Screening Step Research Articles

Is asymptomatic bacteriuria a noticeable risk factor for periprosthetic joint infection following total joint arthroplasty? A systematic review and meta-analysis.

Total joint arthroplasty (TJA) is a common procedure that improves the quality of life for severe arthritis patients. The rising demand for TJA places enormous financial strain on the world's healthcare systems, which is exacerbated by postoperative readmissions for complications such as periprosthetic joint infections (PJIs). We conducted a systematic review and meta-analysis to determine if asymptomatic bacteriuria (ASB) increases infection risk. We conducted searches in three databases: PubMed, Scopus, and Web of Science. Screening steps have been carried out according to PRISMA guidelines. The study focused on patients who had undergone TJA and exhibited ASB. Two reviewers independently screened, assessed quality, and extracted data. Meta-analysis used Mantel-Haenszel method. Following full-text screening, 12 studies were deemed eligible for inclusion in the systematic review, encompassing a total of 42,592 patients. A heightened occurrence of PJI was observed among TJA patients with ASB in comparison to controls (OR [95%CI] = 3.47 [1.42-8.44]). However, microorganisms responsible for the PJIs differed from those identified in the urine cultures of ASB. Additionally, analyses indicate that preoperative antibiotic treatment for ASB does not significantly affect the subsequent risk of PJI (OR [95% CI] = 1.00 [0.42-2.39]). Unlike surgical site infection (SSI), which did not show a difference in the rate of occurrence between the two groups, TJA patients with ASB were more likely to experience superficial wound infection (OR [95%CI] = 3.81 [2.02-7.21]). This review and meta-analysis confirm that ASB correlate with heightened risks of PJI and superficial infection in TJA patients. However, no relationship was found between ASB and PJI microorganisms which raise doubts about the role of ASB microorganisms as the direct cause of infection following TJA.

In Silico Drug Repurposing Uncovered the Antiviral Potential of the Antiparasitic Drug Oxibendazole Against the Chikungunya Virus.

Chikungunya virus (CHIKV) has been reported in over 120 countries and is the causative agent of Chikungunya fever. The debilitating nature of this disease, which can persist months to years after acute infection, drastically impacts the quality of life of patients. Yet, specific antivirals are lacking for the treatment of this disease, which makes the search for new drugs necessary. In this context, the nsP2 protease emerges as an attractive therapeutic target, and drug repurposing strategies have proven to be valuable. Therefore, we combined in silico and in vitro methods to identify known drugs as potential CHIKV nsP2 protease inhibitors with antiviral properties within DrugBank. Herein, we developed a hybrid virtual screening pipeline comprising pharmacophore- and target-based screening, drug-like, and pharmaceutical filtering steps. Six virtual hits were obtained, and two of them, capecitabine (CPB) and oxibendazole (OBZ), were evaluated against CHIKV replication in Vero cells. CPB did not present antiviral activity, whereas OBZ inhibited the replication of two different strains of CHIKV, namely, 181-25 (Asian genotype) and BRA/RJ/18 (clinical isolate from ECSA genotype). OBZ showed potent antiviral activity against the CHIKV BRA/RJ/18 (EC50 = 11.4 μM) with a high selectivity index (>44). Analogs of OBZ (albendazole, fenbendazole, and mebendazole) were also evaluated, but none exhibited anti-CHIKV activity, and further, their stereoelectronic features were analyzed. Additionally, we observed that OBZ acts mainly at post-entry steps. Hence, our results support further in vivo studies to investigate the antiviral potential of OBZ, which offers a new alternative to fight CHIKV infections.

Nutritional risk screening: a need to guide Alice in Nutritionland.

With the shifts in society, healthcare and the profile of the malnourished individual, a re-consideration of the goal of nutritional risk screening is needed: screening for malnutrition, or screening for risk of malnutrition? In this review article, we reflect on the role of nutritional risk screening in relation to prevention and treatment of malnutrition. Within the Global Leadership Initiative on Malnutrition (GLIM) Initiative, modified Delphi studies are currently being conducted to reach global consensus on the conceptual definition and operationalization of 'risk of malnutrition'. This is necessary because various studies have demonstrated that different nutritional screening tools identify different individuals, due to variability in screening tool criteria, which influences GLIM outcomes. Upon screening, three different situations can be distinguished: having risk factors for malnutrition without clear signs of presence of malnutrition, having mild signs of malnutrition (malnutrition in progress), or having obvious signs of malnutrition. The outcomes of the studies on 'risk of malnutrition' will guide the screening step within the GLIM process, and will help professionals to make informed choices regarding screening policy and screening tool(s).

Toxicity of mangrove plant extract (Rhizophora stylosa Griff.) using the brine shrimp lethality test method

Abstract Rhizophora stylosa is a type of mangrove that is very abundant on the coast of Indonesia and contains many active compounds. So far, research on the toxicity of bark, leaf and flower extracts of R. stylosa has not been reported. This research aims to determine the toxic effects of n-hexane, dichloromethane and methanol extracts from the bark, leaves and flowers of R. stylosa through initial screening using the brine shrimp lethality test (BSLT) method and to determine the components contained in the plant. Toxicity testing using the BSLT method is widely used as the first step in extract screening because it is fast, cheap and easy to do. The results obtained were that all R. stylosa plant extracts were classified as non-toxic except for the dichloromethane extract of bark with an LC50 value of 947.76 ppm (toxic). The results of GC-MS analysis show that the compound that has the highest area is lupeol compound.

Exploring EFL Students’ Lived Experiences Working in Single-Gender and Mixed-Gender Groups: A Phenomenological Study

Group work (GW) is one of the strategies teachers employ to organize students and let them participate in collaborative tasks to achieve their learning-centered goals. The formation of single-gender (SG) and mixed-gender (MG) groups cannot be avoided in GW. Thus, this research aimed to explore EFL (English Foreign Language) students’ lived experiences working in SG and MG groups. A qualitative study using a phenomenological approach was conducted. The data were collected from three rounds of semi-structured interviews with eight participants. They were selected through three steps of online screening with some criteria. The research was conducted at one of the state universities in East Java, Indonesia. Van Kaam’s method, modified by Moustakas (1994), was used to analyze the data. This research revealed two main results: positive and negative experiences reported by students in both the SG and MG groups. In the SG group, the themes include effective communication, postponed GW tasks, and a better environment. In contrast, in the MG group, the themes encompass ineffective communication, solid members, and ineffective time management. In short, two positive and one negative theme are mentioned in SG groups, while one positive and two negative themes emerge in MG groups. As a result, the core aspects revealed include communication, individual contributions and responsibility, and productivity and efficiency while working in groups. This present research adds more comprehensive theories of GW related to students' gender and helps students and teachers be more aware of GW related to gender aspects.

RRM2 Is a CTNNB1 Transport Regulator Promoting Colon Cancer Progression.

The cytoplasmic retention and stabilization of CTNNB1 (β-catenin) in response to Wnt is well documented in playing a role in tumor growth. Here, through the utilization of a multiplex siRNA library screening strategy, we investigated the modulation of CTNNB1 function in tumor cell progression by ribonucleoside-diphosphate reductase subunit M2 (RRM2). We conducted a multiplex siRNA screening assay to identify targets involved in CTNNB1 nuclear translocation. In order to examine the effect of inhibition of RRM2, selected from the siRNA screening results, we performed RRM2 knockdown and assayed for colon cancer cell viability, sphere formation assay, and invasion assay. The interaction of RRM2 with CTNNB1 and its impact on oncogenesis was examined using immunoprecipitation, immunoblotting, immunocytochemistry, and RT-qPCR. After a series of screening and filtration steps, we identified 26 genes that were potentially involved in CTNNB1 nuclear translocation. All candidate genes were validated in various cell lines. The results revealed that siRNA-mediated knockdown of RRM2 reduces the nuclear translocation of CTNNB1. This reduction was accompanied by a decrease in cell count, resulting in a suppressive effect on tumor cell growth. High throughput siRNA screening is an attractive strategy for identifying gene functions in cancers and the interaction between RRM2 and CTNNB1 is an attractive drug target for regulating RRM2-CTNNB1-related pathways in cancers.

Identifying potential provenances for climate-change adaptation using spatially variable coefficient models

BackgroundSelection of climate-change adapted ecotypes of commercially valuable species to date relies on DNA-assisted screening followed by growth trials. For trees, such trials can take decades, hence any approach that supports focussing on a likely set of candidates may save time and money. We use a non-stationary statistical analysis with spatially varying coefficients to identify ecotypes that indicate first regions of similarly adapted varieties of Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco) in North America. For over 70,000 plot-level presence-absences, spatial differences in the survival response to climatic conditions are identified.ResultsThe spatially-variable coefficient model fits the data substantially better than a stationary, i.e. constant-effect analysis (as measured by AIC to account for differences in model complexity). Also, clustering the model terms identifies several potential ecotypes that could not be derived from clustering climatic conditions itself. Comparing these six identified ecotypes to known genetically diverging regions shows some congruence, as well as some mismatches. However, comparing ecotypes among each other, we find clear differences in their climate niches.ConclusionWhile our approach is data-demanding and computationally expensive, with the increasing availability of data on species distributions this may be a useful first screening step during the search for climate-change adapted varieties. With our unsupervised learning approach being explorative, finely resolved genotypic data would be helpful to improve its quantitative validation.

Method of Recycling Integrated Circuits from Printed Circuit Boards

This document presents a method for recycling integrated circuits (ICs) from used printed circuit boards (PCBs), providing detailed steps for identification, screening, preconditioning, and testing of target devices. The method aims to salvage functional ICs from discarded PCBs, reducing electronic waste and promoting environmental sustainability. Additionally, it outlines the construction of new PCBs using the recycled ICs, contributing to resource conservation and efficient electronic component utilization.

Sequence-Based Viscosity Prediction for Rapid Antibody Engineering.

Through machine learning, identifying correlations between amino acid sequences of antibodies and their observed characteristics, we developed an internal viscosity prediction model to empower the rapid engineering of therapeutic antibody candidates. For a highly viscous anti-IL-13 monoclonal antibody, we used a structure-based rational design strategy to generate a list of variants that were hypothesized to mitigate viscosity. Our viscosity prediction tool was then used as a screen to cull virtually engineered variants with a probability of high viscosity while advancing those with a probability of low viscosity to production and testing. By combining the rational design engineering strategy with the in silico viscosity prediction screening step, we were able to efficiently improve the highly viscous anti-IL-13 candidate, successfully decreasing the viscosity at 150 mg/mL from 34 cP to 13 cP in a panel of 16 variants.

Association of physical activity and screen time with cardiovascular disease risk in the Adolescent Brain Cognitive Development Study

BackgroundAccording to the Physical Activity Guidelines Advisory Committee Scientific Report, limited evidence is available on sedentary behaviors (screen time) and their joint associations with physical activity (steps) for cardiovascular health in adolescence. The objective of this study was to identify joint associations of screen time and physical activity categories with cardiovascular disease (CVD) risk factors (blood pressure, hemoglobin A1c, cholesterol) in adolescence.MethodsThis study analyzed data from the Adolescent Brain Cognitive Development (ABCD) Study, comprising a diverse sample of 4,718 U.S. adolescents aged 10–15 years between 2018 and 2021. Steps were measured by a Fitbit wearable device and levels were categorized as low (1,000–6,000), medium (> 6,000–12,000), and high (> 12,000) averaged daily step counts. Self-reported recreational screen time hours per day were classified as low (0–4), medium (> 4–8), and high (> 8) hours per day. CVD risk factors including blood pressure, hemoglobin A1c, and cholesterol (total and HDL) were measured.ResultsThe analytical sample averaged 6.6 h of screen time per day and 9,722 steps per day. In models including both screen time and steps, the high screen time category was associated with a 4.27 higher diastolic blood pressure percentile (95% CI 1.83–6.73) and lower HDL cholesterol (B= -2.85, 95% CI -4.77 to -0.94 mg/dL) compared to the low screen time category. Medium (B = 3.68, 95% CI 1.24–6.11) and low (B = 7.64, 95% CI 4.07–11.20) step categories were associated with higher diastolic blood pressure percentile compared to the high step category. The medium step category was associated with lower HDL cholesterol (B= -1.99, 95% CI -3.80 to -0.19 mg/dL) compared to the high step category. Findings were similar when screen time and step counts were analyzed as continuous variables; higher continuous step count was additionally associated with lower total cholesterol (mg/dL).ConclusionsCombinations of low screen time and high steps were generally associated with favorable cardiovascular health markers including lower diastolic blood pressure and higher HDL cholesterol, which can inform future adolescent health guidelines.

Operationalizing open and restricted-access data—Formulating verifiable criteria for the openness of data sets mentioned in biomedical research articles

Abstract Increasing the availability of research data sets is a goal of many stakeholders in science, and monitoring related practices requires definitions of the entity in question. There are several, largely overlapping, definitions for open data. However, they have so far not been translated into operationalizations that would allow us to detect, in a structured and reproducible way, whether, for a specific research article, the underlying data have been shared. Here, we propose a detailed set of criteria to enable such assessments, focusing on biomedical research. We have used these criteria to distribute performance-oriented funding at a large university hospital and to monitor data sharing practices in a dashboard. In addition to fully open data, we include separate criteria for data sets with restricted access, which we also reward. The criteria are partly inspired by the FAIR principles, particularly findability and accessibility, but do not map onto individual principles. The criteria attribute open data status in a binary fashion, both to individual data sets and, ultimately, articles with which they were shared. The criteria allow a verifiable assessment, based on automated and manual screening steps, which we have implemented and validated, as described elsewhere. Here, we focus conceptually on assessing the presence of shared data.

Computational insights into allosteric inhibition of focal adhesion kinase: A combined pharmacophore modeling and molecular dynamics approach

Focal adhesion kinase (FAK) is a non-receptor tyrosine kinase that modulates integrin and growth factor signaling pathways and is implicated in cancer cell migration, proliferation, and survival. Over the past decade various, FAK kinase, FERM, and FAT domain inhibitors have been reported and a few kinase domain inhibitors are under clinical consideration. However, few of them were identified as multikinase inhibitors. In kinase drug design selectivity is always a point of concern, to improve selectivity allosteric inhibitor development is the best choice. The current research utilized a pharmacophore modeling (PM) approach to identify novel allosteric inhibitors of FAK. The all-available allosteric inhibitor bound 3D structures with PDB ids 4EBV, 4EBW, and 4I4F were utilized for the pharmacophore modeling. The validated PM models were utilized to map a database of 770,550 compounds prepared from ZINC, EXIMED, SPECS, ASINEX, and InterBioScreen, aiming to identify potential allosteric inhibitors. The obtained compounds from screening step were forwarded to molecular docking (MD) for the prediction of binding orientation inside the allosteric site and the results were evaluated with the known FAK allosteric inhibitor (REF). Finally, 14 FAK-inhibitor complexes were selected from the docking study and were studied under molecular dynamics simulations (MDS) for 500 ns. The complexes were ranked according to binding free energy (BFE) and those demonstrated higher affinity for allosteric site of FAK than REF inhibitors were selected. The selected complexes were further analyzed for intermolecular interactions and finally, three potential allosteric inhibitor candidates for the inhibition of FAK protein were identified. We believe that identified scaffolds may help in drug development against FAK as an anticancer agent.

Fisher matrix based fault detection for PMUs data in power grids

In this paper, using the data collected from phasor measurement units (PMUs), two methods based on Fisher random matrix are proposed to detect faults in power grids. Firstly, the fault detection matrix is constructed and the event detection problem is reformatted as a testing problem for two-sample covariance matrices, which is related with the so-called Fisher matrix. To save computing resources, the screening step of fault interval based on the test statistic is designed to check the existence of faults. Then two point-by-point methods are proposed to determine the time of the fault in the selected interval. One method detects faults by the limiting spectral distribution of the standard Fisher matrix, which can detect the faults with higher accuracy. The other method tests the faults based on the proposed test statistic , which has a faster detection speed. Compared with existing works, the simulation results illustrate that two methods proposed in this paper cost less computational time and provide a higher degree of accuracy and sensitivity.

ML-Aided Computational Screening of 2D Materials for Photocatalytic Water Splitting.

The exploration of two-dimensional (2D) materials with exceptional physical and chemical properties is essential for the advancement of solar water splitting technologies. However, the discovery of 2D materials is currently heavily reliant on fragmented studies with limited opportunities for fine-tuning the chemical composition and electronic features of compounds. Starting from the V2DB digital library as a resource of 2D materials, we set up and execute a funnel approach that incorporates multiple screening steps to uncover potential candidates for photocatalytic water splitting. The initial screening step is based upon machine learning (ML) predicted properties, and subsequent steps involve first-principles modeling of increasing complexity, going from density functional theory (DFT) to hybrid-DFT to GW calculations. Ensuring that at each stage more complex calculations are only applied to the most promising candidates, our study introduces an effective screening methodology that may serve as a model for accelerating 2D materials discovery within a large chemical space. Our screening process yields a selection of 11 promising 2D photocatalysts.

In silico phylogenetic, physicochemical, and structural characteristics of phytase enzyme from ten Aspergillus species

Phytic acid is a chemical compound consisting of inositol and phosphoric acid and is an antinutrient compound found in monogastric poultry feed ingredients made from cereal crops. Phytase hydrolyzes phosphoester bonds in phytic acid, releasing inorganic phosphate and phosphate esters. Aspergillus is a genus of molds that produce phytase and has been widely used in phytase production because they are easy to culture. This study aims to compare the structures, physicochemical characteristics, and phylogenetic relationships of phytases from several species of Aspergillus in silico as an initial screening step in obtaining the most suitable phytase to be used in poultry feed. Phylogenetic trees were constructed using MEGA 11 and physicochemical characteristics were analyzed using ProtParam. Protein structures were modeled with AlphaFold. The phytase structures were then docked with phytic acid using the YASARA Structure. The results showed that phytase 1QFX from Aspergillus niger, P34755 from A. awamori, and D5HQ11 from A. ficuum have very high similarity in terms of phylogenetics, sequences, physicochemical characteristics, and protein structures. The docking results from the three phytase structures showed that phytase 1QFX has the most negative ΔG value and the lowest Kd, which indicated the highest affinity to the phytic acid substrate. This research concludes that among the three phytase structures that have been compared and docked with phytic acid, phytase 1QFX from A. niger is the most suitable to be applied to poultry feed.

Optimizing MSW incineration bottom ash reuse: A study on treated wastewater washing and leaching control

The current study introduces an innovative methodology by utilizing treated wastewater (TWW) from an effluent treatment plant as a washing agent to enhance the characteristics of incineration bottom ash (IBA). This approach addresses sustainability concerns and promotes the circular economy by reusing wastewater generated in municipal solid waste incineration facilities. Previous research has underscored the challenges of open IBA reuse due to elevated leaching of chlorides, sulfates, and trace metal(loid)s. Thus, the experimental setup explores various combinations of washing, with or without screening, to optimize the properties of soil-like material (SLM < 4.75 mm) and overall material (OM < 31.5 mm) fractions of IBA for unrestricted applications. Batch leaching tests were conducted on treated samples, and leaching characteristics were evaluated in accordance with regulatory standards, primarily the Dutch standard for unrestricted IBA reuse. The findings reveal that washing in isolation proves insufficient to enhance IBA properties; however, washing followed by screening, specifically for removing fines (<0.15 mm), proves effective in reducing contamination. The study identifies that multiple steps of washing and screening (with recirculation) process render OM and SLM fractions suitable for unrestricted reuse with a cumulative liquid-to-solid ratio of 6 L/kg and a total washing time of 15 min. The multi-step treatment was found effective in reducing sulfate contamination by 65–74 % and chloride contamination by 83–89 % in IBA fractions. This approach offers a promising solution for overcoming the limitations associated with IBA leaching, thereby promoting sustainable waste reuse practices.

All-in-one smartphone imaging for visual immunobiosensing of cancer biomarker using electronic gyro-centrifuge and nanozyme

Integrated functionality that combines recognition elements and sensors on a single platform to provide real-time detection capabilities for fifth-generation biosensors is considered one of the dominant tools for addressing point-of-care testing in the community today. Inspired by gyroscope and smartphone-based sensors, we developed a portable and low-cost immunobiosensor for high-throughput detection of cancer-related markers. The main testing process was divided into two parts: blood sample preprocessing based on an electronic gyroscope and signal reading based on nanozyme and smartphone. The feasibility of a commercial electronic gyroscope for serum acquisition from whole blood samples was evaluated. In addition, PtCd nanoclusters (PtCd NCs) were used as excellent peroxidase-like nanozymes to trigger the target signal conversion of the immunosensor. PtCd NCs were able to catalyze the conversion of H2O2 to ·OH, which rapidly oxidized the colorless 3,3′,5,5′-tetramethylbenzidine (TMB) substrate to the blue Oxidized-TMB (oxTMB). Naked eye visible obvious color change and the RGB values of the color metrology system could also be read by smartphone. Prostate-specific antigen (PSA) as a model biomarker was used to validate the utility of an integrated immunoassay system to enable early cancer screening through localization of RGB values. This work focuses on convenient blood sample preprocessing and simplified serologic cancer screening steps to develop an efficient and cost-effective immunoassay platform for community and on-site point-of-care testing (POCT) in resource-limited settings.

Prevalence of Dysbetalipoproteinemia in the UK Biobank According to Different Diagnostic Criteria

Abstract Context Dysbetalipoproteinemia (DBL) is a multifactorial disorder that disrupts the normal metabolism of remnant lipoproteins, causing increased risk of cardiovascular disease. However, establishing a proper diagnosis is difficult, and the true prevalence of the disease in the general population remains unknown. Objective The objectives were to study the prevalence of the disease and to validate the performance of different clinical diagnostic criteria in a large population-based cohort. Methods This study included 453 437 participants from the UK Biobank. DBL was established in participants having an ε2ε2 genotype with mixed dyslipidemia or lipid-lowering therapy use (n = 964). The different diagnostic criteria for DBL were applied in individuals without lipid-lowering medication (n = 370 039, n = 534 DBL), to compare their performance. Results Overall, 0.6% of participants had an ε2ε2 genotype, of which 36% were classified as DBL, for a disease prevalence of 0.2% (1:469). The prevalence of DBL was similar between the different genetic ancestries (≤0.2%). Several diagnostic criteria showed good sensitivity for the diagnosis of DBL (&amp;gt;90%), but they suffered from a very low positive predictive value (0.6-15.4%). Conclusion This study reported for the first time the prevalence of DBL in the UK Biobank according to genetic ancestry. Furthermore, we provided the first external validation of different diagnostic criteria for DBL in a large population-based cohort and highlighted the fact that these criteria should not be used to diagnose DBL alone but should rather be used as a first screening step to determine which individuals may benefit from genetic testing to confirm the diagnosis.

Are national or regional surveys useful for nowcasting regional jobseekers? The case of the French region of Pays‐de‐la‐Loire

AbstactIn this paper we develop nowcasting models for the Pays‐de‐la‐Loire's jobseekers, a dynamic French regional economy. We ask whether these regional nowcasts are more accurate by only using the regional data or by combining the national and regional data. For this purpose, we use penalized regressions, random forest, and dynamic factor models as well as dimension reduction approaches. The best nowcasting performance is provided by the DFM estimated on the regional and regional‐national databases as well as the Elastic‐Net model with a prior screening step for which the national data are the most frequently selected data. For the latter, it appears that the Change in foreign orders in the industry sector, the OECD Composite leading indicator, and the BdF Business sentiment indicator are among the major predictors.

An alternating-intervention pilot trial on the impact of an informational handout on patient-reported outcomes and follow-up after lung cancer screening.

Lung cancer screening (LCS) can reduce lung cancer mortality; however, poor understanding of results may impact patient experience and follow-up. We sought to determine whether an informational handout accompanying LCS results can improve patient-reported outcomes and adherence to follow-up. This was a prospective alternating intervention pilot trial of a handout to accompany LCS results delivery. Patients undergoing LCS in a multisite program over a 6-month period received a mailing containing either: 1) a standardized form letter of LCS results (control) or 2) the LCS results letter and the handout (intervention). A two-sided informational handout on commonly asked questions after LCS created through iterative mixed-methods evaluation with both LCS patients and providers. The primary outcomes of 1)patient understanding of LCS results, 2)correct identification of next steps in screening, and 3)patient distress were measured through survey. Adherence to recommended follow-up after LCS was determined through chart review. Outcomes were compared between the intervention and control group using generalized estimating equations. 389 patients were eligible and enrolled with survey responses from 230 participants (59% response rate). We found no differences in understanding of results, identification of next steps in follow-up or distress but did find higher levels of knowledge and understanding on questions assessing individual components of LCS in the intervention group. Follow-up adherence was overall similar between the two arms, though was higher in the intervention group among those with positive findings (p = 0.007). There were no differences in self-reported outcomes between the groups or overall follow-up adherence. Those receiving the intervention did report greater understanding and knowledge of key LCS components, and those with positive results had a higher rate of follow-up. This may represent a feasible component of a multi-level intervention to address knowledge and follow-up for LCS. ClinicalTrials.gov NCT05265897.