bluuebunny/crossref_metadata_2025 huggingface dataset:
Decarbonizing the Residential Sector with Geothermal District Heating Networks: The Contribution of Hydrocarbon Wells
Authors: Alimonti C. , Scrocca D.
Published: June 2024

Climate change and the energy crises are forcing industrialized countries to contain CO2 emissions and use indigenous renewable energy sources. Geothermal energy has great potential, especially thermal energy, given that 48% of the final energy consumption in the EU20 countries in 2021 concerns heating and cooling systems. The studies on the potential development of district heating (DH) in Italy mainly consider using renewable sources and waste heat from industrial plants. This study highlights how the recovery and reuse of existing hydrocarbon infrastructure can contribute to the diffusion of DH projects that implement the principles of the circular economy.

The Italian geothermal potential in terms of repurposing oil wells is essential. In the 111 onshore hydrocarbon production concessions, 847 active wells have different operational statuses, including 436 productive wells and 399 potentially productive wells.

This study presents two examples of reusing hydrocarbon wells to satisfy the energy demand for heating residential buildings through locally available geothermal renewable energy sources. This study has verified the feasibility, pros, and cons of developing geothermal DH. The context is related to depleted hydrocarbon fields, where oil wells can be converted into geothermal wells.

Regarding the economic sustainability of repurposing projects, the results of this study highlight the viability and profitability of DH development. The analysis based on evaluating the payback time (PBT), the project net present value (NPV), and the internal rate of return (IRR) highlights the dependency on the heat tariff. The evaluated levelized cost of heat (LCOH) has been assumed as a key performance index. It should also be noted that LCOH for the two case studies is less than the reference tariff of 100.5 €/kWh. This result is obtained mainly from the reduced capital expenditure for drilling. However, our results show how reusing hydrocarbon wells in geothermal projects constitutes an excellent opportunity to access geothermal resources, significantly reducing the necessary investment and the mining risk and strongly improving the economics of the projects.

The solutions adopted in this study can also be optimized by considering the application of a binary system to produce electricity. District heating systems using binary plants achieve greater global energy efficiency. It is necessary to revolutionize the residential heating system through solutions such as the one described in this study, creating DH systems that use a zero-emission energy source as the primary source, such as geothermal. The promotion and diffusion of geothermal district heating networks could significantly contribute to achieving the climate objectives set by the European Union.
Automated Rate Control to Prevent Induced Fracturing and Fracture Opening Monitored with Step Rate Tests
Authors: Ambrus A. , Mugisha J. , Shchipanov A. , Aarsnes U. J. F. , Øverland A. M.
Published: June 2024

Designed and occasionally induced fracturing occur during injection and production operations in petroleum, geothermal energy, and geological CO2 storage. Although the goals and perspectives vary across industries, fracture monitoring and control remains crucial. Induced fracturing enhances petroleum and geothermal energy production, whereas its prevention is a key challenge in CO2 storage. Therefore, incorporating fracture management into automated injection control would be beneficial as enabled by the recent wide deployment of permanent well surveillance technologies.

This paper presents a new automated injection control approach based on on-the-fly interpretation of step-rate tests (SRT) commonly used in induced fracture monitoring. The paper explores single-phase isothermal water injection, which is relevant for describing water injection in hydrocarbon reservoirs (after forming the invaded area around the wells) and in saline aquifers. The injection control approach employs time-lapse pressure transient analysis (PTA-SRT) for SRT interpretation and constructing safe operating envelopes (SOE) representing the no-fracture response based on pressure derivative curves. When deviations from the SOE happen, the controller automatically reduces the injection rate to limit fracture growth, and continuously monitors the pressure response for the updated rate.

The paper demonstrates the results of testing of the injection control approach with step-rate test data from synthetic well simulations and a real case for water injection in an oil-bearing formation on the Norwegian Continental Shelf. The simulations use a proxy model for pressure-dependent fracture permeability matched against field measurements. The testing results confirmed the effectiveness of the control approach in detecting induced fractures shortly after their onset and preventing further fracture growth by applying immediate rate adjustments. Through successive rate changes and monitoring of the pressure response, the controller can optimize the injection rate while keeping the well pressure within the safe operating envelope.

The automated injection control workflow differs from existing industry practices through the integration of PTA with the safe operating envelope concept. Fracture detection with the PTA-SRT approach only requires one rate step after fracture opening, whereas standard industry workflows such as p-Q curve analysis typically requires 2-3 rate steps. In addition, the PTA-SRT approach is resistant to noise, step duration and rate stepping, making it appealing to real field SRT applications.
How Resource Classification Systems can Enable the Energy Transition
Authors: Jones A. , Ahadjie J. , Betts I. , Brinnen K. , Hartung M. , Heiberg S. , Ingvald E.
Published: June 2024

The first aim of this work is to show how resource classification systems, such as the Petroleum Resources Management System (PRMS), have provided essential information in the development of traditional energy resources. The challenges of the energy transition will present new demands on stakeholders and on their interactions. The second aim is to understand the new types of common information which the stakeholders will require and how resource classification can address this.

We begin by reviewing the success of systems such as PRMS and how the standardized/common information they provide has assisted in the development of resources. Next we consider the new demands which the energy transition and the Sustainable Development Goals place on stakeholders, and the interactions between them, and examine the types of common project information which will enable them to meet the challenges.

We show how established resource classification has provided key information for capital allocators to facilitate financing, for industry to manage and progress projects, and for governments to manage and improve resource development. Achieving a sustainable energy transition requires effective Public-Private Partnerships. These will be characterized by cooperation between governments, industry, and investors, with support from the public. Whilst integrated solutions are the goal, the focus should be on informed dialogue so as to make mutually beneficial decisions, and to develop the necessary, evolving capabilities. Such a dialogue requires common information. Classification systems can provide this through the expansion of common metrics. Common project metrics are included in the United Nations Framework Classification for Resources (UNFC), which is bridged to other classification systems, including PRMS. These include not only physical quantities of production but also measures of economic, environmental and social costs and benefits, and require consideration of variations with time and cumulative quantities.

The work shows that additional common information can be incorporated into UNFC, so it is an information framework for decisions. This will support dialogue between stakeholders to develop effective Public-Private Partnerships and meet the demands of the energy transition. It shows the potential for professional bodies (such as SPE), academia and industry to contribute to the operationalization of the expanded metrics and to merge the results between classification systems, including with PRMS. In this way, stakeholders and the UN have an opportunity to work together to help facilitate the next stage of energy provision for sustainable development.
Exploring North Sea Fractured Sandstone Properties: Artificial Intelligence, Multiscale Imaging, Pore-Fracture Network Analysis and Experimental Results
Authors: Panaitescu C. T. , Wu K. , Kartal M. E. , Tanino Y. , Starkey A. , Qin G. , Zhao L. , Cao Z. , Wu S.
Published: June 2024

This paper presents a novel integrated workflow that enhances the understanding of matrix pore-fracture flow in lower-margin reservoir engineering. The workflow, which is applied to a North Sea reservoir core sample, combines experiments, imaging, deep-learning segmentation, and pore-scale simulation techniques. Advanced Artificial Intelligence (AI) models are used to analyse images from fractured and unfractured micro-computed tomography (micro-CT) scans. This enables a comprehensive multi-scale analysis crucial for optimising production in challenging reservoirs.

The study commences with an examination of a relatively clean sandstone sample from a depleted North Sea sandstone hydrocarbon reservoir. A specially developed geomechanical-flow experimental cell induces and monitors fractures, offering critical insights. Post-fracture, in-situ imaging accurately captures fracture geometry. The analysis is further enhanced by AI-powered segmentation of image pairs, followed by a multiscale pore-network analysis, which experimentally validates the fracturing-flow processes.

This study's findings have significant implications for reservoir development. By demonstrating how multi-scale, image-derived data can enhance understanding of porous features, the study provides a valuable tool for more efficient resource extraction in marginal fields. The workflow, which includes two-dimensional (2D) and three-dimensional (3D) deep convolutional neural networks (CNNs) with tailored objective functions and a novel algorithm for large-scale domain decomposition and pore network extraction, improves core-scale fracture-pore network modelling (fracture-PNM). The fluid simulation reveals intricate flow behaviours in matrix, fracture, and combined systems, offering crucial insights for advancing subsurface geo-energy processes like hydraulic fracturing, carbon and hydrogen storage, and deep geothermal energy systems.

We introduce cutting-edge segmentation models using 2D and 3D CNNs tailored for multi-scale analysis of fractured systems. A novel 3D large-image PNM extraction and domain decomposition algorithm is proposed, enhancing the fidelity of core-scale PNM modelling. The study offers new perspectives on matrix-fracture flow mechanisms through experimentally validated modelling, enriching the current understanding of fluid dynamics in complex subsurface environments.
Analysis of the Performance of Horizontal Wells in the Long-Term CO2 Sequestration in Saline Aquifers
Authors: Moridis G. J. , Reagan M. T. , Huang T. , Blasingame T. A.
Published: June 2024

The objectives of this study are (a) to analyze the CO2 sequestration performance of horizontal wells in saline aquifers during both the injection period and the subsequent long ‘rest’ period of inactivity, (b) to investigate the potential impact of salinity on the well injectivity, (c) to evaluate the effects of key well and aquifer properties, system conditions, and injection practices, and (d) compare the sequestration performance of horizontal wells to that of vertical wells. The study accounts for all known non-isothermal multiphase flow and transport processes associated with CO2 sequestration, including the potential salt (halite) precipitation, and uses a high-resolution grid to accurately capture the possible emergence and effects of halite near the well. The study models continuous CO2 injection at realistic rates into varying-salinity aquifers until a geomechanically safe maximum pressure is reached, followed by 100 years of shut-in. The results show that (a) large-scale CO2 storage in saline aquifers using horizontal wells is technically feasible, (b) the storage potential of CO2 dissolved in the aqueous phase is substantial (both in terms of the magnitude of the dissolved concentration and its spatial extent) and increases over time during the rest period, but its benefit decreases rapidly with an increasing salinity, (c) unlike the case of vertical wells, salinity appears to have practically no effect on the well injectivity as halite precipitation is negligible even at high CO2 injection rates, requiring no mitigation measures, and (d) horizontal wells appear significantly (if not overwhelmingly) superior to vertical wells for CO2 sequestration in saline aquifers. The results of the study indicate that the overall CO2 sequestration performance of horizontal wells (a) is significantly greater for deeper saline aquifers and, surprisingly, for more permeable upper boundaries, and (b) improves modestly with a decreasing CO2 injection rate, an increasing aquifer permeability, an increasing aquifer salinity, and by positioning the horizontal well closer to the base of the aquifer, but (c) appears practically insensitive to the temperature of the injected CO2. With the exception of the cases of very high-salinity and of a more permeable upper boundary, the long-term amount of CO2 dissolved in the aqueous phase routinely exceeds 45-50% of the total CO2 amount stored in the aquifer. Additionally, with the exception of the cases of more permeable upper boundaries, the long-term losses into the overburden never exceed 1% of the injected CO2. More permeable overburdens may register losses as high as 5%, but these can be acceptable because they occur mostly in the aqueous phase, stagnate very rapidly as they ascend in the subsurface profile—where they pose a limited risk for CO2 exsolution because of lower temperatures—and are associated with much larger amounts of sequestered CO2.
Enhancing Drilling Efficiency for Reduced CO2 Emissions Through Optimization of Mud Motor Power Section
Authors: Belov D. , Du W. , Cantarelli E. , Le K.
Published: June 2024

An approach that combines data-driven analysis and modeling was used to select the optimal mud motor power section tailored to specific drilling requirements. Additionally, a novel methodology was developed for estimating the CO2 footprint of drilling operations based on operational data. Practical demonstrations verified that the strategic utilization of the optimal power section enhances drilling efficiency and mitigates the CO2 impact associated with drilling processes.

Our focus revolves around choosing the optimal power section for the drilling, considering both drilling conditions and limits. Additionally, we estimatethe CO2 footprint for the entire drilling process after the job to underscore the positive impact of selecting the right power section. To achieve these goals, we integrate an advanced physical model of the power section with the capabilities of machine learning and data science. This power section model empowers us to predict its performance and durability in advance, facilitating an optimal choice based on expected drilling conditions. The established workflow for CO2 footprint estimation utilizes surface drilling data, ensuring precise results.

To optimize the power section for drilling, we employ a modeling method coupled with a machine-learning approach. This aids in selecting a suitable power section type and determining drilling parameters based on specific requirements and equipment specifications, contributing to heightened drilling efficiency.

Leveraging digital capabilities enables strategic implementation to minimize greenhouse gas emissions. Enhancing drilling efficiency via optimal mud motor power section selection improves rate of penetration, reduces nonproductive time, and substantially cuts field failures. This cumulatively shortens total drilling time, leading to both direct and indirect CO2 emissions reductions. To gauge the carbon footprint during drilling operations, we construct a physical model. This model predicts rig power consumption by considering surface drilling parameters (as pressure, flow rate, etc.). It estimates power consumption by components like mud pumps, top drive, and draw works, using system efficiency data. Additionally, a model encompassing generator sets and diesel combustion is employed to estimate CO2 emissions.

Application of the proposed methodology to a real field example demonstrated the impact of enhanced drilling performance with optimal power sections versus conventional ones, along with effective greenhouse gas emissions reduction. The entirety of the results and conclusions highlights the substantial value of digital technologies and a smart approach in selecting drilling equipment for the energy transition.
Investigating Rock Material Impact on Low Salinity Water Flooding: Experimental Study with Chalk Samples of a Danish North Sea Oil Reservoir
Authors: Mokhtari R. , Afrough A. , Talaei A. , Feilberg K. L.
Published: June 2024

The low salinity water-flooding technique, an enhanced oil recovery (EOR) method, alters the initial crude oil/brine/rock (COBR) equilibrium, enhancing oil mobilization and potentially reducing CO2 footprint of oil production. Uncertainties arise with natural crude oils and reactive rock surfaces, such as chalk. A study on low salinity waterflooding in Danish Chalk reservoirs yielded results differing from existing literature. To further investigate these results, core flooding experiments were conducted using diverse chalk samples, including outcrop material, dry-stored reservoir core plugs, and preserved reservoir core samples. It's crucial to highlight that existing literature data predominantly relies on outcrop sample experiments, potentially limiting their relevance to reservoir conditions. Computed tomography (CT) images were captured before the experiments to assess rock integrity. Nuclear Magnetic Resonance (NMR) measurements post-experiments gauged the average wettability of the core plug. Contact angle measurements assessed wettability alteration, and ion chromatography analyzed the effluent brine samples. Coreflooding outcomes revealed that preserved core samples initially exhibited an oil-wet state, reflective of reservoir conditions, but underwent changes post-waterflooding and cleaning. This renders direct comparisons challenging, making these cores non-reusable. Conversely, dry-stored reservoir core plugs maintained consistent wetting states across multiple experiments, ensuring reusability and facilitating repeatable, comparable results. However, outcrop samples yielded fundamentally dissimilar results from reservoir samples, proving inadequate analogs. Contact angle measurements highlighted significant differences in wetting conditions and wettability alteration between outcrop and reservoir samples. This discrepancy explains why the negative impact of sulfate ions was evident only in reservoir samples. Ion chromatography results revealed calcite dissolution and concurrent magnesium precipitation during low salinity water injection. While extensively demonstrated in sandstones and some carbonates, the application of low salinity water flooding faces significant challenges in carbonate reservoirs, especially chalk formations. Moreover, the current reported findings regarding chalk samples rely on experiments conducted using outcrop samples. Consequently, there is a lack of experimental data on real reservoir material in existing literature. The conflicting outcomes compared to the literature underscore the significance of conducting experiments on actual reservoir material.
Enhancing Short-Term Production Forecast in Oil Fields: Integrating Data-Driven and Model-Based Approaches to Reduce Uncertainty
Authors: Gonçalves M. M. , Werneck R. , Castro M. , Amaral M. , Mendes P. Ribeiro , Filho L. A. Lusquino , Esmin A. , Moura R. , Morais E. , Linares O. C. , Lustosa A. , Salavati S. , Schiozer D. J. , Ferreira A. Mello , Rocha A. , Davolio A.
Published: June 2024

Reservoir simulation models are usually applied to optimize oil field production across its life cycle but face challenges in short-term forecasting. Data-driven techniques (DD) show promise for short-term predictions but lack reliability over extended periods. This study introduces a Hybrid Methodology (HyM) combining the optimal features of model-based (MB) with DD approaches to select the best simulation models to make short-term decisions, effectively reducing uncertainty in short-term production forecasts for a real field. Using DD techniques, such as Transformers, we forecast 150 days of cumulative water, oil, and gas production for each well of a real field based only on historical production. We also run production forecasts using 200 history-matched simulation models for this field. In our HyM methodology, we propose to select the best simulation models according to their fit to the DD forecasts, assuming the DD forecasts to be more representative of the short-term behavior of the real field. We compare our HyM to a conventional MB model selection based on history-matching quality, referred to as MB. The proposed approach was applied to a heavy oil field from the Campos basin (Brazil), a deep-water turbiditic reservoir. We separated five months of real history data as the ground truth to rank the selected models through the forecast error and rank similarity. With this evaluation, we measured how accurate the forecast of the chosen simulation models was and the efficacy of each approach (HyM and MB) in ranking the simulation models. Our results showed that the subset of models selected using HyM successfully outperformed those chosen by the MB approach since they are better fitted for short-term forecasts for oil, water, and gas rates. The proposed HyM method yields a 75% error reduction, considering the rank similarity, with respect to the MB approach. Consequently, we conclude that better results may be achieved by using a DD forecast as a reference to reduce the uncertainty of oil, water, and gas rates (HyM approach) instead of using the actual history (MB approach). The new methodology showed promising results by selecting models with attenuated errors for oil/water rates during the history-forecast transition, complementing the data assimilation procedure. This study presents an innovative methodology merging the strengths of machine learning (ML) and simulation models to enhance the reliability of short-term production forecasts. The proposed approach is versatile, not tied to any specific ML algorithm, and effectively minimizes uncertainties, particularly in complex fields with numerous wells.
Monitoring Injectivity for CO2 Injection in Depleted Gas Reservoirs
Authors: de Kok J.
Published: June 2024

Determining the injectivity of CO2 injection wells in depleted gas reservoirs is important, because injectivity directly impacts the operating envelope of the injector. Injectivity evolves over time due to changing fluid properties, but it can also change by processes like hydrate formation or fracturing (summarized in the skin parameter). This paper investigates how accurate injectivity monitoring is, based on coupled well-reservoir simulation and identifies threshold values for identifying injectivity loss or gain.

The relation between bottomhole pressure (BHP) and rate is a function of several reservoir properties and fluid properties, each carrying its own uncertainty. This paper quantifies the impact of these uncertainties on injectivity for CO2 injection into depleted gas reservoirs using a coupled wellbore and reservoir simulator. The analysis is performed for three scenarios with varying pipeline conditions and reservoir properties, representative for CCS projects in the Netherlands. The resulting BHP uncertainty is translated to equivalent skin values. This is the threshold value above or below which the observed bottomhole pressure data conclusively suggests injectivity gain or loss.

A good estimate of the fluid properties is important to accurately determine injectivity (changes). Although the uncertainty in density is small when using the appropriate Equation of State (like Span & Wagner for pure CO2), the deviation when using a standard Equation of State (like Peng-Robinson) is significant.

The parameter having the largest impact is reservoir pressure for high injectivity reservoirs and relative permeability for low injectivity reservoirs. The combined effect of all parameters (with the exception of reservoir pressure) is equivalent to a skin value of approximately 3 for the selected cases. Without reducing the uncertainty, it is not possible to distinguish between uncertainty in injectivity and actual wellbore damage of that magnitude.

There are ways to decrease the uncertainty in both estimated average reservoir pressure and fluid properties, like the collection of bottomhole temperature data. Shut-in pressure data will further reduce the average reservoir pressure estimate. However, the presence of skin will be difficult to assess from pressure transient data due to wellbore storage and near-wellbore cooling. Rate dependent skin can be assessed when there is rate variation. Permeability and relative permeability changes in the near-wellbore will be difficult to distinguish from wellbore damage. History matching the observed data by a coupled wellbore-reservoir simulator will further decrease the uncertainties.

Monitoring tools for CO2 injection in depleted reservoirs are yet to be developed. This paper improves the understanding of the uncertainties related to injectivity monitoring, which haven’t been quantified so far. Also, the value of a coupled wellbore and reservoir simulator is demonstrated in this paper.
Coupled Flow and Wellbore-Integrity Geomechanical Effects During Continuous and Cyclic CO2 Injection
Authors: Lee G. , Barreto Y. , Amro M. , Nassan T. , Alkan H. , Solbakken J. , Zamani N. , Burachok O.
Published: June 2024

The sealing potential of the caprock, wellbore and storage formation integrity, are critical elements to ensure CO2 containment and the success of carbon capture storage (CCS) projects. Injection of CO2 for geological storage, results in cooling and increase of pore pressure leading to mechanical deformation in the wellbore cement, reservoir and caprock, which over time can cause leakage due to potential rock failure and irreversible damage. The CO2 chemical interaction with the rock and cement may also lead to mechanical alteration. Coupling thermodynamics, and fluid flow with deviated stress conditions is important for understanding and predicting the geomechanical behaviour of the wellbore, reservoir and caprock formations. This complex interaction was studied with GEM simulator from CMG using dedicated 3D and 2D reservoir models. The study was performed in multiple steps. First, laboratory experiments on the impact of pressure-temperature cyclicity on well integrity were simulated. Results indicated validity of the modelling concept and allowed proper design and constrains of the reservoir scale model for the second step of the study. Due to computational complexity, simulation of geochemical reactions was excluded. The impact of CO2 injection on the geomechanical properties of the cement and rock was approximated by tabulated dependency of Young’s modulus and cohesion against CO2 saturation.

Overall, the results showed that the main failure type in the near-wellbore region is probably tensile, accounted by the tensile safety factor (TSF), extending laterally up to 20 m in the reservoir rock and 1 m in the caprock. The cement also experiences tensile failure up to 3 m in the vertical direction, and above that the TSF tends to increase steadily, posing no risk to wellbore integrity. Shear failure is not expected.
The Impact of Isoinertial Eccentric Strength Training on Knee Related Quality of Life and Function: An Experimental Trial in Post-acl Reconstruction Patients
Authors: Patra Ramesh Chandra , Gupta Som , Mahajan Sanjeev
Published: June 2024

Background: Patients undergoing ACL reconstruction often experience challenges such as limited range of motion caused by swelling and stiffness post-surgery, along with muscle weakness, often leads to poorer functional outcomes scores and decreased activity levels.Previous researches indicated that flywheel resistance training can enhance eccentric muscle loading and strength. However, there is limited study on isoinertial eccentric strength training with conventional rehabilitation affects knee-related quality of life and function in patients who have undergone ACL reconstruction surgery. Thus, the purpose of this study is to investigate the effect of isoinertial eccentric strength training protocol along with conventional rehabilitation protocol and conventional rehabilitation protocol only on knee related quality of life in postoperative patients who underwent ACL reconstruction surgery.Methodology:A total of 96 out of 136 screened patients aged 18-45 years, 3 weeks postop patients were included as per selection criteria and randomly divided into groups. Group A consisted of 47 patients (n=47) which were administered Isoinertial Eccentric Strength Training with Conventional Rehabilitation Protocol whereas 49 patients in group B(n=49) received conventional rehabilitation protocol only over 6 weeks. The patients were assessed for knee related quality of life using Knee injury and Osteoarthritis Outcome Score (KOOS), and knee related function using Cincinnati knee rating System (CKRS) respectively before and after the 6 weeks of interventions.Results: The study demonstrated statistically significant improvements in the score of KOOS and CKRS in both the groups A and B (p < 0.05).However, the comparison between the two groups showed no statistical significance in the difference in improvement of mean values of KOOS and CKRS (p > 0.05).Conclusion: In conclusion, this study investigated the effectiveness of Isoinertial Eccentric Strength Training with Conventional Rehabilitation Protocolcompared to Conventional Rehabilitation Protocol alone for improving knee-related quality of life and function over a six-week period. While both protocols demonstrated effectiveness, neither proved superior to the other.
Right patient to the right place: The impact of a 6-year regional trauma centre-led prehospital education program on EMS triage and patient outcomes
Authors: Choi Donghwan , Huh Yo , Kang Byung Hee , Kim Sora , Song Seoyoung , Jung Kyoungwon , Jung Hohyung
Published: June 2024

Background
In regional trauma systems, emergency medical service (EMS) providers play a crucial role by performing prehospital triage for severely injured patients and transporting them to regional trauma centres. Since 2016, a regional trauma centre has provided prehospital medical guidance to EMS providers through a trauma hotline, facilitated by trauma surgeon, to guide field triage, treatment, and transport. This study analysed the effects and clinical outcomes of a regional trauma centre-led performance improvement program that followed closed-loop principles for EMS providers.
Methods
Data from the regional trauma centre databases (2016–2021) were collected, and patients with trauma with Injury Severity Scores (ISSs) > 15 in the Gyeonggi Province who were injured, regardless of whether they visited the trauma centre directly or not, were included. After severity and baseline demographic adjustments through propensity score matching, clinical outcomes were analysed using the t-test and chi-squared test, and the results were expressed as medians with interquartile ranges (IQRs) or means with standard deviations (SDs).
Results
We included 3017 patients from the 6-year study period. Correct triage and undertriage were performed in 2528 and 489 patients, respectively. Prehospital medical guidance and feedback were provided 432 times (32.1%) in 2016 and increased to 1505 times (96.8%) in 2021 (p < 0.001). The undertriage rate decreased from 32.7% (n = 55/168) to 6.3% (n = 52/820) (p < 0.001), and the overall mortality decreased from 21.4–10% (p < 0.001). After propensity score matching, 484 correctly triaged and 484 undertriaged patients were identified for subgroup analyses. The in-hospital mortality of undertriaged and correctly triaged patients was 20% (n = 99) and 13% (n = 61) (p = 0.001), respectively. The median (IQR) for undertriaged over correctly-triaged patients regarding hospital length of stay (LOS) and ICU day ratio was 17 (8–37) vs 19 (12–36) (p = 0.024) and 0.4 (0.2-1.0) vs 0.3 (0.2–0.6) (p = 0.059), respectively.
Conclusion
Undertriage was associated with increased in-hospital mortality and hospital LOS. There was a statistically significant difference between the increase in prehospital medical guidance and the decreased undertriage rate. Specifically, in the early stages of regional trauma-system development, the trauma center-led trauma hotline feedback and case-based education programs can be effective models to facilitate prehospital patient triage and patient centralisation.
Association of circulating inflammatory proteins with Nonalcoholic Fatty Liver Diseas: a bidirectional Mendelian randomization study
Authors: Jia Ming-Jie , Chen Long , Li Hui-Lin
Published: June 2024

Objective: Investigating the causal relationship between non-alcoholic fatty liver disease (NAFLD) and inflammatory proteins and identifying and quantifying the role of serum metabolites as a potential mediator.
Methods: Using summary-level data from a genome-wide association study (GWAS), a two-sample Mendelian randomization (MR) analysis of genetically predicted Nonalcoholic Fatty Liver Disease and inflammatory proteins (14,824 participants of primarily European descent) was performed. Then we did a MR analysis of inflammatory factors with non-alcoholic steatohepatitis, cirrhosis, and hepatocellular carcinoma.
Results: The IVW method analysis showed that CCL20 (P-value=0.023, OR=0.783, 95% CI=0.644-0.968), IL-1α(P-value=0.039, OR=0.790, 95% CI=0.632-0.988) were protective factors for NAFLD, while TNFRSF9 (P-value<0.001, OR=1.353, 95% CI=1.148-1.594) was a risk factor. IL-10Rα(P-value=0.004, OR=3.274, 95% CI=1.463-7.027), NT-3 (P-value=0.040, OR=2.413, 95% CI=1.040-5.601), SLAM (P-value=0.009, OR=2.183, 95% CI=1.216-3.918) were identified as risk factors for NASH. LIFR (P-value<0.001, OR=0.704, 95% CI=0.595-0.832) was identified as a protective factor for cirrhosis. IL-1α (P-value=0.002, OR=2.274, 95% CI=1.369-3.778) was identified as a risk factor for HCC.
Conclusion:In conclusion, our study identified a causal relationship between inflammatory proteins and Nonalcoholic Fatty Liver Disease.
High Throughput Exsolution Design of CO2 Reduction Reaction Interface in a Copper/High-Entropy Oxide Tandem Electrode
Authors: Bowman William , Xuan ShengQuan , Gurusamy Tamilselvi , Wang Xin , Zheng Hui , Guo Huiming , Vahidi Hasti , Dupuy Alexander , Clarke Liela , Schoenung Julie , Ong Shyue Ping , Ren Hang
Published: June 2024

The entropy design paradigm is yielding advanced materials for many societally crucial applications. While most work focuses on single-phase materials, there are vast opportunities to integrate entropy-designed materials into novel composites. Here we develop a nanocomposite design strategy using exsolution-self-assembly to fabricate Cu nanorods in an entropy-stabilized oxide. Atomic-scale electron probes and energetic calculations elucidate how exsolution-self-assembly is tunable using knowledge of point defect interactions. We leverage this to then demonstrate a high-throughput synthesis and screening strategy to fabricate a library of Cu-ESO tandem CO2 reduction reaction (CO2RR) electrodes. Electrocatalytic mapping and localized physicochemical analyses reveal structure-property relationships between local Cu valence and CO2RR activity, identifying operating potentials and electrode surface chemistries that favor CO2RR over competitive hydrogen evolution. This high-throughput synthesis-screening approach can accelerate development of advanced electrocatalysts and nanocomposite materials for many applications given its compatibility with entropy-designed materials and physical vapor deposition at/near silicon volume manufacturing temperatures.
Development and validation of machine-learning-based survival prediction model for young patients with gastric cancer
Authors: Kang Ha Ye Jin , Ko Minsam , Ryu Kwang Sun
Published: June 2024

Background Despite the global decline in the incidence of gastric cancer, the number of young individuals diagnosed with it continues to rise. Several studies have been conducted to predict the mortality of patients with gastric cancer; however, they employ traditional methodologies and have limitations. Therefore, we propose short-, medium-, and long-term mortality prediction models for young patients with gastric cancer based on a survival machine learning model. Methods Data of 1,200 young (< 50 years) patients diagnosed with gastric cancer between 2013–2015 were obtained from the Gastric Cancer Public Staging Database. Data of 840 and 360 patients were used for training and testing, respectively. We employed the random survival forest (RSF), gradient boosting survival analysis (GBSA), and extra survival tree (EST) prediction models for 1-, 3-, and 5-year survival prediction, and the concordance index (C-index) metric to objectively assess the models. This study also examined the key determinants of mortality based on the prediction time points. Results The results indicate that the EST model (1-year mortality: 97.08 ± 0.01, 3-year mortality: 96.19 ± 0.01, 5-year mortality: 93.68 ± 1) exhibited a slightly better performance than the GBSA (1-year mortality: 96.91 ± 0.01, 3-year mortality: 94.91 ± 0.01, 5-year mortality: 93.57 ± 0.01) and RSF (1-year mortality: 96.67 ± 0.01, 3-year mortality: 95.65 ± 0.01, 5-year mortality: 92.82 ± 0.01) models. Tumour stage and size were the primary variables employed for training the models to predict mortality at different time points. The other variables exhibited varying degrees of consistency for each time point. Conclusions The findings are expected to facilitate the identification of high-risk young patients with gastric cancer who may benefit from aggressive treatment by predicting their risk of death at various time points.
A Novel Highly Invasive Cell-Related Gene Signature for Predicting the Prognosis and Treatment of Osteosarcoma
Authors: Li Zijun , Wang Mengting , Wang Yunlong , Yi Chengfeng , Liu Jun , Han Xie , Bian Erbao , Tian Dasheng
Published: June 2024

Osteosarcoma (OS) is a highly prevalent bone tumor derived from primitive mesenchymal cells that occurs mostly in adolescents and children. OS has a notable propensity for aggressive behavior and resistance to treatment. Additionally, accurately evaluating and predicting the prognosis of OS remains challenging. For this investigation, we utilized scRNA-seq data to identify seven subtypes of OS cells. Survival analysis of each OS cell subtype revealed that highly invasive OS (HIS-OS) had a poorer prognosis. Through differential expression analysis, an entire set of seven genes linked to HIS-OS was identified. Subsequently, these seven genes were employed to construct a predictive model using the LASSO approach. Based on the median risk score, the OS samples in the training set were categorized into high-risk and low-risk groups, and the high-risk group exhibited a significantly shorter survival time. The analysis of immunotherapy and anticancer treatment responsiveness indicated a negative correlation between HIS-OS-related gene signatures and immune checkpoints as well as chemotherapy sensitivity. In addition, functional analysis demonstrated high enrichment of these gene sets throughout the process of tumor invasion. Finally, SERPINE2 was identified as a therapeutically critical gene. Therefore, we subsequently selected an inhibitor, IITZ-01, that targets SERPINE2, and we performed molecular docking simulations. Furthermore, we validated the inhibitory effect of IITZ-01 on OS at the cellular level. The results suggest that HIS-OS-related genes are important for prognostic stratification and therapeutic strategies for OS.
Explainable classification of seizures and other patterns of harmful brain activity in critically ill patients
Authors: A MANIKANDAN , T SANJAY , NAGANDLA CHIRUDEEP
Published: June 2024

Accurate detection and classification of seizures from electroencephalography (EEG) data can potentially enable timely interventions and treatments for neurological diseases. Currently, EEG recordings are exclusively reviewed by human experts, namely neurologists with specialized training. While indispensable, this time-consuming workflow represents a major bottleneck. Review of EEG records is laborious, time-consuming, expensive, prone to fatigue-induced errors, and suffers from inter-rater reliability even among expert reviewers. This paper introduces a new deep neural network (DNN) with interpretable layers for the classification of seizures and other pathologic brain activities such as periodic discharges, rhythmic delta waves and miscellaneous activities. The DNN architecture uses interpretable layers that allow clinicians to evaluate the model’s decision-making pipeline and build trust in the model and support clinical decision making. The combination of deep learning and interpretability layers is novel and addresses the limitations of existing methods. We demonstrate the usefulness of the proposed approach on a publicly available EEG dataset. Our method achieves state-of-the-art performance and provides classification decisions that are interpretable, useful for clinical experts. This paper contributes to the existing body of literature on EEG-based seizure detection and addresses the gap between DNN-based methods and clinical interpretability, leading to accurate and clinically meaningful predictions.
Engineering extracellular vesicles to permeabilize the blood-brain barrier
Authors: Tomatis Francesca , Rosa Susana , Simões Susana , Barão Marta , Barth Emanuel , Marz Manja , Ferreira Lino
Published: June 2024

Background
Drug delivery to the brain is challenging due to the restrict permeability of the blood brain barrier (BBB). Recent studies indicate that BBB permeability increases over time during physiological aging likely due to factors (including extracellular vesicles (EVs)) that exist in the bloodstream. Therefore, inspiration can be taken from aging to develop new strategies for the transient opening of the BBB for drug delivery to the brain.
Results
Here, we evaluated the impact of small EVs (sEVs) enriched with microRNAs (miRNAs) overexpressed during aging, with the capacity to interfere transiently with the BBB. Initially, we investigated whether the miRNAs were overexpressed in sEVs collected from plasma of aged individuals. Next, we evaluated the opening properties of the miRNA-enriched sEVs in a static or dynamic (under flow) human in vitro BBB model. Our results showed that miR-383-3p-enriched sEVs significantly increased BBB permeability in a reversible manner by decreasing the expression of claudin 5, an important tight junction protein of brain endothelial cells (BECs) of the BBB, mediated in part by the knockdown of activating transcription factor 4 (ATF4).
Conclusions
Our findings suggest that engineered sEVs have potential as a strategy for the temporary BBB opening, making it easier for drugs to reach the brain when injected into the bloodstream.
Prevalence and associated factors of psychiatric problems in children aged 6-18 years with type 1 diabetes mellitus in Gondar, Ethiopia: a cross-sectional study
Authors: AlehegnAwoke Birhanu , Alemu Mehretie Kokeb , Genet Geta Bayu , Dubie Addisu Ginbu , Alemayehu Biruk Fanta
Published: June 2024

Background
Diabetes mellitus is one of the most common chronic illnesses in children with multiple psychosocial, economical and developmental effects. Common psychiatric disorders including depression, anxiety, psychological distress, and eating disorders are more common in diabetic patients than their counters without diabetes. The main objective of our study was to assess Prevalence and associated factors of psychiatric problems in children aged 6–18 years with type 1 diabetes mellitus in Gondar, Ethiopia.
Methods
An institution-based prospective cross-sectional study was conducted among 206 children aged 6–18 years with type-1 diabetes mellitus in the University of Gondar comprehensive and specialized hospital (UoGCSH) chronic illness clinic over three months period. Data was collected using a structured questioner by trained general practitioner. Convenient sampling technique was applied and Participants were included taken sequentially until the sample size was achieved. Both bivariate and multivariate logistic regression analyses were used for Model fitness and the strength of association was determined using an OR, p value of < 0.05 and 95% CI.
Results
The prevalence of psychiatric problems was 11.65%and those children living with only either of the parent(AOR = 6.94,95%CI :1.47–32.83), living with other relatives (AOR = 21.49, 95% CI:3.54-130.58), family size > 5 people (AOR = 0.24, 95%CI :0.06–0.97),father attend formal education (AOR = 0.16, 95%CI : 0.03–0.97), patients having good glycemic control (AOR = 0.16, 95% CI : 0.04–0.61)and patients having family history of diabetes mellitus(AOR = 5.07,95%CI:1.20-21.52) were found to have significant association with the occurrence of psychiatric problems in diabetic children.
Conclusion
The prevalence of psychiatric problems in diabetic children were significantly high and children living with single parent, paternal educational status, glycemic control, family history of diabetes, and family size were found to have a significant association with the occurrence of the psychiatric problem in diabetic children.