bluuebunny/crossref_metadata_2025 huggingface dataset:
Maternal postpartum feeding anxiety was associated with infant feeding practices: results from the mother-infant cohort study of China
Authors: Sun Jing , Zhu Yimin , Li Yongjin , Li Niuniu , Liu Tan , Su Xiao , Dai Zhiyong , Zhang Yanchun , Pan Lina , Jiang Wei , Zhu Wenli
Published: December 2020

Background

Maternal feeding anxiety (FA) was prevalent during puerperium and might affect infant feeding practices. This study was aimed to investigate the FA status in Chinese postpartum women and its relationship with infant feeding practices (FPs).

Methods

Participants were from the Mother-Infant Cohort Study of China, in which the dietary and feeding practices, physical and psychiatric health for both mothers and infants were followed up from childbirth to next 2 years. In this study the maternal feeding anxiety (FA) status at 0–3 months postpartum was assessed by Li’s Self-rating Feeding Anxiety Scale (SFAS). Infant feeding practices (FPs) at 0–3 months, including breastfeeding-related behaviors, responsive feeding and infant food refusal were investigated by self-designed questionnaire.

Results

In total 456 mothers the average feeding anxiety scores (FAS) was 41.02 ± 8.02 (mean ± SD), and maternal FA prevalence were 61.4% (FAS>38) with severe FA being 8.6% (FAS>52) at 0–3 months postpartum. The FAS was related with infant FPs, and lower maternal FAS was significantly related with infant colostrum feeding (40.86 ± 8.02 vs 44.74 ± 11.33, P < 0.05), but higher FAS was related with bottle feeding (41.95 ± 8.28 vs 39.69 ± 7.92, P < 0.05). The mothers with severe feeding anxiety (FAS > 53) were more likely to feed infants with bottle (ORs, 95%CI: 2.41, 1.11 ~ 5.19). There were not significant association between FAS and exclusive breastfeeding and responsive feeding practices (P > 0.05). The higher FAS was associated with infant food refusal behaviors, the maternal scores whose infant “never”, “rarely”, “sometimes” and “often” spat out food when feeding were 39.86 ± 8.02, 41.47 ± 8.18, 41.36 ± 7.44 and 42.14 ± 12.03 increasingly (P > 0.05), and the FA prevalence was significantly different among groups (P < 0.05). The infants whose mother was identified as feeding anxiety were more likely to refuse opening the mouth when feeding (P < 0.05). Multivariate analysis indicated maternal FAS was positively related to infant bottle feeding (βi = 2.487, P < 0.05) and outdoor sunshine exposure practice (βi = 1.787, P < 0.05), and negatively related to household income level (βi = − 0.118, P < 0.05).

Conclusions

Maternal postpartum feeding anxiety was associated with some infant feeding practices, including bottle feeding and infant food refusal behaviors.
Problems with Bazett QTc correction in paediatric screening of prolonged QTc interval
Authors: Andršová Irena , Hnatkova Katerina , Helánová Kateřina , Šišáková Martina , Novotný Tomáš , Kala Petr , Malik Marek
Published: December 2020

Background

Bazett formula is frequently used in paediatric screening for the long QT syndrome (LQTS) and proposals exist that using standing rather than supine electrocardiograms (ECG) improves the sensitivity of LQTS diagnosis. Nevertheless, compared to adults, children have higher heart rates (especially during postural provocations) and Bazett correction is also known to lead to artificially prolonged QTc values at increased heart rates. This study assessed the incidence of erroneously increased QTc values in normal children without QT abnormalities.

Methods

Continuous 12-lead ECGs were recorded in 332 healthy children (166 girls) aged 10.7 ± 2.6 years while they performed postural manoeuvring consisting of episodes (in the following order) of supine, sitting, standing, supine, standing, sitting, and supine positions, each lasting 10 min. Detailed analyses of QT/RR profiles confirmed the absence of prolonged individually corrected QTc interval in each child. Heart rate and QT intervals were measured in 10-s ECG segments and in each segment, QTc intervals were obtained using Bazett, Fridericia, and Framingham formulas. In each child, the heart rates and QTc values obtained during supine, sitting and standing positions were averaged. QTc durations by the three formulas were classified to < 440 ms, 440–460 ms, 460–480 ms, and > 480 ms.

Results

At supine position, averaged heart rate was 77.5 ± 10.5 beat per minute (bpm) and Bazett, Fridericia and Framingham QTc intervals were 425.3 ± 15.8, 407.8 ± 13.9, and 408.2 ± 13.1 ms, respectively. At sitting and standing, averaged heart rate increased to 90.9 ± 10.1 and 100.9 ± 10.5 bpm, respectively. While Fridericia and Framingham formulas showed only minimal QTc changes, Bazett correction led to QTc increases to 435 ± 15.1 and 444.9 ± 15.9 ms at sitting and standing, respectively. At sitting, Bazett correction identified 51, 4, and 0 children as having the QTc intervals 440–460, 460–480, and > 480 ms, respectively. At sitting, these numbers increased to 118, 11, and 1, while on standing these numbers were 151, 45, and 5, respectively. Irrespective of the postural position, Fridericia and Framingham formulas identified only a small number (< 7) of children with QT interval between 440 and 460 ms and no children with longer QTc.

Conclusion

During screening for LQTS in children, the use of Bazett formula leads to a high number of false positive cases especially if the heart rates are increased (e.g. by postural manoeuvring). The use of Fridericia formula can be recommended to replace the Bazett correction not only for adult but also for paediatric ECGs.
A randomized controlled trial on the comparative effectiveness of mindfulness-based cognitive therapy and health qigong-based cognitive therapy among Chinese people with depression and anxiety disorders
Authors: Chan Sunny H. W. , Chan Wendy W. K. , Chao June Y. W. , Chan Phyllis K. L.
Published: December 2020

Background

The goal of this study was to investigate treatment outcome and related intervention processes of mindfulness-based cognitive therapy versus health qigong-based cognitive therapy versus waitlist control among individuals with mood disorders.

Methods

A total of 187 individuals with mood disorders were randomized and allocated into mindfulness-based cognitive therapy, health qigong-based cognitive therapy, or waitlist control groups. All participants were assessed at three time points with regard to depressive and anxiety symptoms, physical and mental health status, perceived stress, sleep quality, and self-efficacy. Linear mixed models analysis was used to test the individual growth model by studying the longitudinal data.

Results

Mindfulness-based cognitive therapy and health qigong-based cognitive therapy both produced greater improvements on all outcome measures as compared with waitlist control. Relatively, more reductions of mood symptoms were observed in the health qigong-based cognitive therapy group as compared with the mindfulness-based cognitive therapy group. Health qigong-based cognitive therapy is more conducive to physical health status whereas mindfulness-based cognitive therapy has more favorable mental health outcomes. Individual growth curve models indicated that alterations in perceived stress was the common predictor of mood changes in both intervention groups.

Conclusions

The predominant emphasis on physical health in health qigong-based cognitive therapy makes it more acceptable and effective than mindfulness-based cognitive therapy as applied in Chinese individuals with mood disorders. The influence of Chinese culture is discussed.

Trial registration

HKU Clinical Trials Registry. Identifier:HKUCTR-2558. Registered 21st Nov 2018.
Depression treatment in Germany – using claims data to compare a collaborative mental health care program to the general practitioner program and usual care in terms of guideline adherence and need-oriented access to psychotherapy
Authors: Engels Alexander , König Hans-Helmut , Magaard Julia Luise , Härter Martin , Hawighorst-Knapstein Sabine , Chaudhuri Ariane , Brettschneider Christian
Published: December 2020

Background

Societies strive for fast-delivered, evidence-based and need-oriented depression treatment within budget constraints. To explore potential improvements, selective contracts can be implemented. Here, we evaluate if the German collaborative psychiatry-neurology-psychotherapy contract (PNP), which extends the gatekeeping-based general practitioner (GP) program, improved guideline adherence or need-oriented and timely access to psychotherapy compared to usual care (UC).

Methods

We conducted a retrospective observational cohort study based on health insurance claims data. After we identified patients with depression who were on sick leave due to a mental disorder in 2015, we applied entropy balancing to adjust for selection effects and employed chi-squared tests to compare guideline adherence of the received treatment between PNP, the GP program and UC. Subsequently, we applied an extended cox regression to assess need-orientation by comparing the relationship between accumulated sick leave days and waiting times for psychotherapy across health plans.

Results

N = 23,245 patients were included. Regarding guideline adherence, we found no significant differences for most severity subgroups; except that patients with a first moderate depressive episode received antidepressants or psychotherapy more often in UC. Regarding need-orientation, we observed that the effect of each additional month of sick leave on the likelihood of starting psychotherapy was increased by 6% in PNP compared to UC.

Irrespective of the health plan, we found that within the first 12 months only between 24.3 and 39.7% (depending on depression severity) received at least 10 psychotherapy sessions or adequate pharmacotherapy.

Conclusions

The PNP contract strengthens the relationship between sick leave days and the delay until the beginning of psychotherapy, which suggests improvements in terms of need-oriented access to care. However, we found no indication for increased guideline adherence and – independent of the health plan – a gap in sufficient utilization of adequate treatment options.
Hospital and patient factors influencing the health status among patients with schizophrenia, thirty days after hospital discharge: multi-level analysis
Authors: Smithnaraseth Anantree , Seeherunwong Acharaporn , Panitrat Rungnapa , Tipayamongkholgul Mathuros
Published: December 2020

Background

The time between discharge from hospital and transition to community and home is a critical period for health status among patients with a mental illness, including patients with schizophrenia. This study aimed to investigate crucial patient factors (patient-level) and hospital factors (hospital-level) affecting health status and see whether patient factor effects on health status vary with hospital factors, 30 days after hospital discharge.

Methods

This is a prospective study of 1255 patients with schizophrenia and their primary caregivers from 13 public mental hospitals across Thailand. Logistic regression and multi-level logistic regression was used to investigate the effects of patient and hospital factors simultaneously on health status, 30 days after hospital discharge.

Results

The intraclass correlation coefficient indicated that 14% of the change in health status was explained by the differences between hospital. Poor health status was identified in 14.26% of patients, 30 days after hospital discharge. The majority of participant patients were male (69.8%), single (71.87%), and the average age was 38.09 (SD = 9.74). The finding also showed that the patient factors; being female (ORadj .53, 95%CI .31,.92), perceived moderate and high levels of positive aspect of caregiving (ORadj .24, 95%CI .14,.42 and ORadj .05, 95%CI .02,.09), perceived readiness for hospital discharge (ORadj .21, 95%CI .13,.33), partial and full adherence to treatment (ORadj .24, 95%CI .14,.42 and ORadj .31, 95%CI .20,.47) showed a reduced likelihood of developing poor health status except substance use (ORadj 1.55, 95%CI .98, 2.44). Hospital factors; discharge planning process and nurse-patient ratio (ORadj 1.64, 95%CI 1.17, 2.30 and ORadj 1.16, 95%CI 1.09, 1.22) showed an increased likelihood of developing poor health status, 30 days after hospital discharge.

Conclusions

Findings provide relevant information on how both patient and hospital factors determine health status. These results might lead to better targeting of mental health service policy and enable more precise information gathering and allocation of resources. However, future research should be more focused and continue investigating the pathways through which hospital factors influence health status post-discharge.
Epidemiological and time series analysis on the incidence and death of AIDS and HIV in China
Authors: Xu Bin , Li Jiayuan , Wang Mengqiao
Published: December 2020

Background

To investigate the regional and age-specific distribution of AIDS/HIV in China from 2004 to 2017 and to conduct time series analysis of the epidemiological trends.

Method

Using official surveillance data from publicly accessible database of the national infectious disease reporting system, we described long-term patterns of incidence and death in AIDS/HIV, analyzed age group and regional epidemic characteristics, and established Autoregressive Integrated Moving Average (ARIMA) models for time series analysis.

Result

The incidence and death of AIDS/HIV have increased rapidly from 2004 to 2017, with significant difference regarding age groups and provincial regions (a few provinces appear as hot spots). With goodness-of-fit criteria and using data from 2004 to 2015, ARIMA (0,1,3) × (2,0,0), ARIMA (3,1,0) × (1,0,1), and ARIMA (0,1,2) × (2,0,0) were chosen as the optimal model for the incidence of AIDS, HIV, and combined; ARIMA (0,1,3) × (1,0,0) was chosen as the optimal model for the death of AIDS, HIV, and combined. ARIMA models robustly predicted the incidence and death of AIDS/HIV in 2016 and 2017.

Conclusion

A focused intervention strategy targeting specific regions and age groups is essential for the prevention and control of AIDS/HIV. ARIMA models function as data-driven and evidence-based methods to forecast the trends of infectious diseases and formulate public health policies.
3D simulation of radiographic projections to test and reduce the effect of pelvic tilt on the accuracy of cross-table lateral radiography
Authors: Xu Jie , Su Baohua , Zhang Wenhui , Sun Hao , Li Deng , Cai Zhiqing , Chen Meiyi , Qiu Meiling , Ma Ruofan
Published: December 2020

Background

Cross-table lateral (CL) radiography is a convenient and feasible method to assess cup version angle (VA) after total hip arthroplasty; However, pelvic tilt (PT) may contribute to its measurement inaccuracy. How PT affects CL radiographic measurements have not been well studied. We sought (1) to determine the effect of the PT on cup version measurement on CL radiography and (2) to develop a method for reducing measurement errors caused by the PT.

Methods

We used 3D technique to construct standard model and capture CL radiography simulation. A linear regression model was created to analyze the relationship between PT and VA. CL radiography and computed tomography (CT) were performed for the enrolled patients after surgery. The consistency between CL and CT measurements were verified by intra-class correlation coefficient (ICC).

Results

There was a high correlation between the VA and PT. For each 1-degree increased in the PT, the VA decreased by 0.76° (R2 = 0.995, p < 0.001). Based on the data, we created a corrective formula to convert the radiographic measurements into values approximating the actual VA under a natural pelvic position. The VA measurements corrected by our equation was in high agreement with the CT-measured values with reference to the corresponding PT (ICC = 0.988, p < 0.001), which was in sharp contrast to that without PT control (ICC = 0.454, p = 0.203).

Conclusions

The PT may contribute to cup version measurement inaccuracies on CL radiography. Our mathematical algorithm can serve as a reliable method to improve the accuracy of CL radiography.
Pancreatic serous cystoadenoma (CSA) showing increased tracer uptake at 68-GaDOTA-peptide Positron Emission Tomography (68Ga-DOTA-peptide PET-CT): a case report
Authors: Nappo Gennaro , Funel Niccola , Giudici Simone , Spaggiari Paola , Capretti Giovanni , Carrara Silvia , Pepe Giovanna , Zerbi Alessandro
Published: December 2020

Background

Serous cysto-adenoma (SCA) is a rare benign neoplasm of the pancreas. SCA can mimic other pancreatic lesions, such as neuroendocrine tumours. 68Gallium-DOTA-peptide Positron Emission Tomography (PET) is able to image in vivo the over-expression of the somatostatin receptors, playing an important role for the identification of neuroendocrine neoplasms.

Case presentation

We reported a case of 63-year-old man,with a solid lesion of 7 cm of diameter of the body–tail of the pancreas. Two fine-needle-aspirations (FNA) were inconclusive. A 68Ga-DOTA-peptide PET-CT revealed a pathological uptake of the pancreatic lesion. The diagnosis of a pancreatic neuroendocrine neoplasm was established and a laparoscopic distal splenopancreatectomy and cholecystectomy was performed. Final histopathological report revealed the presence of a micro-cystic SCA.

Conclusions

The current case firstly reports a pancreatic SCA showing increased radiopharmaceutical uptake at 68Ga-DOTA-peptide PET-CT images. This unexpected finding should be taken into account during the diagnostic algorithm of a pancreatic lesion, in order to minimize the risk of misdiagnosis and overtreatment of SCA.
Complete remission of Cdx-2 positive primary testicular carcinoid tumor: 10-years follow-up and literature review
Authors: Widmeier Eugen , Füllgraf Hannah , Waller Cornelius F.
Published: December 2020

Background

The neuroendocrine cells can cause a variety of malignancies throughout the human body known as the neuroendocrine tumors (NETs) or carcinoid tumors. The primary testicular carcinoid tumor (PTCT) accounts for less than 1% of the testicular neoplasms and for only 0.2% of all carcinoid tumors representing already a very rare neoplastic entity. Here, we present a patient with a history of an exceptionally rare primary testicular carcinoid tumor, staining positive for Cdx-2 along with a literature review.

Case presentation

A 44-year old patient without significant past medical history was diagnosed in September 2009 with primary testicular carcinoid tumor, which was surprisingly staining positively for Cdx-2, too. At the time of the initial diagnosis the tumor was already showing histopathological infiltration of veins. DOTA-TATE-PET/CT imaging and endoscopy studies did not show any signs of distant metastases and in particular no gastrointestinal manifestation following no further medical indication for systemic chemotherapy. The continuous and close follow-up of the patient has reached a total of over 10 years at the time of publication remaining in complete remission.

Conclusion

The diagnosis of primary testicular carcinoid is based on histopathology. The detailed histopathologic assessment of biomarkers based on immunohistochemistry is very important for the classification and the prognosis of the primary testicular carcinoid tumor. Primary testicular carcinoid tumor with Cdx-2 positive stain outlines an exceptionally rare neoplastic entity without a consensus about general follow-up guidelines, requiring close clinical and imaging aftercare and consideration in Cdx-2 positive metastatic tumor of unknown origin.
Clinical profiles, outcomes and risk factors among type 2 diabetic inpatients with diabetic ketoacidosis and hyperglycemic hyperosmolar state: a hospital-based analysis over a 6-year period
Authors: Wu Xiao-yan , She Dun-min , Wang Fang , Guo Gang , Li Ran , Fang Ping , Li Ling , Zhou Yun , Zhang Ke-qin , Xue Ying
Published: December 2020

Objective

Diabetic ketoacidosis (DKA) and hyperosmolar hyperglycemic state (HHS) are the two most common hyperglycemic emergencies (HEs) associated with diabetes mellitus. Individuals with HEs can present with combined features of DKA and HHS. The objective of this study is to assess the clinical characteristics, therapeutic outcomes, and associated predisposing factors of type 2 diabetic patients with isolated or combined HEs in China.

Methods

We performed a retrospective analysis of 158 patients with type 2 diabetes (T2DM), complicated with DKA, HHS, or DKA combined with HHS (DKA-HHS) in Shanghai Tongji Hospital, China from 2010 to 2015. Admission clinical features, therapeutic approaches and treatment outcomes of those patients were extracted and analyzed.

Results

Of the 158 patients with T2DM, 65 (41.1%) patients were DKA, 74 (46.8%) were HHS, and 19 (12.0%) were DKA-HHS. The most common precipitants were infections (111, 70.3%), newly diagnosed diabetes (28,17.7%) and non-compliance to medications (9, 5.7%). DKA patients were divided into mild, moderate and severe group, based on arterial blood gas. Spearman correlation analysis revealed that C-reaction protein (CRP) was positively correlated with severity of DKA, whereas age and fasting C peptide were inversely correlated with severity of DKA (P < 0.05). The mortality was 10.8% (17/158) in total and 21.6% (16/74) in the HHS group, 5.9% (1/17) in DKA-HHS. Spearman correlation analysis indicated that death in patients with HHS was positively correlated to effective plasma osmolality (EPO), renal function indicators and hepatic enzymes, while inversely associated with the continuous subcutaneous insulin infusion (CSII) therapy. Logistic regression analysis suggested that elevated blood urea nitrogen (BUN) on admission was an independent predisposing factor of mortality in HHS, while CSII might be a protective factor for patients with HHS. Furthermore, the receiver-operating characteristic (ROC) curve analysis indicated that BUN had the largest area under the ROC curves for predicting death in patients with HHS.

Conclusions

Our findings showed elevated CRP and decreased fasting C-peptide might serve as indicator for severe DKA. Elevated BUN might be an independent predictor of mortality in patients with HHS, whereas CSII might be a protective factor against death in HHS.
Vertical integration in medical education: the broader perspective
Authors: Wijnen-Meijer Marjo , van den Broek Sjoukje , Koens Franciska , ten Cate Olle
Published: December 2020

Curricular integration represents collaborations between disciplines to establish a coherent curriculum and has become the dominant recommendation for medical education in the second half of the twentieth century. Vertical integration specifically is the integration between the clinical and basic science parts throughout the program. Vertically integrated curricula present basic sciences imbedded in a clinical context from the start of medical school.

The authors briefly discuss vertical integration in relationship with context theory, motivation theory, professional identity formation, transition to practice and the continuum of education and practice. They conclude that vertical integration, rather than horizontal integration, extends far beyond curriculum structure. They consider vertical integration a philosophy of education, with impact on students’ maturation and engagement with the profession, and which applies not only to undergraduate education but to the lifelong learning of professionals. The definition of vertical integration as “an educational approach that fosters a gradual increase of learner participation in the professional community through a stepwise increase of knowledge-based engagement in practice with graduated responsibilities in patient care” is more comprehensive than its older conceptualization.
MINDWALC: mining interpretable, discriminative walks for classification of nodes in a knowledge graph
Authors: Vandewiele Gilles , Steenwinckel Bram , Turck Filip De , Ongenae Femke
Published: December 2020

Background

Leveraging graphs for machine learning tasks can result in more expressive power as extra information is added to the data by explicitly encoding relations between entities. Knowledge graphs are multi-relational, directed graph representations of domain knowledge. Recently, deep learning-based techniques have been gaining a lot of popularity. They can directly process these type of graphs or learn a low-dimensional numerical representation. While it has been shown empirically that these techniques achieve excellent predictive performances, they lack interpretability. This is of vital importance in applications situated in critical domains, such as health care.

Methods

We present a technique that mines interpretable walks from knowledge graphs that are very informative for a certain classification problem. The walks themselves are of a specific format to allow for the creation of data structures that result in very efficient mining. We combine this mining algorithm with three different approaches in order to classify nodes within a graph. Each of these approaches excels on different dimensions such as explainability, predictive performance and computational runtime.

Results

We compare our techniques to well-known state-of-the-art black-box alternatives on four benchmark knowledge graph data sets. Results show that our three presented approaches in combination with the proposed mining algorithm are at least competitive to the black-box alternatives, even often outperforming them, while being interpretable.

Conclusions

The mining of walks is an interesting alternative for node classification in knowledge graphs. Opposed to the current state-of-the-art that uses deep learning techniques, it results in inherently interpretable or transparent models without a sacrifice in terms of predictive performance.
Treatment effect prediction with adversarial deep learning using electronic health records
Authors: Chu Jiebin , Dong Wei , Wang Jinliang , He Kunlun , Huang Zhengxing
Published: December 2020

Background

Treatment effect prediction (TEP) plays an important role in disease management by ensuring that the expected clinical outcomes are obtained after performing specialized and sophisticated treatments on patients given their personalized clinical status. In recent years, the wide adoption of electronic health records (EHRs) has provided a comprehensive data source for intelligent clinical applications including the TEP investigated in this study.

Method

We examined the problem of using a large volume of heterogeneous EHR data to predict treatment effects and developed an adversarial deep treatment effect prediction model to address the problem. Our model employed two auto-encoders for learning the representative and discriminative features of both patient characteristics and treatments from EHR data. The discriminative power of the learned features was further enhanced by decoding the correlational information between the patient characteristics and subsequent treatments by means of a generated adversarial learning strategy. Thereafter, a logistic regression layer was appended on the top of the resulting feature representation layer for TEP.

Result

The proposed model was evaluated on two real clinical datasets collected from the cardiology department of a Chinese hospital. In particular, on acute coronary syndrome (ACS) dataset, the proposed adversarial deep treatment effect prediction (ADTEP) (0.662) exhibited 1.4, 2.2, and 6.3% performance gains in terms of the area under the ROC curve (AUC) over deep treatment effect prediction (DTEP) (0.653), logistic regression (LR) (0.648), and support vector machine (SVM) (0.621), respectively. As for heart failure (HF) case study, the proposed ADTEP also outperformed all benchmarks. The experimental results demonstrated that our proposed model achieved competitive performance compared to state-of-the-art models in tackling the TEP problem.

Conclusion

In this work, we propose a novel model to address the TEP problem by utilizing a large volume of observational data from EHR. With adversarial learning strategy, our proposed model can further explore the correlational information between patient statuses and treatments to extract more robust and discriminative representation of patient samples from their EHR data. Such representation finally benefits the model on TEP. The experimental results of two case studies demonstrate the superiority of our proposed method compared to state-of-the-art methods.
A semantic relationship mining method among disorders, genes, and drugs from different biomedical datasets
Authors: Zhang Li , Hu Jiamei , Xu Qianzhi , Li Fang , Rao Guozheng , Tao Cui
Published: December 2020

Background

Semantic web technology has been applied widely in the biomedical informatics field. Large numbers of biomedical datasets are available online in the resource description framework (RDF) format. Semantic relationship mining among genes, disorders, and drugs is widely used in, for example, precision medicine and drug repositioning. However, most of the existing studies focused on a single dataset. It is not easy to find the most current relationships among disorder-gene-drug relationships since the relationships are distributed in heterogeneous datasets. How to mine their semantic relationships from different biomedical datasets is an important issue.

Methods

First, a variety of biomedical datasets were converted into RDF triple data; then, multisource biomedical datasets were integrated into a storage system using a data integration algorithm. Second, nine query patterns among genes, disorders, and drugs from different biomedical datasets were designed. Third, the gene-disorder-drug semantic relationship mining algorithm is presented. This algorithm can query the relationships among various entities from different datasets.

Results and conclusions

We focused on mining the putative and the most current disorder-gene-drug relationships about Parkinson’s disease (PD). The results demonstrate that our method has significant advantages in mining and integrating multisource heterogeneous biomedical datasets. Twenty-five new relationships among the genes, disorders, and drugs were mined from four different datasets. The query results showed that most of them came from different datasets. The precision of the method increased by 2.51% compared to that of the multisource linked open data fusion method presented in the 4th International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019). Moreover, the number of query results increased by 7.7%, and the number of correct queries increased by 9.5%.
KGen: a knowledge graph generator from biomedical scientific literature
Authors: Rossanez Anderson , dos Reis Julio Cesar , Torres Ricardo da Silva , de Ribaupierre Hélène
Published: December 2020

Background

Knowledge is often produced from data generated in scientific investigations. An ever-growing number of scientific studies in several domains result into a massive amount of data, from which obtaining new knowledge requires computational help. For example, Alzheimer’s Disease, a life-threatening degenerative disease that is not yet curable. As the scientific community strives to better understand it and find a cure, great amounts of data have been generated, and new knowledge can be produced. A proper representation of such knowledge brings great benefits to researchers, to the scientific community, and consequently, to society.

Methods

In this article, we study and evaluate a semi-automatic method that generates knowledge graphs (KGs) from biomedical texts in the scientific literature. Our solution explores natural language processing techniques with the aim of extracting and representing scientific literature knowledge encoded in KGs. Our method links entities and relations represented in KGs to concepts from existing biomedical ontologies available on the Web. We demonstrate the effectiveness of our method by generating KGs from unstructured texts obtained from a set of abstracts taken from scientific papers on the Alzheimer’s Disease. We involve physicians to compare our extracted triples from their manual extraction via their analysis of the abstracts. The evaluation further concerned a qualitative analysis by the physicians of the generated KGs with our software tool.

Results

The experimental results indicate the quality of the generated KGs. The proposed method extracts a great amount of triples, showing the effectiveness of our rule-based method employed in the identification of relations in texts. In addition, ontology links are successfully obtained, which demonstrates the effectiveness of the ontology linking method proposed in this investigation.

Conclusions

We demonstrate that our proposal is effective on building ontology-linked KGs representing the knowledge obtained from biomedical scientific texts. Such representation can add value to the research in various domains, enabling researchers to compare the occurrence of concepts from different studies. The KGs generated may pave the way to potential proposal of new theories based on data analysis to advance the state of the art in their research domains.
A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction
Authors: Zhang Zhen , Qiu Hang , Li Weihao , Chen Yucheng
Published: December 2020

Background

Acute myocardial infarction (AMI) is a serious cardiovascular disease, followed by a high readmission rate within 30-days of discharge. Accurate prediction of AMI readmission is a crucial way to identify the high-risk group and optimize the distribution of medical resources.

Methods

In this study, we propose a stacking-based model to predict the risk of 30-day unplanned all-cause hospital readmissions for AMI patients based on clinical data. Firstly,
we conducted an under-sampling method of neighborhood cleaning rule (NCR) to alleviate the class imbalance and then utilized a feature selection method of SelectFromModel (SFM) to select effective features. Secondly, we adopted a self-adaptive approach to select base classifiers from eight candidate models according to their performances in datasets. Finally, we constructed a three-layer stacking model in which layer 1 and layer 2 were base-layer and level 3 was meta-layer. The predictions of the base-layer were used to train the meta-layer in order to make the final forecast.

Results

The results show that the proposed model exhibits the highest AUC (0.720), which is higher than that of decision tree (0.681), support vector machine (0.707), random forest (0.701), extra trees (0.709), adaBoost (0.702), bootstrap aggregating (0.704), gradient boosting decision tree (0.710) and extreme gradient enhancement (0.713).

Conclusion

It is evident that our model could effectively predict the risk of 30-day all cause hospital readmissions for AMI patients and provide decision support for the administration.
Nature of dispensing errors in selected hospitals providing free healthcare: a multi-center study in Sri Lanka
Authors: Dilsha R. A. N. , Kularathne H. M. I. P. , Mujammil M. T. M. , Irshad S. M. M. , Samaranayake N. R.
Published: December 2020

Background

Dispensing errors, known to result in significant patient harm, are preventable if their nature is known and recognized. However, there is a scarcity of such data on dispensing errors particularly in resource poor settings, where healthcare is provided free-of-charge. Therefore, the purpose of this study was to determine the types, and prevalence of dispensing errors in a selected group of hospitals in Sri Lanka.

Methods

A prospective, cross sectional, multi-center study on dispensing errors was conducted, in a single tertiary care, and two secondary care hospitals, in a cohort of 420 patients attending medical, surgical, diabetic and pediatric clinics. The patients were selected according to the population size, through consecutive sampling. The prescription audit was conducted in terms of dispensing errors which were categorized as i) content, ii) labelling, iii) documentation, iv) concomitant, and v) other errors based on in-house developed definitions.

Results

A total of 420 prescriptions (1849 medicines) were analyzed (Hospital-I, 248 prescriptions-1010 medicines; Hospital-II, 84 prescriptions-400 medicines; Hospital-III, 88 prescriptions-439 medicines), and a cumulative total of 16,689 dispensing errors (at least one dispensing error in a prescription) were detected. Labelling errors were the most frequent dispensing error (63.1%; N = 10,523; Mostly missing information on the dispensing label), followed by concomitant prescribing and dispensing errors (20.5%; N = 3425; Missing prescribing information overlooked by the pharmacist), documentation errors (10.6%; N = 1772 Missing identification of pharmacist on dispensing label), clinically significant medication interactions overlooked by pharmacists (0.5%; N = 82), content errors (4.9%; N = 812; Discrepancies between medication dispensed and prescription order), medications dispensed in unsuitable packaging (0.4%; N = 74), and lastly medication dispensed to the wrong patient (0.01%; N = 1).

Conclusions

Dispensing errors are frequent in Sri Lankan hospitals which operate with limited resources and provide free healthcare to all citizenry. Over one half of the errors were labeling errors with minimal content errors. Awareness on common types of dispensing errors and emphasis on detecting them could improve medication safety in Sri Lankan hospitals.
Challenges to effective governance in a low income healthcare system: a qualitative study of stakeholder perceptions in Malawi
Authors: Masefield Sarah C. , Msosa Alan , Grugel Jean
Published: December 2020

Background

All countries face challenging decisions about healthcare coverage. Malawi has committed to achieving Universal Health Coverage (UHC) by 2030, the timeframe set out by the Sustainable Development Goals (SDGs). As in other low income countries, scarce resources stand in the way of more equitable health access and quality in Malawi. Its health sector is highly dependent on donor contributions, and recent poor governance of government-funded healthcare saw donors withdraw funding, limiting services and resources. The 2017 National Health Plan II and accompanying Health Strategic Plan II identify the importance of improved governance and strategies to achieve more effective cooperation with stakeholders. This study explores health sector stakeholders’ perceptions of the challenges to improving governance in Malawi’s national health system within the post-2017 context of government attempts to articulate a way forward.

Methods

A qualitative study design was used. Interviews were conducted with 22 representatives of major international and faith-based non-government organisations, civil society organisations, local government and government-funded organisations, and governance bodies operating in Malawi. Open questions were asked about experiences and perceptions of the functioning of the health system and healthcare decision-making. Content relating to healthcare governance was identified in the transcripts and field notes and analysed using inductive content analysis.

Results

Stakeholders view governance challenges as a significant barrier to achieving a more effective and equitable health system. Three categories were identified: accountability (enforceability; answerability; stakeholder-led initiatives); health resource management (healthcare financing; drug supply); influence in decision-making (unequal power; stakeholder engagement).

Conclusions

Health sector stakeholders see serious political, structural, and financial challenges to improving governance in the national health system in Malawi which will impact the government’s goal of achieving UHC by 2030. Stakeholders identify the need for improved oversight, implementation, service delivery and social accountability of government-funded service providers to communities. Eighteen months after the introduction of the policy documents, they see little evidence of improved governance and have little or no confidence in the government’s ability to deliver UHC. The difficulties stakeholders perceive in relation to building equitable and effective healthcare governance in Malawi have relevance for other resource-limited countries which have also committed to the goal of UHC.
Resilin matrix distribution, variability and function in Drosophila
Authors: Lerch Steven , Zuber Renata , Gehring Nicole , Wang Yiwen , Eckel Barbara , Klass Klaus-Dieter , Lehmann Fritz-Olaf , Moussian Bernard
Published: December 2020

Background

Elasticity prevents fatigue of tissues that are extensively and repeatedly deformed. Resilin is a resilient and elastic extracellular protein matrix in joints and hinges of insects. For its mechanical properties, Resilin is extensively analysed and applied in biomaterial and biomedical sciences. However, there is only indirect evidence for Resilin distribution and function in an insect. Commonly, the presence of dityrosines that covalently link Resilin protein monomers (Pro-Resilin), which are responsible for its mechanical properties and fluoresce upon UV excitation, has been considered to reflect Resilin incidence.

Results

Using a GFP-tagged Resilin version, we directly identify Resilin in pliable regions of the Drosophila body, some of which were not described before. Interestingly, the amounts of dityrosines are not proportional to the amounts of Resilin in different areas of the fly body, arguing that the mechanical properties of Resilin matrices vary according to their need. For a functional analysis of Resilin matrices, applying the RNA interference and Crispr/Cas9 techniques, we generated flies with reduced or eliminated Resilin function, respectively. We find that these flies are flightless but capable of locomotion and viable suggesting that other proteins may partially compensate for Resilin function. Indeed, localizations of the potentially elastic protein Cpr56F and Resilin occasionally coincide.

Conclusions

Thus, Resilin-matrices are composite in the way that varying amounts of different elastic proteins and dityrosinylation define material properties. Understanding the biology of Resilin will have an impact on Resilin-based biomaterial and biomedical sciences.
A Cas9-mediated adenosine transient reporter enables enrichment of ABE-targeted cells
Authors: Brookhouser Nicholas , Nguyen Toan , Tekel Stefan J. , Standage-Beier Kylie , Wang Xiao , Brafman David A.
Published: December 2020

Background

Adenine base editors (ABE) enable single nucleotide modifications without the need for double-stranded DNA breaks (DSBs) induced by conventional CRIPSR/Cas9-based approaches. However, most approaches that employ ABEs require inefficient downstream technologies to identify desired targeted mutations within large populations of manipulated cells. In this study, we developed a fluorescence-based method, named “Cas9-mediated adenosine transient reporter for editing enrichment” (CasMAs-TREE; herein abbreviated XMAS-TREE), to facilitate the real-time identification of base-edited cell populations.

Results

To establish a fluorescent-based assay able to detect ABE activity within a cell in real time, we designed a construct encoding a mCherry fluorescent protein followed by a stop codon (TGA) preceding the coding sequence for a green fluorescent protein (GFP), allowing translational readthrough and expression of GFP after A-to-G conversion of the codon to “TGG.” At several independent loci, we demonstrate that XMAS-TREE can be used for the highly efficient purification of targeted cells. Moreover, we demonstrate that XMAS-TREE can be employed in the context of multiplexed editing strategies to simultaneous modify several genomic loci. In addition, we employ XMAS-TREE to efficiently edit human pluripotent stem cells (hPSCs), a cell type traditionally resistant to genetic modification. Furthermore, we utilize XMAS-TREE to generate clonal isogenic hPSCs at target sites not editable using well-established reporter of transfection (RoT)-based strategies.

Conclusion

We established a method to detect adenosine base-editing activity within a cell, which increases the efficiency of editing at multiple genomic locations through an enrichment of edited cells. In the future, XMAS-TREE will greatly accelerate the application of ABEs in biomedical research.
Feedback inhibition of AMT1 NH4+-transporters mediated by CIPK15 kinase
Authors: Chen Hui-Yu , Chen Yen-Ning , Wang Hung-Yu , Liu Zong-Ta , Frommer Wolf B. , Ho Cheng-Hsun
Published: December 2020

Background

Ammonium (NH4+), a key nitrogen form, becomes toxic when it accumulates to high levels. Ammonium transporters (AMTs) are the key transporters responsible for NH4+ uptake. AMT activity is under allosteric feedback control, mediated by phosphorylation of a threonine in the cytosolic C-terminus (CCT). However, the kinases responsible for the NH4+-triggered phosphorylation remain unknown.

Results

In this study, a functional screen identified protein kinase CBL-Interacting Protein Kinase15 (CIPK15) as a negative regulator of AMT1;1 activity. CIPK15 was able to interact with several AMT1 paralogs at the plasma membrane. Analysis of AmTryoshka, an NH4+ transporter activity sensor for AMT1;3 in yeast, and a two-electrode-voltage-clamp (TEVC) of AMT1;1 in Xenopus oocytes showed that CIPK15 inhibits AMT activity. CIPK15 transcript levels increased when seedlings were exposed to elevated NH4+ levels. Notably, cipk15 knockout mutants showed higher 15NH4+ uptake and accumulated higher amounts of NH4+ compared to the wild-type. Consistently, cipk15 was hypersensitive to both NH4+ and methylammonium but not nitrate (NO3−).

Conclusion

Taken together, our data indicate that feedback inhibition of AMT1 activity is mediated by the protein kinase CIPK15 via phosphorylation of residues in the CCT to reduce NH4+-accumulation.