bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lipid-based nanoparticles mimicking natural killer cells for triple- negative breast cancer therapy
Authors: Won Eun-Jeong , Lee Myungchul , Lee Eui-Kyung , Baek Seung-Hoon , Yoon Tae-Jong 
Published: July 2024

Abstract

Introduction:
Immunotherapy combined with chemicals and genetic engineering tools is emerging as a promising strategy to treat triple-negative breast cancer (TNBC), which is more aggressive with poorer progress than other breast cancer subtypes. In this study, lipid-based nanoparticles (LNPs) possessed an NK cell-like function that could deliver tumor-specific therapeutics and regulate the tumor immune microenvironment.
Method
Our LNP mimicking NK cells consists of three characteristics: i) they are hydrophilic therapeutic substances so that genes can be included inside of LNP; ii) components that make up LNP themselves can exhibit therapeutic effects; and iii) specific membrane protein components of NK cells can be fabricated to the surface of LNP. These particles have both target-based delivery of HIC1 plasmid DNA and immune cell regulation functions.
Results
The drug delivery efficiency of LNP was maximized through the fusion of NK cell membrane protein, which can selectively target TNBC. We demonstrated that combining HIC1 restorations with C18-ceramide delivery can synergistically improve the treatment effect of TNBC. We confirmed that LNPs mimicking NK cells can activate immune cells and induce proinflammatory cytokines, which are known to influence antitumor immunity.
Conclusion
Delivered therapeutic genes can inhibit metastasis of TNBC and then induce apoptotic cell death while targeting macrophages to promote cytokine release. The anti-cancer effect is expected to apply to treating various difficult-to-treat cancers by LNP mimicking NK cells, which can simultaneously deliver therapeutic chemicals and genes.
Teleoperation in Robot-assisted MIS with Adaptive RCM via Admittance Control
Authors: Nasiri Ehsan , Sowrirajan Srikarran , Wang Long 
Published: July 2024

Abstract

This paper presents the development and assessment of a teleoperation framework for robot-assistedminimally invasive surgery (MIS). The framework leverages our novel integration of an adaptive remotecenter of motion (RCM) using admittance control. This framework operates within a redundancyresolution method specifically designed for the RCM constraint. We introduce a compact, low-cost,and modular custom-designed instrument module (IM) that ensures integration with the manipulator,featuring a force/torque sensor, a surgical instrument, and an actuation unit for driving the surgicalinstrument. The paper details the complete teleoperation framework, including the telemanipulationtrajectory mapping, kinematic modelling, control strategy, and the integrated admittance controller.Finally, the system’s capability to perform various surgical tasks was demonstrated, including passinga thread through the rings, picking and placing objects, and trajectory tracking.
Collaborative orchestration of BH3-only proteins governs Bak/Bax-dependent hepatocyte apoptosis under antiapoptotic protein-deficiency in mice
Authors: Takehara Tetsuo , Kudo Shinnosuke , Hikita Hayato , Saito Yoshinobu , Murai Kazuhiro , Kodama Takahiro , Tatsumi Tomohide 
Published: July 2024

Abstract

The fine-tuned balance between anti-apoptotic Bcl-2 family proteins, such as Bcl-xL and Mcl-1, and pro-apoptotic Bcl-2 family proteins, like Bak and Bax, is crucial for maintaining hepatocyte integrity. BH3-only proteins, including Bid, Bim, Puma, Noxa, Bad, Bik, Bmf and Hrk, serve as apoptosis sensors activating Bak and Bax. We previously reported that BH3-only proteins Bid and Bim contribute to hepatocyte apoptosis through Bak/Bax activation in the absence of antiapoptotic proteins, Bcl-xL and/or Mcl-1. However, the comprehensive involvement of all eight BH3-only proteins maintaining hepatocyte integrity in healthy livers remains unclear. Puma disruption suppressed hepatocyte apoptosis in hepatocyte-specific Bcl-xL or Mcl-1 knockout (Bcl-xLΔHep/ΔHep or Mcl-1ΔHep/ΔHep) mice. Disruption of Bid and Bim partially prevented lethality in Mcl-1ΔHep/+ Bcl-xLΔHep/ΔHep mice, although severe hepatocyte apoptosis persisted, which was suppressed by additional Puma disruption. However, hepatocyte apoptosis was still strongly induced compared to that in Mcl-1ΔHep/+ Bcl-xLΔHep/ΔHep BaxΔHep/ΔHep Bak−/− mice. Triple disruption of Bid, Bim and Puma did not prevent induction of hepatocyte apoptosis in tamoxifen-induced Mcl-1iΔHep/iΔHep Bcl-xLiΔHep/iΔHep mice. Primary hepatocytes, isolated from Mcl-1fl/fl Bcl-xLfl/fl Bid−/− Bim−/− Puma−/− mice and immortalized, underwent apoptosis with doxycycline-dependent Cre recombination. Among the remaining five BH3-only proteins, Bik and Hrk were not expressed in this cell line, and Noxa knockdown, but not Bad or Bmf knockdown, reduced apoptosis. Noxa disruption alleviated hepatocyte apoptosis in Mcl-1ΔHep/ΔHep mice and tamoxifen-induced Mcl-1iΔHep/iΔHep Bcl-xLiΔHep/iΔHep Bid−/− Bim−/− Puma−/− mice, prolonging survival. Apoptosis persisted in immortalized primary hepatocytes isolated from Mcl-1fl/fl Bcl-xLfl/fl Bid−/− Bim−/− Puma−/− Noxa−/− mice where doxycycline-dependent Cre recombination was induced, but was completely suppressed by Bak/Bax knockdown, while Bad or Bmf knockdown had no effect. In conclusion, among the eight BH3-only proteins, Puma and Noxa, alongside Bid and Bim, contributed to sustained Bak/Bax-dependent hepatocyte apoptosis in the absence of Mcl-1 and Bcl-xL, elucidating the orchestration of Bcl-2 family proteins in healthy livers.
Factors Affecting Consumers’ Preference for Private Electric Vehicles in Bangladesh: Policy Implications for Adoption
Authors: Nath Tapan Kumar , Nath Shibajee , Paul Alak , Baul Trait Kumar 
Published: July 2024

Abstract

In order to reduce emissions from the transport sector, the government of Bangladesh aims to accelerate the pace of electric vehicles (EVs) adoption. However, research on consumer perceptions of EVs in the country is limited. This study examined the factors that encourage or discourage consumers from adopting private EVs. A pre-tested structured questionnaire was distributed across the country employing purposive sampling, and 427 responses were obtained. Descriptive statistics were computed, and binary regressions were performed to understand the relationship between respondents' characteristics, factors, and adoption of EVs. For more than 80% of respondents, EVs are energy efficient, environmentally friendly, reduce the usage of fossil fuels, stylish, and elegant. About 90% of respondents cited several discouraging factors, including a lack of sufficient service stations and mechanics, a limited driving range, a higher purchase price, and a low resale value. Approximately 60% of respondents are inclined to buy an EV as their next car. Nevertheless, the probability of them embracing an EV was significantly associated with a higher income and a more comprehensive understanding of EVs. These findings could potentially support pertinent authorities in their efforts to accelerate the adoption of EVs. To be more precise, authorities give precedence to the encouragement of EVs purchase through the provision of incentives such as road tax and registration fee reductions. Furthermore, it is essential that authorities allocate resources towards the development of EV-friendly infrastructure, including charging and service stations, while concurrently augmenting public consciousness and understanding of EVs.
Transforming a CO2 Adsorbent to a RWGS Catalyst by Controlling MgO Defects with Ce Single Atoms
Authors: Centi Gabriele , Li Shiyan , Li Na , Qin Tian , Guo Zhanglong , Lv Yuan , Zhang Riguang , Liu Xi , Chen Liwei , Perathoner Siglinda , Liu Yuefeng 
Published: July 2024

Abstract

The reverse water gas shift reaction (RWGS) is an important industrial reaction in the value chain to convert CO2 to enable carbon circularity. MgO is a typical absorbent of CO2 that forms stable surface carbonates. We demonstrate that highly defective MgO nanocrystals (c.a. 20 nanometers) are instead an active catalyst for RWGS. Their performances can be significantly enhanced by adding atomically dispersed Ce atoms anchored at these defective MgO sites. Extensive mechanistic studies and theoretical modelling prove that CO2 catalytic conversion depends on the MgO defective sites. Ce mainly promotes their number. Even if their presence influences the rate-limiting step, the activation energy remains unchanged, with the activity increase related to the pre-exponential factor. The Ce-doped MgO nanocrystal reached the RWGS equilibrium conversion at 600°C with CO product selectivity > 99%, maintained for over 850 hours on stream. The comparison with state-of-the-art evidence of their superior performances.
Edge-based graph centrality measures with spatial analytics to support vulnerability assessment and maintenance planning in sewer networks
Authors: Okwori Emmanuel Jenkeri , Viklander Maria , Hedström Annelie 
Published: July 2024

ABSTRACT

In this study, the spatial relationship between critical pipes identified using edge-based centrality measures and pipes with higher failure probability-based on selected vulnerability indicators were analysed in sanitary sewer networks. By analysing two sub-networks, one residential and the other a central network, significant spatial associations between pipes with high centrality values and those exhibiting adverse conditions (poor CCTV grades, previous blockages, and low self-cleaning capabilities) were identified. Path-based centrality measures, particularly edge betweenness and K-path edge centrality were less influenced by weights when identifying critical pipes. In contrast, non-path-based measures like nearest neighbour edge centrality could identify more localised critical pipes within the sewer networks investigated. The results showed that the spatial patterns between critical pipes and pipes in adverse conditions were not random and could support proactive maintenance planning and the development of more resilient networks. Additionally, the impact of network structure, connectivity, and differences in the composition of pipe attributes could contribute to variations in the strength of observable spatial associations.
Launching Cost-effective Virtual Escape Rooms as a Tool for FormativeAssessment
Authors: Azhar Farah , Adnan Nooreen 
Published: July 2024

:
This paper describes an innovative tool for formative assessment. This involves designing and implementing a Virtual Escape Room for students of
Master’s in Health Professions Education (MHPE) at Gulf Medical University. Escape Room is a type of game-based education where players are
constrained in a room, and they must solve a series of interconnected challenges in the form of puzzles or riddles to escape the room. Escape rooms
are proved to be innovative and engaging learning strategies, although they are resource intensive. We developed cost-effective virtual escape
room with the help of Google Workspace tools. Two cycles of escape rooms were conducted on different courses in similar fashion. Questions
were based on the learning objectives of selected courses of Master’s in Health Professions Education. These tools were interconnected and
revolved around a captivating storyline. Twelve students were grouped into teams of four and were subjected to the game. They played as teams on
a smart device for a time duration of ten minutes. Post-activity students were given feedback on their performance on questions and on team-based
skills. Students were then engaged in open discussion on their experience, and this was documented for analysis. They also gave feedback on
google forms. Analysis of feedback revealed that escape room enhanced students’ engagement and motivation towards learning. It helped foster
leadership and team-building skills along with effectively recalling the learned concepts. In addition, students learned to handle stress when faced
with challenges. Faculty, on the other hand, developed the skills to plan and execute a unique learning activity. We continue to learn from this
experience and will explore further opportunities to expose students and faculty to such engaging and unique learning activities.
In vitro and In vivo Activity of a New N-Oxide Derivative for Acne Vulgaris Treatment
Authors: Delgado Ivone Leila Lima , Scarim Caue Benito , Fernandes Felipe Hugo Alencar , Barbieri Karina Pereira , Polesi Marisa Campos , Pavan Aline Renata , Chiba Diego Eidy , Salgado Herida Regina Nunes , Carlos Iracilda Zeppone , Correa Marcos Antonio , de Andrade Cleverton Roberto , Dos Santos Jean Leandro 
Published: July 2024

Introduction:
Furoxan and benzofuroxan are compounds containing an N-oxide function,
known for their diverse pharmacological properties, including antimicrobial and antiinflammatory
effects. This study aimed to investigate these activities using an in-house library of
N-oxide compounds.

Methods:
Twenty compounds were tested against both Gram-positive and Gram-negative bacteria,
including Cutibacterium acnes (C. acnes), a microorganism implicated in the development of acne
vulgaris. One compound, (E)-4-(3-((2-(3-hydroxybenzoyl)hydrazone)methyl)phenoxy)-3-
(phenylsulfonyl)-1,2,5-oxadiazol-2-N-oxide (compound 15), exhibited selective antimicrobial activity
against C. acnes, with a Minimum Inhibitory Concentration (MIC) value of 2 μg/mL. Indirect
measurement of Nitric Oxide (NO) release showed that compound 15 and isosorbide dinitrate,
when treated with L-cysteine, produced nitrite levels of 20.1% and 9.95%, respectively. Using a
NO scavenger (PTIO) in combination with compound 15 in a culture of C. acnes resulted in reduced
antimicrobial activity, indicating that NO release is part of its mechanism of action. Cytotoxicity
assessments using murine macrophages showed cellular viability above 70% at concentrations
up to 0.78 μg/mL.

Results:
Measurements of Interleukin-1 beta (IL1-β) and Tumor Necrosis Factor-alpha (TNF-α)
indicated that compound 15 did not reduce the levels of these pro-inflammatory cytokines. Sustained
NO production by inducible Nitric Oxide Synthase (iNOS) in macrophages or neutrophils
has been found to be involved in the inflammatory process in acne vulgaris and lead to toxicity in
surrounding tissues. Nitrite levels in the supernatant of murine macrophages were found to be decreased
at a concentration of 0.78 μg/mL of compound 15, indicating an anti-inflammatory effect.
In vivo studies were conducted using Balb/c nude mice inoculated subcutaneously with C. acnes.
Cream and gel formulations of compound 15 were applied to treat the animals, along with commercially
available anti-acne drugs, for 14 days. Animals treated with a cream base containing 5% of
compound 15 exhibited less acanthosis with mild inflammatory infiltration compared to other
groups, highlighting its anti-inflammatory properties.

Conclusion:
Similar results were observed in the benzoyl peroxide group, demonstrating that compound
15 presented comparable anti-inflammatory activity to the FDA-approved drug. These promising
results suggest that compound 15 has a dual mechanism of action, with selective antimicrobial
activity against C. acnes and notable anti-inflammatory properties, making it a potential prototype
for developing new treatments for acne vulgaris.
Using Quality by Design Tools to Study Gel Formulation from Brassica juncea Leaves and Conducting its In vitro, In vivo, Molecular Docking, and ADMET Analyses
Authors: Kanakal Mahibub Mahamadsa , Abbas Syed Atif , Khan Abdullah , Sultana Saleha , Fatima Humaira , Tabasssum Ruheena , Ahmed Mohammed Gulzar , Sultana Rokeya , Mohammed Jaffer Sadik , Salfi Roshan 
Published: July 2024

Introduction:
This research aims to create a gel formulation of Brassica juncea leaf extract and assess its anti-inflammatory properties using an in silico study. The anti-inflamma-tory activity has been compared with Diclofenac molecules in PDB id: 4Z69. Further, the Ab-sorption, Distribution, Metabolism, Excretion, and Toxicity analysis has been performed to en-sure the therapeutic potential and safety of the drug development process. The Quality by De-sign tool has been applied to optimize formulation development.

Methods:
The extracted gel is characterized by performing Fourier transformer infrared, zeta potential, particle size, Scanning Electron Microscope, and entrapment efficiency. Further, the formulation is evaluated by examining its viscosity, spreadability, and pH measurement. An In vitro study of all nine extract suspensions was conducted to determine the drug contents at 276 nm.

Results:
The optimized suspension has shown the maximum percentage of drug release (82%) in 10 hours of study. Animal study for anti-inflammatory activity was performed, and results of all five groups of animals compared the % inhibition of paw edema at three hours; gel (56.70%), standard (47.86%), and (39.72%) were found.

Conclusion:
The research could conclude that the anti-inflammatory activity of gel formulation is high compared to extract, and a molecular docking study validates the anti-inflammatory ther-apeutic effects. ADMET analysis ensures the therapeutic effects and their safety.
COVID-19 Mental Health Impact Analysis using Ensemble-based Classifier
Authors: R Beulah Jayakumari , Jawahar Malathy , Eapen Maya , L Jani Anbarasi , Ravi Vinayakumar , S Lilly Sheeba , Alahmadi Tahani Jaser 
Published: July 2024

Introduction
In the 21st century, human community witnessed a range of biological crises resulting in long-term consequences like loss of life, economic decline, trauma and social disruptions. COVID -19, named the SARs-CoV-2 virus by United Nations, was a similar outbreak in China in the year 2019, which later spread across the world. During the pandemic, as part of preventive measures, the government authorities introduced SOP (standard operating procedures) measures such as social distancing, lockdown, quarantining and closure of educational institutions imposing a great impact on mental health and well-being of humans, especially among the youth.

Materials and Methods
A study was performed on a public dataset containing survey records collected from 1182 students of different educational institutions. The survey data was based on age, region of residence, time spent online and health fitness. The method used in the proposed work is a classifier model based on an ensemble of decision trees called random forest to predict the consequences of online learning on student’s health. The optimum and promising features are selected by using Recursive feature elimination (RFE) method.

Results
Our findings reveal a notable enhancement in predicting human health during a pandemic, as indicated by a significant increase in validation accuracy based on confusion for various classifiers. Experimental validation of the developed classifier model is done through the confusion matrix and receiver operating characteristic (ROC) curve. Further, performance metrics such as accuracy, precision, recall, F1-score, specificity, and error rate were employed. The experimental results established the superiority of the proposed ensemble subspace discriminant classifier compared to traditional classifiers.

Discussions
The RFE feature selection method used in the proposed work helps to select the optimum features as well as more informative features. Moreover, the method employed hyper parameter tuning method to enhance the performance of the classifier model.

Conclusion
This study highlights the importance of taking care of the emotional and physical health of humans during any pandemic. Furthermore, our approach possesses the capacity to significantly influence the field of predicting health, facilitating the development of more effective and advanced prediction strategies in the future.
Infrared Image Human Target Detection Method Based on Deep Learning
Authors: Zhang Le 
Published: July 2024

Introduction:
In this study, we investigate human target detection techniques for infrared
target recognition in complicated backdrops using deep learning approaches. This study
uses the human target detection of infrared images in various scenes as its research object.

Method:
A test was conducted using MatlabR2010a and the real-time C language development
platform. The findings indicate that, in the unoptimized case, the computing speed of the method
was only 0.34 seconds. However, following optimization, its performance significantly increased
to meet the real-time performance requirements.

Result:
The research findings presented in this work will be crucial to the relief and rescue efforts
following engineering mishaps.

Conclusion:
This study is innovative in that it develops a deep learning-based model for human
target detection in infrared photos and thoroughly examines and improves its functionality.
Classifying Residual Stroke Severity Using Robotics-Assisted Stroke Rehabilitation: Machine Learning Approach
Authors: Jeter Russell , Greenfield Raymond , Housley Stephen N , Belykh Igor 
Published: July 2024

Background
Stroke therapy is essential to reduce impairments and improve motor movements by engaging autogenous neuroplasticity. Traditionally, stroke rehabilitation occurs in inpatient and outpatient rehabilitation facilities. However, recent literature increasingly explores moving the recovery process into the home and integrating technology-based interventions. This study advances this goal by promoting in-home, autonomous recovery for patients who experienced a stroke through robotics-assisted rehabilitation and classifying stroke residual severity using machine learning methods.

Objective
Our main objective is to use kinematics data collected during in-home, self-guided therapy sessions to develop supervised machine learning methods, to address a clinician’s autonomous classification of stroke residual severity–labeled data toward improving in-home, robotics-assisted stroke rehabilitation.

Methods
In total, 33 patients who experienced a stroke participated in in-home therapy sessions using Motus Nova robotics rehabilitation technology to capture upper and lower body motion. During each therapy session, the Motus Hand and Motus Foot devices collected movement data, assistance data, and activity-specific data. We then synthesized, processed, and summarized these data. Next, the therapy session data were paired with clinician-informed, discrete stroke residual severity labels: “no range of motion (ROM),” “low ROM,” and “high ROM.” Afterward, an 80%:20% split was performed to divide the dataset into a training set and a holdout test set. We used 4 machine learning algorithms to classify stroke residual severity: light gradient boosting (LGB), extra trees classifier, deep feed-forward neural network, and classical logistic regression. We selected models based on 10-fold cross-validation and measured their performance on a holdout test dataset using F1-score to identify which model maximizes stroke residual severity classification accuracy.

Results
We demonstrated that the LGB method provides the most reliable autonomous detection of stroke severity. The trained model is a consensus model that consists of 139 decision trees with up to 115 leaves each. This LGB model boasts a 96.70% F1-score compared to logistic regression (55.82%), extra trees classifier (94.81%), and deep feed-forward neural network (70.11%).

Conclusions
We showed how objectively measured rehabilitation training paired with machine learning methods can be used to identify the residual stroke severity class, with efforts to enhance in-home self-guided, individualized stroke rehabilitation. The model we trained relies only on session summary statistics, meaning it can potentially be integrated into similar settings for real-time classification, such as outpatient rehabilitation facilities.