bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of Extracranial Metastatic Features on Patients with Breast Cancer Brain Metastasis: A Retrospective Analysis of 284 Patients
Authors: Wang Yan , Xu Hangcheng , Sa Qiang , Li Li , Han Yiqun , Wu Yun , Zhou Yiran , Xu Binghe , Wang Jiayu
Published: February 2024

Purpose
Breast cancer brain metastasis (BCBM) is associated with poor survival outcomes and reduced quality of life. The Graded Prognostic Assessment (GPA) score model serves as a well-established tool for predicting the prognosis of BCBM. Notably, the presence of extracranial metastasis (ECM) is considered as a significant prognostic factor in the breast GPA model. This study aims to further refine other features of extracranial metastasis to enhance the prognostic prediction for BCBM.
Methods
This study included all inpatients diagnosed with BCBM at the Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS), from January 2010 to July 2021. The subsets were categorized by the presence, number, location, and control status of ECM. The primary endpoint was overall survival (OS) after the diagnosis of BCBM. We used the Kaplan–Meier method with log-rank test to compare the survival outcomes. Cox regression analyses were conducted to identify significant prognostic factors, which were then incorporated into a new Breast-GPA model.
Results
A total of 284 patients with BCBM were included in the study. Kaplan–Meier survival curves suggested that patients without ECM when diagnosed with BCBM showed better survival (p = 0.0068). In the subgroups with ECM, more than 3 organs involved, both bone and visceral metastasis and progressive ECM portended dismal OS (p = 0.0032, 0.0014 and 0.00054). Multivariate analysis (MVA) demonstrated that the presence, number, and control status of ECM significantly influenced OS after BCBM (p = 0.01, 0.03, and 0.048, respectively). By modifying the current GPA model to include categories for '3 or fewer controlled ECM', the predictive accuracy of the newly established GPA model was further enhanced.
Conclusion
More extracranial sites, both bone and visceral invasion and uncontrolled ECM were independent prognostic factors for dismal survival outcomes of BCBM patients.
To explore the potential diagnostic and prognostic value of Golgi related genes in prostate cancer
Authors: Chen Hanmin , Li Yanmin , Zeng Qingming , Zhang Guoxi , Huang Jinhua
Published: February 2024

Background
The recurrence rate of prostate cancer (PCa) remains high. Research have shown that high expression of Golgi apparatus (GA) phosphoprotein 3 is related to occurrence of PCa. Therefore, the purpose of this study was to screen hub genes related to GA in PCa.
Methods
TCGA-PRAD, GSE46602 and 1644 golgi apparatus-related genes (GARGs) were performed. Differentially expressed GARGs (DE-GARGs) were obtained by differential expression analysis and Venn analysis. Next, hub genes were screened through protein-protein interaction networks (PPI), further univariate Cox regression and least absolute shrinkage and selection operator (Lasso) regression were performed to obtain prognostic genes and risk models. Afterwards, Kaplan-Meier (KM) curve and receiver operating characteristic (ROC) curve were utilized to evaluate model. Univariate and multivariate Cox regression analyses were executed to evaluate the independent predictive power of models. Further a nomogram was constructed to assess capability of risk score as an independent prognosis. Meanwhile, the correlation analysis of prognostic genes with clinical features and immune cells and drug sensitivity analysis were also carried out. Finally, the expression level of prognostic gene was analyzed.
Results
Among 13 hub genes were screened, and MUC6, PRAME and VGF were obtained by univariate Cox and Lasso regression, further a risk model was constructed. TCGA-PRAD was divided into high and low risk groups according to the median risk score. Firstly, KM curve showed that there was remarkable difference in biochemical recurrence (BCR) between the two groups, next the AUC value of 1, 3 and 5 years was above 0.65. Eventually, in GSE46602, it was also proved that the risk model had better forecasting ability. Meanwhile risk score could be used as an independent prognostic factor, and it was remarkably different in different clinical features. The better predictive ability of the nomogram was proved by calibration curve and DCA curve. Afterwards, there were remarkable differences in BCR between ESTIMATE score and high-low risk group, likewise, there were significant differences in 14 immune cells, 9 immune checkpoints, and 104 drugs between two risk groups. Lastly, the expression of prognostic genes was consistent with univariate Cox analysis when constructing risk model.
Conclusion
A reliable prognostic model based on MUC6, PRAME and VGF was constructed, which provided valuable information for in-depth exploration of the pathogenesis of PCa.
Traffic State Estimation with A multi-head Attention-based Transformer by Spatio-Temporal Autoencoding Transformer (STAT Model)
Authors: Sattarzadeh Ali Reza , Pathiran Pubudu N. , Huynh Van. Thanh
Published: February 2024

Traffic state estimation is an essential component of Intelligent Transportation System (ITS) designed for alleviating traffic congestions. As traffic data is composed of intricate information which can also be impacted by various factors, scholars have been attempting to utilize state-of-the-art deep learning forecasting models in recent years. However, a more complex and robust model is required to extract long-range correlations with large-scale traffic data sequences. In order to overcome the weaknesses of deep learning models, the superior performance of transformers is expected to address this effectively in time-series forecasting with transport data. Employing the capabilities of transformers in extracting long-term trends and dynamic dependencies, proposed model improves the deep learning prediction performance for real datasets. The findings indicate that the transformer-based model exhibited promising performance in forecasting long-term traffic patterns and characteristics with a large quantity of data. In this paper, a comparison across conventional hybrid deep learning models with the Spatio-Temporal Autoencoder Transformer (STAT) model was conducted using real-world datasets. The multi-head attention-based transformer model outperformed all other comparative approaches for large-scale data demonstrating its importance in measuring the error criteria.
Electrocardiographic changes in acute Kawasaki disease in the era of immunoglobulin therapy
Authors: Nakanishi Ryo , Nakamura Toshiki , Koyama Yutaro , Akahoshi Shogo , Hataya Hiroshi , Miura Masaru
Published: February 2024

Background Kawasaki disease (KD), an acute, febrile, self-limiting, multisystem vasculitis of unknown etiology, often involves the cardiac complications of coronary artery aneurysm development, myocarditis, pericarditis, and valvulitis. However, there is insufficient, epidemiological data on electrocardiographic (ECG) changes in acute KD in the IVIG era, and little is known about the association between ECG changes and the prognosis.
Objective The present study aimed to determine the type of ECG changes found in acute KD and to clarify the course of these changes as well as their relationship to disease severity.
Methods The present, retrospective, observational study analyzed 201 patients aged < 15 years with Kawasaki disease who received IVIG from April 2021 to March 2023.
Results In the 156 eligible patients analyzed (male: 62.8%; median age on admission: 24 months), the most common abnormalities were T wave changes (n=141; 90.4%) on any lead followed by T wave inversions (n=63;40.4%), tachycardia (n=36; 23.1%), and a prolonged PR interval (n=23; 14.7%). PR prolongation, abnormal Q wave, QTc prolongation, and ST changes were less common in the present cohort, and arrhythmia occurred less frequently than in previous studies. No significant association was found between T wave inversion and disease severity.
Conclusion T wave changes occurred frequently in the acute phase ECG in KD but were not associated with clinical features, such as unresponsiveness to IVIG therapy or coronary artery abnormalities.
Deciphering the Regulatory Symphony: MicroRNA Orchestration of the RAS/MAPK Signaling Pathway and its Interplay across Cellular Networks in Colorectal Cancer Tumorigenesis
Authors: Pasdar Alireza , Farzadifar Elnaz , Abidi Pouria , Rahmani Farzad , Lotfabadi Narges Nikoonahad , Amerizadeh Forouzan
Published: February 2024

The RAS/MAP kinase pathway, pivotal in cancer development, intertwines with microRNAs (miRNAs), shaping CRC's dynamic cellular landscape. This study utilizes a systems biology approach, employing advanced bioinformatics tools to unravel miRNA-mediated regulatory networks in CRC. Investigating miRNA dysregulation, identifying hub genes, and exploring interplay between signaling pathways aim to provide nuanced insights into CRC's molecular landscape. Protein-protein network analysis uncovers the interconnected web of miRNA-targeted genes within the RAS/MAPK signaling pathway. Utilizing STRING and Cytoscape, we pinpoint 13 hub proteins orchestrating network dynamics. GO and KEGG enrichment analyses reveal intricate regulatory mechanisms. Cluster analysis unveils 817 clusters, emphasizing CRC significance. Hub gene promoter motif analysis delves into transcriptional regulatory elements governing CRC pathogenesis. A comprehensive list of miRNAs targeting the PI3K pathway in CRC is presented. Protein-protein interaction networks highlight 13 hub proteins, predominantly linked to transcriptional regulation. Enriched pathways, such as the MAPK cascade, underscore their pivotal roles. Cluster analysis reveals CRC's significance, emphasizing involvement in glucose metabolism. Promoter motif analysis uncovers significant motifs targeted by dysregulated miRNAs. Transcription factor motifs reveal biological roles, with implications in signal transduction and cell proliferation. Enrichment in nucleus-based terms and disruption of cell polarity underscore potential implications in cancer development. This integrative study unravels the regulatory intricacies of miRNAs in the RAS/MAPK signaling pathway, shedding light on CRC's molecular landscape. Identification of hub genes, enriched pathways, and regulatory motifs provides valuable insights for diagnostics and therapeutics, offering promising targets for simultaneous inhibition in cancer cell growth.
FlightTrackAI: a convolutional neural network-based software for tracking the flight behaviour of Aedes aegypti mosquitoes
Authors: Javed Nouman , López-Denman Adam J. , Paradkar Prasad N. , Bhatti Asim
Published: February 2024

Monitoring the flight behaviour of mosquitoes is crucial for assessing their fitness levels and understanding their potential role in disease transmission. Existing methods for tracking mosquito flight behaviour are challenging to implement in laboratory environments, and they also struggle with identity tracking, particularly during occlusions. Here, we introduce FlightTrackAI, a novel convolutional neural network (CNN)-based software for automatic mosquito flight tracking. FlightTrackAI employs CNN, a multi-object tracking algorithm, and cubic spline interpolation to track flight behaviour. It automatically processes each video in the input folder without supervision and generates tracked videos with mosquito positions across the frames and trajectory graphs before and after interpolation. FlightTrackAI does not require a sophisticated setup to capture videos; it can perform excellently with videos recorded using standard laboratory cages. FlightTrackAI also offers filtering capabilities to eliminate short-lived objects such as reflections. Validation of FlightTrackAI demonstrated its excellent performance with an average accuracy of 99.9% and an average mean absolute error of 0.23 pixels. The percentage of correctly assigned identities after occlusions exceeded 91%. The data produced by FlightTrackAI can facilitate analysis of various flight-related behaviours, including diurnal and nocturnal locomotor activity, host-seeking behaviour, flight distance, volume coverage during flights, and speed measurement. This advancement can help to enhance our understanding of mosquito ecology and behaviour, thereby informing targeted strategies for vector control.
Solvable models for 2+1D quantum critical points: Loop soups of 1+1D conformal field theories
Authors: Moharramipour Amin , Sehayek Dan , Scaffidi Thomas
Published: February 2024

We construct a class of solvable models for 2+1D quantum critical points by attaching 1+1D conformal field theories (CFTs) to fluctuating domain walls forming a “loop soup”. Specifically, our local Hamiltonian attaches gapless spin chains to the domain walls of a triangular lattice Ising antiferromagnet. The macroscopic degeneracy between antiferromagnetic configurations is split by the Casimir energy of each decorating CFT, which is usually negative and thus favors a short loop phase with a finite gap. However, we found a set of 1D CFT Hamiltonians for which the Casimir energy is effectively positive, making it favorable for domain walls to coalesce into a single “snake” which is macroscopically long and thus hosts a CFT with a vanishing gap. The snake configurations are geometrical objects also known as fully-packed self-avoiding walks or Hamiltonian walks which are described by an \mathrm{O}O(n=0) loop ensemble with a non-unitary 2+0D CFT description. Combining this description with the 1+1D decoration CFT, we obtain a 2+1D theory with unusual critical exponents and entanglement properties. Regarding the latter, we show that the log contributions from the decoration CFTs conspire with the spatial distribution of loops crossing the entanglement cut to generate a “non-local area law”. Our predictions are verified by Monte Carlo simulations.
Navigating the technical analysis in stock markets: Insights from bibliometric and topic modeling approaches
Authors: Kumar Inani Sarveshwar , Pradhan Harsh , Kumar Surender , Biswas Baidyanath
Published: February 2024

In stock markets, technical analysis plays a vital role by offering valuable insights into price trends, patterns, and anticipated market movements, aiding investors in making well-informed decisions. This study employs bibliometric and topic modelling approaches on 589 English-language journal articles indexed in Scopus in the last two decades (from 2003 to 2023), exclusively focusing on technical analysis in stock markets. The keyword co-occurrence analysis identifies five topic clusters. The application of structural topic modelling also unravels five prominent thematic clusters, namely pattern-based forecasting, rule-based trading, algorithmic trading, techno-fundamental trading, and machine learning & sentiment analysis. The topic of pattern-based forecasting involves researching the application of various patterns or models to predict stock prices. Rule-based trading concentrates on utilizing technical analysis tools to generate buy and sell signals, aiming for profitability. The algorithmic trading cluster explores the use of algorithms to systematically execute buy and sell actions, especially in high-frequency trading scenarios. Techno-fundamental trading investigates the integration of both fundamental and technical analysis in trading and investment decisions. Lastly, machine learning & sentiment analysis focus on applying advanced machine learning techniques and sentiment analysis for predicting stock prices, highlighting the use of sophisticated methods in this domain. The three predominant topics in the dataset are "rule-based trading," "machine learning & sentiment analysis," and "algorithmic trading" constituting 26.79%, 23.52%, and 21.11% of the dataset, respectively. These findings underscore the prominence and significance of these themes within the context of the research domain.
An Experiment to Prevent Malicious Actors from Compromising Private Digital Assets Over a Public Network
Authors: Hartanto Feliks , Budiman Budiman , Gwei Eldwin , Gunawan Alexander Agung Santoso , Edbert Ivan Sebastian
Published: February 2024

In the current millennium, human society has immensely improved its ability to obtain and distribute information. This change on the other hand, has caused the majority of daily routines to actively involve the usage of computers and mobile devices, which in turn has made people rely heavily on the availability of internet access. This fact was taken advantage of, causing a massive increase in public networks by people or businesses to draw in customers or just as simple public service. This increase gives both ease and risks which this paper will address, specifically on the security measures in network devices that are nearby, and the solution proposed to provide complementary insight on securing the technologies. The authors of this paper supply the main point of the research through experimental efforts i.e., by testing the solution in a real-life scenario. The solution itself involves the configuration of a Raspberry Pi into a VPN server and rerouting all traffic into the Raspberry server so that it will be encrypted and safe from the dangers that will be mentioned in later parts of this paper. The result of the experiment shows that the proposed solution can successfully encrypt the targeted packet so it can’t be read by malicious attackers. Although the solution works it can’t be simply applied to every public network due to internet connection protocols and its inconvenience. Future research will involve the improvement or rework of the solution until the issues mentioned above are solved.
Machine Learning for Predicting Personality using Facebook-Based Posts
Authors: Suhartono Derwin , Ciputri Marcella Marella , Susilo Stefanny
Published: February 2024

Social media contributes a lot to human life. People can share their thoughts through text, photos, and voice through social media. Information from social media can be useful, including in personality research. Personality can generally be known through personality tests. In this research, personality prediction is formed to determine personality through Facebook posts without using a personality test. We create a model based on big five personality traits using 5 machine learning algorithms: Support Vector Machine (SVM), Multinomial Naive Bayes, Decision Tree, K-Nearest Neighbor, and Logistic Regression. Data augmentation was also used for balancing the dataset value and trained using stratified 10-fold cross-validation. This research yields the highest f1 score on Openness using Multinomial Naive Bayes algorithm of 82.31% and the highest average is 68.62%. So the five supervised Machine Learning algorithms used in this research produced Multinomial Naive Bayes as the best algorithm to predict personality based on big five personality traits from user postings on Facebook.
Design of An Intelligent Tutoring System – Student Model: Predicting Learning Style
Authors: Hawari Nubli , Oktavia Tanty
Published: February 2024

Education is very important for everyone, not only for acquiring knowledge but also for improving quality of life and well-being. An Intelligent Tutoring System (ITS) is a computer system that can provide personalized and adaptive learning assistance and support to students. This system is designed to offer effective guidance to students based on their individual abilities and learning styles. ITS utilizes artificial intelligence (AI) technology to understand students' abilities and provide guidance tailored to their needs. Recently, there have been methods to predict learning styles, such as through questionnaires on the EducationPlanner website, but these determinations are often too general. This study aimed to predict the learning styles used by specific students for specific subjects. Researchers conducted this study at XYZ University to determine the learning styles of certain students or groups. With this information, instructional materials and methods can be uniquely designed to cater to the needs of these groups. Based on the evaluation results, the study found that the Logistic Regression model was the best, with a precision of 0.5653 and a hamming loss value of 0.3468. This research demonstrates that information from six selected subjects (English, Religion, Civics, Arts, Physics, and Geography) can be used to determine students' learning styles.
Biological therapy of severe bronchial asthma in a child
Authors: Semernik O. E. , Lebedenko A. A. , Tyurina E. B. , Rudiakova V. S. , Gevorkian N. A. , Kristosturian M. A.
Published: February 2024

Currently, bronchial asthma is a global health problem, due to its high prevalence, economic component, as well as a violation of the social adaptation of children suffering from this disease. This article is devoted to a detailed analysis of the clinical case of a patient born in 2009 with an established diagnosis of Bronchial asthma, atopic form, severe persistent partially controlled course, which initiated therapy with a genetically engineered biological preparation of a humanized monoclonal antibody against IgE – omalizumab. However, in subsequent years, the patient’s condition was unstable, severe seizures were repeated, she was repeatedly hospitalized in the department where infusion therapy was performed and basic therapy was reviewed, dose adjustments and administration regimens of monoclonal antibodies to IgE (omalizumab) were carried out. A retrospective analysis of the results of objective, instrumental and laboratory examinations of the patient was also carried out over the past three years, when the child received the genetically engineered drug omalizumab without interruption. Taking into account the severe course of AD, the lack of control over the disease against the background of basic therapy with combined drugs in combination with therapy with monoclonal antibodies to IgE (omalizumab), it was decided to correct treatment and initiate therapy with a genetically engineered drug, recombinant human monoclonal antibody IgG4 (dupilumab). This clinical example once again shows how personalized the approach should be when prescribing therapy to patients with severe asthma, and also dictates the need to develop new diagnostic methods and management tactics for patients with uncontrolled forms of this disease.
Hereditary periodic fevers in children
Authors: Krutikhina S. B. , Kudryashova M. A.
Published: February 2024

Fevers in children is a pressing challenge in paediatrics, as they are one of the most common symptoms people seek medical help. Autoinflammatory diseases (AIDs) constitute a group of diseases, where fever is one of the main symptoms, and fever attacks have a certain frequency and duration. Autoinflammatory diseases are rooted in the systemic aseptic inflammation associated with activation of the innate immune system, without elevated levels of autoantibodies. The most common autoinflammatory diseases include hereditary periodic fevers, among which are familial Mediterranean fever (FMF), HIDS/MKD, CAPS-, TRAPS-syndromes and rarer diseases (deficiency of natural interleukin receptor antagonists). The pathogenesis of this group of diseases is based on the process of accelerated formation of a supramolecular protein complex (inflammasome), which subsequently leads to the transition of the inactive form of IL1β into its active form. The clinical presentations of these diseases may be similar and include episodes of fever, abdominal pain, arthralgia, various rashes, etc., as well as an increase in acute phase parameters. These diseases are characterized by symptoms at certain intervals; they usually have intervals without showing any symptoms. Familial Mediterranean fever is characterized by a certain ethnic background (Armenians, Turks, Arabs, Jews). Today, molecular genetic testing is the most accurate method for diagnosing familial Mediterranean fever. Current therapy aims to prevent attacks of the disease, but it is also important to use symptomatic therapy to relieve the child’s condition when an attack has already developed. Modern treatment strategies include both drugs that have long been used in paediatric practice (colchicine, non-steroidal anti-inflammatory drugs (ibuprofen), glucocorticosteroids) and novel genetically engineered biological drugs (interleukin antagonists and tumour necrosis factor inhibitors, etc.).
The effect of local use of etiotropic drugs on the oropharyngeal microbiota in children with acute respiratory infection
Authors: Zakharova I. N. , Berezhnaya I. V. , Kuznetsova I. S. , Cherednikova T. A. , Makhaeva A. V. , Odintsova V. E. , Koshechkin S.  I. , Romanov V. A.
Published: February 2024

Introduction. The microbiota of the oral cavity and oropharynx, actively interacts with the epithelium of the mucous membrane and the immune system, creates antagonistic conditions for the growth and reproduction of pathogens. Microbiocenosis-preserving therapy can provide conditions for maintaining colonization resistance and reduce the likelihood of adhesion of respiratory viruses.

Aim. To evaluate the effect of local use of various antimicrobial drugs on the microbiota of the oropharyngeal mucosa in children with acute respiratory infection.

Materials and methods. 120 children (5–10 years old) with acute respiratory infection were divided into three groups, in which they were used as part of complex therapy: in group 1st – hexetidine-containing aerosol, in group 2nd – a children’s spray containing gramicidin S and cetylpyridinium chloride, in the 3rd – 0.01% solution of benzyldimethyl-myristoylamino-propylammonium. On the 1st and 12th days of observation, participants had a throat swab taken to study the state of the oropharyngeal microbiota using 16S rRNA sequencing. The results obtained were compared with those of healthy peers.

Results. According to sequencing data, the microbiota of participants in all groups before treatment differed from the microbiota of healthy peers in terms of alpha and beta diversity, as well as in the results obtained by the nearest balance method. Local use of antiseptic drugs influenced the microbiota at the level of species and genera of microorganisms. After treatment in the 2nd group, in contrast to the 1st and 3rd, statistically significant changes in the balances at the level of microorganism species were revealed towards the indicators of healthy peers.

Conclusions. A combined drug containing cetylpyritinium chloride and the bacteriocin-like antimicrobial peptide gramicidin S demonstrated no negative effect on commensal microbiota and contributed to the stabilization of the regional microbiota.
Deep learning–based short-term water demand forecasting in urban areas: A hybrid multichannel model
Authors: Namdari Hossein , Ashrafi Seyed Mohammad , Haghighi Ali
Published: February 2024

Forecasting short-term water demands is one of the most critical needs of operating companies of urban water distribution networks. Water demands have a time series nature, and various factors affect their variations and patterns, which make it difficult to forecast. In this study, we first implemented a hybrid model of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to forecast urban water demand. These models include a combination of CNN with simple RNN (CNN-Simple RNN), CNN with the gate recurrent unit (CNN-GRU), and CNN with the long short-term memory (CNN-LSTM). Then, we increased the number of CNN channels to achieve higher accuracy. The accuracy of the models increased with the number of CNN channels up to four. The evaluation metrics show that the CNN-GRU model is superior to other models. Ultimately, the four-channel CNN-GRU model demonstrated the highest accuracy, achieving a mean absolute percentage error (MAPE) of 1.65% for a 24-h forecasting horizon. The effects of the forecast horizon on the accuracy of the results were also investigated. The results show that the MAPE for a 1-h forecast horizon is 1.06% in four-channel CNN-GRU, and its value decreases with the amount of the forecast horizon.
Identifying failure types in cyber-physical water distribution networks using machine learning models
Authors: Parajuli Utsav , Shin Sangmin
Published: February 2024

Water cyber-physical systems (CPSs) have experienced anomalies from cyber-physical attacks as well as conventional physical and operational failures (e.g., pipe leaks/bursts). In this regard, rapidly distinguishing and identifying a facing failure event from other possible failure events is necessary to take rapid emergency and recovery actions and, in turn, strengthen system's resilience. This paper investigated the performance of machine learning classification models – support vector machine (SVM), random forest (RF), and artificial neural networks (ANNs) – to differentiate and identify failure events that can occur in a water distribution network (WDN). Datasets for model features related to tank water levels, nodal pressure, and water flow of pumps and valves were produced using hydraulic model simulation (WNTR and epanetCPA tools) for C-Town WDN under pipe leaks/bursts, cyber-attacks, and physical attacks. The evaluation of accuracy, precision, recall, and F1-score for the three models in failure type identification showed the variation of their performances depending on the specific failure types and data noise levels. Based on the findings, this study discussed insights into building a framework consisting of multiple classification models, rather than relying on a single best-performing model, for the reliable classification and identification of failure types in WDNs.
MFAP5 Strengthened the Stem Cell Features of Non-small Cell Lung Cancer Cells by Regulating the FBW/Sox9 Axis
Authors: Du Chun , Qi Zijuan , Zhang Wei
Published: February 2024

Introduction:

Non-small cell lung cancer (NSCLC) is a type of malignant tumor with
high morbidity as well as mortality. The process of lung cancer may be driven by cancer stem
cells. It was known that MFAP5 enhanced the occurrence of diverse types of cancer. Also,
MFAP5 has the potential to induce the degradation of FBW7 which is a tumor suppressor. Lower
levels of FBW7 enhance the stability of Sox9, which is the cancer stem cell-related protein.
However, whether the MFAP5 can modulate the stem cell features of NSCLC cells by modulating
the FBW7/Sox9 axis is unclear. Therefore, this study aimed to explore the role of
MFAP5/FBW7/Sox9 axis on the stem cell features of NSCLC cells and develop a new treatment
of this carcinoma.

Material and Methods:

In this study, we explored the effects of MFAP5 on the stem cell features
of NSCLC cells for the first time. We established MFAP5 overexpression and knockdown
NSCLC cells. Clone formation assays and cell sphere culture assays were conducted for the exploration
of the growth and stem cell features of these cells. Western blotting was applied for the
detection of Sox9 and FBW7 expression in these cells. CHX was applied for the treatment of
these cells for the detection of degradation of Sox9. Finally, we overexpressed the Sox9 in
MFAP5 knockdown NSCLC cells.

Results:

MFAP5 promoted the growth and stem cell features of these cells. Knockdown of
MFAP5 induced higher levels of FBW7 while restricting the expression of Sox9. Knockdown of
MFAP5 aggravated the degradation of Sox9. Overexpression of Sox9 abrogated the efficacy of
MFAP5 inhibition on the growth as well as stem cell features of these cells. The results of this
study clarified the role of MFAP5/FBW7/Sox9 axis on the development of non-small cell lung
cancer cells, providing the potential therapeutic target for the clinical treatment of NSCLC.

Conclusion:

MFAP5 maintained the stem cell features of non-small cell lung cancer cells by
modulating FBW7/Sox9 axis.
Gender Differences in the Predictive Value of Obesity Indices for Insulin Resistance in Adult Mexican Individuals
Authors: Cesar Ivan Elizalde-Barrera , Teresa Estrada-Garcia , Catalina Lopez-Saucedo , Alberto Francisco Rubio-Guerra , Diana Elena Ramirez-Velasco
Published: February 2024

Background:

Obesity-linked insulin resistance (IR) is an important risk factor for
metabolic diseases, and anthropometric indices are commonly used for risk assessment.

Aim:

The study aimed to assess possible differences between women and men in the predictive
value and association of nine obesity indices with IR, as assessed by HOMA-IR, in a nondiabetic
adult population.

Methods:

The cross-sectional study included individuals recruited from a hospital in Mexico
City. Indices evaluated were waist circumference (WC), hip circumference (HC), body mass
index (BMI), waist-to-hip ratio, waist-to-height ratio, visceral adiposity index, body adiposity
index (BAI), relative fat mass (RFM), and conicity index (CI). Fasting plasma glucose and insulin
were measured to calculate HOMA-IR. Correlation analysis was performed between obesity
indices and HOMA-IR. Receiver operating characteristics curve analyses were performed to
determine predictive accuracy and cut-off values of obesity indices for IR. A binary logistic regression
(BLR) analysis with OR calculation was performed to determine the strength of association
with HOMA-IR.

Results:

We included 378 individuals (59% females, mean age 46.38 ±12.25 years). The highest
Pearson coefficient value was observed for BMI among women, while among men, the highest
values were found for BMI and BAI. WC among women, and BAI and RFM among men
showed the highest sensitivity, while the highest specificity was observed for WHR among
women and WC among men with respect to insulin prediction. In the adjusted BLR model,
BMI, WC, and WHR among women and WC and RFM and BAI among men were independently
associated with IR, showing the highest odds ratio (OR).

Conclusion:

In Mexican adults, WC, WHR, RFM and BAI could be complementary tools for
BMI in screening for IR.
Secure Vehicle-to-Vehicle Communication using Routing Protocol based on Trust Authentication Secure Sugeno Fuzzy Inference System Scheme
Authors: K.N. Anupama , Nagaraj R.
Published: February 2024

Introduction:

Vehicular Ad-hoc Network (VANET) is wireless communication between
Roadside vehicles and vehicle infrastructure. Vehicle Ad Hoc Network (VANET) is a promising
technology that effectively manages traffic and ensures road safety. However, communication in an
open-access environment presents real challenges to security and privacy issues, which may affect
large-scale deployments of VANETs. Vehicle identification, classification, distribution rates, and
communication are the most challenging areas in previous methods. Vehicular communications face
challenges due to vehicle interference and severe delays.

Method:

To overcome the drawbacks, this work proposed a new method based on the Artificial Neural
Network Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS). Vehicular Ad Hoc
Networks (VANET) are required to transmit data between vehicles and use traffic safety indicators.
Improved Cluster-Based Secure Routing Protocol (ICSRP). Artificial Neural Network Based Trust
Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) used the symmetric key to increase the
security performance of VANET. Use ANFIS-based Secure Sugeno Fuzzy System for calculating
the node weights for data transferring; reduced the attacks accuracy of network malicious attacks.

Result:

In the improved cluster-based VANET routing protocol, each node obtains an address using a
new addressing scheme between the wireless vehicle-2-vehicle (V2V) exchanges and the Roadside
Units (RSUs). It will explore the effectiveness of the Secure Sugeno Fuzzy System-based adaptation
term Enhanced Cluster-based routing protocol in finding the vehicle's shortest-path for transmission.

Conclusion:

Simulation results show that in the proposed ANN-based Trust Authentication Secure
Sugeno Fuzzy System (AN2-TAS2FS) analysis, the packet delivery ratio is 93%, delay performance
is 0.55sec, throughput performance is 94%, bandwidth is 55bits/sec, Network security is 92%, and
the transmission ratio is 89%, attack detection is 90%.
Materiovigilance as a Health Care Safety in India: A Regulatory Prospective
Authors: Gutte Vaishali , Syed Shoaeb Mohammad , Chaus Wajed
Published: February 2024

Over the past few centuries, life-saving tools have been extensively used across the globe. The use of medical devices has increased adverse events
associated with them. Materiovigilance is the process of identifying, collecting, reporting, and examining unfavorable events connected to medical
devices in order to prevent the recurrence of such occurrences. Several countries keep an eye on medical products once they are commercialized.
Other nations with similar programs include France, Australia, and the United Kingdom, in addition to the Medical Device Reporting (MDR)
programme in the United States. This article addressed how some types of medical equipment might be dangerous and how a Materiovigilance
programme is essential.

:

This page provides information about how to report adverse reactions to these medical devices, as well as what they are.

:

In many countries, post-marketing surveillance for medications has been initiated but is not as reliable and advanced as it is today. By monitoring
adverse events related to medical devices, generating safety data, educating stakeholders, and recommending appropriate procedures and
interventions, the Indian Pharmacopeia Commission established the committee on July 6, 2015.

:

Monitoring adverse events related to medical device usage can increase patient safety. On the basis of safety data gathered and generated, the
Central Drugs Standard Control Organization (CDSCO) will make recommendations about the safe use of medical devices in the Indian
population.
The Role of Local Population in Safeguarding Heritage Case of Chefchaouen
Authors: NAKKOUCH Samia
Published: February 2024

Abstract. Local community engagement in heritage safeguarding has been a growing trend during the last two decades. With international models and charts that introduced the participatory paradigm into public restoration and interventions, community-based conservation approaches have started to emerge. The involvement of local inhabitants into restoration processes has proven effective contribution into sustaining public interventions and developing behaviors that mitigate the risks of deterioration of architectural heritage. The case of Chefchaouen has demonstrated multiples initiatives taken by inhabitants and local council towards chaouni medina aged from de 15th century. Made of stone, earth, wood and lime, the participatory dynamic around safeguarding the fabric has contributed to improve public efforts of restoration and rehabilitation. The contribution has gone beyond raising awareness to tangible actions and engagement. The article purpose is to analyse the modialities and role of inhabitants involvement into sustaining heritage. It presents the results of an empiric research based on qualitative data and analysis, and develops the dynamics around the medina of Chefchaouen, exploring the local initiatives and their effects on safeguarding heritage, with acknoledging the local configurations behind such dynamics.
Preservation of Local Architectural Heritage in Slovenian Istria Against Coastal Flooding
Authors: OBLAK Polona
Published: February 2024

Abstract. This article examines the problem of preserving the local architectural heritage of the town of Piran against of flooding. It presents an overview of the materials and techniques used in traditional construction in Piran and in the Sečovlje salt pans as well as it constitutes a review of the state of the flooding situation in these two places. The gap between what is done on the ground and the needs of the place have their source in the poor management of such emergency situations and the possible erroneous choice of techniques and materials used for the restauration of the infrastructure or buildings. The main conclusion is that, beyond crisis management, flood risks need to be considered in a holistic way, respecting the ancient knowledge or techniques and materials of construction, and including different stakeholders in order to better anticipate, organize and preserve the local and architectural heritage for future generations.
Wall Paintings from The Roman City of Volubilis in Morocco: XRF, Raman and FTIR-ATR Analyses
Authors: FIKRI Imane
Published: February 2024

Abstract The work is an in-depth investigation of painting remains from the roman city of Volubilis in Morocco, classified World Heritage. Raman and ATR-FTIR structural and XRF elemental spectroscopies were crossed to decrypt the pigments adopted by roman craftsmen in the south Mediterranean region. Red-ochre alone or in admixture with cinnabar was used in brown-red paintings, while yellow ochre, green earth and Egyptian blue pigments were used to achieve yellow, green and blue ones. All pigments highlighted had been commonly used in the roman world, among which some ones continue until the medieval period in Morocco. In addition to documenting built heritage in Morocco, the results provide a helpful background for archaeologists interested in Roman sites around the Mediterranean space.
Recycled Glass-Fiber Reinforced Cement (RGFRC) Waste as a Substitute in Concrete Production
Authors: NAIM Amine
Published: February 2024

Abstract. Nowadays, a large amount of glass fiber reinforced cement (GFRC) waste from construction industries and demolition activities presents a significant source of major environmental and economic problems. In order to protect the environment, many studies have been conducted to recycle and reuse these wastes in concrete production. The present work also aims to reach this objective and to show technically the possibility of recycling glass fiber reinforced cement waste (RGFRC) as a partial substitution in concrete production. Three concrete mix variations were formulated: one comprising solely natural aggregate (NC) serving as the control, and two others incorporating a blend of natural and recycled glass fiber reinforced cement (RGFRC) with 20% and 40% replacement of recycled aggregate, respectively. The test of compressive strength behavior was performed on the mixes. The results showed that concrete containing 20% RGFRC has the best mechanical properties compared with the control concrete and that using more RGFRC would have a harmful impact on the mechanical characteristics of concrete.
Geopolymers: An Eco-Friendly Approach to Enhancing the Stability of Earthen Constructions
Authors: MASROUR Ilham
Published: February 2024

Abstract. Earthen constructions, characterized by their historical longevity and adaptability to various environments, constitute an essential part of the global architectural heritage. These structures offer environmental advantages by utilizing local resources, but they also face challenges such as weather sensitivity, vulnerability to earthquakes, and degradation over time. Preserving these constructions while meeting modern sustainability standards poses a crucial challenge. In this context, geopolymers emerge as innovative solutions for stabilizing earthen constructions. A sustainable alternative is provided by geopolymers, which are composed of fly ash and ground granulated blast furnace slag to enhance soil cohesion and strength. This review article aims to provide an insightful perspective on compression tests specific to various types of geopolymers. The objective is to guide the choice of the method for stabilizing earthen constructions based on available resources.
Naturally Strengthening Rammed Earth: The Promising Potential of Biopolymers
Authors: MASROUR Ilham
Published: February 2024

Abstract. Sustainable construction has become a global imperative due to the growing awareness of the harmful environmental impacts of the construction industry. The use of cement and lime in traditional methods of stabilizing earth constructions is a significant problem due to their high carbon footprint. This article examines an ecological alternative to stabilizing earth structures with biopolymers. These Bio-based materials can be used to reduce the environmental impact of the construction industry while also ensuring the structure's stability and durability. The purpose of this article is to examine the mechanical properties of biopolymers in the context of stabilizing earth construction. The objective is to guide the decision on which stabilization method to use for earth construction based on the available resources.
Evaluating the Influence of Shale Extracted from the Settat Khouribga Region on the Characteristics of Concrete
Authors: SOUILEH Ayoub
Published: February 2024

Abstract. The incorporation of indigenous materials in the production of reinforced concrete offers a twofold prospect: the potential reduction of construction costs and environmental impact, along with the stimulation of economic growth in the source regions. To maximize the utility of shale derived from the Settat-Khouribga area and assess its influence on concrete properties, a series of tests were conducted on the material. Samples underwent meticulous characterization, encompassing mineralogical composition, particle size distribution, and mechanical properties. A comprehensive set of assessments was applied to the concrete samples, including tests for compressive strength, flexural strength, water absorption, porosity, and resistance to reinforcement corrosion over an extended timeframe. Concrete samples containing clay shale demonstrated compressive and flexural strength comparable to or even surpassing that of conventional concrete samples. Additionally, the incorporation of clay shale led to a reduction in porosity and water absorption in concrete, indicating an enhancement in durability.
Application of Raw Moroccan Clay as a Potential Adsorbent for the Removal of Malachite Green Dye from an Aqueous Solution: Adsorption Parameters Evaluation and Thermodynamic Study
Authors: CHRACHMY Mohammed
Published: February 2024

Abstract. This study uses raw clay from the Es-sifa (REC) territorial commune in the region of Draa-Tafilalet, Morocco, for malachite green (MG) adsorption from an aqueous medium. The characterization of clay was ensured by Fourier Transform Infrared (FTIR) spectroscopy, X-ray diffraction (XRD), and determining the Point of Zero Charge pH. Moreover, five parameters were considered to regulate the adsorption process, including time of contact, initial dye concentration, mass of the adsorbent, stirring speed, and temperature. The finding displayed that equilibrium was reached after only 40 minutes of contact. The maximum adsorption amount achieved was 89.61 mg/g for a dye concentration of 10-4 M at 25 °C, using a REC mass of 0.10 g. According to the thermodynamic study, the endothermicity and spontaneity of MG adsorption were disclosed. Malachite green's fixation on the REC surface was verified by comparing the clay FTIR spectra before and after adsorption.