Uncovering the multifaceted properties of 6-pentyl-alpha-pyrone for control of plant pathogens.

Some volatile organic compounds (VOCs) produced by microorganisms have the ability to inhibit the growth and development of plant pathogens, induce the activation of plant defenses, and promote plant growth. Among them, 6-pentyl-alpha-pyrone (6-PP), a ketone produced by Trichoderma fungi, has emerged as a focal point of interest. 6-PP has been isolated and characterized from thirteen Trichoderma species and is the main VOC produced, often accounting for >50% of the total VOCs emitted. This review examines abiotic and biotic interactions regulating the production of 6-PP by Trichoderma, and the known effects of 6-PP on plant pathogens through direct and indirect mechanisms including induced systemic resistance. While there are many reports of 6-PP activity against plant pathogens, the vast majority have been from laboratory studies involving only 6-PP and the pathogen, rather than glasshouse or field studies including a host plant in the system. Biopesticides based on 6-PP may well provide an eco-friendly, sustainable management tool for future agricultural production. However, before this can happen, challenges including demonstrating disease control efficacy in the field, developing efficient delivery systems, and determining cost-effective application rates must be overcome before 6-PP's potential for pathogen control can be turned into reality.

On chip synthesis of a pH sensitive gefitinib anticancer drug nanocarrier based on chitosan/alginate natural polymers.

In this research, using a microfluidic chip, a nanocarrier for the anticancer drug gefitinib was synthesized. Chitosan and alginate natural polymers were utilized for the synthesis of the nanocarrier. The synthesis of the nanocarrier comprises the interaction of secondary amine functional groups of gefitinib molecules with carboxylate functional groups of alginate polymer to form the primary nucleus followed by the formation of the nanocarrier through the self-assembly of chitosan and alginate polymers on a fabricated microfluidic chip. The chip was fabricated by laser engraving poly(methyl methacrylate) polymer sheets. The nanocarrier was characterized by FT-IR, DLS, SEM, and TEM techniques. The synthesized nanocarrier had a size distribution of 5.30 ± 2.60 nm and the encapsulation efficiency percent was 68.4% in the optimum conditions. The loading efficiency was calculated as 50.2 mg g-1 of nanocarrier. Drug release studies showed that the nanocarrier is sensitive to pH and releases more gefitinib in acidic environments. Cytotoxicity of the synthesized nanocarrier was studied on the A549 non-small cell lung cancer, and the MTT test showed that the synthesized nanocarrier has a lower IC50 value than the free drug. Also, the cytotoxicity studies showed that the materials used for the synthesis of nanocarrier do not show significant cytotoxicity. Compared to the previously reported method, the developed microfluidic-assisted method showed advantages such as a faster synthesis procedure and comparable encapsulation efficiency and loading capacity.

An osteoinductive and biodegradable intramedullary implant accelerates bone healing and mitigates complications of bone transport in male rats.

Bone transport is a surgery-driven procedure for the treatment of large bone defects. However, challenging complications include prolonged consolidation, docking site nonunion and pin tract infection. Here, we develop an osteoinductive and biodegradable intramedullary implant by a hybrid tissue engineering construct technique to enable sustained delivery of bone morphogenetic protein-2 as an adjunctive therapy. In a male rat bone transport model, the eluting bone morphogenetic protein-2 from the implants accelerates bone formation and remodeling, leading to early bony fusion as shown by imaging, mechanical testing, histological analysis, and microarray assays. Moreover, no pin tract infection but tight osseointegration are observed. In contrast, conventional treatments show higher proportion of docking site nonunion and pin tract infection. The findings of this study demonstrate that the novel intramedullary implant holds great promise for advancing bone transport techniques by promoting bone regeneration and reducing complications in the treatment of bone defects.

Development and characterization of an automated active mixing platform for hydrogel bioink preparation.

Bioink preparation is an important yet challenging step for bioprinting with hydrogels, as it involves fast and homogeneous mixing of various viscous components. In this study, we have developed an automated active mixing platform (AAMP), which allows for high-quality preparation of hydrogel bioinks. The design of AAMP, adapted from syringe pumps, provides many advantages, including low cost, automated control, high precision, customizability, and great cytocompatibility, as well as the potential to intelligently detect the homogeneity. To demonstrate the capability of AAMP, mixing of different hydrogel components, including alginate and xanthan gum with and without Ca2+, alginate and Laponite, PEGDMA and xanthan gum, was performed to investigate an alginate hydrogel preparation process. Colorimetric analyses were carried out to evaluate the mixing outcome with AAMP. Result showed that AAMP can prepare homogeneous hydrogel mixing in a fast and automated fashion. A multiphysics COMSOL simulation is carried out to further validate the results. Moreover, cell viability and proliferation study were performed in a cell encapsulation mixing experiment to validate the cytocompatibility of the AAMP. The AAMP has demonstrated great capability in hydrogel bioink preparation and could therefore holds great promise and wide applications in bioprinting and tissue engineering.

Finite element evaluation of an American football helmet featuring liquid shock absorbers for protecting against concussive and subconcussive head impacts.

Introduction: Concern has grown over the potential long-term effects of repeated head impacts and concussions in American football. Recent advances in impact engineering have yielded the development of soft, collapsible, liquid shock absorbers, which have demonstrated the ability to dramatically attenuate impact forces relative to existing helmet shock absorbers. Methods: To further explore how liquid shock absorbers can improve the efficacy of an American football helmet, we developed and optimized a finite element (FE) helmet model including 21 liquid shock absorbers spread out throughout the helmet. Using FE models of an anthropomorphic test headform and linear impactor, a previously published impact test protocol representative of concussive National Football League impacts (six impact locations, three velocities) was performed on the liquid FE helmet model and four existing FE helmet models. We also evaluated the helmets at three lower impact velocities representative of subconcussive football impacts. Head kinematics were recorded for each impact and used to compute the Head Acceleration Response Metric (HARM), a metric factoring in both linear and angular head kinematics and used to evaluate helmet performance. The head kinematics were also input to a FE model of the head and brain to calculate the resulting brain strain from each impact. Results: The liquid helmet model yielded the lowest value of HARM at 33 of the 36 impact conditions, offering an average 33.0% (range: -37.5% to 56.0%) and 32.0% (range: -2.2% to 50.5%) reduction over the existing helmet models at each impact condition in the subconcussive and concussive tests, respectively. The liquid helmet had a Helmet Performance Score (calculated using a summation of HARM values weighted based on injury incidence data) of 0.71, compared to scores ranging from 1.07 - 1.21 from the other four FE helmet models. Resulting brain strains were also lower in the liquid helmet. Discussion: The results of this study demonstrate the promising ability of liquid shock absorbers to improve helmet safety performance and encourage the development of physical prototypes of helmets featuring this technology. The implications of the observed reductions on brain injury risk are discussed.

Metabolic profile of mesenchymal stromal cells and macrophages in the presence of polyethylene particles in a 3D model.

BACKGROUND: Continuous cross talk between MSCs and macrophages is integral to acute and chronic inflammation resulting from contaminated polyethylene particles (cPE); however, the effect of this inflammatory microenvironment on mitochondrial metabolism has not been fully elucidated. We hypothesized that (a) exposure to cPE leads to impaired mitochondrial metabolism and glycolytic reprogramming and (b) macrophages play a key role in this pathway. METHODS: We cultured MSCs with/without uncommitted M0 macrophages, with/without cPE in 3-dimensional gelatin methacrylate (3D GelMA) constructs/scaffolds. We evaluated mitochondrial function (membrane potential and reactive oxygen species-ROS production), metabolic pathways for adenosine triphosphate (ATP) production (glycolysis or oxidative phosphorylation) and response to stress mechanisms. We also studied macrophage polarization toward the pro-inflammatory M1 or the anti-inflammatory M2 phenotype and the osteogenic differentiation of MSCs. RESULTS: Exposure to cPE impaired mitochondrial metabolism of MSCs; addition of M0 macrophages restored healthy mitochondrial function. Macrophages exposed to cPE-induced glycolytic reprogramming, but also initiated a response to this stress to restore mitochondrial biogenesis and homeostatic oxidative phosphorylation. Uncommitted M0 macrophages in coculture with MSC polarized to both M1 and M2 phenotypes. Osteogenesis was comparable among groups after 21 days. CONCLUSION: This work confirmed that cPE exposure triggers impaired mitochondrial metabolism and glycolytic reprogramming in a 3D coculture model of MSCs and macrophages and demonstrated that macrophages cocultured with MSCs undergo metabolic changes to maintain energy production and restore homeostatic metabolism.

Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics.

BACKGROUND: Traumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are not equally accurate across the variety of impacts that patients may undergo, and the characteristics of different types of impacts are not well studied. We investigated the spectral characteristics of different head impact types with kinematics classification. METHODS: Data were analyzed from 3262 head impacts from lab reconstruction, American football, mixed martial arts, and publicly available car crash data. A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e.g., football, car crash, mixed martial arts). To test the classifier robustness, another 271 lab-reconstructed impacts were obtained from 5 other instrumented mouthguards. Finally, with the classifier, type-specific, nearest-neighbor regression models were built for brain strain. RESULTS: The classifier reached a median accuracy of 96% over 1000 random partitions of training and test sets. The most important features in the classification included both low- and high-frequency features, both linear acceleration features and angular velocity features. Different head impact types had different distributions of spectral densities in low- and high-frequency ranges (e.g., the spectral densities of mixed martial arts impacts were higher in the high-frequency range than in the low-frequency range). The type-specific regression showed a generally higher R2 value than baseline models without classification. CONCLUSION: The machine-learning-based classifier enables a better understanding of the impact kinematics spectral density in different sports, and it can be applied to evaluate the quality of impact-simulation systems and on-field data augmentation.

Association between gout and the development of Parkinson's disease: a systematic review and meta-analysis.

BACKGROUND: As a natural antioxidant, uric acid plays a protective role against neurodegenerative disorders, including Parkinson's disease (PD). Therefore, the risk of PD has been found to be lower in people with hyperuricemia. In this article, we conducted a systematic review and meta-analysis to investigate whether gout affects the future risk of developing PD. METHODS: We searched PubMed, Scopus, the Web of Science, and Google Scholar to find relevant studies, up to March 16, 2022. Studies investigating the risk of PD, following a gout diagnosis, were included if they were cross-sectional, case-control or cohort studies. The Newcastle Ottawa Scale (NOS) checklist was used to assess the quality of all included studies. The meta-analysis was performed using STATA 17.0. RESULTS: Ten studies were included, which were comprised of three case-controls, six cohort studies and one nested case-control study. We found no significant association between gout and the risk of PD among both sexes (RR = 0.94, 95% CI: 0.86-1.04), although the association was significant for females (RR = 1.09; 95% CI: 1.02-1.17). Subgroup analysis also showed no significant findings by age group, whether they were receiving treatment for gout, study design, quality assessment score, and method of gout ascertainment. In contrast, the studies that defined PD according to the use of drugs showed significant results (RR = 0.82; 95% CI: 0.76-0.89). There was a significant publication bias on the association between gout and PD. CONCLUSIONS: The presence of gout had no significant effect on the risk of subsequently developing PD. Further analyses are recommended to investigate the effects of demographic and behavioral risk factors.

Physics-Informed Machine Learning Improves Detection of Head Impacts.

In this work we present a new physics-informed machine learning model that can be used to analyze kinematic data from an instrumented mouthguard and detect impacts to the head. Monitoring player impacts is vitally important to understanding and protecting from injuries like concussion. Typically, to analyze this data, a combination of video analysis and sensor data is used to ascertain the recorded events are true impacts and not false positives. In fact, due to the nature of using wearable devices in sports, false positives vastly outnumber the true positives. Yet, manual video analysis is time-consuming. This imbalance leads traditional machine learning approaches to exhibit poor performance in both detecting true positives and preventing false negatives. Here, we show that by simulating head impacts numerically using a standard Finite Element head-neck model, a large dataset of synthetic impacts can be created to augment the gathered, verified, impact data from mouthguards. This combined physics-informed machine learning impact detector reported improved performance on test datasets compared to traditional impact detectors with negative predictive value and positive predictive values of 88 and 87% respectively. Consequently, this model reported the best results to date for an impact detection algorithm for American football, achieving an F1 score of 0.95. In addition, this physics-informed machine learning impact detector was able to accurately detect true and false impacts from a test dataset at a rate of 90% and 100% relative to a purely manual video analysis workflow. Saving over 12 h of manual video analysis for a modest dataset, at an overall accuracy of 92%, these results indicate that this model could be used in place of, or alongside, traditional video analysis to allow for larger scale and more efficient impact detection in sports such as American Football.

Application of Modified Spent Mushroom Compost Biochar (SMCB/Fe) for Nitrate Removal from Aqueous Solution.

The public is already aware that nitrate pollution caused by nutrient runoff from farms is harmful to aquatic life and human health, and there is an urgent need for a product/technology to solve this problem. A biochar adsorbent was synthesized and used to remove nitrate ions from aqueous media based on spent mushroom compost (SMC), pre-treated with iron (III) chloride hexahydrate and pyrolyzed at 600 °C. The surface properties and morphology of SMCB/Fe were investigated using Fourier transform-infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and scanning electron microscopy (SEM). The effect of main parameters such as the adsorbent dosages, pH of the solutions, contact times, and ion concentrations on the efficiency of nitrate removal was investigated. The validity of the experimental method was examined by the isothermal adsorption and kinetic adsorption models. The nitrate sorption kinetics were found to follow the pseudo-second-order model, with a higher determination coefficient (0.99) than the pseudo-first-order (0.86). The results showed that the maximum percentage of nitrate adsorption was achieved at equilibrium pH 5-7, after 120 min of contact time, and with an adsorbent dose of 2 g L-1. The highest nitrate adsorption capacity of the modified adsorbent was 19.88 mg g-1.

Dexmedetomidine as an adjunctive treatment for acute asthma.

OBJECTIVE: This study aimed to compare the efficacy of using dexmedetomidine with salbutamol and salbutamol nebulization alone in patients with acute exacerbation of asthma presenting to the emergency department. METHODS: This clinical trial included 60 patients, in the age range of 18 to 55 years, with signs of bronchospasm presenting to the emergency department. In the intervention group, dexmedetomidine 0.5 µg/kg was injected intravenously and three doses of salbutamol nebulization were administered over 60 minutes. In the control group, salbutamol nebulization was administered for 60 minutes three times. The patient's clinical status, based on clinical symptoms, consciousness, speech, breathing rate, heart rate, and blood pressure were recorded before the intervention, and peak expiratory flow rate and forced expiratory volume in 1 second were measured at 20, 40, and 60 minutes after intervention. Patients who did not respond to the intervention were excluded from the study within 60 minutes. RESULTS: The increased mean forced expiratory volume in 1 second and mean peak expiratory flow rate were found to be similar in both groups during the treatment (P=0.304). The mean systolic and diastolic blood pressure recorded at 40 and 60 minutes were significantly lower in the intervention group. During this study, no patient was excluded before 60 minutes. CONCLUSION: Administration of dexmedetomidine in addition to standard salbutamol treatment has no beneficial effect in patients with acute asthma attacks and merely causes hypotension in patients.

Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation.

Brain tissue deformation resulting from head impacts is primarily caused by rotation and can lead to traumatic brain injury. To quantify brain injury risk based on measurements of kinematics on the head, finite element (FE) models and various brain injury criteria based on different factors of these kinematics have been developed, but the contribution of different kinematic factors has not been comprehensively analyzed across different types of head impacts in a data-driven manner. To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in (1) the derivative order (angular velocity, angular acceleration, angular jerk), (2) the direction and (3) the power (e.g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events. Ordinary least squares regressions were built from kinematics factors to the 95% maximum principal strain (MPS95), and we compared zero-order correlation coefficients, structure coefficients, commonality analysis, and dominance analysis. The angular acceleration, the magnitude and the first power factors showed the highest predictive power for the majority of impacts including laboratory impacts, American football impacts, with few exceptions (angular velocity for MMA and NASCAR impacts). The predictive power of rotational kinematics about three directions (x: posterior-to-anterior, y: left-to-right, z: superior-to-inferior) of kinematics varied with different sports and types of head impacts.

The relationship between brain injury criteria and brain strain across different types of head impacts can be different.

Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different head impact types (e.g. sports and car crashes) are widely used in risk evaluation. However, the accuracy of using the BIC on brain injury risk estimation across head impact types has not been evaluated. Physiologically, brain strain is often considered the key parameter of brain injury. To evaluate the BIC's risk estimation accuracy across five datasets comprising different head impact types, linear regression was used to model 95% maximum principal strain, 95% maximum principal strain at the corpus callosum and cumulative strain damage (15%) on 18 BIC. The results show significantly different relationships between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain across head impact types. The accuracy of brain strain regression is generally decreasing if the BIC regression models are fitted on a dataset with a different type of head impact rather than on the dataset with the same type. Given this finding, this study raises concerns for applying BIC to estimate the brain injury risks for head impacts different from the head impacts on which the BIC was developed.

Rapid Estimation of Entire Brain Strain Using Deep Learning Models.

OBJECTIVE: Many recent studies suggest that brain deformation resulting from head impacts are linked to the corresponding clinical outcome, such as mild traumatic brain injury (mTBI). Even if several finite element (FE) head models have been developed and validated to calculate brain deformation based on impact kinematics, the clinical application of these FE head models is limited due to the time-consuming nature of FE simulations. This work aims to accelerate the brain deformation calculation and thus improve the potential for clinical applications. METHODS: We propose a deep learning head model with a five-layer deep neural network and feature engineering, and trained and tested the model on 2511 head impacts from a combination of head model simulations and on-field college football and mixed martial arts impacts. RESULTS: The proposed deep learning head model can calculate the maximum principal strain (Green Lagrange) for every element in the entire brain in less than 0.001 s with an average root mean squared error of 0.022 and a standard deviation of 0.001 over twenty repeats with random data partition and model initialization. CONCLUSION: Trained and tested using the dataset of 2511 head impacts, this model can be applied to various sports in the calculation of brain strain with accuracy, and its applicability can even further be extended by incorporating data from other types of head impacts. SIGNIFICANCE: In addition to the potential clinical application in real-time brain deformation monitoring, this model will help researchers estimate the brain strain from a large number of head impacts more efficiently than using FE models.

A Computational Study of Liquid Shock Absorption for Prevention of Traumatic Brain Injury.

Mild traumatic brain injury (mTBI), more colloquially known as concussion, is common in contact sports such as American football, leading to increased scrutiny of head protective gear. Standardized laboratory impact testing, such as the yearly National Football League (NFL) helmet test, is used to rank the protective performance of football helmets, motivating new technologies to improve the safety of helmets relative to existing equipment. In this work, we hypothesized that a helmet which transmits a nearly constant minimum force will result in a reduced risk of mTBI. To evaluate the plausibility of this hypothesis, we first show that the optimal force transmitted to the head, in a reduced order model of the brain, is in fact a constant force profile. To simulate the effects of a constant force within a helmet, we conceptualize a fluid-based shock absorber system for use within a football helmet. We integrate this system within a computational helmet model and simulate its performance on the standard NFL helmet test impact conditions. The simulated helmet is compared with other helmet designs with different technologies. Computer simulations of head impacts with liquid shock absorption predict that, at the highest impact speed (9.3 m/s), the average brain tissue strain is reduced by 27.6% ± 9.3 compared to existing helmet padding when tested on the NFL helmet protocol. This simulation-based study puts forth a target benchmark for the future design of physical manifestations of this technology.

Synthesis, Molecular Docking and Cytotoxic Activity Evaluation of Organometallic Thiolated Gold(I) Complexes.

The complex [(PhCH2NC)AuCl], 1, was prepared by the reaction of [(Me2S)AuCl], A, with an equimolar amount of benzyl isocyanide (PhCH2NC) ligand. Through a salt metathesis reaction, the chloride ligand in 1 was replaced by potassium benzothiazole-2-thiolate (Kbt) and potassium benzoimidazole-2-thiolate (Kbi) to afford complexes (PhCH2NC)Au(κ1-S-bt)], 2a and (PhCH2NC)Au(κ1-S-bi)], 2b, respectively, which were characterized by NMR spectroscopy. The cytotoxic activities of 2a and 2b were evaluated against three human cancer cell lines, including A549 (lung), SKOV3 (ovary), and MCF-7 (breast). Our results indicated that 2a exhibited comparable cytotoxicity on investigated cell lines with cisplatin. It showed a good anti-proliferative activity with IC50 of 19.46, 11.76 and 13.27 µM against A549, SKOV3 and MCF-7 cell lines, respectively. The effects of these complexes on the proliferation of the non-tumorigenic epithelial breast cell line (MCF-10A) showed their good selectivity between the tumorigenic and non-tumorigenic cell lines. Molecular docking simulation studies were also conducted to determine the specific binding site and binding mode of the synthesized gold complexes to DNA and thioredoxinreductase (TrxR) as their proposed targets.

Methods for the Evaluation of the Bioactivity and Biocontrol Potential of Species of Trichoderma.

Members of the genus Trichoderma comprise the majority of commercial fungal biocontrol agents of plant diseases. As such, there is a wealth of information available on the analysis of their biocontrol potential and the mechanisms behind their superior abilities. This chapter aims to summarize the most common methods utilized within a Trichoderma biocontrol program for assessing the biological properties of individual strains.

Improved rapid detection of trypsin isoinhibitors using non-denaturing polyacrylamide gels with immobilised azoalbumin.

INTRODUCTION: Proteinaceous inhibitors of animal trypsin occur naturally as isoforms in seeds and some are of interest as antinutritional or anti-pest agents. OBJECTIVE: To establish a simplified electrophorectic, in-gel method for rapid and direct detection of trypsin isoinhibitors present in crude plant extracts that are particularly suitable for many studies including rapid evaluation of cultivars. METHODOLOGY: Azoalbumin (3%, w/v) is immobilised in 7.5% polyacrylamide gels before electrophoresis under non-denaturing conditions. RESULTS: This improved method eliminates the need for both time-consuming and labourious staining and destaining or renaturation steps. CONCLUSION: Immobilised azoalbumin in polyacrylamide gels, run under non-denaturing electrophoresis conditions, can be used to assist rapid evaluation of trypsin isoinhibitors in numerous crude plant extracts.

Conidiogenic effects of mannose-binding lectins isolated from cotyledons of red kidney bean (Phaseolus vulgaris) on Alternaria alternata.

Effect of proteinaceous extracts from red kidney bean cotyledons on mycelium of Alternaria alternata growing on potato dextrose agar (PDA) plates was investigated. Unexpectedly, conidia formation was induced in response to applied crude extracts. A PDA disc method was developed to quantify conidia formed. A purified fraction retaining conidiation inducing effect (CIE) was obtained following several protein purification procedures including the last step of eluting bound proteins from an Affi-gel blue gel column. Based on MALDI (matrix assisted laser desorption/ionization) mass spectrometric analysis, a previously identified mannose-binding lectin (MBL) called PvFRIL (Phaseolus vulgaris fetal liver tyrosine kinase 3-receptor interacting lectin) was present in this conidiation inducing fraction. The PvFRIL was subsequently purified using a single step mannose-agarose affinity column chromatography. When the lectin was applied exogenously to A. alternata, increased conidiation resulted. The conidia produced in response to the MBL were similar to those induced by other methods and their germ tubes were longer after 12 h growth than those induced under white light. To our knowledge this is the first report of exogenous application of a PvFRIL or another purified protein from a plant inducing conidia formation in a fungus.