bluuebunny/crossref_metadata_2025_split huggingface dataset:
Neuroplasticity Mechanisms in Frontal Brain Gliomas: A Preliminary Study
Authors: Mitolo Micaela , Zoli Matteo , Testa Claudia , Morandi Luca , Rochat Magali Jane , Zaccagna Fulvio , Martinoni Matteo , Santoro Francesca , Asioli Sofia , Badaloni Filippo , Conti Alfredo , Sturiale Carmelo , Lodi Raffaele , Mazzatenta Diego , Tonon Caterina 
Published: June 2022

BackgroundPathological brain processes may induce adaptive cortical reorganization, however, the mechanisms underlying neuroplasticity that occurs in the presence of lesions in eloquent areas are not fully explained. The aim of this study was to evaluate functional compensatory cortical activations in patients with frontal brain gliomas during a phonemic fluency task and to explore correlations with cognitive performance, white matter tracts microstructural alterations, and tumor histopathological and molecular characterization.MethodsFifteen patients with frontal glioma were preoperatively investigated with an MRI study on a 3T scanner and a subgroup underwent an extensive neuropsychological assessment. The hemispheric laterality index (LI) was calculated through phonemic fluency task functional MRI (fMRI) activations in the frontal, parietal, and temporal lobe parcellations. Diffusion-weighted images were acquired for all patients and for a group of 24 matched healthy volunteers. Arcuate Fasciculus (AF) and Frontal Aslant Tract (FAT) tractography was performed using constrained spherical deconvolution diffusivity modeling and probabilistic fiber tracking. All patients were operated on with a resective aim and underwent adjuvant therapies, depending on the final diagnosis.ResultsAll patients during the phonemic fluency task fMRI showed left hemispheric dominance in temporal and parietal regions. Regarding frontal regions (i.e., frontal operculum) we found right hemispheric dominance that increases when considering only those patients with tumors located on the left side. These latter activations positively correlate with verbal and visuo-spatial short-term memory, and executive functions. No correlations were found between the left frontal operculum and cognitive performance. Furthermore, patients with IDH-1 mutation and without TERT mutation, showed higher rightward frontal operculum fMRI activations and better cognitive performance in tests measuring general cognitive abilities, semantic fluency, verbal short-term memory, and executive functions. As for white matter tracts, we found left and right AF and FAT microstructural alterations in patients with, respectively, left-sided and right-side glioma compared to controls.ConclusionsCompensatory cortical activation of the corresponding region in the non-dominant hemisphere and its association with better cognitive performance and more favorable histopathological and molecular tumor characteristics shed light on the neuroplasticity mechanisms that occur in the presence of a tumor, helping to predict the rate of post-operative deficit, with the final goal of improving patients'quality of life.
APOE ε4 Allele Distribution and Association With Scores of Subjective Cognitive Decline Questionnaire 9 in a Large Chinese Memory Clinic Cohort
Authors: Hao Lixiao , Jia Jianguo , Xing Yue , Han Ying 
Published: June 2022

BackgroundPrevious reports on APOE ε4 allele distribution in different populations have been inconclusive. The Subjective Cognitive Decline-Questionnaire 9 (SCD-Q9) was developed to identify those at risk of objective cognitive impairment [OCI; including mild cognitive impairment (MCI) and dementia groups), but its association with APOE ε4 and discriminatory powers for SCDwith subtle cognitive decline (SCDs) and OCI in memory clinics are unclear.ObjectivesTo investigate demographic distribution of APOE ε4, its association with SCD-Q9 scores, and its ability to discriminate SCDs and OCI groups from normal control (NC).MethodsA total of 632 participants were recruited (NC = 243, SCDs = 298, OCI = 91). APOE ε4 allele distribution and association with SCD-Q9 scores were calculated and the effects on cognitive impairment were analyzed. Receiver operating characteristic (ROC) analysis was applied to identify discriminatory powers for NC, SCDs, and OCI.ResultsTotal APOE ε4 frequency was 13.1%. This did not vary by demography but was higher in patients with OCI. The SCD-Q9 scores were higher in APOE ε4 carriers than non-carriers in the OCI group. The area under the curve (AUC) for discriminating from OCI using APOE ε4 were 0.587 and 0.575, using SCD-Q9 scores were 0.738 and 0.571 for NC and SCDs groups, respectively. When we combined APOE ε4 and SCD-Q9 scores into the model, the AUC increased to 0.747 for discriminating OCI from NC. However, when OCI group was split into MCI and dementia groups, only total SCD-Q9 score was the independent affecting factor of MCI.ConclusionThis study demonstrated that the distribution of APOE ε4 alleles did not vary with different demographic characteristics in a large-scale cohort from a memory clinic. APOE ε4 alleles may be associated with scores of SCD-Q9 reflecting the degree of cognitive complaints but their additional contribution to SCD-Q9 scores is marginal in discriminating between NC, SCDs, and OCI.
Metformin in the Treatment of Amisulpride-Induced Hyperprolactinemia: A Clinical Trial
Authors: Zhu Cuifang , Li Ruofan , Ju Mingliang , Xiao Xudong , Yuan Ti-Fei , Jin Zhixing , Zhao Jing 
Published: June 2022

ObjectiveTo evaluate the efficacy and safety of metformin in the treatment of amisulpride-induced hyperprolactinemia.MethodsA total of 86 schizophrenic patients who developed hyperprolactinemia after taking amisulpride were screened and randomly assigned to the metformin group (42 patients) and placebo group (44 patients) and followed up for eight weeks. The patients’ serum prolactin levels, blood glucose and lipids were measured at the baseline and the end of the intervention. The treatment emergent symptom scale (TESS) was also assessed.ResultsAfter eight weeks of intervention, serum prolactin levels in the metformin group decreased from (1737.360 ± 626.918) mIU/L at baseline to (1618.625 ± 640.865) mIU/L, whereas serum prolactin levels in the placebo group increased from (2676.470 ± 1269.234) mIU/L at baseline to (2860.933 ± 1317.376) mIU/L. There was a significant difference in prolactin changes (Fcovariance = 9.982, P = 0.002) between the two groups. There was no significant difference in the incidence of adverse drug reactions (P &gt; 0.05) between the two groups.ConclusionMetformin is able to improve amisulpride-induced hyperprolactinemia with its safety.
DSNN: A DenseNet-Based SNN for Explainable Brain Disease Classification
Authors: Zhu Ziquan , Lu Siyuan , Wang Shui-Hua , Gorriz Juan Manuel , Zhang Yu-Dong 
Published: June 2022

Aims: Brain diseases refer to intracranial tissue and organ inflammation, vascular diseases, tumors, degeneration, malformations, genetic diseases, immune diseases, nutritional and metabolic diseases, poisoning, trauma, parasitic diseases, etc. Taking Alzheimer’s disease (AD) as an example, the number of patients dramatically increases in developed countries. By 2025, the number of elderly patients with AD aged 65 and over will reach 7.1 million, an increase of nearly 29% over the 5.5 million patients of the same age in 2018. Unless medical breakthroughs are made, AD patients may increase from 5.5 million to 13.8 million by 2050, almost three times the original. Researchers have focused on developing complex machine learning (ML) algorithms, i.e., convolutional neural networks (CNNs), containing millions of parameters. However, CNN models need many training samples. A small number of training samples in CNN models may lead to overfitting problems. With the continuous research of CNN, other networks have been proposed, such as randomized neural networks (RNNs). Schmidt neural network (SNN), random vector functional link (RVFL), and extreme learning machine (ELM) are three types of RNNs.Methods: We propose three novel models to classify brain diseases to cope with these problems. The proposed models are DenseNet-based SNN (DSNN), DenseNet-based RVFL (DRVFL), and DenseNet-based ELM (DELM). The backbone of the three proposed models is the pre-trained “customize” DenseNet. The modified DenseNet is fine-tuned on the empirical dataset. Finally, the last five layers of the fine-tuned DenseNet are substituted by SNN, ELM, and RVFL, respectively.Results: Overall, the DSNN gets the best performance among the three proposed models in classification performance. We evaluate the proposed DSNN by five-fold cross-validation. The accuracy, sensitivity, specificity, precision, and F1-score of the proposed DSNN on the test set are 98.46% ± 2.05%, 100.00% ± 0.00%, 85.00% ± 20.00%, 98.36% ± 2.17%, and 99.16% ± 1.11%, respectively. The proposed DSNN is compared with restricted DenseNet, spiking neural network, and other state-of-the-art methods. Finally, our model obtains the best results among all models.Conclusions: DSNN is an effective model for classifying brain diseases.
Poor Nutritional Status and Dynapenia Are Highly Prevalent in Post-Acute COVID-19
Authors: de Blasio Francesco , Scalfi Luca , Castellucci Bianca , Sacco Anna Maria , Berlingieri Giulia Miracco , Capitelli Ludovica , Alicante Paola , Sanduzzi Alessandro , Bocchino Marialuisa 
Published: June 2022

Poor nutritional status is common (estimated prevalence 5–69%) in acute coronavirus disease-2019 (COVID-19), and has been associated with hospitalization, the need for intensive care, and mortality. Body composition (BC) and muscle function have also been related in such patients to poor disease outcomes.As the evidence in the literature is limited, a cross-sectional study was carried out to determine the frequency of malnutrition in a cohort of post-acute COVID-19 patients referred to a rehabilitation center after hospital discharge. BC and muscle strength were assessed and the differences between bedridden and not bedridden patients were specifically evaluated.The study sample was composed of 144 post-acute COVID-19 patients (mean age 64.8 years; males = 95), 37% of whom were bedridden (males = 60%). Nutritional status was evaluated with Mini-Nutritional Assessment (MNA) and Controlling Nutritional status (CONUT). Fat-free mass (FFM) and skeletal muscle mass (SM) were estimated using bioelectrical impedance analysis (BIA). Raw BIA variables (phase angle = PhA and impedance ratios = IRs) were also determined and handgrip strength (HGS) was measured. Dynapenia was identified according to the 2019 EWGSOP criteria.According to MNA, 18% (n. 26) of patients were malnourished and 62% (n. 89) were at risk of malnutrition. As for CONUT, 21% (n. 31) of cases had moderate–severe malnutrition and 58% (n. 83) had light malnutrition. Abnormalities of raw BIA variables (low PhA and high IRs) and low HGS were more common in bedridden patients, in those who were malnourished, or had low FFM or SM. Dynapenic patients were 65% men and 47% women.In conclusion, malnutrition, BC alterations, and low HGS occur in post-acute COVID-19 patients and are more common in bedridden patients. Further studies are needed to identify reliable algorithms for assessing nutritional status in post-acute COVID-19 patients undergoing rehabilitation.
FOXP4-AS1 May be a Potential Prognostic Biomarker in Human Cancers: A Meta−Analysis and Bioinformatics Analysis
Authors: Zhang Guangming , Wang Yongfeng , Han Xiaoyong , Lu Tingting , Fu Liangyin , Jin Haojie , Yang Kehu , Cai Hui 
Published: June 2022

BackgroundCancer is one of the leading causes of death worldwide. Early diagnosis can significantly lower cancer-related mortality. Studies have shown that the lncRNA Forkhead box P4 antisense RNA 1 (FOXP4-AS1) is aberrantly expressed in various solid tumors. A meta-analysis was performed to evaluate the correlation of FOXP4-AS1 with the prognosis of cancer patients and determine the clinical value of FOXP4-AS1 as a potential diagnostic marker.MethodsCorrelational studies from the Web of Science, Embase, OVID, Cochrane and PubMed databases were screened (up to April 1, 2021). Meta-analysis was performed using Stata SE12.0 software.ResultsEleven original studies with 1,332 patients who were diagnosed with a solid cancer (nasopharyngeal carcinoma, hepatocellular carcinoma, colorectal cancer, gastric cancer, osteosarcoma, mantle cell lymphoma, prostate cancer, and pancreatic ductal adenocarcinoma) were included in the meta-analysis. High expression of FOXP4-AS1 was correlated with poor overall survival (OS) (HR = 1.77, 95% CI 1.29–2.44, P &lt; 0.001) and shorter disease−free survival (DFS) (HR = 1.66, 95% CI 1.01–2.72, P = 0.044). Subgroup analysis based on sample size, follow-up time and Newcastle-Ottawa Scale (NOS) score revealed significant differences between FOXP4-AS1 levels and OS (P &lt; 0.05). However, the expression level of FOXP4-AS1 was not significantly correlated with the OS of gastric cancer patients (P = 0.381). High expression of FOXP4-AS1 was predictive of a larger tumor size (OR = 3.82, 95% CI 2.3–6.3, P &lt; 0.001).ConclusionsOverexpression of FOXP4-AS1 correlates with poor prognosis of cancer patients, and is a potential prognostic biomarker and therapeutic target.Systematic Review RegistrationPROSPERO, identifier CRD42021245267.
Molecular Typing of Gastric Cancer Based on Invasion-Related Genes and Prognosis-Related Features
Authors: Guo Haonan , Tang Hui , Zhao Yang , Zhao Qianwen , Hou Xianliang , Ren Lei 
Published: June 2022

BackgroundThis study aimed to construct a prognostic stratification system for gastric cancer (GC) using tumour invasion-related genes to more accurately predict the clinical prognosis of GC.MethodologyTumour invasion-related genes were downloaded from CancerSEA, and their expression data in the TCGA-STAD dataset were used to cluster samples via non-negative matrix factorisation (NMF). Differentially expressed genes (DEGs) between subtypes were identified using the limma package. KEGG pathway and GO functional enrichment analyses were conducted using the WebGestaltR package (v0.4.2). The immune scores of molecular subtypes were evaluated using the R package ESTIMATE, MCPcounter and the ssGSEA function of the GSVA package. Univariate, multivariate and lasso regression analyses of DEGs were performed using the coxph function of the survival package and the glmnet package to construct a RiskScore model. The robustness of the model was validated using internal and external datasets, and a nomogram was constructed based on the model.ResultsBased on 97 tumour invasion-related genes, 353 GC samples from TCGA were categorised into two subtypes, thereby indicating the presence of inter-subtype differences in prognosis. A total of 569 DEGs were identified between the two subtypes; of which, four genes were selected to construct the risk model. This four-gene signature was robust and exhibited stable predictive performance in different platform datasets (GSE26942 and GSE66229), indicating that the established model performed better than other existing models.ConclusionA prognostic stratification system based on a four-gene signature was developed with a desirable area under the curve in the training and independent validation sets. Therefore, the use of this system as a molecular diagnostic test is recommended to assess the prognostic risk of patients with GC.
Deep Learning for Approaching Hepatocellular Carcinoma Ultrasound Screening Dilemma: Identification of α-Fetoprotein-Negative Hepatocellular Carcinoma From Focal Liver Lesion Found in High-Risk Patients
Authors: Zhang Wei-bin , Hou Si-ze , Chen Yan-ling , Mao Feng , Dong Yi , Chen Jian-gang , Wang Wen-ping 
Published: June 2022

BackgroundFirst-line surveillance on hepatitis B virus (HBV)-infected populations with B-mode ultrasound is relatively limited to identifying hepatocellular carcinoma (HCC) without elevated α-fetoprotein (AFP). To improve the present HCC surveillance strategy, the state of the art of artificial intelligence (AI), a deep learning (DL) approach, is proposed to assist in the diagnosis of a focal liver lesion (FLL) in HBV-infected liver background.MethodsOur proposed deep learning model was based on B-mode ultrasound images of surgery that proved 209 HCC and 198 focal nodular hyperplasia (FNH) cases with 413 lesions. The model cohort and test cohort were set at a ratio of 3:1, in which the test cohort was composed of AFP-negative HBV-infected cases. Four additional deep learning models (MobileNet, Resnet50, DenseNet121, and InceptionV3) were also constructed as comparative baselines. To evaluate the models in terms of diagnostic power, sensitivity, specificity, accuracy, confusion matrix, F1-score, and area under the receiver operating characteristic curve (AUC) were calculated in the test cohort.ResultsThe AUC of our model, Xception, achieved 93.68% in the test cohort, superior to other baselines (89.06%, 85.67%, 83.94%, and 78.13% respectively for MobileNet, Resnet50, DenseNet121, and InceptionV3). In terms of diagnostic power, our model showed sensitivity, specificity, accuracy, and F1-score of 96.08%, 76.92%, 86.41%, and 87.50%, respectively, and PPV, NPV, FPR, and FNR calculated from the confusion matrix were respectively 80.33%, 95.24%, 23.08%, and 3.92% in identifying AFP-negative HCC from HBV-infected FLL cases. Satisfactory robustness of our proposed model was shown based on 5-fold cross-validation performed among the models above.ConclusionsOur DL approach has great potential to assist B-mode ultrasound in identifying AFP-negative HCC from FLL found in surveillance of HBV-infected patients.
Fibroblast-Induced Paradoxical PI3K Pathway Activation in PTEN-Competent Colorectal Cancer: Implications for Therapeutic PI3K/mTOR Inhibition
Authors: Conciatori Fabiana , Salvati Erica , Ciuffreda Ludovica , Shirasawa Senji , Falcone Italia , Cognetti Francesco , Ferretti Gianluigi , Zeuli Massimo , Del Bufalo Donatella , Bazzichetto Chiara , Milella Michele 
Published: June 2022

PurposeTumor-microenvironment interactions are important determinants of drug resistance in colorectal cancer (CRC). We, therefore, set out to understand how interactions between genetically characterized CRC cells and stromal fibroblasts might influence response to molecularly targeted inhibitors.TechniquesSensitivity to PI3K/AKT/mTOR pathway inhibitors of CRC cell lines, with known genetic background, was investigated under different culture conditions [serum-free medium, fibroblasts’ conditioned medium (CM), direct co-culture]. Molecular pathway activation was monitored using Western Blot analysis. Immunoprecipitation was used to detect specific mTOR complex activation. Immunofluorescence was used to analyze cellular PTEN distribution, while different mutant PTEN plasmids were used to map the observed function to specific PTEN protein domains.ResultsExposure to fibroblast-CM resulted in increased growth-inhibitory response to double PI3K/mTOR inhibitors in PTEN-competent CRC cell lines harboring KRAS and PI3K mutations. Such functional effect was attributable to fibroblast-CM induced paradoxical PI3K/mTORC1 pathway activation, occurring in the presence of a functional PTEN protein. At a molecular level, fibroblast-CM induced C-tail phosphorylation and cytoplasmic redistribution of the PTEN protein, thereby impairing its lipid phosphatase function and favored the formation of active, RAPTOR-containing, mTORC1 complexes. However, PTEN’s lipid phosphatase function appeared to be dispensable, while complex protein-protein interactions, also involving PTEN/mTOR co-localization and subcellular distribution, were crucial for both mTORC1 activation and sensitivity to double PI3K/mTOR inhibitors.Data InterpretationMicroenvironmental cues, in particular soluble factors produced by stromal fibroblasts, profoundly influence PI3K pathway signaling and functional response to specific inhibitors in CRC cells, depending on their mutational background and PTEN status.
MRI-Based Radiomics and Urine Creatinine for the Differentiation of Renal Angiomyolipoma With Minimal Fat From Renal Cell Carcinoma: A Preliminary Study
Authors: Jian Lian , Liu Yan , Xie Yu , Jiang Shusuan , Ye Mingji , Lin Huashan 
Published: June 2022

ObjectivesStandard magnetic resonance imaging (MRI) techniques are different to distinguish minimal fat angiomyolipoma (mf-AML) with minimal fat from renal cell carcinoma (RCC). Here we aimed to evaluate the diagnostic performance of MRI-based radiomics in the differentiation of fat-poor AMLs from other renal neoplasms.MethodsA total of 69 patients with solid renal tumors without macroscopic fat and with a pathologic diagnosis of RCC (n=50) or mf-AML (n=19) who underwent conventional MRI and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) were included. Clinical data including age, sex, tumor location, urine creatinine, and urea nitrogen were collected from medical records. The apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (f) were measured from renal tumors. We used the ITK-SNAP software to manually delineate the regions of interest on T2-weighted imaging (T2WI) and IVIM-DWI from the largest cross-sectional area of the tumor. We extracted 396 radiomics features by the Analysis Kit software for each MR sequence. The hand-crafted features were selected by using the Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO). Diagnostic models were built by logistic regression analysis. Receiver operating characteristic curve analysis was performed using five-fold cross-validation and the mean area under the curve (AUC) values were calculated and compared between the models to obtain the optimal model for the differentiation of mf-AML and RCC. Decision curve analysis (DCA) was used to evaluate the clinical utility of the models.ResultsClinical model based on urine creatinine achieved an AUC of 0.802 (95%CI: 0.761-0.843). IVIM-based model based on f value achieved an AUC of 0.692 (95%CI: 0.627-0.757). T2WI-radiomics model achieved an AUC of 0.883 (95%CI: 0.852-0.914). IVIM-radiomics model achieved an AUC of 0.874 (95%CI: 0.841-0.907). Combined radiomics model achieved an AUC of 0.919 (95%CI: 0.894-0.944). Clinical-radiomics model yielded the best performance, with an AUC of 0.931 (95%CI: 0.907-0.955). The calibration curve and DCA confirmed that the clinical-radiomics model had a good consistency and clinical usefulness.ConclusionThe clinical-radiomics model may be served as a noninvasive diagnostic tool to differentiate mf-AML with RCC, which might facilitate the clinical decision-making process.