bluuebunny/crossref_metadata_2025 huggingface dataset:
Potential Antidiabetic Activity of β-sitosterol from Zingiber roseum Rosc. via
Modulation of Peroxisome Proliferator-activated Receptor Gamma
(PPARγ)
Authors: Amanat Muhammed , Daula A. F. M. Shahid Ud , Singh Randhir
Published: February 2024

Aim::

To evaluate the antidiabetic potential of β-sitosterol from Zingiber roseum.

Background::

Diabetes mellitus is a cluster of metabolic disorders, and 90% of diabetic patients
are affected with Type II diabetes (DM2). For the treatment of DM2, thiazolidinedione drugs
(TZDs) were proposed, but recent studies have shown that TZDs have several detrimental effects,
such as weight gain, kidney enlargement (hypertrophy), fluid retention, increased risk of bone
fractures, and potential harm to the liver (hepatotoxicity). That is why a new molecule is needed
to treat DM2.

Objective::

The current research aimed to assess the efficacy of β-Sitosterol from methanolic extract
of Zingiber roseum in managing diabetes via PPARγ modulation.

Methods::

Zingiber roseum was extracted using methanol, and GC-MS was employed to analyze
the extract. Through homology modeling, PPARγ structure was predicted. Molecular docking,
MD simulation, free binding energies, QSAR, ADMET, and bioactivity and toxicity scores were
all used during the in-depth computer-based research.

Results::

Clinically, agonists of synthetic thiazolidinedione (TZDs) have been used therapeutically
to treat DM2, but these TZDs are associated with significant risks. Hence, GC-MS identified phytochemicals
to search for a new PPAR-γ agonist. Based on the in-silico investigation, β-sitosterol
was found to have a higher binding affinity (-8.9 kcal/mol) than standard drugs. MD simulations
and MMGBSA analysis also demonstrated that β-sitosterol bound to the PPAR-γ active site stably.

Conclusion::

It can be concluded that β-sitosterol from Z. roseum attenuates Type-II diabetes by
modulating PPARγ activity.
Cu-Co Bimetallic Catalyst-based Electrochemical Sensing Platform for
Determination of Bisoprolol in Clinical Samples
Authors: Salemi Mohammad Reza , Fathi Shahla , Chekin Fereshteh , Norouzi Banafsheh
Published: February 2024

Background::

Bisoprolol (BIS) is a selective beta-blocker. It has been successfully
used to treat hypertension and angina pectoris. An overdose of BIS can lead to serious complications.
An overdose is a medical emergency that requires immediate medical attention to overcome
the adverse effects of the overdose. Hence, sensitive, reliable, and cost-effective methods
are required for the determination of BIS.

Methods::

In this work, a new electrochemical sensing platform based on a bimetallic catalyst was
developed for the determination of BIS. The Cu-Co nanocatalyst was easily synthesized by galvanic
displacement onto a carbon paste electrode (CPE). Then, field emission scanning electron
microscopy (FESEM), energy dispersive spectroscopy (EDS), and cyclic voltammetry (CV) were
utilized for the characterization of the Cu-Co catalyst.

Results::

The galvanic displacement of Cu metal significantly affected the electro-catalytic behavior
of the Cu-Co catalyst and the Cu-Co/CPE electrode displayed a very sensitive and accurate
response towards BIS. Under optimized conditions, the response was linear in the 3 to 120 μM
concentration range, sensitivity of 631.1 μA mM-1 and a detection limit of as low as 0.4 μM using
cyclic voltammetry. The simple proposed method was also successfully employed in the analysis
of BIS in biological and pharmaceutical samples. The advantages of Cu-Co/CPE are its fast and
simple manufacturing and the possibility of a repeated surface regeneration of the sensing platform,
as well as its application for the detection of BIS in tablets and biological samples, making
Cu-Co significant promise for use in clinical diagnostics. Besides, the synthesized catalysts
showed excellent reusability and stability.

Conclusion::

The presence of Cu metal due to galvanic displacement increased the sensitivity. These
findings suggest that the new nanocatalyst has potential applications in sensors and electronics.
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

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.
Mechanisms of Anti-PD Therapy Resistance in Digestive System Neoplasms
Authors: Wu Yuxia , Jiang Xiangyan , Yu Zeyuan , Xing Zongrui , Ma Yong , Qing Huiguo
Published: February 2024

Digestive system neoplasms are highly heterogeneous and exhibit complex resistance
mechanisms that render anti-programmed cell death protein (PD) therapies poorly effective.
The tumor microenvironment (TME) plays a pivotal role in tumor development, apart from
supplying energy for tumor proliferation and impeding the body's anti-tumor immune response,
the TME actively facilitates tumor progression and immune escape via diverse pathways, which
include the modulation of heritable gene expression alterations and the intricate interplay with
the gut microbiota. In this review, we aim to elucidate the mechanisms underlying drug
resistance in digestive tumors, focusing on immune-mediated resistance, microbial crosstalk,
metabolism, and epigenetics. We will highlight the unique characteristics of each digestive
tumor and emphasize the significance of the tumor immune microenvironment (TIME).
Furthermore, we will discuss the current therapeutic strategies that hold promise for combination
with cancer immune normalization therapies. This review aims to provide a thorough
understanding of the resistance mechanisms in digestive tumors and offer insights into potential
therapeutic interventions.
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

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.
Advancing Cognitive Radio in 5G Networks: A Survey of Flexible and Reconfigurable Antenna Technologies
Authors: C Anjani , Muduli Arjuna
Published: February 2024

Nowadays, the communication industry is looking for a cognitive radio platform that can be
used to handle multiple frequency bands, utilize various transmission protocols and be reconfigurable. For
spectrum allocation, two methodologies have been adopted for CR between users who are licensed (PU)
and those who are not (SU). They are spectrum overlay and spectrum underlay. Secondary users need to
operate below the principal users' noise level therefore, significant restrictions resisted on the strength of
their transmission in spectrum underlay methodology. Spectrum underlay is a type of underlay that is used
in the construction of the unoccupied section of the spectrum searched by CR in the spectrum overlay process. A reconfigurable antenna is needed on RF front-end side of CR, which will a range of completely to
span a vast array of frequencies specified sub-bands of frequency, and this is often a major design challenge. An antenna with a flexible monopole for Ultra-wideband equipment competent to function between
the frequency range of 3.1 GHz to 10.6 GHz. Designs of reconfigurable antennas reported in the literature
are mostly observed to be capable of manual tuning or switching between two or a few frequency bands.
Hence, the requirement of new designs of integrated UWB and reconfigurable narrowband antennas that
operate from 2 GHz and up to 12 GHz frequency range with a high degree of automated tunability or
switching facility into different narrow bands would have a strong application potential for CRs.
Performance Analysis of Multiple Interference Suppression of One Small Four Elements Array Antenna used in Satellite Navigation System
Authors: Hao Ronghua , Zhao Xiaobo
Published: February 2024

background:

The anti-interference requirements of miniaturized satellite navigation arrays is increasing, the anti-interference ability reducing of small aperture arrays caused by large mutual coupling, large main lobe width, may be solved through adopting quad-arrays antenna.

objective:

Research of multiple interference suppression method is the aim of the paper, and it makes the research of small four element satellite navigation array antenna

method:

In the paper, it focuses on the structural design of low coupling polarized array antennas, and makes analysis of mutual coupling between array elements, the compensation technology based on auxiliary sources is proposed.

result:

Experimental analysis of antenna narrowband interference suppression are made. Relevant anti-interference results on four element antenna are also obtained. The anti-interference ability of four element antenna is summarized.

conclusion:

The anti-interference ability of four element antenna is analyzed and summarized in the paper, related results show that small four element satellite navigation array antenna can be used in the multiple interference suppression, in the satelliate navigation system.
An Approach to Forecast Quality of Water Effectively Using Machine Learning Algorithms
Authors: Nambiar P.V. Manjusha , Urkude Giridhar
Published: February 2024

Background:

The quality of water directly or indirectly impacts the health and environmental
well-being. Data about water quality can be evaluated using a Water Quality Index (WQI).
Computing WQI is a quick and affordable technique to accurately summarise the quality of water.

Objective:

The objective of this study is to find strategies for data preparation to categorize a dataset
on the water quality in two remote Indian villages in different geographic locations, to predict the quality
of water, and to identify low-quality water before it is made accessible for human consumption.

Methods:

To accomplish this task, four water quality features Nitrate, pH, Residual Chlorine,
and Total Dissolved Solids which are crucial for human consumption, are considered to dictate the
quality of water. Methods used in handling these features include five steps that are data preprocessing
with min-max normalization, finding WQI, using feature correlation to identify parameter
importance with WQI, application of supervised machine learning regression models such as
Random Forest (RF), Multiple Linear Regression (MLR), Gradient Boosting (GB) and Support
Vector Machine (SVM) for WQI prediction. Then, a variety of machine learning classification
models, including K-Nearest Neighbour (KNN), Support Vector Classifier (SVC), and Multi-layer
Perceptron (MLP), are ensembled with Logistic Regression (LR), acting as a meta learner, to create
a stack ensemble model classifier to predict the Water Quality Class (WQC) more accurately.

Results:

The examination of the testing model revealed that RF regression and MLR algorithms
performed best in predicting the WQI with mean absolute error (MAE) of 0.003 and 0.001 respectively.
Mean square error (MSE), root mean square error (RMSE), R squared (R2), and Explained
Variance Score (EVS) findings are 0.002,0.005,0.988 and 0.998 respectively with RF
while 0.001,0.031,0.999 and 0.999 respectively with MLR. Meanwhile, for predicting WQC, the
stack model classifier showed the best performance with an Accuracy of 0.936, F1 score of 0.93,
and Matthews Correlation Coefficient (MCC) of 0.893 for the dataset of Lalpura and Accuracy
of 0.991, F1 Score of 0.991 and MCC of 0.981 respectively for the dataset of Heingang.

Conclusion:

This study explores a method for predicting water quality that combines easy and
feasible water quality measurements with machine learning. The stack model classifier performed
best for multiclass classification, according to this study. To ensure that the highest quality
of water is given throughout the year, information from this study will motivate researchers to
look into the underlying root causes of the quality variations.
Distributed Dynamic State Estimation of Power Systems Considering Dynamic State Constraints based on Phasor Measurements Units
Authors: Juan Li , Jia-ming Wang , Yun-long Jiang , Xiong Yang , Mao-lin Zhu , Hao Liu
Published: February 2024

Background:

Dynamic state estimation can provide detailed information about power
systems. However, there is no clear, dynamic equation for the state transition of bus voltages in
power systems. Currently, smoothing methods based on historical data are commonly used, but
they cannot ensure accurate state prediction in power systems with a large amount of renewable
energy. Moreover, the fast sampling rate of phasor measurement units generates a vast amount of
real-time data for the dispatch center, making it challenging for centralized state estimation to
meet real-time demands.

Objective:

This paper proposes a distributed power system state estimation considering dynamic
state constraints to address the above issues.

Methods:

By incorporating the constraints between the dynamic states of the system dynamic
components and the bus voltage phasors, the state transition equations for bus voltage phasors are
constructed based on the predicted dynamic states and the nodal injection power equations. This
allows taking the dynamic model constraints into account when predicting bus voltage phasors.
Then, based on the principle of hierarchical coordination and distributed state estimation, the
method of estimation-coordination-correction is adopted to acquire system state information
quickly and accurately.

method:

By incorporating the constraints between the dynamic states of the system dynamic components and the bus voltage phasors, the state transition equations for bus voltage phasors are constructed based on the predicted dynamic states and the nodal injection power equations. This allows taking the dynamic model constraints into account when predicting bus voltage phasors. Then, based on the principle of hierarchical coordination and distributed state estimation, the method of estimation-coordination-correction is adopted to acquire system state information quickly and accurately.

Results:

Furthermore, an IEEE 9-bus system and an IEEE 39-bus system are used to validate the
proposed method. The proposed method is compared with other algorithms to prove its superiority.

result:

Furthermore, an IEEE 9-bus system and an IEEE 39-bus system are used to validate the proposed method. And the proposed method is compared with other algorithms to prove its superiority.

Conclusion:

The simulation results show that the proposed method can effectively improve the
accuracy of the state estimation results of power systems under dynamic conditions.

conclusion:

The simulation results show that the proposed method can effectively improve the accuracy of the state estimation results of power systems under dynamic conditions.

other:

None
Prevalent Oral Drug Delivery System for Dental Care
Authors: Frank Amanda , Singh Komal , Singh Preet Amol , Bajwa Neha
Published: February 2024

Oral drug delivery systems, including fluoride supplements, constitute one of the most
prevalent routes for the administration of medications, playing a crucial role in the treatment and
prevention of various diseases, including dental caries. Fluorides, employed as a preventive and
control measure against dental caries, are available in two primary forms: Systemic fluorides and
topical fluorides. Topical fluorides are found in toothpaste, varnish, gels, mouth rinses, and washes,
while systemic fluorides are administered via lozenges, tablets, and liquid formulations.

:

This literature work focuses on the specific challenges associated with fluoride supplement formulations
and aims to shed light on potential setbacks. Among these challenges, poor patient
compliance, especially in pediatric populations, and the associated risk of underdosing or overdosing,
leading to fluorosis, stand out as significant concerns. If left unaddressed, these issues
could compromise the efficacy of one of the most effective caries prevention methods available.

:

The primary objective of this study is to explore advancements in fluoride supplement formulations,
with a particular emphasis on addressing the identified challenges, especially for pediatric
and geriatric patients. By devising improved formulations tailored to the unique needs of these
populations, we aim to mitigate compliance issues and minimize the associated risk factors,
thereby strengthening the effectiveness of dental caries prevention strategies. This research endeavors
to contribute valuable insights to the field of oral healthcare and pharmaceutical development,
ultimately promoting better oral health outcomes for individuals of all ages.
Enhancing Image Captioning Using Deep Convolutional Generative
Adversarial Networks
Authors: Jaiswal Tarun , Pandey Manju , Tripathi Priyanka
Published: February 2024

Introduction::

Introduction: Image caption generation has long been a fundamental challenge in the
area of computer vision (CV) and natural language processing (NLP). In this research, we present
an innovative approach that harnesses the power of Deep Convolutional Generative Adversarial
Networks (DCGAN) and adversarial training to revolutionize the generation of natural
and contextually relevant image captions.

Method::

Our method significantly improves the
fluency, coherence, and contextual relevance of generated captions and showcases the effectiveness
of RL reward-based fine-tuning. Through a comprehensive evaluation of COCO datasets,
our model demonstrates superior performance over baseline and state-of-the-art methods.
On the COCO dataset, our model outperforms current state-of-the-art (SOTA) models
across all metrics, achieving BLEU-4 (0.327), METEOR (0.249), Rough (0.525) and CIDEr
(1.155) scores.

Result::

The integration of DCGAN and adversarial training opens new possibilities
in image captioning, with applications spanning from automated content generation to enhanced
accessibility solutions.

Conclusion::

This research paves the way for more intelligent
and context-aware image understanding systems, promising exciting future exploration and innovation
prospects.
Panchakarma procedures for a healthy life that can be performed at home
Authors: Pradeep Kumar Meena  , Meena Suman
Published: February 2024

Panchakarma, a treasure of our ancient medicinal system, effectively removes toxins from our body that are considered a source of many illnesses. Panchakarma is an Ayurvedic way of purifying our bodies through the detoxification process. The toxins are generally eliminated by the five therapies, namely Vamana, Virechana, Basti, Nasya, and Rakthamoksha along with the Panchakarma regime. Detoxification helps strengthen our immune system and rejuvenate our body. Panchakarma helps in balancing the Tridoshas. In a nutshell, this facilitates achieving overall well-being of mind, body, and soul. While panchakarma is typically performed at an ayurvedic center by a skilled and trained ayurvedic doctor, it can also be carried out at home to accommodate factors like time and logistical limitations. Panchakarma is done at home by consuming a few simple home remedies and following an Ayurvedic regime. A few Panchkarma procedures can be done at home, but first we have to decide the type of procedure that could be useful for an individual. So, it's necessary to consult with the physician about Poorvakarma, Paschatkarma and suitable procedures according to Ritu, Roga and the individual's Prakriti.
Understanding and approach of Ayurveda management towards a unique case of Hashimoto Thyroiditis - A Case Report
Authors: Abdul Khader  , Bhagyashree Kamate
Published: February 2024

Hashimoto thyroiditis is also known as chronic autoimmune thyroiditis and chronic lymphocytic thyroiditis, is an autoimmune disease in which thyroid cells are destroyed via cell and antibody-mediated immune processes. It is the most common cause of hypothyroidism in developed countries. The Pathophysiology of Hashimoto Thyroiditis involves the formation of antithyroid antibodies that attack the thyroid tissue, causing progressive fibrosis. The condition is sometimes not diagnosed until late in the disease process. The most common laboratory findings demonstrate elevated Thyroid-Stimulating Hormone (TSH) and low Thyroxine (T4) levels, coupled with increased anti thyroid peroxidase (anti-TPO) antibodies. In Ayurveda, it can be correlated with Rasavaha Sroto Vikara, Dhatwagnimandya, Vataja Pandu, Pittavruta Vata based on symptoms. Case Report: In this present study a female patient aged 37 years, known case of Hashimoto Thyroiditis since 8 years was treated with Shiro Takradhara, Sarvanga Abhynaga, Virechana and specific Shamanaushadhi’s as per signs and symptoms. Results: After 2 months of treatment there was a significant reduction in the signs and symptoms of the disease with 70% improvement in the condition.
Effectiveness of Telemedicine for Musculoskeletal Disorders: Umbrella Review
Authors: Bargeri Silvia , Castellini Greta , Vitale Jacopo Antonino , Guida Stefania , Banfi Giuseppe , Gianola Silvia , Pennestrì Federico
Published: February 2024

Background

Several systematic reviews (SRs) assessing the use of telemedicine for musculoskeletal conditions have been published in recent years. However, the landscape of evidence on multiple clinical outcomes remains unclear.

Objective

We aimed to summarize the available evidence from SRs on telemedicine for musculoskeletal disorders.

Methods

We conducted an umbrella review of SRs with and without meta-analysis by searching PubMed and EMBASE up to July 25, 2022, for SRs of randomized controlled trials assessing telemedicine. We collected any kind of patient-reported outcome measures (PROMs), patient-reported experience measures (PREMs), and objective measures, including direct and indirect costs. We assessed the methodological quality with the AMSTAR 2 tool (A Measurement Tool to Assess systematic Reviews 2). Findings were reported qualitatively.

Results

Overall, 35 SRs published between 2015 and 2022 were included. Most reviews (n=24, 69%) were rated as critically low quality by AMSTAR 2. The majority of reviews assessed “telerehabilitation” (n=29) in patients with osteoarthritis (n=13) using PROMs (n=142 outcomes mapped with n=60 meta-analyses). A substantive body of evidence from meta-analyses found telemedicine to be beneficial or equal in terms of PROMs compared to conventional care (n=57 meta-analyses). Meta-analyses showed no differences between groups in PREMs (n=4), while objectives measures (ie, “physical function”) were mainly in favor of telemedicine or showed no difference (9/13). All SRs showed notably lower costs for telemedicine compared to in-person visits.

Conclusions

Telemedicine can provide more accessible health care with noninferior results for various clinical outcomes in comparison with conventional care. The assessment of telemedicine is largely represented by PROMs, with some gaps for PREMs, objective measures, and costs.

Trial Registration

PROSPERO CRD42022347366; https://osf.io/pxedm/