bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Oncogenic Role of Disulfidptosis-related Gene SLC7A11 in
Anti-tumor Immunotherapy Response to Human Cancers
Authors: Xu Borui , Liang Jiahua , Fu Liangmin , Wei Jinhuan , Lin Juan 
Published: February 2024

Background:
The protein Solute Carrier Family 7 Member 11 (SLC7A11) plays a pivotal
role in cellular redox homeostasis by suppressing disulfidptosis, which restricts tumor growth.
Yet, its relevance in prognosis, immunity, and cancer treatment efficacy is not well understood.

Methods:
We conducted a comprehensive analysis of the expression of SLC7A11 across 33 cancer
types, employing datasets from public databases. Methods, such as Cox regression and survival
analyses assessed its prognostic significance, while functional enrichment explored the biological
processes tied to SLC7A11. The association between SLC7A11 expression, immune cell infiltration,
and immune-related gene expression was also scrutinized.

Results:
Notably, SLC7A11 expression was more pronounced in cancerous compared to normal
samples and correlated with higher tumor grades. Increased SLC7A11 expression was linked to
poor outcomes, particularly in liver hepatocellular carcinoma (LIHC). This protein's expression also
showcased significant relationships with diverse molecular and immune subtypes.

:
Additionally, a prognostic nomogram was devised, integrating SLC7A11 expression and clinical
variables. High SLC7A11 levels corresponded with cell growth and senescence pathways in various
cancers and with lipid and cholesterol metabolism in LIHC. Furthermore, potential therapeutic
compounds for LIHC with high SLC7A11 were identified. Real-time PCR (qPCR) and Western
blot were conducted to explore the expression of SLC7A11 in tumor tissues and cancer cell
lines.

Conclusion:
In summation, this study emphasizes the prognostic and immunological importance
of SLC7A11, spotlighting its potential as a therapeutic target in LIHC.
Harnessing Computational Modeling for Efficient Drug Design Strategies
Authors: Singh Kuldeep , Bhushan Bharat , Dube Akhalesh Kumar , Jha Anit Kumar , Rani Ketki , Mishra Akhilesh Kumar , Porwal Prateek 
Published: February 2024

Abstract:
Computational modeling has become a crucial tool in drug design, offering efficiency and
cost-effectiveness. This paper discusses the various computational modeling techniques used in drug
design and their role in enabling efficient drug discovery strategies. Molecular docking predicts the
binding affinity of a small molecule to a target protein, allowing the researchers to identify potential
lead compounds and optimize their interactions. Molecular dynamics simulations provide insights into
protein-ligand complexes, enabling the exploration of conformational changes, binding free energies,
and fundamental protein-ligand interactions. Integrating computational modeling with machine learning
algorithms, such as QSAR modeling and virtual screening, enables the prediction of compound
properties and prioritizes potential drug candidates. High-performance computing resources and advanced
algorithms are essential for accelerating drug design workflows, with parallel computing,
cloud computing, and GPU acceleration reducing computational time. The paper also addresses the
challenges and limitations of computational modeling in drug design, such as the accuracy of scoring
functions, protein flexibility representation, and validation of predictive models. It emphasizes the
need for experimental validation and iterative refinement of computational predictions to ensure the
reliability and efficacy of designed drugs.
Machine Learning and Augmented Intelligence Enables Prognosis of Type 2 Diabetes Prior to Clinical Manifestation
Authors: Lakey Jonathan R.T. , Casazza Krista , Lernhardt Waldemar , Mathur Eric J. , Jenkins Ian 
Published: February 2024

Background:
The global incidence of type 2 diabetes (T2D) persists at epidemic proportions.
Early diagnosis and/or preventive efforts are critical to attenuate the multi-systemic
clinical manifestation and consequent healthcare burden. Despite enormous strides in the understanding
of pathophysiology and on-going therapeutic development, effectiveness and access
are persistent limitations. Among the greatest challenges, the extensive research efforts have not
promulgated reliable predictive biomarkers for early detection and risk assessment. The emerging
fields of multi-omics combined with machine learning (ML) and augmented intelligence
(AI) have profoundly impacted the capacity for predictive, preventive, and personalized medicine.

Objective:
This paper explores the current challenges associated with the identification of predictive
biomarkers for T2D and discusses potential actionable solutions for biomarker identification
and validation.

Methods:
The articles included were collected from PubMed queries. The selected topics of inquiry
represented a wide range of themes in diabetes biomarker prediction and prognosis

Results:
The current criteria and cutoffs for T2D diagnosis are not optimal nor consider a myriad
of contributing factors in terms of early detection. There is an opportunity to leverage AI and
ML to significantly enhance the understanding of the underlying mechanisms of the disease and
identify prognostic biomarkers. The innovative technologies being developed by GATC are expected
to play a crucial role in this pursuit via algorithm training and validation, enabling comprehensive
and in-depth analysis of complex biological systems.

Conclusion:
GATC is an emerging leader guiding the establishment of a systems approach towards
research and predictive, personalized medicine. The integration of these technologies
with clinical data can contribute to a more comprehensive understanding of T2D, paving the
way for precision medicine approaches and improved patient outcomes.
In-vitro Interactions between Fluconazole and Diphenyl Diselenide against Various Candida Species
Authors: Dahiya Sweety , Kumar Chhillar Anil 
Published: February 2024

Background:
In the immunocompromised population, Candida species are the most
aetiologic agents causing severe nosocomial fungal infections. Candida species, irrespective of
being commensals in the human microbiome, are the fourth most prevalent source of potentially
fatal yeast infections. Monotherapy is frequently employed to treat invasive fungal infections, but
sometimes, patients do not favor the monotherapy treatment regime. It may be because of the reduced
susceptibility of the pathogen toward traditional antimycotic drugs. Antimycotic drug
combination therapy could be a better choice in such specific circumstances. In our study, we
evaluated the interactions of fluconazole with diphenyl diselenide.

Methods:
The antimycotic susceptibilities of Candida species for fluconazole and diphenyl
diselenide were determined by broth microdilution assay, and the in-vitro interactions of fluconazole
with diphenyl diselenide were studied by using disc diffusion assay and chequerboard assay.
The nature of the interactions was assessed by calculating the fractional inhibitory concentration
index (FICI). The interactions were also analyzed by the response surface approach.

Results:
The minimum inhibitory concentrations (MICs) for fluconazole and diphenyl diselenide
as determined by the broth microdilution assay against Candida species were 4 μg/ml-512 μg/ml
and 1 μg/ml-32 μg/ml, respectively. The FICI values varied from 0.375 to 2.

Conclusion:
Our finding demonstrated that there is no antagonism interaction between fluconazole
and diphenyl diselenide in Candida species. Thus, this innovative combination should be
explored in the future.
SSPH I, A Novel Anti-cancer Saponin, Inhibits EMT and Invasion and
Migration of NSCLC by Suppressing MAPK/ERK1/2 and PI3K/AKT/
mTOR Signaling Pathways
Authors: Zhou Jinling , Luo Jian , Gan Rizhi , Zhi Limin , Zhou Huan , Lv Meixian , Huang Yinmei , Liang Gang 
Published: February 2024

Background:
Saponin of Schizocapsa plantaginea Hance I (SSPH I)，a bioactive
saponin found in Schizocapsa plantaginea, exhibits significant anti-proliferation and antimetastasis
in lung cancer.

Objective:
To explore the anti-metastatic effects of SSPH I on non-small cell lung cancer
(NSCLC) with emphasis on epithelial-mesenchymal transition (EMT) both in vitro and in vivo.

Methods:
The effects of SSPH I at the concentrations of 0, 0.875,1.75, and 3.5 μM on A549 and
PC9 lung cancer cells were evaluated using colony formation assay, CCK-8 assay, transwell
assay and wound-healing assay. The actin cytoskeleton reorganization of PC9 and A549 cells
was detected using the FITC-phalloidin fluorescence staining assay. The proteins related to
EMT (N-cadherin, E-cadherin and vimentin), p- PI3K, p- AKT, p- mTOR and p- ERK1/2 were
detected by Western blotting. A mouse model of lung cancer metastasis was established by utilizing
95-D cells, and the mice were treated with SSPH I by gavage.

Results:
The results suggested that SSPH I significantly inhibited the migration and invasion of
NSCLC cells under a non-cytotoxic concentration. Furthermore, SSPH I at a non-toxic concentration
of 0.875 μM inhibited F-actin cytoskeleton organization. Importantly, attenuation of
EMT was observed in A549 cells with upregulation in the expression of epithelial cell marker
E-cadherin and downregulation of the mesenchymal cell markers vimentin as well as Ncadherin.
Mechanistic studies revealed that SSPH I inhibited MAPK/ERK1/2 and
PI3K/AKT/mTOR signaling pathways.

Conclusion:
SSPH I inhibited EMT, migration, and invasion of NSCLC cells by suppressing
MAPK/ERK1/2 and PI3K/AKT/mTOR signaling pathways, suggesting that the natural compound
SSPH I could be used for inhibiting metastasis of NSCLC.
STNMDA: A Novel Model for Predicting Potential Microbe-Drug Associations with Structure-Aware Transformer
Authors: Fan Liu , Yang Xiaoyu , Wang Lei , Zhu Xianyou 
Published: February 2024

Introduction:
Microbes are intimately involved in the physiological and pathological
processes of numerous diseases. There is a critical need for new drugs to combat microbe-induced
diseases in clinical settings. Predicting potential microbe-drug associations is, therefore, essential for
both disease treatment and novel drug discovery. However, it is costly and time-consuming to verify
these relationships through traditional wet lab approaches.

Methods:
We proposed an efficient computational model, STNMDA, that integrated a StructureAware Transformer (SAT) with a Deep Neural Network (DNN) classifier to infer latent microbedrug associations. The STNMDA began with a “random walk with a restart” approach to construct a
heterogeneous network using Gaussian kernel similarity and functional similarity measures for microorganisms and drugs. This heterogeneous network was then fed into the SAT to extract attribute
features and graph structures for each drug and microbe node. Finally, the DNN classifier calculated
the probability of associations between microbes and drugs.

Results:
Extensive experimental results showed that STNMDA surpassed existing state-of-the-art
models in performance on the MDAD and aBiofilm databases. In addition, the feasibility of
STNMDA in confirming associations between microbes and drugs was demonstrated through case
validations.

Conclusion:
Hence, STNMDA showed promise as a valuable tool for future prediction of microbedrug associations.
DeepPTM: Protein Post-translational Modification Prediction from Protein Sequences by Combining Deep Protein Language Model with Vision Transformers
Authors: Soylu Necla Nisa , Sefer Emre 
Published: February 2024

Introduction:
More recent self-supervised deep language models, such as Bidirectional
Encoder Representations from Transformers (BERT), have performed the best on some language
tasks by contextualizing word embeddings for a better dynamic representation. Their proteinspecific
versions, such as ProtBERT, generated dynamic protein sequence embeddings, which resulted
in better performance for several bioinformatics tasks. Besides, a number of different protein
post-translational modifications are prominent in cellular tasks such as development and differentiation.
The current biological experiments can detect these modifications, but within a longer duration
and with a significant cost.

Methods:
In this paper, to comprehend the accompanying biological processes concisely and more
rapidly, we propose DEEPPTM to predict protein post-translational modification (PTM) sites from
protein sequences more efficiently. Different than the current methods, DEEPPTM enhances the
modification prediction performance by integrating specialized ProtBERT-based protein embeddings
with attention-based vision transformers (ViT), and reveals the associations between different
modification types and protein sequence content. Additionally, it can infer several different modifications
over different species.

Results:
Human and mouse ROC AUCs for predicting Succinylation modifications were 0.793 and
0.661 respectively, once 10-fold cross-validation is applied. Similarly, we have obtained 0.776,
0.764, and 0.734 ROC AUC scores on inferring ubiquitination, crotonylation, and glycation sites,
respectively. According to detailed computational experiments, DEEPPTM lessens the time spent in
laboratory experiments while outperforming the competing methods as well as baselines on inferring
all 4 modification sites. In our case, attention-based deep learning methods such as vision transformers
look more favorable to learning from ProtBERT features than more traditional deep learning and
machine learning techniques.

Conclusion:
Additionally, the protein-specific ProtBERT model is more effective than the original
BERT embeddings for PTM prediction tasks. Our code and datasets can be found at
https://github.com/seferlab/deepptm.
P4PC: A Portal for Bioinformatics Resources of piRNAs and circRNAs
Authors: Liu Yajun , Li Ru , Ding Yulian , Hei Xinhong , Wu Fang-Xiang 
Published: February 2024

Background:
PIWI-interacting RNAs (piRNAs) and circular RNAs (circRNAs) are two
kinds of non-coding RNAs (ncRNAs) that play important roles in epigenetic regulation, transcriptional
regulation, post-transcriptional regulation of many biological processes. Although there exist
various resources, it is still challenging to select such resources for specific research projects on
ncRNAs.

Methods:
In order to facilitate researchers in finding the appropriate bioinformatics sources for
studying ncRNAs, we created a novel portal named P4PC that provides computational tools and data
sources of piRNAs and circRNAs.

Results:
249 computational tools, 126 databases and 420 papers are manually curated in P4PC. All
entries in P4PC are classified in 5 groups and 26 subgroups. The list of resources is summarized in
the first page of each group.

Conclusion:
According to their research proposes, users can quickly select proper resources for their
research projects by viewing detail information and comments in P4PC. Database URL is
http://www.ibiomedical.net/Portal4PC/ and https://43.138.46.5/Portal4PC/.
Cordyceps militaris: A Comprehensive Study on Laboratory Cultivation and
Anticancer Potential in Dalton's Ascites Lymphoma Tumor Model
Authors: Dutta Diksha , Singh Namram Sushindrajit , Aggarwal Rohit , Verma Akalesh Kumar 
Published: February 2024

Background:
Cancer, a predominant cause of mortality, poses a formidable challenge in our pursuit of
elevating life expectancy. Throughout history, individuals have sought natural remedies with minimal side effects
as an appealing substitute for chemotherapeutic drugs. One such remedy is Cordyceps militaris, a renowned medicinal mushroom deeply entrenched in Asian ethnomedicine. Revered for its rejuvenating and curative attributes,
it relied upon for ages.

Objective:
The mushroom’s soaring demand outpaced natural availability, necessitating controlled laboratory
cultivation as the core focus and exploring the potential of methanolic extracts from harvested Cordyceps militaris fruiting bodies against Dalton's Lymphoma Ascites (DLA) cells in vitro, with a specific emphasis on its anticancer traits.

Methods:
For cultivation, we employed a diverse range of rice substrates, among which bora rice showed promising growth of C. militaris fruiting bodies. To assess DLA cell cytotoxicity, several assays, including trypan blue
exclusion assay, MTT assay, and LDH assay, were employed at different time points (24-96 h), which provided
valuable insights on DLA cell viability and proliferation, shedding light on its therapeutic potential against cancer.

Results:
Our studies unveiled that methanolic extract prompts apoptosis in DLA cells via AO/EB dual staining,
manifesting consistent apoptosis indicators such as membrane blebbing, chromatin condensation, nuclei fragmentation, and cellular shrinkage at 48-96 h of treatment. Furthermore, these striking repercussions of apoptosis were
comprehended by an in silico approach having molecular docking simulation against antiapoptotic proteins like
BCL-2, BCL-XL, MCL-1, BFL-1 & HSP100.

Conclusion:
Methanolic C. militaris extracts exhibited cytotoxicity and apoptotic alterations in DLA cells
Marine-derived Compounds: A Powerful Platform for the Treatment of
Alzheimer’s Disease
Authors: Arora Rashmi , Babbar Ritchu , Dabra Abhishek , Chopra Bhawna , Deswal Geeta , Grewal Ajmer Singh 
Published: February 2024

Abstract:
Alzheimer's disease (AD) is a debilitating form of dementia that primarily affects cholinergic
neurons in the brain, significantly reducing an individual's capacity for learning and creative
skills and ultimately resulting in an inability to carry out even basic daily tasks. As the elderly
population is exponentially increasing, the disease has become a significant concern for society.
Therefore, neuroprotective substances have garnered considerable interest in addressing this universal
issue. Studies have shown that oxidative damage to neurons contributes to the pathophysiological
processes underlying AD progression. In AD, tau phosphorylation and glutamate excitotoxicity
may play essential roles, but no permanent cure for AD is available. The existing therapies only
manage the early symptoms of AD and often come with numerous side effects and toxicities. To
address these challenges, researchers have turned to nature and explored various sources such as
plants, animals, and marine organisms. Many historic holy books from different cultures emphasize
that adding marine compounds to the regular diet enhances brain function and mitigates its decline.
Consequently, researchers have devoted significant time to identifying potentially active neuroprotective
substances from marine sources. Marine-derived compounds are gaining recognition due to
their abundant supply of diverse chemical compounds with biological and pharmacological potential
and unique mechanisms of action. Several studies have reported that plants exhibit multitarget
potential in treating AD. In light of this, the current study focuses on marine-derived components
with excellent potential for treating this neurodegenerative disease.
A Review on Role of Inflammation in Coronavirus Disease
Authors: Lotfi Arezoo , Hajian Pouran , Abbasi Laleh , Gargari Morad Kohandel , Fard Najmeh Nameh Goshay , Naderi Delaram 
Published: February 2024

:
The respiratory illness known as COVID-19 is caused by the novel coronavirus,
SARS-CoV-2. While the precise pathogenic mechanism of COVID-19 remains unclear, the occurrence
of a cytokine storm subsequent to viral infection plays a pivotal role in the initiation
and advancement of the disease. The infection of SARS-CoV-2 induces a state of immune system
hyperactivity, leading to an excessive production of inflammatory cytokines. Consequently,
the identification of the various signaling pathways implicated in the inflammation induced by
COVID-19 will enable researchers to investigate new targets for therapeutic intervention.
Organelle Targeted Drug Delivery: Key Challenges, Recent Advancements
and Therapeutic Implications
Authors: Singh Dilpreet 
Published: February 2024

:
Organelle-specific targeted drug delivery has emerged as a promising approach in the
field of drug delivery and therapeutics. This innovative strategy involves the precise delivery of
therapeutic agents to specific organelles within cells, such as the nucleus, mitochondria, endoplasmic
reticulum, or lysosomes, with the aim of enhancing drug efficacy while minimizing offtarget
effects. Despite its tremendous potential, organelle-specific drug delivery faces several
key challenges. One major challenge is the development of delivery systems that can accurately
navigate the complex intracellular environment and deliver drugs exclusively to the desired organelles.
Achieving this level of precision demands advanced nanotechnology and biomaterials
engineering. Furthermore, ensuring the safety and biocompatibility of these delivery systems is
paramount. Recent advancements in this field include the development of nanocarriers, such as
liposomes, nanoparticles, and dendrimers, designed to target specific organelles through ligandreceptor
interactions or pH-responsive mechanisms. Additionally, advancements in molecular
biology and genetic engineering have enabled the design of genetically encoded organellespecific
drug delivery systems. The therapeutic implications of organelle-specific drug delivery
are vast. This approach has the potential to revolutionize the treatment of diseases with organelle-
specific pathologies, such as neurodegenerative disorders, cancer, and mitochondrial diseases.
By precisely targeting the organelles involved in disease progression, the efficacy of therapies
can be significantly improved while minimizing collateral damage to healthy tissues.
Physiologic Effects of Hibiscus sabdariffa (Sorrel) on Biological Systems:
Advances in Sorrel Research
Authors: McCalla Garsha , Smith Bethany 
Published: February 2024

Background::
Hibiscus sabdariffa (sorrel) is a member of the hibiscus genus and Malvaceae
family. An important attribute is its therapeutic characteristic, which has been validated by research
that shows some success in treating several diseases.

Objective::
Owing to limited information and research deficits, this study aimed to investigate the
physiologic effects of Hibiscus sabdariffa on biological systems and the advances made in the last
century.

Methods::
PubMed Central search for “Hibiscus sabdariffa OR sorrel” was conducted from November
2021 to March 2022, covering articles from 1921 to 2020. The results were documented in Microsoft
Excel 2016 and grouped based on physiological systems.

Results::
Sorrel research publications mushroomed in the last decade. Studies from 1921-2010 reported
on the endocrine and cardiovascular systems, while additional biological systems (nervous,
urinary, gastrointestinal, integumentary, hematopoietic, reproductive, and skeletal) were later investigated.
Sorrel positively treated diabetes, hypertension, and a multitude of other illnesses due to its
antioxidant, antibacterial, and anti-inflammatory potential, chiefly via its anthocyanins. Hypertension
was mostly reported, predominantly from Nigeria (Africa), and toxicity exceeded 2000 mg/kg b.w.

Conclusion::
Sorrel has multifaceted uses, mostly involving hypertension treatment. Despite increased
publications and advances in research, all areas of physiology still require sorrel research,
especially in the Caribbean, which lacked publication, amidst Jamaica being known for the consumption
of sorrel drinks.