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Severe acute respiratory syndrome coronavirus 2 infection after coronavirus disease 2019 vaccination raises concerns about the emergence of vaccine escape variants. Here we characterize 14 breakthrough infections among 5860 fully vaccinated Dutch health care workers ≥14 days after the final dose of vaccination with either BNT162b2, mRNA-1273, or Ad26.COV2.S. These breakthrough infections presented with regular B.1.1.7 (Alpha) and B.1.617.2 (Delta) variants and high viral loads, despite normal vaccine-induced B- and T-cell immune responses detected by live virus neutralization assays and ELISpot. High-risk exposure settings, such as in households, indicate a potential risk of viral transmission despite full vaccination.

Contains fulltext : 252227.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 252227.pdf (Publisher’s version ) (Open Access)

The most studied HIV eradication approach is the "shock and kill" strategy, which aims to reactivate the latent reservoir by latency reversing agents (LRAs) and allowing elimination of these cells by immune-mediated clearance or viral cytopathic effects. The CNS is an anatomic compartment in which (persistent) HIV plays an important role in HIV-associated neurocognitive disorder. Restriction of the CNS by the blood-brain barrier is important for maintenance of homeostasis of the CNS microenvironment, which includes CNS-specific cell types, expression of transcription factors, and altered immune surveillance. Within the CNS predominantly myeloid cells such as microglia and perivascular macrophages are thought to be a reservoir of persistent HIV infection. Nevertheless, infection of T cells and astrocytes might also impact HIV infection in the CNS. Genetic adaptation to this microenvironment results in genetically distinct, compartmentalized viral populations with differences in transcription profiles. Because of these differences in transcription profiles, LRAs might have different effects within the CNS as compared with the periphery. Moreover, reactivation of HIV in the brain and elimination of cells within the CNS might be complex and could have detrimental consequences. Finally, independent of activity on latent HIV, LRAs themselves can have adverse neurologic effects. We provide an extensive overview of the current knowledge on compartmentalized (persistent) HIV infection in the CNS and on the "shock and kill" strategy. Subsequently, we reflect on the impact and promise of the "shock and kill" strategy on the elimination of persistent HIV in the CNS.

Item does not contain fulltext, BACKGROUND: Virological failure during antiretroviral treatment (ART) may indicate the presence of drug resistance, but may also originate from nonadherence. Qualitative detection of ART components using drug level testing may be used to differentiate between these scenarios. We aimed to validate and implement qualitative point-of-care drug level tests for efavirenz (EFV), lopinavir (LPV), and dolutegravir (DTG) in rural South Africa. METHODS: Qualitative performance of immunoassays for EFV, LPV, and DTG was assessed by calculating limit of detection (LoD), region of uncertainty, and qualitative agreement with a reference test. Minimum duration of nonadherence resulting in a negative drug level test was assessed by simulation of treatment cessation using validated population pharmacokinetic models. RESULTS: LoD was 0.05 mg/L for EFV, 0.06 mg/L for LPV, and 0.02 mg/L for DTG. Region of uncertainty was 0.01-0.06 mg/L for EFV, 0.01-0.07 mg/L for LPV, and 0.01-0.02 mg/L for DTG. Qualitative agreement with reference testing at the LoD in patient samples was 95.2% (79/83) for EFV, 99.3% (140/141) for LPV, and 100% (118/118) for DTG. After simulated treatment cessation, median time to undetectability below LoD was 7 days [interquartile range (IQR) 4-13] for EFV, 30 hours (IQR 24-36) for LPV, and 6 days (IQR 4-7) for DTG. CONCLUSIONS: We demonstrate that qualitative ART drug level testing using immunoassays is feasible in a rural resource-limited setting. Implementation of this technology enables reliable detection of recent nonadherence and may allow for rapid and cost-effective differentiation between patients in need for adherence counseling and patients who require drug resistance testing or alternative treatment.

Item does not contain fulltext, BACKGROUND: Virological failure during antiretroviral treatment (ART) may indicate the presence of drug resistance, but may also originate from nonadherence. Qualitative detection of ART components using drug level testing may be used to differentiate between these scenarios. We aimed to validate and implement qualitative point-of-care drug level tests for efavirenz (EFV), lopinavir (LPV), and dolutegravir (DTG) in rural South Africa. METHODS: Qualitative performance of immunoassays for EFV, LPV, and DTG was assessed by calculating limit of detection (LoD), region of uncertainty, and qualitative agreement with a reference test. Minimum duration of nonadherence resulting in a negative drug level test was assessed by simulation of treatment cessation using validated population pharmacokinetic models. RESULTS: LoD was 0.05 mg/L for EFV, 0.06 mg/L for LPV, and 0.02 mg/L for DTG. Region of uncertainty was 0.01-0.06 mg/L for EFV, 0.01-0.07 mg/L for LPV, and 0.01-0.02 mg/L for DTG. Qualitative agreement with reference testing at the LoD in patient samples was 95.2% (79/83) for EFV, 99.3% (140/141) for LPV, and 100% (118/118) for DTG. After simulated treatment cessation, median time to undetectability below LoD was 7 days [interquartile range (IQR) 4-13] for EFV, 30 hours (IQR 24-36) for LPV, and 6 days (IQR 4-7) for DTG. CONCLUSIONS: We demonstrate that qualitative ART drug level testing using immunoassays is feasible in a rural resource-limited setting. Implementation of this technology enables reliable detection of recent nonadherence and may allow for rapid and cost-effective differentiation between patients in need for adherence counseling and patients who require drug resistance testing or alternative treatment.

Contains fulltext : 220642.pdf (publisher's version ) (Open Access), INTRODUCTION: When protease inhibitor (PI)-based second-line ART fails, guidelines recommend drug resistance testing and individualized third-line treatment. However, PI-resistant viral strains are rare and drug resistance testing is costly. We investigated whether less costly PI-exposure testing can be used to select those patients who would benefit most from drug resistance testing. METHODS: We performed a retrospective analysis of South African adults living with HIV experiencing failure of ritonavir-boosted-lopinavir (LPV/r)-based second-line ART for whom drug resistance testing results were available. We included patients who received plasma-based drug resistance testing at a central South African reference laboratory in 2017 and patients who received dried blood spots (DBS)-based drug resistance testing at a rural South African clinic between 2009 and 2017. PI-exposure testing was performed on remnant plasma or DBS using liquid chromatography mass spectrometry (LCMS). Additionally, a low-cost immunoassay was used on plasma. Population genotypic drug resistance testing of the pol region was performed on plasma and DBS using standard clinical protocols. RESULTS: Samples from 544 patients (494 plasma samples and 50 DBS) were included. Median age was 41.0 years (IQR: 33.3 to 48.5) and 58.6% were women. Median HIV-RNA load was 4.9 log10 copies/mL (4.3 to 5.4). Prevalence of resistance to the NRTI-backbone was 70.6% (349/494) in plasma samples and 56.0% (28/50) in DBS. Major PI-resistance mutations conferring high-level resistance to LPV/r were observed in 26.7% (132/494) of plasma samples and 12% (6/50) of DBS. PI-exposure testing revealed undetectable LPV levels in 47.0% (232/494) of plasma samples and in 60.0% (30/50) of DBS. In pooled analysis of plasma and DBS samples, detectable LPV levels had a sensitivity of 90% (84% to 94%) and a negative predictive failure of 95% (91% to 97%) for the presence of major LPV/r resistance. CONCLUSIONS: PI-exposure testing reve

Contains fulltext : 220642.pdf (publisher's version ) (Open Access), INTRODUCTION: When protease inhibitor (PI)-based second-line ART fails, guidelines recommend drug resistance testing and individualized third-line treatment. However, PI-resistant viral strains are rare and drug resistance testing is costly. We investigated whether less costly PI-exposure testing can be used to select those patients who would benefit most from drug resistance testing. METHODS: We performed a retrospective analysis of South African adults living with HIV experiencing failure of ritonavir-boosted-lopinavir (LPV/r)-based second-line ART for whom drug resistance testing results were available. We included patients who received plasma-based drug resistance testing at a central South African reference laboratory in 2017 and patients who received dried blood spots (DBS)-based drug resistance testing at a rural South African clinic between 2009 and 2017. PI-exposure testing was performed on remnant plasma or DBS using liquid chromatography mass spectrometry (LCMS). Additionally, a low-cost immunoassay was used on plasma. Population genotypic drug resistance testing of the pol region was performed on plasma and DBS using standard clinical protocols. RESULTS: Samples from 544 patients (494 plasma samples and 50 DBS) were included. Median age was 41.0 years (IQR: 33.3 to 48.5) and 58.6% were women. Median HIV-RNA load was 4.9 log10 copies/mL (4.3 to 5.4). Prevalence of resistance to the NRTI-backbone was 70.6% (349/494) in plasma samples and 56.0% (28/50) in DBS. Major PI-resistance mutations conferring high-level resistance to LPV/r were observed in 26.7% (132/494) of plasma samples and 12% (6/50) of DBS. PI-exposure testing revealed undetectable LPV levels in 47.0% (232/494) of plasma samples and in 60.0% (30/50) of DBS. In pooled analysis of plasma and DBS samples, detectable LPV levels had a sensitivity of 90% (84% to 94%) and a negative predictive failure of 95% (91% to 97%) for the presence of major LPV/r resistance. CONCLUSIONS: PI-exposure testing reve

Contains fulltext : 220642.pdf (publisher's version ) (Open Access), INTRODUCTION: When protease inhibitor (PI)-based second-line ART fails, guidelines recommend drug resistance testing and individualized third-line treatment. However, PI-resistant viral strains are rare and drug resistance testing is costly. We investigated whether less costly PI-exposure testing can be used to select those patients who would benefit most from drug resistance testing. METHODS: We performed a retrospective analysis of South African adults living with HIV experiencing failure of ritonavir-boosted-lopinavir (LPV/r)-based second-line ART for whom drug resistance testing results were available. We included patients who received plasma-based drug resistance testing at a central South African reference laboratory in 2017 and patients who received dried blood spots (DBS)-based drug resistance testing at a rural South African clinic between 2009 and 2017. PI-exposure testing was performed on remnant plasma or DBS using liquid chromatography mass spectrometry (LCMS). Additionally, a low-cost immunoassay was used on plasma. Population genotypic drug resistance testing of the pol region was performed on plasma and DBS using standard clinical protocols. RESULTS: Samples from 544 patients (494 plasma samples and 50 DBS) were included. Median age was 41.0 years (IQR: 33.3 to 48.5) and 58.6% were women. Median HIV-RNA load was 4.9 log10 copies/mL (4.3 to 5.4). Prevalence of resistance to the NRTI-backbone was 70.6% (349/494) in plasma samples and 56.0% (28/50) in DBS. Major PI-resistance mutations conferring high-level resistance to LPV/r were observed in 26.7% (132/494) of plasma samples and 12% (6/50) of DBS. PI-exposure testing revealed undetectable LPV levels in 47.0% (232/494) of plasma samples and in 60.0% (30/50) of DBS. In pooled analysis of plasma and DBS samples, detectable LPV levels had a sensitivity of 90% (84% to 94%) and a negative predictive failure of 95% (91% to 97%) for the presence of major LPV/r resistance. CONCLUSIONS: PI-exposure testing reve

The magnitudes of voltage dips on non-monitored buses can be estimated by applying Bayes's theorem with measured voltage dips on a limited number of monitored buses. Bayes's theorem is used to obtain the grid conditions, including the network impedance, load, distributed generation vector, and short-circuit location, from which the voltage magnitudes along the feeder can be calculated. In order to implement this approach effectively, it is necessary to understand which parameters among the grid conditions are more significant for the estimation performance and how the uncertainties associated with these parameters affect the estimation results. Moreover, since the measurement structure provides observations to calculate the posterior distribution from the prior distribution of the grid conditions. The measurement parameters, quantities, and accuracy levels are varied in simulations to obtain an optimum measurement structure balancing between the cost and the measurement performance. This paper presented the results of the sensitivity studies which have been done on the IEEE 13-Bus and 123-Bus test systems. The results shows the performance of the voltage dip state estimation approach is less sensitive to the uncertainty level of the load while more sensitive to those of the DG, the short-circuit power, the zero sequence impedance of the cable and the short-circuit resistance. The voltage measurement accuracy is more important than that of current and phase angles. The proposed method shows much stronger robustness compared to the conventional one and the estimation performance can be improved through several measures, e.g. adding more measurement points.

With the energy transition, capacity challenges are expected to occur more frequently in low-voltage (LV) distribution networks. In the literature, several direct and indirect load control methods have been suggested as solutions to alleviate network congestion. Direct methods involve the network operator directly controlling appliances at the households, while indirect methods aim to motivate end-users to shift their consumption through price changes. In this paper, the direct and indirect methods are combined into an integrated approach, making use of the advantages of both methods. An agent-based architecture is adopted so that distributed and computational intelligence can be combined to ensure a smooth coordination among the actors. A sensitivity-based curtailment scheme is used to incorporate the unbalanced loading condition of the LV networks. The efficiency of the proposed integrated approach is investigated through simulations in the unbalanced IEEE European LV test feeder. Simulation results reveal up to 94% reduction in congestion by the integrated approach, while maintaining the required levels of supply in the network.

The magnitudes of voltage dips on non-monitored buses can be estimated by applying Bayes's theorem with measured voltage dips on a limited number of monitored buses. Bayes's theorem is used to obtain the grid conditions, including the network impedance, load, distributed generation vector, and short-circuit location, from which the voltage magnitudes along the feeder can be calculated. In order to implement this approach effectively, it is necessary to understand which parameters among the grid conditions are more significant for the estimation performance and how the uncertainties associated with these parameters affect the estimation results. Moreover, since the measurement structure provides observations to calculate the posterior distribution from the prior distribution of the grid conditions. The measurement parameters, quantities, and accuracy levels are varied in simulations to obtain an optimum measurement structure balancing between the cost and the measurement performance. This paper presented the results of the sensitivity studies which have been done on the IEEE 13-Bus and 123-Bus test systems. The results shows the performance of the voltage dip state estimation approach is less sensitive to the uncertainty level of the load while more sensitive to those of the DG, the short-circuit power, the zero sequence impedance of the cable and the short-circuit resistance. The voltage measurement accuracy is more important than that of current and phase angles. The proposed method shows much stronger robustness compared to the conventional one and the estimation performance can be improved through several measures, e.g. adding more measurement points.

With the energy transition, capacity challenges are expected to occur more frequently in low-voltage (LV) distribution networks. In the literature, several direct and indirect load control methods have been suggested as solutions to alleviate network congestion. Direct methods involve the network operator directly controlling appliances at the households, while indirect methods aim to motivate end-users to shift their consumption through price changes. In this paper, the direct and indirect methods are combined into an integrated approach, making use of the advantages of both methods. An agent-based architecture is adopted so that distributed and computational intelligence can be combined to ensure a smooth coordination among the actors. A sensitivity-based curtailment scheme is used to incorporate the unbalanced loading condition of the LV networks. The efficiency of the proposed integrated approach is investigated through simulations in the unbalanced IEEE European LV test feeder. Simulation results reveal up to 94% reduction in congestion by the integrated approach, while maintaining the required levels of supply in the network.

Supplement: 5, Special Issue: SI, Meeting Abstract: P35

Supplement: 103, Special Issue: SI, Meeting Abstract: 0122

Due the many uncertainties present in the evolution of loads and distributed generation, the use of probabilistic load flow in low voltage (LV) networks is essential for the evaluation of the robustness of these networks from a planning perspective. The main challenge with the assessment of LV-networks is the sheer number of networks which need to be analyzed. Moreover, most loads in the LV-network have a volatile nature and are hard to approximate using conventional probability distributions. This can be overcome by the use of a Gaussian mixture distribution in load modeling. Taking advantage of its radial nature and high R/X ratios, the LV-network can be analyzed more efficiently from a computation viewpoint. By the application of simplifications defined in this paper, the backward–forward load flow can be solved analytically. This allows for the direct computation of the load flow equations with a Gaussian mixture distribution as load. When using this new approach, the required calculation time for small networks can be decreased to 3% of the time it takes to generate a similar accuracy with a Monte Carlo approach. The practical application of this load flow calculation method is illustrated with a case study on PV penetration.

With the introduction of more non-linear loads, e.g., compact fluorescent lamps, electric vehicles, photovoltaics, etc., the need to determine the harmonic impact of the residential load is rising, illustrated by the many studies performed on their harmonic impact. Traditionally, these studies are performed for a single new device and single penetration level, neglecting the harmonic interaction between new types of devices, as well as giving little information at which moment in time possible problems may arise. A composite approach to access the impact of harmonic sources on the distribution network is therefore proposed. This method combines a bottom-up stochastic modeling of the residential load with harmonic measurement data and harmonic load-flows all based on a scenario analysis. The method is validated with measurement data and shows a good prediction of the current level of harmonics in a residential neighborhood for the current situation. To demonstrate the applicability of the proposed method, case studies are performed on the IEEE European Low Voltage Test Feeder. These case studies show a marked difference between applying individual device-based models and a composite modeling approach, demonstrating why the proposed approach is an adequate method for the determination of the impact of new devices on the harmonics.

Distribution network operators charge household consumers with a network tariff, so they can recover their network investment and operational costs. With the transition; towards a sustainable energy system, the household load is changing, through the introduction of photovoltaics and electric vehicles. The tariff structures which are currently employed in the EU are either capacity and/or energy consumption based. In light of the changes in the household load the question whether these tariff structures are the most suitable merits renewed attention. In this work, the cost-reflectivity of various tariff structures has been computed based on a distribution network planning approach. Next to this, the predictability of a network tariff, i.e. how much change would a household experience in network charges in two consecutive years has also been computed to gain insight into how well users will be able to react to the tariff. The results show that a peak load based network tariffs score best on the reflectivity while having an acceptable level of predictability. The switch from an energy consumption based network tariff, which is now most often applied, towards a peak load based network tariff should therefore, be considered.

The introduction of electric vehicles and photovoltaics is changing the residential electricity consumption. Distribution network operators (DNO) are investing in an advanced metering infrastructure (AMI) to enable cost reduction through smart grid applications. The DNO also benefits from the additional measurement data the AMI gives for the network planning process. The availability of AMI data can be limited by the cost of communication and by privacy concerns. To determine the social welfare of an AMI, the economic gains should be estimated. For the planning of the low voltage (LV) network, a method for determining the value of an AMI still needs to be developed. Therefore, a planning methodology which allows various levels of measurement data availability has been developed. By applying this approach the value of different levels of AMI from an LV-network planning perspective can be determined. To illustrated the application of this approach a case study for the LV-network of a Dutch DNO is performed. The results show that an increase in measurement data can lead to €49-254 lower LV-network reinforcement costs. A detailed analysis of the results shows that already 50% of the possible cost reduction can be achieved if only 65% of the households have AMI data available.

Distribution network operators charge household consumers with a network tariff, so they can recover their network investment and operational costs. With the transition; towards a sustainable energy system, the household load is changing, through the introduction of photovoltaics and electric vehicles. The tariff structures which are currently employed in the EU are either capacity and/or energy consumption based. In light of the changes in the household load the question whether these tariff structures are the most suitable merits renewed attention. In this work, the cost-reflectivity of various tariff structures has been computed based on a distribution network planning approach. Next to this, the predictability of a network tariff, i.e. how much change would a household experience in network charges in two consecutive years has also been computed to gain insight into how well users will be able to react to the tariff. The results show that a peak load based network tariffs score best on the reflectivity while having an acceptable level of predictability. The switch from an energy consumption based network tariff, which is now most often applied, towards a peak load based network tariff should therefore, be considered.

The introduction of electric vehicles and photovoltaics is changing the residential electricity consumption. Distribution network operators (DNO) are investing in an advanced metering infrastructure (AMI) to enable cost reduction through smart grid applications. The DNO also benefits from the additional measurement data the AMI gives for the network planning process. The availability of AMI data can be limited by the cost of communication and by privacy concerns. To determine the social welfare of an AMI, the economic gains should be estimated. For the planning of the low voltage (LV) network, a method for determining the value of an AMI still needs to be developed. Therefore, a planning methodology which allows various levels of measurement data availability has been developed. By applying this approach the value of different levels of AMI from an LV-network planning perspective can be determined. To illustrated the application of this approach a case study for the LV-network of a Dutch DNO is performed. The results show that an increase in measurement data can lead to €49-254 lower LV-network reinforcement costs. A detailed analysis of the results shows that already 50% of the possible cost reduction can be achieved if only 65% of the households have AMI data available.

With the introduction of more non-linear loads, e.g., compact fluorescent lamps, electric vehicles, photovoltaics, etc., the need to determine the harmonic impact of the residential load is rising, illustrated by the many studies performed on their harmonic impact. Traditionally, these studies are performed for a single new device and single penetration level, neglecting the harmonic interaction between new types of devices, as well as giving little information at which moment in time possible problems may arise. A composite approach to access the impact of harmonic sources on the distribution network is therefore proposed. This method combines a bottom-up stochastic modeling of the residential load with harmonic measurement data and harmonic load-flows all based on a scenario analysis. The method is validated with measurement data and shows a good prediction of the current level of harmonics in a residential neighborhood for the current situation. To demonstrate the applicability of the proposed method, case studies are performed on the IEEE European Low Voltage Test Feeder. These case studies show a marked difference between applying individual device-based models and a composite modeling approach, demonstrating why the proposed approach is an adequate method for the determination of the impact of new devices on the harmonics.

Due the many uncertainties present in the evolution of loads and distributed generation, the use of probabilistic load flow in low voltage (LV) networks is essential for the evaluation of the robustness of these networks from a planning perspective. The main challenge with the assessment of LV-networks is the sheer number of networks which need to be analyzed. Moreover, most loads in the LV-network have a volatile nature and are hard to approximate using conventional probability distributions. This can be overcome by the use of a Gaussian mixture distribution in load modeling. Taking advantage of its radial nature and high R/X ratios, the LV-network can be analyzed more efficiently from a computation viewpoint. By the application of simplifications defined in this paper, the backward–forward load flow can be solved analytically. This allows for the direct computation of the load flow equations with a Gaussian mixture distribution as load. When using this new approach, the required calculation time for small networks can be decreased to 3% of the time it takes to generate a similar accuracy with a Monte Carlo approach. The practical application of this load flow calculation method is illustrated with a case study on PV penetration.

In the residential sector, with the introduction of electric vehicles and photovoltaics, developments are taking place which have an impact on residential load curves. In order to assess the integration of these new types of technologies on both the generation and load side, as well as to develop mitigation strategies like demand side management, detailed information is required about the load curve of a household. To gain knowledge about this load curve a residential load model is developed based on publicly available data. The model utilises a Markov Chain Monte Carlo method to model the occupancy in a household based on time use surveys, which together with weather variables, neighbourhood characteristics and behavioural data are used to model the switching pattern of appliances. The modelling approach described in this paper is applied for the situation in the Netherlands. The resulting load curve probability distributions are validated with smart meter measurements for 100 Dutch households for a week. The validation shows that the model presented in this paper can be employed for further studies on demand side management approaches and integration issues of new appliances in distribution grids.

In the residential sector, with the introduction of electric vehicles and photovoltaics, developments are taking place which have an impact on residential load curves. In order to assess the integration of these new types of technologies on both the generation and load side, as well as to develop mitigation strategies like demand side management, detailed information is required about the load curve of a household. To gain knowledge about this load curve a residential load model is developed based on publicly available data. The model utilises a Markov Chain Monte Carlo method to model the occupancy in a household based on time use surveys, which together with weather variables, neighbourhood characteristics and behavioural data are used to model the switching pattern of appliances. The modelling approach described in this paper is applied for the situation in the Netherlands. The resulting load curve probability distributions are validated with smart meter measurements for 100 Dutch households for a week. The validation shows that the model presented in this paper can be employed for further studies on demand side management approaches and integration issues of new appliances in distribution grids.