bluuebunny/crossref_metadata_2025_split huggingface dataset:
Treatment of direct carotid-cavernous fistula with Willis covered stent with midterm follow-up
Authors: Liu Qinglin , Qi Changjing , Wang Yunyan , Su Wandong , Li Gang , Wang Donghai 
Published: September 2021

Abstract
Background
Willis covered stent is the first stent designed exclusively for intracranial vasculature, and its application in carotid-cavernous fistula is limited. The aim is to evaluate the feasibility and efficacy of this device in treating direct carotid-cavernous fistula.

Methods
Ten consecutive patients with direct carotid-cavernous fistula were treated in our institution with Willis covered stents from September 2013 to December 2015. The characteristics of these patients and the immediate and follow-up results were retrospectively reviewed.

Results
Of the 10 patients, 8 were treated for the first time, and 2 had been treated elsewhere. Willis covered stents were successfully released in 9 patients. Abnormal arteriovenous shunt disappeared in 6 cases immediately after stent deployment and endoleak occurred in 3 cases. Endoleak disappeared at 6-month angiography follow-up in one case and was sealed with coils through a pre-set microcatheter in another case. Parent artery was sacrificed as endoleak remained despite repeated balloon dilation and a second stent deployment in the third case. All patients got clinical follow-ups for at least 24 months and 7 patients received angiographic follow-up. Symptoms were relieved gradually in all cases except for slight oculomotor paralysis and visual acuity in one case, respectively. In-stent stenosis was found in 1 case, and no recurrence was observed.

Conclusions
Willis covered stent is feasible for direct carotid-cavernous fistula.
Grey-box modelling of lithium-ion batteries using neural ordinary differential equations
Authors: Brucker Jennifer , Bessler Wolfgang G. , Gasper Rainer 
Published: September 2021

AbstractGrey-box modelling combines physical and data-driven models to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling, as differential equations given by physical laws and neural networks can be combined in a single modelling framework. This simplifies the simulation and optimization and allows to consider irregularly-sampled data during training and evaluation of the model. We demonstrate this approach using two levels of model complexity; first, a simple parallel resistor-capacitor circuit; and second, an equivalent circuit model of a lithium-ion battery cell, where the change of the voltage drop over the resistor-capacitor circuit including its dependence on current and State-of-Charge is implemented as NODE. After training, both models show good agreement with analytical solutions respectively with experimental data.
Comparison of short-term electrical load forecasting methods for different building types
Authors: Groß Arne , Lenders Antonia , Schwenker Friedhelm , Braun Daniel A. , Fischer David 
Published: September 2021

AbstractThe transformation of the energy system towards volatile renewable generation, increases the need to manage decentralized flexibilities more efficiently. For this, precise forecasting of uncontrollable electrical load is key. Although there is an abundance of studies presenting innovative individual methods for load forecasting, comprehensive comparisons of popular methods are hard to come across.In this paper, eight methods for day-ahead forecasts of supermarket, school and residential electrical load on the level of individual buildings are compared. The compared algorithms came from machine learning and statistics and a median ensemble combining the individual forecasts was used.In our examination, nearly all the studied methods improved forecasting accuracy compared to the naïve seasonal benchmark approach. The forecast error could be reduced by up to 35% compared to the benchmark. From the individual methods, the neural networks achieved the best results for the school and supermarket buildings, whereas the k-nearest-neighbor regression had the lowest forecasting error for households. The median ensemble narrowly yielded a lower forecast error than all individual methods for the residential and school category and was only outperformed by a neural network for the supermarket data. However, this slight increase in performance came at the cost of a significantly increased computation time. Overall, identifying a single best method remains a challenge specific to the forecasting task.
Peak load minimization of an e-bus depot: impacts of user-set conditions in optimization algorithms
Authors: Toniato Enrico , Mehta Prakhar , Marinkovic Stevan , Tiefenbeck Verena 
Published: September 2021

AbstractThe transport sector is responsible for 25% of global CO2emissions. To reduce emissions in the EU, a shift from the currently 745,000 operating public buses to electric buses (EBs) is expected in the coming years. Large-scale deployments of EBs and the electrification of bus depots will have a considerable impact on the local electric grid, potentially creating network congestion problems and spikes in the local energy load. In this work, we implement an exact, offline, modular multi-variable mixed-integer linear optimization algorithm to minimize the daily power load profile peak and optimally plan an electric bus depot. The algorithm accepts a bus depot schedule as input, and depending on the user input on optimization conditions, accounts for varying time granularity, preemption of the charging phase, vehicle-to-grid (V2G) charging capabilities and varying fleet size. The primary objective of this work is the analysis of the impact of each of these input conditions on the resulting minimized peak load. The results show that our optimization algorithm can reduce peak load by 83% on average. Time granularity and V2G have the greatest impact on peak reduction, whereas preemption and fleet splitting have the greatest impact on the computational time but an insignificant impact on peak reduction. The results bear relevance for mobility planners to account for innovative fleet management options. Depot infrastructure costs can be minimized by optimally sizing the infrastructure needs, by relying on split-fleet management or V2G options.
Integrated planning of grids and energy conversion units in municipal multi-energy carrier systems
Authors: Hauk Christopher , Ulbig Andreas , Moser Albert 
Published: September 2021

AbstractThe ongoing energy transition requires the planning of low-emission municipal energy supply systems. These systems comprise distribution grids for electricity, gas, and heat, as well as energy conversion units such as heating systems. This paper presents a linear optimization model considering these elements in order to identify the cost-minimizing system design while achieving a given CO2 emission limit. The model is applied to an exemplary test case comprising 900 buildings. In order to increase scalability of the model to larger system sizes, the effect of reducing the spatial resolution on the optimization results is analyzed. The results show that the effect is small and that spatial aggregation is indeed a valid approach to reduce problem complexity and to allow significant speedups, reaching a factor of 200 for the given case study.
Towards reinforcement learning for vulnerability analysis in power-economic systems
Authors: Wolgast Thomas , Veith Eric MSP , Nieße Astrid 
Published: September 2021

AbstractFuture smart grids can and will be subject of systematic attacks that can result in monetary costs and reduced system stability. These attacks are not necessarily malicious, but can be economically motivated as well. Emerging flexibility markets are of interest here, because they can incite attacks if market design is flawed. The dimension and danger potential of such strategies is still unknown. Automatic analysis tools are required to systematically search for unknown strategies and their respective countermeasures. We propose deep reinforcement learning to learn attack strategies autonomously to identify underlying systemic vulnerabilities this way. As a proof-of-concept, we apply our approach to a reactive power market setting in a distribution grid. In the case study, the attacker learned to exploit the reactive power market by using controllable loads. That was done by systematically inducing constraint violations into the system and then providing countermeasures on the flexibility market to generate profit, thus finding a hitherto unknown attack strategy. As a weak-point, we identified the optimal power flow that was used for market clearing. Our general approach is applicable to detect unknown attack vectors, to analyze a specific power system regarding vulnerabilities, and to systematically evaluate potential countermeasures.
Quantifying geospatial interdependencies of ICT and power system based on open data
Authors: Volkova Anna , Terzer Jonas , de Meer Hermann 
Published: September 2021

AbstractIn order to evaluate the effectiveness of innovative services and technologies spanning over the ICT-enabled power system, realistic models are required. Although nowadays there is a wide range of power system data models, these models do not include a lifelike representation of the ICT system architecture and do not consider the level of interconnectedness of the two systems. In this paper, we propose a methodology for quantification of the geospatial relations between ICT and power system based on openly available data. We describe a graph-theoretic approach, formulate a problem of assessing geospatial relations and discuss the methods required to process publicly available data, retrieve scenarios for different regions and quantify the level of interdependence between ICT and power system.
Impact of Disease Evolution on Efficacy Outcomes From Larotrectinib in Patients With Locally Advanced or Metastatic Tropomyosin Receptor Kinase Fusion–Positive Solid Tumors
Authors: Bokemeyer Carsten , Vassal Gilles , Italiano Antoine , De La Cuesta Esther , Hiemeyer Florian , Fellous Marc , Marian Marisca 
Published: September 2021

PURPOSENeurotrophic tyrosine receptor kinase ( NTRK) gene fusions encode oncogenic, chimeric tropomyosin receptor kinase (TRK) proteins. Larotrectinib, an approved TRK inhibitor, is efficacious in locally advanced or metastatic (adv/met) TRK fusion cancer. We evaluated the time from initial diagnosis to locally advanced or metastatic disease and to initiation of larotrectinib treatment as well as larotrectinib impact on disease course.MATERIALS AND METHODSPatients were grouped by prior lines of therapy (0, 1-2, and ≥ 3) and pre-larotrectinib duration of adv/met disease (short [< 3.5 months], medium [3.5 to < 15.7 months], and long [≥ 15.7 months]). Overall response rate (ORR), duration of response (DOR), and progression-free survival were assessed.RESULTSOne hundred sixty-four patients were evaluated. The median time from initial diagnosis to development of locally adv/met stage was 2.1 months; the duration of pre-larotrectinib adv/met disease was 7.3 months (n = 153). In patients with 0, 1-2, and ≥ 3 prior lines of therapy, the median time from diagnosis to adv/met stage was 0.9, 1.2, and 9.4 months, and 1.5, 5.8, and 29.0 months from adv/met disease to larotrectinib initiation, respectively. Clinical outcomes were independent of line of therapy (ORR: 86%, 63%, and 80%, respectively; median DOR: 27.6, not reached, and 32.9 months), and similar across subgroups of short, medium, and long duration of pre-larotrectinib adv/met disease status (ORR: 88%, 65%, and 69%, respectively; median DOR: not reached, 27.6, and 32.9 months).CONCLUSIONThe short time from initial diagnosis to adv/met stage before larotrectinib suggests that NTRK gene fusion does not generally have a positive prognostic value. Patients on larotrectinib had high, sustained ORR, independent of number of prior therapies or duration of adv/met disease, suggesting that the effect of TRK inhibition in molecularly selected patients is independent of prior treatments or disease course.
Integrating 31-Gene Expression Profiling With Clinicopathologic Features to Optimize Cutaneous Melanoma Sentinel Lymph Node Metastasis Prediction
Authors: Whitman Eric D. , Koshenkov Vadim P. , Gastman Brian R. , Lewis Deri , Hsueh Eddy C. , Pak Ho , Trezona Thomas P. , Davidson Robert S. , McPhee Michael , Guenther J. Michael , Toomey Paul , Smith Franz O. , Beitsch Peter D. , Lewis James M. , Ward Andrew , Young Shawn E. , Shah Parth K. , Quick Ann P. , Martin Brian J. , Zolochevska Olga , Covington Kyle R. , Monzon Federico A. , Goldberg Matthew S. , Cook Robert W. , Fleming Martin D. , Hyams David M. , Vetto John T. 
Published: September 2021

PURPOSE National guidelines recommend sentinel lymph node biopsy (SLNB) be offered to patients with > 10% likelihood of sentinel lymph node (SLN) positivity. On the other hand, guidelines do not recommend SLNB for patients with T1a tumors without high-risk features who have < 5% likelihood of a positive SLN. However, the decision to perform SLNB is less certain for patients with higher-risk T1 melanomas in which a positive node is expected 5%-10% of the time. We hypothesized that integrating clinicopathologic features with the 31-gene expression profile (31-GEP) score using advanced artificial intelligence techniques would provide more precise SLN risk prediction. METHODS An integrated 31-GEP (i31-GEP) neural network algorithm incorporating clinicopathologic features with the continuous 31-GEP score was developed using a previously reported patient cohort (n = 1,398) and validated using an independent cohort (n = 1,674). RESULTS Compared with other covariates in the i31-GEP, the continuous 31-GEP score had the largest likelihood ratio (G2 = 91.3, P < .001) for predicting SLN positivity. The i31-GEP demonstrated high concordance between predicted and observed SLN positivity rates (linear regression slope = 0.999). The i31-GEP increased the percentage of patients with T1-T4 tumors predicted to have < 5% SLN-positive likelihood from 8.5% to 27.7% with a negative predictive value of 98%. Importantly, for patients with T1 tumors originally classified with a likelihood of SLN positivity of 5%-10%, the i31-GEP reclassified 63% of cases as having < 5% or > 10% likelihood of positive SLN, for a more precise, personalized, and clinically actionable SLN-positive likelihood estimate. CONCLUSION These data suggest the i31-GEP could reduce the number of SLNBs performed by identifying patients with likelihood under the 5% threshold for performance of SLNB and improve the yield of positive SLNBs by identifying patients more likely to have a positive SLNB.
Removal efficacy of propolis/calcium hydroxide medicaments from the root canal
Authors: Ahmed Muhammad Adeel 
Published: September 2021

Objective: To compare the removal efficacy of propolis and calcium hydroxide medicaments from the root canal using manual irrigation with sodium hypochlorite.
Methods: A randomized controlled trial was conducted at the Department of Restorative Dentistry and Endodontics, College of Dentistry, King Faisal University for two months. Thirty single-rooted upper or lower permanent anterior teeth with sound root and closed apex were selected. After cleaning and shaping of canal by protaper rotary, teeth were randomly divided into two groups. In group “A,” propolis paste was placed while calcium hydroxide was placed inside root canals in group “B,” followed by temporary restoration. After seven days, intracanal medicament was removed using 25 # K file and irrigated canal by 5 ml of 3.0% sodium hypochlorite. A final irrigation of 2-ml of 17% EDTA for three minutes followed by 1-ml of normal saline was performed. A diamond disc was used to cut the crowns of the teeth from cemento-enamel junction and divide the roots into two halves. These sectioned halves were then observed under a stereomicroscope at 7X magnification and analyzed using Adobe Photoshop. Magnetic lasso tool was used to calculate the percentage of residual medicament in the canals by comparing the pixel proportion of the medicament with the total pixel proportion of the canal. Paired t-test was used to see the difference in the number of remaining remnants between propolis and calcium hydroxide. A p-value of less than 0.5 was considered statistically significant.
Results: A statistically significant difference (p-value < 0.001) was observed in the percentage of remaining remnants between propolis (23.22%) and calcium hydroxide (38.58%) after thorough irrigation with sodium hypochlorite.
Conclusion: Propolis is superior to calcium hydroxide in terms of their removal potency from the root canal after thorough irrigation with sodium hypochlorite.
doi: https://doi.org/10.12669/pjms.37.7.4241
How to cite this:Ahmed MA. Removal efficacy of propolis/calcium hydroxide medicaments from the root canal. Pak J Med Sci. 2021;37(7):---------. doi: https://doi.org/10.12669/pjms.37.7.4241
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Role of topical beta blockers in regression of infantile capillary hemangioma
Authors: Junejo Murtaza Sameen , . Rebecca , Memon Maria Nazish , Shaikh Sajida Parveen 
Published: September 2021

Objective: To observe efficacy of Timolol maleate 0.5% by topical and surface application in infantile superficial capillary hemangioma of eyelid.
Methods: This multi-centered clinical case series was carried out at Ophthalmology Department of Bilawal Medical College and Institute of Ophthalmology, LUMHS, Jamshoro from November 2019 to May 2020. We included 14 subjects. All the patients were subjected to detailed clinical examination. Before starting the topical beta blockers, the enrolled subjects had obtained the expert opinion by pediatrician to rule out any preexisting developmental cardio vascular disease. Topical beta blockers 0.5% drops were thus started with, against the ongoing finding of superficial capillary hemangioma of eyelid.
Results: There was significant regression in size of infantile hemangioma after treating with topical timolol maleate 0.5%. We included 14 subjects in this study. Mean±SD age of patients was 4.94. Complete regression was seen in 08 subjects at the end of 12 weeks (64%) while 03 were completely cured at 08 weeks (21%) and 01 patient lost follow up with us. No significant ocular and systemic side effects were noted.
Conclusion: Topical timolol maleate 0.5% can be the first-line treatment modality for superficial capillary hemangiomas due to its better safety and efficacy.
doi: https://doi.org/10.12669/pjms.37.7.4317
How to cite this:Junejo MS, Rebecca, Memon MN, Shaikh SP. Role of topical beta blockers in regression of infantile capillary hemangioma. Pak J Med Sci. 2021;37(7):---------. doi: https://doi.org/10.12669/pjms.37.7.4317
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Prediction of twin pregnancy preeclampsia based on clinical risk factors, early pregnancy serum markers, and uterine artery pulsatility index
Authors: Lu Yan , Ding Zhongying , Li Wenwen , Mei Lina , Shen Linglong , Shan Huaying 
Published: September 2021

Objectives: To investigate whether a combination of clinical risk factors, early pregnancy serum markers, and uterine artery pulsatility index (UTPI) can be used to predict twin preeclampsia (PE).
Methods: This case control study included women with twin pregnancies who had undergone obstetrics treatments and gave birth at the Huzhou Maternity and Child Health Care Hospital from October 2018 to November 2020. Patients with PE comprised study group, and patients without PE comprised control group based on selection criteria and a 1:1 ratio. Statistical analysis was performed using clinical risk factors, early pregnancy serum markers, and UTPIs, and the area under the receiver operating curve (AUC. Sensitivity, and the specificity of different combinations of these variables were calculated to predict PE in women with twin pregnancy.
Results: Logistic regression analysis revealed four independent predictors for the onset of PE during twin pregnancies: first delivery (OR, 7.51; P=0.045), conception method (OR, 7.11; P=0.036), β-HCG level (per SD OR, 2.73; P=0.026), and UTPI (OR, 0.17; P=0.043). First-delivery and IVF pregnancy methods both lead to a 7-fold increase in the PE risk during twin pregnancies. Every one sigma (standard deviation) increase in the β-HCG level led to a 2.73-fold increase in the PE risk. Every UTPI increment by 1.0 reduces the risk of PE by 83%. The prediction efficiencies were based on an AUC of 0.837, a sensitivity of 69%, and a specificity of 92% for the clinical risk factors; an AUC of 0.800, a sensitivity of 81%, and specificity of 78% for the β-HCG level, and an AUC of 0.814, a sensitivity of 88%, and a specificity of 65% for the UTPI. AUC was 0.928, sensitivity 85%, and a specificity 88% after applying the three types of indicators together for prediction.
Conclusions: By combining early pregnancy serum markers (β-HCG), and UTPI, the predictive value for PE during twin pregnancy is improved together with its sensitivity and specificity.
doi: https://doi.org/10.12669/pjms.37.7.5041
How to cite this:Lu Y, Ding Z, Li W, Mei L, Shen L, Shan H. Prediction of twin pregnancy preeclampsia based on clinical risk factors, early pregnancy serum markers, and uterine artery pulsatility index. Pak J Med Sci. 2021;37(7):---------. doi: https://doi.org/10.12669/pjms.37.7.5041
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
An Instrumental Variable Forest Approach for Detecting Heterogeneous Treatment Effects in Observational Studies
Authors: Wang Guihua , Li Jun , Hopp Wallace J. 
Published: September 2021

This study addresses the ubiquitous challenge of using big observational data to identify heterogeneous treatment effects. This problem arises in precision medicine, targeted marketing, personalized education, and many other environments. Identifying heterogeneous treatment effects presents several analytical challenges including high dimensionality and endogeneity issues. We develop a new instrumental variable tree (IVT) approach that incorporates the instrumental variable method into a causal tree (CT) to correct for potential endogeneity biases that may exist in observational data. Our IVT approach partitions subjects into subgroups with similar treatment effects within subgroups and different treatment effects across subgroups. The estimated treatment effects are asymptotically consistent under a set of mild assumptions. Using simulated data, we show our approach has a better coverage rate and smaller mean-squared error than the conventional CT approach. We also demonstrate that an instrumental variable forest (IVF) constructed using IVTs has better accuracy and stratification than a generalized random forest. Finally, by applying the IVF approach to an empirical assessment of laparoscopic colectomy, we demonstrate the importance of accounting for endogeneity to make accurate comparisons of the heterogeneous effects of the treatment (teaching hospitals) and control (nonteaching hospitals) on different types of patients. This paper was accepted by J. George Shanthikumar, big data analytics.