Miracle-dz/UniFAR-FTUs huggingface dataset:
Corneal UV Protective Effects of a Topical Antioxidant Formulation: A Pilot Study on In Vivo Rabbits

This study aimed to evaluate the protective effect of a topical antioxidant and ultraviolet (UV) shielding action formulation containing riboflavin and D-α-tocopherol polyethylene glycol succinate (TPGS) vitamin E against corneal UV-induced damage in vivo rabbit eyes. In vivo experiments were performed using male albino rabbits, which were divided into four groups. The control group (CG) did not receive any UV irradiation; the first group (IG) was irradiated with a UV-B−UV-A lamp for 30 min; the second (G30) and third (G60) groups received UV irradiation for 30 and 60 min, respectively, and were topically treated with one drop of the antioxidant and shielding formulation every 15 min, starting one hour before irradiation, until the end of UV exposure. The cornea of the IG group showed irregular thickening, detachment of residual fragments of the Descemet membrane, stromal fluid swelling with consequent collagen fiber disorganization and disruption, and inflammation. The cornea of the G30 group showed edema, a mild thickening of the Descemet membrane without fibrillar collagen disruption and focal discoloration, or inflammation. In the G60 group, the cornea showed a more severe thickening, a more abundant fluid accumulation underneath the Descemet membrane with focal detachment, and no signs of severe tissue alterations, as were recorded in the IG group. Our results demonstrate that topical application of eye drops containing riboflavin and TPGS vitamin E counteracts UV corneal injury in exposed rabbits.
Extracellular Vesicles-Based Drug Delivery Systems: A New Challenge and the Exemplum of Malignant Pleural Mesothelioma

Research for the most selective drug delivery to tumors represents a fascinating key target in science. Alongside the artificial delivery systems identified in the last decades (e.g., liposomes), a family of natural extracellular vesicles (EVs) has gained increasing focus for their potential use in delivering anticancer compounds. EVs are released by all cell types to mediate cell-to-cell communication both at the paracrine and the systemic levels, suggesting a role for them as an ideal nano-delivery system. Malignant pleural mesothelioma (MPM) stands out among currently untreatable tumors, also due to the difficulties in achieving an early diagnosis. Thus, early diagnosis and treatment of MPM are both unmet clinical needs. This review looks at indirect and direct evidence that EVs may represent both a new tool for allowing an early diagnosis of MPM and a potential new delivery system for more efficient therapeutic strategies. Since MPM is a relatively rare malignant tumor and preclinical MPM models developed to date are very few and not reliable, this review will report data obtained in other tumor types, suggesting the potential use of EVs in mesothelioma patients as well.
High Fidelity Dynamic Modeling and Nonlinear Control of Fluidic Artificial Muscles

HIGH FIDELITY DYNAMIC MODELING AND NONLINEAR CONTROL OF FLUIDIC ARTIFICIAL MUSCLES Jonathon E. Slightam, M.Sc. Marquette University, 2019 A fluidic artificial muscle is a type of soft actuator. Soft actuators transmit power with elastic or hyper-elastic bladders that are deformed with a pressurized fluid. In a fluidic artificial muscle a rubber tube is encompassed by a helical fiber braid with caps on both ends. One of the end caps has an orifice, allowing the control of fluid flow in and out of the device. As the actuator is pressurized, the rubber tube expands radially and is constrained by the helical fiber braid. This constraint results in a contractile motion similar to that of biological muscles. Although artificial muscles have been extensively studied, physics-based models do not exist that predict their motion. This dissertation presents a new comprehensive lumped-parameter dynamic model for both pneumatic and hydraulic artificial muscles. It includes a tube stiffness model derived from the theory of large deformations, thin wall pressure vessel theory, and a classical artificial muscle force model. Furthermore, it incorporates models for the kinetic friction and braid deformation. The new comprehensive dynamic model is able to accurately predict the displacement of artificial muscles as a function of pressure. On average, the model can predict the quasi-static position of the artificial muscles within 5% error and the dynamic displacement within 10% error with respect to the maximum stroke. Results show the potential utility of the model in mechanical system design and control design. Applications include wearable robots, mobile robots, and systems requiring compact, powerful actuation. The new model was used to derive sliding mode position and impedance control laws. The accuracy of the controllers ranged from ±6 μm to ±50 μm, with respect to a 32 mm and 24 mm stroke artificial muscles, respectively. Tracking errors were reduced by 59% or more when using the high-fidelity model sliding mode controller compared to classical methods. The new model redefines the state-of-the-art in controller performance for fluidic artificial muscles.
Proteinaceous Transformers: Structural and Functional Variability of Human sHsps

The proteostasis network allows organisms to support and regulate the life cycle of proteins. Especially regarding stress, molecular chaperones represent the main players within this network. Small heat shock proteins (sHsps) are a diverse family of ATP-independent molecular chaperones acting as the first line of defense in many stress situations. Thereby, the promiscuous interaction of sHsps with substrate proteins results in complexes from which the substrates can be refolded by ATP-dependent chaperones. Particularly in vertebrates, sHsps are linked to a broad variety of diseases and are needed to maintain the refractive index of the eye lens. A striking key characteristic of sHsps is their existence in ensembles of oligomers with varying numbers of subunits. The respective dynamics of these molecules allow the exchange of subunits and the formation of hetero-oligomers. Additionally, these dynamics are closely linked to the chaperone activity of sHsps. In current models a shift in the equilibrium of the sHsp ensemble allows regulation of the chaperone activity, whereby smaller oligomers are commonly the more active species. Different triggers reversibly change the oligomer equilibrium and regulate the activity of sHsps. However, a finite availability of high-resolution structures of sHsps still limits a detailed mechanistic understanding of their dynamics and the correlating recognition of substrate proteins. Here we summarize recent advances in understanding the structural and functional relationships of human sHsps with a focus on the eye-lens αA- and αB-crystallins.
Prevalence, Incidence, and Years Lived With Disability Due to Gout and Its Attributable Risk Factors for 195 Countries and Territories 1990–2017: A Systematic Analysis of the Global Burden of Disease Study 2017

To describe the levels and trends of point prevalence, annual incidence, and years lived with disability (YLD) for gout and its attributable risk factors in 195 countries and territories from 1990 to 2017 according to age, sex, and Sociodemographic Index (SDI; a composite of sociodemographic factors).
Engineering human ACE2 to optimize binding to the spike protein of SARS coronavirus 2

A decoy receptor for SARS-CoV-2 For severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to enter human cells, the spike protein on the surface of the virus must bind to the host receptor protein, angiotensin-converting enzyme 2 (ACE2). A soluble version of the receptor is being explored as a therapeutic. Chan et al. used deep mutagenesis to identify ACE2 mutants that bind more tightly to the spike protein and combined mutations to further increase binding affinity (see the Perspective by DeKosky). A promising variant was engineered to be a stable dimer that has a binding affinity for the spike protein; it is comparable with neutralizing antibodies and neutralized both SARS-CoV-2 and SARS-CoV-1 in a cell-based assay. In addition, the similarity to the natural receptor may limit the possibility for viral escape. Science, this issue p. 1261; see also p. 1167 A variant of ACE2 based on deep mutagenesis far outcompetes the natural receptor in binding the SARS-CoV-2 spike protein. The spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds angiotensin-converting enzyme 2 (ACE2) on host cells to initiate entry, and soluble ACE2 is a therapeutic candidate that neutralizes infection by acting as a decoy. By using deep mutagenesis, mutations in ACE2 that increase S binding are found across the interaction surface, in the asparagine 90–glycosylation motif and at buried sites. The mutational landscape provides a blueprint for understanding the specificity of the interaction between ACE2 and S and for engineering high-affinity decoy receptors. Combining mutations gives ACE2 variants with affinities that rival those of monoclonal antibodies. A stable dimeric variant shows potent SARS-CoV-2 and -1 neutralization in vitro. The engineered receptor is catalytically active, and its close similarity with the native receptor may limit the potential for viral escape.
Sexual Dimorphism in the Multielemental Stoichiometric Phenotypes and Stoichiometric Niches of Spiders

Nutritional limitations may shape populations and communities of organisms. This phenomenon is often studied by treating populations and communities as pools of homogenous individuals with average nutritional optima and experiencing average constraints and trade-offs that influence their fitness in a standardized way. However, populations and communities consist of individuals belonging to different sexes, each with specific nutritional demands and limitations. Taking this into account, we used the ecological stoichiometry framework to study sexual differences in the stoichiometric phenotypes, reflecting stoichiometric niches, of four spider taxa differing in the hunting mode. The species and sexes differed fundamentally in their elemental phenotypes, including elements beyond those most commonly studied (C, N and P). Both species and sexes were distinguished by the C:N ratio and concentrations of Cu, K and Zn. Species additionally differed in concentrations of Na, Mg and Mn. Phosphorous was not involved in this differentiation. Sexual dimorphism in spiders’ elemental phenotypes, related to differences in their stoichiometric niches, suggests different nutritional optima and differences in nutritional limitation experienced by different sexes and species. This may influence the structure and functioning of spider populations and communities.
Preparation and Evaluation of Eudragit L100-PEG Proliponiosomes for Enhanced Oral Delivery of Celecoxib

PEGylated Eudragit L100 (ELP)-containing proliponiosomes (PLNs) were developed for improved oral delivery of celecoxib (CXB). The successful introduction of PEG 2000 or 5000 to Eudragit L100 (EL) was confirmed via proton nuclear magnetic resonance analysis of which calculated molar substitution ratio of PEG to EL was 36.0 or 36.7, respectively. CXB, ELP, phospholipid, and non-ionic surfactants were dissolved in dimethyl sulfoxide and lyophilized to produce CXB-loaded PLNs (CXB@PLNs). The physical state of CXB@PLNs was evaluated using differential scanning calorimetry and powder X-ray diffractometry, which revealed that crystalline CXB was transformed into amorphous form after the fabrication procedure. The reconstitution of CXB@PLNs in aqueous media generated CXB-loaded liponiosomes with nano-sized mean diameters and spherical morphology. CXB@PLNs displayed enhanced dissolution rate and permeability compared to CXB suspension. In vivo pharmacokinetic studies performed on rats demonstrated the improved oral bioavailability of CXB@PLNs compared to that of CXB suspension. No serious systemic toxicity was observed in the blood biochemistry tests performed on rats. These results suggest that the developed PLNs could be promising oral delivery systems for improving the bioavailability of poorly water-soluble drugs, such as CXB.
On Detecting Cold Storage Transactions on Bitcoin's Blockchain

There is a disparity between Bitcoin addresses and real-world entities: the same entity can have many addresses. In Blockchain’s analysis, a common technique used for clustering addresses is to view addresses present at the input of the same transaction as a single entity. A common practice to make Bitcoin safer is the use of cold wallets. The use of cold wallets by exchanges that control the wallets of many users may disrupt Blockchain’s current methods of analysis. In this work we define these scenarios and introduce an heuristic and an algorithm to detect these occurrences on Blockchain. We show that the data obtained using the proposed heuristic are consistent with what was expected.
Autopolicy: Automated Traffic Policing for Improved IoT Network Security

A 2.3Tbps DDoS attack was recently mitigated by Amazon, which is a new record after the 2018 GitHub attack, or the famous 2016 Dyn DNS attack launched from hundreds of thousands of hijacked Internet of Things (IoT) devices. These attacks may disrupt the lives of billions of people worldwide, as we increasingly rely on the Internet. In this paper, we tackle the problem that hijacked IoT devices are often the origin of these attacks. With the goal of protecting the Internet and local networks, we propose Autopolicy: a system that automatically limits the IP traffic bandwidth—and other network resources—available to IoT devices in a particular network. We make use of the fact that devices, such as sensors, cameras, and smart home appliances, rarely need their high-speed network interfaces for normal operation. We present a simple yet flexible architecture for Autopolicy, specifying its functional blocks, message sequences, and general operation in a Software Defined Network. We present the experimental validation results, and release a prototype open source implementation.
Effects of Microwave-Assisted Extraction Conditions on Antioxidant Capacity of Sweet Tea (Lithocarpus polystachyus Rehd.)

In this study, the effects of microwave-assisted extraction conditions on antioxidant capacity of sweet tea (Lithocarpus polystachyus Rehd.) were studied and the antioxidants in the extract were identified. The influences of ethanol concentration, solvent-to-sample ratio, microwave power, extraction temperature and extraction time on Trolox equivalent antioxidant capacity (TEAC) value, ferric reducing antioxidant power (FRAP) value and total phenolic content (TPC) were investigated by single-factor experiments. The response surface methodology (RSM) was used to study the interaction of three parameters which had significant influences on antioxidant capacity including ethanol concentration, solvent-to-sample ratio and extraction time. The optimal conditions for the extraction of antioxidants from sweet tea were found as follows—ethanol concentration of 58.43% (v/v), solvent-to-sample ratio of 35.39:1 mL/g, extraction time of 25.26 min, extraction temperature of 50 ℃ and microwave power of 600 W. The FRAP, TEAC and TPC values of the extract under the optimal conditions were 381.29 ± 4.42 μM Fe(II)/g dry weight (DW), 613.11 ± 9.32 μM Trolox/g DW and 135.94 ± 0.52 mg gallic acid equivalent (GAE)/g DW, respectively. In addition, the major antioxidant components in the extract were detected by high-performance liquid chromatography with diode array detection (HPLC-DAD), including phlorizin, phloretin and trilobatin. The crude extract could be used as food additives or developed into functional food for the prevention and management of oxidative stress-related diseases.
Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models

Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle–compound/drug complexes having antimalarial activity (against Plasmodium). The aim is to save experimental resources and time by using a virtual screening for DDNPs. The raw data was obtained by the fusion of experimental data for nanoparticles with compound chemical assays from the ChEMBL database. The inputs for the eight Machine Learning classifiers were transformed features of drugs/compounds and nanoparticles as perturbations of molecular descriptors in specific experimental conditions (experiment-centered features). The resulting dataset contains 107 input features and 249,992 examples. The best classification model was provided by Random Forest, with 27 selected features of drugs/compounds and nanoparticles in all experimental conditions considered. The high performance of the model was demonstrated by the mean Area Under the Receiver Operating Characteristics (AUC) in a test subset with a value of 0.9921 ± 0.000244 (10-fold cross-validation). The results demonstrated the power of information fusion of the experimental-centered features of drugs/compounds and nanoparticles for the prediction of nanoparticle–compound antimalarial activity. The scripts and dataset for this project are available in the open GitHub repository.
Non-Cancer Chronic Pain Conditions and Risk for Incident Alzheimer’s Disease and Related Dementias in Community-Dwelling Older Adults: A Population-Based Retrospective Cohort Study of United States Medicare Beneficiaries, 2001–2013

Accumulating evidence suggests that certain chronic pain conditions may increase risk for incident Alzheimer’s disease and related dementias (ADRD). Rigorous longitudinal research remains relatively sparse, and the relation of overall chronic pain condition burden to ADRD risk remains little studied, as has the potential mediating role of sleep and mood disorders. In this retrospective cohort study, we investigated the association of common non-cancer chronic pain conditions (NCPC) at baseline to subsequent risk for incident ADRD, and assessed the potential mediating effects of mood and sleep disorders, using baseline and 2-year follow-up data using 11 pooled cohorts (2001–2013) drawn from the U.S. Medicare Current Beneficiaries Survey (MCBS). The study sample comprised 16,934 community-dwelling adults aged ≥65 and ADRD-free at baseline. NCPC included: headache, osteoarthritis, joint pain, back or neck pain, and neuropathic pain, ascertained using claims data; incident ADRD (N = 1149) was identified using claims and survey data. NCPC at baseline remained associated with incident ADRD after adjustment for sociodemographics, lifestyle characteristics, medical history, medications, and other factors (adjusted odds ratio (AOR) for any vs. no NCPC = 1.21, 95% confidence interval (CI) = 1.04–1.40; p = 0.003); the strength and magnitude of this association rose significantly with increasing number of diagnosed NCPCs (AOR for 4+ vs. 0 conditions = 1.91, CI = 1.31–2.80, p-trend < 0.00001). Inclusion of sleep disorders and/or depression/anxiety modestly reduced these risk estimates. Sensitivity analyses yielded similar findings. NCPC was significantly and positively associated with incident ADRD; this association may be partially mediated by mood and sleep disorders. Additional prospective studies with longer-term follow-up are warranted to confirm and extend our findings.
Evaluierung von Biomarkern im Liquor cerebrospinalis bei paraplegischen Hunden mit Rückenmarksverletzung

Intervertebral disc herniation is a common cause of spinal cord injuries (SCI) in dogs. The prognosis in these dogs is determined by the severity of SCI and the degree of neurological deficits. The prediction of a reliable prognosis can be challenging in paraplegic dogs, particularly when they lost nociception. Tau protein, macrophage inflammatory protein-3s (MIP-3s) and glial fibrillary acidic protein (GFAP) are potential markers of SCI. The aim of this study was to assess the value of these substances as cerebrospinal fluid (CSF) biomarkers for dogs with SCI. Therefore, the potential of these markers to quantify the degree of spinal cord damage was evaluated and whether they could consequently serve as prognostic indicators. Additionally, a calculation of the combination of multiple CSF biomarkers was performed in order to assess whether this could enhance the predictive value. The concentrations of tau protein, MIP-3s and GFAP were measured in cisternal and lumbar CSF samples of 49 paraplegic dogs using enzyme-linked immunosorbent assays (ELISA). The dogs with acute/subacute ( 28 days; n = 13) SCI were classified according to the presence (grade 4) or absence (grade 5) of deep pain perception (Sharp & Wheeler, 2005). Outcome of dogs with acute/subacute SCI was monitored by neurological follow-up exams and was defined to be successful when an improvement of at least one grade was noted within four weeks after surgery. The CSF of seven healthy beagle dogs served as control. Tau protein and MIP-3s concentrations were significantly higher in paraplegic dogs with acute/subacute SCI compared to healthy dogs and to dogs with chronic SCI. GFAP concentration in cisternal CSF of dogs with acute/subacute SCI and grade 5 were significantly higher compared to healthy dogs. However, no difference in GFAP concentrations between paraplegic and healthy dogs was detected in lumbar CSF nor between dogs with acute/subacute and chronic SCI. Paraplegic dogs with chronic SCI had significantly elevated tau protein and MIP-3s concentrations in lumbar CSF compared to control dogs. Dogs with neurological improvement had significantly lower cisternal tau protein values, especially those without deep pain perception. This predictive value of CSF tau protein concentrations could not be enhanced by a combination of multiple CSF biomarkers. In conclusion, tau protein, MIP-3s and GFAP are potential biomarkers in dogs with SCI. The cisternal tau protein concentration may serve as a prognostic indicator in dogs with SCI. A combination of all three CSF biomarkers together could not enhance the prognostic accuracy.
Design and Implementation of a Pressure Monitoring System Based on IoT for Water Supply Networks

Increasing the efficiency of water supply networks is essential in arid and semi-arid regions to ensure the supply of drinking water to the inhabitants. The cost of renovating these systems is high. However, customized management models can facilitate the maintenance and rehabilitation of hydraulic infrastructures by optimizing the use of resources. The implementation of current Internet of Things (IoT) monitoring systems allows decisions to be based on objective data. In water supply systems, IoT helps to monitor the key elements to improve system efficiency. To implement IoT in a water distribution system requires sensors that are suitable for measuring the main hydraulic variables, a communication system that is adaptable to the water service companies and a friendly system for data analysis and visualization. A smart pressure monitoring and alert system was developed using low-cost hardware and open-source software. An Arduino family microcontroller transfers pressure gauge signals using Sigfox communication, a low-power wide-area network (LPWAN). The IoT ThingSpeak platform is used for data analysis and visualization. Additionally, the system can send alarms via SMS/email in real time using the If This, Then That (IFTTT) web service when anomalous pressure data are detected. The pressure monitoring system was successfully implemented in a real water distribution network in Spain. It was able to detect both breakdowns and leaks in real time.