An Automated Robotic Interface for Assays: Facilitating Machine Learning in Drug Discovery by the Automation of Physicochemical Property Assays.

Measuring the physicochemical properties of molecules is an iterative but integral process in the drug development process. A strategy to overcome the challenges in maximizing assay throughput relies on the usage of in silico machine learning (ML) prediction models trained on experimental data. Consequently, the performance of these in silico models are dependent on the quality of the utilized experimental data. To improve the data quality, we have designed and implemented an automated robotic system to prepare and run physicochemical property assays (Automated Robotic Interface for Assays, ARIA) with an increase in sample throughput of 6 to10-fold. Through this process, we overcame major challenges and achieved consistent reproducible assay data compared to semiautomated assay preparation.

Stochastic competitive release and adaptive chemotherapy.

We develop a finite-cell model of tumor natural selection dynamics to investigate the stochastic fluctuations associated with multiple rounds of adaptive chemotherapy. The adaptive cycles are designed to avoid chemoresistance in the tumor by managing the ecological mechanism of competitive release of a resistant subpopulation. Our model is based on a three-component evolutionary game played among healthy (H), sensitive (S), and resistant (R) populations of N cells, with a chemotherapy control parameter, C(t), which we use to dynamically impose selection pressure on the sensitive subpopulation to slow tumor growth and manage competitive release of the resistant population. The adaptive chemoschedule is designed based on the deterministic (N→∞) adjusted replicator dynamical system, then implemented using the finite-cell stochastic frequency dependent Moran process model (N=10K-50K) to ascertain the cumulative effect of the stochastic fluctuations on the efficacy of the adaptive schedules over multiple rounds. We quantify the stochastic fixation probability regions of the R and S populations in the HSR trilinear phase plane as a function of the control parameter C∈[0,1], showing that the size of the R region increases with increasing C. We then implement an adaptive time-dependent schedule C(t) for the stochastic model and quantify the variances (using principal component coordinates) associated with the evolutionary cycles over multiple rounds of adaptive therapy. The variances increase subquadratically through several rounds before the evolutionary cycle begins to break down. Despite this, we show the stochastic adaptive schedules are more effective at delaying resistance than standard maximum tolerated dose and low-dose metronomic schedules. The simplified low-dimensional model provides some insights on how well multiple rounds of adaptive therapies are likely to perform over a range of tumor sizes (i.e., different values of N) if the goal is to maintain a sustained balance among competing subpopulations of cells to avoid chemoresistance via competitive release in a stochastic environment.

CO2 capture by microporous carbon based on Brazil nut shells.

The increase in burning, deforestation, and the exorbitant use of fossil fuels have contributed to the increase in carbon dioxide emissions; this gas is responsible for the intensification of the greenhouse effect and radical climate changes. In this way, it becomes necessary to find alternatives to reduce its emission. Porous carbon materials synthesized from lignocellulosic waste can be employed in technologies for capture and utilization of CO2 due to the advantages such as selectivity, low-cost synthesis, high surface area and pore volume, and thermal and chemical stability. Considering the availability of Brazil nut biomass residues in the Amazon region, this article proposes to synthesize activated carbon from the lignocellulosic residue using physical and chemical activation methods for CO2 capture. The analysis of N2 adsorption-desorption isotherms proves the predominance of a microporous structure when using the two synthesis methods described here. In physical activation, the surface area was 912 m2/g, while, in chemical activation, it was 1421 to 2730 m2/g. The sample treated via the chemical method (BS6-K1) showed better performance in CO2 adsorption, with adsorption results of 3.8 and 6 mmol/g of CO2 at 25 ℃ and 0 °C, respectively, at 101 kPa. CO2 adsorption capacity is due to the high volume of ultramicropores. It is believed that the microporous carbon material synthesized from Brazil nut residues is an alternative precursor for carbon materials used as CO2 capture.

COVID-19 vaccine incentive scheduling using an optimally controlled reinforcement learning model.

We model Covid-19 vaccine uptake as a reinforcement learning dynamic between two populations: the vaccine adopters, and the vaccine hesitant. Using data available from the Center for Disease Control (CDC), we estimate the payoff matrix governing the interaction between these two groups over time and show they are playing a Hawk-Dove evolutionary game with an internal evolutionarily stable Nash equilibrium (the asymptotic percentage of vaccinated in the population). We then ask whether vaccine adoption can be improved by implementing dynamic incentive schedules that reward/punish the vaccine hesitant, and if so, what schedules are optimal and how effective are they likely to be? When is the optimal time to start an incentive program, how large should the incentives be, and is there a point of diminishing returns? By using a tailored replicator dynamic reinforcement learning model together with optimal control theory, we show that well designed and timed incentive programs can improve vaccine uptake by shifting the Nash equilibrium upward in large populations, but only so much, and incentive sizes above a certain threshold show diminishing returns.

Key predictors and burden of meticillin-resistant Staphylococcus aureus infection in comparison with meticillin-susceptible S. aureus infection in an Australian hospital setting.

BACKGROUND: Staphylococcus aureus is associated with significant mortality and increased burden on the healthcare system. Relatively few reliable estimates are available regarding the impact of meticillin-resistant S. aureus (MRSA) infection compared with meticillin-susceptible S. aureus (MSSA) infection. AIMS: To compare patients with MRSA infection and MSSA infection to identify differences in inpatient mortality, length of stay and costs of hospital services, and identify predictors of MRSA as a cause of S. aureus infection. METHODS: An analytical, retrospective, longitudinal study using non-identifiable linked data on adults admitted to hospitals of a health district in Australia with a diagnosis of S. aureus infection over a 10-year period. The main outcome measure was 30-day inpatient mortality. Secondary endpoints included total overnight stays, all-cause inpatient mortality, and hospitalization costs within 1 year of index admission. FINDINGS: Inpatient mortality at 30, 100 and 365 days was estimated to be significantly greater for patients with MRSA infection. The mean additional cost of MRSA infection when controlling for additional factors was $5988 and 4 nights of additional hospital stay per patient within 1 year of index admission. Key predictors of MRSA infection were: date of index admission; higher comorbidity score; greater socio-economic disadvantage; admission to hospital other than via the emergency department; older age; and prior admission to hospital within 28 days of index admission. CONCLUSIONS: MRSA infection is associated with increased inpatient mortality, costs and hospital length of stay compared with MSSA infection. Efforts are required to alleviate the additional burden of MRSA infection on patients and healthcare systems.

Molecular typing of enteroviruses: comparing 5'UTR, VP1 and whole genome sequencing methods.

Enteroviruses (EV) commonly cause hand, foot and mouth disease (HFMD), and can also cause potentially fatal neurological and systemic complications. In our laboratory, sequencing 5' untranslated region (UTR) of the viral genome has been the routine method of genotyping EVs. During a recent localised outbreak of aseptic meningitis, sequencing the 5'UTR identified the causative virus as EV-A71, which did not fit with the clinical syndrome or illness severity. When genotyped using a different target gene, VP1, the result was different. This led us to evaluate the accuracy of the two different target genome regions and compare them against whole genome sequencing (WGS). We aimed to optimise the algorithm for detection and characterisation of EVs in the diagnostic laboratory. We hypothesised that VP1 and WGS genotyping would provide different results than 5'UTR in a subset of samples. Clinical samples from around New South Wales which were positive for EV by commercial polymerase chain reaction (PCR) assays were genotyped by targeting three different viral genome regions: the 5'UTR, VP1 and WGS. Sequencing was performed by Sanger and next generation sequencing. The subtyping results were compared. Of the 74/118 (63%) samples that were successfully typed using both the 5'UTR and the VP1 method, the EV typing result was identical for 46/74 (62%) samples compared to WGS as the gold standard. The same EV group but different EV types were found in 22/74 (30%) samples, and 6/74 (8%) samples belonged to different EV groups depending on typing method used. Genotyping with WGS and VP1 is more accurate than 5'UTR. Genotyping by the 5'UTR method is very sensitive, but less specific.

Optimal dynamic incentive scheduling for Hawk-Dove evolutionary games.

The Hawk-Dove evolutionary game offers a paradigm of the trade-offs associated with aggressive and passive behaviors. When two (or more) populations of players compete, their success or failure is measured by their frequency in the population, and the system is governed by the replicator dynamics. We develop a time-dependent optimal-adaptive control theory for this dynamical system in which the entries of the payoff matrix are dynamically altered to produce control schedules that minimize and maximize the aggressive population through a finite-time cycle. These schedules provide upper and lower bounds on the outcomes for all possible strategies since they represent two extremizers of the cost function. We then adaptively extend the optimal control schedules over multiple cycles to produce absolute maximizers and minimizers for the system.

Role of synergy and antagonism in designing multidrug adaptive chemotherapy schedules.

Chemotherapeutic resistance via the mechanism of competitive release of resistant tumor cell subpopulations is a major problem associated with cancer treatments and one of the main causes of tumor recurrence. Often, chemoresistance is mitigated by using multidrug schedules (two or more combination therapies) that can act synergistically, additively, or antagonistically on the heterogeneous population of cells as they evolve. In this paper, we develop a three-component evolutionary game theory model to design two-drug adaptive schedules that mitigate chemoresistance and delay tumor recurrence in an evolving collection of tumor cells with two resistant subpopulations and one chemosensitive population that has a higher baseline fitness but is not resistant to either drug. Using the nonlinear replicator dynamical system with a payoff matrix of Prisoner's Dilemma (PD) type (enforcing a cost to resistance), we investigate the nonlinear dynamics of this three-component system along with an additional tumor growth model whose growth rate is a function of the fitness landscape of the tumor cell populations. A key parameter determines whether the two drugs interact synergistically, additively, or antagonistically. We show that antagonistic drug interactions generally result in slower rates of adaptation of the resistant cells than synergistic ones, making them more effective in combating the evolution of resistance. We then design evolutionary cycles (closed loops) in the three-component phase space by shaping the fitness landscape of the cell populations (i.e., altering the evolutionary stable states of the game) using appropriately designed time-dependent schedules (adaptive therapy), altering the dosages and timing of the two drugs. We describe two key bifurcations associated with our drug interaction parameter which help explain why antagonistic interactions are more effective at controlling competitive release of the resistant population than synergistic interactions in the context of an evolving tumor.

Maximizing cooperation in the prisoner's dilemma evolutionary game via optimal control.

The prisoner's dilemma (PD) game offers a simple paradigm of competition between two players who can either cooperate or defect. Since defection is a strict Nash equilibrium, it is an asymptotically stable state of the replicator dynamical system that uses the PD payoff matrix to define the fitness landscape of two interacting evolving populations. The dilemma arises from the fact that the average payoff of this asymptotically stable state is suboptimal. Coaxing the players to cooperate would result in a higher payoff for both. Here we develop an optimal control theory for the prisoner's dilemma evolutionary game in order to maximize cooperation (minimize the defector population) over a given cycle time T, subject to constraints. Our two time-dependent controllers are applied to the off-diagonal elements of the payoff matrix in a bang-bang sequence that dynamically changes the game being played by dynamically adjusting the payoffs, with optimal timing that depends on the initial population distributions. Over multiple cycles nT (n>1), the method is adaptive as it uses the defector population at the end of the nth cycle to calculate the optimal schedule over the n+1st cycle. The control method, based on Pontryagin's maximum principle, can be viewed as determining the optimal way to dynamically alter incentives and penalties in order to maximize the probability of cooperation in settings that track dynamic changes in the frequency of strategists, with potential applications in evolutionary biology, economics, theoretical ecology, social sciences, reinforcement learning, and other fields where the replicator system is used.

Crowdsourcing: A novel tool to elicit the student voice in the curriculum design process for an undergraduate diagnostic radiography degree programme.

INTRODUCTION: Stakeholder participation in healthcare curriculum design is an important aspect of higher education with stakeholders including students, staff members, clinical partners, healthcare organisations, patients and members of the public. Significantly, student co-creation, of the curriculum, has become increasingly important. Yet there is limited research which addresses how to engage this group in design processes. METHODS: This paper represents the first phase of a three stage action research spiral whereby the authors evaluated the use of a novel tool for curriculum design processes, anonymised crowdsourcing. This initial phase was open to all students enrolled on an undergraduate diagnostic radiography programme in the UK. To confirm the reliability of the crowdsource design an established eight point crowdsourcing verification tool was applied. RESULTS: Twenty-three unique ideas were generated by participants, 40 comments made and 173 votes cast. Inductive analysis of the comments generated five themes. These included: the role of technology enhanced learning; simulation activities; patient focused curriculum; mental wealth (resilience) authentic assessment approaches. An evaluation of those who had and had not engaged highlighted areas of improvement for the administration of the second and third iterations which will include a wider pool of participants. CONCLUSION: This study from a single programme offers lessons for others wishing to adopt and develop this approach elsewhere. IMPLICATIONS FOR PRACTICE: Several ideas elicited by the crowdsource have been considered by the curriculum design team and will be implemented in the 2020 curriculum thus demonstrating the impact on local education practice of this research approach.

Dietary nitrate attenuates high-fat diet-induced obesity via mechanisms involving higher adipocyte respiration and alterations in inflammatory status.

Emerging evidence indicates that dietary nitrate can reverse several features of the metabolic syndrome, but the underlying molecular mechanisms still remain elusive. The aim of the present study was to explore mechanisms involved in the effects of dietary nitrate on the metabolic dysfunctions induced by high-fat diet (HFD) in mice. Four weeks old C57BL/6 male mice, exposed to HFD for ten weeks, were characterised by increased body weight, fat content, increased fasting glucose and impaired glucose clearance. All these metabolic abnormalities were significantly attenuated by dietary nitrate. Mechanistically, subcutaneous primary mouse adipocytes exposed to palmitate (PA) and treated with nitrite exhibited higher mitochondrial respiration, increased protein expression of total mitochondrial complexes and elevated gene expression of the thermogenesis gene UCP-1, as well as of the creatine transporter SLC6A8. Finally, dietary nitrate increased the expression of anti-inflammatory markers in visceral fat, plasma and bone marrow-derived macrophages (Arginase-1, Egr-2, IL-10), which was associated with reduction of NADPH oxidase-derived superoxide production in macrophages. In conclusion, dietary nitrate may have therapeutic utility against obesity and associated metabolic complications possibly by increasing adipocyte mitochondrial respiration and by dampening inflammation and oxidative stress.

The prevalence and impact of depression and anxiety in cardiac rehabilitation: A longitudinal cohort study.

BACKGROUND: Co-morbid depression and anxiety symptoms are frequently under-recognised and under-treated in heart disease and this negatively impacts self-management. AIMS: The purpose of this study was to determine the prevalence, correlates and predictors of depression and anxiety in cardiac rehabilitation programmes, the impact of cardiac rehabilitation on moderate depression, anxiety and stress symptoms, and the relationship between moderate depression, anxiety and stress symptoms and cardiac rehabilitation adherence. METHODS: This was a retrospective cohort study of 5908 patients entering cardiac rehabilitation programmes from 2006-2017, across two Sydney metropolitan teaching hospitals. Variables included demographics, diagnoses, cardiovascular risk factors, medication use, participation rates, health status (Medical Outcomes Study Short Form-36) and psychological health (Depression Anxiety Stress Scales) subscale scores. RESULTS: Moderate depression, anxiety or stress symptoms were prevalent in 18%, 28% and 13% of adults entering cardiac rehabilitation programmes, respectively. Adults with moderate depression (24% vs 13%), anxiety (32% vs 23%) or stress (18% vs 10%) symptoms were significantly less likely to adhere to cardiac rehabilitation compared with those with normal-mild symptoms (p < 0.001). Anxiety (odds ratio 4.395, 95% confidence interval 3.363-5.744, p < 0.001) and stress (odds ratio 4.527, 95% confidence interval 3.315-6.181, p < 0.001) were the strongest predictors of depression. Depression (odds ratio 3.167, 95% confidence interval 2.411-4.161) and stress (odds ratio 5.577, 95% confidence interval 4.006-7.765, p < 0.001) increased the risk of anxiety on entry by more than three times, above socio-demographic factors, cardiovascular risk factors, diagnoses and quality of life. CONCLUSION: Monitoring depression and anxiety symptoms on entry and during cardiac rehabilitation can assist to improve adherence and may identify the need for additional psychological health support. Exploring the relevance and use of adjunct psychological support strategies within cardiac rehabilitation programmes is warranted.

The temporal dynamics of humoral immunity to Rickettsia typhi infection in murine typhus patients.

OBJECTIVES: This study examined individuals with Rickettsia typhi infection in the Lao People's Democratic Republic (Lao PDR) to (a) investigate humoral immune dynamics; (b) determine the differences in reference diagnostic results and recommend appropriate cut-offs; (c) determine differences in immune response after different antibiotic treatments; and (d) determine appropriate diagnostic cut-off parameters for indirect immunofluorescence assay (IFA). METHODS: Sequential serum samples from 90 non-pregnant, adults were collected at seven time-points (days 0, 7, 14, 28, 90, 180 and 365) as part of a clinical antibiotic treatment trial. Samples were tested using IFA to determine IgM and IgG antibody reciprocal end-point titres against R. typhi and PCR. RESULTS: For all 90 individuals, reciprocal R. typhi IgM and IgG antibody titres ranged from <400 to ≥3200. The median half-life of R. typhi IgM was 126 days (interquartile range 36-204 days) and IgG was 177 days (interquartile range 134-355 days). Overall median patient titres for R. typhi IgM and IgG were significantly different (p < 0.0001) and at each temporal sample collection point (range p < 0.0001 to p 0.0411). Using Bayesian latent class model analysis, the optimal diagnostic cut-off reciprocal IFA titer on patient admission for IgM was 800 (78.6%, 95% CI 71.6%-85.2% sensitivity; 89.9%, 95% CI 62.5%-100% specificity), and for IFA IgG 1600 (77.3%; 95% CI 68.2%-87.6% sensitivity; 99%, 95% CI 95%-100% specificity). CONCLUSIONS: This study suggests suitable diagnostic cut-offs for local diagnostic laboratories and other endemic settings and highlights antibody persistence following acute infection. Further studies are required to validate and define cut-offs in other geographically diverse locations.

Accurate prediction of spontaneous lumbar curve correction following posterior selective thoracic fusion in adolescent idiopathic scoliosis using logistic regression models and clinical rationale.

INTRODUCTION: Accurate prediction of spontaneous lumbar curve correction (SLCC) after selective thoracic fusion (STF) remains difficult. This study sought to improve prediction accuracy of SLCC. The hypothesis was preoperative and intraoperative variables could predict SLCC < 20°. METHODS: A multicenter observational prospective analysis was conducted to determine predictors of SLCC in AIS patients that had posterior STF. Curve types included major thoracic curves (Lenke 1, 3-4).The primary outcome variable was to establish prediction models, and a postoperative lumbar curve (LC) ≤ 20° was defined as the target variable. Multivariate logistic regression models were established to study the relationship between selected variables and a LC ≤ 20° versus a LC > 20° at ≥ 2-year follow-up. Single and dual thresholds models in perspective of clinical rationales were applied to find models with the highest positive/negative predictive values (PPV/NPV). The secondary outcome measure was SRS scores at ≥ 2-year follow-up. RESULTS: 410 patients were included. At ≥ 2-year follow-up 282 patients had LC ≤ 20°. These patients had better SRS-22 scores than those with LC > 20° (P = 0.02). The postoperative LC and LC ≤ 20° were predicted by preoperative LC and LC-bending Cobb angle (P < 0.01, r = 0.4-0.6). Logistic regression models could be established to identify patients at risk for failing the target LC ≤ 20°.For preoperative LC and LC-bending, the prediction model achieved a NPV/PPV of 80%/72%. If the postoperative main thoracic curve is combined with the preoperative LC and a gray area for difficult decisions was allowed, model accuracy could even be improved (NPV/PPV = 96%/81%). CONCLUSION: An accurate prediction model for postoperative SLCC was established based on a large analysis of prospective STF cases. These models can support prediction and understanding of postoperative SLCC aiding in surgical decision making when contemplating a selective thoracic fusion. These slides can be retrieved under Electronic Supplementary Material.

Crowdsourcing in health professions education: What radiography educators can learn from other disciplines.

OBJECTIVES: Crowdsourcing works through an institution outsourcing a function normally performed by an employee or group of individuals. Within a crowdsource users, known as the crowd, form a community who voluntarily undertake a task which involves the pooling of knowledge resources. A literature review was undertaken to identify how the tool is being used in health professions education, and potential for use in radiography education. KEY FINDINGS: 17 papers were returned. Literature identified was assessed against an established crowdsourcing definition. Reviewing these yielded four themes for discussion: student selection procedures, lesson planning, teaching materials and assessment. CONCLUSION: Crowdsourcing is associated with innovative activities through collective solution seeking via a large network of users. It is increasingly being adopted in healthcare training and maybe transferable to educational activities within the field of radiography education.

Nonlinear adaptive control of competitive release and chemotherapeutic resistance.

We use a three-component replicator system with healthy cells, sensitive cells, and resistant cells, with a prisoner's dilemma payoff matrix from evolutionary game theory, to model and control the nonlinear dynamical system governing the ecological mechanism of competitive release by which tumors develop chemotherapeutic resistance. The control method we describe is based on nonlinear trajectory design and energy transfer methods first introduced in the orbital mechanics literature for Hamiltonian systems. For continuous therapy, the basin boundaries of attraction associated with the chemo-sensitive population and the chemo-resistant population for increasing values of chemo-concentrations have an intertwined spiral structure with extreme sensitivity to changes in chemo-concentration level as well as sensitivity with respect to resistant mutations. For time-dependent therapies, we introduce an orbit transfer method to construct continuous families of periodic (closed) orbits by switching the chemo-dose at carefully chosen times and appropriate levels to design schedules that are superior to both maximum tolerated dose (MTD) schedules and low-dose metronomic (LDM) schedules, both of which ultimately lead to fixation of sensitive cells or resistant cells. By keeping the three subpopulations of cells in competition with each other indefinitely, we avoid fixation of the cancer cell population and regrowth of a resistant tumor. The method can be viewed as a way to dynamically shape the average population fitness landscape of a tumor to steer the chemotherapeutic response curve. We show that the method is remarkably insensitive to initial conditions and small changes in chemo-dosages, an important criterion for turning the method into an actionable strategy.

Maresin 1 attenuates neuroinflammation in a mouse model of perioperative neurocognitive disorders.

BACKGROUND: Resolution of inflammation is an active and dynamic process after surgery. Maresin 1 (MaR1) is one of a growing number of specialised pro-resolving lipids biosynthesised by macrophages that regulates acute inflammation. We investigated the effects of MaR1 on postoperative neuroinflammation, macrophage activity, and cognitive function in mice. METHODS: Adult male C57BL/6 (n=111) and Ccr2RFP/+Cx3cr1GFP/+ (n=54) mice were treated with MaR1 before undergoing anaesthesia and orthopaedic surgery. Systemic inflammatory changes, bone healing, neuroinflammation, and cognition were assessed at different time points. MaR1 protective effects were also evaluated using bone marrow derived macrophage cultures. RESULTS: MaR1 exerted potent systemic anti-inflammatory effects without impairing fracture healing. Prophylaxis with MaR1 prevented surgery-induced glial activation and opening of the blood-brain barrier. In Ccr2RFP/+Cx3cr1GFP/+ mice, fewer infiltrating macrophages were detected in the hippocampus after surgery with MaR1 prophylaxis, which resulted in improved memory function. MaR1 treatment also reduced expression of pro-inflammatory cell surface markers and cytokines by in vitro cultured macrophages. MaR1 was detectable in the cerebrospinal fluid of older adults before and after surgery. CONCLUSIONS: MaR1 exerts distinct anti-inflammatory and pro-resolving effects through regulation of macrophage infiltration, NF-κB signalling, and cytokine release after surgery. Future studies on the use of pro-resolving lipid mediators may inform novel approaches to treat neuroinflammation and postoperative neurocognitive disorders.

Clathrin-dependent endocytosis is associated with RNAi response in the western corn rootworm, Diabrotica virgifera virgifera LeConte.

The cellular uptake of dsRNA after dietary exposure is critical for RNAi efficiency; however, the mechanism of its uptake in many insects remains to be understood. In this study, we evaluated the roles of the endocytic pathway genes Clathrin heavy chain (Chc), Clathrin adaptor protein AP50, ADP ribosylation factor-like 1 (Arf72A), Vacuolar H+ ATPase 16 kDa subunit (Vha16), and small GTPase Rab7 and putative sid-1-like genes (silA and silC) in RNAi response in western corn rootworm (WCR) using a two-stage dsRNA exposure bioassay. Silencing of Chc, Vha16, and AP50 led to a significant decrease in the effects of laccase2 dsRNA reporter, indicating that these genes are involved in RNAi response. However, the knockdown of either Arf72A or Rab7 did not suppress the response to laccase2 dsRNA. The silencing of the silC gene did not lead to a significant reduction in mortality or increase in the expression of V-ATPase A reporter. While the silencing of the silA gene significantly decreased insect mortality, significant changes in V-ATPase A expression were not detected. These results suggest that clathrin-dependent endocytosis is a biological mechanism that plays an important role during RNAi response in WCR adults. The fact that no definitive support for the roles of silA or silC in RNAi response was obtained support the idea that RNAi response varies greatly in different insect species, demanding additional studies focused on elucidating their involvement in this mechanism.