Engaging peers in service delivery is an effective strategy to reduce stigma among key populations. In this Viewpoint, we describe an example from Zimbabwe where a community treatment observatory model has been implemented to systematically provide services and track results for people living with HIV and key populations. It is a peer‐based model that has been adopted by the Zimbabwe National Network of People living with HIV (ZNNP+).With an HIV prevalence of 12.8%, which translates to 1.4 million people living with HIV, Zimbabwe remains one of the most HIV endemic countries in sub‐Saharan Africa [In Zimbabwe, the legal context marginalizes key populations by criminalizing same‐sex relationships. The Zimbabwean penal code outlaws same‐sex relations under the “sodomy” and “indecent act” clauses, and Section 81 prohibits the solicitation of sex work . FurtherCommunity‐led monitoring has been an effective instrument for providing HIV prevention and care to key populations while ensuring services are stigma‐free for all recipients. An example of a community‐led monitoring system is the community treatment observatory. This observatory employs community members, such as people living with HIV or those representing key populations without HIV to collect data from their peers about the quantity and quality of HIV prevention, care and treatment services within communities . The colStigma and discrimination negatively impact the quality of life of people living with HIV in various ways, including detracting from mental health and inhibiting access to care , 9, 10. The community treatment observatory model at ZNNP+ involves recruiting and training self‐disclosed community cadres living with HIV to collect data from their peers, coordinate community dialogues, and engage with community leaders and health facilities. The cadres include mentor mothers, community HIV and AIDS support agents and key population peer supporters who are sex workers and men who have sex with men. Mentor mothers are women living with HIV who have successfully delivered infants who are HIV negative through prevention of mother‐to‐child transmission and help their peers achieve the same. Community HIV and AIDS support agents are self‐disclosed people living with HIV who help their peers access services that improve their quality of life. The community treatment observatory model aligns with recent calls to shift away from viral suppression‐oriented care models towards more person‐centred approaches that focus on ensuring the health of people living with HIV throughout their lives .The community treatment observatory model plays two roles: ensuring data‐driven evidence on issues affecting key populations and providing key populations with a platform to share their views confidentially. Community cadres are trained to do community‐led monitoring using mobile data collection. Data collection consists of in‐person qualitative and quantitative interviews using the customer satisfaction survey questionnaires that cover availability, affordability, accessibility, acceptability and appropriateness issues. This provides valuable insights into how key populations perceive the way they are being served at facilities. Community cadres collect data on satisfaction with time spent at health facilities, stigma and discrimination at facilities and in the community, privacy during consultations, and frequency and duration of antiretroviral treatment stock‐outs.In our experience, community‐led monitoring has proved to be an effective mechanism for organized and systematic advocacy for the health and rights of key populations. Engaging peers in service delivery is an effective strategy to reduce stigma among key populations. Getting feedback from receivers of care could encourage healthcare workers to improve person‐centred service and address issues, such as stigma, that cause low uptake of services and poor health outcomes. The example of ZNNP+ illustrates that, when properly resourced, communities can deliver crucial, destigmatized services to their peers. These services ultimately aim to ensure that health systems, governments and other stakeholders are accountable for meeting the needs and upholding the rights of key populations.All authors except KM are employees of ZNNP+.TM, CM and MM contributed to the conceptualization and writeup of the manuscript. GK contributed to the conceptualization, writeup and revision of the manuscript. KM contributed to the writeup and revision of the manuscript. TM, GK, CM, MM and KM all read and approved the final manuscript.There was no specific funding for this Viewpoint.


Ultrasound‐guided peripheral intravenous cannulation (USGPIVC) benefits patients with difficult intravenous access (DIVA) through visualising otherwise non‐visible and non‐palpable veins. Supervised live‐case training is an important component of learning this skill, but supervisor availability can present a barrier limiting or delaying staff completing their training.The aim of this study was to determine the first‐attempt success rate of newly trained USGPIVC inserters using remote supervision and timely written feedback based on app‐based screen recordings taken during insertion. Secondary aims were overall procedural success, and inserter and patient experiences.This study is an observational cohort study carried out between October and December 2021. Fourteen newly trained junior medical officers (JMOs) were eligible to utilise USGPIVC on a minimum of five consenting patients while simultaneously recording the ultrasound screen during insertion to capture their technique. Feedback was generated following expert review of these recordings against a standardised feedback tool.Average first‐attempt success was 71% (n = 72) in the 102 patients recruited. The average time for JMOs to receive feedback was 30 h, and 13 JMOs (93%) felt well supported and completed the remote training pathway. The majority of patients were female , were aged 41–80 years  and had ≥2 risk factors for DIVA .First‐attempt success rates were similar when comparing remote supervision used in this study to direct supervision used by other studies.This finding supports incorporating remote supervision into training guidelines for USGPIVC as an alternative method of supervision, particularly when supervisor availability is limited. Ault The ability to remotely supervise and provide timely standardised feedback to newly trained USGPIVC practitioners could enable staff to overcome the practical barrier of direct supervision during live‐case training. This research investigated whether remote supervision could be an alternative to the current labour‐intensive and costly requirement of having a physically present supervisor during the live‐case training component of USGPIVC training.The aim of this study was to determine the first‐attempt cannulation success rate of newly trained USGPIVC inserters with ultrasound using remote supervision and timely written feedback.A prospective observational cohort study was undertaken between October and December 2021 at the Royal Brisbane and Women's Hospital in Queensland, Australia. This study was approved by the participating hospital's Human Research Ethics Committee (Ref No.: LNR/2021/QRBW/76402) and follows the STROBE guidelines for reporting of observational studies in health care.Junior medical officers (JMOs) competent in landmark/palpation‐based PIVC insertion were eligible to undertake a standardised training pathway Table  for USGPInpatients ≥18 years of age were eligible if they required a PIVC for medical reasons and were able to provide written informed consent for study participation. JMOs recruited patients at their discretion during their after‐hours work and could be low‐, moderate‐ or high‐risk DIVA patients.An attempt was defined as a single percutaneous needle puncture, regardless of the amount of subcutaneous exploration, and success was defined as being able to flush the newly inserted PIVC with 10 ml of 0.9% saline without signs of infiltration.The number of ultrasound‐guided attempts per patient encounter was also recorded. After an unsuccessful first attempt, a new attempt could be undertaken by the JMO by firstly localising a vein, followed by a new percutaneous puncture. A procedure was defined as all attempts using ultrasound by the JMO at establishing intravenous access in a given patient with the number of attempts limited to a maximum of two based on hospital policy. The number of newly trained JMOs completing five USGPIVCs was also assessed from insertion data. JMO's self‐reported confidence and satisfaction with the training pathway were assessed at the end of the study period. Patient experience and PIVC outcomes were also measured including comfort, patient perception of their level of difficulty and inserter skill and PIVC patency.Data were collected and collated using the REDCap software .Immediately before commencing the USGPIVC attempt, JMOs started the inbuilt screen recording application (DU Recorder®) on the Samsung Galaxy® tablet running the Philips Lumify® linear ultrasound transducer. Screen recordings were downloaded off the tablet and stored in a secure location for the CIs  to view and provide feedback in the research database.https://metronorth.health.qld.gov.au/uploads/usgpivcfeedback.pdf) Ultrasound screen recordings were matched to patient encounter data recorded by JMOs at the time of insertion, and feedback was returned to JMOs via email within 48 h. Following the review of the feedback, JMOs were required to complete a feedback acknowledgement form indicating their satisfaction with the feedback (1 = not satisfied to 5 = very satisfied), and whether they would like to be contacted by a supervisor.Prior to study commencement, the research team developed a standardised feedback tool by reviewing previously published literature and identifying important components for training and procedural success.A member of the research team attempted to review enrolled patients the following day or, when not appropriate, as soon as possible. Willing patients completed the Patient Reported Experience Measure (PREM) questionnaire based on the research by Cuthbertson and Ashton.At the conclusion of the study timeframe, JMOs were asked to give feedback on their confidence relating to their USGPIVC skill and overall satisfaction (1 = strongly disagree to 5 = strongly agree) with the remote supervision aspect of the training pathway.Prior to study commencement, sample size was pre‐determined to be a minimum of 50 USGPIVC insertion attempts based on sample size recommendations for feasibility trials.Observational endpoints were analysed by the investigator team with quantitative data identified as continuous variables. No inferential statistical analysis was planned.This study was approved by the participating hospital's Human Research Ethics Committee (Ref No: LNR/2021/QRBW/76402). Informed written consent was obtained from JMOs and patients recruited in the study.Of the 21 JMOs emailed study information, 15 expressed interest and 14 met the eligibility criteria. One JMO was excluded as they had already performed >50 USGPIVC insertions. Most participants were male , with an average age of 30 and were two  or three  years postgraduate.Of the 102 patients recruited, 59 (58%) were female and most were from medical wards . Ages ranged from 18 to 92 years with the 75 (74%) patients aged 41–80 years. Patients were categorised according to the number of risk factors as per the A‐DIVA rating scale, with 45 (44%) at low DIVA risk (0–1 risk factors), 39 (38%) at moderate DIVA risk (2–3 risk factors) and 18 (18%) at high DIVA risk (4–5 risk factors).Of the 14 enrolled JMOs, 13 performed five or more USGPIVC insertions during their 5 weeks of after‐hours work. After completing face‐to‐face training, one recruited JMO did not undertake any USGPIVC insertion attempts during the study due to a large proportion of patients encountered having established central venous access.Average first insertion success by the newly trained JMO using ultrasound was 71% n = 72) in the 102 patients encountered. Of the 27 second attempts, 13 (48%) were successful giving an overall procedural success rate of 83% (n = 85)  patient questionnaires were completed. Reasons for not completing the questionnaire included being absent from the ward (i.e. discharged) or feeling too unwell to give feedback. Patients reported their mean PIVC insertion difficulty as 7.5 out of 10 based on previous cannulation experiences. Patient perception of JMO USGPIVC insertion skill was high, with a mean score of 8.4 out of 10. Complications arising from the USGPIVC inserted by the JMO were noted by 28 (35%) of patients questioned. PIVC failure  was most commonly followed by pain on insertion , fluid leak , bruising  and uncomfortable location . Patient perception of the importance of continuing research into PIVC insertion and management was high, with a mean score of 9.0 of 10.Average time from USGPIVC insertion to feedback completion was 30 hours with one JMO contacting the supervisors for additional assistance during the study period. All 14 JMOs completed a satisfaction survey at the end of the training period, and their responses are shown in Table et al predicting a first‐attempt success rate of >70% following four attempts and van Loon demonstrating a first‐attempt success rate of 81% over the first 10 directly supervised insertions.Our results are important because they demonstrate the success of an USGPIVC training pathway incorporating remotely supervised practice. Most JMOs  met the requirements to progress to unsupervised practice with only one being referred for additional training and direct supervision. Supervised practice fosters further learning and allows newly trained staff to feel supported while demonstrating safe and sufficient quality practice before transitioning to unsupervised practice.Collation of screen recording feedback gave insight into the common difficulties encountered by novice USGPIVC inserters. Owing to default machine settings, this element was overall very well achieved by JMOs. A commonly encountered problem however was the selection of superficial and small diameter veins requiring precise needle tip movements, often difficult for the novice. Needle tip tracking, although impacted by vein selection, demonstrated a gradual decline in achievement through identifying the tip outside the vessel (83%), manoeuvring it inside the vessel (75%) and being able to centre the needle tip within the vessel at the conclusion of the procedure (66%). Needle tip tracking is a key component of USGPIVC, and novices need to focus on developing this skill during their early learning.et al, without ultrasound patients at moderate DIVA risk were reported to have a 63% first‐attempt success rate and high DIVA risk a 6% first‐attempt success rate.Importantly, 83% of patients in this majority DIVA cohort had a PIVC inserted with the aid of ultrasound. As per the A‐DIVA risk assessment tool described by van Loon A limitation of this study is the small sample size, and it was undertaken in a major metropolitan teaching hospital. More motivated JMOs may have been recruited through the expression of interest process, and patients recruited by them may have been subject to some selection bias. Reviewing screen recordings did not allow for observation of some aspects of insertion such as aseptic technique. However, aseptic technique was incorporated into simulation‐based training and required to be demonstrated before the beginning of live‐case training. Simulation‐based training in aseptic technique for PIVC insertion has been previously demonstrated to improve technique and knowledge retention.i.e. JMOs inserting PIVCs after hours), so results may not be transferrable to other centres or other cohorts of healthcare staff. However, a strength of this study was that decision‐making around PIVC need, maintenance and removal were, according to the treating medical team, potentially making results more generalisable to other healthcare settings.Supervisors and JMOs were contained within the same hospital, and results may not be generalisable to environments where both parties are not collocated. Also uncertain is the most beneficial timeframe to provide feedback to newly trained staff. During this study, instructors of the short‐course training programme were also those providing ongoing review and supervision, and results might differ if this was not the case. This study was conducted in a single centre operating within local policies  training results in similar first‐attempt success rates when compared to direct supervision used in other studies, and that this training model is well received by staff and patients. Expanding the ability to supervise newly trained staff who manage difficult intravenous access patients where the availability of supervision is limited results in improvements in staff training and better patient care. As a result, our study supports incorporating remote supervision into training guidelines for USGPIVC as an alternative to the traditional methods of supervision which rely on supervisors to be physically present at the bedside.NP involved in conceptualisation, methodology, project administration, investigation, supervision, writing – original draft and reviewing and editing. JT and CW involved in conceptualisation, methodology, project administration, investigation and writing – review and editing. CR and NM involved in supervision and writing – review and editing.The authors acknowledge that their listing conforms to the AJUM authorship policy. They have all reviewed the manuscript and are in agreement with the contents of the manuscript.No funding was received for the conduct of this study.NP and JT are course facilitators of the Ultrasound Guided Peripheral Intravenous Cannulation course at the Clinical Skills Development Service . CW has no conflicts of interest to declare. CR's employer (Griffith University or The University of Queensland) has received investigator‐initiated research grants or consultancy payments on her behalf. On behalf of NM, Griffith University and The University of Queensland have received investigator‐initiated research grants or consultancy payments.


Here, we use a mathematical framework to explore the link between resource acquisition and sexual selection on an advantageous mate‐acquisition trait across biologically relevant trade‐off scenarios. Our findings provide a set of testable predictions of how resource acquisition can influence sexual selection on mating traits. In general, selection on mate‐acquisition traits is expected to be heavily influenced by: (1) the episode of selection considered, and in particular, whether one considers selection associated with the mating pool only or selection associated with both the mating pool and pre‐mating pool; (2) whether resource‐acquisition and mate‐acquisition traits are positively associated or whether they trade off; and (3) the proportion of males with the resource‐ and mate‐acquisition traits.Sexual selection influences the evolution of phenotypic traits and contributes to patterns of biodiversity. In many animals, mating involves sequential steps. Often, individuals must secure resources that are essential for mating , and then after securing a resource, individuals engage in competition for access to limited opposite sex mates and gametes. A large body of empirical research and some verbal models have illustrated that resource acquisition can influence sexual selection. In general, though, we lack  Resource competition can have cascading effects on sexual selection. For both trade‐off scenarios, we crossed six levels of male resource‐trait abundance with six levels of male mating‐trait abundance and explored the effect of resource‐ and mating‐trait abundance on selection on male traits  crossed with six levels of male mating‐trait abundance   Figure , and in To explore the effect of resource acquisition on mating dynamics, we calculated the strength of selection associated with the possible episodes of selection in our model. In all cases the strength of selection was quantified as the selection differential or sexual selection differential, which quantifies the strength of selection on the phenotypic trait and is proportional to the response to selection assuming heritable variation associated with the trait Jones, . The selsresource) as follows:xresource is the male resource‐trait value, Wresource is relative resource success. All adult males were included in this selection calculation since all adult males either have or lack the resource trait.To explore the selection associated directly with pre‐mating pool resource acquisition, we first calculated the selection differential associated with the resource trait (smating_matpool) as follows:xmating is the male mating trait value, and Wmating is relative mating success , we then calculated the sexual selection differential associated with the mating trait . This measure, sresource could be a measure of natural selection, sexual selection, or some combination of natural and sexual selection, and the relative contribution of natural and/or sexual selection will depend on the specific trait considered for a given system. Bower or territory acquisition, for example, is expected to be under only sexual selection, whereas the acquisition of nests or food resources that are necessary for mating is likely to be under natural selection or a combination of natural and sexual selection. Regardless of whether resource acquisition is under natural and/or sexual selection, the scenarios considered in our model allow us to explore the cascading impact of resource acquisition on subsequent sexual selection on an advantageous mating trait.Importantly, traits associated with resource acquisition can be under natural and/or sexual selection , the strength of sexual selection on the mating trait was influenced by the proportion of males that had the mating trait and the proportion of males that had the resource trait Figure . When thWhen the resource and mating traits traded off, the strength of sexual selection associated with mating pool dynamics was greatest when all males had the resource trait and few males had the mating trait Figure . This sc3.3To explore the overall selection on the mating trait that was associated with both pre‐mating pool resource acquisition and direct mate acquisition, we quantified the selection differential on the mating trait including all adult males . This measure of selection provides a look at the overall strength of selection on the mating trait in relation to the two competitive steps (resource acquisition and direct mate acquisition) in our model. When there was a positive association between the resource and mating traits, the overall strength of selection on the mating trait increased as the proportion of males with the mating trait decreased, and there was no effect of the resource‐trait abundance on selection on the mating trait Figure .When the resource and mating traits traded off, the overall selection on the mating trait was most likely to be positive when a large proportion of males had the resource trait and was most likely to be negative when a small proportion of males had the resource trait Figure . The eff4a priori expectations of the effect of resource acquisition on sexual selection. The results of our model suggest that pre‐mating pool resource acquisition and mate acquisition within the mating pool can act independently or interact to affect selection on phenotypic traits ; data curation (supporting); formal analysis (supporting); funding acquisition (lead); investigation (lead); methodology (lead); project administration (lead); writing – original draft (lead). Chelsea Langley: Data curation ; formal analysis ; writing – review and editing (supporting). Elijah Reyes: Formal analysis (supporting); investigation (supporting); writing – review and editing (supporting).The authors have no conflicts of interest.Appendix S1Click here for additional data file.


Gene drives can potentially be used to suppress pest populations, and the advent of CRISPR technology has made it feasible to engineer them in many species, especially insects. What remains largely unknown for implementations is whether antidrive resistance will evolve to block the population suppression. An especially serious threat to some kinds of drive is mutations in the CRISPR cleavage sequence that block the action of CRISPR, but designs have been proposed to avoid this type of resistance. Various types of resistance at loci away from the cleavage site remain a possibility, which is the focus here. It is known that modest‐effect suppression drives can essentially “outrun” unlinked resistance even when that resistance is present from the start. We demonstrate here how the risk of evolving (unlinked) resistance can be further reduced without compromising overall suppression by introducing multiple suppression drives or by designing drives with specific ecological effects. However, we show that even modest‐effect suppression drives remain vulnerable to the evolution of extreme levels of inbreeding, which halt the spread of the drive without actually interfering with its mechanism. The landscape of resistance evolution against suppression drives is therefore complex, but avenues exist for enhancing gene drive success. Not only can they be used to quickly transform wild populations with genes of social value, even if detrimental to the species, but they can be used to suppress and even extinguish populations  already existing in the population  with a drive that has the best chance of outrunning resistance. The present study builds on our previous study that explored some of these variations . Mating is random unless specified otherwise. Two or three unlinked loci are assumed, each with 2 alleles. Some models assume that the sexes are haploids, other models assume diploid sexes. Computation employed C programs with graphics in R , B/b (homing drive 2), and R/r (resistance), lowercase alleles representing wild‐type; resistance acts equally against both drives. Other assumptions about segregation distortion and fitness effects are provided in Tables A/a and B/b loci; dominant, cost‐free resistance to segregation distortion operates at the R/r locus  is attained using 2 drives with equal fitness effects confined to just females. These figures assume that the two drives are introduced sequentially , but essentially the same curves are obtained for simultaneous introduction, suggesting that the major benefit of two drives is not from some interaction between the combination of two drive alleles and the resistance allele.Figure s disproportionately lead to higher final frequencies of the resistance allele. (2) For the same value of s, higher initial frequencies of the resistance allele yield higher gains of the resistance allele . A third outcome works against resistance‐free suppression by 2 drives: to attain a combined suppression of 1‐s, the selective coefficient per drive needed when using 2 drives is slightly more than s/2, the excess increasing with s. The relative gain from using 2 drives is thus due to the net balance of these three components. A theoretical possibility is that arbitrary resistance‐free population suppression could be obtained by using many drives of very small effect, but the method is not obviously practical beyond two or three drives.The greater impact of resistance‐proof suppression by using 2 drives instead of a single drive begs some understanding. We addressed this question with numerical results using our observation that 2 sequential drives can attain approximately the same total suppression as 2 concurrent drives; properties of gene frequency evolution were analyzed during single drive fixation . We found that two outcomes work in favor of greater resistance‐free suppression by 2 weak drives than by 1 stronger drive. (1) For the same initial frequency of the resistance allele, larger values of 2.1.1s between a single drive and two drives changed less than 0.01 when comparing perfect drives to imperfect drives. A second check on robustness was to introduce a fitness decrement in drive heterozygotes. Surprisingly, a reduction in heterozygote fitness of 0.1 (from 1.0 to 0.9) increased the maximum resistance‐free s. For perfect drive, the gain in resistance‐free s was substantial even for a single drive  and often somewhat larger for two drives (up to 0.09). For imperfect drives , the gain in resistance‐free s by a 0.1 reduction in heterozygote fitness could be somewhat larger or smaller than the gain for perfect drives. The main observation remains that the use of 2 drives can attain a larger net suppression than the use of a single drive despite moderate variations in drive perfection and heterozygote fitness. It is of course understood that drive properties differing between the 2‐drive and 1‐drive cases could affect the benefit of using two drives.The model assumed a few extremes: perfect homing drives and no fitness costs to drive heterozygotes. To address the importance of the first extreme, trials were analyzed with drive distortion of 0.9 (drive heterozygotes produce 90% drive and 10% wild‐type gametes); this analysis was conducted for two male drives introduced simultaneously with equal fitness effects in both sexes and compared to that of a single male drive . This assumption is equivalent to cost‐free type‐M resistance and enables isolating the drive‐blocking effect of sib mating. It is obviously a best‐case scenario for the evolution of type‐P resistance, and although it may be unrealistic, it should also define the conditions under which a suppression drive can be assured of evading sib mating.Q, from low levels of sib mating (m=0.2), medium (m=0.5) to high levels (m=0.95). Each graph displays equilibrium mean fitness , equilibrium drive frequency, and equilibrium frequency of Q for different values of drive fitnesses (1‐s); . There are three important outcomes.m>1‐s). Thus, mild‐effect drives can avoid type‐P resistance evolution if there are only low levels of sib mating types in the population.Sib mating does not evolve (and the drive evolves to fixation) unless the sib‐mating fraction exceeds the fitness of the drive homozygote , sib mating evolves to extinguish all but the most benign gene drives.If the sib‐mating genotype enforces a high degree of sib mating  will select any level of sib mating , although the equilibrium frequency of the drive remains high if the sib‐mating allele enforces only low levels of sib mating , with the heterozygote fitness cost also increasing mean fitness but less so. The basic result that high magnitudes of sib mating purge drives of even small s effect remains. The fact that drive imperfection reduces population suppression in the zones of drive polymorphism is not surprising, and the magnitude of effect may have little permanence given that the polymorphic drive would be prone to select other forms of resistance.These results were based on perfect drive and no heterozygote fitness effect of the drive. Revisiting the three cases in Figure 2.3Inbreeding has a moderately straightforward interaction with homing drives, which are confined to one locus. If the principle behind the evolution of sib mating in response to a suppression drive is that it increases the variance in fitness across families, one would expect sib mating to be favored with other forms of suppression drives. Here, we consider evolution of sib mating in response to toxin‐antidote systems used for suppression.c/C, the C allele encodes both a rescue gene and a nuclease. The nuclease annihilates wild‐type alleles at a second, unlinked, essential‐gene locus, g/G (Table C) is engineered as a one‐locus construct with two functions, the g/G locus merely being the passive “victim” of the nuclease of the first locus. The C allele spreads because it creates nonfunctional G alleles, with GG genotypes dying if not rescued by C . ClvR does not intrinsically suppress populations. To be used for population suppression, the C can be inserted into a gene important to survival/fecundity so that the gene is disrupted and homozygotes impaired . Our interest here is in the evolution of sib mating as a form of type‐P resistance. Sib mating operates as in the previous model /[(1+σ)(1‐f)]. At least when rare, inbreeding would likely be selected against as the population neared this state since less inbred families would produce more descendants. This behavior contrasts with that of homing drives considered above, which have no effect on sib mating once they are fixed.In the absence of resistance, evolution of ClvR leads to a population that is fixed for null homozygotes c/C locus (as noted above) and the g/G locus, for which GG is lethal in the absence of C. With homing drives, only one locus is affected by inbreeding.Inbreeding affects and is affected by two loci with ClvR: the σ on mean fitness is not the same as that of the homing drive parameter s.As elaborated in the next section, ClvR cannot suppress mean fitness below 0.5 (when fitness effects are confined to the homozygote as viability effects). The effect of fitness parameter In contemplating the evolution of inbreeding, use of ClvR for population suppression exhibits some notable differences from homing drives. Note that we are confining our analysis to viability effects of These differences raise the possibility that sib mating will evolve differently under ClvR than under homing drives.σ than for s with homing drives. For example, with m=0.5, ClvR does not select sib mating until σ is almost 0.7, whereas homing drives selected sib mating when s reached 0.5.Alleles enforcing weak sib mating evolve only at higher values of C and sib mating was never observed, although we cannot rule out the possibility. If sib mating evolved, ClvR was lost, at which point the Q allele was neutral. Yet with homing drives, the drive allele could be maintained after Q evolved to fixation (for some conditions).At equilibrium, joint polymorphism of Plots of sib‐mating evolution in response to ClvR evolution show similar patterns as for homing drives in that strong allelic sib mating can evolve to block the suppressing ClvR . In contrast, a 1‐sex homing drive can suppress mean fitness no lower than 0.5 , the most extreme fitness suppression occurs when both homozygotes die, leaving Cc heterozygotes as the only viable genotype and a mean fitness of 0.5  males. To model population size, the number of progeny born is determined by the number of adult females; males are never limiting for female reproduction. Birth rates and viability effects on females, therefore, determine population size in each generation based on numbers of adult females in the previous generation.Fitness effects of the drive affect progeny viability regardless of sex and may operate in a density‐independent manner as well as a density‐dependent manner Table . The denAA. Its density‐dependent viability descends from 1 to 1‐sK across three zones determined by the population size N relative to the thresholds K and K(1‐sK) (Table sb=sK), density‐dependent selection against AA is never more extreme than density‐independent selection and may be considerably less. Whenever N is below the AA viability threshold of K(1‐sK), the drive has no relative fitness effect on AA—providing a ready means for ensuring resistance‐free evolution by (for example) suppressing population size artificially.Much of the evolution in this model can be qualitatively anticipated by translating the information of Table K) Table . CompariK  by the gene drive itself, the latter process being sensitive to intrinsic birth rates. Likewise, conditionally expressed fitness effects can avoid selection during drive spread and thus facilitate avoidance of resistance despite later manifestation of the fitness effects. For example, a drive that reverses pesticide resistance can evolve cost‐free if pesticide use is averted until drive fixation.Density‐independent fitness effects can achieve extinction while avoiding resistance evolution if intrinsic fecundity is low. There may be few pest species for which this is relevant though it may help with suppression of naturalized invasives.Fitness effects that operate in only one sex reduce resistance evolution while possibly enhancing or minimizing ecological effects. Thus, reducing only female fitness will more easily suppress populations than does reducing only male fitness, and fitness effects confined to one sex reduce selection for resistance  can evolve to suppress moderate‐effect drives, but only if genotypes exist that enforce high levels of sib mating. High levels of sib mating can evolve despite being rare in the population, but low genetic levels of sib mating are not favored except with strong‐effect drives. These results apply to both homing drives and toxin‐antidote drives (here represented by ClvR).The realm of resistance‐free evolution can be expanded by manipulating the ecological effects of the drive. Drives with density‐dependent fitness effects can escape much of resistance evolution by externally imposed suppression of the population or by taking advantage of natural declines in population size .The work here expands on the potential robustness of resistance‐free evolution of unlinked suppression drives. Three meaningful elaborations can be offered:There are yet many elaborations to explore with the models, especially on the ecological end. Sib mating might increase dynamically (not through evolution) as the population size declines. Group selection itself  may slow or even stop the spread of the drive  across the heterogeneous environment in the wild is not practical. Then, when the population persists after fixation of a moderate‐effect drive , compensatory evolution can be expected to reduce the fitness effect of the knockout—type‐C resistance .Official releases of synthetic gene drives have yet to be approved. Any release of a suppression drive should anticipate the potential for resistance evolution at least because some types of resistance, once evolved, will thwart other attempts with suppression drives . Thus, the failure of one suppression drive may ensure the failure of subsequent drives. However, delaying the approval of official releases has its own risk in an age when multiple laboratories can create gene drives—the unintentional or unapproved deliberate release of a functional gene drive. There would seem to be some imperative for gaining experience with appropriately monitored gene drive releases before an uncontrolled release happens.4A wide variety of gene drives can now be engineered, and experiments with model organisms in laboratory settings confirm their potential utility in suppressing populations. The unknown that faces releases into natural populations is the evolution of resistance. We previously showed that suppression drives with moderate fitness effects can evade many types of resistance that are unlinked to the drive locus. Here, we have identified additional ways of escaping unlinked resistance. One method is to introduce multiple  unlinked drives of individually mild effect to achieve a greater combined suppression. Another method is to rely on the fact that resistance evolves in response to fitness effects of the drive; careful choice of ecological effects of the drive may thus enable population suppression with only modest fitness effects. One remaining challenge is that sib mating can evolve in response to a suppression drive with any fitness effect; sib mating can limit the suppression and even cause loss of the drive. However, sib mating is a threat for moderate‐effect suppression drives only from alleles for high levels of sib mating (or if sib mating arises as a demographic effect of the reduction in population size). Evolution of resistance to suppression drives may therefore cause problems only on a limited basis.The authors declare no conflicts of interest.Supplement S1Click here for additional data file.


Improving healthcare for people with multiple chronic or ongoing conditions is receiving increased attention, particularly due to the growing number of people experiencing multimorbidity (MM) and concerns about the sustainability of the healthcare system. Primary care has been promoted as an important resource for supporting people with MM to live well with their conditions and to prevent unnecessary use of health care services. However, traditional primary care has been criticized for not centring the needs and preferences of people with MM themselves. Our aim was to conduct a review that centred on the perspective of people with MM in multiple ways, including having patient partners co‐lead the design, conduct and reporting of findings, and focusing on literature that reported the perspective of people with MM, irrespective of it being experimental or nonexperimental.We searched for published literature in CINAHL with Full Text (EBSCOhost) and MEDLINE All (Ovid). Findings from experimental and nonexperimental studies were integrated into collaboration with patient partners.Twenty‐nine articles were included in the review. Findings are described in five categories: (1) Care that is tailored to my unique situation; (2) meaningful inclusion in the team; (3) a healthcare team that is ready and able to address my complex needs; (4) supportive relationships and (5) access when and where I need it.This review supports a reorientation of primary care systems to better reflect the experiences and perspectives of people with MM. This can be accomplished by involving patient partners in the design and evaluation of primary care services and incentivizing collaboration among health and social supports and services for people with MM.Patient partners were involved in the design and conduct of this review, and in the preparation of the manuscript. Their involvement is further elucidated in the manuscript text. In principle, primary healthcare recognizes physical, mental and social well‐being as contributing to a person's overall health and extends beyond disease‐centred healthcare to a whole person approach; it also includes health promotion and illness prevention, along with treatment of disease and rehabilitation.Increasingly, healthcare leaders and researchers are engaging patients—a term used to describe those with personal experience seeking healthcare, and their family and friend caregiversThere is a growing body of literature reporting the perspectives of patients on their PC experiences, and there have been several systematic reviews synthesizing aspects of this literature. For example, there have been reviews examining the perspectives of patients on coordination of PC and oncology care for people with MM,2Integrative reviews are the broadest type of literature synthesis with the purpose of integrating evidence from various types of sources to provide a comprehensive overview of a subject area to inform a specific problem.With support from a librarian trained in systematic and integrative review methods (Boulos), we developed a search strategy using the key concepts: MM, PC and patient perspectives. A series of exploratory scoping searches conducted in 2017 to support the design of our project revealed a broad body of literature related to these key concepts. We determined that it would be difficult to capture the breadth and depth of the literature in a single systematic or scoping review. Therefore, we used scoping searches to develop a search strategy for this integrative review. First, we identified a preliminary set of literature, then harvested the subject terms and keywords from this set of literature and finally used those terms and keywords to develop a targeted search appropriate for this integrative review. The search was run in CINAHL with Full Text (EBSCOhost) and MEDLINE All (Ovid) on 11 February 2020 . Not only did the patient partners have lived experience of MM but they had also been involved in research and advocacy for issues related to MM over time, including collaborations with several other team members. Baxter and Peacock were involved in conceptualizing the objectives of the review, designing the search strategy, screening the literature and interpreting the results. The patient partners were significantly involved in identifying key elements of the review that reflected their experiences and priorities, and they provided insights into how the literature could be interpreted through the lens of a person with MM. To help accomplish meaningful engagement, the review team met weekly over the course of the review, as part of on‐going team meetings for multiple projects over several years.Data analysis progressed through five phases: data reduction, data display, data comparison, conclusion drawing, and verification.33.1N = 11), focus groupsN = 3), a discrete choice experimentN = 1), ethnographic methodsN = 1) and a combination of interviews and focus groupsN = 1). Nine included studies used cross‐sectional surveys to collect data,A total of 1624 sources were identified. Of these, 402 were duplicates. The titles and abstracts of 1222 sources were screened and 1079 were not relevant. The full‐text screening phase included 143 sources of which 115 were excluded. See Figure 3.2In reviewing the literature reporting the perspectives of people with MM on PC experiences, we found five characteristics of PC that were particularly important to people with MM ; formal analysis (lead); writing – original draft preparation (lead). Ruth Martin‐Misener: Conceptualization (lead); formal analysis ; funding acquisition (lead); writing – original draft preparation . Larry Baxter: Conceptualization ; formal analysis (lead); validation (lead). Leah Boulos: Methodology (lead); writing – review and editing . Fred Burge: Conceptualization ; formal analysis ; writing – review and editing . Erin Christian: Conceptualization ; writing – review and editing . Brian Condran: Analysis (supporting); project administration (lead). Adrian MacKenzie: Conceptualization ; formal analysis ; writing – original draft preparation . Elizabeth Michael: Conceptualization ; writing – review and editing . Tanya Packer: Conceptualization ; formal analysis ; writing – review and editing . Kylie Peacock: Conceptualization ; formal analysis (lead); validation (lead); writing – review and editing . Tara Sampalli: Conceptualization ; formal analysis ; funding acquisition (lead). Grace Warner: Conceptualization ; formal analysis ; supervision (lead); writing – review and editing .The authors declare no conflicts of interest.


Behavior during 3–4 Hz spike‐wave discharges (SWDs) in absence epilepsy can vary from obvious behavioral arrest to no detectible deficits. Knowing if behavior is impaired is crucial for clinical care but may be difficult to determine without specialized behavioral testing, often inaccessible in practice. We aimed to develop a pure electroencephalography (EEG)‐based machine‐learning method to predict SWD‐related behavioral impairment. Our classification goals were 100% predictive value, with no behaviorally impaired SWDs misclassified as spared; and maximal sensitivity. First, using labeled data with known behavior (130 SWDs in 34 patients), we extracted EEG time, frequency domain, and common spatial pattern features and applied support vector machines and linear discriminant analysis to classify SWDs as spared or impaired. We evaluated 32 classification models, optimized with 10‐fold cross‐validation. We then generalized these models to unlabeled data (220 SWDs in 41 patients), where behavior during individual SWDs was not known, but observers reported the presence of clinical seizures. For labeled data, the best classifier achieved 100% spared predictive value and 93% sensitivity. The best classifier on the unlabeled data achieved 100% spared predictive value, but with a lower sensitivity of 35%, corresponding to a conservative classification of 8 patients out of 23 as free of clinical seizures. Our findings demonstrate the feasibility of machine learning to predict impaired behavior during SWDs based on EEG features. With additional validation and optimization in a larger data sample, applications may include EEG‐based prediction of driving safety, treatment adjustment, and insight into mechanisms of impaired consciousness in absence seizures. When examining electroencephalography (EEG) recordings, the most apparent marker of generalized epilepsy syndromes is large‐scale synchronous activity, which in the case of absence epilepsy comes as spike‐wave discharges (SWDs) among other activity patterns.A crucial dilemma arises for many people with generalized epilepsy who reach a stage where they are considered clinically seizure‐free but continue to have epileptiform discharges including SWDs on EEG.Recent work evaluating the behavioral impairment of patients afflicted with absence epilepsy during these discharges has shed some light on various characteristics of EEG and fMRI recording that might be indicative of a lapse in consciousness.EEG and behavioral data collected in four different studies that focused on absence epilepsy were employed in this work. In particular, two of these studies were conducted at Yale,Patients were recruited subject to the following criteria: age 6–19 years, diagnosis of either childhood or juvenile absence epilepsy according to International League Against Epilepsy classification, and the presence of 3–4 Hz bilateral SWDs with normal background on EEG. Exclusions were for subjects with additional seizure types , structural brain abnormalities, or other such neurological disorders.EEG was recorded using a high‐density 256‐lead cap at a sampling rate of 500 Hz while patients underwent two behavioral measures of attention during absence episodes: continuous performance (CPT) and repetitive tapping task (RTT). For both behavioral testing paradigms, subjects watched letters on a screen, occurring at a rate of 1 Hz. For the CPT assessment, the task was to press a button whenever an “X” appeared on the screen out of a random sequence of letters. In RTT, subjects were merely asked to press the button every time a letter appeared. With either task, more than one target letter often occurred during any individual SWD. Therefore, with either task, an SWD was defined as “spared” if the correct response rate across all stimuli during the SWD was greater than 75% and “impaired” if it was less than 25%.Inclusion criteria for this study were as follows: (1) age ≥ 15 years, (2) diagnosed with generalized epilepsy, and (3) at least one epileptiform discharge in routine EEG recording taken within a year of recruitment. Furthermore, patients were excluded for any of the following: any clinical seizures in the month leading up to participation (because the study was aimed at asymptomatic patients) or being diagnosed with any other neurological defect which might inhibit driving ability.The task paradigm for the study involved a realistic driving simulator in conjunction with real‐time monitoring of the patient's EEG recording which was performed using a 128‐electrode cap with a sampling rate of 1000 Hz.The behavioral test was conducted while the patient drove around a track in the realistic driving simulator. When the onset of an SWD was detected, the experimenters triggered a red oval (akin to a stop sign) to appear on the screen, prompting the driver to slow down and pull over and thus signal their conscious perception of that stimuli. A “spared” response thus corresponded to an appropriate reaction to the stimuli and an “impaired” response was a lack of reaction, as assessed by gas pedal position, brake pedal force, and the car's velocity.The study inclusion criteria required only that the participant demonstrate a history of absence seizures and 3–4 Hz SWD during scalp EEG recording. The study utilized cup electrodes positioned according to either the 10–20 system or the revised IFCN arrayBehavioral testing involved a line of questioning and assessment of the responsiveness of the patient when it was noted on a live recording of EEG that an SWD had begun.For the present study, video recordings of behavior during SWDs were viewed by two reviewers (S.B. and H.B.) and ratings were decided by consensus. SWDs in which the subject was entirely responsive to testing were deemed “spared” and those SWDs in which subjects were entirely unresponsive to the above testing were thus “impaired.” The instance where the subject was responsive to some parts of testing and not others were classified as edge cases  and not used in further analysis.The study inclusion criteria required the following (1) childhood absence epilepsy diagnoses as described by the International League Against Epilepsy, (2) bilateral synchronous, symmetric spike–wave complexes (2.7–5 Hz) on EEG with a normal background, (3) at least one EEG‐recorded SWD lasting >3 s in a 1‐h session. The data were collected at 31 different sites, all using a sampling rate of 256 Hz with a standard 10–20 electrode system.Inclusion in the present study required the presence of SWDs on EEG recordings performed at the 16/20‐week assessment stage. A patient was classified as spared if no clinical seizures were observed by family or caregivers or induced by hyperventilation. Impaired patients had clinical seizures or seizures induced by hyperventilation reported by family or caregivers.EEGs were reviewed for SWD identification and marking by consensus of two reviewers (M.S. and H.B.) in Cz reference, with SWD onset and end times marked within 0.1 s, blinded to behavioral testing. EEG recordings were then exported and analyzed in MATLAB 2019b (MathWorks).Individual SWDs were labeled according to the behavioral studies described above, with a binary rating of 0 = normal response (spared), or 1 = lack of response (impaired). As noted previously, the marked SWDs from the cohort D data set were not labeled individually, rather the patients were labeled as either without (spared) or with (impaired) clinical seizures.A detailed description of the analysis pipeline including preprocessing, feature extraction, feature selection, classification, and performance evaluation is provided in the Data Our objective was to develop a machine‐learning model to predict whether an SWD will yield a lapse in consciousness, defined as impaired responsiveness to external stimuli. Such a predictor could potentially be used for driving safety, so we required that the predictor should never classify SWDs (or patients) as behaviorally spared if in fact they were impaired. Equivalently, we sought a predictor with a zero false discovery rate  for spared classification. To be conservative about driving safety, this criterion was considered more important than the sensitivity of the detector. In other words, it was essential to only classify SWD or patients as spared if they were truly spared, even if this was at the expense of not successfully detecting a substantial number of other truly spared SWD or patients.To achieve this goal, we tested several classifiers with different strategies Fig. . This inTo determine if preictal or ictal periods or both are needed to achieve the best possible classification performance, we evaluated the performance of the model using the preictal features only, ictal features only, and both preictal and ictal features. Preictal and ictal features were combined using two different approaches. In the first approach, feature vectors of preictal and ictal windows were concatenated to form one feature vector while in the second approach preictal and ictal feature vectors were projected into scalar scores using either LDA or SVM and those scores were combined using weighted Bayesian fusion. This probabilistic fusion approach assumes that the distributions of the preictal and ictal scores are independent but may not have equal importance in determining the severity of the seizure, and therefore, finds the optimal weighting of preictal and ictal feature score distributions to attain correct behavioral classification  and testing data sets (10% of data). Second, we validated the model performance on the unlabeled data set of patients from cohort D. This was done by training the model on the behaviorally labeled SWDs  and then applying the model to the unlabeled SWDs in cohort D.The data sets generated for this study are available from the corresponding author on reasonable request. The codes generated for the analyses in the study are also available from the corresponding author on reasonable request.We first evaluated the performance of the feature sets from the three labeled data sets  in which behavioral impairment was known for each SWD, to find the optimal parameters  that maximize the classification performance. Using the optimal set of parameters, we next evaluated the performance of the proposed machine‐learning approach using 10‐fold cross‐validation on the three labeled data sets. Finally, we demonstrated how the proposed approach generalizes on the unlabeled data set from cohort D that was not included in the parameter optimization phase.The main objective of the proposed machine‐learning approach is to conservatively label an SWD as spared  only if there truly was no behavioral impairment while at the same time detecting as many spared SWD as possible. Therefore, we sought preictal and ictal window sizes yielding the maximum spared predictive value , while also attempting to maximize sensitivity. Preictal window sizes ranging from 100 to 5000 msec and ictal window sizes ranging from 100 to 1000 msec were tested for the extended feature sets. Window size optimization was not performed on basic features separately because basic features are a subset of the extended features. Preictal windows of 100–1000 msec and ictal windows of 100–500 msec were tested for the CSP features (see Methods). We found that the optimal window size for CSP features was 1000 msec preictal and 500 msec ictal, which yielded corresponding spared predictive value maxima of 100.00% for both intervals. Additionally, we found that the optimal length for the extended feature set was 100 msec preictal and 500 msec ictal, yielding spared predictive values of 80.99% and 83.05%, respectively. However, to be consistent with the optimal findings for the CSP features, we increased the preictal window for the extended features as well to 1000 msec, giving a spared predictive value of 73.90%. Thus, for all subsequent analyses, the basic, extended, and CSP features  were ultimately extracted from the same periods of the EEG recordings for each trial.For both basic and extended features, average feature values were calculated Table . To seleAs for CSP features, the optimal number of spatial filters that maximize the classification performance was found to be five filters, resulting in 10 CSP features in total  to be used for classification.Using the optimal preictal and ictal window sizes and other parameters as determined above, we tested the model in a 10‐fold cross‐validation scheme using the three labeled data sets, cohorts A, B, and C consisting of 130 SWD in 34 patients Fig. . We usedα = 0.73 (CSP‐LDA), 0.48 (CSP‐SVM), 0.70 (All‐LDA), and 0.28 (All‐SVM).The optimal metric of 100.00% spared predictive value was achieved only for classifiers using CSP or all features . As described in Methods, classification of the unlabeled data was done on the patient level by considering a patient to be impaired if they had any SWD determined by the model to be impaired, and a patient was considered spared only if all their SWD were determined by the model to be spared. These results were compared to observations by family or caregivers who thought the patients were having clinical seizures (18 of 41 patients) or not (23 of 41 patients). Our conservative criterion for classifier success was once again a spared predictive value of 100%, but now at the patient level, meaning that no patient should be classified as spared if in fact they were reported to have clinical seizures. This 100% spared predictive value criterion was achieved mainly by classifiers using weighted probabilistic fusion  achieved a maximum spared predictive value of 98% with a spared sensitivity of 65%. Inclusion of additional features in the “extended” feature set led to a maximum spared predictive value of 93% but with an increase in sensitivity up to 89%. The use of CSP‐based features alone or in conjunction with the extended feature set as “all features,” yielded the best performance, particularly when using a weighted probabilistic fusion of preictal and ictal data, providing a positive predictive value of 100% and sensitivity of up to 93%. Thus, the machine‐learning approach introduced in this work was highly successful in correctly classifying SWD for which behavioral responsiveness was known based on ictal behavioral testing.In previous studies, the CSP algorithm has been used predominantly for the purposes of brain–computer interfacing and motor control studies.In validating our model on the unlabeled data from cohort D, we see once again that the inclusion of CSP features in the all features‐based classification using probabilistic fusion yielded to the best classifier. This classifier was hesitant to label a patient as entirely spared when experiencing discharges but was absolutely confident when electing to make such a decision . Such a classification approach is optimal as a clinical tool as it would ease restrictions only on patients who are predicted to retain consciousness awareness and responsiveness during a discharge with high confidence.Although we have achieved an optimal value of 100% spared predictive value corresponding to a false discovery rate of zero in validating our model on unlabeled data, the sensitivity of identifying patients with spared behavior in the unlabeled data was quite low (35%). Of note, the patients felt to be spared of clinical absence seizures based on observations by family and caregivers did have SWD, but these were not formally assessed for more subtle behavioral impairment. Such incomplete characterization of the behavioral effects of SWDs might explain the low sensitivity of the model in identifying patients with spared behavior. Moreover, it is possible that some of the patients considered to be free of absence seizures by family and caregivers, but classified as impaired by our model, might in fact have had behavioral impairments that would make driving hazardous during SWD. To investigate this possibility further, additional investigation will be needed in a larger cohort, and with behavioral testing during SWD in all participants. The decreased sensitivity of our classifier in detecting spared patients might also be due to the relatively small sample size of the labeled data sets used for training the proposed model. In particular, the selected features for classification as well as the spared and impaired patterns learned by the classifiers for this data set may not generalize very well to the larger population due to overfitting the small training data set and thus can result in a degradation in some of the metrics determining model performance when applied to a separate patient group. In addition, the fact that the data sets have clinically notable differences because the patients in these data sets have different epilepsy syndromes could be a factor in the degraded model performance due to the existence of various SWD patterns with a small number of training examples per each pattern. In case a large data set is used to train the model, this heterogeneity is expected to yield a classifier with better generalizability as it will be trained on various patterns that are represented by a sufficient number of training examples. Generally, a large sample size that is more representative of SWD global characteristics is expected to allow improved spared sensitivity and overall model performance.Additionally, a large sample size would allow the use of more robust classification approaches that are expected to have better generalizability such as deep‐learning approaches that require large training data sets to perform efficiently. Previously, convolutional neural networks have been used for similar EEG analyses including predicting seizure onset.Another future direction that has the potential to improve classification performance across patients is to use transfer learning for identifying the optimal training data to be used with each subject. This would involve devising a metric for measuring similarity between the data of each subject in the training set and the data in the test set and train on those patients who have the highest similarity with the patient being tested. The methodology would overcome obstacles pertaining to data variability across subjects and sessions within a subject.Although fMRI data are in a sense more difficult and time‐consuming to obtain than a scalp EEG, and thus less efficient as a clinical tool, they could provide greater insight into the underlying mechanisms of impaired consciousness.Future work might also include the generalization of our approach to more broad diagnoses outside of childhood and juvenile absence epilepsy. The present study focuses only on the SWD and its characteristics, however, many other forms of epilepsy exist with a plethora of EEG markers such as poly‐spike wave activity, rhythmic slowing or higher frequency rhythmic activity. Epilepsy is characterized by large‐scale synchronous behavior in the brain and as a result, more advanced machine‐learning strategies should be implemented on a comprehensive data set to pinpoint more generalized characteristics of lapses in consciousness. With additional future work, hopefully, such approaches will increase understanding of epilepsy pathophysiology and lead to better practical guidance that will improve the quality of life for people with epilepsy.The authors declare that they have no competing financial interests.Data S1. Supplementary Materials and Methods.Table S1. Performance metric definitions.Table S2. Performance of support vector machine Classifier for all Feature Sets and Temporal Windows.Table S3. Performance of linear discriminant analysis Classifier for all Feature Sets and Temporal Windows.Click here for additional data file.


Tumor cells have the ability to induce platelet activation and aggregation. This has been documented to be involved in tumor progression in several types of cancers, such as lung, colon, breast, pancreatic, ovarian, and brain. During the process, platelets protect circulating tumor cells from the deleterious effects of shear forces, shield tumor cells from the immune system, and provide growth factors, facilitating metastatic spread and tumor growth at the original site as well as at the site of metastasis. Herein, we present a wider view on the induction of platelet aggregation by specific factors primarily developed by cancer, including coagulation factors, adhesion receptors, growth factors, cysteine proteases, matrix metalloproteinases, glycoproteins, soluble mediators, and selectins. These factors may be presented on the surface of tumor cells as well as in their microenvironment, and some may trigger more than just one simple receptor–ligand mechanism. For a better understanding, we briefly discuss the physiological role of the factors in the platelet activation process, and subsequently, we provide scientific evidence and discuss their potential role in the progression of specific cancers. Targeting tumor cell-induced platelet aggregation (TCIPA) by antiplatelet drugs may open ways to develop new treatment modalities. On the one hand, it may affect patients’ prognosis by enhancing known therapies in advanced-stage tumors. On the other hand, the use of drugs that are mostly easily accessible and widely used in general practice may be an opportunity to propose an unparalleled antitumor prophylaxis. In this review, we present the recent discoveries of mechanisms by which cancer cells activate platelets, and discuss new platelet-targeted therapeutic strategies. The primary hemostatic function of platelets is well known; however, increasing evidence supports the crucial role of platelets in cancer biology , 2. In 1Thrombin is a serine proteaseplaying a pivotal role in blood coagulation . It convTissue factor (TF) is a membrane glycoprotein that is crucial to initiate the extrinsic coagulation cascade . Expressvon Willebrand factor (vWF) is an adhesive and multimeric glycoprotein present in plasmasynthesized by endothelial cells and megakaryocytes. vWF has a central role in primary hemostasis where it mediates platelet adhesion to the exposed extracellular matrix at the site of vascular damage , 33. vWFGPIb–IX–V is a membrane receptor complex originating in megakaryocytes and belonging to the leucine–rich repeat family of proteins. The complex consists of four distinct transmembrane proteinsnamelyGPIbαGPIbβGPIXand GPVand is expressed on platelets. The most important component of the complex is the glycoprotein component GPIbα which contains the binding sites for vWFP–selectinthrombinthrombospondinfactor XIIfactor Xkininogenand integrin αMβ2 (Mac–1). GPIb–IX–V plays a critical role in thrombosisinflammationmetastasisand the life cycle of platelets –55. One Glycoprotein IIb‐IIIa (GPIIb‐IIIa) is an important platelet membrane receptor for fibrinogenfibronectinand vWF. It provides adhesive properties andhenceis necessary for platelet aggregation . The funIntegrin αvβ3 is a transmembrane heterodimer which belongs to the family of cell adhesion receptors . PlateleVEGF is the basic regulator of vascular growth. The actions of VEGF include the regulation of proliferationmigrationand permeability of endothelial cells. VEGF increases the expression of adhesion molecules and coagulation factors. It is stored in high quantities in platelet alpha granules . ElevatePlatelets are a potent reservoir of transforming growth factor–beta (TGF–β) carrying a higher proportion of this molecule in the blood (up to 40%). Its release from TCIPA is widely described in the literature , 84. HowPlatelet–derived growth factor (PDGF) is a two–chain polypeptidewhich belongs to the family of growth factors  and is aCathepsins are a family of globular proteases that primarily were discovered as intracellularly functioning peptide hydrolaseshowevermultiple cathepsins have extracellular activity . The catMMPs are structurally similarzinc–dependent endopeptidases. The major function of the MMP family is controlled degeneration of the extracellular matrix . Their iPodoplanin (PDPN) is a mucin–type protein that mediates effects on cell migration and adhesion through its multiple partners. During embryonic developmentit plays a role in blood and lymphatic vessel separation by binding platelet C–type lectin–like receptor 1B (CLEC1B)triggering CLEC1B activation in platelets and leading to platelet activation and/or aggregation , 140. PD1. PDPN may be released into the circulationeither in soluble form or on the surface of tumor–derived microvesicles.2. Circulating tumor cells may be a source of circulating PDPN. Trapping of tumor cells in the venous system might lead to further local platelet activation and aggregation .PDPN is known to contribute to tumor progression by inducing cancer cell migration and tumor invasion connected to the EMT mechanism 145146) and in the absence of EMT markers . High PD146 and iADP is a strong proaggregatory factoraccumulated in platelet–dense granulesand constitutes a secondary mediator of platelet aggregation . ADP hasTXA2 is considered a powerful modulator of platelet activation and aggregation as well as a stimulator of vascular constrictionwhich acts via binding to the thromboxane prostanoid receptor (TP) . TXA2 isP–selectin is a Ca2+–dependent receptor for myeloid cells that binds to carbohydrates on neutrophils and monocytes . It mediAn abbreviated description of all aforementioned factors is gathered in In recent yearsthe successful adoption and implementation of selective cancer therapies in clinical practice has increased research efforts aimed to identify and target various anticancer mechanisms. TCIPA is one of the pathways explored in the search for new options for cancer treatment. The crosstalk between plateletstheir receptorsreleased moleculesand clotting factors is subject to extensive and long–time research from different medical fields. These extensive studies resulted in the development of blockbuster therapies in cardiology and vascular medicine. The major principle of those protocols is to affect aggregation and clotting in a safe and controlled manner on different regulatory levels. The rich experience gained in the design of antiplatelet and antithrombotic treatment modalities may be transferred to oncology. For instanceTCIPA is a mechanism worthy of further studies as it demonstrates the involvement of platelets in carcinogenesis. It is highly probable that TCIPA inhibition would be beneficial for patients due to reduced risk of cancer–related thrombosis and associated clinical conditions such as strokepulmonary embolismand deep vein thrombosis. Howeverin this articlewe focused on exposing antitumor effects mediated by influencing TCIPA. The proof of concept is several studies reporting the potential benefit of targeting TCIPA in various tumors.In pancreatic cancerthe expression of the PAR–1 receptor in the tumor microenvironment was proven to drive progression and induce chemoresistance . TherefoDabigatran and a direct factor Xa inhibitor rivaroxaban are known as novel oral anticoagulants (NOACs) and have gained widespread use in medicine. The latter was found to inhibit cancer stem cell (CSC) activity in the in–vitro functional CSC assay of mammosphere formation . Thenin The inhibition of adhesion receptors using monoclonal antibodies has been already the subject of several studies. Qi et al. revealed that inhibition of GPIba leads to reduced interaction between platelets and tumor cellswhich results in the diminished metastatic potential of lung cancer cells. The study was performed in vitro and in vivo on animal models . AnotherThe PDPN–CLEC2 axis might provide a potential drug target for both reducing the risk of VTE and improving prognosis. Anti–PDPN therapies are expected to be of robust potential for future treatment strategies. In recent preclinical studiesseveral molecules were evaluated to interfere with the PDPN–CLEC2 pathway. CD9an inhibitor of PDPN–induced platelet aggregationwas recognized to reduce the metastatic potential of human fibrosarcoma cells . The recAnother potential target is the ADP receptor P2Y12. The study of Cho et al. has shown that the growth of ovarian cancer in murine models was reduced with the specific P2Y12 inhibitor ticagrelor . AspirinStudies reported that heparin is an outstanding inhibitor of P–selectinwhich binds to its natural ligandsthus inhibiting the initial platelet–tumor cell interactions. Even a single heparin dose that transiently blocks this interaction is sufficient to prevent long–term organ colonization. These discoveries indicate that P–selectin and its ligands could be a potential therapeutic target .The therapeutic targets among the TCIPA mechanisms are shown in Among the unorthodox mechanisms of tumor progressionTCIPA has established by far a genuine target for potential therapies. The discussed studies demonstrated the encouraging influence of newly manufactured as well as widely used antiplatelet drugs on the inhibition of tumorigenesisprogressionand metastasis. These revelations should lay the groundwork for next–level clinical trials to optimize and determine the oncological efficiency of antiplatelet treatment. Howeverbecause of the simultaneous influence on blood coagulation and the wide variety of individual sensitivity to antiplatelet drugsit is still difficult to specify the optimal criteria for such studies. Hopefullyfurther research in the field of TCIPA will soon give us another instrument for aiding antitumor therapies.The first draft of the manuscript was written by WS, JJ, JD, BK, MK, and AK. Supervision and review were exercised by DG, TD, and JA. All authors contributed to the article and approved the submitted version.This research was funded by Nicolaus Copernicus University in ToruńFaculty of MedicineCollegium Medicum in Bydgoszcz (Research Task No. 141 within the framework of the Basic Research Activity).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In this secondary analysis of data from the Veterans Affairs Augmentation and Switching Treatments for Improving Depression Outcomes (VAST‐D) study, the authors sought to determine the effectiveness of early improvement (or lack thereof) for predicting remission from depression with antidepressant therapy.This study used data from the VAST‐D study, a multisite, randomized, single‐blind trial with parallel assignment to one of three medication interventions for 1,522 veterans whose major depressive disorder was unresponsive to at least one course of antidepressant treatment meeting minimal standards for dosage and duration. The authors calculated the positive predictive value (PPV) and negative predictive value (NPV) of early improvement on remission, response, or greater than minimal improvement from depression for various degrees of improvement (10%–50%) on the Quick Inventory of Depressive Symptomatology–Clinician Rated (QIDS‐C) at 1, 2, 4, and 6 weeks.The end of week 2 of treatment was identified as the best time to evaluate early improvement. The presence of a ≥20% drop from the baseline QIDS‐C score by the end of week 2 resulted in a PPV for remission of 38% and an NPV of 93% by week 12. Extending the observational window to week 6 minimally improved NPV (97%). This association did not differ across treatment groups.A lack of early improvement at the end of week 2 of antidepressant therapy can be used to inform clinical decisions on the likelihood of nonremission of depression during the subsequent 10 weeks, even when dosage optimization is incomplete. The optimal time for evaluating early improvement from an antidepressant medication regimen is at the end of week 2.A lack of early improvement at the end of week 2 of antidepressant therapy can be used to inform clinical decisions on the likelihood of nonremission of depression with that therapy during the subsequent 10 weeks, even when dosage optimization is incomplete.The same factors that influence early improvement also determine whether a patient will show a false negative outcome : lower baseline Quick Inventory of Depressive Symptomatology–Clinician Rated score, fewer adverse childhood experiences, lower baseline anxiety, lower suicidal ideation, and higher baseline quality of life score.The utility of using lack of early improvement to predict lack of remission in antidepressant therapy did not depend on treatment allocation.Major depressive disorder is a significant health concern not only because it is one of the most prevalent psychiatric disorders , but becIn the management of depression of patients who do not adequately respond to initial therapy, it is critical to determine when a patient will need to proceed to a next‐step medication. In a meta‐analysis covering 17 studies and 14,779 patients, the role of early improvement  was assessed , 11. In The Veterans Affairs Augmentation and Switching Treatments for Improving Depression Outcomes (VAST‐D) study is the largest next‐step trial for individuals who did not adequately respond to an initial antidepressant , 12, 13.The U.S. Department of Veterans Affairs (VA) Office of Research and Development and the Central Institutional Review Board (CIRB) approved the VAST‐D study. A certificate of confidentiality was obtained for the study from the National Institutes of Health. The CIRB conducted annual continuing reviews, and a data monitoring committee (DMC) reviewed the study biannually. Adverse events were reviewed by both the CIRB and DMC throughout the study. All participants provided written informed consent and privacy authorization after receiving full explanation of the study procedures.online supplement for a list of participating sites), randomized, single‐blind, parallel‐assignment next‐step trial of veterans whose major depressive disorder was suboptimally responsive to at least one course of antidepressant treatment with an SSRI, serotonin and norepinephrine reuptake inhibitor, or mirtazapine that met or exceeded minimal standards for dosage and duration of treatment. Suboptimal response was defined as a score of ≥16 (indicating severe depression) on the QIDS‐C questionnaire after at least 6 weeks of treatment or a score of ≥11 (indicating moderate depression) after at least 8 weeks of treatment, with the three most recent weeks at a stable, “optimal” dosage  statement and flow diagram) has been published previously , 12, 13.DSM‐IV‐TR. Potential participants who were pregnant; breastfeeding; currently using contraindicated medications, including either study drug; or had a clear history of nonresponse or intolerance to bupropion‐SR or aripiprazole, were excluded from the study. Participants who had a primary diagnosis of bipolar, psychotic, obsessive‐compulsive, dementia, or eating disorders; had general medical conditions contraindicating the use of bupropion‐SR or aripiprazole; had serious, unstable medical conditions requiring acute treatment; met criteria for substance dependence requiring inpatient detoxification; or were considered at high risk for suicide and in need of acute treatment were also excluded.Participants were 1,522 Veterans Health Administration (VHA) patients, 18 years or older and diagnosed as having major depressive disorder, who were referred by their VHA clinicians. Study clinicians confirmed the diagnosis prior to study enrollment. Research staff further established diagnostic eligibility using criteria from the This report addresses the acute phase (first 12 weeks of treatment) of the VAST‐D study, in which 1,522 veterans with nonpsychotic major depressive disorders were randomized to one of three treatment groups: augmentation with bupropion‐SR (Aug‐BUP), augmentation with aripiprazole (Aug‐ARI), or switch to another antidepressant  , 12, 13.DSM‐5, severity of health impairment as measured by the Cumulative Illness Rating Scale (CIRS)  , severite (CIRS) , generale (CIRS) , QIDS‐C e (CIRS) , and durThe primary outcome measure, the QIDS‐C score, was collected by an independent evaluator who was blind to the patients’ treatment assignments at baseline and at each visit following randomization. Standard definitions of “response” (≥50% decrease from baseline QIDS‐C score at the end of week 12), and “remission”  were used. In addition, “greater than minimal improvement” was defined as a >30% decrease from baseline QIDS‐C score at the end of week 12. Except in exploratory analyses, early improvement was defined as a ≥20% drop from baseline QIDS‐C score by the end of week 2.We conducted the statistical analysis by using observed cases. We calculated the PPV and NPV of early improvement on remission. To calculate PPV and NPV, we categorized participant outcomes as true positive (TP), false positive (FP), true negative (TN), and false negative (FN). A TP outcome was defined as having a ≥20% drop from baseline QIDS‐C score by the end of week 2 (early improvement) and achieving remission by the end of week 12. A FP outcome was defined as showing early improvement but not achieving remission by the end of week 12. A TN outcome was one in which the participant did not demonstrate early improvement and did not achieve remission by the end of week 12. A FN outcome was one in which the participant did not show early improvement but achieved remission by the end of week 12. PPV and NPV were calculated as PPV=TP/(TP+FP) and NPV=TN/(TN+FN). We calculated sensitivity as the ratio of true positive outcomes to the total number of patients achieving remission (sensitivity =TP/[TP+FN]) and specificity as the ratio of true negative outcomes to the total number of patients not achieving remission (specificity=TN/[TN+FP]). The relative likelihood of remission, response, and greater than minimal improvement between those displaying early improvement and those who did not was calculated as the unadjusted odds ratios from 2×2 frequency tables.To identify the optimal drop in baseline QIDS‐C score and the observational window to achieve the best PPV and NPV values, we calculated the PPVs and NPVs for multiple percentage drops  and at various observational windows .We identified baseline characteristics associated with early responders and participants exhibiting false negative outcomes by using chi‐square tests for categorical variables and Wilcoxon rank sum tests for continuous variables. We calculated effect sizes (Cohen's d) as the difference of the means divided by the pooled standard deviation. We conducted a chi‐square analysis to compare withdrawal rates between early improvers and those who did not have early improvement. We used chi‐square analysis to perform area‐under‐the‐curve comparisons of receiver operating curves to determine the generalizability of using early improvement to predict remission.Sixty‐two percent of the sample showed a ≥20% drop from the baseline QIDS‐C score by the end of week 2 (early improvement). Table DSM‐5, severity of health impairment (as measured by the CIRS), and duration of index treatment trial did not influence whether early improvement was present. Patients who did not have early improvement but achieved remission during the trial  were more likely to have a lower baseline QIDS‐C score, fewer adverse childhood experiences, lower baseline Beck Anxiety Inventory score, lower C‐SSRS score, and a higher baseline quality of life (Q‐LES‐Q‐SF) score .Of the 940 participants who met the criterion for early improvement, 143 were withdrawn from the study (15%) for various reasons that have been described previously . Of the The PPV for remission was mostly influenced by the magnitude of the percentage drop in QIDS‐C score from baseline, with no obvious benefit provided by the duration of the observation window Figure . The NPVonline supplement).The average prescribed dosages of bupropion at the end of week 2 were 237 mg and 221 mg for the Switch‐BUP and Aug‐BUP groups, respectively. The average dosage of aripiprazole at the end of week 2 was 3 mg  had a PPV of 38% and an NPV of 93% for prediction of remission by the end of week 12. In addition, our data show that those who reached the 20% threshold of early improvement by week 2 were more likely by the end of week 12 to achieve greater than minimal improvement or response, compared with patients who did not show this level of early improvement. In a smaller study of participants who had not responded to an initial antidepressant trial, venlafaxine was the only antidepressant studied . The datThe present study bolsters the proposed use of the lack of early improvement as a predictor of failure to achieve remission with the current medication. In fact, in the VAST‐D study, the NPV for early improvement was over 92%. The lack of early improvement contributes to identifying a majority of those who will not ultimately demonstrate remission of symptoms with the current treatment, even if the dosage is increased to the optimal therapeutic dosage. Therefore, if there is not at least a 20% drop from the baseline QIDS‐C score by the end of week 2, there is <8% chance of achieving remission, just over a one‐in‐three (38%) chance of reaching the response criterion, and a five‐eighths (62%) chance of achieving greater than minimal improvement at the end of week 12 with continuation of the medication. In contrast to the prediction of remission, when predicting response and greater than minimal improvement, PPV is generally a better predictor than NPV . Study withdrawal may account, at least in part, for the low remission and response rates among patients who did not experience early improvement. It would be important to learn whether more perseverance would have resulted in better outcomes for some of these patients. The present results suggest that a change in intervention is likely warranted relatively early in a medication trial if early improvement is not evident. However, specific patient groups may benefit from a longer duration of the intervention.Identifying the characteristics of patients who would benefit from additional time is important, as is developing strategies to enhance treatment adherence when improvement is slower than anticipated. Evaluation of the factors influencing a false negative outcome sheds some light on this issue. Participants who did not show early improvement but achieved remission by the end of week 12  were more likely to have lower baseline QIDS‐C scores, fewer adverse childhood experiences, lower baseline Beck Anxiety Inventory score, lower C‐SSRS scores, and higher baseline quality of life (Q‐LES‐Q‐SF) scores. These findings are similar to the factors influencing inclusion in the early improvement group, but the effect sizes were much larger among the participants classified as having false negative outcomes .The use of early clinical improvement to predict remission has been reviewed by Lam . Four baDoes the use of early improvement as a predictor of remission make a difference in clinical decisions? Only one study has tested a strategy of changing the clinical management when early improvement (in the first 2 weeks) was not achieved during an initial trial of the antidepressant escitalopram . Only 19One of the strengths of the present analysis of the VAST‐D data was the availability of a large patient population who received frequent, closely monitored visits with dosaging guided by measurement‐based care. A second strength is that the study population focused on patients who had inadequate response to prior treatment for depression. These factors suggest that this study was ideally suited to determine the predictive value of early improvement. Because of the large patient population and multiple assessment visits early in the trial, we were able to bolster evidence provided by existing studies on the utility of effectively using early improvement (or lack thereof) as a guide for clinical management. Comparing our findings in a large sample of patients inadequately responding to an initial antidepressant trial with prior studies addressing the role of early improvement in initial antidepressant trials, it is apparent that the utility of determining the presence of early improvement is robust across clinical populations.This study has some limitations. It is possible that some component of early improvement may be associated with the expectation of benefit associated with entering a randomized trial. Despite this concern, treatment duration in the trial had no impact on PPV. In contrast, there were modest changes in the NPV over time, which were greatest at the end of week 6 Figure , consistResults must also be interpreted in the context of VAST‐D being a “next‐step” treatment study of patients who had already experienced inadequate response to at least one antidepressant trial. Thus, overall remission rates were relatively low, ranging from 22% for patients in the Switch‐BUP group to 29% for those in the Aug‐ARI group. These low remission rates resulted in a lower ceiling for the PPV. Higher overall remission rates were achieved in the initial treatment phase of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study with “first‐step” trials  and thesThrough this analysis, we reinforced existing literature that supports the utility of using early improvement in patients taking antidepressant medication to predict later remission, response, and greater than minimal improvement. Also, we were able to identify an optimal time for assessing early improvement. The predictive importance of lack of early improvement is based on the assumption that standard assessments of depression severity are obtained at least at baseline and at the end of week 2 of each new medication intervention.The recognition that lack of early improvement following initiation of an antidepressant medication regimen tells us only that the current therapy—even allowing for dosage escalation—is unlikely to be effective. However, the lack of early improvement does not tell us what the next step should be. The utility of using the absence of early improvement to enhance clinical outcomes should be evaluated in randomized controlled trials that test whether continuing the current treatment for a longer duration or switching to an alternative intervention is more effective for those failing to show early improvement.Supplementary MaterialClick here for additional data file.