bluuebunny/crossref_metadata_2025 huggingface dataset:
Exosomes isolated from IMMUNEPOTENT CRP, a hemoderivative, to accelerate diabetic wound healing
Authors: García Coronado Paola Leonor , Franco Molina Moisés Armides , Zárate Triviño Diana Ginette , Hernández Martínez Sara Paola , Castro Valenzuela Beatriz Elena , Zapata Benavides Pablo , Rodríguez Padilla Cristina
Published: May 2024

The increasing risk of amputation due to diabetic foot ulcer calls for new therapeutic options; for that, we determined the role of IMMUNEPOTENT CRP (ICRP) and its parts in the wound healing process of superficial wounds in diabetic BALB/c mice. A potency test was performed to confirm the batch of ICRP, and then its parts were separated into pellets, supernatants, and exosomes, and another group of exosomes loaded with insulin was added. Viability and scratch healing were assessed in NIH-3T3, HUVEC, and HACAT cell lines. Diabetes was induced with streptozotocin, and wounds were made by dissecting the back skin. Treatments were topically applied, and closure was monitored; inflammatory cytokines in sera were also evaluated by flow cytometry, and histological analysis was performed by Masson’s staining and immunohistochemistry for p-AKT, p-FOXO, p-P21, and p-TSC2. ICRP pellets and exosomes increased cellular viability, and exosomes and exosome–insulin accelerated scratch healing in vitro. Exosome–insulin releases insulin constantly over time in vitro. In vivo, treatments accelerated wound closure, and better performance was observed in pellet, exosome, and exosome–insulin treatments. Best collagen expression was induced by ICRP. P-AKT and p-FOXO were overexpressed in healing tissues. Inflammatory cytokines were downregulated by all treatments. In conclusion, IMMUNEPOTENT CRP components, especially exosomes, and the process of encapsulation of exosome–insulin accelerate diabetic wound healing and enhance cellular proliferation, collagen production, and inflammation modulation through the phosphorylation of components of the AKT pathway.
A comparative study of biomechanical assessments in laboratory and field settings for manual material handling tasks using extractor tools and exoskeletons
Authors: Shakourisalim Maryam , Wang Xun , Beltran Martinez Karla , Golabchi Ali , Krell Sarah , Tavakoli Mahdi , Rouhani Hossein
Published: May 2024

To enhance physical capabilities of workers who regularly perform physically demanding tasks involving heavy lifting and awkward postures, various tools and occupational exoskeletons can be used. Most of the studies aiming to explore the efficiency of these tools and exoskeletons have been performed in confined and controlled laboratory spaces, which do not represent the real-world work environment. This study aimed to compare the outcome of biomechanical assessment of using a back support exoskeleton and assistive tools (Lever and Jake) in the procedure of a high demanding manual material handling task versus the results found by performing the same task in a laboratory. Ten able-bodied participants and ten able-bodied utility workers performed the same manhole removal task in-lab and in-field, respectively, with the aid of an exoskeleton and Lever and Jake tools. Muscle activity and Rapid Entire Body Assessment (REBA) scores were recorded using surface electromyography and inertial measurement units, respectively and compared between in-lab and in-field trials. The field experiments indicated significant differences (p < 0.05) in normalized muscle activity across most muscles when compared to laboratory data. These results revealed how muscle activity is affected by the controlled lab setting compared to real-world field conditions. However, REBA scores indicate similar ergonomic implications regardless of the utilization of exoskeletons or tools. These findings underscore that real-world field assessments are crucial for evaluating ergonomic risks and effects of occupational exoskeletons and tools to account for environmental factors and workers’ skills in ergonomic evaluations of this nature.
TMVR after TA-TAVR: a re-redo surgery—case report
Authors: Pommert Nina Sophie , Puehler Thomas , Voges Inga , Sellers Stephanie , Lutter Georg
Published: May 2024

Introduction

Transcatheter mitral valve replacement (TMVR) is a valuable treatment option in patients with severe mitral regurgitation. Prior transapical transcatheter aortic valve replacement (TA-TAVR) may complicate the procedure and is therefore considered a relative contraindication. In this case report, the authors describe the successful TMVR as a tertiary cardiac surgery and transapical redo procedure.

Case Summary

An 83-year-old male patient, suffering from dyspnoea and angina, was diagnosed with severe mitral valve regurgitation (MR). He had already undergone cardiac surgery in the form of coronary artery bypass grafting at the age of 64 and TA-TAVR at 79 years. After a failed attempt at mitral valve transcatheter edge-to-edge repair, he opted for TMVR. Pre-TMVR computed tomography simulation was used to analyse possible interactions between the prostheses and to predict the neo-left ventricular outflow tract (neo-LVOT). The operation was carried out without complications. There was no bleeding and the LV function remained unchanged. On MRI, the valves were perfectly aligned without any signs of paravalvular leakage or LVOT obstruction. The patient was discharged seven days postoperatively. At the one-year follow up, there was no need for rehospitalisation and the patient had clinically improved (from NYHA IV to II). Echocardiography demonstrated a mean transvalvular gradient of under 5 mmHg and no residual MR.

Conclusion

A redo transapical access for TMVR as a tertiary cardiac operation can be easily performed. Pre-operative CT suggested good alignment of the aortic and mitral valved stent which was confirmed postoperatively.
Anti-apolipoprotein A-1 IgG, incident cardiovascular events, and lipid paradox in rheumatoid arthritis
Authors: Mongin Denis , Pagano Sabrina , Lamacchia Celine , Juillard Catherine , Antinori-Malaspina Paola , Dan Diana , Ciurea Adrian , Möller Burkhard , Gabay Cem , Finckh Axel , Vuilleumier Nicolas
Published: May 2024

Objective

To validate the prognostic accuracy of anti-apolipoprotein A-1 (AAA1) IgG for incident major adverse cardiovascular (CV) events (MACE) in rheumatoid arthritis (RA) and study their associations with the lipid paradox at a multicentric scale.

Method

Baseline AAA1 IgG, lipid profile, atherogenic indexes, and cardiac biomarkers were measured on the serum of 1,472 patients with RA included in the prospective Swiss Clinical Quality Management registry with a median follow-up duration of 4.4 years. MACE was the primary endpoint defined as CV death, incident fatal or non-fatal stroke, or myocardial infarction (MI), while elective coronary revascularization (ECR) was the secondary endpoint. Discriminant accuracy and incidence rate ratios (IRR) were respectively assessed using C-statistics and Poisson regression models.

Results

During follow-up, 2.4% (35/1,472) of patients had a MACE, consisting of 6 CV deaths, 11 MIs, and 18 strokes; ECR occurred in 2.1% (31/1,472) of patients. C-statistics indicated that AAA1 had a significant discriminant accuracy for incident MACE [C-statistics: 0.60, 95% confidence interval (95% CI): 0.57–0.98, p = 0.03], mostly driven by CV deaths (C-statistics: 0.77; 95% CI: 0.57–0.98, p = 0.01). IRR indicated that each unit of AAA1 IgG increase was associated with a fivefold incident CV death rate, independent of models’ adjustments. At the predefined and validated cut-off, AAA1 displayed negative predictive values above 97% for MACE. AAA1 inversely correlated with total and HDL cholesterol.

Conclusions

AAA1 independently predicts CV deaths, and marginally MACE in RA. Further investigations are requested to ascertain whether AAA1 could enhance CV risk stratification by identifying patients with RA at low CV risk.
Big data and its impact on the 3Rs: a home cage monitoring oriented review
Authors: Fuochi Sara , Rigamonti Mara , O'Connor Eoin C. , De Girolamo Paolo , D'Angelo Livia
Published: May 2024

Undisturbed home cage recording of mouse activity and behavior has received increasing attention in recent years. In parallel, several technologies have been developed in a bid to automate data collection and interpretation. Thanks to these expanding technologies, massive datasets can be recorded and saved in the long term, providing a wealth of information concerning animal wellbeing, clinical status, baseline activity, and subsequent deviations in case of experimental interventions. Such large datasets can also serve as a long-term reservoir of scientific data that can be reanalyzed and repurposed upon need. In this review, we present how the impact of Big Data deriving from home cage monitoring (HCM) data acquisition, particularly through Digital Ventilated Cages (DVCs), can support the application of the 3Rs by enhancing Refinement, Reduction, and even Replacement of research in animals.
The cycle of violence: effects of violence experience, behavior, and attitudes on adolescents’ peer rejection networks
Authors: Favre Céline A. , Garrote A. , Kassis W. , Bacher J. , Wullschleger A. , Aksoy D.
Published: May 2024

Previous research on adolescent peer networks has mainly focused on friendship networks and their association with violence, but very limited research is available on peer rejection networks. This lack of knowledge hinders the effectiveness of preventing peer rejection and its negative effects. Based on the theory of the cycle of violence, the present study examines the extent to which parental physical abuse experiences, aggressive behavior toward peers and acceptance of violence are related to peer rejection networks at school. Social network analysis with a stochastic actor-oriented model of longitudinal data collected from high school students (Wave 1, n = 620; Wave 2, n = 590) confirms that adolescents who frequently use aggression toward their peers are more likely to be rejected, especially if those adolescents have experienced abuse. Similarly, peers are more likely to reject adolescents with high levels of accepting violence. The results also show that aggression toward peers generally tends to decrease over time but not for adolescents who reject a larger number of students. For students who reject many peers, aggression frequency increases. From the perspective of resilience theory, peer rejection, when combined with aggression toward peers and acceptance of violence, particularly in female adolescents, creates a significant risk factor for socio-emotional development. Therefore, tackling violence attitudes, experiences and behavior in the school environment and at home is crucial in overcoming the cycle of violence.
Lay advisor interventions for hypertension outcomes: A Systematic Review, Meta-analysis and a RE-AIM evaluation
Authors: Patil Sonal J. , Bhayani Vishwa , Yoshida Yilin , Bushweller Leila , Udoh Eno-Obong , Todorov Irina , Saper Robert , Stange Kurt C. , Bolen Shari
Published: May 2024

Introduction

Lay advisor interventions improve hypertension outcomes; however, the added benefits and relevant factors for their widespread implementation into health systems are unknown. We performed a systematic review to: (1) summarize the benefits of adding lay advisors to interventions on hypertension outcomes, and (2) summarize factors associated with successful implementation in health systems using the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework.

Methods

We systematically searched several databases, including Ovid MEDLINE, CINAHL, PsycINFO from January 1981 to May 2023. All study designs of interventions delivered solely by lay advisors for adults with hypertension were eligible. If both arms received the lay advisor intervention, the study arm with lower intensity was assigned as the low-intensity intervention.

Results

We included 41 articles, of which 22 were RCTs, from 7,267 screened citations. Studies predominantly included socially disadvantaged populations. Meta-analysis (9 RCTs; n = 4,220) of eligible lay advisor interventions reporting outcomes showed improved systolic blood pressure (BP) [−3.72 mm Hg (CI –6.1 to −1.3; I2 88%)], and diastolic BP [−1.7 mm Hg (CI −1 to −0.9; I2 7%)] compared to control group. Pooled effect from six RCTs (n = 3,277) comparing high-intensity with low-intensity lay advisor interventions showed improved systolic BP of −3.6 mm Hg (CI –6.7 to −0.5; I2 82.7%) and improved diastolic BP of −2.1 mm Hg (CI –3.7 to −0.4; I2 70.9%) with high-intensity interventions. No significant difference in pooled odds of hypertension control was noted between lay advisor intervention and control groups, or between high-intensity and low-intensity intervention groups. Most studies used multicomponent interventions with no stepped care elements or reporting of efficacious components. Indicators of external validity (adoption, implementation, maintenance) were infrequently reported.

Discussion

Lay advisor interventions improve hypertension outcomes, with high intensity interventions having a greater impact. Further studies need to identify successful intervention and implementation factors of multicomponent interventions for stepped upscaling within healthcare system settings as well as factors used to help sustain interventions.
GluN2A or GluN2B subunits of the NMDA receptor contribute to changes in neuronal excitability and impairments in LTP in the hippocampus of aging mice but do not mediate detrimental effects of oligomeric Aβ (1–42)
Authors: Südkamp Nicolina , Shchyglo Olena , Manahan-Vaughan Denise
Published: May 2024

Studies in rodent models have revealed that oligomeric beta-amyloid protein [Aβ (1–42)] plays an important role in the pathogenesis of Alzheimer’s disease. Early elevations in hippocampal neuronal excitability caused by Aβ (1–42) have been proposed to be mediated via enhanced activation of GluN2B-containing N-methyl-D-aspartate receptors (NMDAR). To what extent GluN2A or GluN2B-containing NMDAR contribute to Aβ (1–42)-mediated impairments of hippocampal function in advanced rodent age is unclear. Here, we assessed hippocampal long-term potentiation (LTP) and neuronal responses 4–5 weeks after bilateral intracerebral inoculation of 8–15 month old GluN2A+/− or GluN2B+/− transgenic mice with oligomeric Aβ (1–42), or control peptide. Whole-cell patch-clamp recordings in CA1 pyramidal neurons revealed a more positive resting membrane potential and increased total spike time in GluN2A+/−, but not GluN2B+/−-hippocampi following treatment with Aβ (1–42) compared to controls. Action potential 20%-width was increased, and the descending slope was reduced, in Aβ–treated GluN2A+/−, but not GluN2B+/− hippocampi. Sag ratio was increased in Aβ–treated GluN2B+/−-mice. Firing frequency was unchanged in wt, GluN2A+/−, and GluN2B+/−hippocampi after Aβ–treatment. Effects were not significantly different from responses detected under the same conditions in wt littermates, however. LTP that lasted for over 2 h in wt hippocampal slices was significantly reduced in GluN2A+/− and was impaired for 15 min in GluN2B+/−-hippocampi compared to wt littermates. Furthermore, LTP (>2 h) was significantly impaired in Aβ–treated hippocampi of wt littermates compared to wt treated with control peptide. LTP induced in Aβ–treated GluN2A+/− and GluN2B+/−-hippocampi was equivalent to LTP in control peptide-treated transgenic and Aβ–treated wt animals. Taken together, our data indicate that knockdown of GluN2A subunits subtly alters membrane properties of hippocampal neurons and reduces the magnitude of LTP. GluN2B knockdown reduces the early phase of LTP but leaves later phases intact. Aβ (1–42)-treatment slightly exacerbates changes in action potential properties in GluN2A+/−-mice. However, the vulnerability of the aging hippocampus to Aβ–mediated impairments of LTP is not mediated by GluN2A or GluN2B-containing NMDAR.
Prognostic stratification of glioblastoma patients by unsupervised clustering of morphology patterns on whole slide images furthering our disease understanding
Authors: Baheti Bhakti , Innani Shubham , Nasrallah MacLean , Bakas Spyridon
Published: May 2024

Introduction

Glioblastoma (GBM) is a highly aggressive malignant tumor of the central nervous system that displays varying molecular and morphological profiles, leading to challenging prognostic assessments. Stratifying GBM patients according to overall survival (OS) from H&E-stained whole slide images (WSI) using advanced computational methods is challenging, but with direct clinical implications.

Methods

This work is focusing on GBM (IDH-wildtype, CNS WHO Gr.4) cases, identified from the TCGA-GBM and TCGA-LGG collections after considering the 2021 WHO classification criteria. The proposed approach starts with patch extraction in each WSI, followed by comprehensive patch-level curation to discard artifactual content, i.e., glass reflections, pen markings, dust on the slide, and tissue tearing. Each patch is then computationally described as a feature vector defined by a pre-trained VGG16 convolutional neural network. Principal component analysis provides a feature representation of reduced dimensionality, further facilitating identification of distinct groups of morphology patterns, via unsupervised k-means clustering.

Results

The optimal number of clusters, according to cluster reproducibility and separability, is automatically determined based on the rand index and silhouette coefficient, respectively. Our proposed approach achieved prognostic stratification accuracy of 83.33% on a multi-institutional independent unseen hold-out test set with sensitivity and specificity of 83.33%.

Discussion

We hypothesize that the quantification of these clusters of morphology patterns, reflect the tumor's spatial heterogeneity and yield prognostic relevant information to distinguish between short and long survivors using a decision tree classifier. The interpretability analysis of the obtained results can contribute to furthering and quantifying our understanding of GBM and potentially improving our diagnostic and prognostic predictions.
MASMDDI: multi-layer adaptive soft-mask graph neural network for drug-drug interaction prediction
Authors: Lin Junpeng , Hong Binsheng , Cai Zhongqi , Lu Ping , Lin Kaibiao
Published: May 2024

Accurately predicting Drug-Drug Interaction (DDI) is a critical and challenging aspect of the drug discovery process, particularly in preventing adverse reactions in patients undergoing combination therapy. However, current DDI prediction methods often overlook the interaction information between chemical substructures of drugs, focusing solely on the interaction information between drugs and failing to capture sufficient chemical substructure details. To address this limitation, we introduce a novel DDI prediction method: Multi-layer Adaptive Soft Mask Graph Neural Network (MASMDDI). Specifically, we first design a multi-layer adaptive soft mask graph neural network to extract substructures from molecular graphs. Second, we employ an attention mechanism to mine substructure feature information and update latent features. In this process, to optimize the final feature representation, we decompose drug-drug interactions into pairwise interaction correlations between the core substructures of each drug. Third, we use these features to predict the interaction probabilities of DDI tuples and evaluate the model using real-world datasets. Experimental results demonstrate that the proposed model outperforms state-of-the-art methods in DDI prediction. Furthermore, MASMDDI exhibits excellent performance in predicting DDIs of unknown drugs in two tasks that are more aligned with real-world scenarios. In particular, in the transductive scenario using the DrugBank dataset, the ACC and AUROC and AUPRC scores of MASMDDI are 0.9596, 0.9903, and 0.9894, which are 2% higher than the best performing baseline.
Enhanced amphiregulin exposure promotes modulation of the high grade serous ovarian cancer tumor immune microenvironment
Authors: Ebott Jasmine , McAdams Julia , Kim Chloe , Jansen Corrine , Woodman Morgan , De La Cruz Payton , Schrol Christoph , Ribeiro Jennifer , James Nicole
Published: May 2024

High grade serous ovarian cancer (HGSOC) is a lethal gynecologic malignancy in which chemoresistant recurrence rates remain high. Furthermore, HGSOC patients have demonstrated overall low response rates to clinically available immunotherapies. Amphiregulin (AREG), a low affinity epidermal growth factor receptor ligand is known to be significantly upregulated in HGSOC patient tumors following neoadjuvant chemotherapy exposure. While much is known about AREG’s role in oncogenesis and classical immunity, it is function in tumor immunology has been comparatively understudied. Therefore, the objective of this present study was to elucidate how increased AREG exposure impacts the ovarian tumor immune microenvironment (OTIME). Using NanoString IO 360 and protein analysis, it was revealed that treatment with recombinant AREG led to prominent upregulation of genes associated with ovarian pathogenesis and immune evasion (CXCL8, CXCL1, CXCL2) along with increased STAT3 activation in HGSOC cells. In vitro co-culture assays consisting of HGSOC cells and peripheral blood mononuclear cells (PBMCs) stimulated with recombinant AREG (rAREG) led to significantly enhanced tumor cell viability. Moreover, PBMCs stimulated with rAREG exhibited significantly lower levels of IFNy and IL-2. In vivo rAREG treatment promoted significant reductions in circulating levels of IL-2 and IL-5. Intratumoral analysis of rAREG treated mice revealed a significant reduction in CD8+ T cells coupled with an upregulation of PD-L1. Finally, combinatorial treatment with an AREG neutralizing antibody and carboplatin led to a synergistic reduction of cell viability in HGSOC cell lines OVCAR8 and PEA2. Overall, this study demonstrates AREG’s ability to modulate cytotoxic responses within the OTIME and highlights its role as a novel HGSOC immune target.
Endolysins: a new antimicrobial agent against antimicrobial resistance. Strategies and opportunities in overcoming the challenges of endolysins against Gram-negative bacteria
Authors: Khan Fazal Mehmood , Rasheed Fazal , Yang Yunlan , Liu Bin , Zhang Rui
Published: May 2024

Antibiotic-resistant bacteria are rapidly emerging, and the increasing prevalence of multidrug-resistant (MDR) Acinetobacter baumannii poses a severe threat to humans and healthcare organizations, due to the lack of innovative antibacterial drugs. Endolysins, which are peptidoglycan hydrolases encoded by a bacteriophage, are a promising new family of antimicrobials. Endolysins have been demonstrated as an effective therapeutic agent against bacterial infections of A. baumannii and many other Gram-positive and Gram-negative bacteria. Endolysin research has progressed from basic in vitro characterization to sophisticated protein engineering methodologies, including advanced preclinical and clinical testing. Endolysin are therapeutic agent that shows antimicrobial properties against bacterial infections caused by drug-resistant Gram-negative bacteria, there are still barriers to their implementation in clinical settings, such as safety concerns with outer membrane permeabilizers (OMP) use, low efficiency against stationary phase bacteria, and stability issues. The application of protein engineering and formulation techniques to improve enzyme stability, as well as combination therapy with other types of antibacterial drugs to optimize their medicinal value, have been reviewed as well. In this review, we summarize the clinical development of endolysin and its challenges and approaches for bringing endolysin therapies to the clinic. This review also discusses the different applications of endolysins.
How housing burden damages residents’ health: evidence from Chinese cities
Authors: Guo Xiaoxin , Zhong Shihu , Li Lin , Luo Manyi
Published: May 2024

Background

Currently, China is steadily pursuing high-quality development and promoting common prosperity, for which residents’ health is a precondition. However, high housing-price-to-income ratios and rent-to-income ratios have already triggered many social problems and have substantially affected people’s work and life. It is of practical significance to examine the relationship between housing burden and residents’ health.

Methods

Combining city-level housing price-to-income ratio data and residents’ health data from the China Family Panel Studies, this study employs a binary logit model to investigate the impact and mechanism of housing burden on residents’ physical and psychological health.

Results

Overall, a 1% increase in the housing-price-to-income ratio leads to a 1.2% decrease in physical health and a 1.9% decrease in psychological health. In terms of different psychological state indicators, a 1% increase in the housing price-to-income ratio leads to a 1.1% increase in depression, 1.1% increase in nervousness, 1.4% increase in relentlessness, 1.4% increase in hopelessness, 1.0% increase in a sense of incapability, and 1.4% increase in meaninglessness. According to mechanistic analyses, a 1% increase in the housing-price-to-income ratio leads to increases of 0.6 and 0.7% in the smoking rate and late sleep rate, respectively, while it leads to a 0.9% decrease in the noon nap rate.

Conclusion

A growing housing burden significantly negatively impacts both the physical and psychological health of residents and increases the possibility of negative emotions. Further investigation revealed that the housing burden damages residents’ health by increasing their likelihood of smoking and sleeping late and decreasing their likelihood of taking a nap at noon, while exercise alleviates the negative impacts of the housing burden on residents’ physical and psychological health. Finally, we also find that housing burdens’ impacts on physical and psychological health differ significantly in terms of gender, age, and educational attainment. From the perspective of improving livelihoods, governments should consider the relationship between housing burdens and residents’ health when formulating livelihood policies. Location-specific and targeted policies should be followed. Additionally, efforts should be made to promote exercise among citizens.
Unguarded liabilities: Borrelia burgdorferi’s complex amino acid dependence exposes unique avenues of inhibition
Authors: Holly Katrina J. , Kataria Arti , Flaherty Daniel P. , Groshong Ashley M.
Published: May 2024

Recent reports from the Centers for Disease Control and Prevention approximate 500,000 cases of Lyme disease in the United States yearly, a significant economic burden on the healthcare system. The standard treatment for Lyme disease includes broad-spectrum antibiotics, which may be administered for extensive periods of time and result in significant impacts to the patient. Recently, we demonstrated that Borrelia burgdorferi, the causative agent of Lyme disease, is uniquely dependent upon peptide acquisition via an oligopeptide transport (Opp) system. This dependence appears unique to the spirochete; thus, the Opp system may constitute a novel and specific target for the inhibition of B. burgdorferi. For proof of concept, we conducted a pilot screen to determine if the Opp system constitutes a viable inhibitor target. OppA2 was utilized as our target protein as it is the most prolific peptide-binding protein throughout the enzootic cycle. We validated a thermal shift assay (TSA) to detect ligand binding against OppA2 and performed a high-throughput screen of 2,240 molecules from a diversity set library. The TSA results identified eight compounds (C1–8) demonstrating potential binding to OppA2, and growth assays identified C2 and C7 as inhibitors of B. burgdorferi growth. We confirmed by TSA that these two compounds interact with additional B. burgdorferi OppAs, potentially resulting in a cumulative inhibitory effect. Additionally, we showed that these compounds have no effect on Escherichia coli, a bacterium that encodes a dispensable Opp system which serves only as an ancillary nutrient transporter. These data demonstrate that the Opp system of B. burgdorferi acts as a viable drug target, with the potential for targeting multiple OppAs with a single compound. Moreover, the lack of inhibition against E. coli suggests that selective targeting of B. burgdorferi via the Opp system may be possible.
High Blood Pressure and Its Associated Factors Among Aksum University Students, Northern Ethiopia, 2019: A Cross-Sectional Study
Authors: Gebremeskel Gebreamlak Gebremedhn , Haile Teklehaimanot Gereziher , Gebrewahd Gebremeskel Tukue , Tadesse Degena Bahrey
Published: May 2024

Objectives:

This study aimed to assess the burden of high blood pressure and its associated factors among students at Aksum University.

Methods:

A total of 240 participants were included; participants were selected through simple random sampling from May 2019 to July 2019. Logistic regression analysis was performed, with statistical significance set at a p-value <0.05 and a 95% confidence level.

Results:

This study found that 17.9% of the participants had high blood pressure, with higher rates observed in males (62.79%) than in females (37.21%). Several factors were identified as associated with high blood pressure, including a family history of high blood pressure [AOR 1.72, 95% CI (1. 75–4.04)], regular physical exercise [AOR 0.64, 95% CI (0.30–0.94)], alcohol consumption [AOR 2.16, 95% CI (1.07–4.62)], tobacco smoking [AOR 5.46, 95% CI (1.98–15.07)], and central obesity [AOR 2.72, 95% CI (1.12–6.58)].

Conclusion:

This study reveals that one out of six students had high blood pressure. Factors such as a family history of high blood pressure, physical inactivity, tobacco smoking, and central obesity were associated with this condition.
The Knapsack Problem with Conflict Pair Constraints on Bipartite Graphs and Extensions
Authors: Punnen Abraham P. , Dhahan Jasdeep
Published: May 2024

In this paper, we study the knapsack problem with conflict pair constraints. After a thorough literature survey on the topic, our study focuses on the special case of bipartite conflict graphs. For complete bipartite (multipartite) conflict graphs, the problem is shown to be NP-hard but solvable in pseudo-polynomial time, and it admits an FPTAS. Extensions of these results to more general classes of graphs are also presented. Further, a class of integer programming models for the general knapsack problem with conflict pair constraints is presented, which generalizes and unifies the existing formulations. The strength of the LP relaxations of these formulations is analyzed, and we discuss different ways to tighten them. Experimental comparisons of these models are also presented to assess their relative strengths. This analysis disclosed various strong and weak points of different formulations of the problem and their relationships to different types of problem data. This information can be used in designing special purpose algorithms for KPCC involving a learning component.
An Integer-Fractional Gradient Algorithm for Back Propagation Neural Networks
Authors: Zhang Yiqun , Xu Honglei , Li Yang , Lin Gang , Zhang Liyuan , Tao Chaoyang , Wu Yonghong
Published: May 2024

This paper proposes a new optimization algorithm for backpropagation (BP) neural networks by fusing integer-order differentiation and fractional-order differentiation, while fractional-order differentiation has significant advantages in describing complex phenomena with long-term memory effects and nonlocality, its application in neural networks is often limited by a lack of physical interpretability and inconsistencies with traditional models. To address these challenges, we propose a mixed integer-fractional (MIF) gradient descent algorithm for the training of neural networks. Furthermore, a detailed convergence analysis of the proposed algorithm is provided. Finally, numerical experiments illustrate that the new gradient descent algorithm not only speeds up the convergence of the BP neural networks but also increases their classification accuracy.
Spectroscopy in Characterization of Materials—Developments
Authors: Krishnapuram Pavani , Jakka Suresh Kumar
Published: May 2024

The intention of the Special Issue “Advances in Spectroscopy for Materials: Bridging Science and Engineering” is to include various enthusiastic works that focus on the use of various analytical spectroscopic techniques while characterizing materials [...]
Theoretical and Experimental Analysis of the Effect of Vaporization Heat on the Interaction between Laser and Biological Tissue
Authors: Cheng Yuru , Shen Yu , Gao Yuxia , Wen Ya , Lv Ze , Wang Erpeng , Wang Mingli , Zhang Shenjin , Bo Yong , Peng Qinjun
Published: May 2024

A theoretical model, based on the classical Pennes’ bioheat theory, incorporating various boundary conditions, was established and compared to analyze the influence of the latent heat of vaporization via simulation. The aim was to elucidate the extent of its influence. The thermal damage rate, governed by the vaporization heat of biological tissue, is introduced as a key factor. Functional relationships between temperature and incident laser power, spatial position, and time are derived from the classical Pennes’ bioheat equation. According to the theoretical model, numerical simulations and experimental validations are conducted using Comsol Multiphysics 6.0, considering the tissue latent heat of vaporization. The model incorporating the latent heat of vaporization proved more suitable for analyzing the interactions between laser and biological tissue, evident from the degree of fit between simulated and experimental data. The minimum deviations between theoretical and experimental observations were determined to be 2.43% and 5.11% in temperature and thermal damage, respectively. Furthermore, this model can be extended to facilitate the theoretical analysis of the impact of vaporization heat from different primary tissue components on laser-tissue interaction.
Research on CC-SSBLS Model-Based Air Quality Index Prediction
Authors: Wang Lin , Wang Yibing , Chen Jian , Zhang Shuangqing , Zhang Lanhong
Published: May 2024

Establishing reliable and effective prediction models is a major research priority for air quality parameter monitoring and prediction and is utilized extensively in numerous fields. The sample dataset of air quality metrics often established has missing data and outliers because of certain uncontrollable causes. A broad learning system based on a semi-supervised mechanism is built to address some of the dataset’s data-missing issues, hence reducing the air quality model prediction error. Several air parameter sample datasets in the experiment were discovered to have outlier issues, and the anomalous data directly impact the prediction model’s stability and accuracy. Furthermore, the correlation entropy criteria perform better when handling the sample data’s outliers. Therefore, the prediction model in this paper consists of a semi-supervised broad learning system based on the correlation entropy criterion (CC-SSBLS). This technique effectively solves the issue of unstable and inaccurate prediction results due to anomalies in the data by substituting the correlation entropy criterion for the mean square error criterion in the BLS algorithm. Experiments on the CC-SSBLS algorithm and comparative studies with models like Random Forest (RF), Support Vector Regression (V-SVR), BLS, SSBLS, and Categorical and Regression Tree-based Broad Learning System (CART-BLS) were conducted using sample datasets of air parameters in various regions. In this paper, the root mean square error (RMSE) and mean absolute percentage error (MAPE) are used to judge the advantages and disadvantages of the proposed model. Through the experimental analysis, RMSE and MAPE reached 8.68 μg·m−3 and 0.24% in the Nanjing dataset. It is possible to conclude that the CC-SSBLS algorithm has superior stability and prediction accuracy based on the experimental results.
Analysis of the Multi-Dimensional Characteristics of City Weather Forecast Page Views and the Spatiotemporal Characteristics of Meteorological Disaster Warnings in China
Authors: Zhang Fang , Ding Jin , Chen Yu , Yu Tingzhao , Zhang Xinxin , Guo Jie , Liu Xiaodan , Wang Yan , Liu Qingyang , Song Yingying
Published: May 2024

In order to provide insights into how various page views are influenced by public engagement with weather information and to shed light on the patterns of warning issuance across different seasons and regions, this study analyzes the multi-dimensional characteristics of city weather forecast page views and the spatiotemporal characteristics of early warning information in China, from 1 March 2020 to 31 August 2023. This is achieved by utilizing the daily page views of city weather forecasts and meteorological warning data, comparing the public’s attention to weather during holidays versus regular days, assessing the public’s attention to weather under different meteorological warning levels, and performing statistical analysis of the spatiotemporal scale of meteorological disasters. Our analysis shows that compared to weekends and holidays, the public pays more attention to the weather on weekdays, and the difference between weekdays and national statutory holidays is more significant. Due to the widespread impact of heat waves, typhoons, severe convective weather, and geological disasters caused by heavy rainfall, public awareness and participation in flood season weather forecasting have significantly increased. Under red alerts, flash floods, typhoons, and geological risks are the primary concerns. Orange alerts predominantly feature flash floods, rainstorms, typhoons, snowstorms, and cold waves, while sandstorms attract the most attention during yellow alerts. Droughts, however, receive relatively less attention regardless of the warning level. Seasonal patterns in the issuance of meteorological warnings reveal a peak in summer, particularly with typhoons and rainstorms being the main concerns in July, followed by high temperatures and additional typhoon warnings in August. Heavy sea surface wind warnings exhibit a strong seasonal trend, with the majority issued during the winter months. Regionally, southern China experiences the highest frequency of severe convection weather warnings, with provinces such as Jiangxi, Guangxi, and Hunan being the most affected.
Using HawkEye Level-2 Satellite Data for Remote Sensing Tasks in the Presence of Dust Aerosol
Authors: Papkova Anna , Kalinskaya Darya , Shybanov Evgeny
Published: May 2024

This paper is the first to examine the operation of the HawkEye satellite in the presence of dust aerosol. The study region is the Black Sea. Dust transport dates were identified using visual inspection of satellite imagery, back-kinematic HYSPLIT trajectory analysis, CALIPSO aerosol stratification and typing maps, and the global forecasting model SILAM. In a comparative analysis of in-situ and satellite measurements of the remote sensing reflectance, an error in the atmospheric correction of HawkEye measurements was found both for a clean atmosphere and in the presence of an absorbing aerosol. It is shown that, on average, the dependence of the atmospheric correction error on wavelength has the form of a power function of the form from λ−3 to λ−9. The largest errors are in the short-wavelength region of the spectrum (412–443 nm) for the dust and dusty marine aerosol domination dates. A comparative analysis of satellite and in situ measurements of the optical characteristics of the atmosphere, namely the AOD and the Ångström parameter, was carried out. It is shown that the aerosol model used by HawkEye underestimates the Angström parameter and, most likely, large errors and outliers in satellite measurements are associated with this.
Air Pollution, Health Effects Indicators, the Exposome, and One Health
Authors: Contini Daniele , Costabile Francesca
Published: May 2024

Ambient air pollution is the seventh highest risk factor for human health, being responsible for millions of premature deaths per year globally [...]
Quantitative Analysis of Stress–Stretch Curves in Canine Lumbar Vertebrae Using Modified Logistic Functions
Authors: Kostenko Ernest , Stonkus Rimantas , Šengaut Jakov , Višniakov Nikolaj , Maknickas Algirdas
Published: May 2024

Background: The mechanical characteristics of bone are crucial for comprehending its functionality and response to different load conditions, which are essential for advancing medical treatments, implants, and prosthetics. By employing mathematical modeling to analyze the mechanical properties of bone, we can assess stress and deformation under both normal and abnormal conditions. This analysis offers valuable perspectives on potential fracture risks, the effects of diseases, and the effectiveness of various treatments. Therefore, researchers are attempting to find an adequate mathematical description of the mechanical properties of bone. Methods: Experimental stress–stretch external loading curves were obtained through investigations of canine vertebrae. The obtained experimental curves were fitted using the SciPy Python library with a slightly modified logistic function (logistic function plus additional const). Results: The resulting coefficient of determination R2 (R squared) for most curves was near 0.999, indicating that an appropriate fitting function was selected for the description of the experimental stress–stretch curves. Conclusions: The stress–stretch behavior of canine vertebrae can be described using a logistic function modified by adding additional parameters for the most accurate fitting results.
Comparative Biomechanical Stability of the Fixation of Different Miniplates in Restorative Laminoplasty after Laminectomy: A Finite Element Study
Authors: Liu Guoyin , Huang Weiqian , Leng Nannan , He Peng , Li Xin , Lin Muliang , Lian Zhonghua , Wang Yong , Chen Jianmin , Cai Weihua
Published: May 2024

A novel H-shaped miniplate (HSM) was specifically designed for restorative laminoplasties to restore patients’ posterior elements after laminectomies. A validated finite element (FE) model of L2/4 was utilized to create a laminectomy model, as well as three restorative laminoplasty models based on the fixation of different miniplates after a laminectomy (the RL-HSM model, the RL-LSM model, and the RL-THM model). The biomechanical effects of motion and displacement on a laminectomy and restorative laminoplasty with three different shapes for the fixation of miniplates were compared under the same mechanical conditions. This study aimed to validate the biomechanical stability, efficacy, and feasibility of a restorative laminoplasty with the fixation of miniplates post laminectomy. The laminectomy model demonstrated the greatest increase in motion and displacement, especially in axial rotation, followed by extension, flexion, and lateral bending. The restorative laminoplasty was exceptional in preserving the motion and displacement of surgical segments when compared to the intact state. This preservation was particularly evident in lateral bending and flexion/extension, with a slight maintenance efficacy observed in axial rotation. Compared to the laminectomy model, the restorative laminoplasties with the investigated miniplates demonstrated a motion-limiting effect for all directions and resulted in excellent stability levels under axial rotation and flexion/extension. The greatest reduction in motion and displacement was observed in the RL-HSM model, followed by the RL-LSM model and then the RL-THM model. When comparing the fixation of different miniplates in restorative laminoplasties, the HSMs were found to be superior to the LSMs and THMs in maintaining postoperative stability, particularly in axial rotation. The evidence suggests that a restorative laminoplasty with the fixation of miniplates is more effective than a conventional laminectomy due to the biomechanical effects of restoring posterior elements, which helps patients regain motion and limit load displacement responses in the spine after surgery, especially in axial rotation and flexion/extension. Additionally, our evaluation in this research study could benefit from further research and provide a methodological and modeling basis for the design and optimization of restorative laminoplasties.
Survival Prediction of Patients after Heart Attack and Breast Cancer Surgery with a Hybrid Model Built with Particle Swarm Optimization, Stacked AutoEncoders, and the Softmax Classifier
Authors: Bülbül Mehmet Akif , Işık Mehmet Fatih
Published: May 2024

The prediction of patient survival is crucial for guiding the treatment process in healthcare. Healthcare professionals rely on analyzing patients’ clinical characteristics and findings to determine treatment plans, making accurate predictions essential for efficient resource utilization and optimal patient support during recovery. In this study, a hybrid architecture combining Stacked AutoEncoders, Particle Swarm Optimization, and the Softmax Classifier was developed for predicting patient survival. The architecture was evaluated using the Haberman’s Survival dataset and the Echocardiogram dataset from UCI. The results were compared with several Machine Learning methods, including Decision Trees, K-Nearest Neighbors, Support Vector Machines, Neural Networks, Gradient Boosting, and Gradient Bagging applied to the same datasets. The findings indicate that the proposed architecture outperforms other Machine Learning methods in predicting patient survival for both datasets and surpasses the results reported in the literature for the Haberman’s Survival dataset. In the light of the findings obtained, the models obtained with the proposed architecture can be used as a decision support system in determining patient care and applied methods.
A Perspective on the 6th International Conference on Sports Concussion
Authors: Nakayama Haruo , Hiramoto Yu , Iwabuchi Satoshi
Published: May 2024

The International Conference on Sports Concussion, held every four years since 2001, has been instrumental in forming the international consensus on sports-related concussions. However, due to the unprecedented global pandemic of COVID-19, not only the Tokyo Olympics 2020, but also the initially scheduled sixth conference was postponed multiple times. Finally, the 6th International Conference on Sports Concussion took place in Amsterdam at the end of October 2022. In July 2023, the Amsterdam Declaration, reflecting the outcomes of this sixth conference, was released. This paper provides an overview of the conference, in which significant updates were revealed and introduced, including revisions to the definition of sports-related concussions, as well as the latest version of the Sports Concussion Assessment Tool (SCAT), known as SCAT6, the Office Assessment Tool (SCOAT), and the updated staged return-to-play protocol.