bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract
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

Abstract
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

Abstract

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

Abstract
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

Abstract
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.
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

Abstract

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

Abstract

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

Abstract:
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.