Arterial supply of the trigeminal ganglion, a micromorphological study

Background: In this study, we explored the specific microanatomical properties of the trigeminal ganglion (TG) blood supply and its close neurovascular relationships with the surrounding vessels. Possible clinical implications have been discussed. Materials and methods: The internal carotid and maxillary arteries of 25 adult and 4 foetal heads were injected with a 10% mixture of India ink and gelatin, and their TGs subsequently underwent microdissection, observation and morphometry under a stereoscopic microscope. Results: The number of trigeminal arteries varied between 3 and 5 (mean 3.34), originating from 2 or 3 of the following sources: the inferolateral trunk (ILT) (100%), the meningohypophyseal trunk (MHT) (100%), and from the middle meningeal artery (MMA) (92%). In total, the mean diameter of the trigeminal branches was 0.222 mm. The trigeminal branch of the ILT supplied medial and middle parts of the TG, the branch of the MHT supplied the medial part of the TG, and the branch of the MMA supplied the lateral part of the TG. Additional arteries for the TG emerged from the dural vascular plexus and the vascular network of the plexal segment of the trigeminal nerve. Uniform and specific intraganglionic dense capillary network was observed for each sensory trigeminal neuron. Conclusions: The reported features of the TG vasculature could be implied in a safer setting for surgical approach to the skull base, in relation to the surrounding structures. The morphometric data on TG vasculature provide anatomical basis for better understanding the complex TG blood supply from the internal and external carotid arteries

Arterial supply of the trigeminal ganglion, a micromorphological study

Background: In this study, we explored the specific microanatomical properties of the trigeminal ganglion (TG) blood supply and its close neurovascular relationships with the surrounding vessels. Possible clinical implications have been discussed. Materials and methods: The internal carotid and maxillary arteries of 25 adult and 4 foetal heads were injected with a 10% mixture of India ink and gelatin, and their TGs subsequently underwent microdissection, observation and morphometry under a stereoscopic microscope. Results: The number of trigeminal arteries varied between 3 and 5 (mean 3.34), originating from 2 or 3 of the following sources: the inferolateral trunk (ILT) (100%), the meningohypophyseal trunk (MHT) (100%), and from the middle meningeal artery (MMA) (92%). In total, the mean diameter of the trigeminal branches was 0.222 mm. The trigeminal branch of the ILT supplied medial and middle parts of the TG, the branch of the MHT supplied the medial part of the TG, and the branch of the MMA supplied the lateral part of the TG. Additional arteries for the TG emerged from the dural vascular plexus and the vascular network of the plexal segment of the trigeminal nerve. Uniform and specific intraganglionicdense capillary network was observed for each sensory trigeminal neuron. Conclusions: The reported features of the TG vasculature could be implied in a safer setting for surgical approach to the skull base, in relation to the surrounding structures. The morphometric data on TG vasculature provide anatomical basis for better understanding the complex TG blood supply from the internal and external carotid arteries

Complex machine-learning algorithms and multivariable logistic regression on par in the prediction of insufficient clinical response to methotrexate in rheumatoid arthritis

The goals of this study were to examine whether machine-learning algorithms outper-form multivariable logistic regression in the prediction of insufficient response to methotrexate (MTX); secondly, to examine which features are essential for correct prediction; and finally, to in-vestigate whether the best performing model specifically identifies insufficient responders to MTX (combination) therapy. The prediction of insufficient response (3-month Disease Activity Score 28-Erythrocyte-sedimentation rate (DAS28-ESR) > 3.2) was assessed using logistic regression, least absolute shrinkage and selection operator (LASSO), random forest, and extreme gradient boosting (XGBoost). The baseline features of 355 rheumatoid arthritis (RA) patients from the “treatment in the Rotterdam Early Arthritis CoHort” (tREACH) and the U-Act-Early trial were combined for analyses. The model performances were compared using area under the curve (AUC) of receiver operating characteristic (ROC) curves, 95% confidence intervals (95% CI), and sensitivity and specificity. Fi-nally, the best performing model following feature selection was tested on 101 RA patients starting tocilizumab (TCZ)-monotherapy. Logistic regression (AUC = 0.77 95% CI: 0.68–0.86) performed as well as LASSO (AUC = 0.76, 95% CI: 0.67–0.85), random forest (AUC = 0.71, 95% CI: 0.61 = 0.81), and XGBoost (AUC = 0.70, 95% CI: 0.61–0.81), yet logistic regression reached the highest sensitivity (81%). The most important features were baseline DAS28 (components). For all algorithms, models with six features performed similarly to those with 16. When applied to the TCZ-monotherapy group, logistic regression’s sensitivity significantly dropped from 83% to 69% (p = 0.03). In the current dataset, logistic regression performed equally well compared to machine-learning algorithms in the prediction of insufficient response to MTX. Models could be reduced to six features, which are more conducive for clinical implementation. Interestingly, the prediction model was specific to MTX (combination) therapy response

Prediction of Methotrexate Intolerance in Juvenile Idiopathic Arthritis: A prospective, observational cohort study

Background: Methotrexate (MTX) is an effective and safe drug in the treatment of juvenile idiopathic arthritis (JIA). Despite its safety, MTX-related gastrointestinal adverse effects before and after MTX administration, termed MTX intolerance, occur frequently, leading to non-compliance and potentially premature MTX termination. The aim of this study was to construct a risk model to predict MTX intolerance. Methods: In a prospective JIA cohort, clinical variables and single nucleotide polymorphisms were determined at MTX start. The Methotrexate Intolerance Severity Score was employed to measure MTX intolerance in the first year of treatment. MTX intolerance was most prevalent at 6 or 12months after MTX start, which was defined as the outcome for the prediction model. The model was developed in 152 patients using multivariable logistic regression analysis and subsequently internally validated using bootstrapping. Results: The prediction model included the following predictors: JIA category, antinuclear antibody, parent/patient assessment of pain, Juvenile Arthritis Disease Activity Score-27, thrombocytes, alanine aminotransferase and creatinine. The model classified 77.5% of patients correctly, and 66.7% of patients after internal validation by bootstrapping. The lowest predicted risk of MTX intolerance was 18.9% and the highest predicted risk was 85.9%. The prediction model was transformed into a risk score (range 0-17). At a cut-off of 6, sensitivity was 82.0%, specificity 56.1%, positive predictive value was 58.7% and negative predictive value 80.4%. Conclusions: This clinical prediction model showed moderate predictive power to detect MTX intolerance. To develop into a clinically usable tool, it should be validated in an independent cohort and updated with new predictors. Such an easy-to-use tool could then assist clinicians in identifying patients at risk to develop MTX intolerance, and in turn to monitor them closely and intervene timely in order to prevent the development of MTX intolerance

Development and validation of a prognostic multivariable model to predict insufficient clinical response to methotrexate in rheumatoid arthritis

Objective The objective was to predict insufficient response to 3 months methotrexate (MTX) in DMARD naïve rheumatoid arthritis patients. Methods A Multivariable logistic regression model of rheumatoid arthritis patients starting MTX was developed in a derivation cohort with 285 patients starting MTX in a clinical multicentre, stratified single-blinded trial, performed in seven secondary care clinics and a tertiary care clinic. The model was validated in a validation cohort with 102 patients starting MTX at a tertiary care clinic. Outcome was insufficient response (disease activity score (DAS)28 >3.2) after 3 months of MTX treatment. Clinical characteristics, lifestyle variables, genetic and metabolic biomarkers were determined at baseline in both cohorts. These variables were dichotomized and used to construct a multivariable prediction model with backward logistic regression analysis. Results The prediction model for insufficient response in the derivation cohort, included: DAS28>5.1, Health Assessment Questionnaire>0.6, current smoking, BMI>25 kg/m2, ABCB1 rs1045642 genotype, ABCC3 rs4793665 genotype, and erythrocyte-folate<750 nmol/L. In the derivation cohort, AUC of ROC curve was 0.80 (95%CI: 0.73–0.86), and 0.80 (95%CI: 0.69–0.91) in the validation cohort. Betas of the prediction model were transformed into total risk score (range 0–8). At cutoff of 4, probability for insufficient response was 44%. Sensitivity was 71%, specificity 72%, with positive and negative predictive value of 72% and 71%. Conclusions A prognostics prediction model for insufficient response to MTX in 2 prospective RA cohorts by combining genetic, metabolic, clinical and lifestyle variables was developed and validated. This model satisfactorily identified RA patients with high risk of insufficient response to MTX

Towards accurate calculation of supercapacitor electrical variables in constant power applications using new analytical closed-form expressions

Corrigendum (available online 8 March 2022, version of record 24 March 2022 at DOI URL: https://doi.org/10.1016/j.est.2022.104367) On November the 20th 2020 the paper titled “Lambert W function based closed-form expressions of supercapacitor electrical variables in constant power applications” [which we now refer to as ‘Paper A’] was published in the journal “Energy”. On August the 2nd 2021, our paper “Towards accurate calculation of supercapacitor electrical variables inconstant power applications using new analytical closed-form expressions” was published in the Journal of Energy Storage, [which we now refer to as Paper B]. Although our paper does have a novelty in applying Special Trans Function Theory to calculate the Lambert W function, paper A owns the originality of introducing the “thorough mathematical analysis which, by using the Lambert W function, accomplishes the explicit calculation of all the electrical variables involved in the charge/discharge processes of supercapacitors operated at constant power as a function of time.” So, we acknowledge the priority of Paper A and apologize to the authors J.F. Pedrayes, M.G. Melero, J.M. Cano, J.G. Norniella, S.B. Duque, C.H. Rojas, and G. A. Orcajo for any inconvenience

Optimal PID controllers for AVR system considering limited excitation voltage limitations using hybrid equilibrium optimizer

Automatic voltage regulator (AVR) represents the basic voltage regulator loop in power systems. The central part of this loop is the regulator, which has parameters that define the speed of the voltage regulation, quality of responses, and system stability. Furthermore, it has an impact on the excitation voltage change and value, especially during transients. In this paper, unlike literature approaches, the experimental verifications of the impact of regulator parameters on the excitation voltage and current value are presented. A novel hybrid metaheuristic algorithm for obtaining regulator parameters determination of the AVR system, and a novel regulator design taking into account excitation voltage limitation are presented. The proposed algorithm combines the properties and characteristics of equilibrium optimizer and evaporation rate water cycle algorithms. The proposed algorithm is effective, fast, and accurate. Both experimental and simulation results show that the limitation of the excitation voltage increases the settling time of the generator voltage during reference change. Additionally, the simulation results show that the optimal values of PID parameters are smaller for limited excitation voltage values