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Magnetic metal–organic frameworks (MMOFs) are gaining increased attention as emerging adsorbents/water remediation agents. Herein, a facile development of novel MMOFs comprised of coated ferrite nanoparticles (MNPs) and UiO-66 metal–organic framework is reported. In specific, coated Co- and Zn-doped ferrite magnetic nanoparticles were synthesized as building block while the metal–organic framework was grown in the presence of MNPs via a semi-self-assembly approach. The utilization of coated MNPs facilitated the conjugation and stands as a novel strategy for fabricating MMOFs with increased stability and an explicit structure. MMOFs were isolated with 13–25 nm crystallites sizes, 244–332 m2/g specific surface area (SSA) and 22–42 emu/g saturation magnetization values. Establishing the UiO-66 framework via the reported semi-self-assembly resulted in roughly 70% reduction in both magnetic properties and SSA, compared with the initial MNPs building blocks and UiO-66 framework, respectively. Nonetheless, the remaining 30% of the magnetization and SSA was adequate for successful and sufficient adsorption of two different pesticides, 2,4-Dichlorophenoxyacetic acid (2,4-D) and 2,4,5-Trichlorophenoxyacetic acid (2,4,5-T), while the recovery with a commercial magnet and reuse were also found to be effective. Adsorption and kinetic studies for all three MMOFs and both pesticides were performed, and data were fitted to Langmuir–Freundlich isotherm models.

The Central Andes in northwestern Argentina are characterized by steep topographic and climatic gradients. The humid foreland areas at 1 km asl elevation rapidly rise to over 5 km in the eastern Cordillera, and they form an orographic rainfall barrier on the eastern windward side. This topographic setting combined with seasonal moisture transport through the South American monsoon system leads to intense rainstorms with cascading effects such as landsliding and flooding. In order to better quantify the dynamics of water vapour transport, we use high-temporal-resolution global navigation satellite system (GNSS) remote sensing techniques. We are particularly interested in better understanding the dynamics of high-magnitude storms with high water vapour amounts that have destructive effects on human infrastructure. We used an existing GNSS station network with 12 years of time series data, and we installed two new ground stations along the climatic gradient and collected GNSS time series data for three years. For several stations we calculated the GNSS signal delay gradient to determine water vapour transport direction. Our statistical analysis combines in situ rainfall measurements and ERA5 reanalysis data to reveal the water vapour transport mechanism for the study area. The results show a strong relationship between altitude and the water vapour content, as well as between the transportation pathways and the topography.

Chagas disease is an endemic chronic parasitosis in Latin America affecting more than 7 million people. Around 100 million people are currently at risk of acquiring the infection; however, no effective vaccine has been developed yet. Trypanosoma cruzi is the etiological agent of this parasitosis and as an intracellular protozoan it can reside within different tissues, mainly muscle cells, evading host immunity and allowing progression towards the chronic stage of the disease. Considering this intracellular parasitism triggers strong cellular immunity that, besides being necessary to limit infection, is not sufficient to eradicate the parasite from tissues, a differential immune response is required and new strategies for vaccines against Chagas disease need to be explored. In this work, we designed, cloned and expressed a chimeric molecule, named NCz-SEGN24A, comprising a parasite antigen, the N-terminal domain of the major cysteine protease of T. cruzi, cruzipain (Nt-Cz), and a non-toxic form of the staphylococcal superantigen (SAg) G, SEG, with the residue Asn24 mutated to Ala (N24A). The mutant SAg SEGN24A, retains its ability to trigger classical activation of macrophages without inducing T cell apoptosis. To evaluate, as a proof of concept, the immunogenicity and efficacy of the chimeric immunogen vs. its individual antigens, C3H mice were immunized intramuscularly with NCz-SEGN24A co-adjuvanted with CpG-ODN, or the recombinant proteins Nt-Cz plus SEGN24A with the same adjuvant. Vaccinated mice significantly produced Nt-Cz-specific IgG titers after immunization and developed higher IgG2a than IgG1 titers. Specific cell-mediated immunity was assessed by in-vivo DTH and significant responses were obtained. To assess protection, mice were challenged with trypomastigotes of T. cruzi. Both schemes reduced the parasite load throughout the acute phase, but only mice immunized with NCz-SEGN24A showed significant differences against control; moreover, these mice maintained 100% survival. These results encourage testing mutated superantigens fused to specific antigens as immune modulators against pathogens.

Bacterial superantigens (SAgs) are enterotoxins that bind to MHC-II and TCR molecules, activating as much as 20% of the T cell population and promoting a cytokine storm which enhances susceptibility to endotoxic shock, causing immunosuppression, and hindering the immune response against bacterial infection. Since monocytes/macrophages are one of the first cells SAgs find in infected host and considering the effect these cells have on directing the immune response, here, we investigated the effect of four non-classical SAgs of the staphylococcal egc operon, namely, SEG, SEI, SEO, and SEM on monocytic–macrophagic cells, in the absence of T cells. We also analyzed the molecular targets on APCs which could mediate SAg effects. We found that egc SAgs depleted the pool of innate immune effector cells and induced an inefficient activation of monocytic–macrophagic cells, driving the immune response to an impaired proinflammatory profile, which could be mediated directly or indirectly by interactions with MHC class II. In addition, performing surface plasmon resonance assays, we demonstrated that non-classical SAgs bind the gp130 molecule, which is also present in the monocytic cell surface, among other cells.

Nano-brasses are emerging as a new class of composition-dependent applicable materials. It remains a challenge to synthesize hydrophilic brass nanoparticles (NPs) and further exploit them for promising bio-applications. Based on red/ox potential of polyol and nitrate salts precursors, a series of hydrophilic brass formulations of different nanoarchitectures was prepared and characterized. Self-assembly synthesis was performed in the presence of triethylene glycol (TrEG) and nitrate precursors Cu(NO3)2·3H2O and Zn(NO3)2·6H2O in an autoclave system, at different temperatures, conventional or microwave-assisted heating, while a range of precursor ratios was investigated. NPs were thoroughly characterized via X-ray diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), transmition electron microscopy (TEM), Fourier-transform infrared (FTIR) spectroscopy, dynamic light scattering (DLS), and ζ-potential to determine the crystal structure, composition, morphology, size, state of polyol coating, and aqueous colloidal stability. Distinct bimetallic α-brasses and γ-brasses, α-Cu40Zn25/γ-Cu11Zn24, α-Cu63Zn37, α-Cu47Zn10/γ-Cu19Zn24, and hierarchical core/shell structures, α-Cu59Zn30@(ZnO)11, Cu35Zn16@(ZnO)49, α-Cu37Zn18@(ZnO)45, Cu@Zinc oxalate, were produced by each synthetic protocol as stoichiometric, copper-rich, and/or zinc-rich nanomaterials. TEM sizes were estimated at 20–40 nm for pure bimetallic particles and at 45–70 nm for hierarchical core/shell structures. Crystallite sizes for the bimetallic nanocrystals were found ca. 30–45 nm, while in the case of the core-shell structures, smaller values around 15–20 nm were calculated for the ZnO shells. Oxidation and/or fragmentation of TrEG was unveiled and attributed to the different fabrication routes and formation mechanisms. All NPs were hydrophilic with 20–30% w/w of polyol coating, non-ionic colloidal stabilization (−5 mV < ζ-potential < −13 mV) and relatively small hydrodynamic sizes (

Aim: To identify (i) the prevalence of meeting the endpoints of 'stable periodontitis' (probing pocket depth [PPD] ≤ 4 mm, bleeding on probing [BoP] < 10%, no BoP at 4 mm sites), 'endpoints of therapy' (no PPD > 4 mm with BoP, no PPD ≥ 6 mm), 'controlled periodontitis' (≤4 sites with PPD ≥ 5 mm), 'PPD < 5 mm' and 'PPD < 6 mm' at the start of supportive periodontal care [SPC]) and (ii) the incidence of tooth loss in relation to not meeting these endpoints within a minimum of 5 years of SPC. Materials and Methods: Systematic electronic and manual searches were conducted to identify studies where subjects, upon completion of active periodontal therapy, entered into SPC. Duplicate screening was performed to find relevant articles. Corresponding authors were contacted to confirm inclusion and retrieve required clinical data for further analyses to assess the prevalence of reaching endpoints and incidence of subsequent tooth loss, if available, within at least 5 years of SPC. Meta‐analyses were carried out to evaluate risk ratios for tooth loss in relation to not reaching the various endpoints. Results: Fifteen studies including 12,884 patients and 323,111 teeth were retrieved. Achievement of endpoints at baseline SPC was rare (1.35%, 11.00% and 34.62%, respectively, for 'stable periodontitis', 'endpoints of therapy' and 'controlled periodontitis'). Less than a third of the 1190 subjects with 5 years of SPC data lost teeth—a total of 3.14% of all teeth were lost. Statistically significant associations with tooth loss, at the subject‐level, were found for not achieving 'controlled periodontitis' (relative risk [RR] = 2.57), PPD < 5 mm (RR = 1.59) and PPD < 6 mm (RR = 1.98). Conclusions: An overwhelming majority of subjects and teeth do not achieve the proposed endpoints for periodontal stability, yet most periodontal patients preserve most of their teeth during an average of 10–13 years in SPC. [ABSTRACT FROM AUTHOR]

TGF-β signaling regulates a variety of cellular processes, including proliferation, apoptosis, differentiation, immune responses, and fibrogenesis. Here, we describe a lysine methylation-mediated mechanism that controls the pro-fibrogenic activity of TGF-β. We find that the methyltransferase Set9 potentiates TGF-β signaling by targeting Smad7, an inhibitory downstream effector. Smad7 methylation promotes interaction with the E3 ligase Arkadia and, thus, ubiquitination-dependent degradation. Depletion or pharmacological inhibition of Set9 results in elevated Smad7 protein levels and inhibits TGF-β-dependent expression of genes encoding extracellular matrix components. The inhibitory effect of Set9 on TGF-β-mediated extracellular matrix production is further demonstrated in mouse models of pulmonary fibrosis. Lung fibrosis induced by bleomycin or Ad-TGF-β treatment was highly compromised in Set9-deficient mice. These results uncover a complex regulatory interplay among multiple Smad7 modifications and highlight the possibility that protein methyltransferases may represent promising therapeutic targets for treating lung fibrosis.

A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGos own move selections and also the winner of AlphaGos games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa:, our new program AlphaGo Zero achieved superhuman performance, winning 1000 against the previously published, champion-defeating AlphaGo., Author(s): David Silver (corresponding author) [1]; Julian Schrittwieser [1]; Karen Simonyan [1]; Ioannis Antonoglou [1]; Aja Huang [1]; Arthur Guez [1]; Thomas Hubert [1]; Lucas Baker [1]; Matthew Lai [1]; [...]

Dysautonomia in the post-COVID-19 condition appears to affect a significant number of patients, with reports raising the incidence up to 61%, having an overlap with myalgic encephalomyelitis/chronic fatigue syndrome. Quality of life and daily function are significantly impacted and conservative management interventions, despite the lack of high-quality evidence to date, are needed to ameliorate disability. A total of 50 adults with a dysautonomia post-COVID-19 diagnosis based on the Ewing battery and a NASA lean test will be enrolled in a randomized single blinded controlled trial with a crossover design. Feasibility and lack of definite dysautonomia diagnosis will be the primary outcomes, while secondary outcomes will be health-related, clinical and cardiopulmonary exercise test indicators. Safety and acceptance will also be checked, primarily excluding participants with post-exertional malaise. The Long-COVID patients Causal Diagnosis and Rehabilitation study in patients with Dysautonomia (LoCoDiRE-Dys) intervention will consist of an educational module, breathing retraining and an individualized exercise intervention of biweekly sessions for two months with regular assessment of both groups. LoCoDiRe-Dys aims to be the first post-COVID-19 randomized study in people with dysautonomia offering a multimodal intervention both in diagnosis and management. The need for evidence in effectively supporting patients is eminent. [ABSTRACT FROM AUTHOR]

The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses 'value networks' to evaluate board positions and 'policy networks' to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away. A computer Go program based on deep neural networks defeats a human professional player to achieve one of the grand challenges of artificial intelligence. AlphaGo computer beats Go champion The victory in 1997 of the chess-playing computer Deep Blue in a six-game series against the then world champion Gary Kasparov was seen as a significant milestone in the development of artificial intelligence. An even greater challenge remained -- the ancient game of Go. Despite decades of refinement, until recently the strongest computers were still playing Go at the level of human amateurs. Enter AlphaGo. Developed by Google DeepMind, this program uses deep neural networks to mimic expert players, and further improves its performance by learning from games played against itself. AlphaGo has achieved a 99% win rate against the strongest other Go programs, and defeated the reigning European champion Fan Hui 5-0 in a tournament match. This is the first time that a computer program has defeated a human professional player in even games, on a full, 19 x 19 board, in even games with no handicap., Author(s): David Silver [sup.1] , Aja Huang [sup.1] , Chris J. Maddison [sup.1] , Arthur Guez [sup.1] , Laurent Sifre [sup.1] , George van den Driessche [sup.1] , Julian Schrittwieser [...]

A priori registration of randomized clinical trials is crucial to the transparency and credibility of their findings. Aim of this study was to assess the frequency with which registered and completed randomized trials in orthodontics are published. We searched ClinicalTrials.gov and ISRCTN for registered randomized clinical trials in orthodontics that had been completed up to January 2017 and judged the publication status and date of registered trials using a systematic protocol. Statistical analysis included descriptive statistics, chi-square or Fisher exact tests, and Kaplan-Meier survival estimates. From the 266 orthodontic trials registered up to January 2017, 80 trials had been completed and included in the present study. Among these 80 included trials, the majority (76%) were registered retrospectively, while only 33 (41%) were published at the time. The median time from completion to publication was 20.1 months (interquartile range: 9.1 to 31.6 months), while survival analysis indicated that less than 10% of the trials were published after 5 years from their completion. Finally, 22 (28%) of completed trials remain unpublished even after 5 years from their completion. Publication rates of registered randomized trials in orthodontics remained low, even 5 years after their completion date.