bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of screening parameters and machine learning models for the prediction of neonatal sepsis: A systematic review
Authors: Ezeobi Dennis Peace , Musiimenta Angella , Wasswa William , Kyoyagala Stella 
Published: January 2023

Abstract
About 2.9 million neonates die every year worldwide, and most of these deaths occur in low-resource settings. Neonatal sepsis occurs when there is a bacterial invasion in the bloodstream; the immune system begins a systemic inflammatory response syndrome (SIRS) damaging to the body and can quickly advance to severe sepsis, multi-organ failure, and finally, death. Sepsis in neonates can progress more rapidly than in adults; therefore, a timely diagnosis is critical. The standard gold test for diagnosing neonatal sepsis is blood culture, which takes at least 72 hours. Hence, identifying key predictor variables and models that work best can help reduce neonatal morbidity and mortality. The matching articles were identified by searching the PubMed, IEEE, and Cochrane bibliography databases. For the inclusion of articles, the abstract and titles were first screened based on some predetermined criteria and then, the full-text articles were screened. Thirty-one studies met the full inclusion criteria. The duration of ROM was found to be more significant than other maternal risk factors. Heart rate and heart rate variability were found to be more significant than other neonatal clinical signs. C reactive protein and I/T ratio were found to be more significant than other laboratory tests. The main limitation is the variation in the performance measures used in the studies, which made it difficult to perform a quantitative assessment. A combination of predictor variables has been shown to strengthen neonatal sepsis prediction, as shown by some of the reviewed studies. Predictive algorithms that combine multiple variables are urgently needed to improve models for early detection, prognosis, and treatment of neonatal sepsis.
A Clustering-Based Surrogate-Assisted Evolutionary Algorithm (CSMOEA) for Expensive Multi-Objective Optimization
Authors: Wang Wenxin , dong huachao , Wang Peng , Shen Jiangtao 
Published: January 2023

Abstract

In this paper, we present a novel surrogate-assisted evolutionary algorithm CSMOEA for multi-objective optimization problems (MOPs) with computationally expensive objectives. Considering most surrogate-assisted evolutionary algorithms do not make full use of the population information, which only use population information in the objective space or design space independently for tackling expensive MOPs, we propose a new strategy to comprehensive utilization of population information of objective and design space. The proposed algorithm adopts an adaptive clustering strategy to divide the current population into ‘good’ and ‘bad’ groups, and the clustering centers in the design space are obtained respectively. Then, a bi-level sampling strategy is proposed to select the best samples in the design space and objective space, where new samples are screened out according to their distance to the clustering centers and approximated objective values of radial basis functions. After the comparison with five state-of-the-art algorithms on 21 widely-used benchmark problems, CSMOEA shows high efficiency and good balance between convergence and diversity. Finally, CSMOEA is applied on the shape optimization of blend-wing-body underwater glider with 14 decision variables and two objectives, and the results show its effectiveness on the engineering problem.
Prolonged usage of fosaprepitant for prevention of delayed CINV in patients receiving highly emetogenic chemotherapy
Authors: Gao# Ai , Guan# Shasha , Sun Yinjuan , Wang Lingling , Meng Fanlu , Liu Xia , Gu Liyan , Li Guo , Zhong Diansheng , Zhang Linlin 
Published: January 2023

Abstract
Background: Even though chemotherapy-induced nausea and vomiting (CINV) can be well controlled in the acute phase, the incidence of delayedCINV remains high. In this study, we intend to investigate whether prolonged use of NK-1 receptor antagonist (RA) in addition to 5-HT3 RA and dexamethasone (DEX) was more effective in preventing delayed CINV.
Methods: This study was designed to compare the efficacy and safetyof fosaprepitant 150 mg given on days 1,3 (prolonged group) versus on day 1 (regular group) in patients receiving highly emetogenic chemotherapy (HEC). All patients also treated withpalonosetron on day 1 and DEX on days 1-3. The primary endpoint was the incidence of delayed nausea and vomiting. The second endpoint was related AEs. All the above endpoints were defined according to CTCAE 5.0.
Results: Seventy-seven patients were randomly assigned to prolonged group and seventy-nine to regular group. Prolonged group demonstrated superiority in controlling delayed CINV to regular group, with statistically significant lower incidence of nausea (6.17% vs 12.66%, P=0.0056), and slightly lower incidence of grade 1 vomiting (1.62% vs 3.80%, P=0.0953) in the delayed phase. In addition, prolonged use of fosaprepitant was safe. No significant difference was found between the two groups regarding constipation, diarrhea, hiccough, fatigue, palpitation and headache in delayed phase.
Conclusions: Prolonged use of fosaprepitant can effectively and safely prevent delayed CINV in patients receiving HEC.
Machine Learning Approach to Differentiate Excitation Failure in Synchronous Generators from Power Swing
Authors: Hemavathi R. , Geethanjali M. 
Published: January 2023

Abstract

Loss of Excitation (LOE) is the most considerable fault in Synchronous generators since it affects both the generators and power network. The traditional protection method for LOE is based on impedance trajectory of the machine with negative offset mho relay. Meanwhile the traditional method experiences malfunctions and speed dip in LOE detection. This paper presents machine learning approach to detect LOE fault as well as classification logic to discriminate LOE fault from power swing conditions due to Line fault. This paper utilizes Hotelling’s-T2 statistical method to calculate Hotelling’s-T2 based Fault Indices (HT2 -FI) for fault detection and Support Vector Machine (SVM) for classification. The time series data of electrical quantities such as Terminal voltage and Reactive Power of the generator are extracted from simulated Single Machine Infinite Bus test system and used as input data. This data is involved in calculation of HT2 –FI and in development of classification logic. The proposed method is simulated and verified for complete, partial LOE conditions and power swing conditions. Simulation outcomes depict the remarkable signs of the proposed method in LOE identification from power swing. Comparative assessment also reports that the method is capable of saving time in detecting LOE.
Intervention of pharmacist included in multidisciplinary team to reduce adverse drug event: a systematic review
Authors: ZAIJ Sarah , MAIA Kelly PEREIRA , LEGUELINEL-BLACHE Géraldine , ROUX-MARSON Clarisse , KINOWSKI Jean Marie , RICHARD Hélène 
Published: January 2023

Abstract
Background

Preventable harm in healthcare is a growing public health challenge. In addition to the economic costs of safety failures, adverse drug events (ADE) may lead to complication or even death. Multidisciplinary care team involving a pharmacist appears to be an adequate response to prevention of adverse drug event. This systematic review aims to identify and describe multidisciplinary planned team-based care involving at least one pharmacist to limit or prevent adverse drug events in the adult patients.

Methods

To determine the type of interprofessional collaboration to prevent adverse drug event in which a pharmacist was involved, we conducted a systematic review of the literature of randomized controlled trials. Two independent reviewers screened trials in three databases: Medline, Web of Science, ScienceDirect. Prospective studies of at least three different health professionals’ interventions, one of whom was a pharmacist in the last five years were included. Two reviewers performed data extraction and quality appraisal independently. We used TIDieR checklist to appraise articles quality.

Results

In total 803 citations were retrieved, 34 were analysed and 16 full-text articles were reviewed. Only 3 studies published an implementation evaluation. More than half of the interventions (62%) targeted elderly patients including 6 whom lived in nursing homes. Studies outcomes were heterogeneous, and we did not perform a statistical analysis of the impact of these interventions. Most teams are composed of a physician/pharmacist/nurse trio (94%; 100%; 88%). Half of the teams were composed of the primary care physician. Other professionals were included such as physical therapists (25%), social worker (19%), occupational therapists (12%), and community health educator (6%). Multidisciplinary medication review was the most common intervention and was generally structured in four steps: data collection and baseline assessment, appraisal report by health professionals, a multidisciplinary medication review meeting and a patient follow-up.

Conclusions

The most common multidisciplinary intervention to prevent ADE in the adult population is the multidisciplinary drug review meeting at least the physician/pharmacist/nurse trio. Interventions target mostly elderly people in nursing homes, although complex chronic patients could benefit from this type of assessment.

Trial registration

PROSPERO registration: CRD42022334685
CORO2A is a pan-cancer prognostic biomarker and correlates with immune infiltration
Authors: Xie Mingjie , Wang Peng , Zhu Donghui , Wang Xingchao , Ding Xiaoling , Chen Erlin 
Published: January 2023

Abstract
Background.Coronin 2A (CORO2A) is a member of the coronin family and reportedly functions as an oncogene in certain malignancies, although its correlation with prognosis and immune infiltration in different cancers remains unclear.
Methods.Data were collected from the University of California Santa Cruz (UCSC), Human Protein Atlas (HPA), Tumor Immune Estimation Resource (TIMER), Tumor-Immune System Interactions (TISIDB) and Gene Set Enrichment Analysis (GSEA) databases. The differential expression of CORO2A, survival, clinical parameters, tumor mutational burden (TMB), microsatellite instability (MSI), mismatch repair (MMR) genes, DNA methyltransferases (DNMTs), tumor microenvironment (TME), immune-related genes (IRGs), immune infiltration, pathways and functions were analyzed using the R language software.
Results.CORO2A was overexpressed in various malignancies, and correlated with clinical parameters, overall survival, disease-specific survival and progression-free survival in certain cancers. Furthermore, CORO2A was significantly correlated to the TMB, MSI, MMR genes, DNMTs, immune and stromal scores, IRGs and immune infiltration. GSEA further showed that CORO2A was associated with various immune-related pathways and functions in different cancer types.
Conclusion.CORO2A is a promising prognostic and immunological marker for human cancers.
Characteristics of suicidal emergency room patients before and during the COVID-19 pandemic in Japan
Authors: Otani Kyohei , Yoshikawa Ryohei , Naito Atsumi , Fukushima Haruko , Matsuishi Kunitaka 
Published: January 2023

Abstract
Background: Owing to the stress caused by the behavioral restrictions and lifestyle changes during the coronavirus disease 2019 pandemic, suicide rates have increased in Japan, especially among young people. This study identified the differences in the characteristics of patients hospitalized for suicide attempts in the Kobe City Medical Center General Hospital’s emergency room, requiring inpatient care before and during the pandemic over two years.
Methods: This study was a retrospective analysis. Data were collected from 201 patients’ medical records. Patient interviews were conducted bedside or in an interview room in the emergency ward. Data collected from the electronic medical records included sex, age, COVID-19 infection status, admission to a ward with physical complications, psychiatric liaison team intervention, presence or absence of a previous department of psychiatry visit and diagnosis, prescription history of antipsychotic, antidepressant, and anxiolytic or sleeping medication, presence of physical complications, and Charlson Comorbidity Index score to compare the severity of comorbidities. A descriptive survey was conducted to examine changes in the pattern of suicide attempts during the COVID-19 outbreak. Two-sample independent t-tests, chi-square tests, and Fisher’s exact test were used for data analysis.
Results: No significant differences were found in the number of patients hospitalized for suicide attempts, average age, or sex ratio between the pre- and post-pandemic periods. Acute drug intoxication and overmedication in patients increased significantly during the pandemic. The self-inflicted means of injury with high fatality rates were non-significantly different in the two periods. The rate of physical complications significantly increased during the pandemic, while the proportion of unemployed individuals significantly decreased.
Conclusion: Despite studies predicting an increase in suicides based on past statistics of young people and of women, no significant changes were observed. This may have been owing to the effect of suicide prevention measures and mental health measures implemented by the Japanese government after an increase in suicides and after past natural disasters as well as infectious disease pandemics such as the H1N1 influenza.
DMA2022-Topological Design of a Nanosatellite Structure with Optimal Frequency Responses Filled by Non-Uniform Lattices
Authors: Wang Cunyi , Zhang Bin , Huang Songhua , Dou Weiyuan , Xin Shengzhen , Yan Jiayong 
Published: January 2023

Abstract

Traditional structural forms are difficult to meet the lightweight requirements of subsequent spacecraft for load-bearing structures. In the aerospace industry, filling structure with lattices is a popular approach to reduce the weight of a spacecraft. However, this design strategy has deficiencies in the spatial distribution of lattice cells as well as its affection on the mechanical properties. In this study, a two-step topology optimization technique is proposed to solve the spatial distribution problem of nanosatellite. Firstly, an entire nanosatellite box composed of panels which filled with uniform lattices is sent to the vibration test to obtain the frequency data. Then, a finite element (FE) model of the nanosatellite structure which contains the same uniform lattices is built and validated with the obtained frequency data above. For the subsequent calculation of topology optimization. An equivalent model of the verified FE model is established by replacing the lattice cells in the sandwich layer with equivalent fictional elements. Subsequently, a topology optimization problem under the mass constraints is formulated for maximize the nature frequency, and a new light weighted nanosatellite which filled with non-uniform lattices is established by applying the density mapping method and the previous topology configuration result. By separating the design problem of nanosatellite into two steps, the proposed optimization design method achieves the maximum frequency design under the weight constraint. Furthermore, the frequency is also guaranteed to be around the nature frequency. The results reveal that the mass of the new structure with non-uniform lattices is reduced by 50.32% and the frequency is increased by 1.19%. An important technical importance and application value of this proposed technique is that it improves the performance and design efficiency of the load-bearing structures of a nanosatellite, and this method has significant technical significance and application value.
Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions of obesity trends
Authors: Huang Alexander , Huang Samuel Y. 
Published: January 2023

Abstract
Importance: The prevalence of obesity among United States adults has increased from 30.5% in 1999 to 41.9% in 2020. However, despite the recognition of long-term weight gain as an important public health issue, there is a paucity of studies studying the long-term weight gain and building models for long-term projection. Methods: A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES 2017–2020) was conducted in patients who completed the weight questionnaire and had accurate data for both weight at time of survey and weight ten years ago. Multistate gradient boost modeling classifiers were used to generate covariate dependent transition matrices and Markov chains were utilized for multistate modeling. Results: Of the 6,146 patients that met the inclusion criteria, 3,024 (49%) of patients were male and 3,122 (51%) of patients were female. There were 2,252 (37%) White patients, 1,257 (20%) Hispanic patients, 1,636 (37%) Black patients, and 739 (12%) Asian patients. The average BMI was 30.16 (SD = 7.15), the average weight was 83.67 kilos (SD = 22.04), and the average weight change was a 3.27 kg (SD = 14.97) increase in body weight (Fig. 1). A total of 2,411 (39%) patients lost weight, and 3,735 (61%) patients gained weight (Table 1). We observed that 87 (1%) of patients were underweight (BMI < 18.5), 2,058 (33%) were normal weight (18.5 ≤ BMI < 25), 1,376 (22%) were overweight (25 ≤ BMI < 30) and 2,625 (43%) were obese (BMI > 30). From analysis of the transitions between normal/underweight, overweight, and obese, we observed that after 10 years, of the patients who were underweight, 65% stayed underweight, 32% became normal weight, 2% became overweight, and 2% became obese. After 10 years, of the patients who were normal weight, 3% became underweight, 78% stayed normal weight, 17% became overweight, and 2% became obese. Of the patients who were overweight, 71% stayed overweight, 0% became underweight, 14% became normal weight, and 15% became obese. Of the patients who were obese, 84% stayed obese, 0% became underweight, 1% became normal weight, and 14% became overweight. Conclusions: United States adults are at risk of transitioning from normal weight to becoming overweight or obese. Covariate dependent Markov chains constructed with gradient boost modeling can effectively generate long-term predictions.
Preparation of biochar derived from waste cotton woven by low-dosage Fe(NO3)3 activation: characterization, pore development and adsorption
Authors: Xu Zhihua , Wang Yongheng , Wu Mingzhen , Chen Weifang 
Published: January 2023

Abstract
Currently, researchers are looking for efficient and sustainable methods to synthesize biochar for the adsorption of pollutants. In this study, biochar with high specific surface area, tunable pore structure, and abundant functional groups were prepared from waste cotton woven (WCW) using low-dosage Fe(NO3)3 activation at 400-900°C. The biochar obtained at 800°C possessed the excellent specific surface area of 1167.37 m2/g with a unique micro-mesoporous structure. XRD analysis showed that the Fe species changed from Fe2O3 to Fe3O4 and then Fe0 with the increase of pyrolysis temperature. TEM images further confirmed the template effect of iron oxides for mesoporous formation. The effect of Fe(NO3)3 on the pyrolysis pathway of waste cotton woven was systematically investigated by TG and XPS analyses to explore the pore development of biochar. The results indicated that Fe(NO3)3 could enhance the dehydration, decarbonylation and dehydroxylation of WCW components, thereby reducing the temperature required for WCW pyrolysis. Moreover, the synergistic effect of Fe and N species improved the development of microporous and mesoporous structure through carbon structure corrosion and reorganization, and volatile release. Additionally, satisfactory adsorption capacity for Eriochrome Black T (456.01 mg/g) of the prepared biochar was obtained at 25°C. This study demonstrated that low-dosage Fe(NO3)3 activation of waste cotton woven could be used as a facile method to prepare promising inexpensive biochar for contaminants removal.
Multi-objective Optimization with Majority Voting Ensemble of Classifiers for Prediction of HIV-1 Protease Cleavage Site
Authors: Palmal Susmita , Saha Sriparna , Tripathy Somanath 
Published: January 2023

Abstract
HIV-1 protease cleavage site prediction of an amino acid sequence of Human Immune Deficiency Virus (HIV- 1) type 1 has been the subject of intense research for decades to increase the AUC value of the prediction without placing much attention to the accuracy metric by many researchers. Knowledge of the substrate specificity of HIV-1 protease has significant application in HIV-1 protease inhibitors development and in studying novel drug targets. Motivated by this, a multi-objective optimization (MOO) based majority voting ensemble framework combining the outputs from multiple classifiers has been proposed in the current paper to increase both the prediction accuracy and AUC values simultaneously. The optimal set of classifiers that are considered for voting purposes at the time of combining the outputs is determined automatically using the search capability of MOO. Comparatively better results have been attained using various benchmark data sets with average accuracy and AUC (area under the ROC curve) values of 0.92 and 0.96, respectively.
Impact of a recognition package as an incentive to strengthen the motivation, performance, and retention of village health teams in Uganda: A study protocol for a cluster randomized controlled trial
Authors: Agarwal Smisha , Tweheyo Raymond , pandya shivani , Obuya Emmanuel , Kiyomoto Arisa , Mitra Paloma , Schleiff Meike , Nagpal Tanvi , Macis Mario , Rutebemberwa Elizeus 
Published: January 2023

Abstract
Introduction: Uganda’s community health worker (CHW), or village health team (VHT), program faces significant challenges with poor retention and insufficient financial and program investment. Adequate compensation comprising of financial and non-financial components is critical to retaining any workforce, including CHWs. This study evaluates the impact of a recognition-based non-financial incentives package on the motivation, performance, and retention of VHTs, as well as on the utilization of health services by the community. The incentives package and intervention were developed in collaboration with the District-level leadership and awards VHTs who have met predetermined performance thresholds with a certificate and a government branded jacket in a public ceremony.
Methods: A two-armed cluster randomized controlled trial (RCT), conducted at the parish-level in Uganda’s Masindi District, will evaluate the effects of the 12-month intervention. The cluster-RCT will use a mixed-methods approach, which includes a baseline/endline VHT survey to assess impact of intervention on key outcomes, with a sample of 304 VHTs per study arm; VHT performance data will be validated using monthly phone surveys tracking key performance indicators and through abstraction of VHT-submitted health facility data; VHT retention will be tracked through a VHT listing activity; and focus group discussions will be conducted with VHTs and community members to understand how intervention was received. Data collection activities will be administered in local languages. A linear regression analysis (adjusting for cluster-effect) and a difference-in-differences analysis will be utilized to assess impact of the intervention.
Discussion: This study utilized a cluster-RCT design to assess the impact of a recognition-based incentives intervention on the motivation, performance, and retention of VHTs in Uganda’s Masindi District. Utilizing a mixed-methods approach, the study will provide insights on the effectiveness and limitations of the intervention, VHT perspectives on perceived value, and critical insights on how non-financial incentives might support the strengthening of the community health workforce.
Trial Registration: This trial was retrospectively registered on ClinicalTrials.gov (Registration Number: NCT05176106) on January 4, 2022.