bluuebunny/crossref_metadata_2025_split huggingface dataset:
Secondary infection in COVID-19 critically ill patients: a retrospective single-center evaluation
Authors: De Bruyn Astrid , Verellen Stijn , Bruckers Liesbeth , Geebelen Laurien , Callebaut Ina , De Pauw Ilse , Stessel Björn , Dubois Jasperina 
Published: March 2022

Abstract
Background
Patients infected with severe acute respiratory syndrome coronavirus (SARS-CoV-2) can develop severe illness necessitating intensive care admission. Critically ill patients are susceptible for the development of secondary bacterial infections. Due to a combination of virus- and drug-induced immunosuppression, critically ill patients with corona virus disease 2019 (COVID-19) may even have a higher risk of developing a secondary infection. These secondary infections can aggravate the severity of illness and increase the risk of death. Further research on secondary infections in COVID-19 patients is essential. Therefore, the objective of this study was to investigate the incidence and associated risk factors of secondary bacterial infections and to identify the most common groups of pathogens in critically ill COVID-19 patients.

Methods
This mono-center, retrospective observational cohort study was performed at the intensive care unit (ICU) of the Jessa Hospital, Hasselt, Belgium. All adult COVID-19 patients admitted to the ICU from 13th March 2020 until 17th October 2020, were eligible for inclusion in the study. Data from the resulting 116 patients were prospectively entered into a customized database. The resulting database was retrospectively reviewed to investigate three types of secondary bacterial infections (secondary pneumonia, bloodstream infections of unknown origin, catheter-related sepsis).

Results
Of 94 included patients, 68% acquired at least one of the studied secondary bacterial infections during their ICU stay. Almost two thirds of patients (65.96%, n = 62) acquired a secondary pneumonia, whereas 29.79% (n = 28) acquired a bacteremia of unknown origin and a smaller proportion of patients (14.89%, n = 14) acquired a catheter-related sepsis. Male gender (P = 0.05), diabetes mellitus (P = 0.03) and the cumulative dose of corticosteroids (P = 0.004) were associated with increased risk of secondary bacterial infection. The most common pathogens detected in the cultures of patients with secondary pneumonia were Gram-negative bacilli. Bacteremia of unknown origin and catheter-related sepsis were mostly caused by Gram-positive cocci.

Conclusion
This study confirms that the incidence of secondary bacterial infections is very high in critically ill COVID-19 patients. These patients are at highest risk of developing secondary pneumonia. Male gender, a history of diabetes mellitus and the administration of corticosteroids were associated with increased risk of secondary bacterial infection.
The effects of gestational diabetes mellitus with maternal age between 35 and 40 years on the metabolite profiles of plasma and urine
Authors: He Xiao-ling , Hu Xiao-jing , Luo Bai-yu , Xia Yin-Yin , Zhang Ting , Saffery Richard , De Seymour Jamie , Zou Zhen , Xu Ge , Zhao Xue , Qi Hong-bo , Han Ting-Li , Zhang Hua , Baker Philip N. 
Published: March 2022

AbstractBackgroundGestational diabetes mellitus (GDM) is defined as impaired glucose tolerance in pregnancy and without a history of diabetes mellitus. While there are limited metabolomic studies involving advanced maternal age in China, we aim to investigate the metabolomic profiling of plasma and urine in pregnancies complicated with GDM aged at 35–40 years at early and late gestation.MethodsTwenty normal and 20 GDM pregnant participants (≥ 35 years old) were enlisted from the Complex Lipids in Mothers and Babies (CLIMB) study. Maternal plasma and urine collected at the first and third trimester were detected using gas chromatography-mass spectrometry (GC-MS).ResultsOne hundred sixty-five metabolites and 192 metabolites were found in plasma and urine respectively. Urine metabolomic profiles were incapable to distinguish GDM from controls, in comparison, there were 14 and 39 significantly different plasma metabolites between the two groups in first and third trimester respectively. Especially, by integrating seven metabolites including cysteine, malonic acid, alanine, 11,14-eicosadienoic acid, stearic acid, arachidic acid, and 2-methyloctadecanoic acid using multivariant receiver operating characteristic models, we were capable of discriminating GDM from normal pregnancies with an area under curve of 0.928 at first trimester.ConclusionThis study explores metabolomic profiles between GDM and normal pregnancies at the age of 35–40 years longitudinally. Several compounds have the potential to be biomarkers to predict GDM with advanced maternal age. Moreover, the discordant metabolome profiles between the two groups could be useful to understand the etiology of GDM with advanced maternal age.
Exposure–response analyses of cabozantinib in patients with metastatic renal cell cancer
Authors: Krens Stefanie D. , van Erp Nielka P. , Groenland Stefanie L. , Moes Dirk Jan A. R. , Mulder Sasja F. , Desar Ingrid M. E. , van der Hulle Tom , Steeghs Neeltje , van Herpen Carla M. L. 
Published: March 2022

AbstractAimIn the registration trial, cabozantinib exposure ≥ 750 ng/mL correlated to improved tumor size reduction, response rate and progression free survival (PFS) in patients with metastatic renal cell cancer (mRCC). Because patients in routine care often differ from patients in clinical trials, we explored the cabozantinib exposure–response relationship in patients with mRCC treated in routine care.MethodsCabozantinib trough concentrations (Cmin) were collected and average exposure was calculated per individual. Exposure–response analyses were performed using the earlier identified target of Cmin > 750 ng/mL and median Cmin. In addition, the effect of dose reductions on response was explored. PFS was used as measure of response.ResultsIn total, 59 patients were included:10% were classified as favourable, 61% as intermediate and 29% as poor IMDC risk group, respectively. Median number of prior treatment lines was 2 (0–5). Starting dose was 60 mg in 46%, 40 mg in 42% and 20 mg in 12% of patients. Dose reductions were needed in 58% of patients. Median Cminwas 572 ng/mL (IQR: 496–701). Only 17% of patients had an average Cmin ≥ 750 ng/mL. Median PFS was 52 weeks (95% CI: 40–64). No improved PFS was observed for patients with Cmin ≥ 750 ng/mL or ≥ 572 ng/ml. A longer PFS was observed for patients with a dose reduction vs. those without (65 vs. 31 weeks,p = .001). After incorporating known covariates (IMDC risk group and prior treatment lines (< 2 vs. ≥ 2)) in the multivariable analysis, the need for dose reduction remained significantly associated with improved PFS (HR 0.32, 95% CI:0.14–0.70,p = .004).ConclusionIn these explorative analyses, no clear relationship between increased cabozantinib exposure and improved PFS was observed. Average cabozantinib exposure was below the previously proposed target in 83% of patients. Future studies should focus on validating the cabozantinib exposure required for long term efficacy.
In young women, a link between childhood abuse and subliminal processing of aversive cues is moderated by impulsivity
Authors: Regier P. S. , Sinko L. , Jagannathan K. , Aryal S. , Teitelman A. M. , Childress A. R. 
Published: March 2022

AbstractBackgroundChildhood maltreatment is a serious public health concern. The association between child maltreatment, adverse behaviors, mental health outcomes, and alterations to brain function and structure have begun to be characterized. Less is known about the specific associations of maltreatment subtypes with cue-response to evocative cues and the moderating effects of confounding mental health/behavioral variables.MethodsFifty-four emerging adult women (aged 18–24) completed assessments for behaviors, mental health, and childhood maltreatment. They participated in a fMRI task featuring passive viewing of evocative (33 ms) cues presented by “backward masking” to prevent conscious processing. Correlations of abuse/neglect scores, behavioral/mental health factors, and brain function were assessed. Follow-up analyses investigated the moderating effects of behavioral/mental health factors on maltreatment and brain relationships.ResultsGreater frequency of childhood abuse and neglect were correlated with higher scores of impulsivity, depressive symptoms, and anxious attachment. Childhood abuse was positively associated with increased medial orbitofrontal cortical (mOFC) response to aversive (vs. neutral) cues. Among the behavioral/mental health variables, only impulsivity appeared to have a moderating effect on the relationship between childhood abuse and brain response to aversive cues.ConclusionsThe link between childhood abuse and a heightened mOFC response to “unseen” aversive stimuli, moderated by impulsivity, adds to the growing literature on the impact of prior adversity on brain function. These findings offer further understanding for the way in which childhood maltreatment affects the brain processing of negative stimuli, helping to explain the well-documented link between childhood maltreatment and a variety of adverse outcomes in adulthood.
Assessing the depression risk in the U.S. adults using nomogram
Authors: Zhang Yafeng , Tian Wei , Han Xinhao , Yan Guangcan , Ma Yuanshuo , Huo Shan , Shi Yu , Dai Shanshan , Ni Xin , Li Zhe , Fan Lihua , Zhang Qiuju 
Published: March 2022

Abstract
Background
Depression has received a lot of attention as a common and serious illness. However, people are rarely aware of their current depression risk probabilities. We aimed to develop and validate a predictive model applicable to the risk of depression in US adults.

Methods
This study was conducted using the database of the National Health and Nutrition Examination Survey (NHANES, 2017–2012). In particular, NHANES (2007–2010) was used as the training cohort (n = 6015) for prediction model construction and NHANES (2011–2012) was used as the validation cohort (n = 2812) to test the model. Depression was assessed (defined as a binary variable) by the Patient Health Questionnaire (PHQ-9). Socio-demographic characteristics, sleep time, illicit drug use and anxious days were assessed using a self-report questionnaire. Logistic regression analysis was used to evaluate independent risk factors for depression. The nomogram has the advantage of being able to visualize complex statistical prediction models as risk estimates of individualized disease probabilities. Then, we developed two depression risk nomograms based on the results of logistic regression. Finally, several validation methods were used to evaluate the prediction performance of nomograms.

Results
The predictors of model 1 included gender, age, income, education, marital status, sleep time and illicit drug use, and model 2, furthermore, included anxious days. Both model 1 and model 2 showed good discrimination ability, with a bootstrap-corrected C index of 0.71 (95% CI, 0.69–0.73) and 0.85 (95% CI, 0.83–0.86), and an externally validated C index of 0.71 (95% CI, 0.68–0.74) and 0.83 (95% CI, 0.81–0.86), respectively, and had well-fitted calibration curves. The area under the receiver operating characteristic curve (AUC) values of the models with 1000 different weighted random sampling and depression scores of 10–17 threshold range were higher than 0.7 and 0.8, respectively. Calculated net reclassification improvement (NRI) and integrated discrimination improvement (IDI) showed the discrimination or accuracy of the prediction models. Decision curve analysis (DCA) demonstrated that the depression models were practically useful. The network calculators work for participants to make personalized predictions.

Conclusions
This study presents two prediction models of depression, which can effectively and accurately predict the probability of depression as well as helping the U.S. civilian non-institutionalized population to make optimal treatment decisions.
Association of socioeconomic status with hypertension prevalence and control in Nanjing: a cross-sectional study
Authors: Qin Zhenzhen , Li Chao , Qi Shengxiang , Zhou Hairong , Wu Jie , Wang Weiwei , Ye Qing , Yang Huafeng , Wang Chenchen , Hong Xin 
Published: March 2022

Abstract
Background
The role of socioeconomic status (SES) on hypertension prevalence and hypertension control has gotten much attention but with conflicting results. This paper aimed to quantify the association of SES with both hypertension prevalence and hypertension control rate in Nanjing, China.

Methods
A community-based cross-sectional study was conducted using multistage random sampling on 60,283 adults aged more than 18 years between March 2017 and June 2018. Hypertension was defined as systolic blood pressure (BP) ≥ 140 mmHg and/or diastolic BP ≥ 90 mmHg or self-reported diagnosis of hypertension or respondent's report of taking antihypertensive medications. The controlled hypertension was defined by systolic BP < 140 mmHg and diastolic BP of < 90 mmHg among the subjects that self-reported exhibiting hypertensive and taking antihypertensive medications. The associations between SES with hypertension prevalence and hypertension control were quantified using generalized mixed model regression analysis and reported as odds ratios (ORs) and 95% confidence interval (CI).

Results
There was a high prevalence of subjects with primary educational level (49.6%) or unemployed and retired (49.5%) or lower annual household income level (44.9%) in each SES group, respectively. After adjustments for potential confounding factors, there were higher odds of hypertension among those with primary educational level (OR = 1.56), but lower odds for controlled BP (OR = 0.51). Higher odds of hypertension could be found among unemployed and retired, and higher odds of controlled BP was observed in the mental laborers or students (OR = 1.30), compared with the other categories, respectively. The lower-income group was more likely to be hypertensive (OR = 1.35) and less likely to have controlled hypertension (OR = 0.73).

Conclusion
Socioeconomic status played an important role in hypertension prevalence and hypertension control among adults in Nanjing, China. Strategies for hypertension prevention and control should especially focus on people in the vulnerable lower SES groups.
Does the thoracolumbar kyphosis secondary to ankylosing spondylitis affect the iliac trajectory of S2AI screw?
Authors: Zhong Xiao-lin , Qian Bang-ping , Huang Ji-chen , Wang Bin , Qiu Yong 
Published: March 2022

Abstract
Background
The study aimed to evaluate the influence of thoracolumbar kyphosis secondary to ankylosing spondylitis (AS) on parameters of S2AI trajectory and to compare the ideal S2AI trajectory with those of the non-deformity patients with AS, sagittal deformity patients without AS, and the normal population reported in literatures.

Methods
Sagittal parameters including global kyphosis (GK), pelvic tilt (PT) and sacral slope (SS) were measured. Besides, according to the simulated ideal S2AI trajectory on the CT images, trajectory parameters were measured including Sag angle, Tsv angle, Max-length, Sacral distance and Iliac width. Starting-point parameters were also measured including PSIS distance, Skin distance, Iliac wing and S2 midline.

Results
Ninety-four AS-related thoracolumbar kyphosis patients were included. After adjusting the age and gender, twenty non-deformity patients with AS and 20 sagittal deformity patients without AS were selected to compare with patients with AS-related thoracolumbar kyphosis, respectively. Sag angle in deformity patients with AS was smaller than other two groups (P < 0.001). No difference was found in Tsv angle and Sacral distance between AS patients with and without deformity. However, these two parameters were shown significant differences between deformity patients with AS and without AS. In deformity patients with AS, no significant differences were found in all parameters between genders Furthermore, there were strong correlations between PT and the bilateral Sag angle (P < 0.001).

Conclusions
The thoracolumbar kyphosis secondary to AS affects the Sag angle of the ideal S2AI trajectory which was approximately 20° smaller than that in non-deformity patients with AS, sagittal deformity patients without AS, and the normal population. Additionally, the Tsv angle and the Sacral distance in AS patients with thoracolumbar kyphosis were about 10° and 10 mm larger than those in sagittal deformity patients without AS, and the normal population reported in literatures.
Identification of risk factors for postoperative stage 3 acute kidney injury in patients who received surgical repair for acute type A aortic dissection
Authors: Wang Zhigang , Ge Min , Wang Zheyun , Chen Cheng , Lu Lichong , Zhang Lifang , Wang Dongjin 
Published: March 2022

Abstract
Background
Acute kidney injury (AKI) is a serious complication that often occurred after acute type A aortic dissection (ATAAD) surgery. Previous studies proved that the Kidney Disease Improving Global Outcomes (KDIGO) defined stage 3 AKI was associated with lower long-term survival rate. However, the risk factors for developing stage 3 AKI had not been identified. The aim of the study was to explore the risk factors for developing KDIGO stage 3 after ATAAD operation.

Methods
This study included 993 patients who received ATAAD operation from 2014 to 2019 at the Nanjing Drum Tower Hospital. Postoperative AKI was diagnosed according to the KDIGO criteria. Multivariate logistic regression analyses were applied to identify risk factors for stage 3 AKI. Kaplan–Meier survival analyses and Cox proportional hazards regression model were conducted to explore the association between different AKI stages and postoperative survival rate.

Results
The mean age of all enrolled patients was 53.0 ± 13.1 years. A total of 236 (23.8%) patients suffered postoperative stage 3 AKI including 165 patients who required renal replacement therapy. Advanced age (odds ratio [OR] 1.031; 95% confidence interval [CI] 1.005–1.057; P = 0.018), prolonged cardiopulmonary bypass (CPB) duration (OR 1.010; 95% CI 1.002–1.018; P = 0.013), and higher drainage volume 24 h after surgery (OR 1.000; 95% CI 1.000–1.001; P = 0.033) were identified as independent risk factors for developing stage 3 AKI. In addition, our result showed that the mortality rate was correlated significantly with the severity of AKI defined by KDIGO criteria and the Cox regression analysis showed that only stage 3 AKI, but not stage 1 and 2, was an independent risk factor for mortality (Hazard ratio 10.365, 95% CI 4.208 to 25.528; P < 0.001) after adjusting for important confounding factors.

Conclusions
Our study suggested that stage 3 postoperative AKI was significantly associated with decreased postoperative survival rate after ATAAD surgery. Advanced age, increased CPB duration and drainage volume 24 h after surgery were identified as risk factors for developing stage 3 AKI.
An advanced network pharmacology study to explore the novel molecular mechanism of Compound Kushen Injection for treating hepatocellular carcinoma by bioinformatics and experimental verification
Authors: Lu Shan , Meng Ziqi , Tan Yingying , Wu Chao , Huang Zhihong , Huang Jiaqi , Fu Changgeng , Stalin Antony , Guo Siyu , Liu Xinkui , You Leiming , Li Xiaojiaoyang , Zhang Jingyuan , Zhou Wei , Zhang Xiaomeng , Wang Miaomiao , Wu Jiarui 
Published: March 2022

AbstractBackgroundCompound Kushen Injection (CKI) is a Chinese patent drug that exerts curative effects in the clinical treatment of hepatocellular carcinoma (HCC). This study aimed to explore the targets and potential pharmacological mechanisms of CKI in the treatment of HCC.MethodsIn this study, network pharmacology was used in combination with molecular biology experiments to predict and verify the molecular mechanism of CKI in the treatment of HCC. The constituents of CKI were identified by UHPLC-MS/MS and literature search. The targets corresponding to these compounds and the targets related to HCC were collected based on public databases. To screen out the potential hub targets of CKI in the treatment of HCC, a compound-HCC target network was constructed. The underlying pharmacological mechanism was explored through the subsequent enrichment analysis. Interactive Gene Expression Profiling Analysis and Kaplan-Meier plotter were used to examine the expression and prognostic value of hub genes. Furthermore, the effects of CKI on HCC were verified through molecular docking simulations and cell experiments in vitro.ResultsNetwork analysis revealed that BCHE, SRD5A2, EPHX2, ADH1C, ADH1A and CDK1 were the key targets of CKI in the treatment of HCC. Among them, only CDK1 was highly expressed in HCC tissues, while the other 5 targets were lowly expressed. Furthermore, the six hub genes were all closely related to the prognosis of HCC patients in survival analysis. Molecular docking revealed that there was an efficient binding potential between the constituents of CKI and BCHE. Experiments in vitro proved that CKI inhibited the proliferation of HepG2 cells and up-regulated SRD5A2 and ADH1A, while down-regulated CDK1 and EPHX2.ConclusionsThis study revealed and verified the targets of CKI on HCC based on network pharmacology and experiments and provided a scientific reference for further mechanism research.
A treatment recommender clinical decision support system for personalized medicine: method development and proof-of-concept for drug resistant tuberculosis
Authors: Verboven Lennert , Calders Toon , Callens Steven , Black John , Maartens Gary , Dooley Kelly E. , Potgieter Samantha , Warren Robin M. , Laukens Kris , Van Rie Annelies 
Published: March 2022

Abstract
Background
Personalized medicine tailors care based on the patient’s or pathogen’s genotypic and phenotypic characteristics. An automated Clinical Decision Support System (CDSS) could help translate the genotypic and phenotypic characteristics into optimal treatment and thus facilitate implementation of individualized treatment by less experienced physicians.

Methods
We developed a hybrid knowledge- and data-driven treatment recommender CDSS. Stakeholders and experts first define the knowledge base by identifying and quantifying drug and regimen features for the prototype model input. In an iterative manner, feedback from experts is harvested to generate model training datasets, machine learning methods are applied to identify complex relations and patterns in the data, and model performance is assessed by estimating the precision at one, mean reciprocal rank and mean average precision. Once the model performance no longer iteratively increases, a validation dataset is used to assess model overfitting.

Results
We applied the novel methodology to develop a treatment recommender CDSS for individualized treatment of drug resistant tuberculosis as a proof of concept. Using input from stakeholders and three rounds of expert feedback on a dataset of 355 patients with 129 unique drug resistance profiles, the model had a 95% precision at 1 indicating that the highest ranked treatment regimen was considered appropriate by the experts in 95% of cases. Use of a validation data set however suggested substantial model overfitting, with a reduction in precision at 1 to 78%.

Conclusion
Our novel and flexible hybrid knowledge- and data-driven treatment recommender CDSS is a first step towards the automation of individualized treatment for personalized medicine. Further research should assess its value in fields other than drug resistant tuberculosis, develop solid statistical approaches to assess model performance, and evaluate their accuracy in real-life clinical settings.