bluuebunny/crossref_metadata_2025_split huggingface dataset:
Molecular analysis and prenatal diagnosis of seven Chinese families with genetic epilepsy
Authors: Mao Bin , Lin Na , Guo Danhua , He Deqin , Xue Huili , Chen Lingji , He Qianqian , Zhang Min , Chen Meihuan , Huang Hailong , Xu Liangpu 
Published: May 2023

IntroductionGenetic epilepsy is a large group of clinically and genetically heterogeneous neurological disorders characterized by recurrent seizures, which have a clear association with genetic defects. In this study, we have recruited seven families from China with neurodevelopmental abnormalities in which epilepsy was a predominant manifestation, aiming to elucidate the underlying causes and make a precise diagnosis for the cases.MethodsWhole-exome sequencing (WES) combined with Sanger sequencing was used to identify the causative variants associated with the diseases in addition to essential imaging and biomedical examination.ResultsA gross intragenic deletion detected in MFSD8 was investigated via gap-polymerase chain reaction (PCR), real-time quantitative PCR (qPCR), and mRNA sequence analysis. We identified 11 variants in seven genes (ALDH7A1, CDKL5, PCDH19, QARS1, POLG, GRIN2A, and MFSD8) responsible for genetic epilepsy in the seven families, respectively. A total of six variants (c.1408T&gt;G in ALDH7A1, c.1994_1997del in CDKL5, c.794G&gt;A in QARS1, c.2453C&gt;T in GRIN2A, and c.217dup and c.863+995_998+1480del in MFSD8) have not yet been reported to be associated with diseases and were all evaluated to be pathogenic or likely pathogenic according to the American College of Medical Genetics and Genomics (ACMG) guidelines.MethodsBased on the molecular findings, we have associated the intragenic deletion in MFSD8 with the mutagenesis mechanism of Alu-mediated genomic rearrangements for the first time and provided genetic counseling, medical suggestions, and prenatal diagnosis for the families. In conclusion, molecular diagnosis is crucial to obtain improved medical outcomes and recurrence risk evaluation for genetic epilepsy.
Focal cross transformer: multi-view brain tumor segmentation model based on cross window and focal self-attention
Authors: Zongren Li , Silamu Wushouer , Shurui Feng , Guanghui Yan 
Published: May 2023

IntroductionRecently, the Transformer model and its variants have been a great success in terms of computer vision, and have surpassed the performance of convolutional neural networks (CNN). The key to the success of Transformer vision is the acquisition of short-term and long-term visual dependencies through self-attention mechanisms; this technology can efficiently learn global and remote semantic information interactions. However, there are certain challenges associated with the use of Transformers. The computational cost of the global self-attention mechanism increases quadratically, thus hindering the application of Transformers for high-resolution images.MethodsIn view of this, this paper proposes a multi-view brain tumor segmentation model based on cross windows and focal self-attention which represents a novel mechanism to enlarge the receptive field by parallel cross windows and improve global dependence by using local fine-grained and global coarse-grained interactions. First, the receiving field is increased by parallelizing the self-attention of horizontal and vertical fringes in the cross window, thus achieving strong modeling capability while limiting the computational cost. Second, the focus on self-attention with regards to local fine-grained and global coarse-grained interactions enables the model to capture short-term and long-term visual dependencies in an efficient manner.ResultsFinally, the performance of the model on Brats2021 verification set is as follows: dice Similarity Score of 87.28, 87.35 and 93.28%; Hausdorff Distance (95%) of 4.58 mm, 5.26 mm, 3.78 mm for the enhancing tumor, tumor core and whole tumor, respectively.DiscussionIn summary, the model proposed in this paper has achieved excellent performance while limiting the computational cost.
Golgi_DF: Golgi proteins classification with deep forest
Authors: Bao Wenzheng , Gu Yujian , Chen Baitong , Yu Huiping 
Published: May 2023

IntroductionGolgi is one of the components of the inner membrane system in eukaryotic cells. Its main function is to send the proteins involved in the synthesis of endoplasmic reticulum to specific parts of cells or secrete them outside cells. It can be seen that Golgi is an important organelle for eukaryotic cells to synthesize proteins. Golgi disorders can cause various neurodegenerative and genetic diseases, and the accurate classification of Golgi proteins is helpful to develop corresponding therapeutic drugs.MethodsThis paper proposed a novel Golgi proteins classification method, which is Golgi_DF with the deep forest algorithm. Firstly, the classified proteins method can be converted the vector features containing various information. Secondly, the synthetic minority oversampling technique (SMOTE) is utilized to deal with the classified samples. Next, the Light GBM method is utilized to feature reduction. Meanwhile, the features can be utilized in the penultimate dense layer. Therefore, the reconstructed features can be classified with the deep forest algorithm.ResultsIn Golgi_DF, this method can be utilized to select the important features and identify Golgi proteins. Experiments show that the well-performance than the other art-of-the state methods. Golgi_DF as a standalone tools, all its source codes publicly available at https://github.com/baowz12345/golgiDF.DiscussionGolgi_DF employed reconstructed feature to classify the Golgi proteins. Such method may achieve more available features among the UniRep features.
Nutritional status of a group of polish children with FASD: A retrospective study
Authors: Domin Agnieszka , Mazur Artur 
Published: May 2023

IntroductionFetal alcohol spectrum disorders (FASDs) are a leading preventable cause of developmental and psychosocial disorders. Prenatal alcohol exposure can be a cause of growth impairment and metabolic problems. In this study, we analyzed data on the growth, weight, and nutritional status of children with FASD.MethodsPatients were recruited from the Department of Pediatrics, Pediatric Endocrinology and Diabetology, and the Outpatient Endocrinology Clinic in Rzeszów, Poland. Each person referred for evaluation had a diagnosis of FASD based on the recommendations of Polish experts. The population consisted of 59 subjects with measurements of weight and height, and the IGF-1 level test was performed.ResultsChildren with FAS had consistently lower height and weight measurements than children with ND-PAE. In the FAS group, children (&lt;3 percentile) accounted for 42.31%, and in the ND-PAE group – 18.18%. The analysis of the whole group showed the highest prevalence of low body weight (below the third percentile) among subjects with FAS – 53.85%. The prevalence of low body weight and short stature (both parameters &lt;3rd centile) was found to be 27.11% in the whole group. Lower mean BMI values were related to the FAS group (21.71 kg/m2) compared to the ND-PAE group (39.62 kg/m2). In the study group, BMI below the fifth percentile was found in 28.81% of the children, normal weight (5th-85th percentile) in 67.80%.DiscussionDuring the care of children with FASD, a continuous evaluation of nutritional status, height, and weight is necessary. This group of patients is often affected by low birth weight, short stature and weight deficiency, which require differential diagnosis and appropriate dietary and therapeutic management.
Prevalence and mortality risk of low skeletal muscle mass in critically ill patients: an updated systematic review and meta-analysis
Authors: Yang Hui , Wan Xi-Xi , Ma Hui , Li Zhen , Weng Li , Xia Ying , Zhang Xiao-Ming 
Published: May 2023

BackgroundPatients with critical illness often develop low skeletal muscle mass (LSMM) for multiple reasons. Numerous studies have explored the association between LSMM and mortality. The prevalence of LSMM and its association with mortality are unclear. This systematic review and meta-analysis was performed to examine the prevalence and mortality risk of LSMM among critically ill patients.MethodsThree internet databases (Embase, PubMed, and Web of Science) were searched by two independent investigators to identify relevant studies. A random-effects model was used to pool the prevalence of LSMM and its association with mortality. The GRADE assessment tool was used to assess the overall quality of evidence.ResultsIn total, 1,582 records were initially identified in our search, and 38 studies involving 6,891 patients were included in the final quantitative analysis. The pooled prevalence of LSMM was 51.0% [95% confidence interval (CI), 44.5–57.5%]. The subgroup analysis showed that the prevalence of LSMM in patients with and without mechanical ventilation was 53.4% (95% CI, 43.2–63.6%) and 48.9% (95% CI, 39.7–58.1%), respectively (P-value for difference = 0.44). The pooled results showed that critically ill patients with LSMM had a higher risk of mortality than those without LSMM, with a pooled odds ratio of 2.35 (95% CI, 1.91–2.89). The subgroup analysis based on the muscle mass assessment tool showed that critically ill patients with LSMM had a higher risk of mortality than those with normal skeletal muscle mass regardless of the different assessment tools used. In addition, the association between LSMM and mortality was statistically significant, independent of the different types of mortality.ConclusionOur study revealed that critically ill patients had a high prevalence of LSMM and that critically ill patients with LSMM had a higher risk of mortality than those without LSMM. However, large-scale and high-quality prospective cohort studies, especially those based on muscle ultrasound, are required to validate these findings.Systematic review registrationhttp://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022379200.
Case report: An asymptomatic mother with an inborn error of cobalamin metabolism (cblC) detected through high homocysteine levels during prenatal diagnosis
Authors: Liu Yu-Peng , He Ru-Xuan , Chen Zhe-Hui , Kang Lu-Lu , Song Jin-Qing , Liu Yi , Shi Chun-Yan , Chen Jun-Ya , Dong Hui , Zhang Yao , Li Meng-Qiu , Jin Ying , Qin Jiong , Yang Yan-Ling 
Published: May 2023

BackgroundThe most common disorder of the intracellular cobalamin metabolism pathway is the combined methylmalonic acidemia and homocysteinemia, cblC type (cblC). There is a variation in its clinical spectrum ranging from severe neonatal-onset forms that are highly fatal to later-onset forms which are milder. In this study, the first case of an asymptomatic Chinese woman with a defect in congenital cobalamin (cblC type) metabolism at prenatal diagnosis due to elevated homocysteine level is identified.Case presentationThe proband, a male child born to a 29-year-old G1P0 mother, admitted to local hospital with feeding disorder, intellectual disability, seizures, microcephaly, as well as heterophthalmos. The level of the urine methylmalonic was elevated. Equally found were increased blood propionylcarnitine (C3) and propionylcarnitine/free carnitine ratio (C3/C0) and decreased methionine levels. The plasma total homocysteine level was elevated at 101.04 μmol/L (normal &lt; 15 μmol/L). The clinical diagnosis of combined methylmalonic acidemia and homocysteinemia was supported. Four years later, the mother of the boy married again and came to us for prenatal diagnosis exactly 15 weeks after her last menstrual period. Subsequently, there is an increase in the amniotic fluid methylmalonate. The level of the amniotic fluid total homocysteine was marginally high. A considerably elevated amniotic fluid C3 was equally observed. In addition, there is a respective significant increase in the plasma and urine total homocysteine at 31.96 and 39.35 μmol/L. After the sequencing of MMACHC genes, it is found that the boy, a proband carried a homozygous mutation of the MMACHC at c.658_660delAAG. While the boy's mother, she carries two mutations in MMACHC: c.658_660delAAG and c.617G&gt;A. The fetus is a carrier of the MMACHC gene. Following the administration of routine treatment, the mother remained symptom-free in the course of pregnancy, and she gave birth to a healthy boy.ConclusionVariable and nonspecific symptoms characterized the cblC type of methylmalonic acidemia combined with homocysteinemia. Both biochemical assays and mutation analysis are recommended as crucial complementary techniques.
Phytochemical screening of Prunus avium for its antioxidative and anti-mutagenic potential against DMBA-induced hepatocarcinogenesis
Authors: Saeed Raakia Anam , Khan Muhammad Issa , Butt Masood Sadiq , Faisal Muhammad Naeem 
Published: May 2023

ScopePrunus avium fruit is the richer source of phenolics known to exert anticancer and anti-invasive activities. The study aimed at elucidating antiproliferative and chemo-preventive potential of sweet cherries (P. avium) against the in vivo hepatocarcinoma model.Methods and resultsThe quantification of ultrasound-assisted extract (UAE) of P. avium depicted anthocyanins, ferulic acid, gallic acid, quercetin, syringic acid and p- and m-coumaric acids as major phytochemicals. The hepatocarcinoma (HCC) was induced in rats through intraperitoneal administration of DMBA (20 mg/kg B.W) once a week for the period of eight weeks. The intragastric administration of P. avium UAE, as cotreatment (500 mg/Kg B.W) to treatment group, significantly (p &lt; 0.01) attenuated the raised serum alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH) as well as total oxidative stress (TOS) and enhanced total antioxidant capacity TAOC in contrast to diseased rats. Moreover, microscopic examination of hepatic tissues confirmed the pleomorphism, nests of neoplastic hepatocytes and necrosis in HCC-bearing rats as compared to extract-fed rats, where these necrotic changes were suppressed. Besides, qRT-PCR analysis of hepatic tissues demonstrated the higher mRNA expression of CHEK1, CHEK2 and P21/CDKN1α genes, while downexpression of ATM gene in extract fed rats, further denoting the anti-mutagenic potential.ConclusionConsequently, the polyphenol-rich sweet cherries UAE exhibited antiproliferative and chemo-preventive potential by reducing tumor biomarkers, serum transaminases and oxidative stress, as well as enhancing antioxidant status. It further upregulated the downstream targets of ATM signaling cascade.
Development and internal validation of a nomogram for predicting survival of nonoperative EGFR-positive locally advanced elderly esophageal cancers
Authors: Wang Jiayang , Peng Jin , Luo Honglei , Song Yaqi 
Published: May 2023

PurposeThis study aims to develop and validate a prediction model for non-operative, epidermal growth factor receptor (EGFR)-positive, locally advanced elderly esophageal cancer (LAEEC).MethodsA total of 80 EGFR-positive LAEEC patients were included in the study. All patients underwent radiotherapy, while 41 cases received icotinib concurrent systemic therapy. A nomogram was established using univariable and multivariable Cox analyses. The model’s efficacy was assessed through area under curve (AUC) values, receiver operating characteristic (ROC) curves at different time points, time-dependent AUC (tAUC), calibration curves, and clinical decision curves. Bootstrap resampling and out-of-bag (OOB) cross-validation methods were employed to verify the model’s robustness. Subgroup survival analysis was also conducted.ResultsUnivariable and multivariable Cox analyses revealed that icotinib, stage, and ECOG score were independent prognostic factors for LAEEC patients. The AUCs of model-based prediction scoring (PS) for 1-, 2-, and 3-year overall survival (OS) were 0.852, 0.827, and 0.792, respectively. Calibration curves demonstrated that the predicted mortality was consistent with the actual mortality. The time-dependent AUC of the model exceeded 0.75, and the internal cross-validation calibration curves showed good agreement between predicted and actual mortality. Clinical decision curves indicated that the model had a substantial net clinical benefit within a threshold probability range of 0.2 to 0.8. Model-based risk stratification analysis demonstrated the model’s excellent ability to distinguish survival risk. Further subgroup analyses showed that icotinib significantly improved survival in patients with stage III and ECOG score of 1 (HR 0.122, P&lt;0.001).ConclusionsOur nomogram model effectively predicts the overall survival of LAEEC patients, and the benefits of icotinib were found in the clinical stage III population with good ECOG scores.
Sociodemographic disparities in targeted therapy in ovarian cancer in a national sample
Authors: Amin Saber A. , Collin Lindsay J. , Kavecansky Juraj , Setoguchi Soko , Satagopan Jaya M. , Bandera Elisa V. 
Published: May 2023

BackgroundThe treatment landscape for ovarian cancer has changed in recent years with the introduction of targeted therapies to treat patients with advanced disease. We investigated patient demographic and clinical factors associated with use of targeted therapies as a part of the first-line treatment for ovarian cancer.MethodsThis study included patients diagnosed with stage I–IV ovarian cancer between 2012 and 2019 from the National Cancer Database. Information on demographic and clinical characteristics were collected and described using frequency and percent across receipt of targeted therapy. Logistic regression was used to compute the odds ratios (ORs) and 95% confidence intervals (CI) associating patient demographic and clinical factors with receipt of targeted therapy.ResultsAmong 99,286 ovarian cancer patients (mean age 62 years), 4.1% received targeted therapy. The rate of targeted therapy receipt across racial and ethnic groups over the study period was fairly similar; however, non-Hispanic Black women were less likely to receive targeted therapy than their non-Hispanic White counterparts (OR=0.87, 95% CI: 0.76–1.00). Patients who received neoadjuvant chemotherapy were more likely to receive targeted therapy than those who received adjuvant chemotherapy (OR=1.26; 95% CI: 1.15–1.38). Moreover, among patients who received targeted therapy, 28% received neoadjuvant targeted therapy, with non-Hispanic Black women being most likely to receive neoadjuvant targeted therapy (34%) compared with other racial and ethnic groups.ConclusionsWe observed differences in receipt of targeted therapy by factors such as age at diagnosis, stage, and comorbidities present at diagnosis, as well as factors related to healthcare access—including neighborhood education level and health insurance status. Approximately 28% of patients received targeted therapy in the neoadjuvant setting, which could negatively impact treatment outcomes and survival due to the increased risk of complications associated with targeted therapies that may delay or prevent surgery. These results warrant further evaluation in a cohort of patients with more comprehensive treatment information.
A nomogram based on cuproptosis-related genes predicts 7-year relapse-free survival in patients with estrogen receptor-positive early breast cancer
Authors: Fan Yu , Luo Chuanxu , Wang Yu , Wang Zhu , Wang Chengshi , Zhong Xiaorong , Hu Kejia , Wang Yanping , Lu Donghao , Zheng Hong 
Published: May 2023

IntroductionExcess copper induces cell death by binding to lipoylated components of the tricarboxylic acid cycle. Although a few studies have examined the relationship between cuproptosis-related genes (CRGs) and breast cancer prognosis, reports on estrogen receptor-positive (ER+) breast cancer are lacking. Herein, we aimed to analyze the relationship between CRGs and outcomes in patients with ER+ early breast cancer (EBC).MethodsWe conducted a case-control study among patients with ER+ EBC presenting poor and favorable invasive disease-free survival (iDFS) at West China Hospital. Logistic regression analysis was performed to establish the association between CRG expression and iDFS. A cohort study was performed using pooled data from three publicly available microarray datasets in the Gene Expression Omnibus database. Subsequently, we constructed a CRG score model and a nomogram to predict relapse-free survival (RFS). Finally, the prediction performance of the two models was verified using training and validation sets.ResultsIn this case-control study, high expression of LIAS, LIPT1, and ATP7B and low CDKN2A expression were associated with favorable iDFS. In the cohort study, high expression of FDX1, LIAS, LIPT1, DLD, PDHB, and ATP7B and low CDKN2A expression were associated with favorable RFS. Using LASSO-Cox analysis, a CRG score was developed using the seven identified CRGs. Patients in the low CRG score group had a reduced risk of relapse in both training and validation sets. The nomogram included the CRG score, lymph node status, and age. The area under the receiver operating characteristic (ROC) curve (AUC) of the nomogram was significantly higher than the AUC of the CRG score at 7 years.ConclusionsThe CRG score, combined with other clinical features, could afford a practical long-term outcome predictor in patients with ER+ EBC.
Delta-radiomics based on CT predicts pathologic complete response in ESCC treated with neoadjuvant immunochemotherapy and surgery
Authors: Li Kaiyuan , Li Yuetong , Wang Zhulin , Huang Chunyao , Sun Shaowu , Liu Xu , Fan Wenbo , Zhang Guoqing , Li Xiangnan 
Published: May 2023

Background and purposeUnnecessary surgery can be avoided, and more appropriate treatment plans can be developed for patients if the efficacy of neoadjuvant immunochemotherapy for esophageal cancer (EC) can be predicted before surgery. The purpose of this study was to evaluate the ability of machine learning models based on delta features of immunochemotherapy CT images to predict the efficacy of neoadjuvant immunochemotherapy in patients with esophageal squamous cell carcinoma (ESCC) compared with machine learning models based solely on postimmunochemotherapy CT images.Materials and methodsA total of 95 patients were enrolled in our study and randomly divided into a training group (n = 66) and test group (n = 29). We extracted preimmunochemotherapy radiomics features from preimmunochemotherapy enhanced CT images in the preimmunochemotherapy group (pregroup) and postimmunochemotherapy radiomics features from postimmunochemotherapy enhanced CT images in the postimmunochemotherapy group (postgroup). We then subtracted the preimmunochemotherapy features from the postimmunochemotherapy features and obtained a series of new radiomics features that were included in the delta group. The reduction and screening of radiomics features were carried out by using the Mann-Whitney U test and LASSO regression. Five pairwise machine learning models were established, the performance of which was evaluated by receiver operating characteristic (ROC) curve and decision curve analyses.ResultsThe radiomics signature of the postgroup was composed of 6 radiomics features; that of the delta-group was composed of 8 radiomics features. The area under the ROC curve (AUC) of the machine learning model with the best efficacy was 0.824 (0.706-0.917) in the postgroup and 0.848 (0.765-0.917) in the delta group. The decision curve showed that our machine learning models had good predictive performance. The delta group performed better than the postgroup for each corresponding machine learning model.ConclusionWe established machine learning models that have good predictive efficacy and can provide certain reference values for clinical treatment decision-making. Our machine learning models based on delta imaging features performed better than those based on single time-stage postimmunochemotherapy imaging features.