bluuebunny/crossref_metadata_2025 huggingface dataset:
An Enhanced Bidirectional Projection Approach for Fuzzy Credibility Multi-Attribute Group Decision Making: Application in Sports Equipment Supplier Selection
Authors: Liu Longfei
Published: February 2025

This article will introduce the fuzzy credibility bidirectional projection (BP) method and some important basic theories of fuzzy credibility set (FCS). Firstly, we reviewed the definition, operation rules, score function, and comparison methods of fuzzy credibility numbers (FCNs). In addition, in order to better integrate the fuzzy credibility evaluation information of multiple experts, we introduced two aggregation operators, such as FCNWAA and FCNWGA. Then, we proposed the concept of the module of FCS and introduced it into the BP method. Incorporating bounded rationality of decision-makers, we applied cumulative prospect theory (CPT) to this multi-attribute group decision-making (MAGDM) method. We employed the minimum identification information principle (MIIP) to integrate the entropy weight method with the method based on the removal effects of criteria (MEREC), alongside subjective weights, to determine decision attribute weights. This approach balances the subjective input of decision-makers with the significance of objective data, making it more applicable to real-world scenarios. Detailed calculation steps for this MAGDM technique are provided. Finally, we propose the MAGDM technique based on fuzzy credibility bidirectional projection method to selection of sports equipment suppliers (SSES) issue with fuzzy credibility information. This paper evaluates the effectiveness of the MAGDM technique by comparing it with the FCNWAA, FCNWGA, and FCDWG operators.
Effect Of Educational Intervention on The Knowledge of Health Workers Regarding Exposures to Bloodborne Pathogens: A Review
Authors: Jamiu Ishola Garba Olalekan , Alsaidi Nahla Abduljaleel
Published: February 2025

Introduction: Healthcare workers (HCWs) face a multitude of occupational hazards, including the risk of bloodborne infections resulting from sharp instrument injuries and exposure of skin and mucous membranes to contaminated blood and body fluids (BBF). Existing empirical evidence, albeit limited, indicates a deficiency in the knowledge of occupational safety among health workers, consequently raising concerns about their adherence to safety-related practices. Nonetheless, numerous studies have demonstrated that enhanced knowledge regarding safety precautions can mitigate the risk of exposure to diseases transmitted through blood and body fluids.
Objective: The purpose of this study was to find out the effectiveness of educational intervention in improving knowledge of universal precautions among Health workers about exposures to blood pathogens. Specifically, it examines literature published between 2010 and 2024, Methodology: the study conducted a literature review on published articles about the effectiveness of educational programs on knowledge of health workers about safety measures against exposure to blood pathogens.
Results: there were disparities in baseline knowledge levels among healthcare workers. While some studies indicated poor knowledge pre-intervention, others reported relatively high levels, particularly regarding certain aspects such as injection safety or HIV guidelines. However, Educational interventions significantly improved workers' knowledge regarding bloodborne pathogens and reduced exposure risks.
Conclusion: However, all studies highlighted the effectiveness of educational interventions in improving healthcare workers' knowledge of bloodborne pathogens. Significant increases in knowledge scores were observed post-intervention across various studies, indicating the efficacy of educational programs in enhancing understanding and awareness.
An Efficient Fire Detection Algorithm Based on Mamba Space State Linear Attention
Authors: Li Yuming , Wang Yongjie , Shao Xiaorui , Zheng Anbo
Published: February 2025

The Mamba model, as a State Space Model (SSM), enhances the global receptive field and feature extraction capabilities of object detection models through an architecture inspired by Recurrent Neural Networks (RNNs). Compared to traditional Convolutional Neural Networks (CNNs) and Transformers, it excels in handling complex scale variations and multi-view interference, offering a novel approach for object detection tasks in dynamic environments such as fire detection. Therefore, this paper proposes an efficient attention mechanism, the Efficient-Mamba-Attention (EMA) module, which enhances input features through adaptive average pooling and SSM modules. By integrating the EMA module with the YOLOv9 architecture, a highly efficient fire detection algorithm is proposed, particularly suitable for complex scenarios. Additionally, the model introduces the ConvNeXtV2 block to strengthen the backbone network, compensating for the limitations of SSM models in local feature modeling. Finally, the introduction of dynamic non-monotonic focusing and distance penalty mechanisms optimizes the loss function, improving the accuracy of the detection bounding boxes and significantly enhancing the precision and robustness of the fire alarm tasks. Comparative experiments demonstrate that the proposed network excels in terms of AP50 and FPS, achieving an AP50 of 91.0% on the large-scale fire dataset (dataset1), and 87.2% on the small-scale fire dataset (dataset2), with FPS reaching 71 on dataset1 and 64 on dataset2. This demonstrates that the proposed method maintains high detection performance while achieving outstanding efficiency.
Impact of Rainfall Variability on Crop Intensification and Diversification in the Coastal Region of Bangladesh
Authors: Islam H. M. Touhidul , Ahmed Sharif , Kamruzzaman Mohammad , Kumar Virender , Bhandari Humnath
Published: February 2025

The coastal regions of Bangladesh are highly vulnerable to climate change. Crop intensification and diversification in these areas are lower than in other parts of the country due to climate change and multiple stress environments. With other factors, high rainfall variability is one of the regions that impact agricultural production and productivity. The current study investigates the impact of rainfall variability on crop intensification and diversification in the coastal region of Bangladesh. Based on the historical climate data (1980-2020) of six weather stations, this study examines the onset (ON) and withdrawal (WD) of monsoon precipitation by forward and backward accumulation technique and the probability of dry and wet spells utilizing the Markov Chain (MC) probability model. The average ON and WD of monsoon precipitation were found at the 21st‒22nd and 43rd‒44th standard meteorological weeks (SMWs), respectively for all the stations. Extreme rainfall events have increased in recent periods (2001–2020) compared to earlier periods (1981–2000) in the rainfall months (Jun-Oct), potentially contributing to the recurrent flash floods and waterlogging in coastal Bangladesh in that time. The probabilities of dry spells are elevated in the early (1st‒17th SMWs) and late (43rd‒52nd SMWs) of the year. The region's higher likelihood of wet weeks (18th–42nd SMWs) experiencing precipitation ranges from 40‒100%. We also analyzed the effect of rainfall variability on aus (pre-monsoon rice) production, which has great potential in this area. Additionally, the frequency and return periods of extreme rainfall events of different thresholds during the dry months (Nov-Apr) were analyzed to assess the risks of cultivating rabi/non-rice crops due to increased precipitation and waterlogging and to evaluate the potentiality of rabi crops. We found that higher return periods of extreme events might delay the sowing of rabi crops and have adverse effects, particularly during their maturity phases. This study emphasizes the optimizing sowing windows of rice and non-rice crops, and cropping systems innovations based on variable rainfall patterns that will foster sustainable agricultural intensification and diversification in the coastal regions of Bangladesh.
Dynamic Token Masking in Spiking Neural Network
Authors: Fang Yuetong , Wang Ziqing , Zhou Deming , Ren Hongwei , Zhou Shibo , Xu Renjing
Published: February 2025

Spiking Neural Networks (SNNs) offer an energy-efficient alternative to conventional artificial neural networks (ANNs). Although ANN-to-SNN conversion has emerged as a promising direction for developing high-performance spike-driven models with reduced training complexity, it still faces the challenge of maintaining energy efficiency in converted SNNs. In this paper, we address these limitations by introducing a dynamic spiking token mixer, inspired by the strong information redundancy present in the spike self-attention mechanism. Our approach effectively replicates the selective processing capabilities of self-attention through dynamic token masking (DynMask), with layer-specific masking ratios customized according to both spatial and temporal significance. Comprehensive results establish DynMask as a practical step toward efficient deep learning systems. Specifically, DynMask achieves performance gains of up to +3.23% on ImageNet-1K while narrowing the accuracy gaps to as low as +0.02% compared to ANNs, with simultaneous energy consumption reductions of up to 44×. Moreover, our approach successfully extends to complex vision tasks that remain largely unexplored in SNN literature, including COCO detection and ADE20K segmentation.
Biostimulants Alleviate Heat Stress in Organic Hydroponic Tomato Cultivation: A Sustainable Approach
Authors: Dash Prosanta K. , Guo Bing , Leskovar Daniel I.
Published: February 2025

Heat stresses significantly reduce crop yield by 50% to 70%, irrespective of stress exposure time, plant species, or cultivars, posing substantial challenges for organically growing hydroponic tomatoes in controlled environment conditions. Low productivity in tomatoes is attributed to the inadequate stress tolerance of existing cultivars, which hinders their ability to optimize fruit set and yield. In this study, three biostimulants—Liquid Seaweeds, MycoApply®, and MycoLife—were applied to the leaf surface of tomato plants at 7-day intervals starting 4 weeks after planting. Their effects on plant growth, physiology, phenology, fruit yield, and quality were assessed. Each biostimulant was applied at intervals of 4, 8, 12, and 16 weeks to optimize application frequency. The experimental design employed a strip plot, with the biostimulants (Liquid Seaweeds, MycoApply®, MycoLife) as the main plot treatments, and application frequency (4, 8, 12, 16 weeks) randomly assigned to the subplots. We observed that the foliar application of biostimulants promoted plant vigor to varying degrees under heat stress conditions compared with the control. Specifically, MycoApply® applied for 12 weeks enhanced chlorophyll synthesis and photosynthesis rates, thereby boosting plant productivity. Tomato (‘Valdeon RZ’) plants treated with MycoApply® demonstrated an increased net assimilation rate (20%), and stomatal conductance (40%), along with reduced transpiration loss (28%) and electrolyte leakage (31%), while maintaining intercellular CO2 concentrations. Flowering occurred 5 days earlier in tomato plants treated with MycoApply® compared with untreated plants. MycoApply®-treated tomato plants also exhibited superior fruit set (19%), pollen viability (37%), and fewer incidences of flower drop (10%) compared with the control. Among the application frequencies, MycoApply® applied for 12 weeks exhibited superior plant growth and tomato productivity compared with the control. MycoApply® treated for 12 weeks outperformed the control in terms of marketable fruit yields, with a significantly higher yield (30%). In addition, ‘Valdeon RZ’ tomato plants treated with MycoApply® demonstrated superior postharvest quality, including firmness, soluble solids, acidity, and color dynamics. Correlogram, heat map, and cluster analysis further confirmed that under heat stress, biostimulants had various promotional effects on tomato growth and productivity. Therefore, MycoApply® emerged as a promising biostimulant with strong heat stress tolerance capacity in organic hydroponic systems.
HUMAN ANATOMY AND PHYSIOLOGY-1
Authors: Pate Royal , Kumar Vishal , Kumar Hansraj , Chaudhri Dr. Vikash Kumar
Published: February 2025

This Human Anatomy and Physiology Lab Manual is an essential tool for students studying the structure and function of the human body through hands-on, experiential learning. It complements theoretical knowledge gained from textbooks by allowing students to apply concepts in a laboratory setting, reinforcing their understanding of human anatomy and physiology. The core of the lab manual is a series of exercises or activities that are designed to correlate with specific body systems. These exercises usually include:
Microscopic Analysis: Students often use microscopes to examine histological slides (slices of tissue) to observe the cellular structure of various organs, tissues, and body systems. For example, examining muscle tissue, nerve cells, or blood vessels at the microscopic level.
Physiological Experiments: This might involve hands-on activities that demonstrate how the human body works. For example, experiments to measure pulse rate, blood pressure, or lung volume, or to observe how changes in environmental conditions (like temperature) affect bodily functions.
Clinical Applications- This lab manual include clinical case studies or examples that demonstrate how the lab concepts are applicable to medical practice. For example, students might study how a condition like heart disease can alter the normal structure and function of the cardiovascular system. This connects the lab exercises to real-world health issues, helping students see the relevance of their studies.
Correlation Between Students’ Vocabulary Mastery and Reading Comprehension at Junior Islamic School
Authors: Pravitasari Hikmah , Nurfadillah Lena
Published: February 2025

This study investigated the correlation between students' vocabulary mastery and reading comprehension, conducted among eighth-grade students at an Islamic boarding school in Indonesia. The research aims to answer whether there is a correlation between students' vocabulary mastery and reading comprehension at the junior high school level. The population of this study was the eighth-grade students of junior Islamic school. The researcher selected students from classes A and B, with a total sample of 25 students chosen through a random sampling technique. The collected data was analyzed by using Pearson Product Moment correlation. The instruments used were vocabulary and reading tests. The results of this study showed that the calculated r-count was significantly higher than the tabulated r-table. Therefore, r-count (0.895) > r-table (0.396) indicated a positive correlation between vocabulary and reading. These findings suggested that students' vocabulary mastery had a significant influence on their reading comprehension abilities in eighth grade. It is emphasized that by building a strong vocabulary foundation, students are better equipped to comprehend the meaning and the core essence from the texts they read.
The use of artificial intelligence in public administration: Bibliometric analysis
Authors: Rekunenko Ihor , Kobushko Iana , Dzydzyguri Oleksii , Balahurovska Inna , Yurynets Oksana , Zhuk Oleksandr
Published: February 2025

Artificial intelligence in public administration is critical for the modernization of the public sector and adaptation to the challenges of modern society. The paper analyzes studies dedicated to the impact of artificial intelligence on the efficiency, innovation, and transparency of management processes in the public sector using meta- and bibliometric analysis. The goal is to identify the main areas and keywords that highlight theoretical and practical aspects of artificial intelligence in public administration. In total, 879 scientific articles were analyzed, of which 598 works are devoted to artificial intelligence. Dynamic time analysis revealed a significant surge in scientific interest in artificial intelligence in public administration: from 2010 to 2019, 135 publications were devoted to this issue, and from 2020 to 2024, 421. Bibliographic maps of keywords and publication maps showed the main thematic areas of research on artificial intelligence: the application of AI in public administration and the public sector, decision-making in public administration, data management and digital technologies in public administration, and the strategic use of AI to forecast socio-economic trends.The obtained data became the basis for expanding the scientific and practical potential of using artificial intelligence in public administration. The main areas of future research will concern the regulation of ethical issues to ensure the trust of citizens, the development of a regulatory framework and standards, increasing the efficiency of public services, the integration of artificial intelligence into strategic planning, and the use of artificial intelligence to achieve sustainable development goals.
AcknowledgmentThe analysis was carried out within the framework of the implementation of the perspective plan for the development of the scientific area “Social Sciences” of Sumy State University, number d/r 0121U112685.
Inorganic Nanoparticles-based Drug Delivery Systems for Neurodegenerative Diseases Therapy
Authors: Bagherpour Iman , Mozafari M. R. , Naghib Seyed Morteza
Published: February 2025

Neurodegenerative disorders (NDs) are highly prevalent among the aging population. It primarily
affects the central nervous system (CNS), but the effects are also observed in the peripheral nervous system.
Neural degeneration is a progressive loss of structure and function of neurons, which may ultimately involve
cell death. The blood-brain barrier (BBB), which separates peripheral blood circulation from the central nervous
system, is essential for maintaining intracerebral homeostasis. Drug delivery systems based on nanomaterials
(NDDSs) employ nanoparticles (NPs) as their drug transport vehicles. Moreover, nanotechnologybased
methods usually involve numerous nanosized carrier platforms, which potentiate the effect of the therapeutic
agents in the therapy of NDs, especially in diagnosis and drug delivery, with negligible side effects. In
addition, nanotechnology-based techniques have offered several strategies to cross BBB to intensify the bioavailability
of drug moieties in the brain. In the last few years, diverse kinds of nanoparticles (NPs) have been
developed by incorporating various biocompatible components (e.g., polysaccharide-based NPs, polymeric
NPs, selenium NPs, AuNPs, protein-based NPs, gadolinium NPs, etc.), that showed great therapeutic benefits
against NDs. The discussion concluded with a look at the opportunities and problems that come with NDDSs
in modern basic and clinical research.
In silico Study of Antiviral Phytochemicals for the Potential Drug Development Against Wild-type and Omicron Variants of SARS-CoV-2
Authors: Dutta Somenath , Sri Pushan Shilpa , Ghosh Rohan , Jose Maria , Pritam Manisha
Published: February 2025

Aims:

To identify potential phytochemical-based drugs for both wild-type and Omicron
variants of SARS-CoV-2 using virtual screening and molecular dynamic simulation.

Background:

Coronavirus disease 2019 (COVID-19) is an infectious viral disease caused by
SARS-CoV-2. Since 2019, multiple variants have been reported from all over the world and the
emergence of new variants of SARS-CoV-2 is a major concern.

Objective:

To identify potential phytochemicals that can be used as drugs against different variants
of SARS-CoV-2.

Methods:

In our present study, we have selected 594 phytochemicals and performed virtual
screening to identify potential drug candidates. The screening commenced with molecular docking
techniques with both ACE2 (Human) and Spike protein (wild-type and Omicron variant),
followed by prediction of pharmacokinetics parameters and toxicity. The Schrodinger tools,
Swiss ADME, and ProTox-II accomplish the analysis. Further, molecular dynamics simulation,
binding free energy calculation and meta-dynamics study was performed for best protein-ligand
complexes of both proteins using GROMACS and gmx_MMPBSA to validate the stability of the
docked complexes.

Results:

we have identified 6 and 4 drugs as spike protein inhibitors for wild-type and Omicron
variants, respectively. 6 drugs were identified as ACE2 receptor inhibitors. We have identified
silymarin as a common drug inhibitor for both pathogen (Wild-type, and Omicrons spikes) as
well as host (human ACE2) protein that reflects its ability to inhibit the host-pathogen interaction
and prevent infection.

Conclusion:

We have found some potential Phytochemicals that can be used against different
variants of SARS-CoV-2 such as silymarin.
Recent Advancements in Breast Cancer Therapies and Biomarkers: Mechanisms and Clinical Significance
Authors: Husain Arbab , Khan Hamda , Jamale e Fatima  , Siddiqui Shirjeel Ahmad , Singh Akash Deep , Husain Adil , Pandey Shivam , Kant Ravi , Khanam Afreen , Pandey Pratibha , Khan Fahad
Published: February 2025

Breast cancer remains a critical health concern, requiring continual innovation in treatment to
improve patient outcomes. The continuous obstacles in therapy and the need for novel techniques underline
the importance of making advances in this discipline. Precision medicine has emerged as a viable method,
with genomic profiling and molecular subtyping allowing for targeted therapy based on distinct breast
cancer subtypes. This strategy is supplemented by advances in early detection and screening, with
technologies like liquid biopsy promising earlier and more accurate diagnosis. The introduction of
immunotherapy has transformed breast cancer treatment by using the body's immune system to combat
cancer. Recent discoveries, particularly combination medicines, attempt to circumvent resistance
mechanisms and improve treatment success. Furthermore, including lifestyle therapies such as nutrition,
exercise, and psychological support has been demonstrated to reduce breast cancer risk and strengthen
survivability rates. Survivorship programs serve an important role in comprehensive care by addressing
long-term needs and enhancing survivors' quality of life. Investigating innovative therapeutic approaches,
such as developing cancer vaccines, epigenetic modulators, and RNA interference (RNAi) therapy, provides
new treatment options. Fostering collaboration among healthcare personnel through shared decision-making
and tumor committees is essential for the integration of multidisciplinary care, which ensures patientcentered
care. Although advancements have been made, there are still numerous obstacles to overcome in
the implementation of these future directions. To effectively confront these obstacles, it is imperative to
capitalize on opportunities for innovation and collaboration. It is imperative to address ethical, social, and
economic factors in the advancement of breast cancer care to ensure that innovations are equitable and
accessible. In conclusion, the future of breast cancer management is bright since substantial improvements
are on the verge of turning patient treatment into a completely different experience. For these breakthroughs
to become a reality, it is necessary to maintain research efforts, advocate for them, and work together. The
dedication to innovation and the joint effort to overcome current problems are the two important factors that
will determine whether or not breast cancer treatment and surviving will have a better future.
Recent Advances in Co-Condensation and Co-Aggregation of Amyloid Proteins Linked to Neurodegenerative Diseases
Authors: Zhang Xuefeng , Chen Yujie , Tan Yuan , Pan Tong , Wei Guanghong
Published: February 2025

The misfolding and aggregation of amyloid proteins are closely associated with a range
of neurodegenerative diseases. Liquid-liquid phase separation (LLPS) can initiate the aggregation
of proteins, indicating that LLPS may serve as an alternative pathway for the pathological aggregation
of amyloid proteins. The co-occurrence of two or more amyloid pathologies has been observed
in extensive pathophysiological studies and is linked to faster disease progression. The co-
LLPS (also known as co-condensation) and co-aggregation of different disease-related proteins
have been proposed as a potential molecular mechanism for combined neuropathology. Here, we
reviewed the current state of knowledge regarding the co-aggregation and co-condensation of various
amyloid proteins, including Aβ, tau, α-synuclein, TDP-43, FUS, and hnRNPA/B protein family,
C9orf72 dipeptide repeats and prion protein. We briefly introduced the epidemiological correlation
among different neurodegenerative diseases and specifically presented recent experimental
findings about co-aggregation and co-condensation of two different amyloid proteins. Additionally,
we discussed computational studies focusing on the molecular interactions between amyloid
proteins to offer mechanistic insights into the co-LLPS and co-aggregation processes. This review
provides an overview of the synergistic interactions between different disease-related proteins,
which is helpful for understanding the mechanisms of combined neuropathology and developing
targeted therapeutic strategies.
Age- and Genotype-Dependent Effects of Chronic Nicotine on Presenilin1/2 Double Knockout Mice: From Behavior to Molecular Pathways
Authors: Si Youwen , Meng Bo , Qi Feiyan
Published: February 2025

Introduction:

The potential therapeutic role of nicotine in Alzheimer's disease (AD)
remains controversial, particularly regarding its age-dependent effects and underlying mechanisms.

Method:

This study investigated the impact of chronic nicotine administration on cognitive function
and molecular pathways in Presenilin 1/2 double knockout (DKO) mice, an amyloid-β-independent
model of AD. Three-month-old and eight-month-old DKO and wild-type (WT) mice received
oral nicotine treatment (100 μg/ml) for three months. Behavioral assessments revealed that
while the 6-month-old cohort showed no significant differences between nicotine-treated and control
groups regardless of genotype, nicotine improved contextual fear memory in 11-month-old
DKO mice but impaired nest-building ability and cued fear memory in age-matched WT controls.
Transcriptome analysis of the prefrontal cortex identified distinct molecular responses to nicotine
between genotypes.

Result:

In DKO mice, nicotine modulated neuropeptide signaling and reduced astrocyte activation,
while in WT mice, it disrupted cytokine-cytokine receptor interaction and neuroactive ligand-
receptor interaction pathways. Western blot analysis revealed that nicotine treatment significantly
reduced tau hyperphosphorylation and GFAP expression in 11-month-old DKO mice,
which was further confirmed by immunohistochemistry showing decreased astrocyte activation in
multiple brain regions.

Conclusion:

These findings demonstrate that nicotine's effects on cognition and molecular pathways
are both age- and genotype-dependent, suggesting its therapeutic potential may be limited to
specific stages of neurodegeneration while potentially having adverse effects in healthy aging
brains.
Determining the Interaction in a Drug Combination using the Dose-based or Effect-based Method
Authors: Yu Tinghe , Yu Tianyan , Li Xinya , Li Min
Published: February 2025

The interaction in a drug combination can be assessed using either the method of Chou or
Jin’s method. The combination index in the former (i.e., CI-C) is calculated based on doses, while
the latter (i.e., CI-J) is based on effects. This perspective demonstrates a correlation between 1/CI-C
and CI-J when applied to both released and simulated data. Thus, 1/CI-C and CI-J are functionally
equivalent for evaluating drug interaction. Combining these two indices is preferred; consistency
shows a reliable verdict, and inconsistency indicates a requirement for further analyses. However, it
has been observed that evaluating released data raises certain concerns.
Mitochondrial Dysfunction: Potential Therapy For Abdominal Aortic Aneurysms
Authors: Yang Wenfan , He Jianxiong , Yu Hao , Wu Ya , Shi Sen
Published: February 2025

Abdominal Aortic Aneurysm (AAA) is a life-threatening vascular disease. Despite advancements
in understanding the pathogenesis of AAA, significant knowledge gaps persist. Recent
evidence increasingly implicates mitochondrial dysfunction as a contributing factor that exacerbates
AAA, inducing further expansion of aneurysm, rupture, and subsequent death. This review summarizes
the latest research findings and theories associated with AAA pathogenesis, with a particular
focus on mitochondrial dysfunction in AAA, including mitochondrial quality control, mitochondrial
membrane potential, mitochondrial morphology, oxidation and antioxidation, normal functioning of
the respiratory chain, mitochondrial mutations, and the regulation of other mitochondrial signaling
pathways. Moreover, we highlight potential medical interventions based on regulating mitochondrial
function for AAA treatment.
The Future of Gene Expression Studies in HIV Research
Authors: Sevimoğlu Tuba
Published: February 2025

Human Immunodeficiency Virus (HIV) damages or interferes with immune cell function
and remains a serious worldwide public health concern. Many researchers have studied the
virus since its discovery in an effort to better understand its immunopathogenesis and neuropathogenesis.
For those who have access to efficient HIV prevention, diagnosis, treatment, and care,
HIV infection has now evolved into a chronic illness that can be controlled. Despite a decrease in
HIV prevalence in the general population, certain subpopulations continue to exhibit higher-risk
behaviors. This work aims to uncover research gaps in HIV gene expression studies, which is
crucial in finding a cure. For instance, blood samples are used for most of the gene expression
experiments for HIV. However, since there are very few HIV latent reservoir cells in the blood,
it can be difficult to identify and quantify them. Furthermore, blood cell populations might not
accurately represent the features of reservoir cells found throughout the body. Using HIV reservoir
cells from distinct tissue types in gene expression research projects could help us pinpoint
the main cause of the latent HIV resilience. Gene expression studies using potential repurposed
drug candidates, as well as alternative experimental setups with combinations of antiretroviral
therapies, can be utilized in future studies as well. Additionally, large-sample research designs
that specifically investigate intestinal disruption in individuals with HIV and associated comorbidities
may help us better understand the processes behind HIV.
Computational Evaluation of Bioactive Compounds from Bryophyllum pinnatum as a Novel GSK3B Inhibitor in Alzheimer's Disease
Authors: Babalola Olorunfemi Oyewole , Ottu Paul Olamide , Akinola Olufemi Adebisi , Kehinde Idayat Oyinkansola , Oluwatobiloba Ayodeji Folasade , Oluwamodupe Cecilia , Iwaloye Opeyemi , Elekofehinti Olusola Olalekan
Published: February 2025

Background:

Glycogen synthase kinase 3-beta (GSK3-β) is a protein that is linked to the
formation of amyloid-beta (Aβ) plaques and neurofibrillary tangles, both of which are characteristic
of Alzheimer's disease. The enzyme's hyperactivity phosphorylates tau proteins, forming neurofibrillary
tangles that impair material transport between axons and dendrites and disrupt neuronal
function. GSK3-β impacts amyloid precursor protein production, causing Aβ accumulation and neurodegeneration.

Aim/Objective:

This study aimed to investigate the inhibitory mechanism of natural compounds
from Bryophyllum pinnatum against GSK3-β.

Methods:

Computational approach, extra precision glide docking, and the Maestro molecular interface
of Schrodinger suites were utilized to determine the binding free energy of the compounds
against the prepared GSK3-β. The compounds' ADME parameters and Lipinski's rule of five were
also evaluated. Using AutoQSAR, predictive models were constructed for both protein targets.

Results:

Three hit compounds (kaempferol, quercetin, and 5,7,4'-trihydroxy-3,8-dimethoxyflavone)
were found in this study. These compounds met the recommended range for the specified ADME
parameters and passed the rule of five. Additionally, the hit compounds' predicted pIC50 values
were promising.

Conclusion:

Our study suggests that the investigated compounds can be used to design GSK3-β inhibitors.
Unlocking the Biological Potential of 2-Pyridones: Synthesis, Antioxidant and Antimicrobial Activity of N-Phenacylated 5/6-Chloro-2-pyridones
Authors: Sangwan Sarita , Chauhan Sonu , Yadav Neelam , Kumar Ravi , Duhan Anil , Malik Vinod
Published: February 2025

Aim:

A simple and efficient synthesis of 14 new (9a-9n) N-phenacyl-2-pyridones with
good yields (up to 75%), is reported. The synthesized derivatives were screened for their in vitro
radical scavenging activity against 1,1-diphenyl-2-picrylhydrazyl (DPPH), their in vitro antimicrobial
potential was tested against human pathogenic bacterial strains, including Bacillus cereus,
Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa, as well as the fungal strain
Candida albicans.

Method:

All compounds displayed modest antioxidant activity, with compound 9b being the most
potent in the DPPH radical scavenging assay. Most of the synthesized compounds exhibited good
to excellent antimicrobial activity, however, the compounds (9d, and 9b) showed maximum inhibition
zone diameters of 18.75, and 18.25mm respectively, demonstrating better antimicrobial potential
than the standard drug streptomycin against Staphylococcus aureus.

Result:

However, the compound 9f was found most effective against Pseudomonas aeruginosa
with a 23.25 mm zone of inhibition against a 17.50 mm zone of inhibition of the standard, streptomycin.
Molecular docking of the compounds 9d and 9f with tyrosyl-tRNA synthetase revealed
good binding with the target.

Conclusion:

The electron-withdrawing substituents on the aryl ring of synthesized N-phenacyl-2-
pyridones improved the antioxidant activity, however, for Gram-positive bacteria, less lipophilic or
more hydrophilic substituents, such as halogens, displayed better antimicrobial activity. Similarly,
it was the more lipophilic substitutions on the aryl ring that improved the antimicrobial activity
against Gram-negative bacteria.
The Prognostic Role of Interferon Gamma-inducible Protein 30 in Clear Cell Renal Cell Carcinoma with Immune Infiltrates
Authors: Xu Wu , Wu Taihong , Liu Yufeng , Luo Yang , Liu Dawei , Yan Lingfei , Li Qing , Wang Tao
Published: February 2025

Background:

Recent research has demonstrated the significance of Interferon Gamma-
Inducible Protein 30 (IFI30), an interferon gamma-induced protein, in the immune response to
cancerous growths. However, the relationship between IFI30 expression levels, patient prognosis,
and tumor-infiltrating lymphocytes in clear cell renal cell carcinoma (ccRCC) remains inadequately
defined.

Methods:

To ascertain the potential link between IFI30 expression, clinical data, and overall survival
(OS) in ccRCC patients, we employed diverse databases, which include TCGA, Gene Expression
Profiling Interaction Analysis (GEPIA), and UALCAN. Furthermore, an in-depth analysis of
the link between tumor-infiltrating immune cells (TIIC) and IFI30 was carried out using the
TIMER, GEPIA, and TISIDB databases. Immunohistochemistry (IHC) was utilized to identify the
IFI30 and PD-1 expression levels in a tissue microarray. Patents about molecular classification
and drugs in ccRCC were reviewed through Worldwide Espacenet®.

Results:

The expression of IFI30 demonstrated a strong association with sample type, lymph node
stage, tumor grade, and cancer stage. Elevated IFI30 expression was linked to unfavorable Disease-
Specific Survival (DSS) and Overall Survival (OS) outcomes (p <0.01). Furthermore, overexpression
of IFI30 was strongly linked to immunomodulatory molecules, chemokines, and increased
infiltration of regulatory T cells (Tregs), natural killer (NK) CD56 cells, T helper 1 (Th1)
cells, cytotoxic T cells, and T helper cells. IHC analysis confirmed a robust correlation between
IFI30 and PD-1 expression.

Conclusion:

IFI30 is a prognostic biomarker for ccRCC patients. Targeting IFI30 may provide
new strategies for cancer therapy and improve the prognosis of ccRCC patients.
Development and Prospects of Transparent Working Face Construction Technology for Intelligent Coal Mining
Authors: Li Wei , Liu Zaibin , Fan Tao , Wenming Liu , Wang Xiaohui , Yan Junsheng , Wang Huahui
Published: February 2025

Background:

As global energy demand continues to rise, coal remains a critical energy
resource. However, traditional coal mining methods face significant challenges such as low safety,
environmental degradation, and resource wastage. In addition, to address these issues, intelligent
coal mining technologies have emerged, with the construction of transparent working faces receiving
significant attention as a key component.

Objective:

This paper aims to explore and summarize the key technologies and methods involved
in the construction of transparent working faces in coal mining, with a focus on enhancing mining
safety, efficiency, and resource utilization.

Methods:

The research comprehensively reviews the principles and applications of various geological
exploration technologies, including surface 3D seismic techniques, channel wave seismic exploration,
mine resistivity methods, directional drilling, borehole transient electromagnetics, and
borehole seismic CT. Additionally, it discusses critical technologies such as intelligent sensing,
multi-source heterogeneous data fusion, and multi-attribute dynamic visualization modeling.

Results:

The study finds that surface 3D seismic techniques are effective for detecting geological
structures within the working face, while mine geophysical prospecting methods like channel
wave seismic and resistivity methods offer high precision and efficiency. Directional drilling and
borehole techniques provide high detection accuracy for concealed structures and anomalies. Furthermore,
the integration of multi-source data significantly enhances the accuracy and real-time capabilities
of geological modeling.

Conclusion:

The construction of transparent working faces plays a crucial role in the sustainable
development of the coal industry by improving mining safety, efficiency, and resource utilization.
Future advancements will focus on algorithm optimization, the application of machine learning,
and the development of refined multi-source data integration platforms.
A Case of Idiopathic Colonic Varices: A Rare Cause of Hematochezia
Authors: Patel Dhruvkumar , Patel Maitri M. , Prajapati Poojan , Patel Lalitkumar , Hussain Kabir , Zia Hassaan Ashraf , Patel Mukundkumar V.
Published: February 2025

Background:

Colonic varices, typically associated with portal hypertension, are a rare cause of lower gastrointestinal bleeding, with idiopathic colonic varices being exceed-ingly uncommon.

Case Presentation:

We report a case of a 36-year-old male who presented with acute ab-dominal pain, hematochezia (blood-streaked stools), and severe iron deficiency anemia. The patient’s past medical history was unremarkable, with no known liver disease, alcohol use, or family history of gastrointestinal disorders. Esophagogastroduodenoscopy (EGD) and colon-oscopy revealed dilated varices in the colon, though no active bleeding was observed. Com-prehensive laboratory investigations, including liver function tests, coagulation profiles, and viral hepatitis serologies, were within normal limits. Imaging studies, including contrast-en-hanced computed tomography (CT) of the abdomen and Doppler ultrasonography, ruled out portal hypertension and other systemic pathologies. With no identifiable underlying cause, a diagnosis of idiopathic colonic varices was made.

Treatment and Outcome:

The patient was managed conservatively with packed red blood cell transfusions and oral iron supplementation to correct the anemia. A follow-up colonoscopy at three months showed no progression of varices, and the patient remained asymptomatic.

Conclusion:

This case highlights the importance of considering idiopathic colonic varices in patients with hematochezia and iron deficiency anemia, even in the absence of portal hyper-tension. A thorough diagnostic workup, including endoscopy and imaging, is essential to ex-clude more common causes and arrive at this rare diagnosis.
An Algorithm for Textual Classification of News Utilizing Artificial Intelligence Technology
Authors: Mishra Rahul
Published: February 2025

The rate at which technology is improving is increasing all throughout the
world. Every day, a tremendous amount of textual data is produced as a result of the
Internet, websites, business data, medical information, and the media. Extraction of
interesting patterns from text data with varied lengths such as views, summaries, and
facts is a challenging issue. This work provides a deep learning (DL) algorithm-based
approach to news text classification to address the issues of large amounts of text data
and cumbersome features obtaining value in news. Although the relationship among
words as well as categories has a significant impact on the categorization of news text,
previous approaches to text classification relied solely on the knowledge of the
connections between words to make their classification decisions. This research uses
the idea of a tailored algorithm to provide a CNN, LSTM, and MLP-based
customizable ensemble framework for categorising news text data. The proposed
model is based on a parallel representation of word vectors and word dispersion. We
feed the term vector to the CNN module to convey the relationship between words, as
well as nourish the discrete vector corresponding to the relationship between words and
categories into the MLP module to achieve deep learning of the spatial data on features,
the time-series feature information, and the connection words and categories in news
texts. Extensive experimental study confirmed the dependability and efficacy of the
proposed approach. The experimental results demonstrated that the proposed method
improved the most - in terms of precision, recall rate, and comprehensive value, while
also addressing the problems of text length, extraction of features issues with the news
text, and classification of news text.
The Application of an N-Gram Machine Learning Method to the Text Classification of Healthcare Transcriptions
Authors: Sharma Pratibha
Published: February 2025

An integral aspect of natural language processing is text categorization, the
goal of which is to assign a predetermined category to a given text. Feature selection
and categorization models come in a wide variety of forms. Most researchers, however,
would rather utilise the prepackaged functions of existing libraries. In the field of
natural language processing (NLP), automated medical text categorization is very
helpful for decoding the information hidden in clinical descriptions. Machine learning
approaches seem to be fairly successful for medical text categorization problems;
nevertheless, substantial human work is required in order to provide labelled training
data. Clinical and translational research has benefited greatly from the computerised
collection of vast amounts of precise patient information, including illness status, blood
tests, medications taken, and side effects, along with therapy results. As a result, the
medical literature contains a massive amount of information on individual patients,
making it very difficult to digest. In this research, we suggest using N-grams and a
Support Vector Machine (SVM) to classify healthcare-related texts. We conduct
experiments to determine the viability of our code and analyse it across a variety of
categorization methods.
Deep Learning-based Text-Retrieval System with Relevance Feedback
Authors: Kalra Simran
Published: February 2025

We presented an Information Retrieval (IR) system that learns from existing
information and creates a single pertinent document that, we presume, has all its
indexed pertinent details for a query. Deep learning makes such a system viable. We
then asked people to score the query plus word-cloud representation of three randomly
selected relevant texts and our new synthetic document. The synthetic document
topped all inquiries and users. We then trained a CNN using query-relevant data. We
performed ”deep learn” function on a synthetic, relevant material using the CNN. We
used crowdsourcing to compare the “deep-learned” material to related documents.
Users can see a query and four-word cloud (three relevant documents and our deep
learning synthetic document). The synthetic document provides the the most relevant
feedback.
Text Document Preprocessing and Classification Using SVM and Improved CNN
Authors: Sidhu Jaspreet
Published: February 2025

Text categorization is a crucial technology in data mining as well as data
retrieval that has been extensively investigated and is developing at a rapid pace.
Convolutional neural networks (CNNs) are a kind of deep learning modeling that may
reduce the complexity of the model while accurately extracting characteristics from
input text. Support vector machine (SVM) results have always been more trustworthy
and superior to those of other traditional artificial intelligence approaches. Using
enhanced convolutional neural network (CNNs) as well as support vector machines
(SVMs), we offer a novel approach to online text categorization in this study. Our
approach begins with text attribute identification and prediction using a model based on
CNN with a five-layer network structure. Databases including both text and images
will find it to be a major factor in the long run.
Public Opinion Regarding COVID-19 Analyzed for Emotion Using Deep Learning Techniques
Authors: Mishra Abhinav
Published: February 2025

As a result of the COVID-19 epidemic, many individuals are experiencing
extreme worry, dread, and other difficult emotions. Since coronavirus immunizations
were first introduced, people's reactions have gotten more nuanced and varied. In this
study, we will use deep learning methods to decode their emotions. Twitter provides a
glimpse into what is popular and what is on people's minds at any given time, and
social media is presently the finest means to convey sentiments and emotions. Our goal
while conducting this study was to have a better grasp of how different groups of
individuals feel about vaccinations. The research period for this research's tweet was
from December 21st to July 21st. Of the most talked-about vaccines that have recently
been available in various regions of the world were the subject of several tweets. The
term Valence Aware Sentiment Dictionary An NLP program called Believed
(VADER) was used to examine people's sentiments on certain vaccines. We were
better able to see the big picture after categorizing the collected attitudes into positive
(33.96 percent), negative (17.55 percent), and neutral (48.49 percent) camps. We also
included into our study an examination of the tweets' chronology, given that attitudes
changed over time. The performance of the forecasting models was evaluated using an
RNN-oriented design that included bidirectional LSTM (Bi-LSTM) as well as long
short-term memory (LSTM); LSTM attained an accuracy of 90.59% as well as BiLSTM of 90.83%. Additional performance metrics, such as Precision, F1-score, as well
as a matrix of confusion, were used to confirm our hypotheses as well as outcomes.
The findings of this research provide credence to efforts to eradicate coronavirus across
the globe by expanding our knowledge of public opinion on COVID-19 vaccines.