bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comportamiento de la notificación de gastrosquisis y comportamientos inusuales por residencia, Colombia, 2015-2020
Authors: Castaño Mora Sandra 
Published: November 2024

Introducción: la gastrosquisis es un defecto congénito de la pared abdominal secundario al cierre incompleto de los pliegues laterales durante la cuarta semana de gestación. Se ha descrito un incremento de las prevalencias a nivel mundial en los últimos años y su etiología no está ampliamente determinada.
Metodología: estudio descriptivo retrospectivo mediante empleo de registros de notificación individual para gastrosquisis que ingresaron al Sistema de Vigilancia en Salud Pública a través del evento defectos congénitos durante los años 2015 a 2020.
Resultados: la prevalencia de gastrosquisis en Colombia muestra una tendencia al aumento desde el año 2015, pasando de 1,9 a 3,4 casos por cada 10 000 nacidos vivos en 2020. Las condiciones identificadas que pueden considerarse como factor de riesgo son edades maternas tempranas, prematurez, bajo peso al nacer y primera gestación. La mortalidad tiene una tendencia al descenso desde 2017 y frecuentemente se presenta en casos con antecedente de prematurez. Las entidades territoriales que presentan aumento de casos en 2020 con respecto al comportamiento histórico de los años 2015 a 2019 fueron Antioquia, Magdalena, Nariño, Norte de Santander y Cali.
Conclusión: aunque las prevalencias en gastrosquisis en Colombia se encuentran en aumento no superan la prevalencia estimada por el Estudio Colaborativo Latinoamericano de Malformaciones Congénitas (ECLAMC) para población latinoamericana. Los factores de riesgo identificados en los casos notificados pueden ser intervenibles y diagnosticados oportunamente en el marco del acceso a consulta preconcepcional y control prenatal para prevención de mortalidad.
Aproximación a la Muestra Continua de Vidas Laborales como fuente estadística en estudios de Economía Social
Authors: Gallego Borrás Álvaro 
Published: November 2024

Con el objetivo de ampliar la información sobre fuentes estadísticas de Economía Social, este estudio realiza una aproximación a la Muestra Continua de Vidas Laborales (MCVL) realizando dos análisis de contenido sobre las 13 publicaciones que, hasta la fecha, han utilizado la muestra para el estudio de la Economía Social.
Como principales resultados, se destaca que la muestra se ha utilizado principalmente con el objetivo de resaltar aspectos diferenciales de la Economía Social, tratando de medir su impacto en la igualdad de género, la calidad del empleo, la cohesión social o la cohesión territorial. Además, se ha observado que los principales filtros utilizados han sido directos, identificando rápidamente personas empleadas en entidades de la Economía Social. Sin embargo, para una delimitación más exacta del ámbito de la Economía Social en la muestra, es necesario recurrir a filtros indirectos.
Optimizing nitrogen removal in advanced wastewater treatment using biological aerated filters
Authors: Li Juan , Niu Ziru , Li Lei , Zhou Shuting 
Published: November 2024

Reducing total nitrogen (TN) presents a significant challenge for numerous wastewater treatment facilities. In order to address this issue, the current study employed a biological aerated filter for the treatment of wastewater containing low nitrogen concentrations. Both lab-scale and pilot-scale biofilters were constructed to investigate the denitrification performance and maximum denitrification load. The findings indicated that the anaerobic denitrification process of established biofilm adhered to pseudo-first-order kinetics. The results of batch testing and continuous-flow experiments confirmed that the minimum hydraulic retention time (HRT) required for mature biofilm was determined to be 0.5 h. The optimal operating parameters were found to be as follows: influent NO3−-N concentration of 25 mg/L, HRT of 0.5 h, resulting in effluent TN levels below 1 mg/L. Under these conditions, the denitrifying load for the lab-scale I-BAF system was calculated to be 1.26 kg (TN)/(m3·d). Furthermore, it was observed that the maximum denitrifying load could reach 2.2 kg (TN)/(m3·d) when the influent NO3−-N concentration was increased to 50 mg/L while maintaining an HRT of 0.5 h. For the mature biofilter, the appropriate HRT ranged from 2 h to 0.5 h. Microbial diversity analysis revealed that the genus Enterobacter was dominant in all denitrification systems, followed by Comamonas and Rhodococcus. The operational parameters described in the paper could be recommended for a full-scale wastewater treatment facility.
Metabolic syndrome among type 2 Diabetes Mellitus patients in Ethiopia: a systematic review and meta-analysis
Authors: Charkos Tesfaye Getachew , Lemi Hunde , Arero Godana , Getnet Menberu 
Published: November 2024

IntroductionThe prevalence of metabolic syndrome among type 2 diabetes mellitus patients was inconsistent in Ethiopia. Therefore, we aimed to pool the prevalence of metabolic syndrome among type 2 diabetes mellitus patients using a systematic review and meta-analysis.MethodsPubMed, EMBASE, and Cochrane Library databases were systematically searched for relevant articles from January 2023 to January 2024. In addition, a manual search was conducted using published articles’ reference lists. The random-effects model was used to pool prevalence from individual studies. All analysis was performed using R software.ResultsA total of nine articles met the inclusion criteria and were included in the analysis. The participants’ average age was 59.8 ± 3.84 years old. The pooled prevalence of MetS in T2DM patients was 53% (95% CI: 47–58). A significant heterogeneity was found across the included studies (P &lt; 0.001, I2 = 92%). Based on diagnostic criteria, the prevalence of MetS in T2DM patients was 49% (95% CI: 43–56), 57% (95% CI: 47–67), 57% (95% CI: 43–77), and 44% (95% CI: 20-58) based on IDF, NCEP-ATP II, 2009 harmonized, and WHO criteria. By gender, the prevalence of MetS in T2DM patients was 48% (95% CI: 28–68) for females and 32% (95% CI: 17–49) for males.ConclusionThis study found that over half of type 2 diabetes mellitus patients in Ethiopia are affected by metabolic syndrome, with a higher prevalence observed in females compared to males. The NCEP-ATP II and 2009 harmonized criteria consistently yielded similar prevalence rates of metabolic syndrome. These findings highlight the importance of educating T2DM patients on preventing and managing cardiovascular disease and its related complications.
Loss of calcium/calmodulin-dependent protein kinase kinase 2, transferrin, and transferrin receptor proteins in the temporal cortex of Alzheimer’s patients postmortem is associated with abnormal iron homeostasis: implications for patient survival
Authors: Sabbir Mohammad Golam 
Published: November 2024

IntroductionIron is crucial for brain function, but excessive iron is neurotoxic. Abnormally high brain iron accumulation is one of the pathogenic factors in Alzheimer’s disease (AD). Therefore, understanding the mechanistic basis of iron dyshomeostasis in AD is vital for disease mitigation. Calcium, another essential bioelement involved in cell signaling, also exhibits dysregulated homeostasis in AD. Calcium ion (Ca2+) signaling can influence iron homeostasis through multiple effectors. Our previous studies identified Ca2+/calmodulin (CAM)-dependent protein kinase kinase 2 (CAMKK2) as a regulator of transferrin (TF)-bound iron trafficking through the TF receptor (TFRC). Given CAMKK2’s high expression in brain cells, it was hypothesized that abnormal CAMKK2-TF/TFRC signaling may underlie excessive iron deposition in AD brains. This study aims to retrospectively investigate CAMKK2, TF, TFRC proteins, and iron content in temporal cortex tissues from AD patients and cognitively normal (CN) individuals, postmortem.MethodsPostmortem temporal cortex tissues from 74 AD patients, 27 Parkinson’s disease (PD) patients, and 17 CN individuals were analyzed for CAMKK2, TF, and TFRC protein levels by Western blotting. Additionally, prefrontal/temporal cortex tissues from 30 CN individuals of various ages were examined for age-related effects. Iron content in cortical tissues was measured using a colorimetric assay.ResultsCAMKK2, TF, and TFRC levels were significantly decreased in AD patients’ temporal cortices compared to CN individuals, independent of age or postmortem interval-related changes. PD patients’ also exhibited similar reductions in CAMKK2/TF/TFRC levels. The increased iron content in AD brains was significantly correlated with reduced TF/TFRC protein levels.DiscussionBuilding on the previous identification of CAMKK2 as a regulator of TF/TFRC trafficking and iron homeostasis, the findings from this study suggest that downregulation of CAMKK2 in AD cortices may disrupt TF/TFRC signaling and contribute to iron overloading and neurodegeneration through iron-induced toxicity. The decreased levels of TF/TFRC and increased iron in AD brains may result from enhanced clearance or post-trafficking degradation of TF/TFRC due to CAMKK2 downregulation. Restoring CAMKK2 levels in the AD brain could offer a novel therapeutic approach to reestablish iron homeostasis. Further studies are needed to explore the pathways linking CAMKK2 and iron dysregulation in AD and other neurodegenerative diseases.
Altered gut microbiome in convalescent patients with coronavirus disease 2019
Authors: Lee Kyoung Hwa , Kim Yeong Ouk , Dho So Hee , Yong Jen J. H. , Oh Hyun-Seok , Lee Je Hee , Yang Seung-Jo , Cha Inseong , Chun Jongsik , Lee Eun Hwa , Jeong Su Jin , Woo Wonjin , Choi Jae-Phil , Han Sang Hoon , Choi Gloria B. , Huh Jun R. , Kim Lark Kyun , Song Young Goo 
Published: November 2024

IntroductionCoronavirus disease 2019 (COVID-19) alters the gut microbiome. This study aimed to assess the association between the disease severity of COVID-19 and changes in stool microbes through a seven-month follow-up of stool collection.MethodsWe conducted a multicentre, prospective longitudinal study of 58 COVID-19 patients and 116 uninfected controls. Differences in the gut microbiota were analysed using 16S ribosomal RNA sequencing. The first stool samples were collected at an early convalescent phase of COVID-19, and the second sample was collected at least seven months after COVID-19 infection.Results and discussionAt the order level, Eubacteriales and Bifidobacteriales decreased, while Bacteroidales and Burkholderiales increased in the COVID-19 group compared to the controls. Alpha diversity also decreased in COVID-19 patients compared to controls, with imperfect recovery of the gut microbiome after seven months. The compositional change in the gut microbiome between the early and late convalescent phases was largest in the moderate and severe groups. The severity of COVID-19 was the most influential clinical variable for microbiome composition (Sum of Sqs = 0.686, P = 0.006), and its effect persisted even after partialling out other effects such as antibiotic use and age. Thus, our study indicates a possible interaction between respiratory viral infection and the composition of the gut microbiota community, warranting future mechanistic and prospective longitudinal studies. Additionally, we were able to detect microbiome changes in patients who were re-infected with SARS-CoV-2. Notably, the dominant bacteria in the re-infected group were Lachnospiraceae and Faecalimonas umbilicata, compared to the one-time infected group.
The prognostic value of neutrophil-to-lymphocyte ratio in adult carbapenem-resistant Klebsiella pneumoniae infection: a retrospective cohort study
Authors: Wang Zhongjie , Li Renhua , Yuan Zhe , Zhang Zuli , Qian Keli 
Published: November 2024

BackgroundSystemic inflammatory indicators such as neutrophil-to-lymphocyte ratio (NLR) can effectively predict the prognosis of various inflammatory diseases. However, its prognostic effect on patients with carbapenem-resistant Klebsiella pneumoniae (CRKP) infection is little known. The objective of this study was to investigate the risk factors for mortality associated with CRKP infection and the clinical value of NLR in predicting prognosis in these patients.MethodsA total of 190 inpatients with CRKP infection from 1 January 2023 to 31 December 2023 were enrolled in this study, namely, 73 fatal cases and 117 survival cases in hospital. The medical data and examination results of these patients were collected. A logistic regression analysis was performed to assess the association between the NLR on the day of CRKP infection onset and all-cause mortality in hospital.ResultsThe overall mortality rate of patients with CRKP infection was 38.42% (73/190). Of the 190 patients, 91 were co-infected with carbapenem-resistant Acinetobacter baumannii/carbapenem-resistant Pseudomonas aeruginosa (CRAB/CRPA). Multifactor regression analysis confirmed that carbapenem exposure in the past 14 days, central line insertion, and chronic Foley catheter requirement were independent risk factors for carbapenem-resistant bacteria co-infection. The multivariate analysis shows that admission to an ICU, co-infection with CRAB/CRPA, and higher NLR were independent risk factors for the mortality in hospital, while appropriate treatment within 3 days was an independent protective factor. The area under the curve (AUC) of the NLR was 0.696, and the cutoff value of the NLR was 10.73.ConclusionsThe NLR on the day of CRKP infection onset, admission to an ICU, and co-infection with CRAB/CRPA were identified as independent risk factors for all-cause mortality of patients with CRKP infection, while appropriate treatment within 3 days was recognized as an independent protective factor. The NLR serves as a conveniently accessible and independent prognostic biomarker for patients with CRKP infection.
Development and validation of prediction models for death within 6 months after cardiac arrest
Authors: Lu Jianping , Zeng Yuqi , Lin Nan , Ye Qinyong 
Published: November 2024

BackgroundEven in patients with a successful return of spontaneous circulation (ROSC), outcomes after cardiac arrest (CA) remain poor, with some eventually succumbing after several months of treatment. There is a need for early assessment of outcomes in patients with ROSC after CA. Therefore, we developed three models for predicting death within 6 months after CA using early post-arrest factors, performed external validation, and compared their efficiency.MethodsIn this retrospective cohort study, 199 patients aged 18–80 years who experienced either in-hospital cardiac arrest or out-of-hospital cardiac arrest and achieved ROSC were included as the training set. Patients were divided into an “alive” group (95 cases) and a “dead” group (104 cases) according to their survival status 6 months after CA. Demographic data, medical history, and laboratory results were collected. Univariate and multivariate logistic regression analyses were used to identify risk factors. A risk prediction model was constructed using random forest methods, support vector machine (SVM), and a nomogram based on factors with P &lt; 0.1 in the multivariate logistic analyses. An additional 42 patients aged 18–80 years who experienced CA with ROSC were included as the validation set. Receiver operating characteristic (ROC), decision, and calibration curves were used to assess model performance.ResultsDuration of cardiac arrest, lactate level after ROSC, secondary infections, length of hospital stay, and ventilator support were the top five risk factors for death within 6 months after CA (P &lt; 0.1) in sequence. The random forest model [average area under the ROC curve (AUC), training set = 0.991, validation set = 0.703] performed better than the SVM model (AUC, training set = 0.905, validation set = 0.636) and the nomogram model (AUC, training set = 0.893, validation set = 0.682). Decision curve analysis indicated that the random forest model provided the best net benefit. The calibration curve indicated that the prediction for death within 6 months after CA by the random forest model was consistent with actual outcomes. The AUC of the prediction model constructed using random forest, SVM, and nomogram methods was 0.991, 0.893, and 0.905, respectively.ConclusionsThe prediction model established by early post-arrest factors performed well, which can aid in evaluating prognosis within 6 months after cardiac arrest. The predictive model constructed using random forest methods exhibited better predictive efficacy.
Evaluation on nature-connected environment in building embedded landscape: theory, detection, and case design
Authors: Zhong Shu , Ren Jiao 
Published: November 2024

IntroductionA nature-connected environment is important for constructing building infrastructures in a sustainable city. However, its realistic application is rarely utilized in urban planning, primarily due to a lack of accurate regression of emotional perceptions of nature connectedness against existing landscape metrics.MethodIn this study, Changsha was selected as the study area, targeting 50 urban parks as plots, with 165 left unselected for prediction. Twenty pedestrians were randomly approached to assess their emotional perceptions of nature connectedness. Self-reported data were used to evaluate positive and negative emotion scores (Cronbach α: 0.803) and the emotional nonparametric relation index (ENRI) (Pearson correlation in retest: R square = 66.37%).ResultsThree machine-learning algorithms (random forest, AdaBoost, and gradient boosting trees [GBT]) were compared, with GBT being selected (R square: 76.49 ‒ 88.64 %) for further comparison with multivariate linear regression (MLR). Principal component analysis was used for verification, and the GBT algorithm surpassed MLR in regression performance. The green view index (GVI) in large and open green spaces was found to correlate with perceptions of positive emotions (parameter estimate [PE]: 0.02 ‒ 0.13). Conversely, large areas of green and blue spaces with low GVI on the flat plane were found to evoke negative emotions (PE: -0.13 and -0.04, respectively).DiscussionArtificial intelligence was used for describing cases of design using our findings. For the purpose to evoke positive emotion of visitors, building embedded landscape can be designed in distant view with large tree canopies in closer view. Large area of green and blue spaces should not be recommended no matter whether buildings background the view.
LRMAHpan: a novel tool for multi-allelic HLA presentation prediction using Resnet-based and LSTM-based neural networks
Authors: Mi Xue , Li Shaohao , Ye Zheng , Dai Zhu , Ding Bo , Sun Bo , Shen Yang , Xiao Zhongdang 
Published: November 2024

IntroductionThe identification of peptides eluted from HLA complexes by mass spectrometry (MS) can provide critical data for deep learning models of antigen presentation prediction and promote neoantigen vaccine design. A major challenge remains in determining which HLA allele eluted peptides correspond to.MethodsTo address this, we present a tool for prediction of multiple allele (MA) presentation called LRMAHpan, which integrates LSTM network and ResNet_CA network for antigen processing and presentation prediction. We trained and tested the LRMAHpan BA (binding affinity) and the LRMAHpan AP (antigen processing) models using mass spectrometry data, subsequently combined them into the LRMAHpan PS (presentation score) model. Our approach is based on a novel pHLA encoding method that enables the integration of neoantigen prediction tasks into computer vision methods. This method aggregates MA data into a multichannel matrix and incorporates peptide sequences to efficiently capture binding signals.ResultsLRMAHpan outperforms standard predictors such as NetMHCpan 4.1, MHCflurry 2.0, and TransPHLA in terms of positive predictive value (PPV) when applied to MA data. Additionally, it can accommodate peptides of variable lengths and predict HLA class I and II presentation. We also predicted neoantigens in a cohort of metastatic melanoma patients, identifying several shared neoantigens.DiscussionOur results demonstrate that LRMAHpan significantly improves the accuracy of antigen presentation predictions.
Drought-tolerant fungal microbes, Aspergillus oryzae and Aspergillus fumigatus, elevate physiohormonal and antioxidant responses of maize under drought stress
Authors: Niaz Kiran , Rauf Mamoona , Arif Muhammad , Hamayun Muhammad , Gul Humaira , Hashem Abeer , Abd_Allah Elsayed Fathi , Wu Qiang-Sheng 
Published: November 2024

IntroductionTemporary and extended drought stress accelerates phytohormones and reactive oxygen species (ROS) in plants, however, the fate of the plants under stress is mostly determined by the metabolic and molecular reprogramming, which can be modulated by the application of habitat-adapted fungi that triggers resistance to stress upon symbiotic association.MethodsThe present research exhibited the exploitation of the newly isolated, drought habitat-adapted fungal endophytic consortium of SAB (Aspergillus oryzae) and CBW (Aspergillus fumigatus), on maize under drought stress. SAB and CBW primarily hosted the root tissues of Conyza bonariensis L., which have not been reported earlier, and sufficiently produced growth-promoting metabolites and antioxidants.ResultsSAB and CBW adeptly inhabited the maize roots. They promoted biomass, primary metabolites, osmolytes (protein, sugar, lipids, proline, phenolics, flavonoids), and IAA production while reducing tannins, ABA, and H2O2 contents and increasing antioxidant enzyme activities. In addition, the enhanced adventitious root development at the root/stem interface, and elongated main root development optimum stomatal activity of SAB- and CBW-inoculated maize plants were observed under drought stress. SAB and CBW modulated the expression of the ZmBSK1, ZmAPX, and ZmCAT1 genes in the maize shoot and root tissues under drought stress vs. control, signifying an essential regulatory function for SAB/CBW-induced drought stress tolerance via phytohormonal signaling pathway leading to the antioxidant upregulation.DiscussionThese findings imply that the exogenous administration of the SAB/CBW consortium might be a rather efficient strategy that contributes to optimizing the physio-hormonal attributes and antioxidant potential to alleviate the drought stress in maize.
Comparative analysis of antimicrobial resistance and genetic diversity of Bordetella bronchiseptica isolates obtained from swine within the United States
Authors: Nicholson Tracy L. , Shore Sarah M. 
Published: November 2024

IntroductionBordetella bronchiseptica is bacterial pathogen that is pervasive in swine populations and serves multiple roles in respiratory disease.MethodsThis study utilized whole-genome sequencing (WGS) analysis to assess the sequence type (ST), identify the genetic diversity of genes predicted to encode regulatory and virulence factors, and evaluated any potential antimicrobial resistance harbored by B. bronchiseptica isolates obtained from swine within the U.S.ResultsWhile a generally high degree of genomic conservation was observed among the swine B. bronchiseptica isolates, genetic diversity was identified within the fimNX locus and among the sequence type six (ST6) isolates. The majority of B. bronchiseptica isolates exhibited phenotypic resistance to four antibiotic classes, however, only three antimicrobial resistance genes were identified.DiscussionCombined the data suggests that B. bronchiseptica isolates are not serving as a source of antimicrobial resistance gene transference in the swine production environment.