bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Progressive Architectural Distortion in Idiopathic Pulmonary Fibrosis Using Deformable Registration of Sequential CT Images
Authors: Yasuda Naofumi , Iwasawa Tae , Baba Tomohisa , Misumi Toshihiro , Cheng Shihyao , Kato Shingo , Utsunomiya Daisuke , Ogura Takashi
Published: July 2024

Background: Monitoring the progression of idiopathic pulmonary fibrosis (IPF) using CT primarily focuses on assessing the extent of fibrotic lesions, without considering the distortion of lung architecture. Objectives: To evaluate three-dimensional average displacement (3D-AD) quantification of lung structures using deformable registration of serial CT images as a parameter of local lung architectural distortion and predictor of IPF prognosis. Materials and Methods: Patients with IPF evaluated between January 2016 and March 2017 who had undergone CT at least twice were retrospectively included (n = 114). The 3D-AD was obtained by deformable registration of baseline and follow-up CT images. A computer-aided quantification software measured the fibrotic lesion volume. Cox regression analysis evaluated these variables to predict mortality. Results: The 3D-AD and the fibrotic lesion volume change were significantly larger in the subpleural lung region (5.2 mm (interquartile range (IQR): 3.6–7.1 mm) and 0.70% (IQR: 0.22–1.60%), respectively) than those in the inner region (4.7 mm (IQR: 3.0–6.4 mm) and 0.21% (IQR: 0.004–1.12%), respectively). Multivariable logistic analysis revealed that subpleural region 3D-AD and fibrotic lesion volume change were independent predictors of mortality (hazard ratio: 1.12 and 1.23; 95% confidence interval: 1.02–1.22 and 1.10–1.38; p = 0.01 and p < 0.001, respectively). Conclusions: The 3D-AD quantification derived from deformable registration of serial CT images serves as a marker of lung architectural distortion and a prognostic predictor in patients with IPF.
LiverColor: An Artificial Intelligence Platform for Liver Graft Assessment
Authors: Piella Gemma , Farré Nicolau , Esono Daniel , Cordobés Miguel Ángel , Vázquez-Corral Javier , Bilbao Itxarone , Gómez-Gavara Concepción
Published: July 2024

Hepatic steatosis, characterized by excess fat in the liver, is the main reason for discarding livers intended for transplantation due to its association with increased postoperative complications. The current gold standard for evaluating hepatic steatosis is liver biopsy, which, despite its accuracy, is invasive, costly, slow, and not always feasible during liver procurement. Consequently, surgeons often rely on subjective visual assessments based on the liver’s colour and texture, which are prone to errors and heavily depend on the surgeon’s experience. The aim of this study was to develop and validate a simple, rapid, and accurate method for detecting steatosis in donor livers to improve the decision-making process during liver procurement. We developed LiverColor, a co-designed software platform that integrates image analysis and machine learning to classify a liver graft into valid or non-valid according to its steatosis level. We utilized an in-house dataset of 192 cases to develop and validate the classification models. Colour and texture features were extracted from liver photographs, and graft classification was performed using supervised machine learning techniques (random forests and support vector machine). The performance of the algorithm was compared against biopsy results and surgeons’ classifications. Usability was also assessed in simulated and real clinical settings using the Mobile Health App Usability Questionnaire. The predictive models demonstrated an area under the receiver operating characteristic curve of 0.82, with an accuracy of 85%, significantly surpassing the accuracy of visual inspections by surgeons. Experienced surgeons rated the platform positively, appreciating not only the hepatic steatosis assessment but also the dashboarding functionalities for summarising and displaying procurement-related data. The results indicate that image analysis coupled with machine learning can effectively and safely identify valid livers during procurement. LiverColor has the potential to enhance the accuracy and efficiency of liver assessments, reducing the reliance on subjective visual inspections and improving transplantation outcomes.
Deep Learning Based Automatic Left Ventricle Segmentation from the Transgastric Short-Axis View on Transesophageal Echocardiography: A Feasibility Study
Authors: Tian Yuan , Qin Wenting , Zhao Zihang , Wang Chunrong , Tian Yajie , Zhang Yuelun , He Kai , Zhang Yuguan , Shen Le , Zhou Zhuhuang , Yu Chunhua
Published: July 2024

Segmenting the left ventricle from the transgastric short-axis views (TSVs) on transesophageal echocardiography (TEE) is the cornerstone for cardiovascular assessment during perioperative management. Even for seasoned professionals, the procedure remains time-consuming and experience-dependent. The current study aims to evaluate the feasibility of deep learning for automatic segmentation by assessing the validity of different U-Net algorithms. A large dataset containing 1388 TSV acquisitions was retrospectively collected from 451 patients (32% women, average age 53.42 years) who underwent perioperative TEE between July 2015 and October 2023. With image preprocessing and data augmentation, 3336 images were included in the training set, 138 images in the validation set, and 138 images in the test set. Four deep neural networks (U-Net, Attention U-Net, UNet++, and UNeXt) were employed for left ventricle segmentation and compared in terms of the Jaccard similarity coefficient (JSC) and Dice similarity coefficient (DSC) on the test set, as well as the number of network parameters, training time, and inference time. The Attention U-Net and U-Net++ models performed better in terms of JSC (the highest average JSC: 86.02%) and DSC (the highest average DSC: 92.00%), the UNeXt model had the smallest network parameters (1.47 million), and the U-Net model had the least training time (6428.65 s) and inference time for a single image (101.75 ms). The Attention U-Net model outperformed the other three models in challenging cases, including the impaired boundary of left ventricle and the artifact of the papillary muscle. This pioneering exploration demonstrated the feasibility of deep learning for the segmentation of the left ventricle from TSV on TEE, which will facilitate an accelerated and objective alternative of cardiovascular assessment for perioperative management.
The Entropy and Energy for Non-Mechanical Work at the Bose–Einstein Transition of a Harmonically Trapped Gas Using an Empirical Global-Variable Method
Authors: Miotti Marcos , Martins Edmur Braga , Hemmerling Michał , Bagnato Vanderlei Salvador
Published: July 2024

Quantum thermal engines have received much attention in recent years due to their potential applications. For a candidate group, harmonically trapped gases under Bose–Einstein condensation (BEC), we see little investigation on the energy transference around that transition. Therefore, we present an empirical study with rubidium-87 gas samples in a magnetic harmonic trap. We developed an empirical equation of state model to fit to our experimental dataset, expressing the pressure parameter in terms of temperature, and six technical coefficients, functions of the volume parameter and the number of atoms. By using standard thermodynamic relations, we determine the system’s entropy, shown to be constant at the BEC transition, as expected. Being isentropic makes the BEC transition an energy source for non-mechanical work. Hence, we observed that the enthalpy at the BEC transition, at fixed values of the volume parameter, grows fairly linearly with the number of atoms. We fitted a linear function to that data, finding the specific enthalpy of the BEC transformation and the intrinsic enthalpic loss for BEC. We deem this study to be a step closer to practical quantum-based engines.
The Relationship between Entrepreneurship and Sustainable Development in Saudi Arabia: A Comprehensive Perspective
Authors: Elmonshid Lena Bedawi Elfadli , Sayed Omer Ahmed
Published: July 2024

This study examines the relationship between entrepreneurship and sustainable development in Saudi Arabia from 2006 to 2022, focusing on the economic, social, and environmental dimensions. Using the autoregressive distributed lag (ARDL) model, the research investigates both short-run and long-run dynamics to understand how entrepreneurial activities influence sustainable development within the framework of Saudi Arabia’s Vision 2030 initiative. The findings indicate that entrepreneurship significantly contributes to economic growth through job creation and innovation, thereby aiding in the diversification of the economy away from oil dependency. Socially, entrepreneurial initiatives have a positive impact on gender equality and social inclusion by empowering women and integrating youth into the labor market. Environmentally, entrepreneurship helps reduce carbon emissions and promotes sustainable business practices. Despite these positive outcomes, challenges such as regulatory barriers, limited access to finance, and the need for a supportive entrepreneurial ecosystem remain. Recommendations include enhancing entrepreneurial education, providing financial incentives, and streamlining regulatory processes to better support start-ups and SMEs. This study highlights the necessity of addressing these challenges to fully realize the potential of entrepreneurship for sustainable development in Saudi Arabia. By providing empirical evidence on the significant role of entrepreneurship in driving sustainable development, this research offers actionable insights for policymakers aiming to foster a more resilient and diversified economy in line with Vision 2030.
Network Intrusion Detection Based on Deep Belief Network Broad Equalization Learning System
Authors: Deng Miaolei , Sun Chuanchuan , Kan Yupei , Xu Haihang , Zhou Xin , Fan Shaojun
Published: July 2024

Network intrusion detection systems are an important defense technology to guarantee information security and protect a network from attacks. In recent years, the broad learning system has attracted much attention and has been introduced into intrusion detection systems with some success. However, since the traditional broad learning system is a simple linear structure, when dealing with imbalanced datasets, it often ignores the feature learning of minority class samples, leading to a poorer recognition rate of minority class samples. Secondly, the high dimensionality and redundant features in intrusion detection datasets also seriously affect the training time and detection performance of the traditional broad learning system. To address the above problems, we propose a deep belief network broad equalization learning system. The model fully learns the large-scale high-dimensional dataset via a deep belief network and represents it as an optimal low-dimensional dataset, and then introduces the equalization loss v2 reweighing idea into the broad learning system and learns to classify the low-dimensional dataset via a broad equalization learning system. The model was experimentally tested using the CICIDS2017 dataset and fully validated using the CICIDS2018 dataset. Compared with other algorithms in the same field, the model shortens the training time and has a high detection rate and a low false alarm rate.
Investigating and Mitigating the Performance–Fairness Tradeoff via Protected-Category Sampling
Authors: Popoola Gideon , Sheppard John
Published: July 2024

Machine learning algorithms have become common in everyday decision making, and decision-assistance systems are ubiquitous in our everyday lives. Hence, research on the prevention and mitigation of potential bias and unfairness of the predictions made by these algorithms has been increasing in recent years. Most research on fairness and bias mitigation in machine learning often treats each protected variable separately, but in reality, it is possible for one person to belong to multiple protected categories. Hence, in this work, combining a set of protected variables and generating new columns that separate these protected variables into many subcategories was examined. These new subcategories tend to be extremely imbalanced, so bias mitigation was approached as an imbalanced classification problem. Specifically, four new custom sampling methods were developed and investigated to sample these new subcategories. These new sampling methods are referred to as protected-category oversampling, protected-category proportional sampling, protected-category Synthetic Minority Oversampling Technique (PC-SMOTE), and protected-category Adaptive Synthetic Sampling (PC-ADASYN). These sampling methods modify the existing sampling method by focusing their sampling on the new subcategories rather than the class label. The impact of these sampling strategies was then evaluated based on classical performance and fairness in classification settings. Classification performance was measured using accuracy and F1 based on training univariate decision trees, and fairness was measured using equalized odd differences and statistical parity. To evaluate the impact of fairness versus performance, these measures were evaluated against decision tree depth. The results show that the proposed methods were able to determine optimal points, whereby fairness was increased without decreasing performance, thus mitigating any potential performance–fairness tradeoff.
Secure Processing and Distribution of Data Managed on Private InterPlanetary File System Using Zero-Knowledge Proofs
Authors: Shibano Kyohei , Ito Kensuke , Han Changhee , Chu Tsz Tat , Ozaki Wataru , Mogi Gento
Published: July 2024

In this study, a new data-sharing method is proposed that uses a private InterPlanetary File System—a decentralized storage system operated within a closed network—to distribute data to external entities while making its authenticity verifiable. Among the two operational modes of IPFS, public and private, this study focuses on the method for using private IPFS. Private IPFS is not open to the general public; although it poses a risk of data tampering when distributing data to external parties, the proposed method ensures the authenticity of the received data. In particular, this method applies a type of zero-knowledge proof, namely, the Groth16 protocol of zk-SNARKs, to ensure that the data corresponds to the content identifier in a private IPFS. Moreover, the recipient’s name is embedded into the distributed data to prevent unauthorized secondary distribution. Experiments confirmed the effectiveness of the proposed method for an image data size of up to 120 × 120 pixels. In future studies, the proposed method will be applied to larger and more diverse data types.
Advancing Reservoir Evaluation: Machine Learning Approaches for Predicting Porosity Curves
Authors: Ali Nafees , Fu Xiaodong , Chen Jian , Hussain Javid , Hussain Wakeel , Rahman Nosheen , Iqbal Sayed Muhammad , Altalbe Ali
Published: July 2024

Porosity assessment is a vital component for reservoir evaluation in the oil and gas sector, and with technological advancement, reliance on conventional methods has decreased. In this regard, this research aims to reduce reliance on well logging, purposing successive machine learning (ML) techniques for precise porosity measurement. So, this research examines the prediction of the porosity curves in the Sui main and Sui upper limestone reservoir, utilizing ML approaches such as an artificial neural networks (ANN) and fuzzy logic (FL). Thus, the input dataset of this research includes gamma ray (GR), neutron porosity (NPHI), density (RHOB), and sonic (DT) logs amongst five drilled wells located in the Qadirpur gas field. The ANN model was trained using the backpropagation algorithm. For the FL model, ten bins were utilized, and Gaussian-shaped membership functions were chosen for ideal correspondence with the geophysical log dataset. The closeness of fit (C-fit) values for the ANN ranged from 91% to 98%, while the FL model exhibited variability from 90% to 95% throughout the wells. In addition, a similar dataset was used to evaluate multiple linear regression (MLR) for comparative analysis. The ANN and FL models achieved robust performance as compared to MLR, with R2 values of 0.955 (FL) and 0.988 (ANN) compared to 0.94 (MLR). The outcomes indicate that FL and ANN exceed MLR in predicting the porosity curve. Moreover, the significant R2 values and lowest root mean square error (RMSE) values support the potency of these advanced approaches. This research emphasizes the authenticity of FL and ANN in predicting the porosity curve. Thus, these techniques not only enhance natural resource exploitation within the region but also hold broader potential for worldwide applications in reservoir assessment.
Process Modeling and Optimization of Supercritical Carbon Dioxide-Enhanced Geothermal Systems in Poland
Authors: Gładysz Paweł , Pająk Leszek , Andresen Trond , Strojny Magdalena , Sowiżdżał Anna
Published: July 2024

This paper presents a comprehensive analysis of supercritical carbon dioxide (sCO2)-enhanced geothermal systems (EGSs) in Poland, focusing on their energetic performance through process modeling and optimization. EGSs harness the potential of geothermal energy by utilizing supercritical carbon dioxide as the working fluid, offering promising avenues for sustainable power generation. This study investigates two distinct configurations of sCO2-EGS: one dedicated to power generation via a binary system with an organic Rankine cycle and the other for combined power and heat production through a direct sCO2 cycle. Through accurate process modeling and simulation, key parameters influencing system efficiency and performance are identified and optimized. The analysis integrates thermodynamic principles with geological and operational constraints specific to the Polish context. The results highlight the potential of sCO2-EGSs to contribute to the country’s energy transition, offering insights into the optimal design and operation of such systems for maximizing both power and thermal output while ensuring economic viability and environmental sustainability.
A Pan-Asian Energy Transition? The New Rationale for Decarbonization Policies in the World’s Largest Energy Exporting Countries: A Case Study of Qatar and Other GCC Countries
Authors: Abdallah Ismail , Alhosin Hamed , Belarabi Mohamed , Chaouki Sanae , Mahmoud Nousseiba , Tayah Jad
Published: July 2024

Climate change has become a major agenda item in international relations and in national energy policy-making circles around the world. This review studies the surprising evolution of the energy policy, and more particularly the energy transition, currently happening in the Arabian Gulf region, which features some of the world’s largest exporters of oil and gas. Qatar, Saudi Arabia, and other neighboring energy exporters plan to export blue and green hydrogen across Asia as well as towards Europe in the years and decades to come. Although poorly known and understood abroad, this recent strategy does not threaten the current exports of oil and gas (still needed for a few decades) but prepares the evolution of their national energy industries toward the future decarbonized energy demand of their main customers in East and South Asia, and beyond. The world’s largest exporter of Liquefied Natural Gas, Qatar, has established industrial policies and projects to upscale CCUS, which can enable blue hydrogen production, as well as natural carbon sinks domestically via afforestation projects.
SF6 Tracer Technique to Estimate Methane Emission in a Dual-Flow Continuous Culture System: Test and Application
Authors: Lobo Richard R. , Salas-Solis Gerald , Vargas Juan , Monteiro Alyce , Silva Sarah S. da , Silva Kaliu , Arce-Cordero Jose , Vyas Diwakar , DiLorenzo Nicolas , Sarturi Jhones O. , Faciola Antonio P.
Published: July 2024

This study aimed to evaluate the sulfur hexafluoride (SF6) tracer technique for estimating methane (CH4) emissions in dual-flow continuous culture systems (DFCCS). In experiment 1 (Exp1), fermenters were filled with water, and known CH4 concentrations (0, 1.35, 2.93, or 4.43 g/d) were injected using permeation tubes with SF6 release rates (3.30 or 9.65 mg/d). Headspace gas was collected using canisters, and the SF6 technique estimated CH4 recovery. Experiment 2 (Exp2) involved a DFCCS fermentation trial with ruminal fluid from three Holstein cows, testing diets with soybean meal or its partial replacement (50%) by Chlorella or Spirulina. Headspace gas was collected at intervals post-feeding. Standard curves for SF6 and CH4 quantification were inadequate for DFCCS samples, with the CH4:SF6 ratio differing from standards, indicating the data needs further SF6 release rate evaluation. In Exp1, a high correlation (r = 0.97) was found between infused and calculated CH4, indicating good repeatability. Low and high SF6 rates performed similarly at low CH4 infusion, but high SF6 overestimated CH4 at high infusion. Exp2 showed CH4 emissions irrespective of SF6 rate and indicated reduced CH4 emissions and increased NDF degradation with algae-containing diets. Further evaluation of the SF6 tracer technique is warranted for DFCCS.
The Effect of Carbon Nanotubes and Carbon Microfibers on the Piezoresistive and Mechanical Properties of Mortar
Authors: Kanellopoulou Irene , Kartsonakis Ioannis A. , Chrysanthopoulou Athanasia I. , Charitidis Costas A.
Published: July 2024

Sustainability, safety and service life expansion in the construction sector have gained a lot of scientific and technological interest during the last few decades. In this direction, the synthesis and characterization of smart cementitious composites with tailored properties combining mechanical integrity and self-sensing capabilities have been in the spotlight for quite some time now. The key property for the determination of self-sensing behavior is the electrical resistivity and, more specifically, the determination of reversible changes in the electrical resistivity with applied stress, which is known as piezoresistivity. In this study, the mechanical and piezoresistive properties of mortars reinforced with carbon nanotubes (CNTs) and carbon micro-fibers (CMFs) are determined. Silica fume and a polymer with polyalkylene glycol graft chains were used as dispersant agents for the incorporation of the CNTs and CMFs into the cement paste. The mechanical properties of the mortar composites were investigated with respect to their flexural and compressive strength. A four-probe method was used for the estimation of their piezoresistive response. The test outcomes revealed that the combination of the dispersant agents along with a low content of CNTs and CMFs by weight of cement (bwoc) results in the production of a stronger mortar with enhanced mechanical performance and durability. More specifically, there was an increase in flexural and compressive strength of up to 38% and 88%, respectively. Moreover, mortar composites loaded with 0.4% CMF bwoc and 0.05% CNTs bwoc revealed a smooth and reversible change in electrical resistivity vs. compression loading—with unloading comprising a strong indication of self-sensing behavior. This work aims to accelerate progress in the field of material development with structural sensing and electrical actuation via providing a deeper insight into the correlation among cementitious composite preparation, admixture dispersion quality, cementitious composite microstructure and mechanical and self-sensing properties.
Supercritical Technology as an Efficient Alternative to Cold Pressing for Avocado Oil: A Comparative Approach
Authors: Pinheiro Pantoja Kelly Roberta , Melo Aires Giselle Cristine , Ferreira Clara Prestes , Lima Matheus da Costa de , Menezes Eduardo Gama Ortiz , Carvalho Junior Raul Nunes de
Published: July 2024

Avocado oil is rich in nutrients beneficial to human health, such as monounsaturated fatty acids, phenolic compounds, tocopherol, and carotenoids, with numerous possibilities for application in industry. This review explores, through a comparative approach, the effectiveness of the supercritical oil extraction process as an alternative to the conventional cold-pressing method, evaluating the differences in the extraction process steps through the effect of temperature and operating pressure on bioactive quality and oil yield. The results reveal that supercritical avocado oil has a yield like that of mechanical cold pressing and superior functional and bioactive quality, especially in relation to α-tocopherol and carotenoids. For better use and efficiency of the supercritical technology, the maturation stage, moisture content, fruit variety, and collection period stand out as essential factors to be observed during pre-treatment, as they directly impact oil yield and nutrient concentration. In addition, the use of supercritical technology enables the full use of the fruit, significantly reducing waste, and adds value to the agro-industrial residues of the process. It produces an edible oil free of impurities, microorganisms, and organic solvents. It is a green, environmentally friendly technology with long-term environmental and economic advantages and an interesting alternative in the avocado market.
Tectonic Inversion and Deformation Differences in the Transition from Ionian Basin to Apulian Platform: The Example from Ionian Islands, Greece
Authors: Zelilidis Avraam , Bourli Nicolina , Zoumpouli Elena , Maravelis Angelos G.
Published: July 2024

The studied areas (the Ionian Islands: Paxoi, Lefkas, Kefalonia, and Zakynthos), are situated at the western ends of the Ionian Basin in contact with the Apulian Platform and named as Apulian Platform Margins. The proposed model is based on fieldwork, previously published data, and balanced geologic cross-sections. Late Jurassic to Early Eocene NNW–SSE extension, followed by Middle Eocene to Middle Miocene (NNW–SSE compression, characterizes the Ionian basin). The space availability, the distance of the Ionian Thrust from the Kefalonia transform fault and the altitude between the Apulian Platform and the Ionian Basin that was produced during the extensional regime were the main factors for the produced structures due to inversion tectonics. In Zakynthos Island, the space availability (far from the Kefalonia Transform Fault), and the reactivation of normal bounding faults formed an open geometry anticline (Vrachionas anticline) and a foreland basin (Kalamaki thrust foreland basin). In Kefalonia Island, the space from the Kefalonia Transform Fault was limited, and the tectonic inversion formed anticline geometries (Aenos Mountain), nappes (within the Aenos Mountain) and small foreland basins (Argostoli gulf), all within the margins. In Lefkas Island, the lack of space, very close to the Kefalonia Transform Fault, led to the movement of the Ionian Basin over the margins, attempting to overthrust the Apulian Platform. Because the obstacle between the basin and the platform was very large, the moving part of the Ionian Basin strongly deformed producing nappes and anticlines in the external part of the Ionian Basin, and a very narrow foreland basin (Ionian Thrust foreland basin).
The Hemianopia Reading Questionnaire (HRQ): Development and Psychometric Qualities in a Large Community Sample
Authors: Tol Sarah , Timmerman Marieke E. , Goltermann Alina , Heutink Joost , de Haan Gera A.
Published: July 2024

The ability to read is important for daily life functioning. Individuals with homonymous visual field defects (iwHs) after brain injury experience frequent reading difficulties. The current study presents a novel self-report questionnaire aimed at measuring the wide variety of reading difficulties iwHs can experience: the Hemianopia Reading Questionnaire (HRQ). The 24-item HRQ was developed with help from clinical experts and experts by experience and was inspired by existing reading questionnaires for adults. The three tested subscales of the HRQ assess the relationship to reading, reading skills and daily life functional reading. The factor structure, reliability, convergent validity and divergent validity were examined in a large community sample (i.e., individuals without homonymous visual field defects) with a comparable distribution of age, gender and level of education to those who have suffered a stroke (N = 998). Two competing hypothesized models were tested and a good fit was found for a three-bifactor model of the HRQ. The reliability of the three subscales was found to be good (ω range 0.93–0.99), as well as the convergent and divergent validity (9 out of 12 Spearman’s correlations, according to expectations). The results support further use of the HRQ in iwHs, especially in the context of reading rehabilitation. Suggestions for clinical and scientific use and future psychometric research on the HRQ are provided.
A Mixed Methods Exploration of Young Women’s Agency and Mental Health during COVID-19 in Low-Income Communities in Mumbai, India
Authors: Brault Marie A. , Peskin Melissa F. , Jones Anastasia N. , Saikia Amrita , Bhutia Rinchen O. , Cheruvu Sai Sammitha , Jagtap Vaishali M. , Singh Rajendra , Nair Poornima , Vedanthan Rajesh , Vermund Sten H. , Maitra Shubhada
Published: July 2024

Introduction: Adolescent girls and young women (AGYW) in India face additional health inequities compared to their male peers, as gender norms constrain agency for prevention and self-care. The onset of the COVID-19 pandemic and associated lockdowns deepened health inequities and often worsened mental health, but the impacts on agency are unclear. This exploratory sequential mixed methods paper examined mental health and COVID-19 elements that exacerbated or mitigated adverse consequences for AGYW in low-income communities in Mumbai. Methods: We conducted semi-structured interviews with AGYW (aged 15–25 years; N = 60) and adults (parents, healthcare providers, community-based organization representative; N = 30). We administered a structured survey to AGYW (N = 150) to assess health concerns, depression and anxiety symptoms (using the PHQ-8 and GAD-7 scales), and experiences during COVID-19. We analyzed qualitative data using the constant comparative approach in Atlas.ti, and quantitative data using R and SPSS. Results: Qualitative data revealed that AGYW faced stressors and had limited agency during lockdowns due to limited access to education, financial insecurity, and community violence. Quantitative data indicated that limited agency in the context of COVID-19 was significantly associated with depression and anxiety. Financial resources to address COVID-19 created new employment and leadership opportunities for AGYW to become COVID educators and preschool teachers; participation in these opportunities was associated with less anxiety. Discussion: Pandemic stress was difficult for low-income AGYW in Mumbai. Mitigating programs for COVID-19 control helped address acute needs and enable capabilities. Exploring similar themes among a broader population of youth can help design strategies and opportunities for young people in low-income communities during health emergencies.
Low-Penetrance Susceptibility Variants in Colorectal Cancer—Current Outlook in the Field
Authors: Szuman Marcin , Kaczmarek-Ryś Marta , Hryhorowicz Szymon , Kryszczyńska Alicja , Grot Natalia , Pławski Andrzej
Published: July 2024

Colorectal cancer (CRC) is one of the most frequent and mortality-causing neoplasia, with various distributions between populations. Strong hereditary predispositions are the causatives of a small percentage of CRC, and most cases have no transparent genetic background. This is a vast arena for exploring cancer low-susceptibility genetic variants. Nonetheless, the research that has been conducted to date has failed to deliver consistent conclusions and often features conflicting messages, causing chaos in this field. Therefore, we decided to organize the existing knowledge on this topic. We screened the PubMed and Google Scholar databases. We drew up markers by gene locus gathered by hallmark: oncogenes, tumor suppressor genes, genes involved in DNA damage repair, genes involved in metabolic pathways, genes involved in methylation, genes that modify the colonic microenvironment, and genes involved in the immune response. Low-penetration genetic variants increasing the risk of cancer are often population-specific, hence the urgent need for large-scale testing. Such endeavors can be successful only when financial decision-makers are united with social educators, medical specialists, genetic consultants, and the scientific community. Countries’ policies should prioritize research on this subject regardless of cost because it is the best investment. In this review, we listed potential low-penetrance CRC susceptibility alleles whose role remains to be established.
N-Chlorotaurine Solutions as Agents for Infusion Detoxification Therapy: Preclinical Studies
Authors: Murashevych Bohdan , Bilenkyi Gennadii , Girenko Dmitry , Bilenkyi Emil
Published: July 2024

N-chlorotaurine (NCT) is a broad-spectrum antimicrobial agent with outstanding tolerability, effective for topical and inhalation use. This paper presents the results of studies of single and repeated intravenous infusions of NCT to laboratory animals. The studies were conducted on female Wistar Han rats. The effect of NCT infusions on the general condition, behavioral reactions, main biochemical and hematological parameters, hemocoagulation system, cardiovascular system, and on the condition of the internal organs was studied. It was found that NCT infusions do not reveal deviations in the studied parameters that could indicate a toxic effect. The estimated LD50 is more than 80 mg/kg. In a subchronic experiment, a statistically significant decrease in cholesterol (by up to 11%), glucose (by up to 15%) and excess bases (up to four times) in the blood, and an increase in heart rate (by up to 31%) and frequency of defecations (by up to 35%), as well as pronounced antiplatelet effect, were found. In animals with simulated endotoxicosis, a decrease in the cytolysis and oxidative stress markers was observed. Such effects are caused by both chlorine-active compounds and taurine.The results obtained indicate broad prospects for the use of NCT solutions as an infusion detoxifying agent.
The Role of Adipocytes Recruited as Part of Tumor Microenvironment in Promoting Colorectal Cancer Metastases
Authors: Ma Yunxia , Nenkov Miljana , Chen Yuan , Gaßler Nikolaus
Published: July 2024

Adipose tissue dysfunction, which is associated with an increased risk of colorectal cancer (CRC), is a significant factor in the pathophysiology of obesity. Obesity-related inflammation and extracellular matrix (ECM) remodeling promote colorectal cancer metastasis (CRCM) by shaping the tumor microenvironment (TME). When CRC occurs, the metabolic symbiosis of tumor cells recruits adjacent adipocytes into the TME to supply energy. Meanwhile, abundant immune cells, from adipose tissue and blood, are recruited into the TME, which is stimulated by pro-inflammatory factors and triggers a chronic local pro-inflammatory TME. Dysregulated ECM proteins and cell surface adhesion molecules enhance ECM remodeling and further increase contractibility between tumor and stromal cells, which promotes epithelial-mesenchymal transition (EMT). EMT increases tumor migration and invasion into surrounding tissues or vessels and accelerates CRCM. Colorectal symbiotic microbiota also plays an important role in the promotion of CRCM. In this review, we provide adipose tissue and its contributions to CRC, with a special emphasis on the role of adipocytes, macrophages, neutrophils, T cells, ECM, and symbiotic gut microbiota in the progression of CRC and their contributions to the CRC microenvironment. We highlight the interactions between adipocytes and tumor cells, and potential therapeutic approaches to target these interactions.
Fungicide-Loaded Liposomes for the Treatment of Fungal Diseases in Agriculture: An Assessment of Botrytis cinerea
Authors: Agnusdei Angelo , Maurelli Anna Maria , Gerin Donato , Monopoli Donato , Pollastro Stefania , Catucci Lucia , Faretra Francesco , De Leo Vincenzo
Published: July 2024

In this work, liposomes loaded with the fungicide, Fludioxonil (FLUD), for the containment of fungal diseases in agriculture were developed. Three types of vesicles with different compositions were compared: (I) plain vesicles, composed of soy phosphatidylcholine and cholesterol; (II) PEG-coated vesicles, with an additional polyethylene glycol coating; and (III) cationic vesicles, containing didodecyldimethylammonium bromide. Nanometric-sized vesicles were obtained both by the micelle-to-vesicle transition method and by the extrusion technique, and encapsulation efficiency, drug loading content, and Zeta potential were determined for all the samples. The extruded and PEGylated liposomes were the most stable over time and together with the cationic ones showed a significant prolonged FLUD release capacity. The liposomes’ biological activity was evaluated on conidial germination, germ tube elongation and colony radial growth of the ascomycete Botrytis cinerea, a phytopathogenic fungus affecting worldwide many important agricultural crops in the field as well as in the postharvest phase. The extruded and PEGylated liposomes showed greater effectiveness in inhibiting germ tube elongation and colony radial growth of the fungal pathogen, even at 0.01 µg·mL−1, the lowest concentration assessed.
Complete Description of the Three Pathways of the Complement System in a Series of 430 Patients with Rheumatoid Arthritis
Authors: Rodríguez-González Dara , García-González María , Gómez-Bernal Fuensanta , Quevedo-Abeledo Juan C. , González-Rivero Agustín F. , Fernández-Cladera Yolanda , González-López Elena , Ocejo-Vinyals J. Gonzalo , Jiménez-Sosa Alejandro , González-Toledo Beatriz , González-Gay Miguel Á. , Ferraz-Amaro Iván
Published: July 2024

The complement (C) system is implicated in the etiopathogenesis of rheumatoid arthritis (RA). However, there is a lack of studies characterizing all three C pathways in RA patients. This study aimed to evaluate the association between an in-depth examination of the C system and RA patient characteristics, focusing on disease activity and the presence of rheumatoid factor and anti-citrullinated protein autoantibodies (ACPA). In a cohort of 430 RA patients, functional assays of the three C pathways (classical, alternative, and lectin) and serum levels of their components were assessed. Components included C1q (classical); factor D and properdin (alternative); lectin (lectin); C1-inhibitor; C2, C4, and C4b (classical and lectin); C3, C3a, and C4b (common); and C5, C5a, and C9 (terminal). A multivariable linear regression analysis showed significant positive correlations between C-reactive protein and C system proteins and functional assays, especially in the terminal and common pathways. Disease activity, measured by scores with or without acute phase reactants, positively correlated with the classical pathway functional test and terminal pathway products. Conversely, rheumatoid factor or ACPA presence was associated with lower classical pathway values and decreased C3a and C4b levels, suggesting complement depletion. In conclusion, RA disease activity increases C molecules and functional complement assays, while rheumatoid factor or ACPA positivity is linked to C consumption. Our study offers a detailed analysis of the complement system’s role in RA, potentially guiding the development of more targeted and effective treatment strategies.
Thionitrosyl Complexes of Rhenium and Technetium with PPh3 and Chelating Ligands—Synthesis and Reactivity
Authors: Nowak Domenik , Hagenbach Adelheid , Sawallisch Till Erik , Abram Ulrich
Published: July 2024

In contrast to corresponding nitrosyl compounds, thionitrosyl complexes of rhenium and technetium are rare. Synthetic access to the thionitrosyl core is possible by two main approaches: (i) the treatment of corresponding nitrido complexes with S2Cl2 and (ii) by reaction of halide complexes with trithiazyl chloride. The first synthetic route was applied for the synthesis of novel rhenium and technetium thionitrosyls with the metals in their oxidation states “+1” and “+2”. [MVNCl2(PPh3)2], [MVNCl(PPh3)(LOMe)] and [MVINCl2(LOMe)] (M = Re, Tc; {LOMe}− = (η5-cyclopentadienyl)tris(dimethyl phosphito-P)cobaltate(III)) complexes have been used as starting materials for the synthesis of [ReII(NS)Cl3(PPh3)2] (1), [ReII(NS)Cl3(PPh3)(OPPh3)] (2), [ReII(NS)Cl(PPh3)(LOMe)]+ (4a), [ReII(NS)Cl2(LOMe)] (5a), [TcII(NS)Cl(PPh3)(LOMe)]+ (4b) and [TcII(NS)Cl2(LOMe)] (5b). The triphenylphosphine complex 1 is partially suitable as a precursor for ongoing ligand exchange reactions and has been used for the synthesis of [ReI(NS)(PPh3)(Et2btu)2] (3a) (HEt2btu = N,N-diethyl-N′-benzoyl thiourea) containing two chelating benzoyl thioureato ligands. The novel compounds have been isolated in crystalline form and studied by X-ray diffraction and spectroscopic methods including IR, NMR and EPR spectroscopy and (where possible) mass spectrometry. A comparison of structurally related rhenium and technetium complexes allows for conclusions about similarities and differences in stability, reaction kinetics and redox behavior between these 4d and 5d transition metals.
Mapping the Distribution of Curculio davidi Fairmaire 1878 under Climate Change via Geographical Data and the MaxEnt Model (CMIP6)
Authors: Wu Junhao , Wei Xinju , Wang Zhuoyuan , Peng Yaqin , Liu Biyu , Zhuo Zhihang
Published: July 2024

Curculio davidi is a major pest in chestnut-producing regions in China, and there have been many studies on its occurrence, biological characteristics, and management strategies. However, few of them have focused on the distribution changes of the pest under climate change. In this study, the MaxEnt model (version 3.3.4) and ArcGIS software (version 10.8) were first employed to map the current and future (2050 s and 2080 s) suitable habitat distribution of the weevil under climate change (CMIP 6: SSP1-2.6, SSP2-4.5, and SSP5-8.5). The results indicate that the highly suitable areas for C. davidi are mainly concentrated in Hubei, Henan, Anhui, Jiangxi, Jiangsu, Zhejiang, the coastal areas of Shandong, and eastern Guizhou, northwestern Hunan, and northeastern Sichuan provinces in China. Through the Jackknife test of 19 climate factors, six climate factors affecting the distribution of C. davidi were identified, with precipitation from July (Prec7), precipitation of warmest quarter (Bio18), and temperature seasonality (standard deviation × 100) (Bio4) contributing a combined percentage of 86.3%. Under three different climate scenarios (CMIP 6: SSP1-2.6, SSP2-4.5, and SSP5-8.5), the area of moderately suitable regions is projected to increase by 22.12–27.33% in the 2050 s and by 17.80–38.22% in the 2080 s compared to the current distribution, while the area of highly suitable regions shows a shrinking trend. This study provides data support for the management strategies of C. davidi and offers new insights into the dynamic changes of similar forestry pests.
Tailoring Laser Powder Bed Fusion Process Parameters for Standard and Off-Size Ti6Al4V Metal Powders: A Machine Learning Approach Enhanced by Photodiode-Based Melt Pool Monitoring
Authors: Liravi Farima , Soo Sebastian , Toorandaz Sahar , Taherkhani Katayoon , Habibnejad-Korayem Mahdi , Toyserkani Ehsan
Published: July 2024

An integral part of laser powder bed fusion (LPBF) quality control is identifying optimal process parameters tailored to each application, often achieved through time-consuming and costly experiments. Melt pool dynamics further complicate LPBF quality control due to their influence on product quality. Using machine learning and melt pool monitoring data collected with photodiode sensors, the goal of this research was to efficiently customize LPBF process parameters. A novel aspect of this study is the application of standard and off-size powder feedstocks. Ti6Al4V (Ti64) powder was used in three size ranges of 15–53 µm, 15–106 µm, and 45–106 µm to print the samples. This facilitated the development of a process parameters tailoring system capable of handling variations in powder size ranges. Ultimately, per each part, the associated set of light intensity statistical signatures along with the powder size range and the parts’ density, surface roughness, and hardness were used as inputs for three regressors of Feed-Forward Neural Network (FFN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The laser power, laser velocity, hatch distance, and energy density of the parts were predicted by the regressors. According to the results obtained on unseen samples, RF demonstrated the best performance in the prediction of process parameters.
Mental Disorders, Social Media Addiction, and Academic Performance in Romanian Undergraduate Nursing Students
Authors: David Liliana , Ismaiel Abdulrahman , Foucambert Paul , Leucuta Daniel Corneliu , Popa Stefan-Lucian , Fadgyas Stanculete Mihaela , Dumitrascu Dan L.
Published: July 2024

Introduction: We aimed to evaluate social media addiction in Romanian undergraduate nursing students and its association with academic performance, depression, and anxiety. Methods: We used a cross-sectional online survey to collect data among nursing undergraduate students enrolled at the University of Medicine and Pharmacy “Iuliu Hatieganu”, Cluj-Napoca, Romania. The Social Media Addiction Scale-Student Form (SMAS-SF), Beck Depression Inventory (BDI), and State-Trait Anxiety Inventory (STAI) were used to assess social media addiction, depression, and anxiety, respectively. Results: A total of 90 nursing students participated in the study, of which 82 (91.1%) were females and 81 (90%) were of Romanian ethnicity. The median age of participants was 21 years (18–40). Males showed higher BDI total scores, SMAS-SF total scores, and STAI scale A state anxiety compared to females, and females showed higher STAI scale A trait anxiety compared to males, although these results were not statistically significant. Also, we found no significant difference in these variables between participants from different study years. Participants with no or mild depression had a significantly higher academic performance (p-value = 0.001), lower SMAS-SF (p-value = 0.004), and lower STAI scores (p-value < 0.001) compared to participants with borderline, moderate, or severe depression after performing multivariate regression analysis. Conclusions: Our study demonstrated that depression was significantly associated with social media addiction, anxiety, and lower academic performance in Romanian undergraduate nursing students.