bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine learning methods to predict 30-day hospital readmission outcome among US adults with pneumonia: analysis of the national readmission database
Authors: Huang Yinan , Talwar Ashna , Lin Ying , Aparasu Rajender R. 
Published: November 2022

Abstract
Background
Hospital readmissions for pneumonia are a growing concern in the US, with significant consequences for costs and quality of care. This study developed the rule-based model and other machine learning (ML) models to predict 30-day readmission risk in patients with pneumonia and compared model performance.

Methods
This population-based study involved patients aged ≥ 18 years hospitalized with pneumonia from January 1, 2016, through November 30, 2016, using the Healthcare Cost and Utilization Project-National Readmission Database (HCUP-NRD). Rule-based algorithms and other ML algorithms, specifically decision trees, random forest, extreme gradient descent boosting (XGBoost), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to model all-cause readmissions 30 days post-discharge from index pneumonia hospitalization. A total of 61 clinically relevant variables were included for ML model development. Models were trained on randomly partitioned 50% of the data and evaluated using the remaining dataset. Model hyperparameters were tuned using the ten-fold cross-validation on the resampled training dataset. The area under the receiver operating curves (AUROC) and area under precision-recall curves (AUPRC) were calculated for the testing set to evaluate the model performance.

Results
Of the 372,293 patients with an index hospital hospitalization for pneumonia, 48,280 (12.97%) were readmitted within 30 days. Judged by AUROC in the testing data, rule-based model (0.6591) significantly outperformed decision tree (0.5783, p value < 0.001), random forest (0.6509, p value < 0.01) and LASSO (0.6087, p value < 0.001), but was less superior than XGBoost (0.6606, p value = 0.015). The AUPRC of the rule-based model in the testing data (0.2146) was higher than the decision tree (0.1560), random forest (0.2052), and LASSO (0.2042), but was similar to XGBoost (0.2147). The top risk-predictive rules captured by the rule-based algorithm were comorbidities, illness severity, disposition locations, payer type, age, and length of stay. These predictive risk factors were also identified by other ML models with high variable importance.

Conclusion
The performance of machine learning models for predicting readmission in pneumonia patients varied. The XGboost was better than the rule-based model based on the AUROC. However, important risk factors for predicting readmission remained consistent across ML models.
A multicriteria decision analysis (MCDA) tool to purchase implantable medical devices in Egypt
Authors: Elezbawy Baher , Fasseeh Ahmad Nader , Németh Bertalan , Gamal Mary , Eldebeiky Mariam , Refaat Remonda , Taha Amr , Rabiea Shimaa , Abdallah Marwa , Ramadan Soha , Noaman Hasnaa , Eldin Amany Bahaa , Mostafa Hossam , Nouh Sara , Zaki Asmaa , Abdelrahman Mohamed , Abaza Sherif , Kalò Zoltàn 
Published: November 2022

Abstract
Background
With the availability of several similar medical devices performing the same function, choosing one for reimbursement is not easy, especially if purchased for a large number of patients. The objective of this project was to create a multicriteria decision analysis (MCDA) tool, that captures and compares all implantable medical devices’ attributes, to provide an objective method for choosing among the available options in Egypt.

Method
We conducted a systematic review and expert interviews, to identify the relevant criteria for inclusion in the tool. Subsequently, a workshop was conducted, that involved experts in procuring and tendering medical devices. Experts chose the criteria, ranked them, assigned weights and scoring functions for each criterion, and then created the draft tool. A pilot phase followed; then, another workshop was conducted to fine-tune the tool. We readjusted the tool based on experts’ experience with the draft tool.

Results
The final tool included eight criteria, arranged according to their weightage: technical characteristics (29.4%), country of origin (19.5%), use in reference countries (14.9%), supply reliability (11.7%), previous use in tenders (9.0%), instant replacement within product variety (6.9%), pharmacovigilance (4.6%), and refund or replacement (4.0%). Each medical device was assessed on these eight criteria to achieve a final score, that was compared to the alternative devices’ scores. Price is not included in the MCDA tool, but it will be added in the financial evaluation phase.

Conclusion
Decisionmakers could use the MCDA tool, to make evidence-based and objective decisions for purchasing implantable devices, in the Egyptian public sector. Post price evaluation, the product with the best value will be chosen for reimbursement.

Highlights

We created an MCDA tool to help decision makers choose between alternative implantable medical devices in Egypt.

The MCDA tool includes eight criteria, where price is evaluated as a separate step.

“Technical characteristics” and “country of origin” criteria carried the highest weights, thus representing approximately 50% of the decision.
Optimized dose selective HDAC inhibitor tucidinostat overcomes anti-PD-L1 antibody resistance in experimental solid tumors
Authors: Zhang Pei , Du Yang , Bai Hua , Wang Zhijie , Duan Jianchun , Wang Xin , Zhong Jia , Wan Rui , Xu Jiachen , He Xiran , Wang Di , Fei Kailun , Yu Ruofei , Tian Jie , Wang Jie 
Published: November 2022

Abstract
Background
Although immune checkpoint inhibitors (ICIs) have influenced the treatment paradigm for multiple solid tumors, increasing evidence suggests that primary and adaptive resistance may limit the long-term efficacy of ICIs. New therapeutic strategies with other drug combinations are hence warranted to enhance the antitumor efficacy of ICIs. As a novel tumor suppressor, histone deacetylase (HDAC) inhibitor tucidinostat has been successfully confirmed to act against hematological malignancies. However, the underlying mechanisms of action for tucidinostat and whether it can manipulate the tumor microenvironment (TME) in solid tumors remain unclear.

Methods
Three murine tumor models (4T1, LLC, and CT26) were developed to define the significant role of different doses of tucidinostat in TME. The immunotherapeutic effect of tucidinostat combined with anti-programmed cell death ligand 1 antibody (aPD-L1) was demonstrated. Furthermore, the effect of tucidinostat on phenotypic characteristics of peripheral blood mononuclear cells (PBMCs) from lung cancer patients was investigated.

Results
With an optimized dose, tucidinostat could alter TME and promote the migration and infiltration of CD8+ T cells into tumors, partially by increasing the activity of C-C motif chemokine ligand 5 (CCL5) via NF-κB signaling. Moreover, tucidinostat significantly promoted M1 polarization of macrophages and increased the in vivo antitumor efficacy of aPD-L1. Tucidinostat also enhanced the expression of the costimulatory molecules on human monocytes, suggesting a novel and improved antigen-presenting function.

Conclusions
A combination regimen of tucidinostat and aPD-L1 may work synergistically to reduce tumor burden in patients with cancer by enhancing the immune function and provided a promising treatment strategy to overcome ICI treatment resistance.
How often do cancer researchers make their data and code available and what factors are associated with sharing?
Authors: Hamilton Daniel G. , Page Matthew J. , Finch Sue , Everitt Sarah , Fidler Fiona 
Published: November 2022

Abstract
Background
Various stakeholders are calling for increased availability of data and code from cancer research. However, it is unclear how commonly these products are shared, and what factors are associated with sharing. Our objective was to evaluate how frequently oncology researchers make data and code available and explore factors associated with sharing.

Methods
A cross-sectional analysis of a random sample of 306 cancer-related articles indexed in PubMed in 2019 which studied research subjects with a cancer diagnosis was performed. All articles were independently screened for eligibility by two authors. Outcomes of interest included the prevalence of affirmative sharing declarations and the rate with which declarations connected to data complying with key FAIR principles (e.g. posted to a recognised repository, assigned an identifier, data license outlined, non-proprietary formatting). We also investigated associations between sharing rates and several journal characteristics (e.g. sharing policies, publication models), study characteristics (e.g. cancer rarity, study design), open science practices (e.g. pre-registration, pre-printing) and subsequent citation rates between 2020 and 2021.

Results
One in five studies declared data were publicly available (59/306, 19%, 95% CI: 15–24%). However, when data availability was investigated this percentage dropped to 16% (49/306, 95% CI: 12–20%), and then to less than 1% (1/306, 95% CI: 0–2%) when data were checked for compliance with key FAIR principles. While only 4% of articles that used inferential statistics reported code to be available (10/274, 95% CI: 2–6%), the odds of reporting code to be available were 5.6 times higher for researchers who shared data. Compliance with mandatory data and code sharing policies was observed in 48% (14/29) and 0% (0/6) of articles, respectively. However, 88% of articles (45/51) included data availability statements when required. Policies that encouraged data sharing did not appear to be any more effective than not having a policy at all. The only factors associated with higher rates of data sharing were studying rare cancers and using publicly available data to complement original research.

Conclusions
Data and code sharing in oncology occurs infrequently, and at a lower rate than would be expected given the prevalence of mandatory sharing policies. There is also a large gap between those declaring data to be available, and those archiving data in a way that facilitates its reuse. We encourage journals to actively check compliance with sharing policies, and researchers consult community-accepted guidelines when archiving the products of their research.
Antibiotic-resistant status and pathogenic clonal complex of canine Streptococcus canis-associated deep pyoderma
Authors: Imanishi Ichiro , Iyori Keita , Také Akira , Asahina Ryota , Tsunoi Manami , Hirano Ryuji , Uchiyama Jumpei , Toyoda Yoichi , Sakaguchi Yoshihiko , Hayashi Shunji 
Published: November 2022

AbstractBackgroundStreptococcus caniscauses deep pyoderma in canines, which raises concerns about the risk of isolates from lesions acquiring an antibiotic-resistant phenotype. It is necessary to identify effective antibiotics and the characteristics of the pathogenic cluster forS. canis-associated deep pyoderma.ResultsThe signalment, molecular typing, and antibiotic-resistant status ofS. canisisolated from deep pyoderma lesions (27 strains) and oral cavities (26 strains) were analyzed. Older dogs tended to haveS. canis-associated deep pyoderma (15 of 27 dogs over 10 years old). Veterinarians chose quinolones for 10/16 cases (63%), even though the rate of quinolone-resistant strains ofS. canisis 38–59%. Although 70% of the strains showed resistance to three or more antibiotic classes (37/53), 94% (50/53) strains showed sensitivity for penicillins. We also identified β-lactamase activity among penicillin-resistant strains ofS. canis. Clonal complex 13 (CC13) was detected only in lesions and formed independent clusters in the phylogenetic tree. One strain of CC13 was resistant to the anti-methicillin-resistantStaphylococcus aureusdrugs, vancomycin and linezolid.ConclusionAlthough antibiotic-resistant strains ofS. canisare isolated at a high rate, they can currently be treated with β-lactamase-inhibiting penicillins. CC13 may be a pathogenic cluster with high levels of antibiotics resistance.
Comparison of machine learning models for bluetongue risk prediction: a seroprevalence study on small ruminants
Authors: Gouda Hagar F. , Hassan Fardos A. M. , El-Araby Eman E. , Moawed Sherif A. 
Published: November 2022

Abstract
Background
Bluetongue (BT) is a disease of concern to animal breeders, so the question on their minds is whether they can predict the risk of the disease before it occurs. The main objective of this study is to enhance the accuracy of BT risk prediction by relying on machine learning (ML) approaches to help in fulfilling this inquiry. Several risk factors of BT that affect the occurrence and magnitude of animal infection with the virus have been reported globally. Additionally, risk factors, such as sex, age, species, and season, unevenly affect animal health and welfare. Therefore, the seroprevalence study data of 233 apparently healthy animals (125 sheep and 108 goats) from five different provinces in Egypt were used to analyze and compare the performance of the algorithms in predicting BT risk.

Results
Logistic regression (LR), decision tree (DT), random forest (RF), and a feedforward artificial neural network (ANN) were used to develop predictive BT risk models and compare their performance to the base model (LR). Model performance was assessed by the area under the receiver operating characteristics curve (AUC), accuracy, true positive rate (TPR), false positive rate (FPR), false negative rate (FNR), precision, and F1 score. The results indicated that RF performed better than other models, with an AUC score of 81%, ANN of 79.6%, and DT of 72.85%. In terms of performance and prediction, LR showed a much lower value (AUC = 69%). Upon further observation of the results, it was discovered that age and season were the most important predictor variables reported in classification and prediction.

Conclusion
The findings of this study can be utilized to predict and control BT risk factors in sheep and goats, with better diagnostic discrimination in terms of accuracy, TPR, FNR, FPR, and precision of ML models over traditional and commonly used LR models. Our findings advocate that the implementation of ML algorithms, mainly RF, in farm decision making and prediction is a promising technique for analyzing cross-section studies, providing adequate predictive power and significant competence in identifying and ranking predictors representing potential risk factors for BT.
Motor neuron survival is associated with reduced neuroinflammation and increased autophagy after brachial plexus avulsion injury in aldose reductase-deficient mice
Authors: Zhong Ke , Huang Yu , Zilundu Prince last Mudenda , Wang Yaqiong , Zhou Yingying , Yu Guangyin , Fu Rao , Chung Sookja Kim , Tang Yamei , Cheng Xiao , Zhou Lihua 
Published: November 2022

AbstractBrachial plexus root avulsion (BPRA) is frequently caused by high-energy trauma including traffic accident and birth trauma, which will induces massive motoneurons (MNs) death as well as loss of motor and sensory function in the upper limb. The death of MNs is attributed to energy deficiency, neuroinflammation and oxidative stress at the injured ventral horn of spinal cord triggered by BPRA injury. It has been reported which aldose reductase (AR), an endogenous enzyme that catalyzes fructose synthesis, positively correlates with the poor prognosis following cerebral ischemic injury, diabetic retinopathy and diabetic peripheral neuropathy. However, the role of AR in BPRA remains unknown. Herein, we used a mouse model and found that in the spinal cord of BPRA mice, the upregulation of AR correlated significantly with (1) an inactivated SIRT1–AMPK–mTOR pathway and disrupted autophagy; (2) increased byproducts accumulation of lipid peroxidation metabolism and neuroinflammation; and (3) increased MNs death. Furthermore, our results demonstrated the role of AR in BPRA injury whereby the absence of AR (AR knockout mice, AR−/−) prevented the hyper-neuroinflammation and disrupted autophagy as well as motor neuron death caused by BPRA injury. Finally, we further demonstrate that AR inhibitor epalrestat is neuroprotective against BPRA injury by increasing autophagy level, alleviating neuroinflammation and rescuing MNs death in mice. Collectively, our data demonstrate that the AR upregulation in the spinal cord is an important factor contributing to autophagy disruption, neuroinflammation and MNs death following brachial plexus roots avulsion in mice. Our study also provides a promising therapy drug to assist re-implantation surgery for the treatment of BPRA.
Head movement kinematics are altered during balance stability exercises in individuals with vestibular schwannoma
Authors: Zobeiri Omid A. , Wang Lin , Millar Jennifer L. , Schubert Michael C. , Cullen Kathleen E. 
Published: November 2022

Abstract
Background
Balance stabilization exercises are often prescribed to facilitate compensation in individuals with vestibular schwannoma (VS). However, both the assessment and prescription of these exercises are reliant on clinical observations and expert opinion rather than on quantitative evidence. The aim of this study was to quantify head motion kinematics in individuals with vestibular loss while they performed commonly prescribed balance stability exercises.

Methods
Using inertial measurement units, head movements of individuals with vestibular schwannoma were measured before and after surgical deafferentation and compared with age-matched controls.

Results
We found that individuals with vestibular schwannoma experienced more variable head motion compared to healthy controls both pre- and postoperatively, particularly in absence of visual input, but that there was little difference between preoperative and postoperative kinematic measurements for our vestibular schwannoma group. We further found correlations between head motion kinematic measures during balance exercises, performed in the absence of visual input, and multiple clinical measurements for preoperative VS subjects. Subjects with higher head motion variability also had worse DVA scores, moved more slowly during the Timed up and Go and gait speed tests, and had lower scores on the functional gait assessment. In contrast, we did not find strong correlations between clinical measures and postoperative head kinematics for the same VS subjects.

Conclusions
Our data suggest that further development of such metrics based on the quantification of head motion has merit for the assessment and prescription of balance exercises, as demonstrated by the calculation of a “kinematic score” for identifying the most informative balance exercise (i.e., “Standing on foam eyes closed”).
Comprehensive virome analysis of the viral spectrum in paediatric patients diagnosed with Mycoplasma pneumoniae pneumonia
Authors: Guo Qiong , Li Lili , Wang Chao , Huang Yiman , Ma Fenlian , Cong Shanshan , Tan Jingjing , Yao Lihong , Chen Aijun , Zheng Lishu 
Published: November 2022

Abstract
Background
Among hospitalized children suffering from community-acquired pneumonia, Mycoplasma pneumoniae (MP) is one of the most common pathogens. MP often exists as a co-infection with bacteria or viruses, which can exacerbate the clinical symptoms. We investigated the pathogen spectrum in MP-positive and MP-negative samples from hospitalized children with respiratory tract infections in Beijing, China.

Method
This study included 1038 samples of nasopharyngeal aspirates obtained between April, 2017 and March, 2018 from hospitalized children under 6 years of age with respiratory tract infections. To explore the impact of MP infection on the composition of the pathogen spectrum, 185 nasopharyngeal aspirates (83 MP-positive/102 MP-negative) were randomly selected for next-generation sequencing and comprehensive metagenomics analysis. Real-time PCR was used to detect and verify common respiratory viruses.

Results
Of the 1038 samples, 454 (43.7%) were infected with MP. In children < 6 years of age, the MP infection rate gradually increased with age, with the highest rate of 74.2% in 5–6-year-olds. The results of metagenomics analysis revealed 11 human, animal and plant virus families, and bacteriophages, including common respiratory viruses, enteroviruses and anelloviruses. The virus family with the highest number of reads in both MP-positive and MP-negative samples was the Pneumoviridae, and the number of reads for human respiratory syncytial virus (HRSV) in MP-positive samples was higher than that in MP-negative samples. Among the 83 MP-positive samples, 47 (56.63%) were co-infected with viruses, the most common of which was influenza virus (IFV). The durations of hospitalization and fever were higher in patients with MP co-infection than MP single infection, but the difference was not statistically significant.

Conclusion
The viral family with the highest number of reads in both groups was Pneumoviridae, and the number of reads matched to HRSV in MP-positive samples was much higher than MP-negative samples. Co-infection of MP and IFV infection were the most cases.
Carrier frequency and incidence estimation of RPE65-associated inherited retinal diseases in East Asian population by population database-based analysis
Authors: Cho Eun Hye , Park Jong Eun , Lee Taeheon , Ha Kyeongsu , Ki Chang-Seok 
Published: November 2022

Abstract
Background
Inherited retinal diseases (IRDs) are clinically and genetically heterogenous disorders leading to visual impairment and blindness. Because gene therapy for RPE65-associated IRDs was recently approved, it is necessary to predict the carrier frequency and prevalence for RPE65-associated IRDs. This study aimed to analyze the carrier frequency and expected incidence of RPE65-associated IRDs in East Asians and Koreans using exome data from the Genome Aggregation Database (gnomAD) and the Korean Reference Genome Database (KRGDB).

Methods
We analyzed 9,197 exomes for East Asian populations from gnomAD comprising 1,909 Korean and 1,722 Korean genomes from KRGDB. All identified RPE65 variants were classified according to the 2015 American College of Medical Genetics and Genomics and the Association for Molecular Pathology guidelines.

Results
The total carrier frequencies of East Asians and Koreans from both gnomAD and KRGDB were 0.10% (11/10,919) and 0.06% (2/3,631), respectively. The estimated incidence of RPE65-associated IRDs was 1/3,941,308 in East Asians and 1/13,184,161 in Koreans.

Conclusion
This study identified carrier frequencies of RPE65-associated IRDs in East Asians and Koreans using gnomAD and KRGDB. We confirmed that the carrier frequency of RPE65-associated IRDs patients was low in Koreans among all East Asian populations, and the incidence was also predicted to be lower than in other East Asian populations. The variant spectrum of RPE65 gene in East Asian and Korean populations differed greatly from those of other ethnic groups.