bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification of Aging-related genes and prognostic model construction in gastric cancer
Authors: Li Jiajun , Xie Wei , Zhang Kunchi , Geng Haigang 
Published: July 2022

Abstract
Background Gastric cancer (GC) is one of the most common tumors and has a poor prognosis. Many studies have shown that aging is closely related to the occurrence and development of cancer. This paper aims to construct an aging gene-related prognostic risk model for early diagnosis and prognosis prediction of gastric cancer.Methods Transcriptome data and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Synthesis (GEO), and aging-related genes (ARGs) were obtained from Human 3 Aging Genomic Resources. The prognostic model was constructed using the GEO database as the training set, and the TCGA database was used as the validation set to validate the prognostic model. To analyze the predictive value and potential clinical application of ARGs in gastric cancer.Results we analyzed ARGs expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Synthesis (GEO). After LASSO and Cox regression analysis, 5 ARG-based features (A2M, AGRT1, CTF1, SNCG, and SERPINE1) were constructed using the GEO dataset to significantly classify cases into high- and low-risk groups according to overall survival (OS). Cox regression analysis showed that ARG features were independent prognostic factors for GC. A nomogram based on ARG characteristics and clinicopathological factors was developed in the GSE62254 (ACRG) cohort and validated in the TCGA dataset. Calibration plots show good agreement between the nomogram's predictions and actual observations. Receiver decision curve analysis showed that ARG nomogram had better OS prediction and clinical net benefit than staging system.Conclusions We developed and validated five aging-related genes prognostic models for gastric cancer, including A2M, AGRT1, CTF1, SNCG, and SERPINE1. This model can be used as a new molecular marker for prognosis prediction of gastric cancer,
Combined Pollution of Heavy Metals and Polycyclic Aromatic Hydrocarbons in the soil in Shenfu Region, China - A Case of Three Different Cities
Authors: Lu Yin-tao , Zhang Yue , Xiang Xin-xin , Zhang Shi-chao , Yao Hong 
Published: July 2022

Abstract
It is a challenging issue to investigate the combined pollution of heavy metals and polycyclic aromatic hydrocarbons (PAHs) in urban soils. The purpose of this study was to determine the concentrations of these two pollutants in soils in Shenyang, Fushun, and Fushun New District, to analyze their distribution, and to assess their interaction and co-contamination levels. The concentrations of heavy metals were measured by inductively coupled plasma mass spectrometry (ICP-MS), while the concentrations of 21 kinds of PAH were analyzed by GC-MS. Based on the analysis of pollution concentrations and distribution patterns, the intrinsic links between heavy metals and PAHs in three different cities were assessed using a variety of multivariate analysis methods. Compared to Shenfu New Distract, the concentration of pollutants in Shenyang and Fushun shows a higher level. Moreover, the results of RDA analysis of samples may quantify the possibility of combined pollution of different heavy metal elements and PAHs. This study also affirms the important role of multivariate analysis in being used to reveal the complex interactions and spatial distribution of different pollutants.
Parental Factors related to Development of Non-Syndromic Craniosynostosis in their Children
Authors: Webb Timothy , O’Brien Patrick , Orndahl Christine M. , Rhodes Jennifer , Navarrete Rene Olivares , Ritter Ann M. 
Published: July 2022

Abstract
Purpose: There are case reports on potential parental risk factors regarding children with non-syndromic craniosynostosis implicating multiple possible pathologic contributions. A case control study was undertaken to elucidate any underlying trends of parental risk factors which yield craniosynostosis in their offspring. Methods: An IRB approved case-control study was performed by administering a survey to parents of children with and without craniosynostosis at the Children’s Hospital of Richmond from March 2018 to December 2019. To determine important risk factors for craniosynostosis, a classification and regression tree (CART) algorithm was employed. Results: Survey responses for 128 children were obtained, 78 (60.93%) of which had craniosynostosis. The remaining 50 children served as the control group. The classification tree produced splits based on the following five predictors: prematurity of the child, gender of the child, educational status of the mother, mother’s history of smoking and maternal diabetes. The most important factor was preterm birth of the infant. The second factor was the gender of the child followed by educational level and history of smoking, and finally presence of diabetes in the mother. The resulting classification tree was 78.9% accurate with high sensitivity (81.1%) and specificity (78.0%).Conclusion: Potential important predictors for craniosynostosis were identified through the classification tree including prematurity, sex of the child, maternal education level, maternal smoking status, and maternal diabetes status. These findings may help guide future research efforts into the pathophysiology of non-syndromic craniosynostosis.
Co-design of a cancer nutrition care pathway by patients, carers and health professionals: The CanEAT pathway
Authors: Loeliger Jenelle , Dewar Sarah , Kiss Nicole , Dumbrell Jodi , Elliott Andrea , Kaegi Kate , Kelaart Amber , McIntosh Rebecca , Swan Wendy , Stewart Jane 
Published: July 2022

Abstract
Purpose: Limited practical resources exist to guide optimal nutrition care for patients, carers and health professionals (HPs). This study aimed to co-design a cancer nutrition care pathway to guide and improve the provision of consistent, evidence-based care with consumers and HPs. Methods: This study utilised an experienced-based co-design (EBCD) approach over five stages. Stage one involved stakeholder engagement and a literature review. Stage two included a survey and focus groups with patients/carers. Co-design workshops were conducted within stage three, key stakeholder consultation within stage four and the finalisation and dissemination of the cancer nutrition care pathway formed stage five. Results of stages three to five are the focus of this paper.Results: Two co-design workshops were held with patients, carers and HPs (n=32 workshop one; n=32 workshop two), who collectively agreed on areas of focus and key priorities. Following this, a consultation period was completed with patients, carers and HPs (n=45) to refine the pathway. The collective outcome of all study stages was the co-design of a cancer nutrition care pathway (The CanEAT pathway) defining optimal cancer nutrition care that combines evidence-based practice tips into a centralised suite of resources, tools and clinical guidance.Conclusion: The CanEAT pathway was co-designed by patients, carers and HPs. The EBCD approach is a meaningful way to develop targeted improvements in cancer care. The CanEAT pathway is freely available to guide and support patients, carers and HPs to aid the implementation of optimal nutrition care into clinical practice.
Smartwatch digital phenotypes predict positive and negative symptom variation in a longitudinal monitoring study of patients with psychotic disorders
Authors: Smyrnis Nikolaos , Kalipserakis Emmanouil , karantinos Thomas , Lazaridi Marina , Garyfali Vasiliki , Filntisis Panagiotis , Zlatintsi Athanasia , Efthymiou Niki , Mantas Asimakis , Mantonakis Leonidas , Mougiakos Theodoros , Maglogiannis Ilias , Tsanakas Panayiotis , Maragos Petros 
Published: July 2022

Abstract
Monitoring biometric data using smartwatches (digital phenotypes) provides a novel approach for quantifying behavior in patients with psychiatric disorders. We tested whether such digital phenotypes predict changes in psychopathology of patients with psychotic disorders. We continuously monitored digital phenotypes from 35 patients (20 with schizophrenia and 15 with bipolar spectrum disorders) using a commercial smartwatch for a period of up to 14 months. These included 5-minute measures of fine motor activity from an accelerometer (FMA), average Heart Rate (HRA) and heart rate variability (HRV) from a plethysmography-based sensor, gross motor activity (GMA) measured as number of total steps per day and sleep/wake ratio (SWR). A self-reporting questionnaire (IPAQ) assessed weekly physical activity. After pooling phenotype data, their monthly mean and variance was correlated within each patient with psychopathology scores (PANSS) assessed monthly. Our results indicate that increased HRA during wakefulness and sleep correlated with increases in positive psychopathology. Besides, decreased HRV and increase in its monthly variance correlated with increases in negative psychopathology. Self-reported physical activity did not correlate with changes in psychopathology. These effects were independent from a series of demographic and clinical (including diagnosis) variables as well as changes in antipsychotic medication dose. Our findings suggest that distinct digital phenotypes derived passively from a smartwatch can predict variations in positive and negative dimensions of psychopathology of patients with psychotic disorders, over time, providing ground evidence for their potential clinical use.
The protective effect of Lasia spinosa (Linn.) dissipates chemical-induced cardiotoxicity in an animal model
Authors: Aktar Rasheda , Rahman Md. Atiar , Rafi Md. Khalid Juhani , Siddique Tanvir Ahmed , Bithy Farhana Yesmin , Akter Sumaiya , Nisa Fatema Yasmin , Khan Md. Asif Nadim , Sultana Farjana 
Published: July 2022

Abstract
Lasia spinosa (L.) Thwaites is a medicinal plant of enormous traditional use with insufficient scientific evidence. This research screened the antioxidative effect of L. spinosa extracts by measuring the total phenolic content, total flavonoid content, DPPH free radical scavenging activity, ABTS scavenging activity, Iron chelating activity, and Ferric reducing power followed by an evaluation of in-vivo cardioprotective effect in doxorubicin-induced Wistar albino rats. Phytochemical characterization was made by Gas-Chromatography Mass Spectroscopic analysis. L. spinosa showed an excellent antioxidative effect while Methanol leaf extract (LSM) was found to be more potent than Ethyl acetate leaf extract (LSE) in scavenging the free radicals. Intraperitoneal injection of doxorubicin caused a significant (P < 0.001) increase in lactate dehydrogenase (LDH), creatine kinase (CK-MB), C-reactive protein(CRP), and Cardiac troponin I. Pretreatment with orally administrated (LSM100 and LSM200 mg/kg b.w) daily for 10 days showed a decrease in the cardiac markers, lipid profiles, especially triglyceride (TG), total cholesterol (TC), Low-density lipoprotein (LDL), an increase of High-density lipoprotein (HDL) compared to the disease control group. LSM200 was found to significantly (P < 0.05) decrease the levels of CK-MB and LDH. It also restored TC, TG, and LDL levels compared to the doxorubicin-induced cardiac control group. The protective role of LSM was further confirmed by histopathological examination. This study thus demonstrates that L. spinosa methanol extract could be approached as an alternative supplement for cardiotoxicity, especially in the chemical-induced toxicity of cardiac tissues.
The contribution of penguin guano to the Southern Ocean iron pool
Authors: Korolev Oleg , Sparaventi Erica , Navarro Gabriel , Rodríguez-Romero Araceli , Tovar-Sanchez Antonio 
Published: July 2022

Abstract
Iron plays a crucial role in the high-nutrient, low-chlorophyll Southern Ocean regions, promoting phytoplankton growth and enhancing atmospheric carbon sequestration(1–5). In this area, the recycling of biogenic iron is partially driven by the iron-rich Antarctic krill (Euphausia superba), and baleen whale species as one of their main predators(6–10). However, although oceanic iron fertilization by Antarctic seabirds has been addressed by previous studies(11–13), penguins have received limited attention, despite their representing the largest seabird biomass in the southern polar region(14). Here, we use breeding site guano volumes estimated from drone images and cross-validated with deep learning-powered penguin census, in combination with guano chemical composition to assess the relative iron export to the Antarctic waters from one the most abundant penguin species, the Chinstrap penguin (Pygoscelis antarcticus). Our results show that these seabirds are a relevant but previously ignored contributor to the iron remobilization pool in the Southern Ocean. With an average guano concentration of 3 mg iron g^-1, we estimate that the Chinstrap penguin population is recycling 514 tonnes iron yr^-1. The current iron contribution represents half of the input these penguins were able to recycle four decades ago, as they have declined in more than 50% since then(15). Our results evidence the need for in-depth understanding how these seabirds’ population dynamics impact the surrounding Antarctic marine ecosystems.
A new model to improve decision-making indicators of bank customers' based on neural networks and particle swarm optimization algorithm
Authors: Asghari Ali , Azgomi Hossein , negahi Maryam 
Published: July 2022

Abstract
Due to the large volume of customers' banking information, classical algorithms face challenges in extracting knowledge and the decision-making process, so algorithms based on artificial intelligence and data mining can help this area. In this paper, we have presented a new model based on the artificial neural network and particle swarm optimization algorithm to improve bank customers' decision-making indicators. The proposed method is based on artificial neural networks and particle swarm optimization (PSO) algorithm. Due to the large volume of available data, the pure neural network can not achieve proper convergence in this area and suffers divergence or overfit. In this paper, the PSO algorithm is used to optimize neural network weights in extracting knowledge from user's banking data to improve the decision indicators. To evaluate the proposed method, two scenarios of optimized neural network using PSO and pure neural networks are used to make decisions between good and bad customers. Then, to evaluate the efficiency of the proposed method, the parameters of accuracy, Sensitivity, and Specificity for both scenarios were extracted and compared. The evaluations show that the accuracy of the optimized neural network is about 97%. In contrast, this value in the pure neural network is about 94%, indicating the proposed method's better performance. The proposed method also has better performance in the sensitivity parameter and feature selection and avoids overfitting than primary neural networks.
The Prognosis Risk Model Based on Immune-related Genes Is Closely Related to the Prognosis of Oral Cancer
Authors: Feng ZhiYuan , He Kang , Zhang Lin , Li Ji Hua 
Published: July 2022

Abstract
Background: Oral cancer (OC) is one of the malignant diseases with high morbidity and mortality worldwide. The prognosis of OC patients has not improved with the development of technology, and treatment outcomes are still particularly poor. Therefore, we screened immune-related genes (IRGs) to construct a risk model for the prognosis of OC patients.Methods: We downloaded and analyzed the clinical materials and gene coexpression network of a total of 321 OC samples from The Cancer Genome Atlas (TCGA) OC dataset. Stromal and immune scores and tumor purity were calculated based on Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data. In addition, CIBERSORT and single-sample Gene Set Enrichment Analysis (ssGSEA) were applied to analyze the immune infiltration in each TCGA-OC cohort. Then we used the R package NbClust to divide the samples into two immune subtypes (S1 and S2). The control group was S2, and the treatment group was S1. Differences in genetic screening conditions were | a fold change | > 2 and FDR < 0.01. We obtained and analyzed 1111 IRGs from the two immune subtypes based on the ImmPort database. Then, 18 IRGs were selected from the 1111 IRGs for further analysis through the least absolute shrinkage and selection operator (LASSO) and univariate Cox regression analysis. Next, CCL22, CCL21, CD79A, CTLA4, MS4A2 and MS4A1 were screened out through the multivariate Cox regression analysis to develop a precise IRG prognosis risk model (IRGPRM) with area under curves (AUCs) of 0.639, 0.620 and 0.586 after 1, 3and 5 years, respectively. We divided the samples into high-risk and low-risk groups using the IRGPRM. Kaplan-Meier analysis was used to determine the survival rate of the two subtypes. The expression data of head and neck squamous cell carcinoma samples were downloaded from Gene Expression Omnibus (GEO) database to test the prognosis capability of the IRGPRM.Results: A six IRGPRM was established. Based on the model, TCGA-OC cohorts and testing sets were divided into high-risk and low-risk groups. The Kaplan-Meier analysis indicated poor overall survival (OS) in the high-risk group. Furthermore, the TIMMER analysis demonstrated that the expression of CCL22, CCL21, CD79A, CTLA4, MS4A2 and MS4A1 was positively correlated with the immune infiltration level based on the Tumor Immune Estimation Resource (TIMER) database. In addition, a nomogram based on risk score and clinical stage showed good calibration and moderate discrimination.Conclusions: Our findings suggest that this model can be a prognostic indicator for OC patients.
Clinical Significance of Prostate Volume and Testosterone Reduction on Lower Urinary Tract Symptoms in Patients with Prostate Cancer Undergoing Androgen Deprivation Therapy
Authors: Bang Woo Jin , Kim Hwanik , Oh Cheol Young , Jo Jung Ki , Cho Jin Seon , Shim Myungsun 
Published: July 2022

Abstract
Background: To investigate the effect of both prostate volume and serum testosterone changes on lower urinary tract symptoms in patients with prostate cancer undergoing androgen deprivation therapy.Methods: A total of 167 patients who received androgen deprivation therapy for prostate cancer treatment from January 2010 to August 2020 were enrolled in this retrospective study. Changes in the International Prostate Symptom Score (IPSS) in the patient groups stratified by prostate volume and the amount of testosterone reduction were assessed every 4 weeks until 12 weeks after androgen deprivation therapy initiation. Longitudinal mixed models were used to assess the adjusted effects of prostate volume and testosterone reduction on IPSS change.Results: All mean values of IPSS-total score (IPSS-total), voiding subscore (IPSS-vs), and storage subscore (IPSS-ss) significantly decreased from baseline to week 12 in both patients with small (<33 mL) and large (≥33 mL) prostates. The mean values of IPSS-total, IPSS-vs, and IPSS-ss similarly decreased in patients with large prostate with a baseline IPSS-total of ≥13. However, in those with small prostate, IPSS-ss specifically remained unchanged, while IPSS-total and IPSS-vs significantly decreased. In addition, only in patients with small prostate (<33 mL), patients with lesser testosterone reduction (<Δ400 ng/dL) showed greater improvement in IPSS-ss by 6.3% compared with those with greater testosterone reduction (≥ Δ400 ng/dL).Conclusion: Although androgen deprivation therapy generally improves lower urinary tract symptoms, it may worsen specifically storage symptoms in patients with relatively small prostate and greater testosterone reduction. Our finding suggests that testosterone may influence lower urinary tract symptoms in these patients.
4D visualization of the light-induced coherent magnon by an X-ray free electron laser
Authors: Jang Hoyoung , Ueda Hiroki , Kim Hyeong-Do , Kim Minsek , Shin Kwang Woo , Kim Kee Hoon , Park Sang-Youn , Shin Hee Jun , Borisov Pavel , Rosseinsky Matthew , Jang Dogeun , Choi Hyeongi , Eom Intae , Staub Urs , Chun Sae Hwan 
Published: July 2022

Abstract
Ultrafast optical manipulation of magnetic phenomena is an exciting achievement of mankind, expanding one’s horizon of knowledge towards functional non-equilibrium states. The dynamics acting on an extremely short timescale push the limit of detection, and ultimately require four-dimensional (4D) views, i.e., 3D space and 1D time to keep sight of the evolving magnetic structure thoroughly. Here we introduce time-resolved resonant magnetic X-ray diffraction with an X-ray free electron laser to accomplish the 4D visualization of a light-induced coherent magnon. The trajectory of antiferromagnetic precession in a Y-type hexaferrite manifests a photoinduced effective magnetic-field that lasts long after the photoirradiation, being attributed to nonthermal inter-site electron transfer among the magnetic ions. With the unprecedented capability of direct quantifying the field for the photoexcitation above the band gap, we find a remarkable amplification of the photomagnetic effect reaching an order of magnitude higher than that below the gap with the same laser fluence. This result illuminates a highly energy-efficient route to achieve substantial photoinduced field in antiferromagnetic dielectrics desired for optospintronics applications. The X-ray methodology established in this work has no restriction on net-magnetization, offering a great capability to uncover ultrafast magnetic phenomena in every class of magnets.
Automatic detection of COVID-19 and community-acquired pneumonia on CT images with artificial intelligence
Authors: Liu Chenglong , Yuan Mingyuan , Wang Mengxing , He Peizhong , Wang Xiaoyang , Peng Wenxian 
Published: July 2022

Abstract
The outbreak of COVID-19 plunged the world into unprecedented difficulties, bringing life to a terrible standstill around the world and claiming thousands of lives. It seriously endangers human life and health. Thus, a high-efficiency COVID-19 and community-acquired pneumonia (CAP) detection method based on computed tomography (CT) images were developed to combat the pandemic. Sixty-one confirmed COVID-19 cases were consecutively enrolled together with 27 confirmed community-acquired pneumonia patients and 87 normal control patients with no finding of COVID-19 and CAP from Ruian People’s Hospital, from January 2020 to March 2020. The final cohort of 175 patients (1611 CT slices) was divided into training and test sets in an 8:2 and equal ratio. Three senior radiologists assessed clinical Characteristics of COVID-19 patients and abnormalities in chest CT images independently. The proposed model, EfficientNet-B1 was applied for the classification task, which consists of 16 mobile inverted bottleneck convolutions (MBConv), 2 convolutional (Conv) layers, 1 global average-pooling layer, and 1 fully connected layer, obtained a test accuracy of 94.24% and 93.64% for binary and three-class classification, respectively. It also achieved a sensitivity of 92.35%, a specificity of 95.93%, a positive predictive value of 95.26%, negative predictive value of 93.39% with an area under the receiver operating characteristic curve of 0.9414 for COVID-19 and CAP classification. Fever (58 [95.1%]) with a mean axillary temperature of 37.3 ± 0.8 °C, cough (52 [85.2%]), and sputum production (35 [57.4%]) are the most common symptoms of COVID-19 patients at the onset of illness. More importantly, more than 50% of COVID-19 images show ground-glass opacities. The proposed model achieves state-of-the-art performance and can be applied as a supplementary tool for radiologists, which is beneficial for pandemic control.