bluuebunny/crossref_metadata_2025 huggingface dataset:
Unrealized “build back better” potential in the Era of “Polycrisis”: City climate and energy action learnings during COVID19
Authors: Constabile Kerry , Taylor Nicholas , Phillips Charlotte
Published: February 2025

Mitigating climate change in cities is one of the most important global climate policy aims due to the majority of emissions stemming from city production and consumption (IPCC 2022). Before COVID-19, thousands of city governments set ambitious climate action plans, including net zero and renewable energy commitments. This study investigates how COVID-19 and recovery packages affected city-level plans and actions. This paper draws on net zero, resilience and COVID-19 recovery literature, an original survey of 30 cities from countries in the global north and south: Argentina, Indonesia, Kenya, Mexico, Mozambique, the Philippines, South Africa, Sweden, Turkey the United Kingdom and the United States, and initial interviews with city leaders in select megacities: Jakarta and London. Results show that cities’ climate ambitions vary widely by region, as do the impacts of COVID-19 and recovery progress towards those goals. Many cities experienced delays as they pivoted to crisis response. Others benefited from new national green recovery policy frameworks and financing streams. Almost two years since the declaration of the end of the pandemic, COVID-19 has not fundamentally transformed cities in either the negative or promising ways that were predicted. While these lessons for city transformations are clear, lessons for cities in tackling climate crises in the ongoing era of the “polycrisis” seem useful to underscore. This paper identifies recommendations that would enable cities to leverage their COVID-19 experiences for tackling climate mitigation in the context of a projected increase in frequency and severity of shocks.
Practice abstract
Mitigating climate change in cities is one of the most important global climate policy aims due to the majority of emissions stemming from city production and consumption (IPCC 2022). Before COVID-19, thousands of cities set ambitious climate action plans, including net zero and renewable energy commitments. This study investigates how COVID-19 and recovery packages affected plans and actions. Results show that cities’ climate ambitions vary widely by region, as do the impacts of COVID-19 and recovery progress towards those goals. Many cities experienced delays as they pivoted to crisis response. Others benefited from new national green recovery policy frameworks and financing streams. This paper identifies recommendations that suggest cities might leverage COVID-19 learnings as well as potentially realise more effective responses to ongoing crises.
Improving Elderly Healthcare by Health Monitoring and Access to Social Welfare Services by IoTs integrationin Thailand
Authors: Chokphukhiao Chaturapron , tun Wonn Shweyi Thet , Duankhan Poomin , Masa Sakaowrat , Hongthong Patcharee , Pongskul Cholatip , Chaiayuth Somporn , Loeiyood Jugsun , Kuhirunyaratn Piyathida , Jindawong Bangonsri , Seeooppalat Nipitphon , Chiewchanwattana Sirapat , Patramanon Rina , Sunat Khamron
Published: February 2025

The rapid aging population in Thailand necessitates innovative solutions to improve healthcare access and monitoring for the elderly. In this study, technology access, social welfare rights, and health monitoring systems among elderly populations in Thailand were investigated by employing a mixed-methods approach. A quantitative survey was conducted with 2,005 participants aged 60 and above across four provinces (Khon Kaen, Lampang, Phra Nakhon Si Ayutthaya, and Songkhla), achieving a 96.95% response rate. The study developed a three-tier IoT-based health monitoring system, focusing on smartwatches and blood pressure monitors. High satisfaction rates were found with medical devices. Technology access was significant, with 78.8% owning internet-enabled smartphones and 92% receiving monthly living allowances. The study analyzed quantitative data using multiple regression statistics, revealing that health problems and concerns about technology are the top two factors restricting access to welfare services for the elderly. In addition, the challenges in digital access were noted, including concerns about online fraud (29.9%) and limited technological infrastructure. Access to efficient welfare services was the most strongly associated factor with increased satisfaction among the elderly in using digital health technology. This research encourages developing digital skills and providing appropriate support to enhance elderly individuals’ ability to access services, emphasizing the importance of addressing these factors within the aging population. It contributes to understanding the intersection of IoT technologies, social welfare, and elderly care, offering insights for future advancements in healthcare services for older adults. Clinical Trial Number: not applicable.
Narratives of Divide: The Polarizing Power of Large Language Models in a Turbulent World
Authors: Saqr Khalid
Published: February 2025

Large language models (LLMs) are reshaping information consumption and influencing public discourse, raising concerns about their potential to empower narrative control and amplify polarisation. This study examines the embedded worldviews of four LLMs across key themes using Wittgenstein’s theory of language games to interpret meaning and narrative structures. A two-tiered methodology—Surface (-S) and Deep (-D) analyses—is applied using Natural Language Processing (NLP) to investigate four different LLMs. The -S analysis, evaluating general differences in thematic focus, semantic similarity, and sentiment pattern, found no significant variability across the four LLMs. However, the -D analysis, employing zero-shot classification across geopolitical, ideological, and philosophical dimensions, revealed alarming differences. Liberalism (H = 12.51, p = 0.006), conservatism (H = 8.76, p = 0.033), and utilitarianism (H = 8.56, p = 0.036) emerged as key points of divergence between LLMs. For example, the narratives constructed by one LLM exhibited strong pro-globalization and liberal leanings, while another generated pro-sovereignty narratives, introducing meaning through national security and state autonomy frames. Differences in philosophical perspectives further highlighted contrasting preferences for utilitarian versus deontological reasoning across justice and security themes. These findings demonstrate that LLMs, when deployed at a sufficient scale and connectivity, could be employed as stealth weapons in narrative warfare.
Cognitive impairment and brain atrophy in patients with newly diagnosed aggressive lymphoma undergoing standard chemotherapy: a normative analysis
Authors: Ditchfield Charlotte , Gates Priscilla , Duque Juan Dominguez , Dhillon Haryana M , Dore Vincent , Wilson Carlene , Caeyenberghs Karen
Published: February 2025

Objective
Cancer-related cognitive impairment (CRCI) can impact daily-life functioning of people with aggressive lymphoma. While many studies have examined the neural substrates implicated in CRCI, most have used group-based analyses, which may mask individual differences. In the present study, we used normative analysis to examine longitudinal changes in cognitive functioning and brain morphology at the level of the individual patient.
Methods
Nine participants with newly diagnosed aggressive lymphoma underwent neuropsychological assessment and anatomical MR before and 6–8 weeks after chemotherapy. Cognitive test scores were converted to T-scores and classified as impaired if ≤ 30. Deviations in cortical thickness and surface area in the superior frontal gyrus (SFG) and anterior cingulate cortex (ACC) were computed at the level of the individual using the novel CentileBrain tool, with z-scores below \(\:-\)1.96 and above 1.96 classified as infranormal and supranormal, respectively.
Results
Analyses revealed substantial between-subject variability over time and across outcome measures. Cognitive impairments in executive function and verbal memory were identified in three participants before and/or after chemotherapy. CentileBrain results showed seven participants had infranormal cortical thickness and/or surface area in the SFG at one or both time points, and one patient had infranormal values in the ACC. No participants exhibited supranormal values in either region at any time point.
Conclusions
Our findings provide a valuable foundation for developing more personalised interventions tailored to the specific cognitive and neural profiles of lymphoma survivors, paving the way for improved clinical care to alleviate and mitigate the impact of CRCI on long-term quality of life.
CO₂ Emissions Projections for 2100: A Comparative Machine Learning Study of U.S. and Multimodal Approach of Global Trends
Authors: Murgod Sanjana , Garg Kartik , Magadum Triveni , Yadav Vivek , Mittal Harshit , Kushwaha Omkar Singh
Published: February 2025

The continuous rise in CO₂ emissions is a major contributor to climate change, affecting ecosystems, economies, and public health. Predicting future emissions accurately is crucial for designing effective policies and mitigation strategies. This study explores multiple machine learning models for CO₂ emissions forecasting, comparing traditional methods like Support Vector Machines (SVM), Linear Regression, and Decision Trees with advanced deep learning techniques such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Multi-Layer Perceptron (MLP). Using a time-series approach, we forecast emissions up to 2100 and assess model performance through key metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), and R² score. Our results indicate that deep learning models, especially LSTM and GRU, outperform traditional methods in capturing complex patterns and trends in emissions data. Additionally, we generate geospatial visualizations to highlight regions facing the highest risks. These insights provide valuable guidance for policymakers and environmental researchers, enabling data-driven decisions for emission reduction, resource allocation, and long-term sustainability planning in the fight against climate change.
Vegetative Propagation of Perennial Cut Flower Flax (Linum spp.) in a Controlled Environment
Authors: Goodman Elizabeth A. , Anderson Neil O.
Published: February 2025

Perennial flax (Linum austriacum, Linum perenne, L. austriacum) has a wide range of uses for the horticultural and agronomic markets. Protocols for vegetative propagation culture and production scheduling of perennial flax are needed to advance development of potential ornamental or specialty cut flower (CF) cultivars, specifically for the floriculture market. The purpose of this research was to establish vegetative propagation practices for perennial flax CF selections. Fifteen CF genotypes were tested in four experiments between week 8 of 2022 and week 37 of 2023, using 5-cm cuttings from greenhouse (GH) or field cuttings. We tested mist house (MH) rooting time (2 or 3 weeks) and rooting hormone concentrations [1000 or 2000 ppm indole-3-butyric acid (IBA)]. Experiments tested rooting time over 3 or 4 total weeks, which included 2 or 3 weeks under mist, respectively, and 1 week in GH culture before rating. Rooting success was rated using a six-point Likert scale to gauge number and length of roots per cutting. Expt. 1 rooted for 3 weeks using 1000 ppm IBA, resulting in 66.5% rooting. Expt. 2 (4 weeks, 1000 ppm IBA) had 96.4% rooting. Experiment 3 (4 weeks, 2000 ppm IBA) had 82.0% rooting. Expt. 4 (replicate of Expt. 2 but rated using a binary rooted vs. unrooted rating scale, as done in a commercial setting), resulted in 85.4% rooting. Overall, Expts. 2 and 4 resulted in the greatest mean success rates, despite the differences in root rating methods. An increased IBA concentration is not recommended for Linum genotypes, as it decreased rooting success in this study. Perennial flax vegetative propagation may be successful using 1000 ppm IBA for 4 weeks to multiply GH stock plants and produce plugs (liners) for shipping and CF production. Additional research will be needed to define the timing and cultural requirements for successful shipping of vernalized, finished (rooted, robust growth) plugs for perennial CF flax cultivars.
Non-unitarity maximizing unraveling of open quantum dynamics
Authors: Daraban Ruben , Salas-Ramírez Fabrizio , Schachenmayer Johannes
Published: February 2025

The dynamics of many-body quantum states in open systems is commonly numerically simulated by unraveling the density matrix into pure-state trajectories. In this work, we introduce a new unraveling strategy that can adaptively minimize the averaged entanglement in the trajectory states. This enables a more efficient classical representation of trajectories using matrix product decompositions. Our new approach is denoted non-unitarity maximizing unraveling (NUMU). It relies on the idea that adaptively maximizing the averaged non-unitarity of a set of Kraus operators leads to a more efficient trajectory entanglement destruction. Compared to other adaptive entanglement lowering algorithms, NUMU is computationally inexpensive. We demonstrate its utility in large-scale simulations with random quantum circuits. NUMU lowers runtimes in practical calculations, and it also provides new insight on the question of classical simulability of quantum dynamics. We show that for the quantum circuits considered here, unraveling methods are much less efficient than full matrix product density operator simulations, hinting to a still large potential for finding more advanced adaptive unraveling schemes.
La negociación colectiva del empleado público en Colombia
Authors: Mendoza Leal Carlos Eduard , Vera Gómez Sandra
Published: February 2025

La negociación colectiva en Colombia históricamente ha contado con amplias garantías en el ordenamiento jurídico constitucional, legal y reglamentario, apoyado en el derecho internacional. El Estado colombiano por tradición honra y protege los derechos humanos, de lo cual no han sido ajenos el derecho de asociación sindical y de negociación colectiva, hoy consagrados como derechos fundamentales.
Academic Capitalism in Southeast Asia: Lessons from Islamic Universities in Indonesia
Authors: Fathana Hangga , Herdianto Enggar Furi , Dewi Karina Utami
Published: February 2025

Indonesia’s demographic potential has fueled increasing demand for higher education, creating opportunities for private universities to address gaps left by public institutions. Many private universities in Indonesia, particularly Islamic institutions, integrate religious values as a core identity while navigating the pressures of academic capitalism. This research investigated how academic capitalism reshapes Islamic universities, compelling them to balance their religious values with global market demands and aspirations for world-class status. This research explored the strategies Islamic universities adopt to navigate these challenges by employing qualitative methods. The findings identify two dominant responses: a pragmatic model, which prioritizes global rankings and revenue generation as indicators of institutional success, and an idealistic model, which integrates rankings cautiously while emphasizing the preservation of Islamic values. These models reflect the diverse ways institutions reconcile external pressures with their missions. Despite challenges, Islamic universities demonstrate resilience, crafting strategies to balance their educational mission with the demands of globalization. The research emphasizes the need for context-sensitive approaches that align global frameworks with local religious and cultural values, offering insights into the evolving landscape of higher education in Southeast Asia. This balance underscores the critical role of identity and values in shaping the future of Islamic higher education.
Libro: Alvira, Rafael. El Lugar al que se Vuelve. Reflexiones sobre la familia. Pamplona: EUNSA, 1998, 112 pp.
Authors: Hurtado Domínguez Rafael
Published: February 2025

Book review
The analysis of implications in academic discourse: through evaluative means towards researcher’s individual style
Authors:   Foreign Languages of Moscow City University, Vodyanitskaya Albina A.
Published: February 2025

The paper focuses on the scientific heritage of S. T. Nefedov in terms of the scientist’s views on linguistic evaluation. The author aims at analyzing Nefedov’s views on linguistic evaluation that functions in research papers, as well as studying academic discourse through the prism
of Nefedov’s approach towards evaluation (e. g., the analysis of evaluative implications in research
papers). The paper also focuses on researching into Nefedov’s individual style based on the analysis the evaluative strategies he uses in his research papers. Research results by Nefedov in the field of research discourse (for example, the analysis of evaluative implications in linguistic articles, study of the language of the evaluating researcher) are regarded during the analysis of evaluative statements of participants in academic discourse. Research revealed that
in a number of cases evaluation in research papers acts similarly to that of spoken academic discourse evaluative means. The paper also studies the evaluative strategies used by Nefedov as an evaluating scientist. The linguistic means he uses when putting forward theories, when
analyzing the scientific views of various linguists, and when comparing different scientific points of view, are being analyzed. The analysis of Nefedov’s evaluation strategies is based on the evaluative styles of scientists that he had previously identified and classified according to
the most frequently used linguistics means used by the scientists and named them “scientistdidactician”,
“scientist-argumentator”, “scientist-diplomat”. The research revealed that Nefedov acts in most cases as a didactic scientist who precedes the disclosure of the essence of his
and other scientific views with an evaluative statement. The methodology of the research is
based on the studies of evaluative semantics, event-based interpretation of academic discourse as well as papers studying the individual style of a scientist. The research relies on methods of discourse analysis, a digital method for identifying phonosemantic characteristics of a text (carried out in the VAAL-mini program), and comparative analysis. The linguistic material of oral academic discourse is analyzed through the prism of event-based interpretation of this type of discourse.
Emotivity in popular science media discourse (on the material of German online articles and podcasts about artificial intelligence)
Authors:   St. Petersburg State University, Kovtunova Elena A. , Jesan Irina E. ,   St. Petersburg State University
Published: February 2025

The paper focuses on the study of verbal and non-verbal means of creating emotionality in the media discourse of artificial intelligence. The material of the study is a corpus of Germanlanguage online publications about artificial intelligence (AI) on the websites of the magazine “Der Spiegel” and the newspaper “Süddeutsche Zeitung”, as well as a corpus of transcriptions of podcasts from the audio libraries of the German TV channels ARD and ZDF. The novelty of this study reflects an investigation of the phenomenon of emotionality through a multidimensional linguodiscourse approach on the material of modern popular scientific media discourse, with a special attention to podcast as a distinct media form, which remains insufficiently studied in modern Russian linguistics. In this work we use the DIMEAN method of linguodiscourse analysis, methods of corpus analysis and automatic processing of textual data, contextual-semantic analysis, method of dictionary definitions, component analysis. As a result of the research the modern definitions of mediated and popular science discourses, emotionality and emotionisation of media discourse were clarified. While undergoing a contextual-semantic and corpus analysis the following means and techniques of emotionality of the AI discourse were revealed: direct, secondary and indirect nominations of emotions, emotives expressing emotions (interjections), implicit emotives (metaphors, comparisons, allusions, irony, sarcasm), non-verbal emotives (illustrations). The authors’ personal contribution consists in establishing the polarity of emotional attitudes towards AI in the two considered network formats: negative emotional attitudes prevail, formed on the basis of such emotions as fear, panic, anxiety.
Evaluation catalysts in German and Russian academic reviews in linguistics
Authors:   St. Petersburg State University, Kondratenko Polina I.
Published: February 2025

The concept of evaluation catalysts (EC), introduced by Sergey T. Nefedov to describe the evaluative acts of the author of an academic article/monograph, is applied in this article for the first time to the study of evaluation language in academic journal reviews. The aim of the article is to identify and describe the EC (connectors, prepositions, modal words and constructions, etc.) in accordance with the strategies of evaluative implication operated by German and Russian reviewers. To achieve this goal, we conducted a comprehensive comparative sociocommunicative and discourse-based (context-semantic, axiological, structural-grammatical, functional-pragmatic and communicative) analysis of 120 reviews published in 2016–2023 in German and Russian linguistic journals “Zeitschrift für Angewandte Linguistik”, “Zeitschrift für Sprachwissenschaft”, “Zeitschrift für Rezensionen”, “Topics in the Study of Language”, “Problems of Onomastics”, “Russian Journal of Linguistics”. Based on the analysis results, three levels of EC have been identified: EC providing the realization of each strategy of evaluative implication; EC participating in the realization of macro-strategies of implicit evaluation; EC accompanying implicitly evaluative acts regardless of the specific strategy of evaluative implication. It is proved that EC differ depending on the strategies. The known classifications of implicit evaluation strategies are supplemented with new varieties of implicit evaluation mechanisms. The cases of using not only grammatical but also lexical units guiding the evaluative
interpretation of the contexts of academic expert communication are established; asymmetry in the operation of EC is revealed, manifested both at the strategy level and also in interlingual and -cultural comparison.
Streamflow prediction using Long Short-Term Memory networks: a case study at the Kratie Hydrological Station, Mekong River Basin
Authors: Nguyen Nhu Y , Kha Dang Dinh , Ninh Luu Van , Anh Vu Tuan , Anh Tran Ngoc
Published: February 2025

Accurate streamflow prediction is vital for hydropower operations, agricultural planning, and water resource management. This study assesses the effectiveness of Long Short-Term Memory (LSTM) networks in daily streamflow prediction at the Kratie station, investigate different network structures and hyperparameters to optimize predictive accuracy while considering computational efficiency. Our findings underscore the significance of LSTM models in addressing streamflow prediction challenges. Training LSTM on historical streamflow data reveals the significance of the training dataset size; spanning 2013–2022 yields optimal results. Incorporating a hidden layer with a nonlinear activation function, and adding a fully connected layer improve prediction ability. However, increasing the number of neurons and layers introduces complexity and computational overhead. Careful parameter tuning, including epochs, dropout, and the number of LSTM units, is crucial for optimal performance without sacrificing efficiency. The stacked LSTM with sigmoid activation demonstrates exceptional performance, boasting a high Nash–Sutcliffe Efficiency of 0.95 and a low relative root mean square error (rRMSE) of approximately 0.002%. Moreover, the model excels in forecasting streamflow for 5–15 antecedent days, with 5 days exhibiting particularly high accuracy. These findings offer valuable insights into LSTM networks for streamflow prediction for water management in the Vietnam Mekong Delta.
Tree-based machine learning and global models for long-term rainfall estimation: intercomparison and evaluation over Bahir Dar, Ethiopia
Authors: Yirga Getnet , Raju U. Jaya Prakash , Gedifaw Assaye , Nigusie Ayanew
Published: February 2025

This study aims to provide an efficient and accurate model by comparing the tree-based machine learning approach and the global prediction model with the European Center for Medium Weather Forecast (ECMWF) model for predicting long-term rainfall. Light gradient boosting (LGB) and regression tree (RT) tree-based machine learning algorithms are utilized in this study and compared with the global model. Local metrological parameters such as relative humidity, dew point temperature, minimum temperature, maximum temperature, wind speed, convective available potential energy, and sunshine and large-scale climate variable (sea surface temperature) were used as input during model development. Initially, the database was preprocessed and then partitioned into a training set and a testing set. GridsearchCV technique was used for tuning the parameters of the models. For daily rainfall variation, LGB exhibits strong performance with the highest coefficient of determination (R2 = 0.991; 0.996), lowest root mean squared error (RMSE = 1.14 mm; 0.383 mm), lowest mean squared error (MSE = 1.992; 0.146), and lowest mean absolute error (MAE = 0.899 mm; 0.302 mm) for daily and monthly time scales. For both temporal variations, the LGB model shows significantly higher accuracy than both RT and ECMWF. Relative humidity is the most influential meteorological parameter for rainfall prediction identified by the important random forest (RF) feature with a value of 0.4129. An agricultural decision support system that is still in development will incorporate the suggested models in Ethiopia.
Temporal analysis of the Hooghly River's water quality: investigating pre- and post-monsoon scenarios in West Bengal, India
Authors: Shamim Farzana , Chakraborty Barna
Published: February 2025

This research aimed to investigate the influence of ferry operations and religious activities on the water quality of the Hooghly River in Kolkata, West Bengal, during 2022, while pinpointing pollution hotspots and suggesting mitigation strategies. As a crucial distributary, the Hooghly River is significantly impacted by human activities, particularly near ferry terminals and pilgrimage locations. Water samples (4 L) were taken from eight critical sites during pre- and post-monsoon periods at high tide, concentrating on important physico-chemical and biological attributes. Though pre-monsoon water quality largely adhered to BIS guidelines, heightened chemical oxygen demand and total coliform (TC) levels indicated potential pollution hotspots, likely caused by ferry movements and religious gatherings. Post-monsoon data revealed acceptable pH and temperature ranges but flagged concerns due to increased biochemical oxygen demand and TC counts, especially in high-traffic zones. The water quality index varied between 213 and 282, with a higher pre-monsoon value of 282 and a post-monsoon value of 213, primarily influenced by total dissolved solids, hardness, alkalinity, and conductivity. The comprehensive pollution index ranged from 1.30 to 1.33, surpassing the acceptable limit. These assessments reveal the impact of ferry and religious activities on the Hooghly River's water quality, stressing the need for targeted pollution control.
Avaliação do uso de técnicas de agrupamento na busca e recuperação de imagens
Authors:   , Silva Filho Antonio Fernandes da
Published: February 2025

Atualmente quase todos os serviços e tarefas cotidianas envolvem algum aparato
computacional, acarretando a criação e o consequente acúmulo de dados. Essa progressiva
quantidade de dados representa uma importante oportunidade de exploração para os
ramos científico e comercial, que passaram a valorizar e utilizar essas informações de
forma mais intensa e objetiva. Aliado a isso, o processo natural de exposição da
vida pública e privada através das redes sociais e dos dispositivos eletrônicos tende
a gerar uma quantidade expressiva de imagens que podem e devem ser aproveitadas
com os mais diversos fins, como na área de segurança pública. Nesse contexto, o
reconhecimento facial tem avançado e atraído estudos e aplicações específicas, que visam a
identificação de indivíduos por meio de características paramétricas. No entanto, alguns
entraves ainda são encontrados, dificultando a realização eficiente dessa operação, como
o custo computacional relativo ao tempo de busca e recuperação de imagens em bases
de dados de grandes proporções. Baseado nisso, este trabalho propõe a utilização de
algoritmos de agrupamento na organização dos dados de imagens, proporcionando assim
um direcionamento e “encurtamento” nas buscas de imagens faciais. Mais especificamente,
é feita uma análise relacionada à otimização imposta pelo uso de técnicas de agrupamento
aplicadas na organização automatizada das imagens, como etapa preparativa para realização
de buscas. O método proposto foi aplicado em bases de dados de imagens faciais reais, e
utilizou dois algoritmos de agrupamento (k-means e EM) com variações para as medidas de
similaridade (distância euclidiana e correlação de Pearson). Os resultados obtidos revelam
que o emprego do agrupamento na organização dos dados mostrou-se eficiente, levando a
uma redução significativa no tempo de busca, e sem prejuízos na precisão do processo.
Construction of a Subcutaneous Fat Transplantation Model Infected with Mycobacterium
Authors: Diao Wuliang , Hu Yang , Chen Xiangyu , Li Weidong , Kpegah Julius K.S.K. , Chen Xuan , Wang Mingzhu , Zhou Jianda , Li Ping
Published: February 2025

Introduction:

Fat grafting procedures for body contouring and cosmetic reconstruction
have received widespread attention.

Method:

In recent years, there has been an increase in post-fat grafting infections caused
by Mycobacterium abscessus (MA), and there is a lack of representative and standardized
murine models of infection; therefore, there has been limited research on the
treatment of post-fat grafting MA infections. To overcome this challenge, we constructed
an MA infection model after fat grafting.

Result:

By evaluating skin charge, dermatopathology, and inflammatory markers, we
found that the fat graft + 1 × 109 CFU/mL bacterial suspension infection group had significant
inflammatory symptoms and elevated inflammatory factors on postoperative day
10.

Conclusion:

The model construction process was simple and reproducible, which paves
the way for further studies on the impact of MA pathogenesis and the efficacy of new
treatments.
Systems Biology Approach to Unraveling Transcriptomic Mechanisms of Ganfule Capsules in Ameliorating Nonalcoholic Fatty Liver Disease
Authors: Pan Yu , Zhang Yunkun , R Sooranna Suren R. , Yang Xiaonan , Zhou Yaqin , Chen Lu , Xu Fei , Huang Danna
Published: February 2025

Aims:

The primary objective of this study is to explore the impact of Ganfule (GFL), a traditional
Chinese medicine, on differentially expressed genes (DEGs) linked to nonalcoholic fatty liver disease
(NAFLD). By identifying potential biomarkers, we seek to enhance GFL's clinical efficacy through targeted
pharmaceutical design.

Background:

NAFLD a prevalent liver disorder, is often associated with obesity and metabolic syndrome.
While GFL has demonstrated clinical efficacy in treating NAFLD, its precise targets and mechanisms of action
remain elusive. Understanding these mechanisms could pave the way for more effective treatments.

Objectives:

GFL, a long-standing traditional Chinese medicine (TCM), has demonstrated clinical effectiveness
in treating NAFLD. However, its precise targets and mechanism of action remain elusive. In this study,
we aim to explore GFL's impact on differentially expressed genes, which could potentially serve as biomarkers
for developing targeted therapies. This approach is intended to enhance GFL's clinical efficacy by
identifying key genes that respond to its treatment.

Methods:

To induce NAFLD, 23 Sprague-Dawley rats were fed a high-fat diet. These rats were then categorized
into three groups: normal diet (NOR), high-fat diet model (HFD), and those treated with GFL. Highthroughput
sequencing was employed to identify DEGs in their livers. Utilizing the STRING and DAVID
databases, we analyzed potential protein interactions expressed by these genes. Furthermore, the KEGG, Reactome,
and Wiki databases aided in determining their biological roles and signaling pathways. Key DEGs'
mRNA expression levels and corresponding proteins were further screened and confirmed through haematoxylin-
eosin staining (HE), immunohistochemistry (IHC), Real-Time Quantitative Reverse Transcription
Polymerase Chain Reaction (RT-qPCR), and western blotting.

Results:

Significant variations in DEGs were observed across the three groups, with 19 intersecting genes
identified within a cluster of 90 NAFLD-related genes. GFL was found to adjust the expression of nine core
DEGs, including Abcg1, Igfgb1, Lepr, Pdk4, Socs3, and Stat3. These genes-related proteins are tied to proteins
such as FABP4, LEPR, SCD1, SOCS3, and STAT3, which are intimately connected to adipocytokine
and adipogenesis pathways. Our study reveals that GFL modifies the expression of IGFBP1, LEPR, PDK4,
SCD1, and SOCS3, thereby regulating the adipocytokine, JAK-STAT, leptin-insulin signaling, and adipogenesis
metabolic pathways, respectively.

Conclusions:

This study enhances understanding of GFL's efficacy and identifies potential biomarkers for
NAFLD treatment. Optimizing GFL's efficacy and elucidating its mechanism provides a methodological reference
for traditional Chinese medicine exploration.
Research on Neuroimmune Gastrointestinal Diseases Based on Artificial Intelligence: Molecular Dynamics Analysis of Caffeine and DRD3 Protein
Authors: Qin Yi , Huo Shuran , González Ana María , Guo Lizhong , Santos Javier , Li Liangyu
Published: February 2025

Objectives:

The aim of this study was to develop a clinical application model for the rational use
of caffeine.

Background:

Caffeine is related to the incidence of neuro immune gastrointestinal diseases. Coffee consumption
needs to be optimized in order to reduce the incidence rate.

Purpose:

By using KEEG analysis to explore potential molecular signaling pathways involved in the progression
of neurological immune gastrointestinal diseases, and analyzing the details of this signaling Pathway
using molecular simulation results, which can support AI system for doctor.

Methods:

The research team designed a controlled experiment to analyze the differences in reward and reinforcement
of Brain pleasure/addiction and dopamine related signaling pathways function between multiple
groups of people with different coffee drinking habits and a blank control group. The study team used molecular
dynamics methods to investigate the signaling route that links coffee with the binding of dopamine
receptor D3.AI is used to predict the prevalence of gastric reflux disease.

Results:

Human experiments have shown a correlation between caffeine intake and gastroesophageal reflux
disease. AI algorithm results can provide clinical support, and molecular simulation results are consistent
with human experimental results. Caffeine and DRD3 protein have a stable interaction system.

Conclusion:

The research team elucidated the intermolecular interaction between caffeine and DRD3, and
AI algorithms can predict the likelihood of disease occurrence, providing a new strategy for clinical practice.
This study has passed ethical approval at Chifeng Cancer Hospital, and the ethical documents for this study
have been submitted to the World Health Organization for filing.
I-Brainer: Artificial intelligence/Internet of Things (AI/IoT)-Powered Detection of Brain Cancer
Authors: Ibrahim Abdullahi Umar , Nwaneri Ikedichukwu Onyemaucheya , Vubangsi Mercel , Al-Turjman Fadi
Published: February 2025

Background/Objective:

Brain tumor is characterized by its aggressive nature and low survival rate and therefore, it is regarded as one of the deadliest diseases. Thus,
misdiagnosis or miss-classification of brain tumors can lead to miss-treatment or incorrect treatment and reduce survival chances. Therefore, there
is a need to develop a technique that can identify and detect brain tumors at early stages.

Methods:

Here, we proposed a framework titled I-Brainer which is an Artificial Intelligence/Internet of Things (AI/IoT)-powered classification of MRI into 4
classes. We employed a Br35H+SARTAJ brain MRI dataset which contains 7023 total images including no tumor, pituitary, meningioma, and
glioma. To accurately classify MRI into 4 classes, we developed the LeNet model from scratch, and implemented 2 pre-trained models which
include EfficientNet and ResNet-50 as well as feature extraction of these models coupled with 2 Machine Learning (ML) classifiers namely; k-
Nearest Neighbours (KNN) and Support Vector Machine (SVM).

Results:

Evaluation and comparison of the performance of the 3 models have shown that ResNet-50 achieved the best result in terms of AUC (99%) and
ResNet-50-KNN ranked higher in terms of accuracy (94%) on the testing set.

Conclusion:

This framework can be harnessed by patients residing in remote areas and as a confirmatory approach for medical experts.
Exploring Structural Requirement of Curcumin-Based CK2 Inhibitors as
Anticancer Agents: 3D-QSAR, Pharmacophore Modeling, Virtual
Screening, and Molecular Docking
Authors: Fatima Firdous , Nema Priyanshu , Garhwal Anushka , Kashaw Sushil Kumar
Published: February 2025

Introduction:

Casein Kinase 2 (CK2), discovered as one of the earliest protein kinases,
is a ubiquitous Ser/Thr protein kinase-specific to acidic environments. CK2 has been implicated in
regulating diverse cellular processes and has been linked to the onset of various diseases, including
cancer.

Method:

Consequently, modulating CK2 function has emerged as a potential therapeutic strategy.
However, currently, available CK2 inhibitors or modulators often lack sufficient specificity and
potency

Results:

The results were validated through QSAR of curcumin derivatives, Pharmacophore modeling, virtual screening performed for filtered curcumin-like featured derivatives from the database,
and Molecular Docking approaches. Since there is a solved crystal structure of high-resolution Xray crystal structures of Human protein kinase CK2 alpha in complex with ferulic aldehyde.Results: The results were validated through QSAR of curcumin derivatives, Pharmacophore modeling, virtual screening performed for filtered curcumin-like featured derivatives from the database,
and Molecular Docking approaches. Since there is a solved crystal structure of high-resolution Xray crystal structures of Human protein kinase CK2 alpha in complex with ferulic aldehyde.

Conclusion:

Also, structure-based virtual screening was performed against a total of 3253 compounds from different libraries, and only the top 4 best-hit compounds with exceptional docking
scores exceeding >-7 kcal/mol (more than 7 kcal/mol) were screened and analyzed. However, to
validate their therapeutic potential, these compounds require in-vitro evaluation to assess their CK2
targeting ability
Qi-Gui-Jian-Gu Decoction Accelerates Osteogenesis and Fracture Healing by Activating the Wnt/β-Catenin Signaling Pathway
Authors: Wu Siluo , Wang Jiayang , Luo Ziheng , Li Bifeng , Xu Liangliang , Hu Liuchao , Hu Rihe
Published: February 2025

Background:

Qi-Gui-Jian-Gu decoction (QGJG), as a clinical empirical formula, has
clinical benefits in promoting bone formation, but the underlying mechanism for its application in
treating fractures has not been investigated.

Methods:

The potential therapeutic target and signaling pathway of QGJG for treating fractures
were analyzed by network pharmacology. In vitro, we used bone marrow mesenchymal stem cells
(MSCs) to evaluate osteogenic differentiation and mineralization by alizarin red staining, quantitative
real-time polymerase chain reaction (qRT-PCR), western blot (WB), and immunofluorescence
staining. In vivo, the 8w male SPF C57BL/6J mouse femoral fracture model was constructed,
and the therapeutic effects of QGJG were evaluated.

Results:

By network pharmacology analysis, we found that glycogen synthase kinase 3 beta
(GSK3β) was a potential therapeutic target of QGJG for treating fractures. The canonical Wnt signaling
pathway was selected as the potential molecular mechanism. QGJG was confirmed to upregulate
the mRNA levels of alkaline phosphatase (ALP) and bone morphogenetic protein 2 (BMP2),
thereby promoting osteogenic differentiation and mineralization. Mechanistically, QGJG inhibited
GSK3β while increasing p-Ser9-GSK3β to increase β-catenin protein expression and its nuclear
translocation, implying the activation of the canonical Wnt signaling pathway. In vivo,
QGJG administration promoted fracture healing, as demonstrated by the up-regulation of OPN
and Osx, and accelerated the progression of ossification at 2 and 3 weeks after surgery.

Conclusion:

QGJG promotes osteogenic differentiation and fracture healing by activating the
canonical Wnt pathway.
Exploring the Synthetic Challenges of Functionalized 2-iminopyrans
Authors: Sharma Pragati , Choudhary Tanisha , Sinha Pragya
Published: February 2025

The review paper presents an overview of the emerging trends in the synthesis of 2-
iminopyrans. Iminopyran derivatives have shown significant potential in various fields such as
pharmacology, cosmetics, and agrochemicals. Among the functionalized iminopyrans, 2-
iminopyrans have gained significant attention due to their diverse biological activities and crucial
role as intermediates in chemical synthesis. However, the traditional approach has limitations such
as low yields and long reaction times. The paper explores the recent advancements in the synthesis
of 2-iminopyrans, particularly emphasizing the reaction mechanisms involved, of these compounds.
The review aims to spark readers' interest in developing more practical and environmentally friendly
synthetic routes by providing a roadmap for synthesizing 2-iminopyrans
The Convergence of Nanotechnology and Artificial Intelligence: Unlocking Future Innovations
Authors: Zafar Sarvat , Rana Nadim
Published: February 2025

This review article explores the integration of artificial intelligence (AI) and
nanotechnology, focusing on their combined potential to drive advancements in nanomaterial
discovery, drug delivery systems, and nano-electronic component design. It also
examines the transformative effects of AI-enhanced nanotechnology in medicine, diagnostics,
bioengineering, and other scientific domains, emphasizing its future implications
across various sectors. This article examines the synergy between AI and nanotechnology,
focusing on recent innovations in nanomaterial discovery, AI-driven material design, and
precision medicine. It reviews case studies and research highlighting AI's role in accelerating
nanomaterial development and its applications in medicine, electronics, diagnostics,
and robotics, using a multidisciplinary approach. AI-enhanced nanotechnology has enabled
the development of novel nanomaterials with unprecedented properties tailored for specific
applications, such as highly efficient drug delivery systems and next-generation nanoelectronic
components. In medicine, AI-driven nanotechnology offers promising solutions
for highly personalized treatments, improving therapeutic efficacy and reducing side effects.
Additionally, AI is driving innovation in diagnostics and robotics, leading to more
sensitive diagnostic tools and the development of nanoscale-precision robotic systems. The
integration of AI and nanotechnology presents vast opportunities for scientific and technological
advancements. As AI algorithms continue to evolve, their impact on nanotechnology
will lead to breakthroughs in diverse fields, such as medicine, electronics, diagnostics,
and robotics. This interdisciplinary synergy will open new frontiers in research, driving
transformative changes in bioengineering, neuroscience, and beyond.
An Assessment of BHLHE40 Transcription Factor Level and its Target Cytokines in Patients with Rheumatoid Arthritis
Authors: Ghotloo Somayeh , Zamani Batol , Osatadian Amir-Reza , Marzhoseyni Zeynab
Published: February 2025

Background:

Basic helix-loop-helix protein 40 (BHLHE40) can function as both a
transcriptional activator and repressor. Recent studies have reported its regulatory functions in T
helper (Th)1 and Th17 immune responses. Rheumatoid arthritis (RA) is an autoimmune inflammatory
disease in which joints are involved. Both Th1 and Th17 contribute to the pathogenesis of the
disease. In the present study, the levels of BHLHE40, interleukin 17 (IL-17), and interferon-gamma
(IFN-γ) were assessed in the RA patients.

Methods:

Two groups, including RA patients and healthy individuals, were included in the study.
The relative expression levels of BHLHE40, IL-17, and IFN-γ were quantified in peripheral blood
mononuclear cells (PBMCs) using real-time PCR.

Results:

The results showed that the level of BHLHE40 was significantly higher in RA patients
compared to the healthy control (P < 0.001) (11.1-fold increase). Accordingly, a significant increase
in the levels of IL-17 (8.1 folds increase) (P < 0.021) and IFN-γ (12.7 folds) (P < 0.001)
was observed.

Conclusion:

Evaluation of the expression level of BHLHE40 in RA patients showed a significant
increase. In line with the elevated level of BHLHE40, a significant increase in the expression level
of IL-17 and IFN-γ was also detected. These findings point to the possible role of BHLHE40 in
the disease course or severity by elevating the levels of inflammatory cytokines, including IL-17
and IFN-γ. Therefore, BHLHE40 might be considered either as a putative biomarker or as a candidate
for therapy in a variety of human diseases.
Penambangan Ilegal Di Kecamatan Saronggi Kabupaten Sumenep dan Faktor-Faktor Yang Mempengaruhinya
Authors: Amalia Bhenneta Bela , Istislam Istislam , Qurbani Indah Dwi
Published: February 2025

This study aims to examine the effectiveness of the regulation found in Article 35 of Law No. 3 of 2020 in Sumenep Regency. The chosen research method is socio-legal, utilizing a sociological juridical approach. Data collection was conducted through interviews and literature studies. The findings of this study indicate that the implementation of the regulation in Article 35 of Law No. 3 of 2020 is ineffective. This ineffectiveness may be attributed to cultural factors, legal factors, infrastructure, law enforcement, and community factors. In this issue, the government's role has not been substantial in imposing legal sanctions on illegal mine owners. Additionally, the community tends to view mining as a source of employment, with those who are less tolerant and permissive toward illegal mining not considering the long-term consequences. The obstacles encountered include the community's difficulty in obtaining permits, even though the land used is privately owned.