bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cell-based therapies have disease-modifying effects on osteoarthritis in animal models. A systematic review by the ESSKA Orthobiologic Initiative. Part 1: adipose tissue-derived cell-based injectable therapies
Authors: Perucca Orfei Carlotta , Boffa Angelo , Sourugeon Yosef , Laver Lior , Magalon Jérémy , Sánchez Mikel , Tischer Thomas , Filardo Giuseppe , de Girolamo Laura 
Published: September 2022

Abstract
Purpose
The aim of this systematic review was to determine if adipose tissue-derived cell-based injectable therapies can induce disease-modifying effects in joints affected by osteoarthritis (OA).

Methods
A systematic review was performed on three electronic databases (PubMed, Web of Science, Embase) according to PRISMA guidelines. A synthesis of the results was performed investigating disease-modifying effects in preclinical studies comparing injectable adipose-derived products with OA controls or other products, different formulations or injection intervals, and the combination with other products. The risk of bias was assessed according to the SYRCLE’s tool.

Results
Seventy-one studies were included (2,086 animals) with an increasing publication trend over time. Expanded cells were used in 65 studies, 3 studies applied point of care products, and 3 studies investigated both approaches. Overall, 48 out of 51 studies (94%) reported better results with adipose-derived products compared to OA controls, with positive findings in 17 out of 20 studies (85%) in macroscopic, in 37 out of 40 studies (93%) in histological, and in 22 out of 23 studies (96%) in immunohistochemical evaluations. Clinical and biomarker evaluations showed positive results in 14 studies out of 18 (78%) and 12 studies out of 14 (86%), while only 9 studies out of 17 (53%) of the imaging evaluations were able to detect differences versus controls. The risk of bias was low in 38% of items, unclear in 51%, and high in (11%).

Conclusion
The current preclinical models document consistent evidence of disease-modifying effects of adipose-derived cell-based therapies for the treatment of OA. The high heterogeneity of the published studies highlights the need for further targeted research to provide recommendations on the optimal methodologies for a more effective application of these injective therapies for the treatment of OA in clinical practice.

Level of evidence
II.

Graphical Abstract
On convergence rates of adaptive ensemble Kalman inversion for linear ill-posed problems
Authors: Parzer Fabian , Scherzer Otmar 
Published: September 2022

AbstractIn this paper we discuss a deterministic form of ensemble Kalman inversion as a regularization method for linear inverse problems. By interpreting ensemble Kalman inversion as a low-rank approximation of Tikhonov regularization, we are able to introduce a new sampling scheme based on the Nyström method that improves practical performance. Furthermore, we formulate an adaptive version of ensemble Kalman inversion where the sample size is coupled with the regularization parameter. We prove that the proposed scheme yields an order optimal regularization method under standard assumptions if the discrepancy principle is used as a stopping criterion. The paper concludes with a numerical comparison of the discussed methods for an inverse problem of the Radon transform.
Comparison of extraction methods for active biomolecules using sub-critical dimethyl ether and n-butane
Authors: Ciulla Michele , Canale Valentino , Wolicki Rafal Damian , Ferrone Vincenzo , Carlucci Giuseppe , Fontana Antonella , Siani Gabriella , D’Alessandro Nicola , Di Profio Pietro 
Published: September 2022

AbstractSeveral extraction methods are used to isolate natural compounds, and recent approaches utilize subcritical or supercritical extraction media. In this paper we compare extraction methods based on subcritical eluents, dimethyl ether (sC-DME) and n-butane (sC-nB), under mild conditions, using coffee beans and powder as an exemplary raw material. The parameters to be controlled to improve the extraction are considered, and the resulting data discussed. The results obtained display higher selectivity of sC-DME for caffeine (1.9%w/w sC-DME vs. 1.7%w/w sC-nB, on dry extract) and a good yield (0.479 mg/g of caffeine from green coffee beans) compared to, e.g., supercritical carbon dioxide (SC-CO2), which shows 0.32 mg/g of caffeine at higher pressure and temperature (25 MPa, 40 °C). We also discuss some technical implementations for optimizing the use of sub-critical eluents through proper combinations of pressure and temperature. We show that extraction processes based on sub-critical eluents are easy to operate and efficient, and can be easily automated.
Analysis of General Shape Optimization Problems in Nonlinear Acoustics
Authors: Meliani Mostafa , Nikolić Vanja 
Published: September 2022

AbstractIn various biomedical applications, precise focusing of nonlinear ultrasonic waves is crucial for efficiency and safety of the involved procedures. This work analyzes a class of shape optimization problems constrained by general quasi-linear acoustic wave equations that arise in high-intensity focused ultrasound (HIFU) applications. Within our theoretical framework, the Westervelt and Kuznetsov equations of nonlinear acoustics are obtained as particular cases. The quadratic gradient nonlinearity, specific to the Kuznetsov equation, requires special attention throughout. To prove the existence of the Eulerian shape derivative, we successively study the local well-posedness and regularity of the forward problem, uniformly with respect to shape variations, and prove that it does not degenerate under the hypothesis of small initial and boundary data. Additionally, we prove Hölder-continuity of the acoustic potential with respect to domain deformations. We then derive and analyze the corresponding adjoint problems for several different cost functionals of practical interest and conclude with the expressions of well-defined shape derivatives.
Non-contrast-enhanced CT texture analysis of primary and metastatic pancreatic ductal adenocarcinomas: value in assessment of histopathological grade and differences between primary and metastatic lesions
Authors: Janisch Michael , Adelsmayr Gabriel , Müller Heimo , Holzinger Andreas , Janek Elmar , Talakic Emina , Fuchsjäger Michael , Schöllnast Helmut 
Published: September 2022

Abstract
Purpose
To evaluate the utility of non-contrast-enhanced CT texture analysis (CTTA) for predicting the histopathological differentiation of pancreatic ductal adenocarcinomas (PDAC) and to compare non-contrast-enhanced CTTA texture features between primary PDAC and hepatic metastases of PDAC.

Methods
This retrospective study included 120 patients with histopathologically confirmed PDAC. Sixty-five patients underwent CT-guided biopsy of primary PDAC, while 55 patients underwent CT-guided biopsy of hepatic PDAC metastasis. All lesions were segmented in non-contrast-enhanced CT scans for CTTA based on histogram analysis, co-occurrence matrix, and run-length matrix. Statistical analysis was conducted for 372 texture features using Mann–Whitney U test, Bonferroni–Holm correction, and receiver operating characteristic (ROC) analysis. A p value < 0.05 was considered statistically significant.

Results
Three features were identified that differed significantly between histopathological G2 and G3 primary tumors. Of these, “low gray-level zone emphasis” yielded the largest AUC (0.87 ± 0.04), reaching a sensitivity and specificity of 0.76 and 0.83, respectively, when a cut-off value of 0.482 was applied. Fifty-four features differed significantly between primary and hepatic metastatic PDAC.

Conclusion
Non-contrast-enhanced CTTA of PDAC identified differences in texture features between primary G2 and G3 tumors that could be used for non-invasive tumor assessment. Extensive differences between the features of primary and metastatic PDAC on CTTA suggest differences in tumor microenvironment.

Graphical Abstract
Fascial Dehiscence and Incisional Hernia Prediction Models: A Systematic Review and Meta‐analysis
Authors: Tansawet Amarit , Numthavaj Pawin , Techapongsatorn Thawin , Techapongsatorn Suphakarn , Attia John , McKay Gareth , Thakkinstian Ammarin 
Published: September 2022

AbstractBackgroundFascial dehiscence (FD) and incisional hernia (IH) pose considerable risks to patients who undergo abdominal surgery, and many preventive strategies have been applied to reduce this risk. An accurate predictive model could aid identification of high‐risk patients, who could be targeted for particular care. This study aims to systematically review existing FD and IH prediction models.MethodsPrediction models were identified using pre‐specified search terms on SCOPUS, PubMed, and Web of Science. Eligible studies included those conducted in adult patients who underwent any kind of abdominal surgery, and reported model performance. Data from the eligible studies were extracted, and the risk of bias (RoB) was assessed using the PROBAST tool. Pooling of C‐statistics was performed using a random‐effect meta‐analysis. [Registration: PROSPERO (CRD42021282463)].ResultsTwelve studies were eligible for review; five were FD prediction model studies. Most included studies had high RoB, especially in the analysis domain. The C‐statistics of the FD and IH prediction models ranged from 0.69 to 0.92, but most have yet to be externally validated. Pooled C‐statistics (95% CI) were 0.80 (0.74, 0.86) and 0.81 (0.75, 0.86) for the FD (external‐validation) and IH prediction model, respectively. Some predictive factors such as body mass index, smoking, emergency operation, and surgical site infection were associated with FD or IH occurrence and were included in multiple models.ConclusionsSeveral models have been developed as an aid for FD and IH prediction, mostly with modest performance and lacking independent validation. New models for specific patient groups may offer clinical utility.
Scenario-based verification of uncertain parametric MDPs
Authors: Badings Thom , Cubuktepe Murat , Jansen Nils , Junges Sebastian , Katoen Joost-Pieter , Topcu Ufuk 
Published: September 2022

AbstractWe consider parametric Markov decision processes (pMDPs) that are augmented with unknown probability distributions over parameter values. The problem is to compute the probability to satisfy a temporal logic specification with any concrete MDP that corresponds to a sample from these distributions. As solving this problem precisely is infeasible, we resort to sampling techniques that exploit the so-called scenario approach. Based on a finite number of samples of the parameters, the proposed method yields high-confidence bounds on the probability of satisfying the specification. The number of samples required to obtain a high confidence on these bounds is independent of the number of states and the number of random parameters. Experiments on a large set of benchmarks show that several thousand samples suffice to obtain tight and high-confidence lower and upper bounds on the satisfaction probability.
Effect of interaction network structure in a response threshold model
Authors: Shiraishi Masashi , Yamanaka Osamu , Nishimori Hiraku 
Published: September 2022

AbstractTask allocation is a characteristic feature of social insects. This phenomenon is self-organized by workers in a colony without central instructions. Workers perform the necessary tasks while communicating and determining their colony’s local situations or outside environments. They can also allocate the workload of the task to finish quickly. To explain the self-organized phenomenon, we develop a theoretical model that includes the local interaction mechanism among the workers. We extend the fixed response threshold model by introducing the interaction network among workers. The new model is called the interaction network response threshold model. This model analyzes the effects of the interaction network structure on the workload allocation mechanism using the Gini coefficient. We find that the network structure affects the information diffusion process, and the network features affect workload allocation. Our results indicate that communication among the workers in a colony is an essential system for workload allocation.
Global optimization using random embeddings
Authors: Cartis Coralia , Massart Estelle , Otemissov Adilet 
Published: September 2022

AbstractWe propose a random-subspace algorithmic framework for global optimization of Lipschitz-continuous objectives, and analyse its convergence using novel tools from conic integral geometry. X-REGO randomly projects, in a sequential or simultaneous manner, the high-dimensional original problem into low-dimensional subproblems that can then be solved with any global, or even local, optimization solver. We estimate the probability that the randomly-embedded subproblem shares (approximately) the same global optimum as the original problem. This success probability is then used to show almost sure convergence of X-REGO to an approximate global solution of the original problem, under weak assumptions on the problem (having a strictly feasible global solution) and on the solver (guaranteed to find an approximate global solution of the reduced problem with sufficiently high probability). In the particular case of unconstrained objectives with low effective dimension, we propose an X-REGO variant that explores random subspaces of increasing dimension until finding the effective dimension of the problem, leading to X-REGO globally converging after a finite number of embeddings, proportional to the effective dimension. We show numerically that this variant efficiently finds both the effective dimension and an approximate global minimizer of the original problem.
Transmembrane TNF-α as a Novel Biomarker for the Diagnosis of Cytokine Storms in a Mouse Model of Multiple Organ Failure
Authors: Yang Peng , Zeng Yimin , Yang Fang , Peng Xin , Hu Yongsheng , Tan Xuhong , Zhang Ruping 
Published: September 2022

Abstract
A cytokine storm (CS) is an out-of-control inflammatory response closely associated with the progression of diseases, such as multiple organ failure (MOF), severe sepsis, and severe or critical COVID-19. However, there is currently a lack of reliable diagnostic markers to distinguish CS from normal inflammatory responses. Tumor necrosis factor-α (TNF-α) includes transmembrane TNF-α (tmTNF-α) and secreted TNF-α (sTNF-α). The MOF mouse model in this study showed that the tmTNF-α expression changes in the neutrophils differed from the serum TNF-α and serum IL-18, INF-γ, IL-4, and IL-6. Furthermore, tmTNF-α, instead of serum TNF-α, IL-18, INF-γ, IL-4, and IL-6, reflected liver and kidney tissue damage and increased with the aggravation of these injuries. Analysis of the ROC results showed that tmTNF-α effectively distinguished between inflammatory responses and CS and efficiently differentiated between surviving and dead mice. It also significantly improved the diagnostic value of the traditional CRP marker for CS. These results indicated that the tmTNF-α expressed in the neutrophils could be used to diagnose CS in MOF mice, providing an experimental basis to further develop tmTNF-α for diagnosing CS patients.
Analysis of 2,5-dimethoxy-amphetamines and 2,5-dimethoxy-phenethylamines aiming their determination in biological matrices: a review
Authors: Nieddu Maria , Baralla Elena , Sodano Federica , Boatto Gianpiero 
Published: September 2022

Abstract
Purpose
The present review aims to provide an overview of methods for the quantification of 2,5-dimethoxy-amphetamines and -phenethylamines in different biological matrices, both traditional and alternative ones.

Methods
A complete literature search was carried out with PubMed, Scopus and the World Wide Web using relevant keywords, e.g., designer drugs, amphetamines, phenethylamines, and biological matrices.

Results
Synthetic phenethylamines represent one of the largest classes of “designer drugs”, obtained through chemical structure modifications of psychoactive substances to increase their pharmacological activities. This practice is also favored by the fact that every new synthetic compound is not considered illegal by existing legislation. Generally, in a toxicological laboratory, the first monitoring of drugs of abuse is made by rapid screening tests that sometimes can occur in false positive or false negative results. To reduce evaluation errors, it is mandatory to submit the positive samples to confirmatory methods, such as gas chromatography or liquid chromatography combined to mass spectrometry, for a more specific qualitative and quantitative analysis.

Conclusions
This review highlights the great need for updated comprehensive analytical methods, particularly when analyzing biological matrices, both traditional and alternative ones, for the search of newly emerging designer drugs.
Bifurcation in car-following models with time delays and driver and mechanic sensitivities
Authors: Padial Juan Francisco , Casal Alfonso 
Published: September 2022

AbstractIn this work, we study a model of traffic flow along a one-way, one lane, road or street, the so-called car-following problem. We first present a historical evolution of models of this type corresponding to a successive improvement of requirements, to explain some real traffic phenomena. For both mathematical reasons and a better explanation of some of those phenomena, we consider more convenient and accurate requirements which lead to a better non-linear model with reaction delays, from several sources. The model can be written as an ordinary nonlinear delay differential equation. It has equilibrium solutions, which correspond to steady traffic. The mentioned reaction delays introduce perturbation terms in the equation, leading to of instabilities of equilibria and changes of the structure of the solutions. For some of the values of the delays, they may become oscillatory. We make a number of simulations to show these changes for different values of delays. We also show that, for certain values of the delays the above mentioned change of structure (representing regimes of real traffic) corresponds to a Hopf bifurcation.
Graph neural networks and cross-protocol analysis for detecting malicious IP addresses
Authors: Huang Yonghong , Negrete Joanna , Wagener John , Fralick Celeste , Rodriguez Armando , Peterson Eric , Wosotowsky Adam 
Published: September 2022

AbstractAn internet protocol (IP) address is the foundation of the Internet, allowing connectivity between people, servers, Internet of Things, and services across the globe. Knowing what is connecting to what and where connections are initiated is crucial to accurately assess a company’s or individual’s security posture. IP reputation assessment can be quite complex because of the numerous services that may be hosted on that IP address. For example, an IP might be serving millions of websites from millions of different companies like web hosting companies often do, or it could be a large email system sending and receiving emails for millions of independent entities. The heterogeneous nature of an IP address typically makes it challenging to interpret the security risk. To make matters worse, adversaries understand this complexity and leverage the ambiguous nature of the IP reputation to exploit further unsuspecting Internet users or devices connected to the Internet. In addition, traditional techniques like dirty-listing cannot react quickly enough to changes in the security climate, nor can they scale large enough to detect new exploits that may be created and disappear in minutes. In this paper, we introduce the use of cross-protocol analysis and graph neural networks (GNNs) in semi-supervised learning to address the speed and scalability of assessing IP reputation. In the cross-protocol supervised approach, we combine features from the web, email, and domain name system (DNS) protocols to identify ones which are the most useful in discriminating suspicious and benign IPs. In our second experiment, we leverage the most discriminant features and incorporate them into the graph as nodes’ features. We use GNNs to pass messages from node to node, propagating the signal to the neighbors while also gaining the benefit of having the originating nodes being influenced by neighboring nodes. Thanks to the relational graph structure we can use only a small portion of labeled data and train the algorithm in a semi-supervised approach. Our dataset represents real-world data that is sparse and only contain a small percentage of IPs with verified clean or suspicious labels but are connected. The experimental results demonstrate that the system can achieve $$85.28\%$$

85.28
%

accuracy in detecting malicious IP addresses at scale with only $$5\%$$

5
%

of labeled data.
Revisiting the Role of Physical Confinement and Chemical Regulation of 3D Hosts for Dendrite-Free Li Metal Anode
Authors: Ye Shufen , Chen Xingjia , Zhang Rui , Jiang Yu , Huang Fanyang , Huang Huijuan , Yao Yu , Jiao Shuhong , Chen Xiang , Zhang Qiang , Yu Yan 
Published: September 2022

AbstractLithium metal anode has been demonstrated as the most promising anode for lithium batteries because of its high theoretical capacity, but infinite volume change and dendritic growth during Li electrodeposition have prevented its practical applications. Both physical morphology confinement and chemical adsorption/diffusion regulation are two crucial approaches to designing lithiophilic materials to alleviate dendrite of Li metal anode. However, their roles in suppressing dendrite growth for long-life Li anode are not fully understood yet. Herein, three different Ni-based nanosheet arrays (NiO-NS, Ni3N-NS, and Ni5P4-NS) on carbon cloth as proof-of-concept lithiophilic frameworks are proposed for Li metal anodes. The two-dimensional nanoarray is more promising to facilitate uniform Li+ flow and electric field. Compared with the NiO-NS and the Ni5P4-NS, the Ni3N-NS on carbon cloth after reacting with molten Li (Li-Ni/Li3N-NS@CC) can afford the strongest adsorption to Li+ and the most rapid Li+ diffusion path. Therefore, the Li-Ni/Li3N-NS@CC electrode realizes the lowest overpotential and the most excellent electrochemical performance (60 mA cm−2 and 60 mAh cm−2 for 1000 h). Furthermore, a remarkable full battery (LiFePO4||Li-Ni/Li3N-NS@CC) reaches 300 cycles at 2C. This research provides valuable insight into designing dendrite-free alkali metal batteries.
Inner Co Synergizing Outer Ru Supported on Carbon Nanotubes for Efficient pH-Universal Hydrogen Evolution Catalysis
Authors: Chen Jian , Ha Yuan , Wang Ruirui , Liu Yanxia , Xu Hongbin , Shang Bin , Wu Renbing , Pan Hongge 
Published: September 2022

AbstractExploring highly active but inexpensive electrocatalysts for the hydrogen evolution reaction (HER) is of critical importance for hydrogen production from electrochemical water splitting. Herein, we report a multicomponent catalyst with exceptional activity and durability for HER, in which cobalt nanoparticles were in-situ confined inside bamboo-like carbon nanotubes (CNTs) while ultralow ruthenium loading (~ 2.6 µg per electrode area ~ cm−2) is uniformly deposited on their exterior walls (Co@CNTsǀRu). The atomic-scale structural investigations and theoretical calculations indicate that the confined inner Co and loaded outer Ru would induce charge redistribution and a synergistic electron coupling, not only optimizing the adsorption energy of H intermediates (ΔGH*) but also facilitating the electron/mass transfer. The as-developed Co@CNTsǀRu composite catalyst requires overpotentials of only 10, 32, and 63 mV to afford a current density of 10 mA cm−2 in alkaline, acidic and neutral media, respectively, representing top-level catalytic activity among all reported HER catalysts. The current work may open a new insight into the rational design of carbon-supported metal catalysts for practical applications.