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Cold atmospheric plasma (CAP) devices generate reactive oxygen and nitrogen species, have antimicrobial and antiviral properties, but also affect the molecular and cellular mechanisms of eukaryotic cells. The aim of this study is to investigate CAP treatment in the upper respiratory tract (URT) to reduce the incidence of ventilator-associated bacterial pneumonia (especially superinfections with multi-resistant pathogens) or viral infections (e.g., COVID-19). For this purpose, the surface-microdischarge-based plasma intensive care (PIC) device was developed by terraplasma medical GmbH. This study analyzes the safety aspects using in vitro assays and molecular characterization of human oral keratinocytes (hOK), human bronchial-tracheal epithelial cells (hBTE), and human lung fibroblasts (hLF). A 5 min CAP treatment with the PIC device at the "throat" and "subglottis" positions in the URT model did not show any significant differences from the untreated control (ctrl.) and the corresponding pressurized air (PA) treatment in terms of cell morphology, viability, apoptosis, DNA damage, and migration. However, pro-inflammatory cytokines (MCP-1, IL-6, and TNFα) were induced in hBTE and hOK cells and profibrotic molecules (collagen-I, FKBP10, and αSMA) in hLF at the mRNA level. The use of CAP in the oropharynx may make an important contribution to the recovery of intensive care patients. The results indicate that a 5 min CAP treatment in the URT with the PIC device does not cause any cell damage. The extent to which immune cell activation is induced and whether it has long-term effects on the organism need to be carefully examined in follow-up studies in vivo.

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We report a prospective consecutive series of CarpoFit® (Implantcast, Buxtehude, Germany) trapeziometacarpal prostheses used between 2006 and 2014 for 292 patients with stage I-III trapeziometacarpal arthritis who remained symptomatic after conservative treatment. Patients were assessed at 3 months, 6 months and 1 year postoperatively for thumb movement, pinch strength and by validated patient-derived outcome scores and radiographs. Follow-up at 5 and 10 years was by outcome scores alone. Complications and revisions were recorded. Of the patients, 91% were either satisfied or very satisfied with their treatment at 10 years. Pain relief and functional outcomes were significantly improved within 3 months. Results were sustained during the entire observation period. The implant survival was 95% after 10 years. Prosthetic arthroplasty is a reliable treatment option for trapeziometacarpal osteoarthritis and the CarpoFit® prosthesis has excellent long-term results for patient satisfaction, functional scores and implant survivorship.Level of evidence: III.

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In synaptic molecular communication (MC), the activation of postsynaptic receptors by neurotransmitter (NTs) is governed by a stochastic reaction-diffusion process. This randomness of synaptic MC contributes to the randomness of the electrochemical downstream signal in the postsynaptic cell, called postsynaptic membrane potential (PSP). Since the randomness of the PSP is relevant for neural computation and learning, characterizing the statistics of the PSP is critical. However, the statistical characterization of the synaptic reaction-diffusion process is difficult because the reversible bi-molecular reaction of NTs with receptors renders the system nonlinear. Consequently, there is currently no model available which characterizes the impact of the statistics of postsynaptic receptor activation on the PSP. In this work, we propose a novel statistical model for the synaptic reaction-diffusion process in terms of the chemical master equation (CME). We further propose a novel numerical method which allows to compute the CME efficiently and we use this method to characterize the statistics of the PSP. Finally, we present results from stochastic particle-based computer simulations which validate the proposed models. We show that the biophysical parameters governing synaptic transmission shape the autocovariance of the receptor activation and, ultimately, the statistics of the PSP. Our results suggest that the processing of the synaptic signal by the postsynaptic cell effectively mitigates synaptic noise while the statistical characteristics of the synaptic signal are preserved. The results presented in this paper contribute to a better understanding of the impact of the randomness of synaptic signal transmission on neuronal information processing.

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Synaptic communication is based on a biological Molecular Communication (MC) system which may serve as a blueprint for the design of synthetic MC systems. However, the physical modeling of synaptic MC is complicated by the possible saturation of the molecular receiver caused by the competition of neurotransmitters (NTs) for postsynaptic receptors. Receiver saturation renders the system behavior nonlinear in the number of released NTs and is commonly neglected in existing analytical models. Furthermore, due to the ligands' competition for receptors (and vice versa), the individual binding events at the molecular receiver are in general not statistically independent and the commonly used binomial model for the statistics of the received signal does not apply. Hence, in this work, we propose a novel deterministic model for receptor saturation in terms of a state-space description based on an eigenfunction expansion of Fick's diffusion equation. The presented solution is numerically stable and computationally efficient. Employing the proposed deterministic model, we show that saturation at the molecular receiver effectively reduces the peak-value of the expected received signal and accelerates the clearance of NTs as compared to the case when receptor occupancy is neglected. We further derive a statistical model for the received signal in terms of the hypergeometric distribution which accounts for the competition of NTs for receptors and the competition of receptors for NTs. The proposed statistical model reveals how the signal statistics are shaped by the number of released NTs, the number of receptors, and the binding kinetics of the receptors, respectively, in the presence of competition. In particular, we show that the impact of these parameters on the signal variance is qualitatively different depending on the relative numbers of NTs and receptors. Finally, the accuracy of the proposed deterministic and statistical models is verified by particle-based computer simulations.

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The possibility to guide and control magnetic nanoparticles in a non-invasive manner has spawned various applications in biotechnology, such as targeted drug delivery and sensing of biological substances. These applications are facilitated by the engineering of the size, selective chemical reactivity, and general chemical composition of the employed particles. Motivated by their widespread use and favorable properties, in this paper, we provide a theoretical study of the potential benefits of magnetic nanoparticles for the design of molecular communication systems. In particular, we consider a magnetic nanoparticle-based communication in a microfluidic channel where an external magnetic field is employed to attract the information-carrying particles to the receiver. We show that the particle transport affected by the Brownian motion, fluid flow, and an external magnetic field can be mathematically modeled as diffusion with drift. Thereby, we reveal that the key parameters determining the magnetic force are the particle size and the magnetic field gradient. Moreover, we derive an analytical expression for the channel impulse response, which is used to evaluate the potential gain in the expected number of observed nanoparticles due to the magnetic field. Furthermore, adopting the symbol error rate as performance metric, we show that using magnetic nanoparticles can enable a reliable communication in the presence of disruptive fluid flow. The numerical results obtained by the particle-based simulation validate the accuracy of the derived analytical expressions.

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In this paper, we propose using mobile nanosensors (MNSs) for early stage anomaly detection. For concreteness, we focus on the detection of cancer cells located in a particular region of a blood vessel. These cancer cells produce and emit special molecules, so-called biomarkers, which are symptomatic for the presence of anomaly, into the cardiovascular system. Detection of cancer biomarkers with conventional blood tests is difficult in the early stages of a cancer due to the very low concentration of the biomarkers in the samples taken. However, close to the cancer cells, the concentration of the cancer biomarkers is high. Hence, detection is possible if a sensor with the ability to detect these biomarkers is placed in the vicinity of the cancer cells. Therefore, in this paper, we study the use of MNSs that are injected at a suitable injection site and can move through the blood vessels of the cardiovascular system, which potentially contain cancer cells. These MNSs can be activated by the biomarkers close to the cancer cells, where the biomarker concentration is sufficiently high. Eventually, the MNSs are collected by a fusion center (FC), where their activation levels are read and exploited to declare the presence of anomaly. We analytically derive the biomarker concentration in a network of interconnected blood vessels as well as the probability mass function of the MNSs' activation levels and validate the obtained results via particle-based simulations. Then, we derive the optimal decision rule for the FC regarding the presence of anomaly assuming that the entire network is known at the FC. Finally, for the FC, we propose a simple sum detector that does not require knowledge of the network topology. Our simulations reveal that while the optimal detector achieves a higher performance than the sum detector, both proposed detectors significantly outperform a benchmark scheme that uses fixed nanosensors at the FC.

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Although many exciting applications of molecular communication (MC) systems are envisioned to be at microscale, the MC testbeds reported in the literature so far are mostly at macroscale. This may partially be due to the fact that controlling an MC system at microscale is challenging. To link the macroworld to the microworld, we propose and demonstrate a biological signal conversion interface that can also be seen as a microscale modulator. In particular, the proposed interface transduces an optical signal, which is controlled using a light-emitting diode, into a chemical signal by changing the pH of the environment. The modulator is realized using Escherichia coli bacteria as microscale entity expressing the light-driven proton pump gloeorhodopsin from Gloeobacter violaceus. Upon inducing external light stimuli, these bacteria locally change their surrounding pH level by exporting protons into the environment. To verify the effectiveness of the proposed optical-to-chemical signal converter, we analyze the pH signal measured by a pH sensor, which serves as a receiver. We develop an analytical parametric model for the induced chemical signal as a function of the applied optical signal. Using this model, we derive a training-based channel estimator that estimates the parameters of the proposed model to fit the measurement data based on a least square error approach. We further derive the optimal maximum likelihood detector and a suboptimal low-complexity detector to recover the transmitted data from the measured received signal. It is shown that the proposed parametric model is in good agreement with the measurement data. Moreover, for an example scenario, we show that the proposed setup is able to successfully convert an optical signal representing a sequence of binary symbols into a chemical signal with a bit rate of 1 bit/min and recover the transmitted data from the chemical signal using the proposed estimation and detection schemes. The proposed modulator may form the basis for future MC testbeds and applications at microscale.

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In this paper, we consider abnormality detection via diffusive molecular communications (MCs) for a network consisting of several sensors and a fusion center (FC). If a sensor detects an abnormality, it injects a number of molecules into the medium which is proportional to its sensing output. Two transmission schemes for releasing molecules into the medium are considered. In the first scheme, each sensor releases a different type of molecule (DTM), whereas in the second scheme, all sensors release the same type of molecule (STM). The molecules released by the sensors propagate through the MC channel and some may reach the FC where the final decision regarding whether or not an abnormality has occurred is made. We derive the optimal decision rules for both DTM and STM. However, the optimal detectors entail high computational complexity as log-likelihood ratios (LLRs) have to be computed. To overcome this issue, we show that the optimal decision rule for STM can be transformed into an equivalent low-complexity decision rule. Since a similar transformation is not possible for DTM, we propose simple low-complexity sub-optimal detectors based on different approximations of the LLR. The proposed low-complexity detectors are more suitable for practical MC systems than the original complex optimal decision rule, particularly when the FC is a nano-machine with limited computational capabilities. Furthermore, we analyze the performance of the proposed detectors in terms of their false alarm and missed detection probabilities. Simulation results verify our analytical derivations and reveal interesting insights regarding the tradeoff between complexity and performance of the proposed detectors and the considered DTM and STM schemes.

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Symbol synchronization refers to the estimation of the start of a symbol interval and is needed for reliable detection. In this paper, we develop several symbol synchronization schemes for molecular communication (MC) systems where we consider some practical challenges, which have not been addressed in the literature yet. In particular, we take into account that in MC systems, the transmitter may not be equipped with an internal clock and may not be able to emit molecules with a fixed release frequency. Such restrictions hold for practical nanotransmitters, e.g., modified cells, where the lengths of the symbol intervals may vary due to the inherent randomness in the availability of food and energy for molecule generation, the process for molecule production, and the release process. To address this issue, we develop two synchronization-detection frameworks which both employ two types of molecule. In the first framework, one type of molecule is used for symbol synchronization and the other one is used for data detection, whereas in the second framework, both types of molecule are used for joint symbol synchronization and data detection. For both frameworks, we first derive the optimal maximum likelihood (ML) symbol synchronization schemes as performance upper bounds. Since ML synchronization entails high complexity, for each framework, we also propose three low-complexity suboptimal schemes, namely a linear filter-based scheme, a peak observation-based scheme, and a threshold-trigger scheme, which are suitable for MC systems with limited computational capabilities. Furthermore, we study the relative complexity and the constraints associated with the proposed schemes and the impact of the insertion and deletion errors that arise due to imperfect synchronization. Our simulation results reveal the effectiveness of the proposed synchronization schemes and suggest that the end-to-end performance of MC systems significantly depends on the accuracy of the symbol synchronization.

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This paper studies the problem of receiver modeling in molecular communication systems. We consider the diffusive molecular communication channel between a transmitter nano-machine and a receiver nano-machine in a fluid environment. The information molecules released by the transmitter nano-machine into the environment can degrade in the channel via a first-order degradation reaction and those that reach the receiver nano-machine can participate in a reversible bimolecular reaction with receiver receptor proteins. Thereby, we distinguish between two scenarios. In the first scenario, we assume that the entire surface of the receiver is covered by receptor molecules. We derive a closed-form analytical expression for the expected received signal at the receiver, i.e., the expected number of activated receptors on the surface of the receiver. Then, in the second scenario, we consider the case where the number of receptor molecules is finite and the uniformly distributed receptor molecules cover the receiver surface only partially. We show that the expected received signal for this scenario can be accurately approximated by the expected received signal for the first scenario after appropriately modifying the forward reaction rate constant. The accuracy of the derived analytical results is verified by Brownian motion particle-based simulations of the considered environment, where we also show the impact of the effect of receptor occupancy on the derived analytical results.

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Visible light communication (VLC) networks, consisting of multiple light-emitting diodes (LEDs) acting as optical access points (APs), can provide low-cost high-rate data transmission to multiple users simultaneously in indoor environments. However, the performance of VLC networks is severely limited by the interference between different users. In this paper, we establish a distributed user-centric scheduling framework based on stable marriage theory, and propose a novel decentralized scheduling method to manage interference by forming flexible amorphous cells for all users. The proposed scheduling method has provable low computational complexity and requires only the exchange of a few 1-bit messages between the APs and the users but not the feedback of the channel state information of the entire network. We further show that the proposed method can achieve both user-wise and system-wise optimality as well as a certain level of fairness. Simulation results indicate that our decentralized user-centric scheduling method outperforms existing centralized approaches in terms of throughput, fairness, and computational complexity.

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In diffusion-based molecular communication (DMC), a transmitter nanomachine is responsible for signal modulation. Thereby, the transmitter has to be able to control the release of the signaling molecules employed for representing the transmitted information. In nature, an important class of control mechanisms for releasing molecules from cells utilizes ion channels which are pore-forming proteins across the cell membrane. The opening and closing of the ion channels is controlled by a gating parameter. In this paper, an ion channel based modulator for DMC is proposed which controls the rate of molecule release from the transmitter by modulating a gating parameter signal. Exploiting the capabilities of the proposed modulator, an on-off keying modulation technique is introduced and the corresponding average modulated signal, i.e., the average release rate of the molecules from the transmitter, is analyzed. However, since the modulated signal is random in nature, it may deviate from its average. Therefore, the concept of modulator noise is introduced and the statistics of the modulated signal are investigated. Finally, by assuming a simple transparent receiver, the performance of the proposed on-off keying modulation format is studied. The derived analytical expressions for the average modulated signal are confirmed with particle based simulations. Our numerical results reveal that performance estimates of DMC systems obtained based on the assumption of instantaneous molecule release at the transmitter may substantially deviate from the performance achieved with practical modulators.

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In this paper, we perform receiver design for a diffusive molecular communication environment. Our model includes flow in any direction, sources of information molecules in addition to the transmitter, and enzymes in the propagation environment to mitigate intersymbol interference. We characterize the mutual information between receiver observations to show how often independent observations can be made. We derive the maximum likelihood sequence detector to provide a lower bound on the bit error probability. We propose the family of weighted sum detectors for more practical implementation and derive their expected bit error probability. Under certain conditions, the performance of the optimal weighted sum detector is shown to be equivalent to a matched filter. Receiver simulation results show the tradeoff in detector complexity versus achievable bit error probability, and that a slow flow in any direction can improve the performance of a weighted sum detector.

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This paper studies the mitigation of intersymbol interference in a diffusive molecular communication system using enzymes that freely diffuse in the propagation environment. The enzymes form reaction intermediates with information molecules and then degrade them so that they cannot interfere with future transmissions. A lower bound expression on the expected number of molecules measured at the receiver is derived. A simple binary receiver detection scheme is proposed where the number of observed molecules is sampled at the time when the maximum number of molecules is expected. Insight is also provided into the selection of an appropriate bit interval. The expected bit error probability is derived as a function of the current and all previously transmitted bits. Simulation results show the accuracy of the bit error probability expression and the improvement in communication performance by having active enzymes present.

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BACKGROUND: Heme oxygenase 1 (HMOX1) is the rate limiting enzyme in heme degradation and a key regulator of inflammatory processes. In animal models the course of pancreatitis was ameliorated by up-regulation of HMOX1 expression. Additionally, carbon monoxide released during heme breakdown inhibited proliferation of pancreatic stellate cells and might thereby prevent the development of chronic pancreatitis (CP). Transcription of HMOX1 in humans is influenced by a GT-repeat located in the promoter. As such, HMOX1 variants might be of importance in the pathogenesis of pancreatitis. METHODS: The GT-repeat and SNP rs2071746 were investigated with fluorescence labelled primers and by melting curve analysis in 285 patients with acute pancreatitis, 208 patients with alcoholic CP, 207 patients with idiopathic/hereditary CP, 147 patients with alcoholic liver cirrhosis, and in 289 controls, respectively. GT-repeat analysis was extended to a total of 446 alcoholic CP patients. In addition, we performed DNA sequencing in 145 patients with alcoholic CP, 138 patients with idiopathic/hereditary CP, 147 patients with alcoholic liver cirrhosis, and 151 controls. Exon 3 screening was extended to additional patients and controls. RESULTS: S- and L-alleles of the GT-repeat, genotypes and alleles of SNP rs2071746 and non-synonymous variants detected by sequencing were found with similar frequencies in all groups. CONCLUSIONS: Although functional data implicate a potential influence of HMOX1 variants on the pathogenesis of pancreatitis, we did not find any association. As rare non-synonymous HMOX1 variants were found in patients and controls, it is rather unlikely that they will have functional consequences essential for pancreatitis development.

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BACKGROUND: Chronic pancreatitis (CP) is an inflammatory disease that in some patients leads to exocrine and endocrine dysfunction. In industrialized countries the most common aetiology is chronic alcohol abuse. Descriptions of associated genetic alterations in alcoholic CP are rare. However, a common PNPLA3 variant (p.I148M) is associated with the development of alcoholic liver cirrhosis (ALC). Since, alcoholic CP and ALC share the same aetiology PNPLA3 variant (p.I148M) possibly influences the development of alcoholic CP. METHODS: Using melting curve analysis we genotyped the variant in 1510 patients with pancreatitis or liver disease (961 German and Dutch alcoholic CP patients, 414 German patients with idiopathic or hereditary CP, and 135 patients with ALC). In addition, we included in total 2781 healthy controls in the study. RESULTS: The previously published overrepresentation of GG-genotype was replicated in our cohort of ALC (p-value <0.0001, OR 2.3, 95% CI 1.6-3.3). Distributions of genotype and allele frequencies of the p.I148M variant were comparable in patients with alcoholic CP, idiopathic and hereditary CP and in healthy controls. CONCLUSIONS: The absence of an association of PNPLA3 p.I148M with alcoholic CP seems not to point to a common pathway in the development of alcoholic CP and alcoholic liver cirrhosis.

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