bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comprehensive In Silico Analysis of Uncaria Tomentosa Extract: Chemical Profiling, Antioxidant Assessment, and CLASP Protein Interaction for Drug Design in Neurodegenerative Diseases
Authors: Kumar Sanjesh , Panda Siva Prasad 
Published: February 2024

Background:
Uncaria tomentosa is a traditional medicinal herb renowned for its
anti-inflammatory, antioxidant, and immune-enhancing properties. In the realm of
neurodegenerative diseases (NDDS), CLASP proteins, responsible for regulating microtubule
dynamics in neurons, have emerged as critical players. Dysregulation of CLASP proteins is
associated with NDDS, such as Alzheimer's, Parkinson's, and Huntington's diseases.
Consequently, comprehending the role of CLASP proteins in NDDS holds promise for the
development of innovative therapeutic interventions.

Objectives:
The objectives of the research were to identify phytoconstituents in the
hydroalcoholic extract of Uncaria tomentosa (HEUT), to evaluate its antioxidant potential
through in vitro free radical scavenging assays and to explore its potential interaction with
CLASP using in silico molecular docking studies.

Methods:
HPLC and LC-MS techniques were used to identify and quantify phytochemicals in
HEUT. The antioxidant potential was assessed through DPPH, ferric reducing antioxidant
power (FRAP), nitric oxide (NO) and superoxide (SO) free radical scavenging methods.
Interactions between conventional quinovic acid, chlorogenic acid, epicatechin, corynoxeine,
rhynchophylline and syringic acid and CLASP were studied through in silico molecular docking
using Auto Dock 4.2.

Results:
The HEUT extract demonstrated the highest concentration of quinovic acid derivatives.
HEUT exhibited strong free radical-scavenging activity with IC50 values of 0.113 μg/ml
(DPPH) and 9.51 μM (FRAP). It also suppressed NO production by 47.1 ± 0.37% at 40 μg/ml
and inhibited 77.3 ± 0.69% of SO generation. Additionally, molecular docking revealed the
potential interaction of quinovic acid with CLASP for NDDS.

Conclusion:
The strong antioxidant potential of HEUT and the interaction of quinovic acid with
CLASP protein suggest a promising role in treating NDDS linked to CLASP protein dysregulation.
A Review on N-nitrosamine Impurity
Authors: Pal Debangana , B. Babu 
Published: February 2024

Abstract:
Nitrosamines are a category of substances that have a nitroso group linked to an amino
group in their molecular structure. They are a category of carcinogens developed as a by-product of
drug synthesis and also constituted in the presence of sodium nitrite or nitric acid and secondary
amines (like dimethyl amines and diethyl amine) that must be monitored in drug production. In
mid-June 2018, the Food and Drug Administration and the European Medicines Agency became
aware of the existence of an impurity called N-nitrosamine in some frequently used medications,
such as those for blood pressure, antacids, and diabetes. N-nitrosamines are of major concern because
the ICH M7 (R1)2 guideline categorizes them as Class 1 impurities or mutagenic carcinogens,
and the International Agency for Cancer Research categorizes them as potential carcinogens.
The existence of N-nitrosamines in pharmaceutical drugs must be minimized to the greatest extent
possible and must be at or below the threshold based on ICH M7(R1)2 fundamentals for materials
in the "cohort of concern" described in this guideline and measured based on lifetime constant exposure.
Evaluation of Stability and In vitro Anti-Cancer Activity of Dihydroquercetin Nanoemulsion
Authors: Nguyen Mai Huong Thi , Nguyen Thu Huong Thi , Phan Thuy Thi , Bach Son Thanh , Phan Thien Xuan , Nguyen Tinh Trong , Le Huong Thi , Nguyen Binh Thanh 
Published: February 2024

Background:
Dihydroquercetin (DHQ), also known as taxifolin, is a flavonoid commonly
found in many plants. Dihydroquercetin has been documented to have powerful antioxidant
activity and many beneficial properties for human health, especially its ability to inhibit
certain types of cancer cells. However, its low solubility and bioavailability are major obstacles
to biomedical applications. Moreover, DHQ is chemically unstable and quickly degrades when
exposed to alkaline conditions.

Objective:
In the present study, a DHQ nanoemulsion formulation was prepared by Self Nano-
Emulsifying Drug Delivery System (SNEDDS) technique to overcome the above disadvantages.

Methods:
The obtained nanoemulsion system was evaluated for its micro-properties, stability,
and in vitro cytotoxic activity against some cancer cells using tetrazolium dyes (MTS assay).

Results:
Measurement results showed that the DHQ nanoemulsion was successfully synthesized
with typical mean droplet sizes from 9 to 11 nm, and revealed excellent stability over time. Dihydroquercetin
in nanoemulsion form is more stable than the non-encapsulated form, as evidenced
by the maintenance of droplet size in the nanometer range when dispersed in aqueous
solution for up to 48 hours. This stability is particularly pronounced in both acidic and neutral
environments. In vitro experiments on cytotoxic activities against A549, Hela, and HepG2 cancer
cell lines indicated that the prepared DHQ nanoemulsion effectively inhibited the growth of
all these cell lines with IC50 values (μg/mL) of 8.0, 20.4, and 29.5 respectively.

Conclusion:
From the detailed results above, it is evident that the solubility and bioavailability
of DHQ can be improved by creating its nanostructure in the form of nanoemulsions. Furthermore,
the nano form of DHQ carried within stable nanoemulsions exhibited better performance
in inhibiting cancer cells compared to free DHQ. Therefore, further research is required to explore
the development of cancer therapeutics utilizing nano DHQ emulsions.
Exploring the Evolving Significance of lncRNA TUG1-mediated Signaling
Pathways in Breast Cancer
Authors: Abouali Gale Dari Mahrokh , Anbiyaiee Amir , Moghanibashi Mehdi , Mohammad Jafari Razieh , Moramezi Farideh , Farzaneh Maryam 
Published: February 2024

Abstract:
Breast cancer is one of the most common malignancies in women worldwide. Invasive
ductal carcinoma (IDC) and invasive lobular carcinoma (ILC) are the most common kinds
of invasive breast cancer. Several genetic, epigenetic, and environmental factors could trigger
the pathogenesis of breast cancer. Breast cancer treatment generally includes surgery, radiation
therapy, chemotherapy, hormonal treatment, targeted therapy, immunotherapeutic, neoadjuvant
systemic therapy, and systemic therapy. Although several classical treatment methods are used
in cancer therapy, molecular-based strategies can open a new perspective for breast cancer
treatment. Previous studies reported that long non-coding RNAs (lncRNAs) play important roles
in cancer development and progression. LncRNA TUG1 was found to target several miRNAs
and regulate breast cancer cell behavior. TUG1 can induce cell proliferation and invasion of
breast cancer cells via downregulation of some miRNAs. Therefore, TUG1 might be a potent
biomarker for the treatment of human cancer. In this review, we summarized the functional roles
of TUG1 in breast cancer.
Discovery of β-carboline based Derivatives through Computational Aid for the Treatment of Leishmania
Authors: Mwamafupa Asifiwe , Arora Pinky , Singh Jagtar , Seksaria Kriti , Kumar Shubham 
Published: February 2024

Background::Leishmaniasis is a phagocytic host cell invading, caused by leishmania species mostly found in developing nations. To treat leishmaniasis, a wide range of medications and potential vaccines are available, such as pentavalent antimonials, amphotericin, and miltefosined, but due to the lack of effective treatments, the high toxicity of chemotherapy and the growth of drug resistance linked to these diseases necessitate the urgent development of innovative therapeutic agents. β-carboline is a group of chemical compounds, and its derivatives have shown to be one of the potential candidates for the treatment of Leishmania. The carboline derivatives may have antileishmanial activity. Which act by interfering with the parasite’s DNA replication or metabolic processes, inhibiting the enzymes that are mainly responsible for the reproduction and metabolism of leishmania.Objective::This research undertaking's present focus is on determining the existence and virtual screening of potential β-carboline derivative, which can act as an antileishmanial agent that can prevent or stop the progression of leishmania by the integration of different computational technologies such as in silico ADMET analysis and docking.Methods::After synthesizing molecules using virtual screening, all designed compounds underwent molecular docking, and hit molecules underwent ADMET analysis.Results::Using AutoDock Vina 1.5.6, molecular docking was carried out in the arginase receptor's active site (PDBID: 2AEB). The research and creation of compounds used virtual screening. Fifteen hits from docking experiments had high binding affinity as, in comparison to the commercially available molecule pentamidine (binding score -5.5Kcal/mol), 15 compounds (binding affinity - 7.9 to 7.0Kcal/mol). These Fifteen best hits were further examined for their ADME activity using SwissADME, and the ADME analysis identified 15 medicines as having the ideal ADME profile and improved bioavailability.Conclusion::This research will bring up a broad spectrum of prospects for investigation into the disciplines of computational and medical research. This will facilitate the development of new antileishmanial agents that have better stability, bioavailability, and less toxicity, side effects for use in future research studies.
Framework for the Classification of Facial Emotions Using Soft Computing
Techniques
Authors: Maity Sourav , Veer Karan 
Published: February 2024

Background::
Facial emotion recognition (FER) technology is enumerated as a productive
interface in several operations, which has been specifically focused on as a substitute
communication path among a user and an appliance for human computer interface in the previous
decade. The efficiency of the facial identification model straightaway relies on the capability
of classification methods. In addition, an appropriate swap between recognition efficiency
and computational cost is reckoned as the most important factor for planning such models.

Methods::
The objective of this paper was to classify the facial emotion electromyogram (EMG)
signals by means of a neural network algorithm (NN), support vector machine (SVM) algorithm,
and Naive-Bayes algorithm. This research work was directed towards the correlation among the
classification accuracies by applying distinct feature extraction procedures on fEMGs. At first,
eight participants (six male and two female) were recruited for data recording. Four electrodes
were placed on each participant's face for capturing facial gestures (happy, angry, sad, and fear)
and two electrodes were placed on the wrist for grounding purposes. Data were recorded by using
BIOPAC MP150. After this, the signals were filtered using a band-pass filter and segmentation
techniques for enhanced processing. After that, the time-domain and frequency-domain feature
extraction procedures were carried out. Time domain and frequency domain features were
applied to recorded signals. In this research, we used LabVIEW and MATLAB to produce a set
of characteristics from fEMG signals for four emotional conditions, such as anger, sad, fear, and
happy. After the feature extraction process, the extracted features were aligned into respective
emotions by applying classifiers. The extracted features were further trained and classified by
applying the SVM classifier, neural network classifier, and Naive Bayes classifier in MATLAB
2020.

Results::
The SVM classifier and neural network classifier generated an accuracy of 93.80% and
96.90%, respectively, whereas the Naive Bayes classifier generated an accuracy of 90.60%.

Conclusion::
Facial emotion recognition (FER) is foresighted as a progressive or futuristic model,
which has attracted the attention of researchers in several areas of learning due to its higher
prospects in distinct applications. Acknowledgment of the emotions through biomedical signals
produced from movements of facial muscles is lately presented using an explicit and authentic
route.
Assessment of Neurologic Safety Profile of Immune Checkpoint Inhibitors:
Evaluation of Adverse Drug Reaction Reports
Authors: Khurana Atul , Dubey Harikesh , Arora Mandeep Kumar 
Published: February 2024

Background::
Immune checkpoint inhibitors (ICIs) used in immunotherapy have revolutionized
cancer management. However, ICI therapy can come with serious neurologic risks.

Objective::
The objective of our study is to analyze the occurrence of neurologic events with ICIs.

Methods::
We referred to EudraVigilance (EV) and VigiAccess to evaluate the frequency of individual
case safety reports (ICSRs), including neurologic events with ICIs. Data was gathered for
a period from the date of ICI’s marketing authorization till 30 January 2023. The computational
assessment was conducted with the help of reporting odds ratio (ROR) and its 95% confidence
interval (CI).

Results::
Overall, 8181 ICSRs in EV and 15905 ICSRs from VigiAccess were retrieved for neurologic
events, with at least one ICI as the suspected drug. The majority of the ICSRs were reported
for nivolumab, pembrolizumab, and ipilimumab, whereas frequently reported events were neuropathy
peripheral, myasthenia gravis, seizure, Guillain-Barre syndrome, paraesthesia, syncope,
encephalopathy, somnolence. Under EV, 92% of ICSRs were reported as serious, 10% included
fatal outcomes, and nearly 61% cited patient recovery. Atezolizumab (ROR 1.64, 95% CI 1.75-
1.52), cemiplimab (ROR 1.61, 95% CI 1.98-1.3), and nivolumab (ROR 1.38, 95% CI 1.44-1.31)
had a considerable increase in the frequency of ICSR reporting. Cerebrovascular accident, posterior
reversible encephalopathy syndrome, tremor, and somnolence were identified as potential
signals.

Conclusion::
ICIs were significantly associated with neurologic risks, which cannot be generalized.
A considerable increase in ICSR reporting frequency was observed with atezolizumab,
cemiplimab, and nivolumab, while avelumab, pembrolizumab, durvalumab, and cemiplimab were
linked with four potential signals. These findings suggest the consideration of a revision of the
neurologic safety profile of ICIs. Furthermore, the necessity for additional ad-hoc research is
emphasized.
A Novel Multi-compartment Rotating Bioreactor for Improving ADSC-Spheroid Formation and its Application in Neurogenic Erectile Dysfunction
Authors: Wang Peng , Liu Yang , Duan Xiao-Feng , Pan Xiao-Ying , Kong Xiang-Rui , Yang Yong 
Published: February 2024

Objective:
The aim of this study was to construct a multicompartment synchronous rotating
bioreactor (MCSRB) for batch-production of homogenized adipose-derived stem cell (ADSC)
microspheres and treat neurogenic erectile dysfunction (ED).

Methods:
Firstly, an MCSRB was constructed using a centrifugal device and hinged trays. Secondly,
influence factors (density, rotational speed) on the formation of ADSC-spheroids were explored.
Finally, a neurogenic ED model was established to verify the effectiveness and safety of
ADSC-spheroids for ED treatment.

Results:
An MCSRB promoted ADSCs to gather microspheres, most of which were 90-130 μm in
diameter. Supernatant from three-dimensional culture led to a significant increase in cytokine expression
in ADSCs and migration rate in human umbilical vein endothelial cells (HUVECs) compared
to control groups. The erectile function and pathological changes of the penis were improved
in the ADSC-spheroids treatment group compared to the traditional ADSCs treatment
group (p < 0.01).

Conclusion:
Efficient, batch, controlled and homogenized production of ADSC stem cell microspheres,
and effective improvement of erectile dysfunction in neurogenic rats can be achieved using
the MCSRB device.
Evaluation of Pancreatic β-cell Differentiation Efficiency of Human iPSC Lines for Clinical Use
Authors: Horikawa Ayumi , Tsuda Kyoko , Yamamoto Takayoshi , Michiue Tatsuo 
Published: February 2024

Background:
Transplantation of pancreatic β-cells generated from human induced
pluripotent stem cells (hiPSCs) has great potential as a root treatment for type 1 diabetes. However,
their current level of efficiency to differentiate into β-cells is still not at par for clinical use. Previous
research has shown that differentiation efficiency varies among human embryonic stem
cells and mouse-induced pluripotent stem cell lines. Therefore, selecting a suitable cell line for efficient
induction into desired tissues and organs is crucial.

Methods:
In this study, we have evaluated the efficiency of 15 hiPSC lines available for clinical
use to differentiate into pancreatic β-cells.

Results:
Our investigation has revealed induction efficiency to differ among the hiPSC lines, even
when derived from the same donor. Among the hiPSC lines tested, the 16A01 cell line exhibited
the highest insulin expression and low glucagon expression, suggesting that this cell line is suitable
for differentiation into β-cells.

Conclusion:
Our study has demonstrated the importance of selecting a suitable hiPSC line for effective
differentiation into β-cells.
Mechanisms of Stem Cells and Their Secreted Exosomes in the Treatment of Autoimmune Diseases
Authors: Lin Shu-Qian , Wang Kai , Pan Xing-Hua , Ruan Guang-Ping 
Published: February 2024

:
Stem cells play a therapeutic role in many diseases by virtue of their strong self-renewal
and differentiation abilities, especially in the treatment of autoimmune diseases. At present, the
mechanism of the stem cell treatment of autoimmune diseases mainly relies on their immune regulation
ability, regulating the number and function of auxiliary cells, anti-inflammatory factors and
proinflammatory factors in patients to reduce inflammation. On the other hand, the stem cell-
derived secretory body has weak immunogenicity and low molecular weight, can target the site
of injury, and can extend the length of its active time in the patient after combining it with the
composite material. Therefore, the role of secretory bodies in the stem cell treatment of autoimmune
diseases is increasingly important.
Transformer-based Named Entity Recognition for Clinical Cancer Drug Toxicity by Positive-unlabeled Learning and KL Regularizers
Authors: Xie Weixin , Xu Jiayu , Zhao Chengkui , Li Jin , Han Shuangze , Shao Tianyu , Wang Limei , Feng Weixing 
Published: February 2024

Background:
With increasing rates of polypharmacy, the vigilant surveillance of clinical
drug toxicity has emerged as an important With increasing rates of polypharmacy, the vigilant surveillance of clinical
drug toxicity has emerged as an important concern. Named Entity Recognition (NER) stands as
an indispensable undertaking, essential for the extraction of valuable insights regarding drug safety
from the biomedical literature. In recent years, significant advancements have been achieved in the
deep learning models on NER tasks. Nonetheless, the effectiveness of these NER techniques relies on
the availability of substantial volumes of annotated data, which is labor-intensive and inefficient.

Methods:
This study introduces a novel approach that diverges from the conventional reliance on
manually annotated data. It employs a transformer-based technique known as Positive-Unlabeled
Learning (PULearning), which incorporates adaptive learning and is applied to the clinical cancer
drug toxicity corpus. To improve the precision of prediction, we employ relative position embeddings
within the transformer encoder. Additionally, we formulate a composite loss function that integrates
two Kullback-Leibler (KL) regularizers to align with PULearning assumptions. The outcomes
demonstrate that our approach attains the targeted performance for NER tasks, solely relying
on unlabeled data and named entity dictionaries.

Conclusion:
Our model achieves an overall NER performance with an F1 of 0.819. Specifically, it
attains F1 of 0.841, 0.801 and 0.815 for DRUG, CANCER, and TOXI entities, respectively. A
comprehensive analysis of the results validates the effectiveness of our approach in comparison to
existing PULearning methods on biomedical NER tasks. Additionally, a visualization of the associations
among three identified entities is provided, offering a valuable reference for querying their
interrelationships.
Inferring Gene Regulatory Networks from Single-Cell Time-Course Data Based on Temporal Convolutional Networks
Authors: Tan Dayu , Wang Jing , Cheng Zhaolong , Su Yansen , Zheng Chunhou 
Published: February 2024

Background:
Time-course single-cell RNA sequencing (scRNA-seq) data represent dynamic
gene expression values that change over time, which can be used to infer causal relationships
between genes and construct dynamic gene regulatory networks (GRNs). However, most of the existing
methods are designed for bulk RNA sequencing (bulk RNA-seq) data and static scRNA-seq data,
and only a few methods, such as CNNC and DeepDRIM can be directly applied to time-course
scRNA-seq data.

Objective:
This work aims to infer causal relationships between genes and construct dynamic gene
regulatory networks using time-course scRNA-seq data.

Methods:
We propose an analytical method for inferring GRNs from single-cell time-course data
based on temporal convolutional networks (scTGRN), which provides a supervised learning approach
to infer causal relationships among genes. scTGRN constructs a 4D tensor representing gene expression
features for each gene pair, then inputs the constructed 4D tensor into the temporal convolutional
network to train and infer the causal relationship between genes.

Results:
We validate the performance of scTGRN on five real datasets and four simulated datasets,
and the experimental results show that scTGRN outperforms existing models in constructing GRNs.
In addition, we test the performance of scTGRN on gene function assignment, and scTGRN outperforms
other models.

Conclusion:
The analysis shows that scTGRN can not only accurately identify the causal relationship
between genes, but also can be used to achieve gene function assignment.
Radiation-induced Testicular Damage in Mice: Protective Effects of Apigenin
Revealed by Histopathological Evaluation
Authors: Azmoonfar Rasool , Mirzaei Fatemeh , Najafi Masoud , Varkeshi Maryam , Ghazikhanlousani Karim , Momeni Sara , Saber Korosh 
Published: February 2024

Background:
Radiation exposure poses a significant threat to reproductive health,
particularly the male reproductive system. The testes, being highly sensitive to radiation, are
susceptible to damage that can impair fertility and overall reproductive function. The study aims
to investigate the radioprotective effects of apigenin on the testis through histopathological
evaluation.

Materials and Methods:
This research involved utilizing a total of 40 mice, which were randomly
divided into eight groups of five mice each. The groups were categorized as follows: A)
negative control group, B, C, and D) administration of apigenin at three different doses (0.3
mg/kg, 0.6 mg/kg, and 1.2 mg/kg) respectively, E) irradiation group, and F, H, and I) administration
of apigenin at three different doses (0.3 mg/kg, 0.6 mg/kg, and 1.2 mg/kg) in combination
with irradiation. The irradiation procedure involved exposing the mice to a 2Gy X-ray
throughout their entire bodies. Subsequently, histopathological assessments were conducted
seven days after the irradiation process.

Results:
The findings indicated that radiation exposure significantly impacted the spermatogenesis
system. This research provides evidence that administering apigenin to mice before ionizing
radiation effectively mitigated the harmful effects on the testes. Apigenin demonstrated radioprotective
properties, positively influencing various parameters, including the spermatogenesis
process and the presence of inflammatory cells within the tubular spaces.

Conclusion:
Apigenin can provide effective protection for spermatogenesis, minimize the adverse
effects of ionizing radiation, and safeguard normal tissues.