bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mesenchymal Stem Cell Secretome Effectiveness on Healing of Chronic Tendon Injury: Analysis of Procollagen Type I N- Terminal Peptide and Procollagen Type Iii N-terminal Peptide and Histopathology in Rat’s Tendon (Rattus Norvegicus)
Authors: Sam Andi Dhedie Prasatia , Warsinggih Warsinggih , Usman Muhammad Andry , Johan Muhammad Phetrus , Suroto Heri , Saleh M. Ruksal , Sakti Muhammad , Zainuddin Andi Alfian , Mubarak Andi Firman 
Published: October 2024

Abstract

Background
Chronic tendon injuries, such as Achilles tendinopathy, are common and challenging to treat due to the limited regenerative capacity of tendon tissue. Using mesenchymal stem cell (MSC) secretome, which contains a rich array of growth factors, holds promise for enhancing tendon healing. This study aimed to evaluate the effectiveness of MSC secretome, derived from tendon-derived stem cells (TDSCs) and adipose-derived stem cells (ASCs), on the healing of chronic Achilles tendon injuries in a rat model, focusing on the levels of Procollagen Type I N-Terminal Peptide (PINP) and Procollagen Type III N-Terminal Peptide (PIIINP), and histopathological changes.
Methods
A chronic tendinopathy model was induced in 16 males of Rattus norvegicus via mechanical overloading and collagenase injection. Rats were divided into four groups: TDSC secretome, ASC secretome, combined TDSC + ASC secretome, and a control group. Secretomes were administered intratendinously. Tendon healing was assessed after four weeks using enzyme-linked immunosorbent assays (ELISA) to measure PINP and PIIINP levels and histopathological analysis to evaluate collagen deposition and tissue structure.
Results
PINP levels were significantly higher in the TDSC + ASC group compared to the control group (p = 0.004), indicating enhanced Type I collagen synthesis. However, no significant differences were observed in PIIINP levels between the groups. The histopathological analysis did not reveal significant structural differences in tendon healing among the groups, though increased collagen alignment was observed in the TDSC + ASC group.
Conclusions
The combined TDSC and ASC secretome promotes Type I collagen synthesis in chronic tendon injuries, but histological improvements were insignificant. Further studies are needed to confirm the long-term benefits of secretome therapy.
On the Reliability of Large Language Models to Misinformed and Demographically-Informed Prompts
Authors: Aremu Toluwani Samuel , Akinwehinmi Oluwakemi , Nwagu Chukwuemeka , Ahmed Syed Ishtiaque , Orji Rita , Amo Pedro Arnau Del , Saddik Abdulmotaleb El 
Published: October 2024

Abstract

We investigate and observe the behaviour and performance of Large Language Model (LLM)-backed chatbots in addressing misinformed prompts and questions with demographic information within the domains of Climate Change and Mental Health. Through a combination of quantitative and qualitative methods, we assess the chatbots’ ability to discern the veracity of statements, their adherence to facts, and the presence of bias or misinformation in their responses. Our quantitative analysis using True/False questions reveals that these chatbots can be relied on to give the right answers to these close-ended questions. However, the qualitative insights, gathered from domain experts, shows that there are still concerns regarding privacy, ethical implications, and the necessity for chatbots to direct users to professional services. We conclude that while these chatbots hold significant promise, their deployment in sensitive areas necessitates careful consideration, ethical oversight, and rigorous refinement to ensure they serve as a beneficial augmentation to human expertise rather than an autonomous solution.
A Study of an EPQ Model of Deterioration Items with Effect of Carbon Emission under Pentagonal Intuitionistic Fuzzy Number
Authors: Dey Agniva , Ray Partha , Seal Somen 
Published: October 2024

Abstract

These days, a lot of inventory experts focus especially on green inventory challenges,
which include global carbon emission difficulties. This study explores a green
inventory EPQ model in which the selling price, inventory level, and degree of green
awareness determine the demand rate. The use of conservation technology has helped
to control carbon emissions; the holding cost is assumed to be a quadratic function of
time, while the deterioration cost is assumed to be a linear function of time. Initially, a
crisp model for minimizing average total cost is developed. Then, if the idea of

membership value and nonmembership value exists, the demand rate assumes

unpredictability in nature as a result of market versatility. Therefore, a fuzzy model is
developed with the assumption that the pentagonal intuitionistic dense fuzzy number
represents the demand rate. To solve the suggested model, a defuzzification method
and a solution algorithm have been developed. A comparative analysis is provided

between crisp and various fuzzy environments in the numerical demonstration.
Moreover, LINGO 21.0 software has been used for numerical computing. Lastly,
sensitivity analysis and graphical depiction have been used to support the proposed
model.
The Mitigation of Excessive Retrieval Augmentation and Knowledge Conflicts in Large Language Models
Authors: Galway Michael , DiRenzo Matteo , Esposito Dominik , Marchand Rafael , Grigoriev Valentin 
Published: October 2024

Abstract

There have been significant advances with large language models in generating coherent and contextually relevant responses, but their limitations in accessing and integrating real-time or specialized information have driven the development of retrieval augmentation techniques. Retrieval augmentation offers the ability to enhance a model’s responses through external knowledge, yet the challenge of managing knowledge conflicts between retrieved data and internal model predictions remains unresolved. A systematic examination of varying levels of retrieval augmentation has revealed that excessive reliance on external information not only introduces factual inconsistencies but also degrades the coherence of model outputs. The experiments conducted on the Llama model demonstrate that while moderate augmentation improves accuracy and relevance, high retrieval augmentation significantly increases the risk of knowledge conflicts, complicating the response generation process. The conflict detection and resolution mechanisms employed showed promise in mitigating some of these inconsistencies, although their effectiveness diminished as the volume of retrieval data increased. These findings highlight the delicate balance required between external knowledge integration and internal model coherence, emphasizing the need for more sophisticated conflict management strategies to optimize the potential of retrieval-augmented models.
Qualitative serum microRNA signatures for lung cancer screening
Authors: Wu Dandan , Yang Nianlin , Yan Haidan , Yu Shuiyan , Zhang Yuchen , Shen Xiaopei , He Jun 
Published: October 2024

Abstract

Background: Lung cancer is the leading cause of morbidity and mortality among cancers worldwide. The early detection of lung cancer can effectively reduce the mortality rates among patients. Therefore, this study aims to construct signatures for the screening of lung cancer.
Methods: The serum miRNA expression profiles of 5078 non-cancer, 1951 lung cancer and 3504 other cancer samples from four datasets were used. Subsequently, the samples were classified into one training dataset and two validation datasets.
Results: In this study, we firstly demonstrate the differential expression pattern of serum miRNAs between lung cancer and non-cancer in four datasets, respectively. Subsequently, two qualitative serum microRNA signatures were established. The first signature, designated as LC-MPS2, was employed to distinguish lung cancer from non-cancer samples. The sensitivity and specificity of the signature were all over 99.0% in both the training and validation datasets. However, LC-MPS2 was unable to distinguish lung cancer from other types of cancer. Thus, an additional signature, comprising of six miRNA pairs designated as LC-MPS6, was constructed. This signature demonstrated 90.9% sensitivity and 91.1% specificity in the training datasets for discriminating lung cancer from multiple other types of cancers. In an independent validation dataset, LC-MPS6 achieved 80.2% sensitivity and 93.9% specificity.
Conclusions:This study demonstrates that the qualitative serum microRNA signatures can accurately identify lung cancer from multiple cancer and non-cancer samples.
Performance analysis of different signal representations and optimizers for CNN based automatic modulation classification
Authors: Chahil Sardar Tamoor Hussain , Zakwan Muhammad , Khan Khurram , Fazil Adnan 
Published: October 2024

Abstract

Automatic Modulation Classification (AMC) plays a crucial role in non-cooperative communication systems by identifying modulation types of received signals without prior information. Recently, Convolutional Neural Networks (CNN) based AMC techniques have shown great promise in achieving high classification accuracy for multiple modulation schemes. In this regard, researchers have used different input signal representations and optimizers for training CNN models. This paper investigates the effectiveness of using constellation diagrams and spectrograms as input representations along with various optimizers for CNN based AMC using SqueezeNet model. The optimizers assessed include Stochastic Gradient Descent with Momentum (SGDM), Adaptive Moment Estimation (ADAM), and Root Mean Square Propagation (RMSprop). Eleven modulation schemes were analyzed, and classification performance was measured using accuracy, F1 score, and specificity. The best results were obtained with constellation diagrams for RMSprop optimizer, achieving an accuracy of 99.55%, an F1 score of 99.54%, and a specificity of 99.95% at 20 dB SNR. The ADAM optimizer with constellation diagrams followed closely, yielding 96.81% accuracy, 96.82% F1 score, and 99.63% specificity. These configurations also provided the best results over wide range of SNRs from − 20 dB to 20 dB. Additionally, a comparative analysis with previous approach utilizing IQ sequential signal frames demonstrated a significant improvement in classification accuracy with the mentioned configurations. These findings highlight the effectiveness of specific input types and optimizers in enhancing classification performance for CNN-based AMC applications.
Desenvolvimento do Dispositivo de Segurança para Embalagens Plásticas
Authors: Fernandes Renan Lopes , Pinheiro Cristina Fidalgo Affonso , Junior Adauri Silveira Rodrigues , Trajano Eduardo Tavares Lima , Souza Maria Cristina Almeida de 
Published: October 2024

Introdução: As intoxicações exógenas são definidas como manifestações clínicas dos efeitos nocivos à saúde produzidos no organismo vivo, decorrente da ingestão ou não de alguma substância química tóxica para o organismo. As crianças com faixa etária entre zero a quatro anos, são as principais vítimas dessas intoxicações, uma que vez que estão num período de desenvolvimento de habilidades, tendo uma curiosidade relacionada aos objetos ao seu redor. Materiais e métodos: Foi utilizado o SolidWorks® 2013 para desenvolvimento do “Dispositivo de Segurança para Embalagens Plásticas”, elaborado para evitar a abertura acidental do recipiente de produtos químicos de 5 litros. Resultados: O produto é constituído por dois componentes principais: uma trava da base e uma tampa com trava de segurança. Quanto à aplicabilidade, o dispositivo possui simplicidade de manuseio, podendo ser aplicado nas bombonas de 5 litros de capacidade, após o rompimento do lacre. Discussão: Uma busca mundial no banco de patentes analisou cinco produtos com diferentes graus de similaridade usados para fechamento de domissanitários. Entretanto, nenhum tinha a finalidade proposta pelo dispositivo aqui descrito, corroborando sua inventividade. Conclusão: O dispositivo possui caráter inovador, uma vez que pode ajudar na redução das intoxicações por domissanitários.
Palavras-chave: Acidentes; Crianças; Exposição a Produtos Químicos; Intoxicações; Segurança de Dispositivos.
Curcumin Modulates the Differential Effects of Fructose and High-fat Diet on Renal Damage, Inflammation, Fibrosis, and Lipid Metabolism
Authors: Melendez-Salcido Cecilia Gabriela , Ramirez-Emiliano Joel , Garcia-Ramirez Juana Rosalba , Gomez-García Anel , Perez-Vazquez Victoriano 
Published: October 2024

Background:
Dyslipidemia and obesity hypercaloric diet-induced lead to kidney damage. We investigated
the effect of curcumin on the expression of proteins related to inflammation, fibrosis, fatty acids
metabolism, kidney damage, and morphological changes in the kidneys of mice hypercaloric diets-fed.

Methods:
Groups of 5-week-old C57BL/6 mice (n=6) were formed: Control (C), High-fructose diet (F), Highfructose
diet and curcumin (F+Cur), High-fat diet (HFD), High-fat diet and curcumin (HFD+Cur), High-fat diet
and fructose (HFD+F), High-fat diet, fructose and curcumin (HFD+F+Cur), treated for 16 weeks with 30%
(w/v) fructose, 60% (w/w) fat and 0.75% (w/w) curcumin. Kidneys were obtained for histomorphological and
Western Blot analysis.

Results:
Curcumin prevented TNF-α overexpression in the F and HFD+F groups. VLCAD expression was
higher in the F, HFD, and HFD+F groups. PPARγ expression was lower in the F+Cur, HFD+Cur, and
HFD+F+Cur groups. Curcumin prevented overexpression of CPT1 and KIM1 in the HFD+F and HFD groups.
Curcumin prevented morphological lesions, fibrosis, and lipid deposition that were hypercaloric diet-induced.

Conclusion:
Chronic consumption of hypercaloric diets causes inflammation, fibrosis, and lipid deposition in
the kidney. It is suggested that curcumin prevents renal structural damage, limits tissue lipid deposition, and
differentially modulates renal injury depending on diet composition in mice fed high-fat and/or high-fructose
diets.
Effective Components and Molecular Mechanism of Biejiaruangan Capsule Against Liver Fibrosis: High-resolution Mass Spectrometry, Network Pharmacological Analysis and Experimental Verification
Authors: Cao Kefeng , Jiang Hui , Zhang Lili , Fan Chang , Feng Zhigang , Li Biao , Song Laicheng , Zhang Qun 
Published: October 2024

Background:
Biejiaruangan capsule (BJRGC) is a commonly used traditional Chinese
medicine preparation for treating oftreating liver fibrosis (LF), but its specific molecular mechanism
is unclear. This study used mass spectrometry, network pharmacology and experimental
verification to explore the mechanism of BJRGC against LF.

Methods:
Ultrahigh-performance liquid chromatography-quadrupole-exactive-orbitrap-mass
spectrometry (UHPLC-Q-Exactive-Orbitrap-MS) and network pharmacology were employed to
identify and screen the potential components, targets, and signaling pathways of BJRGC against
LF. The interaction between the active ingredients and targets was validated using molecular
docking. Finally, 5-ethynyl-2'-deoxyuridine (EDU) staining, western blotting (WB), and flow cytometry
(FCM) were utilized to further verify the mechanism of BJRGC against LF.

Results:
A total of 9 prototype components of BJRGC were identified in serum, most derived
from iridoid glycosides and triterpenes in Gardenia jasminoides Ellis and Artemisia scoparia
Waldst.et Kit. Network pharmacology predicts that medicine prototype components in serum
mostly influence targets such as CDK2, CDK6, and PIK3CG, with the key route being the
PI3K/AKT signaling pathway. Molecular docking showed that the major components have good
binding properties with key target proteins. The experimental results showed that BJRGC could
inhibit the proliferation of HSCs, induce cell cycle arrest and reduce the protein expression of
CDK2, CDK6 and PIK3CG.

Conclusions:
BJRGC can inhibit the proliferation of HSCs by targeting the protein expression of
CDK2, CDK6, and PIK3CG in the PI3K/AKT signaling pathway through its prototype components,
such as hyperoside, tumulosic acid, and hederagenin, thereby alleviating LF disease.
Integrated Transcriptomics and Metabolomics Studies Reveal Steroid Biosynthesis Pathway and BCL2 Inhibitory Diazo-Progesterone of Drimia
indica for Conservation and Sustainable Utilization
Authors: Shit Vivek , Dhakar Mahesh Kumar , Kumar Manoj 
Published: October 2024

Background:
This study is the first report on the sequence of the transcriptome of
Drimia indica, a non-model plant with medicinal properties found in a forest tribal belt, using
the Illumina NovaSeq platform. The primary objectives of this study were to elucidate the gene
expression profiles in different tissues, identify key regulatory genes and pathways involved in
secondary metabolite biosynthesis, and explore the plant's potential pharmacological properties.

Methods:
The study generated 670087 unigenes from both leaves and roots and identified putative
homologs of annotated sequences against UniProt/Swiss-Prot and KEGG databases. The
functional annotation of the identified unigenes revealed the secondary metabolite biosynthetic
process as the most prominent pathway, with gene enrichment analysis predominantly accounting
for secondary metabolite pathways, such as terpenoid, steroid, flavonoid, alkaloid, selenocompound,
and cortisol synthesis. The study also identified regulatory genes NAC, Bhlh,
WRKY, and C2H2 on the transcriptome dataset.

Results:
The functionally annotated unigenes suggested phytocompounds in Drimia indica to
have multi-potent properties, such as anti-cancer, anti-inflammatory, and anti-diabetic activities,
which has been further validated by GC-MS-based metabolite profiling. Notably, we have identified
two novel molecules, di-azo progesterone and 4H-pyran-4-one 2,3-dihydro-3,5-dihydroxy-
6-methyl, with potential BCL2 inhibitory anticancer properties, supported by stable binding interactions
observed in molecular docking and dynamics simulations. Additionally, an abundance
of mono-nucleotide SSR markers has been identified, useful for genetic diversity studies.

Conclusion:
This study provides a foundational understanding of the molecular mechanisms in
Drimia indica, highlighting its potential as a source for novel therapeutic agents and contributing
valuable insights for future pharmacological and agricultural applications. However, further in
vivo studies are warranted to confirm these findings and validate their pharmacological efficacy
and therapeutic potential. The SSR markers identified also offer valuable tools for molecular
genetics, plant breeding, and sustainable drug development.
The Apoptotic, Cytotoxic, and Anti-migration Effects of Sodium Deoxycholate in a Breast Cancer Cell Line and its Modulation on PON1 as a Predictive Risk Marker
Authors: Tülüce Yasin , Alhammud Huda , Keleş Ahmet Yasin , Köstekci Sedat 
Published: October 2024

Introduction:
Breast cancer is the most prevalent cancer among women
and is usually treated with antineoplastic drugs. The present study examines the
influence of sodium deoxycholate on the molecular pathways underlying apoptosis,
cytotoxicity, and the modulation of PON1 in the MCF-7 breast cancer cell line. Various
doses were administered to test the hypothesis that it could potentially affect cancer
cells.

Method:
The study examined the cytotoxic effect of sodium deoxycholate on MCF-7
cells and human mammary epithelial cells (CRL-4010) using the MTT method to detect
its anticancer properties. Subsequently, the efficacy of the active dose on DNA
fragmentation and apoptosis was examined using the apoptotic DNA ladder and
Western blot methods. Additionally, oxidative stress index and cell migration tests
were conducted. Notably, sodium deoxycholate did not cause DNA damage despite
demonstrating cytotoxic effects on cells.

Results:
The study found that sodium deoxycholate increased the levels of several
pro-apoptotic proteins, leading to apoptosis. Moreover, it markedly diminishes the
activity of paraoxonase and arylesterase of PON1, which are predictive risk markers
for cancer. Furthermore, it was found to delay cell migration in a time-dependent
manner.

Conclusion:
These findings suggest that sodium deoxycholate exhibits an
antimetastatic effect in breast cancer cells, could be a valuable subject for further
cancer research.
Cognitive Behavioral Therapy in Cardiovascular Disease
Authors: Manolis Theodora A. , Manolis Antonis A. , Manolis Antonis S. 
Published: October 2024

Introduction/Objective:
The influence of cognitive behavioral therapy (CBT) and its
modalities on various neuropsychiatric conditions is herein explored together with their impact on
specific cardiovascular (CV) diseases (CVD).

Methods:
A comprehensive review of the literature was undertaken via the PubMed, Scopus and
Google Scholar on the above relevant topics. The focus was on large randomized controlled trials
and meta-analyses.

Results:
Among the various neuropsychiatric disorders, depression and anxiety commonly occur
in CVD patients, frequently eluding clinician's attention. This reciprocal liaison may incur higher
rates of morbidity/mortality, through physiological and behavioral mechanisms. Multimodal
psychiatric interventions, using medications and psychotherapies, such as CBT, seem promising.
Such mindfulness-based interventions have the potential to be an efficacious complementary strategy
to address psychological stress in CVD patients. As the cost of CBT is relatively low, such a
supportive approach for stress management provides high patient acceptability, with a positive impact
on improving quality of life, by promoting CV health and mitigating CV complications.

Conclusion:
There is ample evidence of a reciprocal liaison between heart and mind. Several CV
risk factors are strongly affected by diseases of the mind, and the clinical course of various CVDs
is influenced by affective or other psychiatric disorders. CBT and relevant mindfulness-based interventions
have a significant supportive role in patients with various CVDs by targeting CV risk
factor(s) or the underlying specific CVD and by identifying and addressing psychosocial issues. In
this direction, various CBT interventions can provide the means to favorably influence both CV
risk factors and CVDs.