bluuebunny/crossref_metadata_2025 huggingface dataset:
The Quest for an Integrated Set of Neural Mechanisms Underlying Object Recognition in Primates
Authors: Kar Kohitij , DiCarlo James J.
Published: July 2024

Inferences made about objects via vision, such as rapid and accurate categorization, are core to primate cognition despite the algorithmic challenge posed by varying viewpoints and scenes. Until recently, the brain mechanisms that support these capabilities were deeply mysterious. However, over the past decade, this scientific mystery has been illuminated by the discovery and development of brain-inspired, image-computable, artificial neural network (ANN) systems that rival primates in these behavioral feats. Apart from fundamentally changing the landscape of artificial intelligence, modified versions of these ANN systems are the current leading scientific hypotheses of an integrated set of mechanisms in the primate ventral visual stream that support core object recognition. What separates brain-mapped versions of these systems from prior conceptual models is that they are sensory computable, mechanistic, anatomically referenced, and testable (SMART). In this article, we review and provide perspective on the brain mechanisms addressed by the current leading SMART models. We review their empirical brain and behavioral alignment successes and failures, discuss the next frontiers for an even more accurate mechanistic understanding, and outline the likely applications.
Glucosamine Hydrochloride and Glucosamine‐Gallic Acid Nanoparticles for the Treatment of Osteoarthritis: Synthesis, Antioxidant, and Anti‐Inflammatory
Authors: Jafari Alika , Tabarsa Mehdi , Naderi-Manesh Hossein , Gavlighi Hassan Ahmadi , You SangGuan , Vaezi Zahra
Published: July 2024

This study aimed to evaluate the antioxidant and anti‐inflammatory effects of glucosamine nanoparticles (GNPs) grafted with gallic acid (GNPs‐g‐GA). Glucosamine hydrochloride (G‐HCl) was produced from shrimp shell, and then GNPs synthesized using ionic gelation method. GNPs‐g‐GA was prepared by coupling GNPs with GA via 1‐ethy‐3‐(3‐dimethylaminopropyl)‐carbodiimide (EDC) in combination with N‐hydroxysuccinimide (NHS) cross‐linking agents. The results indicated that the grafting of GA onto GNPs at different ratios increased the average size of the nanoparticles from 195.7 to 294.2 nm with various grafting degrees ranging from 73.3 to 146.4 mg GA/g GNPs‐g‐GA. The SEM images revealed the formation of spherical‐shaped GNPs‐g‐GA nanoparticles with approximate sizes ranging from 275.3 to 303.6 nm. The appearance of characteristic signals in the FT‐IR (C=C, C–O/C–C, and NH2) and 1H‐NMR (H‐2 and H‐6 at 6.95 ppm) spectra and the red shift in UV‐Vis spectrum provided further support of GNPs‐g‐GA successful synthesis. DPPH radical scavenging (from 20.0 to 70.4%) and ABTS radical scavenging (from 18.7 to 79.0%) activities and reducing power (nearly fivefold) sharply improved in GNPs‐g‐GA. Moreover, GNPs‐g‐GA was found nontoxic and drastically reduced the level of nitric oxide release and downregulated the synthesis of TNF‐α, IL‐1β, and IL‐6 in LPS‐induced RAW2647 murine macrophage cells through NF‐κB and MAPKs signaling pathways. Overall, these results suggested that the grafting of GNPs and GA is an effective strategy for the suppression of inflammation response and oxidation reaction in osteoarthritis.
The Role of Anxiety Scales and Serum Copeptin Levels in Determining the Preprocedure Anxiety Status of Patients Who Undergo Fiberoptic Bronchoscopy and Endobronchial Ultrasonography
Authors: Kodalak Cengiz Sümeyye , Yılmaz Canan , Yurdakul Ahmet Selim
Published: July 2024

Introduction. Fiberoptic bronchoscopy (FOB) and endobronchial ultrasonography (EBUS) are safe and frequently used minimally invasive procedures. However, the decision to undergo an interventional procedure causes anxiety development and negative consequences in the individual. Objective. To determine the role of the State‐Trait Anxiety Inventory (STAI), Beck Anxiety Inventory (BAI), visual analog scale (VAS), and serum copeptin levels, their sensitivity, specificity, and the correlation between them in the anxiety levels of patients before undergoing FOB and EBUS. Method. A total of 219 patients who were scheduled to undergo FOB/EBUS were included in this prospective observational study. Demographic data were recorded. Patients’ anxiety levels before the procedure were evaluated using STAI‐SA, STAI‐TA, BAI, and VAS. Copeptin levels were measured before and after the procedure. Results. Patients were moderately anxious according to STAI‐SA, STAI‐TA, and VAS and were mildly anxious according to the BAI. Women and patients aged under 65 years were significantly more anxious. All scales were positively correlated. Serum copeptin levels before the procedure were 5.8 ± 4.8 and 4.3 ± 3.9 ng/mL afterwards, with a significant difference between them. The specificity and sensitivity of VAS were found to be the highest. Conclusion. All anxiety scales are correlated with each other, and we recommend VAS as a powerful alternative for determining anxiety levels in terms of easier understanding and implementation. Serum copeptin levels before the procedure were significantly higher than afterwards and may be used as a potential biomarker for preprocedural anxiety.
Mathematical Modeling of COVID‐19 Disease Dynamics With Contact Tracing: A Case Study in Ethiopia
Authors: Zerefe Shimelis Bekele , Ayele Tigabu Kasie , Tilahun Surafel Luleseged
Published: July 2024

In this paper, we developed a mathematical model for the dynamics of coronavirus disease (COVID‐19) transmission. The model embraces the notion of contact tracing and contaminated surfaces which are vital for disease control and contribute to disease transmission, respectively. We analyzed the model properties such as the positivity of the solution, invariant region, existence, and stability nature of equilibria. Besides, we computed the basic reproduction number R0. The local stability and the global stability of disease‐free equilibrium (DFE) points are proved by using the Routh–Hurwitz criteria and the Castillo‐Chavez and Song approach, respectively. LaSalle’s invariant principle is applied to prove the stability of an endemic equilibrium (EE) point. The possibility of bifurcation is discussed using the center manifold theory. We used real data on the spread and control of COVID‐19 disease in Ethiopia. Based on the data reported, we estimated the values of the parameters using the least squares method together with the fmin function in the MATLAB optimization toolbox. The sensitivity analysis of the model is explored numerically to illustrate the impact of the parameters on disease transmission. The study addressed that contact tracing is especially important because COVID‐19 often has asymptomatic carriers, and there are many asymptomatic individuals unaware in Ethiopia. The new infections would decrease in the communities by detecting and isolating COVID‐19 cases before they could spread the virus to others. Moreover, the study endorsed that the contaminated surface has contributed to disease transmission. The sensitivity analysis shows that if the rate of disinfected contaminated objects (ϕ) rises, then the transmission of the disease is reduced. Consequently, this study will aid in the fight against COVID‐19 policymakers and NGOs. It can also be used as a policy input for different countries under this crisis. Because of the mathematical modeling of this global pandemic, there is another point of view rather than public health research outputs. Additionally, with the concept of contact tracing and contaminated surfaces incorporated into the model, the result provides insight for disease prevention.
Fractional Integration via Picard Method for Solving Fractional Differential‐Algebraic Systems
Authors: Mohammad Susan H. , Mohammed Al-Rozbayani Abdulghafor
Published: July 2024

In this paper, we applied an efficient integrative method called the fractional Picard method to find the approximate solution to a system of fractional algebraic differential equations (SFADEs). By comparing the results with the exact solution, it was found that this method is highly efficient for finding solutions. Also, a genetic algorithm (GA) was used to speed up the approximate solutions when choosing the best values for the fractional derivative, which increased the efficiency of Picard’s fractional integral method in finding the best solutions. The MATLAB program was used to find approximate solutions.
Data from A Machine Learning Algorithm Facilitates Prognosis Prediction and Treatment Selection for Barcelona Clinic Liver Cancer Stage C Hepatocellular Carcinoma
Authors: Han Ji W. , Lee Soon K. , Kwon Jung H. , Nam Soon W. , Yang Hyun , Bae Si H. , Kim Ji H. , Nam Heechul , Kim Chang W. , Lee Hae L. , Kim Hee Y. , Lee Sung W. , Lee Ahlim , Chang U I. , Song Do S. , Kim Seok-Hwan , Song Myeong J. , Sung Pil S. , Choi Jong Y. , Yoon Seung K. , Jang Jeong W.
Published: July 2024

AbstractPurpose:
Given its heterogeneity and diverse clinical outcomes, precise subclassification of Barcelona Clinic Liver Cancer stage C (BCLC-C) hepatocellular carcinoma (HCC) is required for appropriately determining patient prognosis and selecting treatment.
Experimental Design:
We recruited 2,626 patients with BCLC-C HCC from multiple centers, comprising training/test (n = 1,693) and validation cohorts (n = 933). The XGBoost model was chosen for maximum performance among the machine learning (ML) models. Patients were categorized into low-, intermediate-, high-, and very high-risk subgroups based on the estimated prognosis, and this subclassification was named the CLAssification via Machine learning of BCLC-C (CLAM-C).
Results:
The areas under the receiver operating characteristic curve of the CLAM-C for predicting the 6-, 12-, and 24-month survival of patients with BCLC-C were 0.800, 0.831, and 0.715, respectively—significantly higher than those of the conventional models, which were consistent in the validation cohort. The four subgroups had significantly different median overall survivals, and this difference was maintained among various patient subgroups and treatment modalities. Immune-checkpoint inhibitors and transarterial therapies were associated with significantly better survival than tyrosine kinase inhibitors (TKI) in the low- and intermediate-risk subgroups. In cases with first-line systemic therapy, the CLAM-C identified atezolizumab–bevacizumab as the best therapy, particularly in the high-risk group. In cases with later-line systemic therapy, nivolumab had better survival than TKIs in the low-to-intermediate-risk subgroup, whereas TKIs had better survival in the high- to very high-risk subgroup.
Conclusions:
ML modeling effectively subclassified patients with BCLC-C HCC, potentially aiding treatment allocation. Our study underscores the potential utilization of ML modeling in terms of prognostication and treatment allocation in patients with BCLC-C HCC.
Supplementary Figure 2 from CD8-Targeted IL2 Unleashes Tumor-Specific Immunity in Human Cancer Tissue by Reviving the Dysfunctional T-cell Pool
Authors: Kaptein Paulien , Slingerland Nadine , Metoikidou Christina , Prinz Felix , Brokamp Simone , Machuca-Ostos Mercedes , de Roo Guido , Schumacher Ton N.M. , Yeung Yik A. , Moynihan Kelly D. , Djuretic Ivana M. , Thommen Daniela S.
Published: July 2024

Gating strategy of PDTFs.
Figure S3 from Genotoxicity Associated with Retroviral CAR Transduction of ATM-Deficient T Cells
Authors: Rozenbaum Meir , Fluss Reut , Marcu-Malina Victoria , Sarouk Ifat , Meir Amilia , Elitzur Sarah , Zinger Tal , Jacob-Hirsch Jasmine , Saar Efrat G. , Rechavi Gideon , Jacoby Elad
Published: July 2024

Retroviral transduction of GFP or CAR in ATM+/+ and ATM-/- T-cells
Design and implementation of an automated irrigation control for home plantations
Authors: Pérez-Baca Molly Scarlet , Sambrano-Luna Karina Lizeth , Sánchez-Ramírez Jhony Miguel , Cabana-Cáceres Maritza , Castro-Vargas Cristian
Published: July 2024

In today's society, keeping our gardens attractive is complex, especially if you need more time to care for them. This can cause the plant to wilt if it is not watered occasionally to keep the soil moist. In summer, this problem tends to get worse because the temperature tends to rise and reach high degrees. The objective is to design an automatic and manual irrigation system with a humidity detector through hardware programming and free software to solve this. The necessary components will be identified and selected, humidity thresholds will be established, and the adoption of technologies such as internet of things (IoT), Arduino, and humidity sensors will be promoted to solve the problem in automated irrigation systems. The technical specifications of the components are described, and the circuit design is presented. A programming algorithm will be developed to control the frequency and duration of irrigation, as well as the state of the water pump. Implementing the automated system will allow precise water supply control, contributing to the healthy growth of plants and crops in green areas.
Mathematical model enhancing flash memory reliability through DFT-driven error correction coding
Authors: Sukanya Poornima Huchegowda , Chowdaiah Nagaraju
Published: July 2024

Flash memory, ubiquitous in diverse electronic devices, confronts persistent challenges stemming from inherent errors that jeopardize data integrity. This research situates itself at the intersection of these challenges and advancements, proposing an inventive error correction coding framework that harnesses the unique capabilities of analysis with a hybrid error control coding (HECC) approach. In the proposed work, a mathematical model aimed at enhancing the flash memory by identifying the error pattern within the pages using the discrete fourier transform (DFT). By incorporating distinctive DFT mathematical properties, the proposed technique intends to improve flash memory error correction beyond traditional methods. The flash storage defect detection and rectification results with hybrid error correction coding achieved bit error rate (BER) of 4.3e-6, latency 14.1, mean 15.1 and standard deviation 1.0. Error correction efficiency 98% and storage overhead 10%. With this approach results are significantly improving the error correction efficiency, reduce storage overhead and enhanced adaptability to diverse error patterns.
Hybrid deep learning with pelican optimization algorithm for M2M communication on UAV image classification
Authors: Sharmili Kasturi Chandrahaasan , Kumar Chevella Anil , Subbaiyan Arunmurugan , Beena Bethel Gundemadugula Nelson , Puliyanjalil Ezudheen , Sapkale Pallavi
Published: July 2024

Machine-to-machine (M2M) communication for unmanned aerial vehicle (UAVs) and image classification is essential to current remote sensing and data processing. UAVs and ground stations or other linked devices exchange information seamlessly using M2M communication. M2M connectivity helps UAVs with cameras and sensors communicate aerial pictures in real time or post-mission for image categorization and analysis. During flight, UAVs acquire massive volumes of picture data. Image classification, commonly using deep learning (DL) methods like convolutional neural network (CNN), automatically categorizes and annotates photos based on predetermined classes or attributes. This work uses UAV photos to produce hybrid deep learning with pelican optimization algorithm for M2M communication (HDLPOA-M2MC). HDLPOA-M2MC automates UAV picture class identification. GhostNet model is used to derive features in HDLPOA-M2MC. The HDLPOA-M2MC approach leverages pelican optimization algorithm (POA) for hyperparameter adjustment in this investigation. Finally, autoencoder-deep belief network (AE-DBN) model can classify. The HDLPOA-M2MC method’s enhanced outcomes were shown by several studies. The complete results showed that HDLPOA M2MC performed better across measures.
Predicting vulnerability for brain tumor: data-driven approach utilizing machine learning
Authors: Effendi Yutika Amelia , Sofiah Amila , Hidayat Niko Azhari , Ebrie Awol Seid , Hamzah Zainy
Published: July 2024

Brain tumors, whether benign or malignant, present a complex and multifaceted challenge in healthcare, affecting individuals across various age groups. Predicting the vulnerability of brain tumors using health risk factors and symptoms is crucial, yet there have been limited research studies, particularly those integrating artificial intelligence (AI) technology. This research explores machine learning models such as support vector machines (SVMs), multi-layer perceptrons (MLPs), and logistic regression (LR) for the early detection of brain tumors. Evaluation metrics, including accuracy, precision, recall, and F1-score, are employed to assess model performance. The results indicate that the SVM outperforms other models, providing a robust foundation for predictive accuracy. To enhance accessibility and usability, the research also integrates these models into a mobile application predictor. The application is beneficial for assisting individuals in early detection by identifying potential risk factors and symptoms that may lead to a brain tumor. In conclusion, the integration of machine learning through a mobile application represents a transformative approach to personalized healthcare. By empowering individuals with cutting-edge technology, this research strives to enhance early detection and decision-making regarding potential brain tumor risks and symptoms, ultimately contributing to improved patient outcomes and quality of life.
Reviewing approaches employed in Arabic chatbots
Authors: Bouhlali Abdelmounaim , Elmansouri Adil , El Mhouti Abderrahim , Fahim Mohamed , Boudaa Tarik
Published: July 2024

The field of chatbots has witnessed a remarkable evolution in recent years, marked by a transition from simplistic rule-based structures to sophisticated systems employing advanced natural language processing (NLP) techniques. While most languages benefit from NLP support, the majority of chatbot research and development has been conducted in English, leaving a notable scarcity of comparable works in Arabic. This scarcity is attributed to the myriad challenges posed by the linguistically intricate nature of Arabic, encompassing orthographic variations and diverse dialects. This study systematically reviews articles that represent implementations of Arabic chatbots, revealing a discernible shift from rule-based frameworks to the predominant adoption of machine learning (ML) and deep learning (DL) methods. The results highlight the dynamic trajectory of chatbot technology, with a notable emphasis on the pivotal role of DL, as evidenced by a significant peak in 2023. Looking forward, the study anticipates a more sophisticated future for chatbot development, driven by ongoing advancements in artificial intelligence (AI) and NLP, offering valuable insights into the current state of Arabic chatbot research and laying the foundation for continued exploration in this evolving and dynamic field.
Detecting COVID-19 from chest X-ray images using machine learning and deep convolutional neural networks
Authors: Vibhute Amol D. , Patil Chandrashekhar H. , Saini Jatinderkumar R. , Patil Harshali P.
Published: July 2024

The world was affected by a novel coronavirus in December 2019 that changed human life. Several types of research have been done, substantial scientific advances have been made, and millions of dollars have been spent on bringing scholars and scientists to one platform to end this critical pandemic. Ascertaining COVID-19 diagnoses in the initial stage of the pandemic was critical, specifically for patients with no manifestations. In this case, artificial intelligence-based systems were proposed to identify the virus at an earlier phase. Thus, the present study suggests a machine vision scheme to identify COVID-19 from chest X-ray images. Three machine learning approaches, such as logistic regression (LR), decision tree (DT), and random forest (RF), were implemented with more than 95% accuracy. The deep convolutional neural network (CNN) architecture was also proposed and implemented with a 99.99% detection rate. Therefore, the present work can effectively detect COVID-19 cases in the early stages.
Improving k-nearest neighbor performance using permutation feature importance to predict student success in study
Authors: Jana Satvika Gd. Aditya , Sukajaya I. N. , Gunadi I Gede Aris
Published: July 2024

The timely graduation of students is a critical indicator of academic quality assessment. Therefore, universities should use effective predictive systems to identify earlier potential lateness of graduation. This study aimed to improve the K-nearest neighbor (K-NN) algorithm’s ability to predict student on-time graduation. It evaluated K-NN algorithm performance with and without the permutation feature importance (PFI) technique, using a dataset of 460 student graduation records from 2014 to 2017. The training data was oversampled, adjusting the ratio of minority class samples from 13% to 100% of the majority class samples. The result shows that integrating PFI into the K-NN model improved K-NN performance by 10 iterations of the PFI process, N-shuffle varying from 10 to 100 for each iteration, and a minority class sample ratio of 25%. The accuracy score improved from 90.22% to 92.39%, precision from 50.00% to 62.50%, F1-score from 52.63% to 58.82%, while recall remained consistent at 55.56%. The PFI analysis showed that achievement index for the 1st semester or IPS 1 had the least impact on the model. The study suggested using a comprehensive approach to determine the n-shuffle of PFI based on the number of test data for a more accurate feature contribution pattern.
Optimizing feature extraction for tampering image detection using deep learning approaches
Authors: Muniappan Ramaraj , Sabareeswaran Dhendapani , Jothish Chembath , Raja Joe Arun , Selvaraj Srividhya , Nainan Thangarasu , Ilango Bhaarathi , Sumbramanian Dhinakaran
Published: July 2024

Tamper image detection approach using deep learning involves, creating a model that can accurately identify and localize instances of image tampering, by employing advanced feature extraction methods, object detection algorithms, and optimization techniques that could be manipulated on need basis. Enhance the integrity of visual content by automating the detection of unauthorized alterations, to ensure the reliability of digital images across various applications and domains. The problem addressing the optimization feature extraction techniques involves the detection of subtle manipulations, handling diverse tampering techniques, and achieving robust performance across different types of images and scenarios. The proliferation of sophisticated image editing tools makes it challenging to detect tampered regions within images, necessitating proposed techniques for automated tamper image detection. The research work will focus on four different feature extraction algorithms such as non-negative factorization (NNF), singular value decomposition (SVD), explicit semantic analysis (ESA), principal component analysis (PCA), which are outsourced. Detecting tampered images through deep learning necessitates the meaningful selection and adjustment of several parameters to enhance the model's effectiveness. Integrating the feature extraction algorithm with the suggested methods effectively identifies critical features within the dataset, thereby improving the detection capabilities and achieving higher accuracy.
Analysis of Village Asset Management at The Naga Timbul Village Office, Tanjung Morawa District, Deli Serdang Regency
Authors: Galih Supraja  , Fiqri Haikal  , M. G. Fahluzi
Published: July 2024

This research aims to find out and describe the role of village asset management carried out by village officials. Apart from that, it also describes the inhibiting factors in managing village assets. This research was carried out at Naga Timbul Village Office, Tanjung Morawa District, Deli Serdang Regency. This type of research is qualitative research with a descriptive approach to describe the role of village asset management in improving the welfare of village people. The data collection techniques used by researchers are mostly interviews and observations. From the research results, it can be concluded that in managing village assets, village officials lack skilled skills in managing village assets so that this is a major inhibiting factor for village government. Apart from that, village officials should be more active in providing outreach and guidance regarding the important role of managing village assets for the welfare of village people. Village asset management is carried out by planning, utilization, maintenance, administration, reporting as well as guidance and supervision. With this stage of managing village assets, it is hoped that the welfare of village people can increase.
Optimization of cervical cord synthetic T1-weighted MRI for enhancing clinical application in neurodegenerative spinal cord disorders
Authors: Schading-Sassenhausen Simon , Seif Maryam , Weiskopf Nikolaus , Freund Patrick
Published: July 2024

Synthetic MRI offers the advantage of reducing acquisition time and enhancing flexibility through the reconstruction of various contrast weightings from a single set of MRI scans. However, the use of synthetic T1-weighted (synT1-w) MRI can lead to potentially biased measurements of the cross-sectional area (CSA) in the spinal cord when compared to conventionally acquired T1-weighted MRI. This disparity can have implications for comparability and sensitivity of MRI in assessing disease progression or treatment effects in neurodegenerative spinal cord disorders. Thus, this study aimed at improving the accuracy (i.e., difference between synthetic and acquired MRI) of cervical cord CSA measurements (C1-C3 level) based on synT1-w MRI implementing a longitudinal data set acquired from 23 acute spinal cord injury (SCI) patients and 21 healthy controls over 2 years. Moreover, the validity of using synT1-w MRI for tracking cervical cord atrophy following SCI over 2 years was verified. SynT1-w images were reconstructed from quantitative maps of proton density, longitudinal, and effective transverse relaxation rates derived from a multi-parameter mapping protocol. The results showed a minimal bias of -0.31 mm2 (-0.5%) in CSA measurements based on synT1-w compared to acquired MRI. Estimates of atrophy rates and average CSA were comparable between synthetic and acquired MRI. A sample size estimation for detecting treatment effects on CSA atrophy after 2 years following SCI revealed that the required sample size is reduced by 13.5% using synT1-w instead of acquired MRI. This study shows high accuracy of synT1-w MRI and demonstrates its applicability in clinical studies for optimizing long MRI protocols.
Brachystele guayanensis is a Cyclopogon (Orchidaceae): notes on its biology and taxonomy
Authors: MENEGUZZO THIAGO E. C. , CARVALHO BRUNO M. DE , BATISTA JOÃO A. N. , GOMES SUELI M. , PROENÇA CAROLYN E. B.
Published: July 2024

Brachystele guayanensis is hereby transferred to the genus Cyclopogon based on molecular systematics and morphological evidence. The major change in the circumscription of Cyclopogon with the inclusion of this species is an increase in the number of flowers per inflorescence, a reduction in flower size, and the fact that sepals may become petaloid.
Machine Learning Approach for Spatiotemporal Multivariate Optimization of Environmental Monitoring Sensor Locations
Authors: Siddiquee Masudur R. , Meray Aurelien O. , Xu Zexuan , Gonzalez-Raymat Hansell , Danielson Thomas , Upadhyay Himanshu , Lagos Leonel E. , Eddy-Dilek Carol , Wainwright Haruko M.
Published: July 2024

Long-term environmental monitoring is critical for managing the soil and groundwater at contaminated sites. Recent improvements in state-of-the-art sensor technology, communication networks, and artificial intelligence have created opportunities to modernize this monitoring activity for automated, fast, robust, and predictive monitoring. In such modernization, it is required that sensor locations be optimized to capture the spatiotemporal dynamics of all monitoring variables as well as to make it cost-effective. The legacy monitoring datasets of the target area are important to perform this optimization. In this study, we have developed a machine-learning approach to optimize sensor locations for soil and groundwater monitoring based on ensemble supervised learning and majority voting. For spatial optimization, Gaussian process regression (GPR) is used for spatial interpolation, while the majority voting is applied to accommodate the multivariate temporal dimension. Results show that the algorithms significantly outperform the random selection of the sensor locations for predictive spatiotemporal interpolation. While the method has been applied to a four-dimensional dataset (with two-dimensional space, time, and multiple contaminants), we anticipate that it can be generalizable to higher-dimensional datasets for environmental monitoring sensor location optimization.
Taxonomic and phenotypic analysis of bifidobacteria isolated from IBD patients as potential probiotic strains
Authors: Bosselaar Sabine , Dhelin Lucile , Dautel Ellena , Titecat Marie , Duthoy Stéphanie , Stelmaszczyk Marie , Delory Nathan , De Sousa Violante Madeleine , Machuron François , Ait-Abderrahim Hassina , Desreumaux Pierre , Foligné Benoit , Monnet Céline
Published: July 2024

Background

Inflammatory Bowel Diseases (IBD) are a major public health issue with unclear aetiology. Changes in the composition and functionality of the intestinal microbiota are associated with these pathologies, including the depletion of strict anaerobes such as Feacalibacterium prausnitzii. Less evidence is observed for depletion in other anaerobes, among which bifidobacteria. This study characterized the taxonomic and functional diversity of bifidobacteria isolated from the human intestinal microbiota in active and non-active IBD patients by a culturomics approach and evaluated if these bifidobacteria might be used as probiotics for gut health.

Results

A total of 341 bifidobacteria were isolated from the intestinal microbiota of IBD patients (52 Crohn’s disease and 26 ulcerative colitis patients), with a high proportion of Bifidobacterium dentium strains (28% of isolated bifidobacteria). In ulcerative colitis, the major species identified was B. dentium (39% of isolated bifidobacteria), in active and non-active ulcerative colitis. In Crohn’s disease, B. adolescentis was the major species isolated from non-active patients (40%), while similar amounts of B. dentium and B. adolescentis were found in active Crohn’s disease patients. The relative abundance of B. dentium was increased with age, both in Crohn’s disease and ulcerative colitis and active and non-active IBD patients. Antibacterial capacities of bifidobacteria isolated from non-active ulcerative colitis against Escherichia coli LF82 and Salmonella enterica ATCC 14028 were observed more often compared to strains isolated from active ulcerative colitis. Finally, B. longum were retained as strains with the highest probiotic potential as they were the major strains presenting exopolysaccharide synthesis, antibacterial activity, and anti-inflammatory capacities. Antimicrobial activity and EPS synthesis were further correlated to the presence of antimicrobial and EPS gene clusters by in silico analysis.

Conclusions

Different bifidobacterial taxonomic profiles were identified in the microbiota of IBD patients. The most abundant species were B. dentium, mainly associated to the microbiota of ulcerative colitis patients and B. adolescentis, in the intestinal microbiota of Crohn’s disease patients. Additionally, the relative abundance of B. dentium significantly increased with age. Furthermore, this study evidenced that bifidobacteria with probiotic potential (antipathogenic activity, exopolysaccharide production and anti-inflammatory activity), especially B. longum strains, can be isolated from the intestinal microbiota of both active and non-active Crohn’s disease and ulcerative colitis patients.
Optimizing placebo and minimizing nocebo effects through communication: e-learning and virtual reality training development
Authors: Westendorp Janine , van Vliet Liesbeth M. , Meeuwis Stefanie H. , olde Hartman Tim C. , Sanders Ariëtte R. J. , Jutten Eric , Dirven Monique , Peerdeman Kaya J. , Evers Andrea W. M.
Published: July 2024

Background

The effects of many treatments in healthcare are determined by factors other than the treatment itself. Patients’ expectations and the relationship with their healthcare provider can significantly affect treatment outcomes and thereby play a major role in eliciting placebo and nocebo effects. We aim to develop and evaluate an innovative communication training, consisting of an e-learning and virtual reality (VR) training, for healthcare providers across all disciplines, to optimize placebo and minimize nocebo effects through healthcare provider-patient communication. The current paper describes the development, mid-term evaluation, optimization, and final evaluation of the communication training, conducted in The Netherlands.

Methods

The development of both the e-learning and the VR training consisted of four phases: 1) content and technical development, 2) mid-term evaluation by healthcare providers and placebo/communication researchers, 3) optimization of the training, and 4) final evaluation by healthcare providers. To ensure the success, applicability, authenticity, and user-friendliness of the communication training, there was ongoing structural collaboration with healthcare providers as future end users, experts in the field of placebo/communication research, and educational experts in all phases.

Results

Placebo/communication researchers and healthcare providers evaluated the e-learning positively (overall 7.9 on 0–10 scale) and the content was perceived as useful, accessible, and interesting. The VR training was assessed with an overall 6.9 (0–10 scale) and was evaluated as user-friendly and a safe method for practicing communication skills. Although there were some concerns regarding the authenticity of the VR training (i.e. to what extent the virtual patient reacts like a real patient), placebo and communication researchers, as well as healthcare providers, recognized the significant potential of the VR training for the future.

Conclusions

We have developed an innovative and user-friendly communication training, consisting of an e-learning and VR training (2D and 3D), that can be used to teach healthcare providers how to optimize placebo effects and minimize nocebo effects through healthcare provider-patient communication. Future studies can work on improved authenticity, translate the training into other languages and cultures, expand with additional VR cases, and measure the expected effects on providers communication skills and subsequently patient outcomes.
Resistance of Nile tilapia fed with Padina boergesenii extract to Pseudomonas putida infection
Authors: Bakry Karima A. , Nasr Mahmoud , Al-Amgad Zeinab , kondos Ereen , Kondos Malak K. N. , Mehanny Pierre E. , Alghamdi Abdullah A. A. , Khormi Mohsen A. , Abd-ElHafeez Hanan H. , Emeish Walaa F.A.
Published: July 2024

The aim of this research was to estimate the immunopotentiation effect of brown algae Padina boergesenii water extract on Nile tilapia, Oreochromis niloticus through resistance to Pseudomonas putida infection. Gas Chromatography Mass Spectrometry was utilized to characterize the seaweed phytoconstituents. One hundred and twenty-six fish were divided in triplicates into two equal groups corresponding to two diet variants that used to feed Nile tilapia for 20 successive days: a basal (control), and P. boergesenii water extract supplemented group. Fish samples were collected at 10-days intervals throughout the experiment. Serum biochemical constituents, total antioxidant capacity (TAC), and some immune related genes expression of the spleen and intestinal tissues of experimental fish were studied, as well as histological examination of fish immune tissues. Moreover, following 20 days of feeding, the susceptibility of Nile tilapia to P. putida infection was evaluated to assess the protective effect of the used extract. The findings indicated that the studied parameters were significantly increased, and the best immune response profiles were observed in fish fed P. boergesenii water extract for 20 successive days. A bacterial challenge experiment using P. putida resulted in higher survival within the supplemented fish group than the control. Thus, the lowered post-challenge mortality of the fish may be related to the protection provided by the stimulation of the innate immune system, reduced oxidative stress by higher activity of TAC, and elevated levels of expression of iterleukin-1beta (IL-1β), beta-defensin (β-defensin), and natural killer-lysin (NKl). Moreover, the constituents of the extract used showed potential protective activity for histological features of the supplemented fish group when compared to the control. Collectively, this study presents a great insight on the protective role of P. boergesenii water extract as an additive in Nile tilapia feed which suggests its potential for improving the immune response against P. putida infection.
Thonzonium bromide inhibits progression of malignant pleural mesothelioma through regulation of ERK1/2 and p38 pathways and mitochondrial uncoupling
Authors: Dell’Anno Irene , Morani Federica , Patergnani Simone , Daga Antonio , Pinton Paolo , Giorgi Carlotta , Mutti Luciano , Gemignani Federica , Landi Stefano
Published: July 2024

Background

Malignant Pleural Mesothelioma (MPM) is a rare malignancy with a poor prognosis. Current therapies are unsatisfactory and novel cures are urgently needed. In a previous drug screening, we identified thonzonium bromide (TB) as one of the most active compounds against MPM cells. Since the biological effects of TB are poorly known, in this work we departed from some hints of previous studies and investigated several hypotheses. Moreover, we evaluated the efficacy of TB in an in vivo xenograft rodent model.

Methods

In vitro assessment was made on five MPM (Mero-14, Mero-25, Ren, NCI-H28, MSTO-211H) and one SV40-immortalized mesothelial cell line (MeT-5A). We evaluated TB ability to affect proliferation, apoptosis, mitochondrial functions and metabolism, and the mevalonate pathway. In vivo assay was carried out on MPM-xenograft NOD-SCID mice (4 mg/kg delivered intraperitoneally, twice a week for 4 weeks) and the overall survival was analysed with Kaplan-Meier curves.

Results

After TB treatment, we observed the suppression of ERK 1/2 phosphorylation, the increase of BAX expression and p38 phosphorylation. TB affected Ca2+ homeostasis in both mitochondrial and cytosolic compartments, it regulated the mitochondrial functioning, respiration, and ATP production as well as the mevalonate pathway. The in vivo study showed an increased overall survival for TB treated group vs. vehicle control group (P = 0.0076).

Conclusions

Both in vitro and in vivo results confirmed the effect of TB on MPM and unravelled novel targets with translational potential.
Spatial distribution pattern of immune cells is associated with patient prognosis in colorectal cancer
Authors: Shen Rongfang , Huang Ying , Kong Deyang , Ma Wenhui , Liu Jie , Zhang Haizeng , Cheng Shujun , Feng Lin
Published: July 2024

Background

The spatial context of tumor-infiltrating immune cells (TIICs) is important in predicting colorectal cancer (CRC) patients’ clinical outcomes. However, the prognostic value of the TIIC spatial distribution is unknown. Thus, we aimed to investigate the association between TIICs in situ and patient prognosis in a large CRC sample.

Methods

We implemented multiplex immunohistochemistry staining technology in 190 CRC samples to quantify 14 TIIC subgroups in situ. To delineate the spatial relationship of TIICs to tumor cells, tissue slides were segmented into tumor cell and microenvironment compartments based on image recognition technology, and the distance between immune and tumor cells was calculated by implementing the computational pipeline phenoptr.

Results

MPO+ neutrophils and CD68+IDO1+ tumor-associated macrophages (TAMs) were enriched in the epithelial compartment, and myeloid lineage cells were located nearest to tumor cells. Except for CD68+CD163+ TAMs, other cells were all positively associated with favorable prognosis. The prognostic predictive power of TIICs was highly related to their distance to tumor cells. Unsupervised clustering analysis divided colorectal cancer into three subtypes with distinct prognostic outcomes, and correlation analysis revealed the synergy among B cells, CD68+IDO1+TAMs, and T lineage cells in producing an effective immune response.

Conclusions

Our study suggests that the integration of spatial localization with TIIC abundance is important for comprehensive prognostic assessment.
Advancing targeted combination chemotherapy in triple negative breast cancer: nucleolin aptamer-mediated controlled drug release
Authors: Ma Yuan , Xie Duoli , Chen Zefeng , Shen Xinyang , Wu Xiaoqiu , Ding Feng , Ding Shijian , Pan Yufei , Li Fangfei , Lu Aiping , Zhang Ge
Published: July 2024

Background

Triple-negative breast cancer (TNBC) is a recurrent, heterogeneous, and invasive form of breast cancer. The treatment of TNBC patients with paclitaxel and fluorouracil in a sequential manner has shown promising outcomes. However, it is challenging to deliver these chemotherapeutic agents sequentially to TNBC tumors. We aim to explore a precision therapy strategy for TNBC through the sequential delivery of paclitaxel and fluorouracil.

Methods

We developed a dual chemo-loaded aptamer with redox-sensitive caged paclitaxel for rapid release and non-cleavable caged fluorouracil for slow release. The binding affinity to the target protein was validated using Enzyme-linked oligonucleotide assays and Surface plasmon resonance assays. The targeting and internalization abilities into tumors were confirmed using Flow cytometry assays and Confocal microscopy assays. The inhibitory effects on TNBC progression were evaluated by pharmacological studies in vitro and in vivo.

Results

Various redox-responsive aptamer-paclitaxel conjugates were synthesized. Among them, AS1411-paclitaxel conjugate with a thioether linker (ASP) exhibited high anti-proliferation ability against TNBC cells, and its targeting ability was further improved through fluorouracil modification. The fluorouracil modified AS1411-paclitaxel conjugate with a thioether linker (FASP) exhibited effective targeting of TNBC cells and significantly improved the inhibitory effects on TNBC progression in vitro and in vivo.

Conclusions

This study successfully developed fluorouracil-modified AS1411-paclitaxel conjugates with a thioether linker for targeted combination chemotherapy in TNBC. These conjugates demonstrated efficient recognition of TNBC cells, enabling targeted delivery and controlled release of paclitaxel and fluorouracil. This approach resulted in synergistic antitumor effects and reduced toxicity in vivo. However, challenges related to stability, immunogenicity, and scalability need to be further investigated for future translational applications.
Correlation between circulating advanced glycation end products and thioredoxin-interacting protein levels and renal fat content in type 2 diabetes mellitus patients
Authors: Hua Yulin , Yin Zaifei , Li Mingming , Sun Hong , Shi Bimin
Published: July 2024

Background

This study sought to explore the clinical relevance of the associations of serum levels of advanced glycation end products (AGEs), soluble receptor for AGEs (sRAGE), and thioredoxin-interacting protein (TXNIP) with the renal fat fraction (RFF) in individuals with type 2 diabetes mellitus (T2DM).

Methods

A total of 133 patients with T2DM were enrolled in the study. RFF, which represents the renal fat level, was determined utilizing Dixon magnetic resonance imaging (MRI). Serum levels of AGEs, sRAGE, TXNIP, and other biochemical parameters were measured in patients who fasted.

Results

RFF in T2DM patients was positively correlated with the fasting levels of C-peptide (CP), triglycerides (TG), AGEs, TXNIP, and sRAGE (P < 0.05) and negatively correlated with the high-density lipoprotein cholesterol (HDL-c) level (P < 0.05). Pearson’s correlation analysis indicated that the serum levels of AGEs, sRAGE, and TXNIP were interrelated and positively correlated (P < 0.05). Then, all patients were assigned to four groups according to the RFF quartile. The HC, CP, TG, AGEs, sRAGE, TXNIP, and DKD percentages tended to increase as the RFF quartiles increased, while the HDL-c level tended to decrease (p for trend < 0.05). Next, multiple linear regression analysis was performed using RFF as the dependent variable. After controlling for covariates related to RFF, the results showed that the serum levels of AGEs and TXNIP were still significantly correlated with RFF.

Conclusion

These results suggest that circulating AGEs and TXNIP levels may be associated with ectopic fat accumulation in the kidneys of T2DM patients and may serve as indicators of the severity of renal fat deposition.
Epigenetic scores derived in saliva are associated with gestational age at birth
Authors: Mckinnon Katie , Conole Eleanor L. S. , Vaher Kadi , Hillary Robert F. , Gadd Danni A. , Binkowska Justyna , Sullivan Gemma , Stevenson Anna J. , Corrigan Amy , Murphy Lee , Whalley Heather C. , Richardson Hilary , Marioni Riccardo E. , Cox Simon R. , Boardman James P.
Published: July 2024

Background

Epigenetic scores (EpiScores), reflecting DNA methylation (DNAm)-based surrogates for complex traits, have been developed for multiple circulating proteins. EpiScores for pro-inflammatory proteins, such as C-reactive protein (DNAm CRP), are associated with brain health and cognition in adults and with inflammatory comorbidities of preterm birth in neonates. Social disadvantage can become embedded in child development through inflammation, and deprivation is overrepresented in preterm infants. We tested the hypotheses that preterm birth and socioeconomic status (SES) are associated with alterations in a set of EpiScores enriched for inflammation-associated proteins.

Results

In total, 104 protein EpiScores were derived from saliva samples of 332 neonates born at gestational age (GA) 22.14 to 42.14 weeks. Saliva sampling was between 36.57 and 47.14 weeks. Forty-three (41%) EpiScores were associated with low GA at birth (standardised estimates |0.14 to 0.88|, Bonferroni-adjusted p-value < 8.3 × 10−3). These included EpiScores for chemokines, growth factors, proteins involved in neurogenesis and vascular development, cell membrane proteins and receptors, and other immune proteins. Three EpiScores were associated with SES, or the interaction between birth GA and SES: afamin, intercellular adhesion molecule 5, and hepatocyte growth factor-like protein (standardised estimates |0.06 to 0.13|, Bonferroni-adjusted p-value < 8.3 × 10−3). In a preterm subgroup (n = 217, median [range] GA 29.29 weeks [22.14 to 33.0 weeks]), SES–EpiScore associations did not remain statistically significant after adjustment for sepsis, bronchopulmonary dysplasia, necrotising enterocolitis, and histological chorioamnionitis.

Conclusions

Low birth GA is substantially associated with a set of EpiScores. The set was enriched for inflammatory proteins, providing new insights into immune dysregulation in preterm infants. SES had fewer associations with EpiScores; these tended to have small effect sizes and were not statistically significant after adjusting for inflammatory comorbidities. This suggests that inflammation is unlikely to be the primary axis through which SES becomes embedded in the development of preterm infants in the neonatal period.

Graphical abstract