bluuebunny/crossref_metadata_2025_split huggingface dataset:
Outcomes and mechanical complications of acute myocardial infarction during the second wave pandemic in a Milan HUB center for cardiac emergencies
Authors: Penso Marco , Frappampina Antonio , Cosentino Nicola , Tamborini Gloria , Celeste Fabrizio , Ianniruberto Monica , Ravagnani Paolo , Troiano Sarah , Marenzi Giancarlo , Pepi Mauro 
Published: October 2022

AimsCOVID-19 has dramatically impacted the healthcare system. Evidence from previous studies suggests a decline in in-hospital admissions for acute myocardial infarction (AMI) during the pandemic. However, the effect of the pandemic on mechanical complications (MC) in acute ST-segment elevation myocardial infarction (STEMI) has not been comprehensively investigated. Therefore, we evaluated the impact of the pandemic on MC and in-hospital outcomes in STEMI during the second wave, in which there was a huge SARS-CoV-2 diffusion in Italy.Methods and resultsBased on a single center cohort of AMI patients admitted with STEMI between February 1, 2019, and February 28, 2021, we compared the characteristics and outcomes of STEMI patients treated during the pandemic vs. those treated before the pandemic. In total, 479 STEMI patients were included, of which 64.5% were during the pandemic. Relative to before the pandemic, primary percutaneous coronary intervention (PCI) declined (87.7 vs. 94.7%, p = 0.014) during the pandemic. Compared to those admitted before the pandemic (10/2019 to 2/2020), STEMI patients admitted during the second wave (10/2020 to 2/2021) presented with a symptom onset-to-door time greater than 24 h (26.1 vs. 10.3%, p = 0.009) and a reduction of primary PCI (85.2 vs. 97.1%, p = 0.009). MC occurred more often in patients admitted during the second wave of the pandemic than in those admitted before the pandemic (7.0 vs. 0.0%, p = 0.032). In-hospital mortality increased during the second wave (10.6 vs. 2.9%, p = 0.058).ConclusionAlthough the experience gained during the first wave and a more advanced hub-and-spoke system for cardiovascular emergencies persists, late hospitalizations and a high incidence of mechanical complications in STEMI were observed even in the second wave.
The impact of BMI on long-term anthropometric and metabolic outcomes in girls with idiopathic central precocious puberty treated with GnRHas
Authors: Bruzzi Patrizia , Valeri Lara , Sandoni Marcello , Madeo Simona Filomena , Predieri Barbara , Lucaccioni Laura , Iughetti Lorenzo 
Published: October 2022

BackgroundGonadotropin-releasing hormone analogs (GnRHas) are effective in increasing the final height of children with idiopathic central precocious puberty (ICPP). However, in previous years, some transient metabolic complications have been described during this treatment, for which there are no long-term outcome data. Our study aimed to evaluate the efficacy of GnRHas and clarify if body mass index (BMI) at diagnosis of ICPP could influence long-term outcomes.MethodsThis was an observational, retrospective study that recruited a cohort of girls with ICPP. Data for anthropometric measures, fasting lipid profile, and glucose metabolism were collected at baseline [when GnRHas treatment started (T1)], at the end of the treatment (T2), and near-final height (nFH) or final height (FH) (T3). Predicted adult height (PAH) was calculated at T1 following Bayley and Pinneau’s method. Analysis was carried out using BMI standard deviation score (SDS) categories at T1 (group A, normal weight, vs. group B, overweight/obese).ResultsFifty-seven girls with ICPP who were treated with GnRHas were enrolled in the study (group A vs. group B: 33 vs. 24 patients, aged 7.86 ± 0.81 vs. 7.06 ± 1.61 years, respectively; p &lt; 0.05). In the study population, nFH/FH was in line with the target height (TH) (p = 0.54), with a mean absolute height gain of 11.82 ± 5.35 cm compared with PAH. Even if the length of therapy was shorter (group A vs. group B: 1.84 ± 2.15 vs. 2.10 ± 0.81 years, respectively; p &lt; 0.05) and the age at menarche was younger (group A vs. group B: 10.56 ± 1.01 vs. 11.44 ± 0.85 years, respectively; p &lt; 0.05) in group B than in group A, the nFH/FH gain was still comparable between the two groups (p = 0.95). At nFH/FH, BMI SDS was still greater in group B than in group A (p = 0.012), despite the fact that BMI SDS significantly increased in group A only (p &lt; 0.05). Glucose metabolism got worst during GnRHa with a complete restoring after it, independently from pre-treatment BMI. The ratio of low-density to high-density lipoprotein cholesterol transiently deteriorated during treatment with GnRHas in group A only (p = 0.030).ConclusionsOur results confirm the effectiveness of treatment with GnRHas on growth and do not support the concern that being overweight and obese can impair the long-term outcomes of GnRHas therapy. However, the observed transient impairment of metabolic parameters during treatment suggests that clinicians should encourage ICPP girls treated with GnRHas to have a healthy lifestyle, regardless of their pretreatment BMI.
Macrophage-related molecular subtypes in lung adenocarcinoma identify novel tumor microenvironment with prognostic and therapeutic implications
Authors: Wen Heng , Chen Hanjian , Xie Liwei , Li Zetao , Zhang Qian , Tian Qiping 
Published: October 2022

Background: Lung adenocarcinoma (LUAD) is a life-threatening malignant tumor, contributing for the largest cancer burden worldwide. Tumor microenvironment (TME) is composed of various immune cells, stromal cells and tumor cells, which is highly associated with the cancer prognosis and the response to immunotherapy, in which macrophages in TME have been revealing a potential target for cancer treatment. In this study, we sought to further explore the role of macrophages in LUAD progression and establish a risk model related to macrophages for LUAD.Methods: We explored immune-related pathways that might be affected by counting positively associated genes in macrophages. Molecular typing was also constructed by mining macrophage-associated genes with prognostic value through COX regression and other analyses. RiskScore prognostic models were constructed using lasso regression and stepwise multifactorial regression analysis. The differences on clinical characteristics among three subtypes (C1, C2, and C3) and RiskScore subtypes were analyzed in TCGA dataset. Immunological algorithms such as TIMER, ssGSEA, MCP-Counter, ESTIMATE, and TIDE were used to calculate the level of difference in immune infiltration between the different subtypes. The TCGA mutation dataset processed by mutect2 was used to demonstrate the frequency of mutations between different molecular subtypes. Finally, nomograms, calibration curves, and decision curves were created to assess the predictive accuracy and reliability of the model.Results: The C1 subtype demonstrated the best prognostic outcome, accompanied by higher levels of immune infiltration and lower mutation frequency, while the majority of patients in the C1 subtype were women under 65 years of age. Myeloid-derived suppressor cell (MDSC) scores were higher in the C3 subtype, suggesting a more severe immune escape, which may have contributed to the tumor evading the immune system resulting in a poorer prognosis for patients. In addition, our RiskScore prognostic model had good predictive accuracy and reliability.Conclusion: This paper provides a study of macrophage-related pathways, immunosuppression, and their mechanisms of action in lung cancer, along with targets for future treatment to guide the optimal treatment of lung cancer.
Construction and validation of a prognostic marker and risk model for HCC ultrasound therapy combined with WGCNA identification
Authors: Bi Yunlong , Jing Yu , Guo Lingling 
Published: October 2022

Background: Hepatocellular carcinoma (HCC) is a malignant tumor with a highly aggressive and metastatic nature. Ultrasound remains a routine monitoring tool for screening, treatment and post-treatment recheck of HCC. Therefore, it is of great significance to explore the role of ultrasound therapy and related genes in prognosis prediction and clinical diagnosis and treatment of HCC.Methods: Gene co-expression networks were developed utilizing the R package WGCNA as per the expression profiles and clinical features of TCGA HCC samples, key modules were identified by the correlation coefficients between clinical features and modules, and hub genes of modules were determined as per the GS and MM values. Ultrasound treatment differential expression genes were identified using R package limma, and univariate Cox analysis was conducted on the intersection genes of ultrasound differential expression genes and hub genes of key HCC modules to screen the signatures linked with HCC prognosis and construct a risk model. The median risk score was used as the threshold point to classify tumor samples into high- and low-risk groups, and the R package IOBR was used to assess the proportion of immune cells in high- and low-risk groups, R package maftools to assess the genomic mutation differences in high- and low-risk groups, R package GSVA’s ssgsea algorithm to assess the HALLMARK pathway enrichment analysis, and R package pRRophetic to analyze drug sensitivity in patients with HCC.Results: WGCNA analysis based on the expression profiles and clinical data of the TCGA LIHC cohort identified three key modules with two major clinical features associated with HCC. The intersection of ultrasound-related differential genes and module hub genes was selected for univariate Cox analysis to identify prognostic factors significantly associated with HCC, and a risk score model consisting of six signatures was finally developed to analyze the prognosis of individuals with HCC. The risk model showed strength in the training set, overall set, and external validation set. The percentage of immune cell infiltration, genomic mutations, pathway enrichment scores, and chemotherapy drug resistance were significantly different between high- and low-risk groups according to the risk scores. Expression of model genes correlated with tumor immune microenvironment and clinical tumor characteristics while generally differentially expressed in pan-cancer tumor and healthy samples. In the immunotherapy dataset, patients in the high-risk group had a worse prognosis with immunotherapy, indicating that subjects in the low-risk group are more responsive to immunotherapy.Conclusion: The 6-gene signature constructed by ultrasound treatment of HCC combined with WGCNA analysis can be used for prognosis prediction of HCC patients and may become a marker for immune response.
A randomized clinical trial to stimulate the cholinergic anti-inflammatory pathway in patients with moderate COVID-19-pneumonia using a slow-paced breathing technique
Authors: Balint Elisabeth Maria , Grüner Beate , Haase Sophia , Kaw-Geppert Mandakini , Thayer Julian F. , Gündel Harald , Jarczok Marc N. 
Published: October 2022

PurposeA characteristic problem occurring in COVID-19 is excessive elevations of pro-inflammatory cytokines (e.g. IL-6 and CRP) which are associated with worse clinical outcomes. Stimulation of the vagally-mediated cholinergic anti-inflammatory reflex by slow paced breathing with prolonged exhalation may present a clinically relevant way to reduce circulating IL-6.MethodSingle-center randomized controlled clinical trial with enrolment of 46 patients hospitalized with confirmed severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) infection and moderate COVID-19 pneumonia (primary diagnosis). Differences between intervention (4sec inhalation, 6sec exhalation for 20 minutes 3x daily) and control group in IL-6 calculated using multilevel mixed-effect linear regression models with random slope including the covariates relevant comorbidities, COVID-19 medication, and age. Both groups received standard care.ResultsMean age was 57 years ± 13 years, N= 28 (60%) male, N=30 (65%) with relevant comorbidities. The model including group-by-time interaction revealed a significantly lower trajectory of IL-6 in the intervention group (effect size Cohens f2 = 0.11, LR-test p=.040) in the intention-to-treat sample, confirmed by per-protocol analysis (f2 = 0.15, LR-test p=.022). Exploratory analysis using the median split of practice time to predict IL-6 of the next morning indicated a dose-response relationship with beneficial effects of practice time above 45 minutes per day. Oxygen saturation remained unchanged during slow-paced breathing (95.1% ± 2.1% to 95.4% ± 1.6%).ConclusionPatients practicing slow-paced breathing had significantly lower IL-6 values than controls with a small to medium effect size and without relevant side effects. Further trials should evaluate clinical outcomes and an earlier start of the intervention. Slow-paced breathing could be an easy to implement, low-cost, safe and feasible adjuvant therapeutic approach to reduce circulating IL-6 in moderate COVID-19 pneumonia.Clinical Trial Registrationhttps://www.drks.de, identifier DRKS00023971, Universal Trial Number (UTN) U1111-1263-8658.
The white blood cell count to mean platelet volume ratio for ischemic stroke patients after intravenous thrombolysis
Authors: Weng Yiyun , Gao Yufan , Zhao Mingyue , Zeng Tian , Huang Jiaqi , Xie Haobo , Huang Jiexi , Chen Yiqun , Hu Xiaoya , Xu Jiahan , Zhu Jinrong , Lin Suichai , Ke Tingting , Li Xiang , Zhang Xu 
Published: October 2022

Background and PurposeWhite blood cell count to mean platelet volume ratio (WMR) is increasingly recognized as a promising biomarker. However, its predictive capability for acute ischemic stroke (AIS) patients is relatively less researched. The primary aim of this study is to explore its prognostic value in AIS patients after reperfusion regarding 3-month poor functional outcome.MethodsA total of 549 AIS patients who had undergone vascular reperfusion procedure with complete 3-month follow-up were retrospectively recruited in this study. White blood cell count, mean platelet volume at 24 h of admission were recorded. Stroke severity had been estimated using the National Institutes of Health Stroke Scale (NIHSS) and poor outcome was defined as modified Rankin Scale (mRS) 3–6 at 3 months.ResultsAIS patients with poor functional outcome at 3 months displayed higher WMR. A positive correlation between WMR and NIHSS score was found (r = 0.334, p &lt; 0.001). After adjusting potential confounders, WMR was still an independent risk factor for poor prognosis at 3 months (OR = 2.257, 95% CI [1.117-4.564], p = 0.023) in multivariate logistic regression model. Subgroup analyses further suggested a significant association between WMR and poor outcome in high baseline NIHSS (per 0.1-point increase: OR = 1.153, 95% CI [1.014-1.312], p = 0.030) group. Receiver operating characteristic (ROC) curves analysis was utilized to assess the predictive ability of WMR, indicating a cut-off value of 0.86. A nomogram that includes age, sex, NIHSS on admission, high WMR for predicting 1-year all-cause survival was also developed (C-index = 0.628).ConclusionsWMR is significantly correlated with stroke severity on admission and is proved to be an important prognostic indicator for AIS outcomes, especially in high NIHSS on admission group. Additionally, the developed nomogram that includes high WMR for predicting 1-year survival provides us with an effective visualization tool.
Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer
Authors: Zeng Qingwen , Li Hong , Zhu Yanyan , Feng Zongfeng , Shu Xufeng , Wu Ahao , Luo Lianghua , Cao Yi , Tu Yi , Xiong Jianbo , Zhou Fuqing , Li Zhengrong 
Published: October 2022

BackgroundThis study aims to develop and validate a predictive model combining deep transfer learning, radiomics, and clinical features for lymph node metastasis (LNM) in early gastric cancer (EGC).Materials and methodsThis study retrospectively collected 555 patients with EGC, and randomly divided them into two cohorts with a ratio of 7:3 (training cohort, n = 388; internal validation cohort, n = 167). A total of 79 patients with EGC collected from the Second Affiliated Hospital of Soochow University were used as external validation cohort. Pre-trained deep learning networks were used to extract deep transfer learning (DTL) features, and radiomics features were extracted based on hand-crafted features. We employed the Spearman rank correlation test and least absolute shrinkage and selection operator regression for feature selection from the combined features of clinical, radiomics, and DTL features, and then, machine learning classification models including support vector machine, K-nearest neighbor, random decision forests (RF), and XGBoost were trained, and their performance by determining the area under the curve (AUC) were compared.ResultsWe constructed eight pre-trained transfer learning networks and extracted DTL features, respectively. The results showed that 1,048 DTL features extracted based on the pre-trained Resnet152 network combined in the predictive model had the best performance in discriminating the LNM status of EGC, with an AUC of 0.901 (95% CI: 0.847–0.956) and 0.915 (95% CI: 0.850–0.981) in the internal validation and external validation cohorts, respectively.ConclusionWe first utilized comprehensive multidimensional data based on deep transfer learning, radiomics, and clinical features with a good predictive ability for discriminating the LNM status in EGC, which could provide favorable information when choosing therapy options for individuals with EGC.
Comprehensive landscape of the functions and prognostic value of RNA binding proteins in uterine corpus endometrial carcinoma
Authors: Yao Yong , Liu Kangping , Wu Yuxuan , Zhou Jieyu , Jin Heyue , Zhang Yimin , Zhu Yumin 
Published: October 2022

Background: The dysregulation of RNA binding proteins (RBPs) is involved in tumorigenesis and progression. However, information on the overall function of RNA binding proteins in Uterine Corpus Endometrial Carcinoma (UCEC) remains to be studied. This study aimed to explore Uterine Corpus Endometrial Carcinoma-associated molecular mechanisms and develop an RNA-binding protein-associated prognostic model.Methods: Differently expressed RNA binding proteins were identified between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues by R packages (DESeq2, edgeR) from The Cancer Genome Atlas (TCGA) database. Hub RBPs were subsequently identified by univariate and multivariate Cox regression analyses. The cBioPortal platform, R packages (ggplot2), Human Protein Atlas (HPA), and TIMER online database were used to explore the molecular mechanisms of Uterine Corpus Endometrial Carcinoma. Kaplan-Meier (K-M), Area Under Curve (AUC), and the consistency index (c-index) were used to test the performance of our model.Results: We identified 128 differently expressed RNA binding proteins between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues. Seven RNA binding proteins genes (NOP10, RBPMS, ATXN1, SBDS, POP5, CD3EAP, ZC3H12C) were screened as prognostic hub genes and used to construct a prognostic model. Such a model may be able to predict patient prognosis and acquire the best possible treatment. Further analysis indicated that, based on our model, the patients in the high-risk subgroup had poor overall survival (OS) compared to those in the low-risk subgroup. We also established a nomogram based on seven RNA binding proteins. This nomogram could inform individualized diagnostic and therapeutic strategies for Uterine Corpus Endometrial Carcinoma.Conclusion: Our work focused on systematically analyzing a large cohort of Uterine Corpus Endometrial Carcinoma patients in the The Cancer Genome Atlas database. We subsequently constructed a robust prognostic model based on seven RNA binding proteins that may soon inform individualized diagnosis and treatment.
Prognostic value of mesorectal package area in patients with locally advanced rectal cancer following neoadjuvant chemoradiotherapy: A retrospective cohort study
Authors: Guan Bingjie , Huang Xinmin , Xia Huang , Guan Guoxian , Xu Benhua 
Published: October 2022

BackgroundThe aim of this study is to explore the most effective inflammation, magnetic resonance imaging (MRI), and nutrition markers for survival and pathology complete response (pCR) in patients with locally advanced rectal cancer (LARC).MethodsA total of 278 patients with LARC undergoing neoadjuvant chemoradiotherapy (NCRT) and radical surgery from 2016 to 2019 were included. The X-tile method was used to select the optimal cutoff points for the mesorectal package area (MPA), advanced lung cancer inflammation index (ALI), prognostic nutritional index (PNI), systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR) scores. Cox regression analysis was used to identify risk factors of disease-free survival (DFS). To discover pCR risk factors, logistic regression analysis was employed. A predictive nomogram for DFS was constructed.ResultsAccording to the least absolute shrinkage and selection operator analysis, the MPA was the only significant predictor for the DFS in patients with LARC. Kaplan-Meier (K-M) analysis demonstrated that groups with higher MPA, PNI, SII, NLR, MLR, and ALI score had improved DFS (all P &lt; 0.05). Receiver operating characteristic (ROC) analysis revealed that the MPA and PNI could accurately predict the pCR in patients with LARC after NCRT. The MPA score and NLR score were found to be independent predictors of DFS after NCRT using Cox regression analysis. Logistical regression analysis demonstrated that the MPA score, PNI score, and pre-NCRT cN stage were all independent predictors of pCR in patients with LARC after NCRT. Recursive partitioning analysis and time-independent ROC curve analysis demonstrated that MPA score was the most important predictor of pCR and prognosis in patients with LARC after NCRT.ConclusionsMPA was identified as the most effective marker for MRI, and the prognostic value was further confirmed by time–ROC analysis. More intense adjuvant treatment could be considered for lower–MPA score patients with LARC after NCRT. Obesity in the pelvis encourages the understanding of the prognosis prediction of patients with LARC after NCRT.