Average Purity Research Articles

An Entropy-Based Clustering Algorithm for Real-Time High-Dimensional IoT Data Streams.

The rapid growth of data streams, propelled by the proliferation of sensors and Internet of Things (IoT) devices, presents significant challenges for real-time clustering of high-dimensional data. Traditional clustering algorithms struggle with high dimensionality, memory and time constraints, and adapting to dynamically evolving data. Existing dimensionality reduction methods often neglect feature ranking, leading to suboptimal clustering performance. To address these issues, we introduce E-Stream, a novel entropy-based clustering algorithm for high-dimensional data streams. E-Stream performs real-time feature ranking based on entropy within a sliding time window to identify the most informative features, which are then utilized with the DenStream algorithm for efficient clustering. We evaluated E-Stream using the NSL-KDD dataset, comparing it against DenStream, CluStream, and MR-Stream. The evaluation metrics included the average F-Measure, Jaccard Index, Fowlkes-Mallows Index, Purity, and Rand Index. The results show that E-Stream outperformed the baseline algorithms in both clustering accuracy and computational efficiency while effectively reducing dimensionality. E-Stream also demonstrated significantly less memory consumption and fewer computational requirements, highlighting its suitability for real-time processing of high-dimensional data streams. Despite its strengths, E-Stream requires manual parameter adjustment and assumes a consistent number of active features, which may limit its adaptability to diverse datasets. Future work will focus on developing a fully autonomous, parameter-free version of the algorithm, incorporating mechanisms to handle missing features and improving the management of evolving clusters to enhance robustness and adaptability in dynamic IoT environments.

Multi-Rotor Drone-Based Thermal Target Tracking with Track Segment Association for Search and Rescue Missions

Multi-rotor drones have expanded their range of applications, one of which being search and rescue (SAR) missions using infrared thermal imaging. This paper addresses thermal target tracking with track segment association (TSA) for SAR missions. Three types of associations including TSA are developed with an interacting multiple model (IMM) approach. During multiple-target tracking, tracks are initialized, maintained, and terminated. There are three different associations in track maintenance: measurement–track association, track–track association for tracks that exist at the same time (track association and fusion), and track–track association for tracks that exist at separate times (TSA). Measurement–track association selects the statistically nearest measurement and updates the track with the measurement through the IMM filter. Track association and fusion fuses redundant tracks for the same target that are spatially separated. TSA connects tracks that have become broken and separated over time. This process is accomplished through the selection of candidate track pairs, backward IMM filtering, association testing, and an assignment rule. In the experiments, a drone was equipped with an infrared thermal imaging camera, and two thermal videos were captured of three people in a non-visible environment. These three hikers were located close together and occluded by each other or other obstacles in the mountains. The drone was allowed to move arbitrarily. The tracking results were evaluated by the average total track life, average mean track life, and average track purity. The track segment association improved the average mean track life of each video by 99.8% and 250%, respectively

Static expansion fracturing mechanism for enhancing gas permeability in low permeability coal seams

Extraction of gas from low-permeability thick coal seams poses challenges globally, attributed to low extraction efficiency, limited enrichment content, and extraction complexities. Investigating static fracturing in low-permeability thick coal seams holds substantial engineering significance and practical utility. This research delves into the current conditions in the Western region, characterized by low gas permeability and challenging extraction in thick coal seams. Through the utilization of FLAC3D and COMSOL, simulation schemes are devised to analyze the influence of borehole parameters on fracture efficacy, elucidating the mechanisms by which external loads and internal gas pressure impact coal seam permeability. Field monitoring tests are employed to evaluate and model gas extraction enhancement via borehole positioning. The results suggest that the application of a static fracturing agent inducing 40 MPa expansion stress, along with a 75 mm borehole diameter and 0.5 m spacing, effectively fractures low-permeability thick coal seams. The spacing between extraction and fracturing holes adversely affects gas extraction efficiency due to the limited range of static fracturing. Field experiments demonstrate that static fracturing significantly improves gas extraction from low-permeability thick coal seams, resulting in a twofold increase in average gas extraction purity post-fracturing. This study establishes a robust theoretical foundation for optimizing gas extraction and mining activities in challenging low-permeability thick coal seam environments.

Imaging Mutant Huntingtin Aggregates using [18F]CHDI-650 PET in a preclinical model of Huntington’s disease

Huntington’s disease (HD) is a fatal neurodegenerative disorder caused by a CAG repeat expansion in the huntingtin gene (HTT) that encodes pathogenic mutant huntingtin (mHTT) protein. Therapeutic strategies aimed at lowering mHTT would benefit from non-invasive PET-based methods to evaluate treatment response. The CHDI foundation developed the first-generation PET ligands labelled with 11C, which advanced to clinical evaluation. Further optimization generated an 18F radioligand with improved metabolic stability, brain exposure and specificity, shown by PET imaging in wild-type mice and autoradiography in HD brain samples1,2. Here, we characterised the PET radioligand [18F]CHDI-650 in the R6/1 mouse model of HD to assess its sensitivity for monitoring treatment response of mHTT lowering therapies. [18F]CHDI-650 was produced according to an established method3 using a TRACERlab system. Radiochemical purity/molar activity of the final [18F]CHDI-650 product was detected by reverse-phase HPLC analysis. Dynamic PET imaging was conducted using R6/1 mice and wild-type littermates at various disease stages. Image processing and kinetic modelling (2TCM) analysis were performed using PMOD software. Ex vivo brain radioactivity was measured using gamma counting. Brains were cryo-sectioned and processed for immunofluorescence to identify HTT load. [18F]CHDI-650 was produced (1.3 ± 0.2 GBq) with an average radiochemical purity of 99.9 ± 0.1 % and a molar activity of 32.4 ± 5.2 GBq/µmol (n = 9). The radioligand was able to discriminate R6/1 HET from WT mice at an early presymptomatic age for both dynamic PET imaging (striatal) and ex vivo whole brain gamma. A well-defined age associated increase was also evident using both measures. Dual immunofluorescence spatially defined the level of aggregated HTT in striatal and hippocampal regions across the age groups. [18F]CHDI-650 PET shows great potential as a non-invasive tool for visualising the development of mHTT pathology and as a biomarker for preclinical efficacy studies. Please click on the 'PDF' for the full abstract!

Modelling of a continuous sorption-enhanced methanation process in an adiabatic packed-bed reactor system

The present study investigates a sorption-enhanced methanation process and successive solid regeneration stages using a dual-function catalyst containing nickel as a catalyst and zeolite 13X for water removal. An adiabatic packed bed, modelled by a dynamical and heterogeneous model, has been considered, and a five-stage sequence describes the sorption-enhanced methanation and successive solid regeneration. First, methanation occurs with in-situ water removal; then, the catalyst drying using a pressure and temperature swing approach implemented by blowdown, purge, cooling, and pressurisation stages. In adiabatic operation, heat management is crucial; on the other hand, the heat produced can be efficiently used to dry the zeolite. Process intensification is pursued by addressing the effect of gas inlet temperature, pressure, and GHSV on system performance. Achieving an average purity of 99 % of the methane for pressures greater than 2 bar is possible for long-time operation, with productivity averaging 0.8 mol/(kgads min) at the highest gas hourly space velocity investigated.

A Double Lysis Method for Animal Rotavirus RNA Extraction From Stool Samples.

Globally, porcine rotavirus is a leading cause of gastroenteritis in nursing and post-weaning piglets, as well as adult pigs. Between February 2015 and June 2016, 156 fecal samples were collected from pigs in the Northeastern part of Accra, Ghana, and screened for Group A rotavirus using the ProflowTM Kit. Here, we describe different extraction methods that were employed to recover high-quality RNA for downstream analysis, with emphasis on a novel hybrid extraction method. The hybrid approach with a kit and manual extraction method led to a 10-fold greater RNA yield versus the kit-based method alone. The new extraction method gave an average purity ratio (A260/A280) of 1.8, which was also significantly higher than that obtained solely from the manual or kit-based extraction methods. Our novel hybrid approach will be useful in the extraction of rotavirus from animal fecal samples, thus improving the yield of RNA for downstream analysis. © 2024 Wiley Periodicals LLC. Basic Protocol: Hybrid 2: A double lysis method for RNA extraction from animal stool samples Support Protocol 1: The GenElute extraction method Support Protocol 2: Hybrid 1 extraction method.

High-purity ethylene production via indirect carbon dioxide electrochemical reduction

High-purity ethylene production from CO2 electroreduction (CO2RR) is a coveted, yet arduous feat because the product stream comprises a blend of unreacted CO2, H2, and other off-target CO2 reduction products. Here we present an indirect reduction strategy for CO2-to-ethylene conversion, one that employs 2-bromoethanol (Br-EO) as a mediator. Br-EO is initially generated from CO2RR and subsequently undergoes reduction to ethylene without the need for energy-intensive separation steps. The optimized AC-Ag/C catalyst with Cl incorporation reduces the energy barrier of the debromination step during Br-EO reduction, and accelerates the mass-transfer process, delivering a 4-fold decrease of the relaxation time constant. Resultantly, AC-Ag/C achieved a FEethylene of over 95.0 ± 0.36% at a low potential of −0.08 V versus reversible hydrogen electrode (RHE) in an H-type cell with 0.5 M KCl electrolyte, alongside a near 100% selectivity within the range of −0.38 to −0.58 V versus RHE. Through this indirect strategy, the average ethylene purity within 6-hour electrolysis was 98.00 ± 1.45 wt%, at −0.48 V (vs RHE) from the neutralized electrolyte after CO2 reduction over the Cu/Cu2O catalyst in a flow-cell.

An electrochemical generator for the continual supply of 213Bi from 225Ac for use in targeted alpha therapy applications

Bismuth-213 is a radionuclide of interest for targeted alpha therapy and is supplied via a radiochemical generator system through the decay of 225Ac. Radionuclide generators employ longer lived "parent" radionuclides to routinely supply shorter-lived "daughter" radionuclides. The traditional 225Ac/213Bi radiochemical generator relies on an organic cation exchange resin where 225Ac binds to the resin and 213Bi is routinely eluted. These resins degrade when they absorb large doses of ionizing radiation (>1×106Gy/mg), which has been observed when the loading activity of 225Ac exceeds 2.59*109Bq (70mCi). Herein we report the development of an electrochemical generator for the supply of 213Bi that has the potential to overcome this limitation. Bismuth-213 spontaneously electrodeposits onto nickel foils in 0.1M hydrochloric acid at 70°C. Using this method, we were able to plate an average of 73±4% of the 213Bi in solution and obtain a final 213Bi recovery of 65±8% in 0.1M citrate pH4.5 via reverse electrolysis using titanium as the cathode. The recovered 213Bi had an average radiochemical purity of >99.8% and was successfully used to radiolabel DOTATATE with an average radiochemical yield of 85.1% (not optimized).

Structural, thermal, and physicochemical properties of ultrasound-assisted extraction of faba bean protein isolate (FPI)

This study aimed to understand the impact of ultrasonication (24 kHz) at a fixed power (100 W) and duration (5–60 min) on the physicochemical, structural, and thermal properties of faba bean protein isolates. Ultrasound-relevant parameters such as acoustic density, intensity, and yield conversion were estimated. The average protein purity (∼85% protein) and yield (up to 22 %) of the ultrasound-assisted protein isolate were higher than those of the control protein isolate. Compared to the original flour, the isolate had significantly lower levels of vicine and convicine. Electrophoresis revealed no substantial alterations in the primary structure of the native and sonicated faba bean isolates. SDS-PAGE and SE-HPLC showed that sonicated samples had profiles similar to those of the native protein isolate. FTIR spectra and X-ray diffraction (XRD) patterns were used to measure structural changes in all faba bean protein isolates (FBPI). XRD study revealed two distinct diffraction peaks at 2 θ = 10° and 2 θ = 20° for protein isolates, indicating that alteration in native conformational crystallite size was altered after the ultrasound application.

Anti-inflammatory Evaluation of Cannabidiol from Cannabis sativa L. Obtained Using Supercritical Fluid Extraction and Cannabidiol Extract-based Cream

Purpose: This study evaluated the anti-inflammatory properties of a cannabidiol (CBD) extract obtained by supercritical fluid extraction (SFE) from <i>Cannabis sativa</i> L. and a cream containing the extract. Methods: The CBD extract was quantitatively analyzed using high-performance liquid chromatography. We used a lipopolysaccharide (LPS)-induced RAW 264.7 cell model of inflammation to determine the anti-inflammatory effect of the CBD extract by measuring nitric oxide (NO) production. We evaluated the anti-inflammatory properties of a cream containing the CBD extract by measuring interleukin-1 alpha (IL-1α) levels induced by sodium dodecyl sulfate (SDS) in KeraSkin<sup>TM</sup>, a human skin model. The cytotoxicity of the CBD and CBD-containing cream was assessed by MTT assay in each model. Results: The average purity of the CBD extracted and refined by SFE was 99.40%. The CBD extract and CBD cream showed concentration-dependent inhibition of inflammation. At 4 μg/mL, NO production in the RAW 264.7 cell model was significantly decreased by 64.98% compared with the negative control. IL-1α production induced by SDS in the KeraSkin<sup>TM</sup> model decreased by 54.17% in the group treated with cream containing 0.4% CBD compared with the negative control and 22.09% compared with the positive control groups. Conclusion: The medicinal use of CBD is attracting interest in Korea. Our study confirmed its exceptional anti-inflammatory properties and effectiveness in a cream product. Should additional research confirm its safety and efficacy, CBD is expected to replace existing anti-inflammatory ingredients.

Using unsupervised learning to classify inlet water for more stable design of water reuse in industrial parks.

The water reuse facilities of industrial parks face the challenge of managing a growing variety of wastewater sources as their inlet water. Typically, this clustering outcome is designed by engineers with extensive expertise. This paper presents an innovative application of unsupervised learning methods to classify inlet water in Chinese water reuse stations, aiming to reduce reliance on engineer experience. The concept of 'water quality distance' was incorporated into three unsupervised learning clustering algorithms (K-means, DBSCAN, and AGNES), which were validated through six case studies. Of the six cases, three were employed to illustrate the feasibility of the unsupervised learning clustering algorithm. The results indicated that the clustering algorithm exhibited greater stability and excellence compared to both artificial clustering and ChatGPT-based clustering. The remaining three cases were utilized to showcase the reliability of the three clustering algorithms. The findings revealed that the AGNES algorithm demonstrated superior potential application ability. The average purity in six cases of K-means, DBSCAN, and AGNES were 0.947, 0.852, and 0.955, respectively.

A Multi-step High-Performance Liquid Chromatography (HPLC) Technique for the Extraction and Purification of Medical-Grade Cyclosporine A

Cyclosporine A (CsA), a potent immunosuppressive drug, is used to treat autoimmune diseases and prevent organ transplant recipients from rejecting their new organs. Maintaining CsA's uniformity and purity is essential to maintaining its therapeutic efficacy and safety. Impurities and contaminants in CsA formulations can exacerbate side effects and patient toxicity. Purification processes can improve the safety profile of the drug and reduce the likelihood of adverse effects by eliminating impurities. Using High-performance liquid chromatography (HPLC) methods, CsA has been isolated and refined from crude in recent years. However, after the procedure, the CsA purity was not at its best. To solve this issue, this work employs a multi-step HPLC method to increase purity levels to above 95%. When exploring the HPLC detection conditions of CsA, we found that the method from the Chinese pharmacopoeia: acetonitrile-water-tert-butyl methyl ether-phosphoric acid (430 : 520 : 50 : 1) was a better mobile phase solution, our further research, including the substitution of tert-butyl methyl ether with other buffer solvents, showed no significant improvement. At the same time, we established that the key factor affecting its separation was temperature. When the temperature is lower than 70 ℃, the HPLC separation effect worsens, the retention time increases, and the peak width becomes longer. Finally, the utilization of acetonitrile-water-tert-butyl methyl ether-phosphoric acid (430 : 520 : 50 : 1) as the mobile phase with a column temperature of 70 °C, petroleum ether: acetic acid Ethyl ester (70 : 30) as the mobile phase, and filling the φ 40 mm × 500 mm chromatography column using a 40~60 μm silica gel so that a 10 : 1 height-to-diameter ratio of the packed part is obtained, under normal temperature conditions, resulted in an average cyclosporine A yield of 80.1%. The corresponding average purity was 97.2%, of which the first isolation yield was 82.3%, and the purity was 98.1%.

Thermal Image Tracking for Search and Rescue Missions with a Drone

Infrared thermal imaging is useful for human body recognition for search and rescue (SAR) missions. This paper discusses thermal object tracking for SAR missions with a drone. The entire process consists of object detection and multiple-target tracking. The You-Only-Look-Once (YOLO) detection model is utilized to detect people in thermal videos. Multiple-target tracking is performed via track initialization, maintenance, and termination. Position measurements in two consecutive frames initialize the track. Tracks are maintained using a Kalman filter. A bounding box gating rule is proposed for the measurement-to-track association. This proposed rule is combined with the statistically nearest neighbor association rule to assign measurements to tracks. The track-to-track association selects the fittest track for a track and fuses them. In the experiments, three videos of three hikers simulating being lost in the mountains were captured using a thermal imaging camera on a drone. Capturing was assumed under difficult conditions; the objects are close or occluded, and the drone flies arbitrarily in horizontal and vertical directions. Robust tracking results were obtained in terms of average total track life and average track purity, whereas the average mean track life was shortened in harsh searching environments.

SarCTAB: an efficient and cost-effective DNA isolation protocol from geophytes.

Geophytes are herbaceous plants that grow anew from underground buds and are excellent models to study storage organ formation. However, molecular studies involving geophytes are constrained due to the presence of a wide spectrum of polysaccharides and polyphenols that contaminate the genomic DNA. At present, several protocols exist for the extraction of genomic DNA from different plant species; however, isolating high-quality DNA from geophytes is challenging. Such challenges are further complexed by longer incubation time and multiple precipitation steps involved in existing DNA isolation methods. To overcome such problems, we aimed to establish a DNA extraction method (SarCTAB) which is an economical, quick, and sustainable way of DNA isolation from geophytes. We improved the traditional CTAB method by optimizing key ingredients such as sarcosine, β-mercaptoethanol, and high molar concentration of sodium chloride (NaCl), which resulted in high concentration and good-quality DNA with lesser polysaccharides, proteins, and polyphenols. This method was evaluated to extract DNA from storage organs of six different geophytes. The SarCTAB method provides an average yield of 1755ng/µl of high-quality DNA from 100mg of underground storage tissues with an average standard purity of 1.86 (260/280) and 1.42 (260/230). The isolated genomic DNA performed well with Inter-simple sequence repeat (ISSR) amplification, restriction digestion with EcoRI, and PCR amplification of plant barcode genes viz. matK and rbcL. Also, the cost involved in DNA isolation was low when compared to that with commercially available kits. Overall, SarCTAB method works effectively to isolate high-quality genomic DNA in a cost-effective manner from the underground storage tissues of geophytes, and can be applied for next-generation sequencing, DNA barcoding, and whole genome bisulfite sequencing.

A novel member enhancement‐based clustering ensemble algorithm

SummaryClustering ensemble is a popular approach for identifying data clusters that combines the clustering results from multiple base clustering algorithms to produce more accurate and robust data clusters. However, the performance of clustering ensemble algorithms is highly dependent on the quality of clustering members. To address this problem, this paper proposes a member enhancement‐based clustering ensemble (MECE) algorithm that selects the ensemble members by considering their distribution consistency. MECE has two main components, called heterocluster splitting and homocluster merging. The first component estimates two probability density functions (p.d.f.s) estimated on the sample points of an heterocluster and represents them using a Gaussian distribution and a Gaussian mixture model. If the random numbers generated by these two p.d.f.s have different probability distributions, the heterocluster is then split into smaller clusters. The second component merges the clusters that have high neighborhood densities into a homocluster, where the neighborhood density is measured using a novel evaluation criterion. In addition, a co‐association matrix is presented, which serves as a summary for the ensemble of diverse clusters. A series of experiments were conducted to evaluate the feasibility and effectiveness of the proposed ensemble member generation algorithm. Results show that the proposed MECE algorithm can select high quality ensemble members and as a result yield the better clusterings than six state‐of‐the‐art ensemble clustering algorithms, that is, cluster‐based similarity partitioning algorithm (CSPA), meta‐clustering algorithm (MCLA), hybrid bipartite graph formulation (HBGF), evidence accumulation clustering (EAC), locally weighted evidence accumulation (LWEA), and locally weighted graph partition (LWGP). Specifically, MECE algorithm has the nearly 23% higher average NMI, 27% higher average ARI, 15% higher average FMI, and 10% higher average purity than CSPA, MCLA, HBGF, EAC, LWEA, and LWGA algorithms. The experimental results demonstrate that MECE algorithm is a valid approach to deal with the clustering ensemble problems.

Anaerobic digestion biogas upgrading using a two-stage membrane system under pilot-scale conditions.

In the present work, the construction, and operation of a pilot-scale biogas upgrading system is presented, employing 2 commercial polyimide (PI) membranes. The Upgrading system treats biogas produced via anaerobic digestion of the sludge, produced from the treatment of municipal wastewater in the facilities of Thessaloniki's Wastewater Treatment Plant. The goal of the separation unit is the production of high purity biomethane (>95%) for potential reuse in terms of energy. The fabrication of the pilot scale system includes the scale up of a laboratory setup separating CO2 from binary CH4–CO2 gas mixture. After the stability tests of the process, for the operation of 5 months (February to June 2023) the purity and recovery of CH4 in the final gas product. The experimental results showed an average recovery of CH4 of 95.7% for an average 55% feed composition, whereas the average purity in the final product was equal to 82.4%. The purity results were lower because of the N2 presence in the product stream (average 17.5%). After normalization with the help of the lab-scale binary results, the expected results assuming N2 absence would be 99.8% CH4 purity and 67% CH4 recovery. Finally, 3 different membrane configurations are compared in terms of their energy production, concluding to the efficiency of 2-stage configuration with recycling stream for the optimal combination of theoretical stage cut fractions.

Pereskia bleo augments NK cell cytotoxicity against triple-negative breast cancer cells (MDA-MB-231).

Natural killer cells (NK cells) are essential in cancer immunosurveillance in the body as they can recognize cancer cells that lacking MHC class 1 on their surface. Regulatory cytokines, including interleukin (IL)-18, IL-12, IL-10, IL-8, interferon-γ (IFN-γ), and secretory granules like perforin and granzyme are involved in NK cell-mediated cytotoxicity. Stimulating NK cells cytotoxicity towards cancer cells is an ideal strategy to combat cancer naturally. Medicinal plants have been reported to enhance immunity, with Pereskia bleo (P. bleo) particularly noteworthy due to its abundant bioactive compounds and ability to activate immune cells. This study aimed to evaluate the potential of methanol extract of P. bleo leaves (MEPB) for enhancing NK cell cytotoxicity against triple-negative human breast cancer cells (MDA-MB-231). The optimal concentration of MEPB to activate NK cells was determined using healthy blood samples, assessing the expression of IL-12, IL-18, IL-10, IL-8, IFN-γ, perforin, and granzyme B via an enzyme-linked immunosorbent assay (ELISA). NK cell purity from healthy donors and breast cancer patients was determined using specific antibodies, and the number of NK cells was assessed using flow cytometry and a hemocytometer. A co-culture experiment, ELISA, and apoptosis assay were used to evaluate NK-mediated cytotoxicity pathways. ELISA data indicated that MEPB at 7.5 µg/ml significantly increased the expression of IFN-γ, IL-12, IL-18, perforin, and granzyme B while decreasing IL-8 and IL-10 expression after 20 hrs of incubation. The average NK cell purity was 87.09 ± 0.043%. Breast cancer patients exhibited lower NK cell counts than healthy donors. Co-culture experiments demonstrated that NK cells induced apoptosis in MDA-MB-231 breast cancer cells in the presence of MEPB by increasing perforin, granzyme B, and IFN-γ expression in both healthy donors and breast cancer patients-experimental groups. P. bleo enhances NK cell activation, promoting the apoptosis of triple-negative human breast cancer cells (MDA-MB-231), suggesting the potential use of MEPB leaves as an anti-cancer immunostimulant.

A Scalable Total Synthesis of Deoxyrhapontigenin from Low-Cost α-Resorcylic Acid

A scalable total synthesis for deoxyrhapontigenin (DRG) was successfully accomplished using low-cost α-resorcylic acid as a starting material. Our approach initiates with the protection of phenolic groups using methoxymethyl, resulting in the synthesis of a derivative of the expensive 3,5-hydroxybenzaldehyde, a critical component for Wittig and Perkin approaches in stilbenoid synthesis. Additionally, the corresponding olefin for Heck reaction and formal synthesis of resveratrol were produced in over 85% yield. The synthesis of deoxyrhapontigenin was accomplished in seven steps from the initial carboxylic acid, yielding a product with an average purity higher than 98%, as confirmed by quantitative nuclear magnetic resonance (NMR) analysis.

Production and purification of 43Sc and 47Sc from enriched [46Ti]TiO2 and [50Ti]TiO2 targets

The radioscandium isotopes, 43Sc and 47Sc, compose a promising elementally matched theranostic pair that can be used for the development of imaging and therapeutic radiopharmaceuticals with identical structures. This study aimed to investigate the production of high radionuclidic purity 43Sc from enriched [46Ti]TiO2 targets and 47Sc from enriched [50Ti]TiO2 targets and establish a target recycling technique. Enriched [46Ti]TiO2 targets were irradiated with 18 MeV protons, and enriched [50Ti]TiO2 targets were bombarded with 24 MeV protons. 43Sc and 47Sc were purified using ion chromatography attaining recovery yields of 91.7 ± 7.4% and 89.9 ± 3.9%, respectively. The average radionuclidic purity for 43Sc was 98.8 ± 0.3% and for 47Sc 91.5 ± 0.6%, while the average recovery of enriched titanium target material was 96 ± 4.0%. The highest apparent molar activity for [43Sc]Sc-DOTA was 23.2 GBq/µmol and 3.39 GBq/µmol for [47Sc]Sc-DOTA. This work demonstrates the feasibility of using enriched recycled [46Ti]TiO2 and [50Ti]TiO2 targets to produce high purity 43Sc and 47Sc as an elementally matched theranostic isotope pair.

Irgafos 168 and Irganox 1076 as new cocaine cutting agents: A COVID-19 pandemic impact on cocaine profiling and trafficking in Brazil

BackgroundsRestrictions in movement and closure of borders imposed by the Sars-Cov- 2 worldwide pandemic have affected the global illicit drug market, including cocaine trafficking. In this scenario, comparing cutting agents added to the cocaine and the drug purity are valuable strategies to understand how the drug trade has been impacted by the pandemic. MethodsIn this work, 204 cocaine salt materials seized in the Brazilian Federal District, before (2019) and during COVID-19 pandemics (2020) were analyzed by two analytical techniques: gas chromatography-mass spectrometry (GC–MS) and Fourier-transform infrared spectroscopy (FTIR). Statistical analyses, including Principal Component Analysis (PCA), were applied to evaluate the COVID-19 pandemic impact in the local market. Bibliometric analysis was performed as a forensic intelligence tool. Results: From 2019–2020, cocaine average purity decreased 26 % while the frequency of cutting agents, as caffeine and anesthetics (lidocaine, tetracaine) increased. The high percentage of unknown were increased. Different cocaine profiling seized in 2020 showed new cutting agents, such as Irganox 1076, and Irgafos 168, indicating a trend on new adulterants/diluents introduced in the local market to mitigate the local drug shortage. Also in 2020, there was an increase in the local cocaine seizures, despite of the cocaine drug purity decreased by 26 % compared to 2019. Conclusions: Taken together, these data showed that the covid-19 pandemics has impacted cocaine trafficking in the Brazilian Federal District, an increase in cocaine seizures, which may indicate greater demand for the drug and, specially, changes in the cocaine purity and cutting agents profiling showing how traffickers tried to minimize difficulties in crossing the Brazilian border during COVID-19 restrictions. The information is relevant since Brazil is one of the major departure points for traded cocaine to the world. Bibliometric analysis showed that Irgafos 168 and Irganox 1076 were consistently identified as cocaine cutting agents for the first time.