bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multivariable Prediction Models for Health Care Spending Using Machine Learning: a Protocol of a Systematic Review
Authors: Huang Andrew W. , Haslberger Martin , Coulibaly Neto , Galárraga Omar , Oganisian Arman , Belbasis Lazaros , Panagiotou Orestis A. 
Published: November 2021

Abstract
Background

With rising cost pressures on health care systems, machine-learning (ML) based algorithms are increasingly used to predict health care costs. Despite their potential advantages, the successful implementation of these methods could be undermined by biases introduced in the design, conduct, or analysis of studies seeking to develop and/or validate ML models. The utility of such models may also be negatively affected by poor reporting of these studies. In this systematic review, we aim to evaluate the reporting quality, methodological characteristics, and risk of bias of ML-based prediction models for individual-level health care spending.
Methods

We will systematically search PubMed and Embase to identify studies developing, updating, or validating ML-based models to predict an individual’s health care spending for any medical condition, over any time period, and in any setting. We will exclude prediction models of aggregate-level health care spending, models used to infer causality, models using radiomics or speech parameters, models of non-clinically validated predictors (e.g. genomics), and cost-effectiveness analyses without predicting individual-level health care spending. We will extract data based on the CHARMS checklist, previously published research, and relevant recommendations. We will assess the adherence of ML-based studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) and examine the inclusion of transparency and reproducibility indicators (e.g. statements on data sharing). To assess the risk of bias, we will apply the Prediction model Risk Of Bias Assessment Tool (PROBAST). Findings will be stratified by study design, ML methods used, population characteristics, and medical field.
Discussion

Our systematic review will appraise the quality, reporting, and risk of bias of ML-based models for individualized health care cost prediction. This review will provide an overview of the available models and give insights into the strengths and limitations of using ML methods for the prediction of health spending.
Trial registration:
Not applicable.
The Association between Caffeine Intake and Testosterone: NHANES 2013-2014
Authors: Glover Frank , Caudle W. Michael , Giudice Francesco Del , Belladelli Federico , Mulloy Evan , Lawal Eniola , Eisenberg Michael L. 
Published: November 2021

Abstract
Background

Investigations into the link between caffeine consumption and testosterone levels in men have recently gained more attention, although studies are limited and the results are inconclusive.
Methods

Using data from a cross-sectional study of 372 adult men in the 2013-2014 NHANES survey cycle, we set out to characterize the association between serum testosterone levels, caffeine, and 14 caffeine metabolites.
Results

Multivariable, weighted linear regression revealed a significant inverse association between caffeine and testosterone. Multivariable linear regression revealed significant, inverse associations between 6 xanthine metabolic products of caffeine and testosterone. Inverse associations were observed between 5-methyluric acid products and testosterone, as well as between 5-acetlyamino-6-amino-3-methyluracil and testosterone. A significant, positive association was observed for 7-methyl xanthine, 3,7-dimethyluric acid, and 7-methyluric acid. Logistic regression models to characterize the association between 2 biologically active metabolites of caffeine (theobromine and theophylline) and odds of low testosterone (<300 ng/dL) were non-significant.
Conclusions

These findings suggest a potential role for caffeine’s contribution to low testosterone and biochemical androgen deficiency. Future studies are warranted to corroborate these findings, determine dose-response effects of caffeine on testosterone, and evaluate biological mechanisms underlying this association.
Trans-oral Trans-Sheath Forceps Biopsy For Patients With Severe Esophageal Obstruction Under Fluoroscopy
Authors: Jiao Dechao , Xu Kaihao , Liu Yiming , Li Zongming , Wang Yanli , Ren Jianzhuang , Han Xinwei 
Published: November 2021

Abstract
Background: To investigate the safety and effectiveness of trans-oral trans-sheath forceps biopsy (TTFB) for patients with severe esophageal obstruction under fluoroscopy.Methods: From November 2016 to November 2019, 35 patients with level Ⅲ or Ⅳ dysphagia and a Karnofsky score of less than 60 were enrolled to undergo TTFB and esophageal nutrition tube insertion or stenting simultaneously. Data on diagnostic performance, early complications, and radiation dose were collected, and Karnofsky scores before and after the procedures were compared.Results: The technical success of TTFB was 100%. The sensitivity, specificity and accuracy were 92.3% (24/26), 77.8% (7/9), and 88.6% (31/35), respectively. Complications occurred in two cases (5.7%). The mean procedure duration and irradiation dose were 23.2 min and 7.2 mSv, respectively. The Karnofsky scores significantly increased after 2-4 weeks (t = -8.9, P < 0.01).Conclusions: TTFB is a safe and effective method for patients with severe esophageal obstruction under fluoroscopy, especially in those who cannot undergo or refuse endoscopy.
Reduced Brain Connectivity in Clinical and Dimensional Autism Phenotypes Beyond Familial Confounding – A Twin Study
Authors: Neufeld Janina , Maier Simon , Revers Mirian , Reisert Marco , Kuja-Halkola Ralf , Ludger Tebartz van Elst , Bölte Sven 
Published: November 2021

Abstract
BackgroundPrevious studies on brain connectivity in clinical and dimensional autism have largely focused on selective connections and yielded inconsistent results. This study aimed to overcome these limitations. Global fiber tracking allowed a more unbiased assessment of white matter connectivity and utilizing a within-twin pair design introduced implicit control for genetic and environmental factors shared by twins and allowed conclusions regarding their impact. MethodsThe study examined the within-twin pair associations between structural brain connectivity of anatomically defined brain regions and both clinical autism spectrum diagnoses and dimensional autistic traits in 85 twin pairs (n=170; 56% monozygotic; 25 individuals with autism spectrum diagnosis). Structural connectivity was estimated using diffusion tensor imaging and linear regression models were fit, adjusted for IQ, other neurodevelopmental and psychiatric conditions and multiple testing. ResultsOverall, both clinical and dimensional autism phenotypes were associated with localized reductions in structural connectivity, despite comprehensively controlling for possible confounders, including all factors shared by twins. Twins fulfilling autism spectrum diagnostic criteria showed decreased brainstem-cuneus connectivity compared to their co-twins without the diagnosis. Further, twins with higher autistic traits showed decreased connectivity of the left hippocampus with the left fusiform and parahippocampal areas. These associations pointed into the same direction in mono- and dizygotic sub-cohorts, but were only significant in dizygotic twins.LimitationsThe recruitment approach of selecting primarily twin pairs discordant for autistic traits prevented a quantitative estimation of genetic and environmental contributions to brain correlates of clinical and dimensional autism. Further, assessing twins and excluding individuals with an IQ below 75 limited the generalizability of the findings. The statistical power allowed detecting medium-size or larger effects of dimensional autism. Finally, due the relatively small number of twin pairs discordant for a clinical autism, the results for clinical autism need to be interpreted with caution.ConclusionsReduced brainstem-cuneus connectivity might point towards alterations in low-level visual processing in clinical autism while reduced connectivity in networks crucial for visual and especially face processing seem to be more associated with dimensional aspects of autism. The results further suggest that the observed associations were potentially influenced by both genes and environment.
Synthesis of Dendritic Magnetic Graphene Oxide By Radical Polymerization As Adsorbent For Rapid Removal of Malachite Green From Aqueous Solutions
Authors: Razavikia Seyyed Ali , Faramarzi Mehdi , Parsa Seyed Aboutaleb Mousavi , Karimi Hajir 
Published: November 2021

Abstract
Disposal of Malachite green containing sewage from related industries has resulted in global concern. Thus, removing Malachite green from aqueous solutions is highly significant and necessary. during this study, magnetic graphene oxide coated with dendrimer (G (1) -MGO-chitosan) was prepared successfully and applied for removing cationic malachite green in various conditions. The properties of the synthesized adsorbent (G (1) -MGO-chitosan) were evaluated using XRD, FTIR, BET, FESEM, TEM and TGA. Furthermore, the effect of different parameters on malachite green removal was studied. The results indicated that at pH = 8, temperature of 40 °C, initial concentration 600 µg mL-1 and contact time 10 min were obtained as optimal values for removing malachite green with nanoadsorbent (G (1) -MGO-chitosan) with maximum adsorption capacity of malachite green was obtained at 38.71 µg mg-1. The high correlation coefficient (R2 = 0.9947) for the Freundlich model confirmed that the Freundlich model is appropriate for fitting laboratory data. Based on the model, Temkin heat adsorption for malachite green j mol-1 is B = 8.1447, indicating that the process of dye adsorption with Nano adsorbent is of physical type. Based on the results of fitting the kinetic models of Malachite green adsorption by Nano adsorbent, Hu and McKay’s model with higher correlation coefficient (R2 = 0.9994) is more consistent with experimental data than other models. Since no large decrease is observed in Malachite green removal in seven consecutive recovery cycles, thus Nano adsorbent has a high stability and can be used several times.
Potential causes of Acaricide Resistance in Rhipicephalus and Amblyomma Ticks (Acari: Ixodidae) in Namwala District, Zambia
Authors: Sichibalo Karen , Changula Katendi , Mumba Chisoni , Mwila Natasha , Chibesa Kennedy , Mubemba Benjamin , Nalubamba King S , Muleya Walter , Simulundu Edgar , Chitanga Simbarashe 
Published: November 2021

Abstract
Acaricide resistance in ticks poses a great threat to livestock production in many parts of the world where ticks are a problem. The objectives of this study were to screen for acaricide resistance in Rhipicephalus and Amblyomma ticks using phenotypic and molecular assays, and to assess current tick control practices used by cattle farmers in the traditional sector of Namwala District. The larval packet test was used to screen for acaricide resistance in ticks covering concentrations up to twice the discriminatory dose for amitraz, diazinon and cypermethrin. Genetic mutations associated with resistance to amitraz (A22C-T8P and T65C-L22S), and organophosphates/ pyrethroids (G1120A) were screened using polymerase chain reaction and sequencing. Information on tick control practices at the household level was collected using a semi-structured questionnaire. Intermediate resistance (21-50%) to amitraz and cypermethrin was detected in both Rhipicephalus and Amblyomma ticks, with both tick genera showing susceptibility to diazinon (<10%). None of the ticks in this study had the reported acaricide resistance-conferring molecular markers that were screened for. The findings suggest that the resistance detected in the studied tick population may be due to other mechanisms that are yet to be identified. Tick control practices observed amongst the farmers, such as incorrect use of acaricide concentrations and rotations could be fuelling the development of acaricide resistance. It is critical to comprehensively unravel the factors that could be contributing to acaricide treatment failure as this would allow for application of appropriate remedial actions for effective tick control in Zambia.
Relationship Between Diabetes Mellitus and the Prognosis of Ischemic Heart Failure
Authors: Li Huiying , Li Chunlin , Bai Yongyi , Li Man , Chen Jianqiao , Cai Yulun , Hao Benchuan , Zhu Zifan , Wang Wei , Liu Hongbin 
Published: November 2021

Abstract
Background: Ischemic heart failure (IHF) is an important type of heart failure (HF). The aim of this study is to analyze the clinical features of IHF, investigate the prognostic value of clinical risk factors, especially DM-related factors, on the IHF patients, so as to provide reference for the high-quality health management of IHF patients.Methods: A total of 430 IHF patients were divided into comorbid (IHF+ DM) group or IHF group according to whether they had DM in this cross sectional and prospective study. The patients were collected for their clinical information, and were followed up for 3.3 years to record the composite clinical endpoints including all-cause death, stroke, myocardial infarction and decompensated heart failure. The prognostic value of DM-related and other traditional risk factors with IHF were evaluated. Results: Among 430 patients with IHF, 176 had DM. Compared with the IHF group, the IHF + DM group had higher prevalence of hypertension and overweight, higher levels of FPG and HbA1c, higher proportion of patients in NYHA III – IV grade, larger left ventricular end diastolic diameter (LVEDD) in male group, higher Gensini score and shorter distance of 6-minute walk test (6-MWT). After a median follow-up of 3.3 years, a total of 156 patients (36.3%) had experienced clinical endpoint events. Kaplan Meier analysis showed that the IHF patients with DM history, and higher HbA1c or FPG level would significantly reduce the cumulative survival rates of composite clinical endpoints, as well as the cumulative survival rates of all-cause death and cardiovascular death (all P < 0.05). Cox regression analysis indicated that DM history and higher PFG or HbA1c levels were independently associated with the all-cause death, cardiovascular death, and composite endpoints (all P < 0.05). The incorporation of diabetes related factors (diabetes history, FPG and HbA1c) significantly increased the area under the receiver operating characteristic (ROC) curve of the risk model established by the traditional risk factors (0.803 vs. 0.775, P= 0.039). In addition to DM history, male sex, uric acid ≥ 400 μmol / L, creatinine ≥ 100 μmol / L, Gensini score > 80 and ACEF score were all independent risk factors for the occurrence of composite clinical endpoints in IHF patients.Conclusion: There existed a high incidence rate of DM in the IHF population. The diabetes related factors (diabetes history, FPG, HbA1c) significantly increased the predictive value of the risk model made by the basic risk factors in predicting the IHF outcomes. DM history, male, uric acid ≥ 400 μmol / L, creatinine ≥ 100 μmol / L, Gensini score > 80 and ACEF score were independent risk factors for the occurrence of composite clinical endpoints in IHF patients.
Development and Psychometrics of a Theory-Based Physical Activity Assessment Questionnaire for the Pregnant Women (PA2Q-PW)
Authors: Kianfard Leila , Niknami Shamsaddin , Shokravi Farkhonde Amin , Rakhshanderou Sakineh 
Published: November 2021

Abstract
Backgrounds & Aims: There is evidence that physical activity plays an essential role in preventing pregnancy-related diseases. This study aimed to develop and design a theory-based physical activity assessment questionnaire for pregnant women (PA2Q-PW). Methods: The PEN-3 cultural model provided the theoretical foundations for the four subscales of our designed instrument; knowledge, attitude, nurture, and enabling. The primary questionnaire was designed with 41 items, and the questionnaire was modified according to the experts' reviews. At last, a group of 400 pregnant women participated in a study to calculate the psychometrics of the tool. Results: The highest score was seen in the knowledge subscale of PA2Q-PW, and the value of Cronbach's alpha for the subscales was from 0.89 to 0.97, and the value of ICC was from 0.76 to 0.89. The correlation (CVI & CVR indices) between the experts was satisfactory in all items of PA2Q-PW. The minimum loading factor for varimax rotation in the knowledge subscale was 0.41; the attitude was 0.56, nurtures were 0.38, and enabling was 0.27. Conclusion: PA2Q-PW is the first standard tool based on the PEN-3 cultural model that researchers can utilize to gather data and conduct the desired education interventions to change physical activity behavior in pregnant women during pregnancy.
Investigation of satellite precipitation product driven rainfall-runoff model using deep learning approaches in two different catchments of India
Authors: Yeditha Pavan Kumar , Rathinasamy Maheswaran , Neelamsetty Sai Sumanth , Bhattacharya Biswa , Agarwal Ankit 
Published: November 2021

AbstractRainfall–runoff models are valuable tools for flood forecasting, management of water resources, and drought warning. With the advancement in space technology, a plethora of satellite precipitation products (SPPs) are available publicly. However, the application of the satellite data for the data-driven rainfall–runoff model is emerging and requires careful investigation. In this work, two satellite rainfall data sets, namely Global Precipitation Measurement-Integrated Multi-Satellite Retrieval Product V6 (GPM-IMERG) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS), are evaluated for the development of rainfall–runoff models and the prediction of 1-day ahead streamflow. The accuracy of the data from the SPPs is compared to the India Meteorological Department (IMD)-gridded precipitation data set. Detection metrics showed that for light rainfall (1–10 mm), the probability of detection (POD) value ranges between 0.67 and 0.75 and with an increasing rainfall range, i.e., medium and heavy rainfall (10–50 mm and &gt;50 mm), the POD values ranged from 0.24 to 0.45. These results indicate that the satellite precipitation performs satisfactorily with reference to the IMD-gridded data set. Using the daily precipitation data of nearly two decades (2000–2018) over two river basins in India's eastern part, artificial neural network, extreme learning machine (ELM), and long short-time memory (LSTM) models are developed for rainfall–runoff modelling. One-day ahead runoff prediction using the developed rainfall–runoff modelling confirmed that both the SPPs are sufficient to drive the rainfall–runoff models with a reasonable accuracy estimated using the Nash–Sutcliffe Efficiency coefficient, correlation coefficient, and the root-mean-squared error. In particular, the 1-day streamflow forecasts for the Vamsadhara river basin (VRB) using LSTM with GPM-IMERG inputs resulted in Nash-Sutcliffe Efficiency Coefficient (NSC) values of 0.68 and 0.67, while ELM models for Mahanadhi river basin (MRB) with the same input resulted in NSC values of 0.86 and 0.87, respectively, during training and validation stages. At the same time, the LSTM model with CHIRPS inputs for the VRB resulted in NSC values of 0.68 and 0.65, and the ELM model with CHIRPS inputs for the MRB resulted in NSC values of 0.89 and 0.88, respectively, in training and validation stages. These results indicated that both the SPPs could reliably be used with LSTM and ELM models for rainfall–runoff modelling and streamflow prediction. This paper highlights that deep learning models, such as ELM and LSTM, with the GPM-IMERG products can lead to a new horizon to provide flood forecasting in flood-prone catchments.
Deep learning model for daily rainfall prediction: case study of Jimma, Ethiopia
Authors: Endalie Demeke , Haile Getamesay , Taye Wondmagegn 
Published: November 2021

Abstract
Rainfall prediction is a critical task because many people rely on it, particularly in the agricultural sector. Rainfall forecasting is difficult due to the ever-changing nature of weather conditions. In this study, we carry out a rainfall predictive model for Jimma, a region located in southwestern Oromia, Ethiopia. We propose a Long Short-Term Memory (LSTM)-based prediction model capable of forecasting Jimma's daily rainfall. Experiments were conducted to evaluate the proposed models using various metrics such as Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Nash–Sutcliffe model efficiency (NSE), and R2, and the results were 0.01, 0.4786, 0.81 and 0.9972, respectively. We also compared the proposed model with existing machine-learning regressions like Multilayer Perceptron (MLP), k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT). The RMSE of MLP was the lowest of the four existing learning models i.e., 0.03. The proposed LSTM model outperforms the existing models, with an RMSE of 0.01. The experimental results show that the proposed model has a lower RMSE and a higher R2.
How to Optimize the Effectiveness and Safety of Parkinson’s Disease
Therapy? – A Systematic Review of Drugs Interactions with Food and Dietary
Supplements
Authors: Agnieszka Wiesner , Paweł Paśko , Małgorzata Kujawska 
Published: November 2021

Background:
Despite increasing worldwide incidence of Parkinson’s disease, the therapy
is still suboptimal due to the diversified clinical manifestations, lack of sufficient treatment, the poor
adherence in advanced patients, and varied response. Proper intake of medications regarding food
and managing drug-food interactions may optimize Parkinson’s disease treatment.

Objectives:
We investigated potential effects that food, beverages, and dietary supplements may
have on the pharmacokinetics and pharmacodynamics of drugs used by parkinsonian patients; identified
the most probable interactions; and shaped recommendations for the optimal intake of drugs
regarding food.

Methods:
We performed a systematic review in adherence to PRISMA guidelines, and included a
total of 81 studies in the qualitative synthesis.

Results and Conclusions:
We found evidence for levodopa positive interaction with coffee, fiber and
vitamin C, as well as for the potential beneficial impact of low-fat and protein redistribution diet.
Contrastingly, high-protein diet and ferrous sulfate supplements can negatively affect levodopa
pharmacokinetics and effectiveness. For other drugs, the data of food impact are scarce. Based on
the available limited evidence, all dopamine agonists (bromocriptine, cabergoline, ropinirole), tolcapone,
rasagiline, selegiline in tablets, safinamide, amantadine and pimavanserin can be taken with
or without a meal. Opicapone and orally disintegrating selegiline tablets should be administered on
an empty stomach. Of monoamine oxidase B inhibitors, safinamide is the least susceptible for interaction
with the tyramine-rich food, whereas selegiline and rasagiline may lose selectivity to monoamine
oxidase B when administered in supratherapeutic doses. The level of presented evidence is
low due to the poor studies design, their insufficient actuality, and missing data.