Heavy Metal(oid)s Contamination and Potential Ecological Risk Assessment in Agricultural Soils.

Soil pollution caused by heavy metal(oid)s has generated great concern worldwide due to their toxicity, persistence, and bio-accumulation properties. To assess the baseline data, the heavy metal(oid)s, including manganese (Mn), iron (Fe), Cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), lead (Pb), mercury (Hg), chromium (Cr), and cadmium (Cd), were evaluated in surface soil samples collected from the farmlands of Grand Forks County, North Dakota. Samples were digested via acid mixture and analyzed via inductively coupled plasma mass spectrometry (ICP MS) analysis to assess the levels, ecological risks, and possible sources. The heavy metal(oid) median levels exhibited the following decreasing trend: Fe > Mn > Zn > Ni > Cr > Cu > Pb > Co > As > Cd > Hg. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) suggested the main lithogenic source for the studied metal(oid)s. Metal(oid) levels in the current investigation, except Mn, are lower than most of the guideline values set by international agencies. The contamination factor (Cf), geo accumulation index (Igeo) and enrichment factor (EF) showed considerable contamination, moderate contamination, and significant enrichment, respectively, for As and Cd on median value basis. Ecological risk factor (Er) results exhibited low ecological risk for all studied metal(oid)s except Cd, which showed considerable ecological risk. The potential ecological risk index (PERI) levels indicated low ecological risk to considerable risk. Overall, the results indicate the accumulation of As and Cd in the study area. The high nutrients of the soils potentially affect their accumulation in crops and impact on consumers' health. This drives the impetus for continued environmental monitoring programs.

An Unprecedented Metal Distribution in Silica Nanoparticles Determined by Single-Particle Inductively Coupled Plasma Mass Spectrometry.

Metal-containing nanoparticles are now common in applications ranging from catalysts to biomarkers. However, little research has focused on per-particle metal content in multicomponent nanoparticles. In this work, we used single-particle inductively coupled plasma mass spectrometry (ICP-MS) to determine the per-particle metal content of silica nanoparticles doped with tris(2,2'-bipyridyl)ruthenium(II). Monodispersed silica nanoparticles with varied Ru doping levels were prepared using a water-in-oil microemulsion method. These nanoparticles were characterized using common bulk-sample methods such as absorbance spectroscopy and conventional ICP-MS, and also with single-particle ICP-MS. The results showed that averaged concentrations of metal dopant measured per-particle by single-particle ICP-MS were consistent with the bulk-sample methods over a wide range of dopant levels. However, the per-particle amount of metal varied greatly and did not adhere to the usual Gaussian distribution encountered with one-component nanoparticles, such as gold or silver. Instead, the amount of metal dopant per silica particle showed an unexpected geometric distribution regardless of the prepared doping levels. The results indicate that an unusual metal dispersal mechanism is taking place during the microemulsion synthesis, and they challenge a common assumption that doped silica nanoparticles have the same metal content as the average measured by bulk-sample methods.

Vagus Nerve Stimulation Paired With Upper-Limb Rehabilitation After Stroke: 2- and 3-Year Follow-up From the Pilot Study.

OBJECTIVE: To assess whether a long-term home-based intervention using Paired VNS therapy is feasible and whether the benefits of Paired VNS therapy are maintained beyond 1 year. DESIGN: A long-term follow-up study. SETTING: Three centers in the United States and 1 in the United Kingdom. PARTICIPANTS: Adults with chronic ischemic stroke (n=15) with moderate to severe arm and hand impairment. INTERVENTIONS: Participants were implanted with a VNS device followed by 6 weeks of in-clinic therapy with Paired (Active) or control VNS followed by home-based rehabilitation through day 90 (blinded phase). The control VNS group then crossed over to receive 6 weeks of in-clinic Active VNS. Participants in both groups then continued a long-term home exercise program with self-administered Active VNS. MAIN OUTCOME MEASURES: Fugl-Meyer Assessment for Upper Extremity (FMA-UE) and Wolf Motor Function Test (WMFT) Functional scores were evaluated at the end of in-clinic therapy and day 90. Since both groups were subsequently receiving home-based rehabilitation with Active VNS during the long term, follow-up outcome assessments were pooled for the analyses at 6, 9, and 12 months, as previously reported. Here, we report pooled analysis of outcomes beyond 1 year. RESULTS: One year after Paired VNS therapy, FMA-UE improved by an average of 9.2±8.2 points, as previously reported. Overall, the 2- and 3-year FMA-UE gain from baseline was 11.4±8.7 (P<.001) and 14.8±10.2 points (P<.001), respectively. At years 2 and 3, FMA-UE improved by an additional 2.9 (P=.03 for change vs year 1, n=14) and 4.7 (P=.02 for change vs year 1, n=14) points, respectively. At year 1, 73% (11/15) of participants were responders (FMA-UE change ≥6) and by year 3, 85.7% (12/14) were responders. At years 2 and 3, the WMFT score improved by an additional 0.21 points (P=.03 for change vs year 1, n=15) and 0.42 points (P=.01 for change vs year 1, n=13), respectively. Responder rate (WMFT change ≥0.4) was 46.6% (7/15), 73.3% (11/15), and 69.2% (9/13) at years 1, 2, and 3, respectively. Long-term significant improvements were also observed for Motor Activity Log (MAL) and Stroke Impact Scale, Hand section (SIS-Hand). There were no serious long-term adverse events from the stimulation. CONCLUSIONS: Significant effects of Paired VNS therapy at 1 year were maintained at years 2 and 3, and further improvements in both impairment and function were observed in years 2 and 3. These changes were associated with improvements in measures of activity and participation.

Contamination Assessment and Potential Human Health Risks of Heavy Metals in Urban Soils from Grand Forks, North Dakota, USA.

Heavy metal (HM) pollution of soil is an increasingly serious problem worldwide. The current study assessed the metal levels and ecological and human health risk associated with HMs in Grand Forks urban soils. A total 40 composite surface soil samples were investigated for Mn, Fe, Co, Ni, Cu, Zn, As, Pb, Hg, Cr, Cd and Tl using microwave-assisted HNO3-HCl acid digestion and inductively coupled plasma mass spectrometry (ICP-MS) analysis. The enrichment factor (EF), contamination factor (CF), geoaccumulation index (Igeo), ecological risk and potential ecological risk index were used for ecological risk assessment. The park soils revealed the following decreasing trend for metal levels: Fe > Mn > Zn > Cr > Ni > Cu > Pb > As > Co > Cd > Tl > Hg. Based on mean levels, all the studied HMs except As and Cr were lower than guideline limits set by international agencies. Principal component analysis (PCA) indicated that Mn, Fe, Co, Ni, Cu, Zn, As, Cd, Pb, Cr and Tl may originate from natural sources, while Hg, Pb, As and Cd may come from anthropogenic/mixed sources. The Igeo results showed that the soil was moderately polluted by As and Cd and, based on EF results, As and Cd exhibited significant enrichment. The contamination factor analysis revealed that Zn and Pb showed moderate contamination, Hg exhibited low to moderate contamination and As and Cd showed high contamination in the soil. Comparatively higher risk was noted for children over adults and, overall, As was the major contributor (>50%), followed by Cr (>13%), in the non-carcinogenic risk assessment. Carcinogenic risk assessment revealed that As and Cr pose significant risks to the populations associated with this urban soil. Lastly, this study showed that the soil was moderately contaminated by As, Cd, Pb and Hg and should be regularly monitored for metal contamination.

A chemo-mechano-biological modeling framework for cartilage evolving in health, disease, injury, and treatment.

BACKGROUND AND OBJECTIVE: Osteoarthritis (OA) is a pervasive and debilitating disease, wherein degeneration of cartilage features prominently. Despite extensive research, we do not yet understand the cause or progression of OA. Studies show biochemical, mechanical, and biological factors affect cartilage health. Mechanical loads influence synthesis of biochemical constituents which build and/or break down cartilage, and which in turn affect mechanical loads. OA-associated biochemical profiles activate cellular activity that disrupts homeostasis. To understand the complex interplay among mechanical stimuli, biochemical signaling, and cartilage function requires integrating vast research on experimental mechanics and mechanobiology-a task approachable only with computational models. At present, mechanical models of cartilage generally lack chemo-biological effects, and biochemical models lack coupled mechanics, let alone interactions over time. METHODS: We establish a first-of-its kind virtual cartilage: a modeling framework that considers time-dependent, chemo-mechano-biologically induced turnover of key constituents resulting from biochemical, mechanical, and/or biological activity. We include the "minimally essential" yet complex chemical and mechanobiological mechanisms. Our 3-D framework integrates a constitutive model for the mechanics of cartilage with a novel model of homeostatic adaptation by chondrocytes to pathological mechanical stimuli, and a new application of anisotropic growth (loss) to simulate degradation clinically observed as cartilage thinning. RESULTS: Using a single set of representative parameters, our simulations of immobilizing and overloading successfully captured loss of cartilage quantified experimentally. Simulations of immobilizing, overloading, and injuring cartilage predicted dose-dependent recovery of cartilage when treated with suramin, a proposed therapeutic for OA. The modeling framework prompted us to add growth factors to the suramin treatment, which predicted even better recovery. CONCLUSIONS: Our flexible framework is a first step toward computational investigations of how cartilage and chondrocytes mechanically and biochemically evolve in degeneration of OA and respond to pharmacological therapies. Our framework will enable future studies to link physical activity and resulting mechanical stimuli to progression of OA and loss of cartilage function, facilitating new fundamental understanding of the complex progression of OA and elucidating new perspectives on causes, treatments, and possible preventions.

Dynamic biophysical responses of neuronal cell nuclei and cytoskeletal structure following high impulse loading.

Cells are continuously exposed to dynamic environmental cues that influence their behavior. Mechanical cues can influence cellular and genomic architecture, gene expression, and intranuclear mechanics, providing evidence of mechanosensing by the nucleus, and a mechanoreciprocity between the nucleus and environment. Force disruption at the tissue level through aging, disease, or trauma, propagates to the nucleus and can have lasting consequences on proper functioning of the cell and nucleus. While the influence of mechanical cues leading to axonal damage has been well studied in neuronal cells, the mechanics of the nucleus following high impulse loading is still largely unexplored. Using an in vitro model of traumatic neural injury, we show a dynamic nuclear behavioral response to impulse stretch (up to 170% strain per second) through quantitative measures of nuclear movement, including tracking of rotation and internal motion. Differences in nuclear movement were observed between low and high strain magnitudes. Increased exposure to impulse stretch exaggerated the decrease in internal motion, assessed by particle tracking microrheology, and intranuclear displacements, assessed through high-resolution deformable image registration. An increase in F-actin puncta surrounding nuclei exposed to impulse stretch additionally demonstrated a corresponding disruption of the cytoskeletal network. Our results show direct biophysical nuclear responsiveness in neuronal cells through force propagation from the substrate to the nucleus. Understanding how mechanical forces perturb the morphological and behavioral response can lead to a greater understanding of how mechanical strain drives changes within the cell and nucleus, and may inform fundamental nuclear behavior after traumatic axonal injury. STATEMENT OF SIGNIFICANCE: The nucleus of the cell has been implicated as a mechano-sensitive organelle, courting molecular sensors and transmitting physical cues in order to maintain cellular and tissue homeostasis. Disruption of this network due to disease or high velocity forces (e.g., trauma) can not only result in orchestrated biochemical cascades, but also biophysical perturbations. Using an in vitro model of traumatic neural injury, we aimed to provide insight into the neuronal nuclear mechanics and biophysical responses at a continuum of strain magnitudes and after repetitive loads. Our image-based methods demonstrate mechanically-induced changes in cellular and nuclear behavior after high intensity loading and have the potential to further define mechanical thresholds of neuronal cell injury.

Vagus Nerve Stimulation Paired With Rehabilitation for Upper Limb Motor Impairment and Function After Chronic Ischemic Stroke: Subgroup Analysis of the Randomized, Blinded, Pivotal, VNS-REHAB Device Trial.

BACKGROUND: Vagus Nerve Stimulation (VNS) paired with rehabilitation improved upper extremity impairment and function in a recent pivotal, randomized, triple-blind, sham-controlled trial in people with chronic arm weakness after stroke. OBJECTIVE: We aimed to determine whether treatment effects varied across candidate subgroups, such as younger age or less injury. METHODS: Participants were randomized to receive rehabilitation paired with active VNS or rehabilitation paired with sham stimulation (Control). The primary outcome was the change in impairment measured by the Fugl-Meyer Assessment Upper Extremity (FMA-UE) score on the first day after completion of 6-weeks in-clinic therapy. We explored the effect of VNS treatment by sex, age (≥62 years), time from stroke (>2 years), severity (baseline FMA-UE score >34), paretic side of body, country of enrollment (USA vs UK) and presence of cortical involvement of the index infarction. We assessed whether there was any interaction with treatment. FINDINGS: The primary outcome increased by 5.0 points (SD 4.4) in the VNS group and by 2.4 points (SD 3.8) in the Control group (P = .001, between group difference 2.6, 95% CI 1.03-4.2). The between group difference was similar across all subgroups and there were no significant treatment interactions. There was no important difference in rates of adverse events across subgroups. CONCLUSION: The response was similar across subgroups examined. The findings suggest that the effects of paired VNS observed in the VNS-REHAB trial are likely to be consistent in wide range of stroke survivors with moderate to severe upper extremity impairment.

Democratization of deep learning for segmenting cartilage from MRIs of human knees: Application to data from the osteoarthritis initiative.

In this study, we aimed to democratize access to convolutional neural networks (CNN) for segmenting cartilage volumes, generating state-of-the-art results for specialized, real-world applications in hospitals and research. Segmentation of cross-sectional and/or longitudinal magnetic resonance (MR) images of articular cartilage facilitates both clinical management of joint damage/disease and fundamental research. Manual delineation of such images is a time-consuming task susceptible to high intra- and interoperator variability and prone to errors. Thus, enabling reliable and efficient analyses of MRIs of cartilage requires automated segmentation of cartilage volumes. Two main limitations arise in the development of hospital- or population-specific deep learning (DL) models for image segmentation: specialized knowledge and specialized hardware. We present a relatively easy and accessible implementation of a DL model to automatically segment MRIs of human knees with state-of-the-art accuracy. In representative examples, we trained CNN models in 6-8 h and obtained results quantitatively comparable to state-of-the-art for every anatomical structure. We established and evaluated our methods using two publicly available MRI data sets originating from the Osteoarthritis Initiative, Stryker Imorphics, and Zuse Institute Berlin (ZIB), as representative test cases. We use Google Colabfor editing and adapting the Python codes and selecting the runtime environment leveraging high-performance graphical processing units. We designed our solution for novice users to apply to any data set with relatively few adaptations requiring only basic programming skills. To facilitate the adoption of our methods, we provide a complete guideline for using our methods and software, as well as the software tools themselves. Clinical significance: We establish and detail methods that clinical personal can apply to create their own DL models without specialized knowledge of DL nor specialized hardware/infrastructure and obtain results comparable with the state-of-the-art to facilitate both clinical management of joint damage/disease and fundamental research.

One-Pot Synthesis of Ruthenium-Based Nanocatalyst Using Reduced Graphene Oxide as Matrix for Electrochemical Synthesis of Ammonia.

Conventional ammonia production consumes significant energy and causes enormous carbon dioxide (CO2) emissions globally. To lower energy consumption and mitigate CO2 emissions, a facile, environmentally friendly, and cost-effective one-pot method for the synthesis of a ruthenium-based nitrogen reduction nanocatalyst has been developed using reduced graphene oxide (rGO) as a matrix. The nanocatalyst synthesis was based on a single-step simultaneous reduction of RuCl3 into ruthenium-based nanoparticles (Ru-based NPs) and graphene oxide (GO) into rGO using glucose as the reducing agent and stabilizer. The obtained ruthenium-based nanocatalyst with rGO as a matrix (Runano-based/rGO) has shown much higher catalytic activity at lower temperatures and pressures for ammonia synthesis than conventional iron catalysts. The rGO worked as a promising promoter for the electrochemical synthesis of ammonia due to its excellent electrical and thermal conductivity. The developed Runano-based/rGO nanocatalyst was characterized using transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM), scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), ultraviolet-visible (UV-vis) absorption spectroscopy, X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), dynamic light scattering (DLS), inductively coupled plasma mass spectrometry (ICP-MS), and X-ray photoelectron spectroscopy (XPS). The results demonstrated that the size of the Ru-based NPs on the surface of rGO was 1.9 ± 0.2 nm and the ruthenium content was 25.03 wt %. Bulk electrolysis measurements were conducted on thin-layer electrodes at various cathodic potentials in a N2-saturated 0.1 M H2SO4 electrolyte at room temperature. From the chronoamperometric measurements, the maximum faradic efficiency (F.E.) of 2.1% for ammonia production on the nanostructured Runano-based/rGO electrocatalyst was achieved at a potential of -0.20 V vs reversible hydrogen electrode (RHE). This electrocatalyst has attained a superior ammonia production rate of 9.14 µg·h-1·mgcat.-1. The results demonstrate the feasibility of reducing N2 into ammonia under ambient conditions and warrant further exploration of the nanostructured Runano-based/rGO for electrochemical ammonia synthesis.

Spatial Gradients of Quantitative MRI as Biomarkers for Early Detection of Osteoarthritis: Data From Human Explants and the Osteoarthritis Initiative.

BACKGROUND: Healthy articular cartilage presents structural gradients defined by distinct zonal patterns through the thickness, which may be disrupted in the pathogenesis of several disorders. Analysis of textural patterns using quantitative MRI data may identify structural gradients of healthy or degenerating tissue that correlate with early osteoarthritis (OA). PURPOSE: To quantify spatial gradients and patterns in MRI data, and to probe new candidate biomarkers for early severity of OA. STUDY TYPE: Retrospective study. SUBJECTS: Fourteen volunteers receiving total knee replacement surgery (eight males/two females/four unknown, average age ± standard deviation: 68.1 ± 9.6 years) and 10 patients from the OA Initiative (OAI) with radiographic OA onset (two males/eight females, average age ± standard deviation: 57.7 ± 9.4 years; initial Kellgren-Lawrence [KL] grade: 0; final KL grade: 3 over the 10-year study). FIELD STRENGTH/SEQUENCE: 3.0-T and 14.1-T, biomechanics-based displacement-encoded imaging, fast spin echo, multi-slice multi-echo T2 mapping. ASSESSMENT: We studied structure and strain in cartilage explants from volunteers receiving total knee replacement, or structure in cartilage of OAI patients with progressive OA. We calculated spatial gradients of quantitative MRI measures (eg, T2) normal to the cartilage surface to enhance zonal variations. We compared gradient values against histologically OA severity, conventional relaxometry, and/or KL grades. STATISTICAL TESTS: Multiparametric linear regression for evaluation of the relationship between residuals of the mixed effects models and histologically determined OA severity scoring, with a significance threshold at α = 0.05. RESULTS: Gradients of individual relaxometry and biomechanics measures significantly correlated with OA severity, outperforming conventional relaxometry and strain metrics. In human explants, analysis of spatial gradients provided the strongest relationship to OA severity (R2  = 0.627). Spatial gradients of T2 from OAI data identified variations in radiographic (KL Grade 2) OA severity in single subjects, while conventional T2 alone did not. DATA CONCLUSION: Spatial gradients of quantitative MRI data may improve the predictive power of noninvasive imaging for early-stage degeneration. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

Genipin does not reduce the initiation or propagation of microcracks in collagen networks of cartilage.

Objective: We recently initiated microcracks, i.e. micron-scale cracks in the collagen networks of cartilage, using both single low-energy impacts and unconfined, cyclic compressions. We also tracked the propagation of microcracks after cyclic compressions simulating 12,000 walking strides. In this study, we aimed to determine the effect of one or more genipin treatments on: (1) the initiation of microcracks under mechanical impacts and (2) the subsequent propagation of microcracks under cyclic, unconfined compression. We hypothesized that treatments with genipin would improve the resistance of cartilage to microdamage, specifically reducing both the initiation of microcracks under impact loading and the propagation of microcracks under cyclic compression. Design: We tested 49 full-thickness, cylindrical osteochondral specimens. We incorporated one or two doses of genipin in between mechanical treatments, i.e. single low-energy mechanical impacts to initiate microcracks and unconfined, cyclic compressions to propagate microcracks. We also imaged specimens using second harmonic generation confocal microscopy, and analyzed the resulting images to quantify changes in morphologies (length, width, and depth) and orientations of microcracks. Finally, we used separate mixed-regression modeling to evaluate the effects of genipin treatments on mechanically induced microcracks. Results: Specimens treated with genipin presented significantly longer and marginally deeper microcracks after mechanical impacts. Two doses of genipin caused significantly longer and wider microcracks under propagation verses one dose. Conclusions: Our results do not support our hypothesis: unfortunately treatments with genipin, and the resulting mechanisms of cross-linking, do not provide resistance to microdamage, quantified as the initiation and propagation of microcracks.

Vagus nerve stimulation paired with rehabilitation for stroke: Implantation experience from the VNS-REHAB trial.

OBJECTIVE: Vagus Nerve Stimulation (VNS) paired with rehabilitation delivered by the Vivistim® Paired VNS™ System was approved by the FDA in 2021 to improve motor deficits in chronic ischemic stroke survivors with moderate to severe arm and hand impairment. Vagus nerve stimulators have previously been implanted in over 125,000 patients for treatment-resistant epilepsy and the surgical procedure is generally well-tolerated and safe. In this report, we describe the Vivistim implantation procedure, perioperative management, and complications for chronic stroke survivors enrolled in the pivotal trial. METHODS: The pivotal, multisite, randomized, triple-blind, sham-controlled trial (VNS-REHAB) enrolled 108 participants. All participants were implanted with the VNS device in an outpatient procedure. Thrombolytic agents were temporarily discontinued during the perioperative period. Participants were discharged within 48 hrs and started rehabilitation therapy approximately 10 days after the Procedure. RESULTS: The rate of surgery-related adverse events was lower than previously reported for VNS implantation for epilepsy and depression. One participant had vocal cord paresis that eventually resolved. There were no serious adverse events related to device stimulation. Over 90% of participants were taking antiplatelet drugs (APD) or anticoagulants and no adverse events or serious adverse events were reported as a result of withholding these medications during the perioperative period. CONCLUSIONS: This study is the largest, randomized, controlled trial in which a VNS device was implanted in chronic stroke survivors. Results support the use of the Vivistim System in chronic stroke survivors, with a safety profile similar to VNS implantations for epilepsy and depression.

A specialized protocol for mechanical testing of isolated networks of type II collagen.

The mechanical responses of most soft biological tissues rely heavily on networks of collagen fibers, thus quantifying the mechanics of both individual collagen fibers and networks of these fibers advances understanding of biological tissues in health and disease. The mechanics of type I collagen are well-studied and quantified. Yet no data exist on the tensile mechanical responses of individual type II collagen fibers nor of isolated networks comprised of type II collagen. We aimed to establish methods to facilitate studies of networked and individual type II collagen fibers within the native networked structure, specifically to establish best practices for isolating and mechanically testing type II collagen networks in tension. We systematically investigated mechanical tests of networks of type II collagen undergoing uniaxial extension, and quantified ranges for each of the important variables to help ensure that the experiment itself does not affect the measured mechanical parameters. Specifically we determined both the specimen (establishing networks of isolated collagen, the footprint and thickness of the specimen) and the mechanical test (both the device and the strain rate) to establish a repeatable and practical protocol. Mechanical testing of isolated networks of type II collagen fibers leveraging this protocol will lead to better understanding of the mechanics both of these networks and of the individual fibers. Such understanding may aid in developing and testing therapeutics, understanding inter-constituent interactions (and their roles in bulk-tissue biomechanics), investigating mechanical/biochemical modifications to networked type II collagen, and proposing, calibrating, and validating constitutive models for finite element analyses.

Predicting the micromechanics of embedded nerve fibers using a novel three-layered model of mouse distal colon and rectum.

Mechanotransduction plays a central role in evoking pain from the distal colon and rectum (colorectum) where embedded sensory nerve endings convert micromechanical stresses and strains into neural action potentials. The colorectum displays strong through-thickness and longitudinal heterogeneity with collagen concentrated in the submucosa thus indicating the significant load-bearing role of this layer. The density of sensory nerve endings is also significantly the greatest in the submucosa, suggesting a nociceptive function. Thus biomechanical heterogeneity in the colorectum influences the micromechanical stresses and strains surrounding afferent endings embedded within different layers of the colorectum which is critical for the mechanotransduction of various mechanical stimuli. In this study we aimed to: (1) calibrate and validate a three-layered computational model of the colorectum; (2) predict intra-tissue distributions of stresses and strains during mechanical stimulation of the colorectum ex vivo (i.e. circumferential stretching, punctuate probing, and mucosal shearing); and (3) establish a methodology to calculate local micromechanical stresses and strains surrounding afferent nerve endings embedded in the colorectum. We established three-layered FE models that include mucosa, submucosa, and muscular layers, and incorporated residual stretches, to calculate intra-tissue stresses and strains when the colorectum undergoes the mechanical stimuli used to characterize afferent neural encoding ex vivo. Finally, we established a methodology for detailed calculations of the local micromechanical stresses and strains surrounding afferent endings embedded in the colorectum and demonstrated this with a representative example. Our novel methodologies will bridge the existing neurophysiological and biomechanical evidence from experiments to advance our mechanistic understanding of colorectal mechanotransduction.

The CORE study-An adapted mental health experience codesign intervention to improve psychosocial recovery for people with severe mental illness: A stepped wedge cluster randomized-controlled trial.

BACKGROUND: Mental health policies outline the need for codesign of services and quality improvement in partnership with service users and staff (and sometimes carers), and yet, evidence of systematic implementation and the impacts on healthcare outcomes is limited. OBJECTIVE: The aim of this study was to test whether an adapted mental health experience codesign intervention to improve recovery-orientation of services led to greater psychosocial recovery outcomes for service users. DESIGN: A stepped wedge cluster randomized-controlled trial was conducted. SETTING AND PARTICIPANTS: Four Mental Health Community Support Services providers, 287 people living with severe mental illnesses, 61 carers and 120 staff were recruited across Victoria, Australia. MAIN OUTCOME MEASURES: The 24-item Revised Recovery Assessment Scale (RAS-R) measured individual psychosocial recovery. RESULTS: A total of 841 observations were completed with 287 service users. The intention-to-treat analysis found RAS-R scores to be similar between the intervention (mean = 84.7, SD= 15.6) and control (mean = 86.5, SD= 15.3) phases; the adjusted estimated difference in the mean RAS-R score was -1.70 (95% confidence interval: -3.81 to 0.40; p = .11). DISCUSSION: This first trial of an adapted mental health experience codesign intervention for psychosocial recovery outcomes found no difference between the intervention and control arms. CONCLUSIONS: More attention to the conditions that are required for eight essential mechanisms of change to support codesign processes and implementation is needed. PATIENT AND PUBLIC INVOLVEMENT: The State consumer (Victorian Mental Illness Awareness Council) and carer peak bodies (Tandem representing mental health carers) codeveloped the intervention. The adapted intervention was facilitated by coinvestigators with lived-experiences who were coauthors for the trial and process evaluation protocols, the engagement model and explanatory model of change for the trial.

Where is the center of resistance of a maxillary first molar? A 3-dimensional finite element analysis.

INTRODUCTION: The center of resistance (CRes) is regarded as the fundamental reference point for predictable tooth movement. Accurate estimation can greatly enhance the efficiency of orthodontic tooth movement. Only a handful of studies have evaluated the CRes of a maxillary first molar; however, most had a low sample size (in single digits), used idealized models, or involved 2-dimensional analysis. The objectives of this study were to: (1) determine the 3-dimensional (3D) location of the CRes of maxillary first molars, (2) evaluate its variability in a large sample, and (3) investigate the effects of applying orthodontic load from 2 directions on the location of the CRes. METHODS: Cone-beam computed tomography scans of 50 maxillary molars from 25 patients (mean age, 20.8 ± 8.7 years) were used. The cone-beam computed tomography volume images were manipulated to extract 3D biological structures via segmentation. The segmented structures were cleaned and converted into virtual mesh models made of tetrahedral triangles having a maximum edge length of 1 mm. The block, which included the molars and periodontal ligament, consisted of a mean of 7753 ± 2748 nodes and 38,355 ± 14,910 tetrahedral elements. Specialized software was used to preprocess the models to create an assembly and assign material properties, interaction conditions, boundary conditions, and load applications. Specific loads were applied, and custom-designed algorithms were used to analyze the stress and strain to locate the CRes. The CRes was measured in relation to the geometric center of the buccal surface of the molar and the trifurcation of the molar roots. RESULTS: The average location of the CRes for the maxillary first molar was 4.94 ± 1.39 mm lingual, 2.54 ± 2.7 mm distal, and 7.86 ± 1.66 mm gingival relative to the geometric center of the buccal surface of the molar and 0.136 ± 1.51 mm lingual (P <0.01), 1.48 ± 2.26 mm distal (P <0.01), and 0.188 ± 1.75 mm gingival (P >0.01) relative to the trifurcation of the molar roots. In the anteroposterior (y-axis) and the vertical (z-axis) planes, the CRes showed significant association with root divergence (P <0.01). CONCLUSIONS: The CRes of the maxillary first molar was located apical and distal to the trifurcation area. It showed significant variation in its location. The 3D location of and also varied with the force direction. In some samples, this deviation was large. For accurate and predictable movement, tooth-specific CRes need to be calculated.

Ignitions explain more than temperature or precipitation in driving Santa Ana wind fires.

Autumn and winter Santa Ana wind (SAW)-driven wildfires play a substantial role in area burned and societal losses in southern California. Temperature during the event and antecedent precipitation in the week or month prior play a minor role in determining area burned. Burning is dependent on wind intensity and number of human-ignited fires. Over 75% of all SAW events generate no fires; rather, fires during a SAW event are dependent on a fire being ignited. Models explained 40 to 50% of area burned, with number of ignitions being the strongest variable. One hundred percent of SAW fires were human caused, and in the past decade, powerline failures have been the dominant cause. Future fire losses can be reduced by greater emphasis on maintenance of utility lines and attention to planning urban growth in ways that reduce the potential for powerline ignitions.