•The membranes containing PVP demonstrated significant modifications in its thermal properties.•BMV release followed Korsmeyer and Peppas model (n > 0.89) suggesting that its diffusion in the swollen ...matrix is driven by polymer relaxation.•The developed mucoadhesive membranes are an interesting and promising system to deliver BMV for the treatment of RAS.
Mucoadhesive membranes were proposed in this study as drug delivery system for betamethasone-17-valerate (BMV) in the treatment of recurrent aphthous stomatitis (RAS). The membranes were obtained by using the polymers chitosan (CHI) in both presence and absence of polyvinilpyrrolidone (PVP), following the solvent evaporation method. The presence of PVP in the membranes causes significant modifications in its thermal properties. Changes in the thermal events at 114 and 193 °C (related to BMV melting point), and losses in mass (39.38 and 30.68% for CH:PVP and CH:PVP-B, respectively), suggests the incorporation of BMV in these membranes. However, the morphological aspects of the membranes do not change after adding PVP and BMV. PVP causes changes in swelling ratios (>80%) of the membranes, and it is suggested that the reorganization of the polymer mesh was highlighted by the chemical interactions between the polymers leading to different percentages of BMV released ∼40% and ∼80% from CH-B and CH:PVP-B. BMV release profile follows Korsmeyer and Peppas model (n > 0.89) which suggests that the diffusion of the drug in the swollen matrix is driven by polymer relaxation. In addition, the membranes containing PVP (higher swelling ability) present high rates of tensile strength, and therefore, higher mucoadhesion. Moreover, given the results presented, the developed mucoadhesive membranes are a promising system to deliver BMV for the treatment of RAS.
Seven fuels (four types of wood pellets and three agro-fuels) were tested in an automatic pellet stove (9.5 kWth) in order to determine emission factors (EFs) of gaseous compounds, such as carbon ...monoxide (CO), methane (CH4), formaldehyde (HCHO), and total organic carbon (TOC). Particulate matter (PM10) EFs and the corresponding chemical compositions for each fuel were also obtained. Samples were analysed for organic carbon (OC) and elemental carbon (EC), anhydrosugars and 57 chemical elements. The fuel type clearly affected the gaseous and particulate emissions. The CO EFs ranged from 90.9 ± 19.3 (pellets type IV) to 1480 ± 125 mg MJ−1 (olive pit). Wood pellets presented the lowest TOC emission factor among all fuels. HCHO and CH4 EFs ranged from 1.01 ± 0.11 to 36.9 ± 6.3 mg MJ−1 and from 0.23 ± 0.03 to 28.7 ± 5.7 mg MJ−1, respectively. Olive pit was the fuel with highest emissions of these volatile organic compounds. The PM10 EFs ranged from 26.6 ± 3.14 to 169 ± 23.6 mg MJ−1. The lowest PM10 emission factor was found for wood pellets type I (fuel with low ash content), whist the highest was observed during the combustion of an agricultural fuel (olive pit). The OC content of PM10 ranged from 8 wt.% (pellets type III) to 29 wt.% (olive pit). Variable EC particle mass fractions, ranging from 3 wt.% (olive pit) to 47 wt.% (shell of pine nuts), were also observed. The carbonaceous content of particulate matter was lower than that reported previously during the combustion of several wood fuels in traditional woodstoves and fireplaces. Levoglucosan was the most abundant anhydrosugar, comprising 0.02–3.03 wt.% of the particle mass. Mannosan and galactosan were not detected in almost all samples. Elements represented 11–32 wt.% of the PM10 mass emitted, showing great variability depending on the type of biofuel used.
•The highest emission factors were obtained for agro-fuels.•Organic carbon contributed no more than 30% of the PM10 mass.•Mannosan and galactosan were not detected in almost all samples.•Treated wood in pellets generated high contents of Pb, Zn and As in PM10.
Macrophages play a very important role in the conduction of several regenerative processes mainly due to their plasticity and multiple functions. In the muscle repair process, while M1 macrophages ...regulate the inflammatory and proliferative phases, M2 (anti‐inflammatory) macrophages direct the differentiation and remodelling phases, leading to tissue regeneration. The aim of this study was to evaluate the effect of red and near infrared (NIR) photobiomodulation (PBM) on macrophage phenotypes and correlate these findings with the repair process following acute muscle injury. Wistar rats were divided into 4 groups: control; muscle injury; muscle injury + red PBM; and muscle injury + NIR PBM. After 2, 4 and 7 days, the tibialis anterior muscle was processed for analysis. Macrophages phenotypic profile was evaluated by immunohistochemistry and correlated with the different stages of the skeletal muscle repair by the qualitative and quantitative morphological analysis as well as by the evaluation of IL‐6, TNF‐α and TGF‐β mRNA expression. Photobiomodulation at both wavelengths was able to decrease the number of CD68+ (M1) macrophages 2 days after muscle injury and increase the number of CD163+ (M2) macrophages 7 days after injury. However, only NIR treatment was able to increase the number of CD206+ M2 macrophages (Day 2) and TGF‐β mRNA expression (Day 2, 4 and 7), favouring the repair process more expressivelly. Treatment with PBM was able to modulate the inflammation phase, optimize the transition from the inflammatory to the regeneration phase (mainly with NIR light) and improve the final step of regeneration, enhancing tissue repair.
Bees have the potential to be used as indicators of environmental quality. The parameters typically analyzed in this context include species diversity, colony condition and foraging behavior. Bees ...explore the area around their nests, whose size and location vary based on the flight ranges and nesting preferences of the respective species. The environment around the nest must contain appropriate resources, which are collected by bees during foraging. Therefore, the internal nest environment is connected to the external environment via foraging. Until early 2000, direct observations and/or video recording of the foraging activity of social bees were the predominant techniques for studying foraging behavior, and paint marks or labels were used to distinguish individuals. Although these techniques are still used, radio-frequency identification (RFID) technology has been used for bee monitoring and can automatically count the inbound and outbound movements of bees from the nest and perform individual recognition. Here, we review the applications of RFID technology in bee research and discuss the advantages and disadvantages of RFID compared with those of other techniques.
Some of the well-known drawbacks of clinically approved Pt
complexes can be overcome using six-coordinate Pt
complexes as inert prodrugs, which release the corresponding four-coordinate active Pt
...species upon reduction by cellular reducing agents. Therefore, the key factor of Pt
prodrug mechanism of action is their tendency to be reduced which, when the involved mechanism is of outer-sphere type, is measured by the value of the reduction potential. Machine learning (ML) models can be used to effectively capture intricate relationships within Pt
complex data, leading to highly accurate predictions of reduction potentials and other properties, and offering significant insights into their electrochemical behavior and potential applications. In this study, a machine learning-based approach for predicting the reduction potentials of Pt
complexes based on relevant molecular descriptors is presented. Leveraging a data set of experimentally determined reduction potentials and a diverse range of molecular descriptors, the proposed model demonstrates remarkable predictive accuracy (MSE = 0.016 V
, RMSE = 0.13 V,
= 0.92). Ab initio calculations and a set of different machine learning algorithms and feature engineering techniques have been employed to systematically explore the relationship between molecular structure and similarity and reduction potential. Specifically, it has been investigated whether the reduction potential of these compounds can be described by combining ML models across different combinations of constitutional, topological, and electronic molecular descriptors. Our results not only provide insights into the crucial factors influencing reduction potentials but also offer a rapid and effective tool for the rational design of Pt
complexes with tailored electrochemical properties for pharmaceutical applications. This approach has the potential to significantly expedite the development and screening of novel Pt
prodrug candidates. The analysis of principal components and key features extracted from the model highlights the significance of structural descriptors of the 2D Atom Pairs type and the lowest unoccupied molecular orbital energy. Specifically, with just 20 appropriately selected descriptors, a notable separation of complexes based on their reduction potential value is achieved.
Some of the well-known drawbacks of clinically approved PtII complexes can be overcome using six-coordinate PtIV complexes as inert prodrugs, which release the corresponding four-coordinate active ...PtII species upon reduction by cellular reducing agents. Therefore, the key factor of PtIV prodrug mechanism of action is their tendency to be reduced which, when the involved mechanism is of outer-sphere type, is measured by the value of the reduction potential. Machine learning (ML) models can be used to effectively capture intricate relationships within PtIV complex data, leading to highly accurate predictions of reduction potentials and other properties, and offering significant insights into their electrochemical behavior and potential applications. In this study, a machine learning-based approach for predicting the reduction potentials of PtIV complexes based on relevant molecular descriptors is presented. Leveraging a data set of experimentally determined reduction potentials and a diverse range of molecular descriptors, the proposed model demonstrates remarkable predictive accuracy (MSE = 0.016 V2, RMSE = 0.13 V, R 2 = 0.92). Ab initio calculations and a set of different machine learning algorithms and feature engineering techniques have been employed to systematically explore the relationship between molecular structure and similarity and reduction potential. Specifically, it has been investigated whether the reduction potential of these compounds can be described by combining ML models across different combinations of constitutional, topological, and electronic molecular descriptors. Our results not only provide insights into the crucial factors influencing reduction potentials but also offer a rapid and effective tool for the rational design of PtIV complexes with tailored electrochemical properties for pharmaceutical applications. This approach has the potential to significantly expedite the development and screening of novel PtIV prodrug candidates. The analysis of principal components and key features extracted from the model highlights the significance of structural descriptors of the 2D Atom Pairs type and the lowest unoccupied molecular orbital energy. Specifically, with just 20 appropriately selected descriptors, a notable separation of complexes based on their reduction potential value is achieved.
Wood combustion experiments were carried out to determine the effect of ignition technique, biomass load and cleavage, as well as secondary air supply, on carbon monoxide (CO), total hydrocarbon ...(THC), particulate matter (PM10) and particle number emissions from a woodstove. Wood from two typical tree species in the Iberian Peninsula was selected: pine (Pinus pinaster) and beech (Fagus sylvatica). The highest CO and total hydrocarbon emission factors (EFs) were observed, respectively, for pine and beech, for high and low fuel loads. The highest PM10 EF was recorded for the operation with low loads for both woods. Secondary air supply produced the lowest PM10 emission factors. The top ignition can decrease the PM10 EF to less than half when compared with the common technique of lighting from the bottom. The lowest particle number emission factors were observed when operating with high loads of split beech logs and when using secondary air supply during the combustion of pine. Regarding particle number distributions, the highest geometric mean diameter (Dg), for both woods, were observed when operating with high loads (with split and non-split wood).
•Operating conditions have great influence on emissions.•Top ignition can reduce by 50% the PM10 emissions in relation to bottom lighting.•Secondary air supply produced the lowest PM10 emission factors.•Highest Dg was observed for operation with high loads.
Euthanasia of infected dogs is one of the measures adopted in Brazil to control visceral leishmaniasis (VL) in endemic areas. To detect infected dogs, animals are screened with the rapid test DPP® ...Visceral Canine Leishmaniasis for detection of antibodies against K26/K39 fusion antigens of amastigotes (DPP). DPP-positives are confirmed with an immunoenzymatic assay probing soluble antigens of promastigotes (ELISA), while DPP-negatives are considered free of infection. Here, 975 dogs from an endemic region were surveyed by using DPP, ELISA and real-time PCR (qPCR) for the diagnosis of VL. When DPP-negative dogs were tested by qPCR applied in blood and lymph node aspirates, 174/887 (19·6%) were positive in at least one sample. In a second sampling using 115 cases, the DPP-negative dogs were tested by qPCR in blood, lymph node and conjunctival swab samples, and 36/79 (45·6%) were positive in at least one sample. Low-to-moderate pairwise agreement was observed between all possible pair of tests. In conclusion, the official diagnosis of VL in dogs in Brazilian endemic areas failed to accuse an expressive number of infected animals and the impact of the low accuracy of serological tests in the success of euthanasia-based measure for VL control need to be assessed.
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