes in patients with HICH one year after onset were analyzed. Quantitative data that conforms to normal distribution were represented by xˉ±s, and independent sample t-test was used for comparison between two groups; The measurement data of skewed distribution was represented by M ( Q1, Q3), and Mann Whitney U test was used for comparison between the two groups; Count data was presented as an example (%), and comparison between groups was conducted using the χ2 test. Multivariate logistic regression analysis was used to analyze the influencing factors of poor prognosis in HICH patients one year after onset. Results:BMI, high density lipoprotein cholesterol(HDL-C) levels and baseline Glasgow coma score(GCS) score in the poor outcome group were lower than those in the good outcome group [23.8 (22.4, 26.1) kg/m 2 vs. 25.0 (22.5, 27.4) kg/m 2, Z=-2.31, P=0.021; 1.1 (1.0,1.4) mmol/L vs. 1.3 (1.0,1.6) mmol/L, Z=-4.18, P<0.001; 14 (13,15) score vs. 10 (7,13) score, Z=-10.20, P<0.001]. The incidence of hemorrhage into the ventricle, cerebral hernia, pulmonary infection and hydrocephalus [43.3%(78/180) vs. 23.7% (68/287). 5.6%(10/180) vs. 0.7% (2/287), 48.9%(88/180) vs. 6.6% (19/287), 5.0%(9/180) vs. 1.4% (4/287), χ2=19.86, P<0.001, χ2=10.43, P<0.001, χ2=111.90, P<0.001, χ2=5.32, P=0.021], proportion of surgical removal of hematoma [41.1%(74/180) vs. 19.5% (56/287), χ2=25.69, P<0.001], systolic blood pressure [158 (141,173) mmHg vs. 152 (138,169) mmHg, Z=-2.18, P=0.029] and fasting blood glucose [6.9 (5.7,8.2) mmol/L vs. 6.3 (5.4,7.8) mmol/L, Z=-2.08, P=0.038] were higher than those in good outcome group. The metabolic phenotypes in the poor conversion group were as follows: 41 cases (22.8%) of MH-NW, 23 cases (12.8%) of MH-OW, 9 cases (5.0%) of MHO, 54 cases (30.0%) of MU-NW, 33 cases (18.3%) of MU-OW, and 20 cases (11.1%) of MUO. Conversely, the metabolic phenotypes in the good conversion group were as follows: 67 cases (23.3%) of MH-NW, 77 cases (26.8%) of MH-OW, 31 cases (10.8%) of MHO, 40 cases (13.9%) of MU-NW, 46 cases (16.0%) of MU-OW, and 26 cases (9.1%) of MUO. Regarding metabolic types, the poor conversion group comprised 73 healthy cases (40.6%) and 107 unhealthy cases (59.4%), whereas the good conversion group had 177 healthy cases (61.7%) and 110 unhealthy cases (38.3%). In terms of body mass, the poor conversion group included 94 cases (52.2%) of normal weight, 57 cases (31.7%) of overweight, and 29 cases (16.1%) of obesity. Conversely, the good conversion group had 108 cases (37.6%) of normal weight, 122 cases (42.5%) of overweight, and 57 cases (19.9%) of obesity.There were statistically significant differences in the composition ratios of physical metabolic phenotype, metabolic type, and xBMI type between the two groups of patients ( χ2=29.56, P<0.001, χ2=19.83, P<0.001, χ2=9.68, P=0.008). Multivariate Logistic regression analysis showed that after adjusting for other risk factors related to the prognosis of HICH, HDL-C ( OR=0.30, 95% CI: 0.12-0.75, P=0.010), admission GCS score ( OR=0.71, 95% CI:0.64-0.79, P<0.001), MH-OW ( OR=0.38, 95% CI: 0.17-0.82, P=0.013) and MHO ( OR=0.30, 95% CI:0.09-0.99, P=0.048) were all protective factors for adverse outcomes in patients with HICH 1 year after the onset of the disease, and hemorrhage into the ventricle ( OR=2.46, 95% CI:1.41-4.32, P=0.002) and pulmonary infection ( OR=9.13, 95% CI: 4.78- 17.44, P<0.001) were risk factors for adverse outcomes. Conclusions:MH-OW and MHO are beneficial to the prognosis of HICH patients 1 year after the onset of HICH. The secondary prevention of HICH patients should pay attention to the BMI level and comprehensive metabolic status of the patients.

Objective:To analyze the differentially expressed messenger RNA(mRNA), microRNA(miRNA), and long non-coding RNA(lncRNA)during the browning of mouse subcutaneous adipose tissue, construct a competitive endogenous RNA(ceRNA)network, and provide a theoretical basis for investigating the regulatory mechanisms of white adipose tissue browning.Methods:A cold-stimulated mouse model was established for transcriptome sequencing.Bioinformatics tools were employed to screen for differentially expressed mRNAs, miRNAs, and lncRNAs.An integrated mRNA-miRNA-lncRNA analysis was performed to construct a ceRNA network.Gene Ontology(GO), Kyoto Encyclopedia of Genes and Genomes(KEGG), and Gene Set Enrichment Analysis(GSEA)were conducted on the differentially expressed mRNAs and ceRNA networks to explore transcriptional regulation during the cold-induced browning of subcutaneous adipose tissue.Results:Transcriptomic analysis of the cold-stimulated model identified 4, 256 differentially expressed RNAs, which include 3, 600 mRNAs, 588 lncRNAs, and 68 miRNAs.GO and KEGG analyses revealed that the browning of white adipose tissue involves immune-related processes, such as immune system processes, immune responses, adaptive and innate immune responses, and the positive regulation of T-cell activation.A ceRNA network associated with browning regulation was constructed, comprising 233 nodes(188 mRNAs, 34 miRNAs, and 11 lncRNAs)and 351 edges.Protein-protein interaction(PPI)analysis of the mRNAs within the ceRNA network highlighted pathways including apoptosis, intracellular signaling transduction, hypoxia-inducible factor-1(HIF-1), AMP-activated protein kinase(AMPK), Janus kinase-signal transducer and activators of transcription(JAK-STAT)signaling, carbon metabolism, glycolysis, and thyroid hormone pathways, all of which regulate lipid metabolism, hypoxia, and glycolysis.Cytohubba analysis identified the top 10 hub genes: Bcl2, Src, Cebpb, Creb1, Runx1, Foxo3, Ets1, Socs3, Slc2 a4, and Pkm. Conclusions:The ceRNA network that regulates the browning of white adipose tissue is involved in various pathways, including carbon metabolism, glycolysis, thyroid hormone signaling, growth hormone signaling, prolactin signaling, as well as the HIF-1, AMPK, and JAK-STAT pathways.Key regulatory miRNAs in this context include miR-30e-5p, miR-182-5p, miR-20b-5p, miR-144-3p, miR-363-3p, miR-141-3p, miR-203-3p, and miR-107-3p.These miRNAs may serve as critical targets for inducing browning in response to cold exposure.

Objectives:To explore the clinicopathological characteristics and prognostic risk factors in differentiated thyroid cancer (DTC) patients with lung metastasis.Methods:Patients of differentiated thyroid cancer with lung metastasis in Tianjin Medical University Cancer Institute & Hospital were enrolled from Jan 1, 2010 to Dec 31, 2016. The clinicopathological characteristics and risk factors affecting the prognosis were analyzed retrospectively.Results:A total of 65 DTC patients with lung metastasis were collected in this study, including 56 patients with papillary thyroid carcinoma and 9 patients with follicular thyroid carcinoma; 23 patients died and 42 patients survived. Median follow-up time was 99.4 months. There were 18 males, 47 females. Age 14-73 years, median age 51.0 years. High incidence of DTC lung metastasis was 50-59 years for males and 40-49 years for females. Based on AJCC 8th edition TNM staging, there were 37 patients in stage Ⅱ (age <55 years) and 28 patients in stage Ⅳb (age ≥55 years). The number of 131Ⅰ treatments performed ranged from 1 to 13 times, with a mean of 3.9 times. Firty-five patients were with lung metastasis alone, and 10 patients with lung metastasis and distant metastasis in other organs. Eleven patients suffered from hypoparathyroidism after 131Ⅰ treatment. COX multifactorial regression analysis found that age was independent risk factor affecting prognosis, multiple organs distant metastasis and pathologic subtype were relative risk factors affecting prognosis. There was no correlation between gender, number of 131Ⅰ treatments and poor prognosis. Conclusions:DTC has a high survival even with the occurrence of lung metastasis, but the prognosis is poor when combined with multi-organ metastasis. Age and multiple organ distant metastatic are independent risk factors affecting prognosis.

Objective Bioinformatics tools were used to conduct a prediction analysis of common CYP2C9 variants(*3)and new variants(*76-*85)in our population,aiming to illustrate the impact of amino acid variants on the multidimensional aspects of CYP2C9 protein structure,properties,and functions.Methods The physicochemical properties,glycosylation and phosphorylation mod-ification,important structural domains,spatial structure and functional changes,and docking mode with the probe drug,tolbutamide,were predicted for each variant by applying various analytical tools.Results Compared with CYP2C9*1,the variants showed different degrees and multiple levels of alterations in amino acid sequence,local structural domains,phosphorylation sites,physicochemical proper-ties,tertiary structure,and docking patterns with substrates.*76 and*84 showed local structure,overall spatial structure,and function disruptions,and they were also correlated with disease outbreaks.*78-*79 and*81-*82 variants showed significant heterogeneity in the predicted results at different levels;*85 had an early termination codon and deletion of most critical structural domains due to a code-shift mutation,and the results suggested that it might affect the protein translation and the assembly of the whole enzyme.The pre-dicted results of the physicochemical properties,local structural domains,and stability of*3 were all non-significantly altered in com-parison with*1;however,docking results showed that*3 protein and tolbutamide were significantly changed in the shape,three-di-mensional size,and contact pattern of the docking pocket.Conclusion This study analyzed the effects of amino acid variants on pheno-types from multiple perspectives and levels,including protein primary,secondary,and tertiary structures,holoenzyme-drug docking,physicochemical properties,and functions,which provides essential references and new interpretative perspectives for future structural elu-cidation,ex vivo and in vivo drug metabolism experiments,and individualized dosing of CYP2C9 variants.

Objective:To study the emergency cerebral hemorrhage grading scale (EDICH) score and the modified emergency intracerebral hemorrhage grading scale (mEDICH) score on the adverse prognosis of patients with cerebral hemorrhage 90 days after discharge.Methods:The clinical data of 248 patients with cerebral hemorrhage in the Department of Neurosurgery, Lianyungang Hospital Affiliated to Xuzhou Medical University from January 2022 to December 2022 were retrospectively analyzed. According to the modified Rankin scale (mRS) 90 days after discharge, patients were divided into a good prognosis group (212 cases) and a poor prognosis group (36 cases). Independent-samples t-test and rank-sum test were used to compare the basic information, vital signs, laboratory tests, imaging characteristics, EDICH score and mEDICH score of the two groups. Multivariate Logistic regression was used to analyze the risk factors affecting the poor prognosis of patients with cerebral hemorrhage. The receiver operating characteristic (ROC) curve was drawn to analyze the predictive value of EDICH score and mEDICH score for the poor prognosis of patients with intracerebral hemorrhage 90 days after discharge.Results:The diameter of intracerebral hematoma (5.53±0.32) cm, the rate of intraventricular hemorrhage (61.11%,22/36) and the rate of subtentorial hemorrhage (27.78%, 10/36) in poor prognosis group were significantly higher than those in good prognosis group (3.50±0.07) cm, intraventricular hemorrhage (30.19%, 64/212) and infratentorial hemorrhage (14.15%, 30/212), the differences were statistically significant ( t=6.19, P<0.001; χ 2=12.99, P<0.001; χ 2=4.22, P=0.040). GCS scores in the poor prognosis group were lower than those in the good prognosis group ( H=72.01, P<0.001). EDICH scores and mEDICH scores in the poor prognosis group were higher than those in the good prognosis group (EDICH score: H=65.79, P<0.001; mEDICH score: H=76.56, P<0.001). Multivariate Logistic regression analysis showed EDICH score ( OR=5.869,95% CI 3.451-9.981, P<0.05) and mEDICH score ( OR=10.353,95% CI 4.842-22.137, P<0.05)could predict the prognosis of patients with cerebral hemorrhage 90 days after discharge. ROC curve analysis showed that the sensitivity and specificity of EDICH score and mEDICH score were 0. 861,0.889 and 0. 821,0.953, respectively, but the predictive effect of mEDICH score was significantly better than that of EDICH score ( Kappa values were 0. 787 and 0. 494, respectively). The predicted results are highly consistent with the actual results. Conclusion:MEDICH score can accurately predict the prognosis of patients with intracerebral hemorrhage 90 days after discharge, which has clinical application value.

Spontaneous intracerebral hemorrhage (ICH) is a neurological disease with high mortality and morbidity rates, and early neurological deterioration (END) is one of the key factors in evaluating outcome of patients. Early identification and effective intervention of END in patients with ICH is of great significance for their treatment and prognosis. This article reviews the predictive factors for END in patients with ICH from the perspectives of blood markers, hematoma characteristics, blood pressure variability, and imaging signs.