bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comorbidity network analysis using graphical models for electronic health records
Authors: Zhao Bo , Huepenbecker Sarah , Zhu Gen , Rajan Suja S. , Fujimoto Kayo , Luo Xi 
Published: August 2023

ImportanceThe comorbidity network represents multiple diseases and their relationships in a graph. Understanding comorbidity networks among critical care unit (CCU) patients can help doctors diagnose patients faster, minimize missed diagnoses, and potentially decrease morbidity and mortality.ObjectiveThe main objective of this study was to identify the comorbidity network among CCU patients using a novel application of a machine learning method (graphical modeling method). The second objective was to compare the machine learning method with a traditional pairwise method in simulation.MethodThis cross-sectional study used CCU patients' data from Medical Information Mart for the Intensive Care-3 (MIMIC-3) dataset, an electronic health record (EHR) of patients with CCU hospitalizations within Beth Israel Deaconess Hospital from 2001 to 2012. A machine learning method (graphical modeling method) was applied to identify the comorbidity network of 654 diagnosis categories among 46,511 patients.ResultsOut of the 654 diagnosis categories, the graphical modeling method identified a comorbidity network of 2,806 associations in 510 diagnosis categories. Two medical professionals reviewed the comorbidity network and confirmed that the associations were consistent with current medical understanding. Moreover, the strongest association in our network was between “poisoning by psychotropic agents” and “accidental poisoning by tranquilizers” (logOR 8.16), and the most connected diagnosis was “disorders of fluid, electrolyte, and acid–base balance” (63 associated diagnosis categories). Our method outperformed traditional pairwise comorbidity network methods in simulation studies. Some strongest associations between diagnosis categories were also identified, for example, “diagnoses of mitral and aortic valve” and “other rheumatic heart disease” (logOR: 5.15). Furthermore, our method identified diagnosis categories that were connected with most other diagnosis categories, for example, “disorders of fluid, electrolyte, and acid–base balance” was associated with 63 other diagnosis categories. Additionally, using a data-driven approach, our method partitioned the diagnosis categories into 14 modularity classes.Conclusion and relevanceOur graphical modeling method inferred a logical comorbidity network whose associations were consistent with current medical understanding and outperformed traditional network methods in simulation. Our comorbidity network method can potentially assist CCU doctors in diagnosing patients faster and minimizing missed diagnoses.
Comprehensive analysis identifies novel targets of gemcitabine to improve chemotherapy treatment strategies for colorectal cancer
Authors: Zeng Xinxin , Sun Liyue , Ling Xiaomei , Jiang Yuying , Shen Ju , Liang Lei , Zhang Xuhui 
Published: August 2023

BackgroundGemcitabine (GEM) is a second-line anticancer drug of choice for some colorectal cancer (CRC) patients, and GEM inability to be commonly available in the clinic due to the lack of clarity of the exact action targets.MethodsThe half maximal inhibitory concentration (IC50) of GEM treatment for 42 CRC cell lines were accessed from the Genomics of Drug sensitivity in Cancer (GDSC) database. High-throughput sequencing data of CRC patients were captured in The Cancer Genome Atlas (TCGA) and Weighted correlation network analysis (WGCNA) was conducted. Pearson correlations were derived for GEM potency-related genes. Differential analysis was conducted in the TCGA cohort to obtain CRC development-related genes (CDRGs), and univariate COX model analysis was performed on CDRGs overlapping with GEM potency-related genes to obtain CDRGs affecting CRC prognosis. Hub genes affecting GEM potency were identified by Spearman correlation.ResultsCALB2 and GPX3 were identified as potential targets for GEM treatment of CRC via prognostic analysis, which we also observed to be elevated with elevated clinical stage in CRC patients. The enhanced expression of CALB2 and GPX3 genes identified in the pathway analysis might inhibit the body metabolism as well as activate immune and inflammation related pathways. In addition, we found that CALB2 and GPX3 could also be considered as prognostic biomarkers in pan-cancer. Finally, we found that CALB2 and GPX3 were remarkably associated with the drug sensitivity of MG-132, Dasatinib, Shikonin, Midostaurin, MS-275, and Z-LNle-CHO, which were expected to be the drugs of choice for GEM combination.ConclusionCALB2 and GPX3 represent prognostic biomarkers for CRC and they might be potential action targets for GEM. Our study offered innovative ideas for GEM administration strategies.
Bioactive oxylipins in type 2 diabetes mellitus patients with and without hypertriglyceridemia
Authors: Xiao Yanan , Pietzner Anne , Rohwer Nadine , Jung Adelheid , Rothe Michael , Weylandt Karsten H. , Elbelt Ulf 
Published: August 2023

ObjectiveDyslipidemia, in particular elevated triglycerides (TGs) contribute to increased cardiovascular risk in type 2 diabetes mellitus (T2DM). In this pilot study we aimed to assess how increased TGs affect hepatic fat as well as polyunsaturated fatty acid (PUFA) metabolism and oxylipin formation in T2DM patients.Methods40 patients with T2DM were characterized analyzing routine lipid blood parameters, as well as medical history and clinical characteristics. Patients were divided into a hypertriglyceridemia (HTG) group (TG ≥ 1.7mmol/l) and a normal TG group with TGs within the reference range (TG &lt; 1.7mmol/l). Profiles of PUFAs and their oxylipins in plasma were measured by gas chromatography and liquid chromatography/tandem mass spectrometry. Transient elastography (TE) was used to assess hepatic fat content measured as controlled attenuation parameter (CAP) (in dB/m) and the degree of liver fibrosis measured as stiffness (in kPa).ResultsMean value of hepatic fat content measured as CAP as well as body mass index (BMI) were significantly higher in patients with high TGs as compared to those with normal TGs, and correlation analysis showed higher concentrations of TGs with increasing CAP and BMI scores in patients with T2DM. There were profound differences in plasma oxylipin levels between these two groups. Cytochrome P450 (CYP) and lipoxygenase (LOX) metabolites were generally more abundant in the HTG group, especially those derived from arachidonic acid (AA), eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), γ-linolenic acid (γ-LA), and α-linolenic acid (α-LA), and a strong correlation between TG levels and plasma metabolites from different pathways was observed.ConclusionsIn adult patients with T2DM, elevated TGs were associated with increased liver fat and BMI. Furthermore, these patients also had significantly higher plasma levels of CYP- and LOX- oxylipins, which could be a novel indicator of increased inflammatory pathway activity, as well as a novel target to dampen this activity.
Macroinvertebrate traits in Arctic streams reveal latitudinal patterns in physiology and habits that are strongly linked to climate
Authors: Lento Jennifer , Lau Danny C. P. , Brittain John E. , Culp Joseph M. , Goedkoop Willem 
Published: August 2023

IntroductionArctic freshwater ecosystems are undergoing rapid environmental transformation because of climate change, which is predicted to produce fundamental alterations in river community structure and function.MethodsWe explored how climate change affects benthic invertebrate communities of Arctic streams by examining patterns of their biological traits along latitudinal and climatic gradients in eastern North America (Canada) and northwestern Europe (Sweden, Norway).ResultsDespite differences in taxonomic composition between continents, we identified similarities in the functional trait niche (FTN) of predominant macroinvertebrate taxonomic groups. Trait composition differed by latitude in eastern Canada, with a predominance of cold-tolerant taxa, tubular body shape, and cased and attached habits at the highest latitudes. Differences in trait composition were evident among ecoregions in Europe, with trait dominance at the highest latitudes that was comparable to North America. There was a similar increase in the relative abundance of cold tolerance and tubular body shape and a decrease in obligate shredders and trait richness with decreasing temperatures across both continents.DiscussionThese patterns are indicative of FTNs that include physiological traits and habits that are advantageous for the low temperatures, short ice-free period, and low riparian vegetation cover at the highest latitudes. We predict that climate change will lead to an increase in functional diversity at high latitudes, as organisms with trait modalities that are currently only found at lower latitudes move northward. However, this change in trait composition will be mediated by the effect of spatial connectivity on dispersal ability, with slower change occurring on Arctic islands. These findings can support modelling of future change in Arctic freshwater assemblages in response to ongoing climate change.
Understory vegetation supports more abundant and diverse butterfly communities in oil palm plantations
Authors: Reiss-Woolever Valentine Joy , Advento Andreas Dwi , Aryawan Anak Agung Ketut , Caliman Jean-Pierre , Foster William A. , Naim Mohammad , Pujianto  , Purnomo Dedi , Soeprapto  , Suhardi  , Tarigan Ribka Sionita , Wahyuningsih Resti , Rambe Tuani Dzulfikar Siguga , Ps Sudharto , Widodo Rudy Harto , Luke Sarah H. , Snaddon Jake L. , Turner Edgar C. 
Published: August 2023

IntroductionThe cultivation of oil palm, from which palm oil, the world’s most widely traded vegetable oil, is processed, has had marked effects on ecosystems and native species across the tropics. While declines in biodiversity due to conversion to oil palm have been well recorded across plant and animal taxa, less work has been done to identify approaches to plantation management which will enable producers to satisfy growing global demand while limiting environmental damage.MethodsThrough a large-scale understory management experiment, we investigated the long- and short-term effects of varying vegetation management regimes on the abundance, richness, and diversity of day-flying Lepidoptera.ResultsOver the long-term, the lowest levels of vegetation complexity resulted in significantly lower Lepidoptera abundance, species richness and evenness. Less intensive understory clearing resulted in healthier communities, with limited differences between removal by herbicide application or chemical-free removal. Over the short-term, biodiversity was not directly affected by vegetation complexity, suggesting that manual removal of vegetation may be equally damaging to butterfly or moth communities as removal by intermediate levels of herbicide spraying.DiscussionThese findings substantiate calls to limit vegetation clearing and maintain habitat heterogeneity on both a local and landscape scale, while also suggesting that a hard “no-spray” guideline may not be the only option to support butterfly friendly plantations.
Identification of molecular subtypes and immune infiltration in endometriosis: a novel bioinformatics analysis and In vitro validation
Authors: Lv Si-ji , Sun Jia-ni , Gan Lei , Sun Jing 
Published: August 2023

IntroductionEndometriosis is a worldwide gynacological diseases, affecting in 6–10% of women of reproductive age. The aim of this study was to investigate the gene network and potential signatures of immune infiltration in endometriosis.MethodsThe expression profiles of GSE51981, GSE6364, and GSE7305 were obtained from the Gene Expression Omnibus (GEO) database. Core modules and central genes related to immune characteristics were identified using a weighted gene coexpression network analysis. Bioinformatics analysis was performed to identify central genes in immune infiltration. Protein-protein interaction (PPI) network was used to identify the hub genes. We then constructed subtypes of endometriosis samples and calculated their correlation with hub genes. qRTPCR and Western blotting were used to verify our findings.ResultsWe identified 10 candidate hub genes (GZMB, PRF1, KIR2DL1, KIR2DL3, KIR3DL1, KIR2DL4, FGB, IGFBP1, RBP4, and PROK1) that were significantly correlated with immune infiltration. Our study established a detailed immune network and systematically elucidated the molecular mechanism underlying endometriosis from the aspect of immune infiltration.DiscussionOur study provides comprehensive insights into the immunology involved in endometriosis and might contribute to the development of immunotherapy for endometriosis. Furthermore, our study sheds light on the underlying molecular mechanism of endometriosis and might help improve the diagnosis and treatment of this condition.
ErbB2 (HER2)-CAR-NK-92 cells for enhanced immunotherapy of metastatic fusion-driven alveolar rhabdomyosarcoma
Authors: Heim Catrin , Moser Laura M. , Kreyenberg Herman , Bonig Halvard B. , Tonn Torsten , Wels Winfried S. , Gradhand Elise , Ullrich Evelyn , Meister Michael T. , Koerkamp Marian Groot , Holstege Frank C. P. , Drost Jarno , Klusmann Jan-Henning , Bader Peter , Merker Michael , Rettinger Eva 
Published: August 2023

IntroductionMetastatic rhabdomyosarcoma (RMS) is a challenging tumor entity that evades conventional treatments and endogenous antitumor immune responses, highlighting the need for novel therapeutic strategies. Applying chimeric antigen receptor (CAR) technology to natural killer (NK) cells may offer safe, effective, and affordable therapies that enhance cancer immune surveillance. MethodsHere, we assess the efficacy of clinically usable CAR-engineered NK cell line NK-92/5.28.z against ErbB2-positive RMS in vitro and in a metastatic xenograft mouse model.ResultsOur results show that NK-92/5.28.z cells effectively kill RMS cells in vitro and significantly prolong survival and inhibit tumor progression in mice. The persistence of NK-92/5.28.z cells at tumor sites demonstrates efficient antitumor response, which could help overcome current obstacles in the treatment of solid tumors.DiscussionThese findings encourage further development of NK-92/5.28.z cells as off-the-shelf immunotherapy for the treatment of metastatic RMS.
Adherence to a healthy sleep pattern is associated with lower risks of incident falls and fractures during aging
Authors: Zhou Tao , Dai Xue , Yuan Yu , Xue Qiaochu , Li Xiang , Wang Mengying , Ma Hao , Heianza Yoriko , Qi Lu 
Published: August 2023

BackgroundAutoimmune diseases are more common among people with unhealthy sleep behaviors, and these conditions have been linked to aging-related bone health. However, there have been few studies that examined the correlation between recently developed sleep patterns based on sleep duration, sleepiness, chronotype, snoring, insomnia, and the incidence of falls and fractures.MethodsWe used a newly developed sleep pattern with components of sleep 7 to 8 h per day, absence of frequent excessive daytime sleepiness, early chronotype, no snoring, and no frequent insomnia as healthy factors to study their relationship with the incidence of falls and fractures. The analysis was conducted among 289,000 participants from the UK Biobank.ResultsThe mean follow-up period was 12.3 years (3.5 million person-years of follow-up), and 12,967 cases of falls and 16,121 cases of all fractures were documented. Compared to participants exhibiting an unfavorable sleep pattern, those adhering to a healthy sleep pattern experienced a 17% and 28% reduction in the risks of incident falls (hazard ratio [HR], 0.83; 95% CI, 0.74–0.93) and all fractures (HR, 0.72; 95% CI, 0.66–0.79) during follow-up. In addition, participants exhibiting a healthy sleep pattern, together with a high genetically determined bone mineral density (BMD), showed the lowest risks of falls and fractures.ConclusionA healthy sleep pattern was significantly linked to decreased risks of incident falls and fractures. The protective association was not modified by genetically determined BMD.
Deep learning assisted diagnosis system: improving the diagnostic accuracy of distal radius fractures
Authors: Zhang Jiayao , Li Zhimin , Lin Heng , Xue Mingdi , Wang Honglin , Fang Ying , Liu Songxiang , Huo Tongtong , Zhou Hong , Yang Jiaming , Xie Yi , Xie Mao , Lu Lin , Liu Pengran , Ye Zhewei 
Published: August 2023

ObjectivesTo explore an intelligent detection technology based on deep learning algorithms to assist the clinical diagnosis of distal radius fractures (DRFs), and further compare it with human performance to verify the feasibility of this method.MethodsA total of 3,240 patients (fracture: n = 1,620, normal: n = 1,620) were included in this study, with a total of 3,276 wrist joint anteroposterior (AP) X-ray films (1,639 fractured, 1,637 normal) and 3,260 wrist joint lateral X-ray films (1,623 fractured, 1,637 normal). We divided the patients into training set, validation set and test set in a ratio of 7:1.5:1.5. The deep learning models were developed using the data from the training and validation sets, and then their effectiveness were evaluated using the data from the test set. Evaluate the diagnostic performance of deep learning models using receiver operating characteristic (ROC) curves and area under the curve (AUC), accuracy, sensitivity, and specificity, and compare them with medical professionals.ResultsThe deep learning ensemble model had excellent accuracy (97.03%), sensitivity (95.70%), and specificity (98.37%) in detecting DRFs. Among them, the accuracy of the AP view was 97.75%, the sensitivity 97.13%, and the specificity 98.37%; the accuracy of the lateral view was 96.32%, the sensitivity 94.26%, and the specificity 98.37%. When the wrist joint is counted, the accuracy was 97.55%, the sensitivity 98.36%, and the specificity 96.73%. In terms of these variables, the performance of the ensemble model is superior to that of both the orthopedic attending physician group and the radiology attending physician group.ConclusionThis deep learning ensemble model has excellent performance in detecting DRFs on plain X-ray films. Using this artificial intelligence model as a second expert to assist clinical diagnosis is expected to improve the accuracy of diagnosing DRFs and enhance clinical work efficiency.
Nomogram based on computed tomography images and clinical data for distinguishing between primary intestinal lymphoma and Crohn’s disease: a retrospective multicenter study
Authors: Xiao Mengjun , Tan Jiahe , Li Haiou , Qiu Chenyang , Ma Yinchao , Wang Haiyan 
Published: August 2023

BackgroundDifferential diagnosis of primary intestinal lymphoma (PIL) and Crohn’s disease (CD) is a challenge in clinical diagnosis.AimsTo investigate the validity of the nomogram based on clinical and computed tomography (CT) features to identify PIL and CD.MethodsThis study retrospectively analyzed laboratory parameters, demographic characteristics, clinical manifestations, and CT imaging features of PIL and CD patients from two centers. Univariate logistic analysis was performed for each variable, and laboratory parameter model, clinical model and imaging features model were developed separately. Finally, a nomogram was established. All models were evaluated using the area under the curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA).ResultsThis study collected data from 121 patients (PIL = 69, CD = 52) from Center 1. Data from 43 patients (PIL = 24, CD = 19) were collected at Center 2 as an external validation cohort to validate the robustness of the model. Three models and a nomogram were developed to distinguish PIL from CD. Most models performed well from the external validation cohort. The nomogram showed the best performance with an AUC of 0.921 (95% CI: 0.838–1.000) and sensitivities, specificities, and accuracies of 0.945, 0.792, and 0.860, respectively.ConclusionA nomogram combining clinical data and imaging features was constructed, which can effectively distinguish PIL from CD.
HAR_Locator: a novel protein subcellular location prediction model of immunohistochemistry images based on hybrid attention modules and residual units
Authors: Zou Kai , Wang Simeng , Wang Ziqian , Zhang Zhihai , Yang Fan 
Published: August 2023

Introduction: Proteins located in subcellular compartments have played an indispensable role in the physiological function of eukaryotic organisms. The pattern of protein subcellular localization is conducive to understanding the mechanism and function of proteins, contributing to investigating pathological changes of cells, and providing technical support for targeted drug research on human diseases. Automated systems based on featurization or representation learning and classifier design have attracted interest in predicting the subcellular location of proteins due to a considerable rise in proteins. However, large-scale, fine-grained protein microscopic images are prone to trapping and losing feature information in the general deep learning models, and the shallow features derived from statistical methods have weak supervision abilities.Methods: In this work, a novel model called HAR_Locator was developed to predict the subcellular location of proteins by concatenating multi-view abstract features and shallow features, whose advanced advantages are summarized in the following three protocols. Firstly, to get discriminative abstract feature information on protein subcellular location, an abstract feature extractor called HARnet based on Hybrid Attention modules and Residual units was proposed to relieve gradient dispersion and focus on protein-target regions. Secondly, it not only improves the supervision ability of image information but also enhances the generalization ability of the HAR_Locator through concatenating abstract features and shallow features. Finally, a multi-category multi-classifier decision system based on an Artificial Neural Network (ANN) was introduced to obtain the final output results of samples by fitting the most representative result from five subset predictors.Results: To evaluate the model, a collection of 6,778 immunohistochemistry (IHC) images from the Human Protein Atlas (HPA) database was used to present experimental results, and the accuracy, precision, and recall evaluation indicators were significantly increased to 84.73%, 84.77%, and 84.70%, respectively, compared with baseline predictors.
Neuroanatomical and neurochemical effects of prolonged social isolation in adult mice
Authors: Heng Vibol , Zigmond Michael , Smeyne Richard Jay 
Published: August 2023

IntroductionAs social animals, our health depends in part on interactions with other human beings. Yet millions suffer from chronic social isolation, including those in nursing/assisted living facilities, people experiencing chronic loneliness as well as those in enforced isolation within our criminal justice system. While many historical studies have examined the effects of early isolation on the brain, few have examined its effects when this condition begins in adulthood. Here, we developed a model of adult isolation using mice (C57BL/6J) born and raised in an enriched environment.MethodsFrom birth until 4 months of age C57BL/6J mice were raised in an enriched environment and then maintained in that environment or moved to social isolation for 1 or 3 months. We then examined neuronal structure and catecholamine and brain derived neurotrophic factor (BDNF) levels from different regions of the brain, comparing animals from social isolation to enriched environment controls.ResultsWe found significant changes in neuronal volume, dendritic length, neuronal complexity, and spine density that were dependent on brain region, sex, and duration of the isolation. Isolation also altered dopamine in the striatum and serotonin levels in the forebrain in a sex-dependent manner, and also reduced levels of BDNF in the motor cortex and hippocampus of male but not female mice.ConclusionThese studies show that isolation that begins in adulthood imparts a significant change on the homeostasis of brain structure and chemistry.