bluuebunny/crossref_metadata_2025_split huggingface dataset:
CONHECIMENTO DOS PROFESSORES NO USO DE TECNOLOGIAS NA EDUCAÇÃO INCLUSIVA
Authors: Barbosa de Oliveira. Janine , Jose Alves Tiago , Paganini da Silva Eliane , Izabel Hummel Eromi 
Published: February 2025

Resumo: A presente pesquisa tem como objetivo refletir sobre os conhecimentos dos professores na utilização de tecnologias na educação inclusiva, reflexão necessária para compreender a atual sociedade. Por meio de revisão bibliográfica, apresenta-se conceitos e definições da cibercultura e da inclusão pela cultura digital. Neste contexto, a acessibilidade torna-se imprescindível, valorizando a equidade de interação entre as pessoas com deficiência e o conhecimento. A justificativa para essa abordagem reside na importância de compreender o impacto da cibercultura na educação, assegurando a acessibilidade e inclusão de acordo com as normas legais, com o propósito de fomentar um ambiente educacional igualitário e enriquecedor para todos os envolvidos. A pesquisa adotou uma abordagem exploratória, utilizando um questionário com questões abertas e fechadas, e envolveu estudantes do Mestrado Profissional em Educação Inclusiva. O objetivo é compreender os saberes dos professores no uso de tecnologias na educação inclusiva. Os resultados revelam que a maioria dos participantes possui conhecimentos digitais sólidos, incluindo seleção crítica de informações online e ética digital. A valorização da formação continuada foi evidente, com professores engajados em programas de capacitação. Laboratórios de informática e ambientes virtuais de aprendizagem são identificados como recursos de apoio. A pesquisa destaca a importância do desenvolvimento dos conhecimentos digitais para uma inclusão eficaz e colaborativa, crucial para educadores e a educação inclusiva.
Production, Characterization, and Molecular Dynamic Study of Antidiabetic
and Antioxidative Peptides of Fermented Cheese Whey with Anti-inflammatory
Properties using Limosilactobacillus fermentum
Authors: Das Bhagyashree , Basaiawmoit Bethsheba , Sakure Amar A. , Maurya Ruchika , Bishnoi Mahendra , Kondepudi Kanthi Kiran , Tiwary Bipransh Kumar , Mankad Pooja M. , Patel Ashish , Hati Subrota 
Published: February 2025

Introduction:
This study aims to valorise cheese whey waste by converting it into bioactive peptides
that have several health benefits, potentially leading to the development of nutraceuticals and functional
foods and also used in pharmaceutical industry.

Methods:
The study evaluates the antidiabetic, antioxidative, and anti-inflammatory properties of fermented
cheese whey with Limosilactobacillus fermentum (M4), along with the production of antioxidative and antidiabetic
peptides. SDS PAGE and 2D PAGE were also performed to identify proteins by molecular weight and
isoelectric point, while RP-HPLC distinguished peptide fractions. Peptide sequences from 2D gel spots were
identified using RPLC/MS, and RP-HPLC analyzed 3 kDa and 10 kDa permeates. Peakview software characterized
the LC/MS results, and FTIR analysis measured structural changes in bioactive peptides.

Results:
The antioxidative and antidiabetic properties in cheese whey fermented with M4 showed a progressive
growth over extended incubation periods, higher effects were observed after fermentation for 48 hours.
Inhibitory activities in α-glucosidase, α-amylase & lipase were found to be 65.39%, 66.09%, and 56.74% respectively.
ABTS assay was performed to measure antioxidant activity (63.39%) and the highest proteolytic
activity (7.62 mg/ml) was measured at 2.5% inoculation rate for 48 hours. In SDS-PAGE, protein bands between
10 & 30 kDa were observed, whereas peptide spots within the range of 10 to 70 kDa were also visualized
on the 2D PAGE. RP-HPLC was used to distinguish different fractions of a peptide. Peptide sequences
from 2D gel spots were identified using RP-HPLC & RPLC/MS. Peakview software was utilized to characterize
the LC/MS results. Sequences of peptides generated from α-lactalbumin and β-lactoglobulin were
searched in the BIOPEP database to validate the antidiabetic and antioxidative activities of fermented cheese
whey peptides. 0.50 mg/mL of fermented cheese whey significantly LPS suppressed the production of proinflammatory
cytokines as well as the mediators that govern them including IL-6, IL-1β, NO, and TNF-α in
RAW 264.7 cells. FTIR was used to analysis of protein secondary structure and conformational changes.

Conclusion:
This study aims to the production of antidiabetic and antioxidative peptides from dairy waste,
and cheese whey.
Coptis chinensis Franch Relies on Berberine to Alleviate Stomach Heat Syndrome
through the JAK2-STAT3-NF-κB Signaling Pathway and Amino Acid Metabolism Regulation
Authors: Wang Benye , Shu Yuhang , Wang JingJing , Nisar Muhammad Farrukh , Wang Chunli , Wang Guangzhong , Xu Kang 
Published: February 2025

Background:
Stomach Heat Syndrome (SHS) arises from the excessive consumption of spicy and
greasy foods, resulting in the infiltration of pathogens and increased gastric activity, ultimately culminating in
gastric injury. Coptis chinensis Franch (CC), a frequently employed remedy in Traditional Chinese Medicine
(TCM) for dampness elimination, fire purging, and detoxification, has been extensively utilized.

Objective:
This study aimed to explore the functional role and in-depth molecular mechanism of CC in treating
SHS.

Methods:
The CC Alcohol Extract (CCAE) was obtained and analyzed using HPLC. A rat model of SHS
hemorrhagic lesions was established using a combination of 8% chili powder and 60% ethanol. After oral administration
of CCAE, gastric histology, Myeloperoxidase (MPO), Malondialdehyde (MDA), and Superoxide
Dismutase (SOD) levels were evaluated. Further, the gene expressions of Interleukin-6 (IL-6), Interleukin-1β
(IL-1β), and Tumor Necrosis Factor-α (TNF-α) were assessed, respectively, using H&E staining, colorimetry,
qRT-PCR, and immunohistochemistry analyses. The network pharmacology in multiple databases, transcriptome
sequencing, and non-targeted metabolomic analyses were used to identify signal pathways and molecular
targets. Subsequent cellular and molecular experiments were also conducted to validate the mechanisms.

Results:
The findings demonstrated that the CCAE administration effectively mitigated SHS symptoms in rats
and reduced inflammatory cytokines levels and oxidative stress. Berberine (Bbr) was identified as the primary
active component responsible for the anti-SHS effects of CC. Additionally, the multi-omics analysis revealed
that Bbr primarily regulates amino acid metabolism, unsaturated fatty acid metabolism, the TNF signaling
pathway, and the JAK-STAT signaling pathway. Furthermore, Bbr was found to inhibit the phosphorylation
of JAK2, STAT3, and p65.

Conclusion:
CC alleviated SHS-related gastric disease by suppressing inflammatory responses and enhancing
gastric mucosal function through the JAK2-STAT3-NF-κB signaling pathway and amino acid metabolism,
with Bbr serving as the key component.
The Antimicrobial Peptide Pipeline: A Bacteria-Centric AMP Predictor
Authors: Veldsman Werner Pieter , Zhang Qi , Zhao Qian , Zhang Lu , Yuanjie Zou 
Published: February 2025

Introduction:
Antimicrobial peptides (AMPs), unlike antibiotics, are encoded in genomes.
AMPs are exported from the cell after expression and translation. In the case of bacteria, the
exported peptides target other microbes to give the producing bacterium a competitive edge. While
AMPs are sought after for their similar antimicrobial activity to traditional antibiotics, it is difficult
to predict which combinations of amino acids will confer antimicrobial activity. Many computer algorithms
have been designed to predict whether a sequence of amino acids will exhibit antimicrobial
activity, but the vast majority of validated AMPs in databases are still of eukaryotic origin. This
defies common sense since the vast majority of life on Earth is prokaryotic.

Methods:
The antimicrobial peptide pipeline, presented here, is a bacteria-centric AMP predictor
that predicts AMPs by taking design inspiration from the sequence properties of bacterial genomes
with the intention to improve the detection of naturally occurring bacterial AMPs. The pipeline integrates
multiple concepts of comparative biology to search for candidate AMPs at the primary,
secondary, and tertiary peptide structure levels.

Results:
Results showed that the antimicrobial peptide pipeline identifies known AMPs that are
missed by state-of-the-art AMP predictors and that the pipeline yields more AMP candidates from
real bacterial genomes than from fake genomes, with the rate of AMP detection being significantly
higher in the genomes of six nosocomial pathogens than in the fake genomes.

Conclusion:
This bacteria-centric AMP pipeline enhances the detection of bacterial AMPs by incorporating
sequence properties unique to bacterial genomes. It complements existing tools, addressing
gaps in AMP detection and providing a promising avenue for discovering novel antimicrobial
peptides.
Nanophase: An Effective Dispersion System for the Decoction of Kushen Huaihua for the Treatment of Ulcerative Colitis
Authors: Liu Jingrui , Tang Haixia , Yang Liansheng , Wang Haibo , Li Xiuyan , Yang Zhixin 
Published: February 2025

Objective:
In traditional Chinese medicine, the decoction turns into a complex multiphase
system following exposure to high temperatures and a complex chemical environment. However, the
effective dispersion system of the decoction of Kushen Huaihua (DKH) for the treatment of ulcerative
colitis (UC) has yet to be elucidated.

Methods:
DKH was separated into precipitated phase (DKH-P), nanophase (DKH-N), and solution
phase (DKH-S) according to the particle size by ultracentrifugation dialysis, and the physicochemical
properties of each phase group, such as particle size, morphology, chemical composition, and content,
were analysed by TEM and HPLC. The anti-UC effects of the different phases were evaluated
by ELISA and HE staining. Furthermore, the composition of the effective dispersion system and
release characteristics were investigated by UV and HPLC.

Results:
The fingerprint analysis of DKH recognized 11 key components, namely Ru, Qu, Ka, Fo,
Iso, Kur, SFG, OMT, OSC, MT, and SC. The content of these components in DKH-N was found to
be 69.51%, 88.30%, 84.60%, 82.92%, 73.35%, 77.03%, 74.02%, 89.95%, 85.99%, 79.53%, and
85.24% of the corresponding levels in DKH, respectively. Pharmacodynamic results demonstrated
that DKH-N exerted the same anti-UC effect as DKH, decreased DAI and CMDI scores, increased
IL-4 and IL-10 activities, and reduced expression of IL-6, TNF-α, and MPO, which were significantly
different from those of the model group (**P<0.01). Additionally, DKH-N was found to comprise
30.30% polysaccharides and 24.93% protein components. Furthermore, 11 components in DKH-N
demonstrated more than 80% release in enzyme-containing simulated colonic fluid in 24 h.

Conclusion:
DKH-N may be an effective dispersion system for DKH treatment of UC.
Use of Virtual Reality to Improve Spatial Orientation in Alzheimer's Disease and Mild Cognitive Impairment: A Systematic Review
Authors: Gulin Wojciech , Oziemblewska Monika , Zając-Lamparska Ludmiła 
Published: February 2025

Background:
Alzheimer's disease is a chronic, neurodegenerative condition that leads
to a significant cognitive decline. One of the symptoms that greatly reduces the quality of daily
functioning is the deterioration of spatial orientation abilities. A non-pharmacological treatment
option for Alzheimer's disease, which is also employed to improve the cognitive functioning of individuals
with mild cognitive impairment, is virtual reality training.

Objective:
To the best of the authors' knowledge, there is no existing systematic review on the use
of virtual reality training to enhance spatial orientation in individuals with Alzheimer’s disease or
mild cognitive impairment. The review was therefore conducted to fill this gap. The findings of
this review may support the efficacy of virtual reality in enhancing spatial orientation.

Methods:
Five databases were searched. The primary inclusion criteria were study participants
aged over 60 years with a diagnosis of Alzheimer's disease or mild cognitive impairment and the
use of virtual reality for improving spatial orientation. Six studies meeting these criteria were ultimately
included in the review.

Results:
All included studies demonstrated an improvement in the spatial orientation of individuals
with Alzheimer's disease or mild cognitive impairment following virtual reality training. This
indicates the effectiveness of virtual reality technology in cognitive rehabilitation.

Conclusion:
As virtual reality cognitive training has proven effective, its use should be more widely
adopted. Further research on the application of virtual reality for enhancing spatial orientation
in individuals with dementia is recommended.
A Non-invasive Cell-free DNA Diagnosis Method for Hepatocellular Carcinoma Based on Deep Learning
Authors: Li Xue-Yi , Zhang Wei , Liu Zhi-Ping 
Published: February 2025

Background:
Hepatocellular Carcinoma (HCC) is a major disease that seriously threatens
human health. Early screening can significantly improve the five-year survival rate of HCC patients.
Cell-free DNA (cfDNA), as a potential carrier of cancer signals in body fluids, can be used
for early cancer detection. However, current early HCC detection methods based on cfDNA sequencing
require deep sequencing data, limiting their application and usage in routine disease
screening.

Objective:
We proposed a foundational DNA language model, called CLHCC, for analyzing DNA
sequences and methylation patterns to detect HCC at low sequencing depths.

Methods:
CLHCC randomly selected 1500 DNA fragments from HCC-specific differentially methylated
regions identified by cd-score. The model then performed a one-hot encoding strategy on
these DNA fragments and input the data into a CNN combined with an LSTM neural network for
classification.

Results:
We tested CLHCC on 2139 target-BS data samples, achieving an accuracy of 84.59% (precision:
83.44%, recall: 81%) under 10-fold cross-validations. This performance is better than DNA
language models built using CNN or LSTM alone.

Conclusion:
Our study applies deep learning to analyze DNA sequences in specific methylation regions
without the need for complex alignment processes. This provides new theoretical and practical
guidance for clinical applications and holds promise for non-invasive early HCC screening via
cfDNA.

other:
We proposed a non-invasive method for early screening for HCC and achieved a precise diagnosis of HCC at ultra-low DNA sequencing depths. In the meantime, we innovatively apply DNA sequences from specific methylation regions for analysis, saving alignment time. Expanding datasets, improving model refinement, and broadening model applications are essential for future development.
The Role of Serotonin Modulators in Nicotine Reward and Reinforcement: A Conditioned Place Preference Investigation
Authors: Tiwari Rajkumar , Sharma Bhawna , Pandey Rohit , Kumar Gaurav , Bhardwaj Khushboo 
Published: February 2025

Background:
Nicotine addiction remains a significant public health challenge, driving the need for effective treatment strategies. While various pharmacological ap-proaches have been explored, targeting neurotransmitter and neuromodulator systems of-fers a promising avenue. These systems, particularly those involving dopamine, play a pivotal part in interceding the rewarding effects of nicotine and the development of de-pendence. Serotonin, a pivotal neuromodulator, exerts a profound influence on dopamine pathways. By interacting with these pathways, serotonin can significantly impact the sub-stantiating effects of nicotine. Understanding the intricate interplay between serotonin and dopamine systems is pivotal for developing new remedial interventions that effectively address the intricacies of nicotine dependence.

Objective:
The purpose of this research study was to examine the potential roles played by serotonin and its receptors in nicotine addiction by examining the involvement of seroto-nin modulators in the development of conditioned location preference in mice after expo-sure to nicotine.

Methods:
Mice were subjected to conditioned place preference (CPP) training with nico-tine. After induction of CPP, the extinction session was given for the disappearance of CPP. There were a total of five groups, each having six animals that participated in the ex-periments, including group 1 (controls; normal saline), group 2 (nicotine A; 0.25 mg/kg), group 3 (nicotine B; 0.5 mg/kg), group 4 (nicotine C; 0.75 mg/kg), and group 5 (standard drug; fluoxetine). All drugs were given through the subcutaneous route. The treatment of serotonin modulators, i.e., fluoxetine (100mg/kg), a selective serotonin reuptake inhibitor (SSRI), was given before the priming dose of nicotine, and the effect of the serotonin modulator was observed.

Results:
On day 17th, the CPP values were 390.16s, 235.66s, and 219.16s, respectively, for nicotine A, nicotine B, and nicotine C in the black compartment groups; however, in the white compartment groups, the CPP values were 347.16s, 588s, and 549.66s, respectively, for nicotine A, nicotine B, and nicotine C.

Conclusion:
Using the conditioned place preference (CPP) paradigm, we administered both drugs in a context in which their rewarding properties could be measured. Fluoxetine produced a significant but less robust CPP than nicotine. A single injection of fluoxetine was found to reduce nicotine-induced CPP. Moreover, the rewarding properties of nicotine were completely abolished in response to repeated fluoxetine injections. Long-term fluox-etine users may benefit more from the drug than those who have only used it occasionally or in small doses, according to the study.
Tumor Suppressor and Oncogenic miRNA Expressions in Patients with Type I Gaucher Disease and Carriers
Authors: Üzen Ramazan , Bayram Fahri , Dursun Huseyin , Kardas Fatih , Cakir Mustafa , Hasan Sohel Md Mahmodul , Cucer Nurhan , Eken Ahmet , Donmez-Altuntas Hamiyet 
Published: February 2025

Background:
Gaucher disease (GD) occurs due to a mutation in the glucosylcerami-dase (GBA) gene and is a common lysosomal storage disease. It is well known that there is a strong association between the abnormal expression of miRNAs and various diseases including cancer. These abnormally expressed miRNAs can be used as biomarkers. Interestingly, several studies have reported a linkage between GD with an increased risk of cancer. Therefore, in the current study, we investigated the expression levels of selected miRNAs that are associated with cancers that might have potential use as biomarkers in GD.

Methods:
Blood samples were collected from 24 healthy volunteers, 6 carriers, and 20 treated patients with type 1 GD. A reverse transcription-quantitative real-time PCR (RT-qPCR) platform was used to analyze the miRNA expression levels.

Results:
While carriers had lower relative expressions of miRNA-15a with tumor suppressor ef-fect, and miRNA-150 and miRNA-181b with oncogene effect, treated patients with type 1 GD had lower relative expressions of miRNA-15a and miRNA-125b with tumor suppressor effect and higher relative expression miRNA-21 with oncogene effect (p<0.001, p<0.05, p<0.01, p<0.05, p<0.001, and p<0.05, respectively).

Conclusion:
The results suggested that the downregulation of miRNA-15a and miRNA-125b expressions with tumor suppressor effect and the upregulation of miRNA-21 expression with on-cogene effect can be indicated to an increased risk for cancers such as multiple myeloma (MM), B-cell lymphoma, leukemia, and hepatocellular carcinoma (HCC) in GD.
Demystifying the Risk of Long COVID-19: Clinical, Psychological, and Lung Imaging Abnormalities
Authors: Rosyid Alfian Nur , Puspitasari Arina Dery , Fithriyah Izzatul , Sensusiati Anggraini Dwi , Nugraha Jusak , Amin Muhammad 
Published: February 2025

Objective:
The objective of this study was to analyse clinical symptoms, quality of life,
chest X-rays, and factors associated with post-COVID-19 symptoms.

Methods:
This prospective cohort study involved hospitalised COVID-19 patients who were followed
up until 3 months after discharge. Clinical symptoms, examinations, imaging, etc., were evaluated
during admission and 3 months after. Quality of life (QOL) was assessed using the SF-36
questionnaire.

Results:
About 76 adult COVID-19 hospitalised patients were followed up during treatment for up to
3 months. Mortality rate was 30.26%. About 69.81% of patients experienced residual symptoms
consisting of 54.71% acute-post-COVID-19, 9.4% ongoing-post-COVID-19, and 5.6% long-
COVID-19. The most common residual symptoms were fatigue, dyspnea, myalgia, cough, weakness,
anxiety, and headache. Brixia score was higher in deceased and residual symptom groups compared
to the no residual symptom group. QOL of post-COVID-19 patients decreased in all domains and
was lower in long-COVID-19. Social functioning and pain were domains that differed significantly
in all residual symptom groups. Bivariate analysis found that age >50 years (OR=3.472), history of
mechanical ventilator use (OR=0.644), and RR ≥ 28 times/minute (OR=3.529) were factors associated
with the occurrence of post-COVID-19 syndrome. All degrees of severity during COVID-19 contributed
to the occurrence of post-COVID-19 syndrome.

Conclusion:
Almost 70% of hospitalised post-COVID-19 patients with various severities will experience
residual symptoms with decreased quality of life. Several influencing factors are age >50,
history of ventilator use, RR ≥28x/minute, and extent of lung abnormalities.
Local Mean Gradient Pattern (LMGP): A Novel Approach for the
Classification of Brain CT Scan Images
Authors: Singh Kavya , Koundal Anil Kumar , Kaur Navjeet 
Published: February 2025

Objective:
Visual descriptor methods like Local Binary Pattern (LBP) capture anatomical
structures in captured images along with their disparities, which can be exploited by suitable methods
for the diagnosis of medical anomalies. In our study, we have proposed a Local Mean Gradient
Pattern (LMGP), based partly on LBP, a feature extraction algorithm for the classification of Computed
Tomography (CT) images of the brain into normal, ischemic, or hemorrhage categories.

Methods:
The AISD and Kaggle datasets containing patients’ brain CT scan images (acute ischemic
stroke, hemorrhage, and normal cases) were taken. Initially, adaptive histogram equalization (AHE)
techniques were applied as preprocessing operations to enhance the quality of the CT
images. Furthermore, features were extracted from the preprocessed data using several feature
extraction techniques, including our proposed LMGP feature descriptor. The features were then
scaled using the standard scaling technique. Subsequently, preprocessed images were fed into
different classifiers to build models for classifying brain CT scan images.

Results:
The effectiveness of our methodology LMGP was determined using different metrics, such
as recall, precision, F1 score, logarithmic loss, accuracy (ACC), and area under the curve (ROC).
Conclusively, LMGP performed best when the RBF-SVM classifier was used for the classification
and gave an accuracy of 94% and 96% in the case of five-fold and ten-fold cross-validation, respectively.

Conclusion:
LMGP offers a distinctive and robust method of feature extraction from CT scan
images by combining local information along with gradient change in pixels of the image. The efficacy
of our proposed methodology (LMGP) was evaluated by using distinct classifiers, and the results
were compared with eight different feature extraction methods. Overall, LMGP effectively
outperformed all other feature descriptor methods in this study.