Screening Methodology Research Articles

Cost Effectiveness of Screening & Diagnostic Methods for Cervical Cancer in Women aged 30-65 Years Globally: A Systematic Review Protocol to Guide Policy Documents

Background: Cervical cancer is the fourth most common cancer in females, with high prevalence in developing countries, and the most common cause (99%) is the Human Papillomavirus (HPV). The global incidence of cervical cancer is increasing every year, with an incidence of 13.3 cases per 100,000 women and a mortality rate of 7.2 deaths per 100,000 women in 2020. There are various screening and diagnostic methods; however, the availability of any strong evidence on the most cost-effective method is limited. This review systematically analyzes the available screening and diagnostic methodologies for cervical cancer, sensitivity, specificity, and cost implications. Methods and Analysis: Electronic databases like PubMed, Cochrane, and Google Scholar will be systematically searched for relevant articles using medical subject headings (MeSH) terms. Eight reviewers (DL, SW, PP, AK, AR, MS, TK, NT) will independently assess titles and abstracts against the selection criteria during the first phase. Five reviewers will assess all full-text papers before the final decision is made. The studies will be selected based on the predefined inclusion and exclusion criteria. The selected systematic reviews will undergo a quality assessment using the PRISMA checklist and RCTs using CONSORT and CHEERS statements to examine economic evaluations. A narrative synthesis will be formulated from the studies based on two types of outcomes- Clinical and Economical. If sufficient data is available, a meta-analysis will be performed. Conclusion: The results from this study may aid in identifying factors influencing test performance and have evidence of the cost-effectiveness of the screening and diagnostic tests for cervical cancer. This review will generate a platform for more studies that will provide answers to crucial research concerns about cervical cancer screening.

High-Throughput Screening to Advance In Vitro Toxicology: Accomplishments, Challenges, and Future Directions.

Traditionally, chemical toxicity is determined by in vivo animal studies, which are low throughput, expensive, and sometimes fail to predict compound toxicity in humans. Due to the increasing number of chemicals in use and the high rate of drug candidate failure due to toxicity, it is imperative to develop in vitro, high-throughput screening methods to determine toxicity. The Tox21 program, a unique research consortium of federal public health agencies, was established to address and identify toxicity concerns in a high-throughput, concentration-responsive manner using a battery of in vitro assays. In this article, we review the advancements in high-throughput robotic screening methodology and informatics processes to enable the generation of toxicological data, and their impact on the field; further, we discuss the future of assessing environmental toxicity utilizing efficient and scalable methods that better represent the corresponding biological and toxicodynamic processes in humans.

Comparison of in silico, in vitro, and in vivo toxicity benchmarks suggests a role for ToxCast data in ecological hazard assessment.

Large repositories of in vitro bioactivity data such as US EPA's Toxicity Forecaster (ToxCast) provide a wealth of publicly accessible toxicity information for thousands of chemicals. These data can be used to calculate point-of-departure (POD) estimates via concentration-response modeling that may serve as lower bound, protective estimates of in vivo effects. However, the data are predominantly based on mammalian models and discussions to date about their utility have largely focused on potential integration into human hazard assessment, rather than application to ecological risk assessment. The goal of the present study was to compare PODs based on (1) quantitative structure-activity relationships (QSARs), (2) the 5th centile of the activity concentration at cutoff (ACC), and (3) lower-bound cytotoxic burst (LCB) from ToxCast, with the distribution of in vivo PODs compiled in the Ecotoxicology Knowledgebase (ECOTOX). While overall correlation between ToxCast ACC5 and ECOTOX PODs for 649 chemicals was weak, there were significant associations among PODs based on LCB and ECOTOX, LCB and QSARs, and ECOTOX and QSARs. Certain classes of compounds showed moderate correlation across datasets (eg, antimicrobials/disinfectants), while others, such as organophosphate insecticides, did not. Unsurprisingly, more precise classifications of the data based on ECOTOX effect and endpoint type (eg, apical vs biochemical; acute vs chronic) had a significant effect on overall relationships. Results of this research help to define appropriate roles for data from new approach methodologies in chemical prioritization and screening of ecological hazards.

Prioritization of organic contaminants in a reclaimed water irrigation system using wide-scope LC-HRMS screening

A prioritization procedure was developed and implemented at the local level to identify the most relevant organic contaminants of emerging concern (CECs) in an agricultural area irrigated with reclaimed water. A wide-scope screening methodology based on UPLC-HRMS analysis was applied to holistically characterize the CEC footprint in water and its spatial and temporal variations. One hundred and fifty-eight CECs, including pharmaceuticals, industrial chemicals, and pesticides, among others, were identified with a confidence level of 2 in the water samples investigated. After water treatment in the reclamation plant and transport within the irrigation channel network, more than a hundred compounds were still detected at the location where water is abstracted for crop irrigation. Compound ecotoxicity and occurrence (semi-quantified concentrations or peak intensity) were the parameters used to prioritize CECs in the water used for irrigation. Results pointed at venlafaxine, O-desmethyl-venlafaxine, galaxolidone, theophylline/paraxanthine, oxybenzone, and N-phenyl-1-naphtylamine, among others, as CECs of concern in the investigated area. This study provides a simple and cost-effective approach to detecting site-specific priority pollutants that could otherwise be overlooked by national or European regulations. The prioritization tool provided contributes to rationally designing monitoring and attenuation programs and efficiently managing water resources, by ensuring the safety of reclaimed water applications.

A multi-analyte screening method for the rapid detection of illicit adulterants in dietary supplements using a portable SERS analyzer

The popularity and number of dietary supplements on the health market have experienced an unprecedented boost in recent years. Simultaneously, their increased use has been accompanied by an increase in acute intoxication cases linked to the adulteration of these products with illicit and undeclared substances. In this study, a SERS-based screening methodology was developed to rapidly detect illegally added pharmaceutically active substances to dietary supplements. A portable analyzer and silver printed-SERS substrates were used to enhance the signal, requiring less than 20 min of sample preparation prior to the analysis. The method was successful in the qualitative identification of eleven out of twenty-three illicit adulterants in the dietary supplements; it could detect the target compounds at realistic adulteration levels (0.1–5.0% w/w), demonstrating the potential of SERS-based methodologies for forensic rapid screening applications. The developed method is quick, easy to use, requires no skilled technicians and little sample preparation, and allows in-situ analyses. For these reasons, it is suitable for quick screening to be performed by inspectors at customs. Moreover, the low specificity of spectroscopic methods, to which SERS belongs, would benefit the detection of newly synthesized analogues of the target adulterants, which would otherwise be more difficult using common mass spectrometry methods in absence of reference standards.

Novel educational training of para medical professionals in cervical cancer screening

BackgroundCervical cancer is a public health problem in India due to weak national screening policy compounded by lack of resources including scarcity of trained personnel to carry out community-based screening program. Para medical professionals (PMPs) are closely related to women in local communities. Hence, training PMPs by incorporating novel technology and reduced time duration to achieve adequate competence in screening is an area underutilized and needs to be explored. Materials and methodsA pilot cross sectional analytical study was conducted at a tertiary referral cancer center using a shorter version of educational intervention of 2 weeks duration (EI2W) involving PMPs. Pre- and post-training assessment of knowledge, attitude, and practice (KAP) was done using questionnaires consisting of 5 domains viz. awareness of cervical cancer, awareness of cervical pre-cancer, practical screening methodology (practice oriented), data management and aspects of human papilloma virus (HPV). Wilcoxon signed-rank test was used for comparison and the degree of change was measured using analysis of covariance (ANCOVA). A p value of <0.05 was considered significant. Results118 PMPs were included. There was a significant improvement in scores of all domains (except cervical pre-cancer domain), following introduction of EI2W. Knowledge scores, post EI2W was better in Auxiliary Nurse Midwives (ANMs) than other participants. Awareness regarding cervical cancer was higher with more years of experience. The KAP analysis showed excellent interrater reliability in the practice 0.726 (0.649–0.792) followed by knowledge domain 0.711 (0.626–0.783). ConclusionEI2W was effective in significantly improving the competence of PMPs, thus reducing human resource constraints in cervical cancer prevention and elimination.

Developing a Data-Driven Network Screening Procedure for Systemic Safety Approach

Systemic analysis is considered an important safety analysis approach that is complementary to the Highway Safety Manual hotspot analysis. The network screening step in systemic analysis is to identify sites with characteristics that are associated with specific types of severe crashes. Traditionally, determining the risk scores involves subjective criteria. This research aims to develop a data-driven approach to replace the subjective methods used in the past. To achieve the research objective, this study collected roadway and crash data from the Connecticut Department of Transportation. A data-driven crash risk factor categorization methodology is proposed to estimate accurately the performance measures indicating crash risks. Moreover, this study proposes and compares four different risk scoring matrices to identify an optimal risk scoring method that is attuned with the principles of the systemic approach to safety as well as providing additional insights on justifying the systemic safety analysis results. The proposed methodology is implemented to conduct network screening for severe roadway departure crashes and later validated using severe aggressive-driving related crashes. Risk-based network screening results indicate that risk scores derived from normalized crash over-representation provide additional emphasis on sites with low traffic volume that are associated with high severe crash counts. The highest modified crash rate was obtained using normalized crash over-representation based risk scores indicating that the proposed network screening methodology can not only identify roadway attributes that are correlated with severe crashes but also account for low-volume roadway sites with severe crashes. The validation analysis indicated proposed method is transferrable to different emphasis area related crashes.

Novel Tetrazole Derivatives Targeting Tubulin Endowed with Antiproliferative Activity against Glioblastoma Cells.

Increasing awareness of the structure of microtubules has made tubulin a relevant target for the research of novel chemotherapies. Furthermore, the particularly high sensitivity of glioblastoma multiforme (GBM) cells to microtubule disruption could open new doors in the search for new anti-GBM treatments. However, the difficulties in developing potent anti-tubulin drugs endowed with improved pharmacokinetic properties necessitates the expansion of medicinal chemistry campaigns. The application of an ensemble pharmacophore screening methodology helped to optimize this process, leading to the development of a new tetrazole-based tubulin inhibitor. Considering this scaffold, we have synthesized a new family of tetrazole derivatives that achieved remarkable antimitotic effects against a broad panel of cancer cells, especially against GBM cells, showing high selectivity in comparison with non-tumor cells. The compounds also exerted high aqueous solubility and were demonstrated to not be substrates of efflux pumps, thus overcoming the main limitations that are usually associated with tubulin binding agents. Tubulin polymerization assays, immunofluorescence experiments, and flow cytometry studies demonstrated that the compounds target tubulin and arrest cells at the G2/M phase followed by induction of apoptosis. The docking experiments agreed with the proposed interactions at the colchicine site and explained the structure-activity relationships.

A Five-Factor Asset Pricing Model of Shariah Compliant Firms in the United States

Shariah compliant firms operating in an environment with little to no access to a robust Islamic capital market (such as in the United States (US) stock market) will exhibit a consistent bias towards certain corporate financial behaviour. Does this bias subsequently lead to a skewed asset pricing behaviour? To answer this question, this paper investigates the asset pricing behaviour of multiple samples of Shariah compliant firms listed in the US as compared to an overall conventional sample by employing the Fama &amp; French Five-Factor Model. By applying contemporary Shariah stock screening methodology on a sample of all stocks listed in the NYSE, NASDAQ and the IEX from January 2000 to December 2019, this paper shows that asset pricing behaviour differs not only between Shariah compliant and conventional samples, but also amongst Shariah compliant samples themselves. Ultimately, this paper shows that when deriving the appropriate expected return for Shariah compliant portfolios in the US, there are evidence to suggest that the Fama &amp; French Five-Factor model is more suitable compared to the traditional Capital Asset Pricing Model (CAPM) since the additional risk premiums show consistent significance across groups of Shariah compliant firms.

Bibliometric Analysis of the Thinking Styles in Math and Its' Implication on Science Learning

The exploration of mathematical thinking styles is a vital area of investigation, particularly concerning its influence on science education within the classroom, given the significant role mathematics plays in advancing scientific understanding. The examination of this subject holds great interest, and its pertinence strongly bolsters prospective teaching and research endeavors. This research aims to perform a bibliometric scrutiny of mathematical thinking styles and their implication on science learning. The focus of this bibliometric inquiry is to elucidate and scrutinize literature congruent with the concept of mathematical thinking styles and their alignment with science learning. The SCOPUS repository is employed as the primary source of document references. Document selection and analysis were conducted using specific keywords in the 'document search' section. Employing diverse document screening methodologies pertinent to mathematical thinking styles and their implication on science learning, a corpus of relevant documents addressing the subject matter was identified. The sequential screening procedures and document findings are discussed comprehensively in this article. Fundamentally, articles pertinent to the bibliometric analysis theme, 'mathematical thinking styles and their implications for science learning,' underscore the significance of delving into students' mathematical thinking styles. Variances in these cognitive styles pose significant challenges for educators' pedagogical approaches in both mathematics and science instruction. This constitutes a pivotal implication of the present study, necessitating educators to adeptly navigate diverse mathematical thinking styles when structuring pedagogy in science and mathematics. Ultimately, this study stands as a pivotal reference for future investigations delving into themes associated with mathematical thinking styles.

Development of an Imaging Flow Cytometry Method for Fungal Cytological Profiling and Its Potential Application in Antifungal Drug Development.

Automated imaging techniques have been in increasing demand for the more advanced analysis and efficient characterization of cellular phenotypes. The success of the image-based profiling method hinges on assays that can rapidly and simultaneously capture a wide range of phenotypic features. We have developed an automated image acquisition method for fungal cytological profiling (FCP) using an imaging flow cytometer that can objectively measure over 250 features of a single fungal cell. Fungal cells were labeled with calcofluor white and FM4-64FX, which bind to the cell wall and lipophilic membrane, respectively. Images of single cells were analyzed using IDEAS® software. We first acquired FCPs of fungal cells treated with fluconazole, amphotericin B, and caspofungin, each with a distinct mode of action, to establish FCP databases of profiles associated with specific antifungal treatment. Once fully established, we investigated the potential application of this technique as a screening methodology to identify compounds with novel antifungal activity against Candida albicans and Cryptococcus neoformans. Altogether, we have developed a rapid, powerful, and novel image-profiling method for the phenotypic characterization of fungal cells, also with potential applications in antifungal drug development.

A novel method for blood detection using fluorescent dye

Detection of the body fluid present at a crime scene is essential for any forensic investigation. Amongst all the body fluids (sweat, semen, vaginal fluid, saliva, etc.,) blood is the most frequently encountered evidence at the crime scene. Currently, reagents like benzidine, ortho-toluidine, tetramethylbenzidine, phenolphthalein, leucomalachite green, luminol, and fluorescein are used to screen the presence of blood on different surfaces (porous/nonporous). Most of these tests are based on colorimetric changes owing to the nature of hemoglobin to catalyze the oxidation of chromogenic compounds. Apart from aiding the investigation, these reagents show toxic behavior (DNA damage, carcinogenic, etc.) and false-positive results. Hence, to circumvent this issue, the present study attempts to develop a state-of-art methodology for preliminary blood detection and screening using fluorescein-derived 2′,7′-dichlorofluorescein di-acetate (DCFDA) dye. It is hereby proposed that the fluorescein-based dye can successfully detect blood and bloodstains aged up to 20 days. Moreover, supplemental experiments have suggested that the dye doesn't interfere with DNA integrity- causing any damage to DNA. Parallelly, no false-positive reactions have been observed as tested against similar-looking substances.

3 QUESTIONS ON . . . Using Artificial Intelligence in Breast Cancer Risk Prediction

Celine Vachon, PhD: Celine Vachon, PhDA rtificial intelligence (AI) technology is making headlines in many areas of research and sectors of business. Now, epidemiologists and others who study cancer screening methodology say they've found new evidence it could improve breast cancer detection efforts, too. In a study published in the Journal of Clinical Oncology (JCO), researchers found that using an AI algorithm designed to improve breast cancer detection on mammograms could improve breast cancer risk prediction, including interval and advanced cancers—particularly when combined with consideration of breast density (2023; doi: 10.1200/JCO.22.01153). “This adds further evidence to support computer-aided imaging assessments and will help motivate prospective studies to disentangle improved earlier diagnosis from an improved risk model for subsequent cancer,” Paul H. Frankel, PhD, JCO Associate Editor, wrote in a relevance statement published with the article. “Such prospective studies will also provide estimates that can more easily guide clinical practice.” In an interview with Oncology Times, the study's lead author, Celine Vachon, PhD, Professor and Consultant in the Division of Epidemiology within the Department of Quantitative Health Sciences at Mayo Clinic, shared more about the work. 1 What were the key findings from this research? “We evaluated whether the AI measure assessed on mammograms taken years prior to the cancer (2-5.5 years prior to the cancer diagnosis) contributed additional information to breast density for risk of breast cancer—in particular interval and advanced cancers. The AI algorithm was developed to be used on all women, not just those with dense breasts. It was run on the mammograms from women in our clinical practices who did and did not develop breast cancer. “AI score assessed on mammograms 2-5.5 years before cancer diagnosis was associated with invasive breast cancer, and similarly predictive of interval and advanced cancers in dense and non-dense breasts. These findings imply that the AI score is robust for long-term risk prediction by severity of cancer and independent of breast density for predicting cancer. “Furthermore, the AI score improved prediction of all cancer types in models with breast density measures. AI imaging algorithms coupled with breast density independently contribute to long-term risk prediction of invasive breast cancers, including advanced cancers.” 2 What are the implications of these findings and how might they improve real-world screening efforts? “Data from our study and others suggests that AI algorithms will help improve both breast cancer detection and breast cancer risk prediction, including interval cancers and advanced cancers. These will likely help better identify women for supplemental screening and, potentially, for identification of women who would benefit from early interventions. “The AI algorithm we used is commercially available. And there are several similar AI algorithms from multiple vendors available that likely would provide similar findings. However, it will be important to understand how these algorithms work among women from various races, BMI (those with obesity, for example), and how they contribute to factors already known for risk prediction (such as genetic factors). Also, these algorithms need to be able to identify prognostically poor cancers, not just DCIS or early cancers that may not progress. This research is underway by many groups to inform how best to use these algorithms in the clinical setting.” 3 What is the next step of this work? “We are pursuing additional research to understand the imaging factors that determine the high-risk score, including calcifications. We are also examining other AI models, in particular those designed for longer-term risk, and how the combination of these algorithms with genetic factors and breast density can better inform future risk. The bottom line that practicing oncologists and cancer care providers need to know now is that this research confirms the importance of both breast density and AI models to predict invasive breast cancer, including both interval and advanced cancers. Our data suggest that this AI algorithm is not only relevant at the time of cancer [for detection], but also 2-5.5 years prior to the breast cancer.”

Evaluation of rapid visual screening assessment of existing buildings using nonlinear numerical analysis

Different methodologies are available in the literature to perform seismic assessment of existing buildings. Amongst the simplified ones, Rapid Visual Screening (RVS) methodologies have been recently developed and proposed. RVS methods are simplified procedures that can be implemented rather quickly for preliminary assessment of building vulnerability without the need to perform a detailed model and numerical analysis. Their application is particularly useful in regions characterised by high seismicity or where an important share of the buildings was not designed according to seismic provisions. The outcome of RVS methods is a safety index, based on the data from simplified forms that are filled during visual inspection of the buildings. For this reason, the accuracy of the results could be significantly affected by building features that cannot be considered by such forms. To address this issue and to evaluate the effectiveness of RVS methods, this paper focuses on the comparison between the results of a specific RVS method and those from advanced numerical modelling and nonlinear analyses of masonry-infilled reinforced concrete buildings. Several case-study buildings were selected from a database developed in the framework of a recently concluded European project, which focused on the seismic risk assessment of residential buildings in Northern Algeria, a seismic prone region. The buildings are analysed using detailed structural finite element models, through both nonlinear static and dynamic analysis, considering also the influence of mechanical and geometrical properties of the masonry infills. The comparison results show that, despite the significant variability observed in the RVS vulnerability assessment outcomes, there is a fair overall match with the nonlinear analysis results, confirming the utility of RVS methods at an initial assessment stage, as well as the need for further extended validation research efforts.

Genomic screening methodology not requiring barcoding: Single nucleotide polymorphism-based, mixed-cell screening (SMICS)

Although high-throughput, cancer cell-line screening is a time-honored, important tool for anti-cancer drug development, this process involves the testing of each, individual drug in each, individual cell-line. Despite the availability of robotic liquid handling systems, this process remains a time-consuming and costly investment. The Broad Institute developed a new method called Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) to screen a mixture of barcoded, tumor cell-lines. Although this methodology significantly improved the efficiency of screening large numbers of cell-lines, the barcoding process itself was tedious that requires gene transfection and subsequent selection of stable cell-lines. In this study, we developed a new, genomic approach for screening multiple cancer cell-lines using endogenous “tags” that did not require prior barcoding: single nucleotide polymorphism-based, mixed-cell screening (SMICS). The code for SMICS is available at https://github.com/MarkeyBBSRF/SMICS.

The son-killer microbe Arsenophonus nasoniae is a widespread associate of the parasitic wasp Nasonia vitripennis in Europe

Heritable microbes that exhibit reproductive parasitism are common in insects. One class of these are the male-killing bacteria, which are found in a broad range of insect hosts. Commonly, our knowledge of the incidence of these microbes is based on one or a few sampling sites, and the degree and causes of spatial variation are unclear. In this paper, we examine the incidence of the son-killer microbe Arsenophonus nasoniae across European populations of its wasp host, Nasonia vitripennis. In preliminary work, we noticed two female N. vitripennis producing highly female biased sex ratios in a field study from the Netherlands and Germany. When tested, the brood from Germany was revealed to be infected with A. nasoniae. We then completed a broad survey in 2012, in which fly pupal hosts of N. vitripennis were collected from vacated birds’ nests from four European populations, N. vitripennis wasps allowed to emerge and then tested for A. nasoniae presence through PCR assay. We then developed a new screening methodology based on direct PCR assays of fly pupae and applied this to ethanol-preserved material collected from great tit (Parus major) nests in Portugal. These data show A. nasoniae is found widely in European N. vitripennis, being present in Germany, the UK, Finland, Switzerland and Portugal. Samples varied in the frequency with which they carry A. nasoniae, from being rare to being present in 50% of the pupae parasitised by N. vitripennis. Direct screening of ethanol-preserved fly pupae was an effective method for revealing both wasp and A. nasoniae infection, and will facilitate sample transport across national boundaries. Future research should examine the causes of variation in frequency, in particular testing the hypothesis that N. vitripennis superparasitism rates drive the variation in A. nasoniae frequency through providing opportunities for infectious transmission.

Exploiting the potential of natural polyphenols as antivirals against monkeypox envelope protein F13 using machine learning and all-atoms MD simulations

The re-emergence of monkeypox (MPX), in the era of COVID-19 pandemic is a new global menace. Regardless of its leniency, there are chances of MPX expediting severe health deterioration. The role of envelope protein, F13 as a critical component for production of extracellular viral particles makes it a crucial drug target. Polyphenols, exhibiting antiviral properties have been acclaimed as an effective alternative to the traditional treatment methods for management of viral diseases. To facilitate the development of potent MPX specific therapeutics, herein, we have employed state-of-the-art machine learning techniques to predict a highly accurate 3-dimensional structure of F13 as well as identify binding hotspots on the protein surface. Additionally, we have effectuated high-throughput virtual screening methodology on 57 potent natural polyphenols having antiviral activities followed by all-atoms molecular dynamics (MD) simulations, to substantiate the mode of interaction of F13 protein and polyphenol complexes. The structure-based virtual screening based on Glide SP, XP and MM/GBSA scores enables the selection of six potent polyphenols having higher binding affinity towards F13. Non-bonded contact analysis, of pre- and post- MD complexes propound the critical role of Glu143, Asp134, Asn345, Ser321 and Tyr320 residues in polyphenol recognition, which is well supported by per-residue decomposition analysis. Close-observation of the structural ensembles from MD suggests that the binding groove of F13 is mostly hydrophobic in nature. Taken together, this structure-based analysis from our study provides a lead on Myricetin, and Demethoxycurcumin, which may act as potent inhibitors of F13. In conclusion, our study provides new insights into the molecular recognition and dynamics of F13-polyphenol bound states, offering new promises for development of antivirals to combat monkeypox. However, further in vitro and in vivo experiments are necessary to validate these results.

Classifying micropollutants by environmental risk in groundwater using screening analysis associated to a hybrid multicriteria method combining (Q)SAR tools, fuzzy AHP and ELECTRE

This study aimed to evaluate and monitor pesticides in groundwater of the Serra Geral aquifer, located in the Paraná Basin 3 (southern Brazil), using Liquid Chromatography coupled with a Quadrupole-Time-of-Flight Mass Spectrometer (LC-QTOF MS). A total of 117 samples, collected in three different moments, were analyzed over 36 months. Groundwater samples from 35 wells and four surface water points were monitored in each sampling campaign. A pesticide screening methodology was proposed with the tentative identification of 1607 pesticides and pesticide metabolites. The application of the proposed methodology resulted in the verification of 29 pesticides and pesticide metabolites, 7 as confirmed analytes and 22 as suspect compounds. (Q)SAR in silico predictions and GUS index calculation provided data on the potential environmental risk of the identified compounds, with eight endpoints considered. After in silico predictions, an alternative hybrid multicriteria method was applied, combining the weighting of endpoints of fuzzy AHP and micropollutants classification by environmental risk using ELECTRE. The fuzzy AHP results indicated the greatest importance of mutagenicity among the eight evaluated indicators, while the scarce influence of the physicochemical properties on the environmental risk suggested their exclusion from the model. Accordingly, the ELECTRE results highlighted the prevalence of thiamethoxam and carbendazim as the most dangerous for the environment. The application of the proposed method enabled the selection of the compounds that must be monitored, considering mutagenicity and toxicity predictions for environmental risk analysis.

A dual-cure approach for the ultraviolet-assisted material extrusion of highly loaded opaque suspensions

Ultraviolet-Assisted Material Extrusion (UV-MEX) is an additive manufacturing (AM) technology in which a viscous ink, is selectively extruded and solidified via in-situ UV irradiation (photocuring) in a layer-by-layer fashion to create three-dimensional parts. UV-MEX is an attractive AM technology due to its ability to (i) extrude highly viscous inks that are not processable via other photo-enabled AM technologies, (ii) leverage the broad photopolymer material library and chemistries established for other photo-enabled AM technologies and (iii) process a wide range of loaded inks to enable fabrication of metal, ceramic, polymer, bio-based, and multi-material parts. UV-MEX is well positioned to process highly loaded and highly viscous photocurable inks, a class of materials that cannot be processed by other AM modalities due to their process-induced viscosity constraints. However, opaque particles in the highly loaded systems may absorb and scatter UV irradiation, which will hinder final part properties due to limited cure depth and degree of conversion of the photopolymer matrix. To enable the robust UV-MEX printing of highly loaded inks containing opaque fillers, this work introduces a framework for formulating and characterizing resins that are both photo (UV irradiation) and thermally (heat) curable. This dual-cure strategy is enabled by the facile addition of free radical photo- and thermal initiators into acrylate-based resins. The resin’s responsiveness to UV irradiation enables rapid solidification and stiffening of the resin to reduce the spreading and slumping of the extruded bead. However, due to the absorption of the UV irradiation by the opaque fillers, only a thin cured skin (20-100um) is formed on the surface of the extruded bead. Thus, the secondary thermal cure is used to fully cure the ink and develop the final properties of the printed part. Heating during the printing process is shown to provide in-situ thermal curing of the deposited ink, which further strengthens the deposited material and enables the printing of tall structures. In this work, the dual-cure ink formulation strategy is assessed for its ability to enable the printing of dimensionally stable and high-aspect ratio parts from inks highly loaded with opaque solids. A methodology for characterizing the curing of the highly loaded inks using rheological and differential scanning calorimetry experiments is presented, along with a framework for assessing their printing performance in UV-MEX. The dual-cure ink concept and screening methodology is demonstrated on a dual-cure urethane acrylate ink loaded with 60 vol% aluminum particles.

EXISTING STATE OF HYPERTENSION IN PAKISTAN - A REVIEW

Objective: This review aims to describe existing literature on the state of hypertension in Pakistan, including the prevalence, associated risk factors, preventive strategies, barriers, and facilitators of the management of hypertension Design and method: A comprehensive systematic literature search was conducted electronically using PubMed and Google Scholar. Using specific screening methodology, 55 articles were selected to be included. Results: There is a lack of recent data on prevalence of hypertension in Pakistan, although several smaller studies revealed a high prevalence. Lifestyle risk factors such as obesity, unhealthy diet, decreased physical activity, and low socioeconomic status and lack of access to care were the main associated factors with hypertension. Lack of blood pressure monitoring practices and medication non-adherence were also linked to uncontrolled hypertension in Pakistan and were more evident in primary care setups. The evidence presented is essential for delineating the burden of the disease, hence allowing for better management of this underserved population Conclusions: There is a need for updated surveys to depict the true prevalence and management of hypertension in Pakistan. Cost-effective implementation strategies and policies at the national level must be conducted for both prevention and control of hypertension.