Peripheral nociceptors and their synaptic partners utilize neuropeptides for signal transmission. Such communication tunes the excitatory and inhibitory function of nociceptor-based circuits, eventually contributing to pain modulation. Corticotropin-releasing hormone (CRH) is the initiator hormone for the conventional hypothalamic-pituitary-adrenal axis, preparing our body for stress insults. Although knowledge of the expression and functional profiles of CRH and its receptors and the outcomes of their interactions has been actively accumulating for many brain regions, those for nociceptors are still under gradual investigation. Currently, based on the evidence of their expressions in nociceptors and their neighboring components, several hypotheses for possible pain modulations are emerging. Here we overview the historical attention to CRH and its receptors on the peripheral nociception and the recent increases in information regarding their roles in tuning pain signals. We also briefly contemplate the possibility that the stress-response paradigm can be locally intrapolated into intercellular communication that is driven by nociceptor neurons. Such endeavors may contribute to a more precise view of local peptidergic mechanisms of peripheral pain modulation. Pain and touch sensations occur through the relay of electrical impulses generated by somatosensory neural circuits, which comprise peripheral and central components . In thisMany molecular factors including neuropeptides tune nociceptor sensitivity in an autocrine or paracrine manner. Because pain exacerbation or attenuation often arises from such tuning, knowledge of their identities and tuning mechanisms at work may help not only to better understand the physiological aspect of pain, but also to devise pain control strategies that target nociceptors .Local neuropeptides such as calcitonin-gene related peptide (CGRP) and a tachykinin substance P (SP) are currently well known to be secreted by nociceptors and act as slow neurotransmitters, which lead us to experience tonic pain whereas the typical ones like glutamate and aspartate evoke sharp and fast pain when released synaptically. Moreover, those local peptides may strengthen synapses in the spinal cord or medulla and may occasionally cause peripheral neurogenic inflammation when retrogradely secreted toward innervated tissues or blood vessels . GastrinInformation on the localized interactions of peptide hormones is also increasing. As well as CGRP and SP, nociceptors can also secrete pituitary adenylate cyclase-activating peptide (PACAP) and vasoactive intestinal peptide (VIP) to tissues when hyper-activated and aggravate neurogenic inflammation via vascular dilation and lymphocyte activation . BradykiCRH is one of the most upstream players in the hypothalamic-pituitary-adrenal (HPA) axis of hormonal networks. CRH, when released from the hypothalamus upon stress, evokes the subsequent secretion of adrenocorticotropic hormone (ACTH) by stimulating the anterior pituitary. ACTH circulates, reaches, and stimulates the adrenal gland, which finally secretes corticosteroids, preparing many different tissues and cells to cope with stress including pain . BesidesEven earlier than maturation of the information on CRH and its receptors, a multitude of interesting approaches proposed identification of CRH-like molecules that possibly imply dorsal root ganglion (DRG) or nearby tissues as a location. When incubated with the duck spinal cord extract in vitro, the dissected pituitary generated ACTH, which suggests that components of the spinal cord, in which the central termini of nociceptors form synapses, contain some stimulant molecules that may or may not be CRH . The medSensory ganglionic immunostaining eventually confirmed dorsal root and trigeminal ganglionic neurons, which appeared to have relatively smaller diameter somas, indicating that probably C- and Aδ-fiber nociceptors predominantly express CRH . ImmunocTo date, two pathologic models have been tried for CRH expression in nociceptors. The Hwang lab, before they used spinal nerve injury (SNI)-induced neuropathic pain models, confirmed by using immunohistochemistry that a number of small-diameter DRG neurons that were presumably among nociceptors were CRH-positive under normal condition . These Ccrh [Later, an approach to sorting out potentially pain-associated genes has generated interesting data. Quantitative analyses on mRNA levels in DRG neurons from chronic constriction injury (CCI)-induced neuropathic pain models using mice found dozens of genes with altered expression, particularly in neuropathic neurons compared to undamaged contralateral or adjacent neurons, and the most increased one was crh . Thus coThis concept may require an integrated view regarding receptor expression, and we overview this in the next section.125I-labeled ovine CRH were the first to show its specific binding in the superficial dorsal horn of the spinal cord and the central termini of nociceptors in rats [The CRH receptors (CRHRs) are among members of the secretin-like (class B) family of G-protein-coupled receptors (GPRs) and there are two subtypes known as CRHR1 and CRHR2 . Even be in rats ,38  analysis using rat lumbar DRGs as well as the relevant spinal cord section [After a series of cloning reports in 1993 and 1995, the Vale lab first reported the spinal trigeminal nucleus as a major expression site for CRHR1, which was confirmed by Rivest et al., suggesting a possible interaction with a ligand secreted from neighbor regions and the potential nociception-related role of this receptor ,41. CRHRdetected . mRNA de section .crhr1 and crhr2 in their in situ hybridization assays using murine spinal cords [Korosi et al. aimed to show the laminae distribution of al cords . Interesal cords . CRHR1 wal cords ,56. HoweEven in the early stage when most studies covered expressions, Wei et al. focused on a functional aspect. When they observed the rat paw, CRH administration inhibited neurogenic extravasation, which is one of the important mechanisms of nociceptor-induced neurogenic inflammation . This inMousa et al. systematically compared the CNS and peripheral effects of CRH injection on pain using three different local routes  in one study and all of those exhibited analgesia at doses that unlikely diffuse to other regions at a significant level . The effIn the spinal cord, more or less controversial results have been reported. Intrathecal treatment with CRH and UCN2 effectively attenuated duodenal distension-induced pain behaviors and related visceromotor responses (VMRs) . The CRHExpression profiles predicted possible pain modulation by CRH and its receptors in the nociceptor-based circuit. However, many questions remain to be clarified, for example, how CRHR expressing cells undergo nociceptive insults, what the endogenous ligands result in, and how quantitatively and qualitatively these interactions contribute to pain modulation.Largely, CRH has been detected in nociceptors while CRHRs mostly appear to be present in synaptic interactor neighbors such as dorsal horn neurons or innervated peripheral tissues. But at least a subpopulation of nociceptor neurons, particularly under pathological states including neuropathic conditions also seem to express CRHRs. This suggests that this ligand may play a role in an autocrine and/or paracrine manner, otherwise that the sensory neurons themselves may also have the potential to respond to CRH from other sources. Because both CRHRs are well known to drive Gs or Gq/11 signal transduction that typically promotes neuronal excitation, it can be assumed that those CRHR-(+) neurons, when stimulated by the ligands, may increase their firing and eventually contribute to pain aggravation, which has not been experimentally confirmed yet. In addition, CRHR2-specific Gi/o-coupled downstream first found in trophoblasts, may complicate its data translation . More prRegional discrepancies, as mentioned above, between ligand producer and receiver components in the spinal dorsal horn layers are another question. When exclusively focusing on superficial laminae, one can simply hypothesize that inhibitory interneurons, excited upon CRH from nociceptors, can downregulate nociceptive signals. Given this notion, it is unlikely to fully explain the raison d’être of broadly expressed CRHRs throughout the laminae. Possibly, complex local interactions between interneuronal ligands and receptors may exist. Otherwise, Aδ nociceptors, which form synapses also in the deeper laminae may contribute to CRH signals, particularly when their ligand expression is pathologically upregulated. Also, UCN positivity needs to be compared among whole DRG populations including mechanoreceptors as well as nociceptors because the possibility as to whether the collateral terminals of mechanoreceptors participate in the transmission based on the gate-control theory should be checked.A comparative validation as an analgesic target is another way to interrogate neuropeptides and their receptors. Neuropeptides in nociceptors such as CGRP, PACAP, SP, VIP, GAL, NPY, VIP, SST, AVP, and OXT all appear to play a crucial role in pain transmission. On the other hand, their specificities to some pain phenotypes, the extent of their expression and also its breadth, and the quantities of their effects on pain intensity especially under pathologic conditions, frequently open or limit opportunities to employ proof of concept regarding their roles. Quantitative approaches for nociceptive synaptic transmissions and pain outcomes due to controlling CRH paradigms may help precise definition of its validity as an analgesic target. Accessibility to peptide and receptor expressers can also be important. Basically, nociceptors are surrounded by thinner blood brain barriers and even their peripheral termini are exposed to tissues, which can be an advantage in that regard . Future Besides the conventional HPA axis, many other tissues are now known to express CRH and its receptors, which appear to play a specific role dependent on location. Regarding pain transmission, not only the nociceptor-based circuit, which is reviewed here, but also other regions such as the amygdala in the CNS and the gastrointestinal tract in the innervated periphery draw attention as places where the ligand and receptor interact ,71. DespOpioids-producing leukocytes may be an effector that dampens the pain mediation itself. So may spinal inhibitory interneurons although this has not been thoroughly confirmed. Regarding outcomes from the CRHR-mediated responses in visceral and vascular epithelia, also assumed to be effectors, their preparation might lean more on the removal of potential damage, because these generally promote inflammation or motility for the defense. This latter paradigm may help pain resolution from a relatively long-term perspective but can acutely aggravate pain owing to pro-inflammatory processes and hypersensitivity, particularly during the developmental stage of inflammation. This can be a possible explanation for why CRH antagonism, but not its agonism has been shown to contribute to pain reduction via epithelial CRHR activation in visceral hypersensitivity, endometriosis, and cystitis, etc. ,72,73 F. In otheAlthough study of CRH has started at an early stage, the knowledge of the CRH function in peripheral nociception is immature compared to that for other neuropeptides. Not only the systemic effect of CRH from the HPA axis, its local and specific action occurring in the nociceptor-based circuit appears to be potentially important in pain modulation according to the accumulating evidence to date. It will be interesting to see whether the HPA paradigm of coping with stress can also be applied to pain transmission or neurogenic management of pain-producing damage. Future efforts defining CRH- and UCN-specific labeled lines, which likely arise more conspicuously under neuropathic or inflammatory conditions, may shed light on unknown aspects of the physiology of pathologic pain. This will not only academically contribute to the mapping of peripheral somatosensory circuits where other peptidergic information is currently preceding, but also therapeutically to the devising of new strategies for controlling pain diseases possibly by providing a novel proof of concept.


Rho GTPases are a family of small G-proteins of the Ras superfamily. The ubiquitously expressed Rho GTPases RhoA, Rac1, and Cdc42 are major regulators of the actin cytoskeleton, with essential functions in cell migration. However, Rho GTPases also regulate other processes than actin polymerization and are involved in the regulation of proliferation, differentiation, polarity, and stemness. These effects are mediated by a large number of effector molecules, which are often co-regulated by other pathways, resulting in a complex signaling network and considerable cell-type specificity. Not only with respect to effectors but also to the regulatory side, Rho GTPase signaling is complex due to the large number of activating (guanine nucleotide exchange factors (GEFs)) and inactivating (GTPase activating proteins (GAPs) and guanine nucleotide dissociation inhibitors (GDIs)) molecules, which in turn are controlled by many different signaling pathways. This complexity explains why many aspects of Rho GTPase signaling are poorly understood, in particular with respect to the in vivo function. In this Special Issue entitled Rho GTPase Signaling in Health and Disease, we collected review articles and original papers that shed light on specific aspects of Rho GTPase function during development and disease, often with a focus on certain Rho GTPases. The role of Rho GTPase signaling in cancer has been investigated thoroughly for many years. Whereas Rho GTPases initially were simply considered invasion-promoting, pro-oncogenic molecules, recent research including cancer genome sequencing has resulted in a more differentiated view of Rho GTPase functions in cancer, revealing both promoting and inhibiting effects and high cancer-type specificity. In this Special Issue, De et al. review the role of Rac1 in solid cancers , an alteIn addition to cancer, Rho GTPase signaling has emerged as a key regulatory pathway in a number of other diseases. Guo et al. describe the role of Rho GTPase signaling in autism spectrum disorders, where 20 Rho GTPase regulator or effector genes are suggested to be disease-associated . MulherkSeveral articles in this Special Issue describe tissue-specific roles of Rho GTPase signaling. Bros et al. analyze RhoA as a crucial regulator of innate and adaptive immunity, where it regulates the migration and activation of immune cells . Rho GTPAnother set of reviews highlights specific effectors and regulators of Rho GTPases. The Cdc42-binding protein Cdc42SE1 is identified as a negative regulator of Cdc42-dependent growth signaling with potential importance in skin cancer by Kalailingam et al. . BusteloMore than three decades after their recognition as master regulators of the actin cytoskeleton, Rho GTPases and their signaling remain an exciting and active research area with many important open questions and implications in many different diseases.


Peste des petits ruminants virus (PPRV) causes an infectious disease of high morbidity and mortality among sheep and goats which impacts millions of livestock keepers globally. PPRV transmission risk varies by production system, but a deeper understanding of how transmission scales in these systems and which husbandry practices impact risk is needed. To investigate transmission scaling and husbandry practice-associated risk, this study combined 395 household questionnaires with over 7115 cross-sectional serosurvey samples collected in Tanzania among agropastoral and pastoral households managing sheep, goats, or cattle . Although self-reported compound-level herd size was significantly larger in pastoral than agropastoral households, the data show no evidence that household herd force of infection  increased with herd size. Seroprevalence and FOI patterns observed at the sub-village level showed significant spatial variation in FOI. Univariate analyses showed that household herd FOI was significantly higher when households reported seasonal grazing camp attendance, cattle or goat introduction to the compound, death, sale, or giving away of animals in the past 12 months, when cattle were grazed separately from sheep and goats, and when the household also managed dogs or donkeys. Multivariable analyses revealed that species, production system type, and goat or sheep introduction or seasonal grazing camp attendance, cattle or goat death or sales, or goats given away in the past 12 months significantly increased odds of seroconversion, whereas managing pigs or cattle attending seasonal grazing camps had significantly lower odds of seroconversion. Further research should investigate specific husbandry practices across production systems in other countries and in systems that include additional atypical host species to broaden understanding of PPRV transmission. Peste des petits ruminants virus (PPRV), the contact rate assumption chosen may have important impacts on modelling outcomes and control strategies. More empirical data is needed to inform contact rate pattern selection.A key assumption made when modeling disease transmission is whether the per capita contact rate within populations is density-dependent (linearly related to population size) or frequency-dependent (independent of population size), which, respectively, results in increasing or unchanging transmission with the number of hosts. Directly estimating transmission-relevant contact rates is challenging, and empirical data on contact rate or proportion of successful transmission given contact is not commonly collected. However, calculating the force of infection (FOI), the per capita rate at which susceptible hosts become infected, from commonly collected age-specific serological studies is more tractable. Considering the case of animal diseases, under the assumption of density-dependent transmission, as the number of infected hosts increase, the FOI increases with herd size . Under fsmall ruminant morbillivirus (SRMV), impacts an estimated 330 million farmers worldwide across varying production systems [PPRV, or  systems . Livesto systems . This so systems . The vir systems . Further systems , the Foo systems .Successful eradication may depend on the ability to model PPRV transmission correctly at a herd-by-herd level to inform decision making in the context of varying ecosystems, production systems and practices, herd immunity, and the presence of atypical hosts  that may be involved in interspecific transmission . PPRV trMorbillivirus, it is believed to be a fully immunizing infection that provides life-long immunity, so seroprevalence is expected to rise over age  at a raThis study aims to better understand the nature and impact of contact patterns and specific husbandry practices on PPRV transmission to better inform eradication efforts. The dataset is comprised of 395 household questionnaires and accompanying cross-sectional serosurvey data of sheep, goats, and cattle managed together in households under agropastoral and pastoral production systems across 20 villages in northern Tanzania. Pastoral households were those in which livestock rearing was considered the primary livelihood activity, while agropastoral households were those in which a mix of crops and livestock were important. Given that PPRV is transmitted via aerosols and close contact, and that previous work suggests an association of larger herd size with increased PPRV seroprevalence , we testBlood samples for the cross-sectional serosurvey and household questionnaires were collected as part of the “Social, Economic, and Environmental Drivers of Zoonoses” (SEEDZ) study. The SEEDZ study data collection, data cleaning, and laboratory testing methods have been described in detail previously ,21,26. TOvis aries), goats (Capra aegagrus hircus), and cattle (Bos taurus indicus) in 20 northern Tanzanian villages in Arusha and Manyara Regions. Northern Tanzania was selected as it is a region with large livestock populations and diverse production systems. A total of 7576 animals from 417 households were sampled, and 404 household surveys were conducted using multistage random sampling , and location data, a total of 7538 serum samples were tested in duplicate using a commercially available competitive ELISA kit  directed against the hemagglutinin protein of PPRV . In housIn total, 404 household questionnaires were conducted, which included a variety of questions about the demographics and socio-economic characteristics of the household, household herd composition and demographics, and livestock health and husbandry practices. Questionnaires were piloted in Ngorongoro district in 13 households and were conducted in Swahili or local Masaai language Maa among households that were sampled during the advertised sampling event at a central location to the village. Details of variables used in this study (a subset of the full SEEDZ questionnaire) are in n = 268).Some variables were measured at a level different than household herd response, and these included herd size, birth counts, and counts of animal species other than cattle, sheep, and goats. These variables were all at a compound level. As previously mentioned, although a compound may be comprised of several households, only one household per compound was sampled and surveyed in the SEEDZ study. A compound was considered the relevant epidemiological unit, given that household herd animals mix in and near an individual compound and are typically held in a central enclosure overnight. In this analysis, we make the simplifying assumption that household herd was representative of the compound herd, and so the terms compound and household are used interchangeably. For the grazing variable, three responses were recorded, but the third response  only had 3 households responding, so these 3 household responses for this variable were not included in analysis when comparing the other two reported species combinations of grazing  were explored for use in this study and ultimately not selected for inclusion . For conAn alternate classification system with smallholder as a third category, matching that of de Glanville et al. , was useThe final analysis dataset included 395 households from which both household questionnaire data and serosurvey data were available, and the household did not report PPRV vaccination in the past 24 months (2 households excluded). This includes a total of 7115 animals, representing 1975 sheep, 2302 goats, and 2838 cattle and these denominators apply to all analyses.λ), the rate of infection of susceptible hosts, using the catalytic framework [P(a), can be expressed as:P(a)) is the proportion of hosts still susceptible at age a, and λ(a) is the age-specific FOI. Integrating and rearranging yields:P(a) represents the probability of having been infected before age a. Age-specific seroprevalence curves are direct empirical observations on this probability. This model assumes endemicity and that seronegatives are fully susceptible.To understand the role of contact pattern assumptions and husbandry practices in PPRV transmission, we calculated the force of infection , represents an estimate of the log FOI when log(a) is used as a regression offset . By using this generalized linear model, the impact of age is controlled for in this analysis of PPRV transmission heterogeneities between production systems. All sampled animals within a household herd were used to calculate the household herd FOI.In Equation (4), the intercept, log with sex and production system type as fixed effects, log(age) as an offset, and the geographic scale  as a random effect were used to calculate the FOI for the analysis in the main text and geographic scale analyses in For calculating the household herd FOI to compare to variables from the household questionnaire (husbandry practices and births), a GLMM without fixed effects, with log(age) as an offset and household ID as a random effect was used. While the serosurvey data contained serostatus, species, age, and location information for sampled animals at various geographic scales, the household questionnaire solely contained about the entire household herd so household level GLMMs were used to calculate FOI for production system practice variables. Three single-species GLMMs and an all-species GLMM were used to calculate specific and total household (compound) herd size. Comparing the distribution of household herd FOI is more advantageous than comparing the distribution of household herd seroprevalence, as the modeling framework controls for the effects of increasing PPRV seroprevalence with age, which is expected for a fully immunizing  infection. In our univariate analysis, differences in the distributions of household herd FOI for each husbandry practice variable response were tested with the Kruskal–Wallis test, if the assumption for heteroscedasticity was first met by the Levene’s Test. This non-parametric test was selected because husbandry practice variable response distributions did not meet the normality assumption of parametric tests. This was the preferred test through the study. If the assumption was not met, the non-parametric Wilcoxon test, and parametric oneway test and t-test as well as permutation tests (2000 permutations) were conducted to compare if the qualitative response was different between tests (significant or not).While all aforementioned production practice variables were considered in univariate analysis to assess their relationship to the distribution of household herd FOI, a subset of these variables selected based on expert opinion were considered in multivariable logistic regression models with age group as an offset  to determine their impact on PPRV seroprevalence. Variables were included based on previous knowledge from the literature  and the National Institute of Medical Research  in Tanzania; and the College of Medical, Veterinary, and Life Sciences, University of Glasgow in the United Kingdom . Approval for animal work was provided by the Clinical Research Ethics Committee at the University of Glasgow School of Veterinary Medicine , which authorizes research under The Veterinary Surgeons Act UK 1996 and oversees research regulated under the Animal (Scientific Procedures) Act 1986. Permission to publish this work was granted by the Director of Veterinary Services, Tanzania.Herd demographic characteristics of the 7496 serosurvey samples analyzed have been published previously . Demogran = 284, 71.9%). Two-species households accounted for 18.5%, with 47 households that managed cattle and goats only (11.9%), 18 that managed cattle and sheep only (4.6%), and 8 managed only sheep and goats (2%). Single-species households comprised the remaining 9.6%, with 30 households that only managed cattle, 8 that managed only goats, and none that managed only sheep. Thus, 16 households (4%) did not manage cattle, 48 (12%) did not manage goats, and 85 (22%) did not manage sheep. The highest median household herd FOI was among the sheep and goat two-species households (n = 8), followed by the three-species households, the rest of the two species households, and then the single-species households . Additionally, pastoral households generally had more females than male animals  households had significantly larger self-reported compound herd sizes than agropastoral (AP) households, both overall and when stratified by species, sex, or age category, than agropastoral herd sizes  with compound-level birth counts in the past 12 months (Compound (household) herd FOI did not have a relationship with increasing values of self-reported compound herd size  and household level . A Local Indicators of Spatial Association (LISA) analysis conducted at the village, sub-village, and household herd level (data not shown) indicated significant spatial clusters of increased and decreased FOI only at the sub-village scale  among households that reported any species in their herd attended a seasonal grazing camp .The majority of households stated that they did not keep livestock elsewhere . Among households who did, 72%, 31%, and 30% reported cattle, sheep, and goats, respectively, being kept elsewhere, with grazing as the top reason for all three species. The mean number kept elsewhere was 18.4 cattle, 17.1 goats, 25.9 sheep among agropastoral households and 74.4 cattle, 43.5 goats, and 76.1 sheep among pastoral households. Owners also kept animals they did not own  with the top three reasons being a favor, milk loan (cattle) or breeding loan , or grazing. For households that reported introducing animals, the most common practice was to mix new animals into the herd without a period of quarantine (47.7% cattle and 65.3% sheep and goats), or both mix and provide medicine to new animals (6.3% cattle and 10.6% sheep and goats). Sample sizes for categories other than mixing were not sufficiently large to conduct statistical testing , cattle or goat introduction to the compound, grazing cattle separately from sheep and goats vs. all together, presence of donkeys or dogs kept by the household, the report of any animal deaths, sales, or gifting (given away) in past 12 months. Multivariable analyses found goat or sheep introduction or seasonal grazing camp attendance, cattle or goat death or sales, and goats given away in the past twelve months to be significant production practices that increased odds of PPRV seroconversion, whereas managing pigs, and cattle seasonal grazing camp attendance decreased the odds of seroconversion. Additionally, spatial patterns in PPRV seroprevalence and FOI as well as paired species FOI comparisons observed at the sub-village and household herd level were consistent with previous village-level work .Morbillivirus systems such as human Measles virus [This study quantified and used herd size variation among households in northern Tanzania to explore hypotheses about contact rate assumptions on PPRV transmission. The findings are interesting in that evidence of frequency-dependent scaling of transmission was present down to a finer scale than expected: the compound (household) scale. This pattern was consistent for both agropastoral and pastoral systems. The geographic scale at which transmission is assessed is important—within a herd, one might expect to see density-dependent transmission as larger numbers of animals may be herded more closely, but at a larger geographic scale, one may expect to see frequency-dependent scaling of transmission as animals are managed in discrete herd units. In fact, both the mode of transmission and the underlying contact network structure drive patterns seen at different scales (within and between populations) and have been previously shown to give paradoxical predictions in other es virus . Future es virus ,42.In a further refinement of the distinction between pastoral and agropastoral households regarding growing crops, de Glanville et al.  used mulThis study explored husbandry practices for cattle managed alongside sheep and goats and their relationship to PPRV transmission risk. The univariable finding that cattle introduction to the compound increases household herd FOI may result from cattle introductions occurring at the same time as sheep or goat introductions (which may be PPRV infected and shedding virus). This finding is counter balanced by the univariable evidence presented in this study that grazing cattle with sheep and goats was associated with a significantly lower household herd FOI compared to when cattle are separated, and is consistent with the prevailing dogma and experimental transmission findings ,45 that Overall, the husbandry practices that increase PPR transmission risk that were identified in this study align with and expand upon previously published studies . Like thThis study made several assumptions, consistent with previous work in this population ,21, inclThis study explored evidence for density- or frequency-dependent transmission scaling of PPRV and husbandry practices that impact PPRV transmission risk among households in northern Tanzania. Taken together, the data presented here support the use of frequency-dependent transmission scaling in PPRV modelling work at finer geographic scales and identified specific husbandry practices associated with increased household herd transmission risk. This analysis also quantified the distribution of herd sizes in pastoral and agropastoral production systems in the study region in total and by species, sex, and age group stratifications. Future research should assess model sensitivity to variations in contact rate assumptions, and the resulting impact on proposed PPR control strategies. Additionally, future studies should explore the relationship between FOI and herd size in settings where both household-level and compound-level herd sizes can be confirmed by counting animals instead of self-reporting in order to reduce potential bias. This study found that specific husbandry practices such as seasonal grazing camp (‘ronjo’) attendance and livestock introduction practices are associated with increased household herd PPRV transmission risk. Given that an affordable vaccine is available, vaccination should be the cornerstone of control efforts to reduce transmission at camps and during new animal introductions when available. Furthermore, if additional husbandry practices are found to be associated with increased PPRV transmission risk in future research, then vaccination could be targeted to settings in which these practices are common. In settings lacking access to veterinary services and vaccination, further research may be needed to determine which alterations to husbandry practices reduce transmission risk, such as modifications at seasonal grazing camps without restricting attendance and standardizing best practices for animal introductions to reduce PPRV transmission.


The technological complexities and broad operational scope of eSource impede coordinated, inter-organizational action on advancing at-scale solutions.We introduce an architectural framework for articulating technological considerations across organizations. The architecture neither implies nor endorses solution implementations; rather, it proposes solution functionality based upon principles and good clinical practices.Key technology considerations include patterns of anticipated use, implications to the current state of clinical trial operations, and the need for new technologies .Technology considerations drive implications beyond technology—influencing regulatory, process, and ethical realms of clinical research.The online version of this article (10.1007/s43441-020-00132-4) contains supplementary material, which is available to authorized users. Adoption of digital technologies into clinical research simultaneously disrupts and transforms how pharmaceutical companies (Sponsors) approach development of new therapies. It drives innovation, provides for planning and execution of higher value trials, drives discovery of correlations between digital signals and biological events, and has the potential to personalize therapeutic health outcomes.Technology professionals should understand how digital methodologies disrupt current clinical research models and respond with solutions that address the new needs while simultaneously supporting global regulatory expectations, data integrity, and security and privacy concerns.data initially recorded in electronic format [eSource is the digital vanguard of clinical research. Simply stated, eSource is c format . Since 2c format –5. eSourBig Data—with potential data volumes that far surpass those associated with main-line clinical trials.It moves clinical research into the realm of It builds an interoperability bridge with the Healthcare IT domain, allowing Sponsors to coordinate trial data with electronic health records (EHR) and improve the power of clinical research analysis.It drives the need for algorithmic data interpretation and reduction to transform raw digital signals into clinically meaningful, data-driven results.It requires use of internet-scale techniques for collection of coordinated time-series data from sensors, wearables, and mobile devices.eSource brings four significant technology disruptions into the clinical research domain.Stakeholder community—i.e., trial sites, technology vendors, standards organizations, regulatory authorities, payers, ethics boards, healthcare professionals (HCPs), and Sponsors. The challenges span the space of trial design, data collection and exchange, pharmacovigilance, data security and privacy, workforce skills, resource allocation, and regulatory expectations [These disruptions unlock new opportunities to improve drug development effectiveness and usher in new classes of digitally enabled healthcare therapies. They also create a new class of challenges for the ctations .Previous TransCelerate publications  identifyDirect Data Capture, Non-CRF, Devices & Apps, and EHR  member companies collaborated on a logical-level architecture, based upon previous TCB reference architecture work and the experience gained from internal efforts at the TCB member pharmaceutical companies. The resulting Logical Architecture provides a conveyance for Stakeholder discussion and evaluation of eSource technology considerations.Logical architectures strive to provide as much detail as possible for resulting solutions without constraining those solutions to particular designs, environments, vendors, or technologies. This is in contrast to conceptual architectures, which explain what solutions should do in terms non-technical stakeholders can understand, and physical architectures which provide enough detail to implement and deploy resulting solutions.The Logical Architecture conveys technology considerations, asserted functionality, and patterns of use without mandating specific solution implementations. The intent is to spur contemplation and discussion rather than supply recipes for solution construction. The completeness and absolute correctness of the Logical Architecture at this juncture is less important than the discussion it generates, the implications it asserts and the degree of alignment it drives.The Logical Architecture , data might be un-mappable to CRF fields because it lacks self-evident clinical significance , and incoming data volumes may exceed the EDC system storage capacity.Main-line clinical trials utilize EDC (electronic data capture) systems to acquire data at trial sites and exchange it with Sponsors. EDC systems digitize the traditional CRF (case report form) process where clinicians record discrete data values on a per-patient basis during site visits. Digital data collection—aside from the Things in this scenario are the networked devices—ranging from personal health mobile devices, environmental sensors, central lab data feeds and institutional EHR systems. The acquired digital data can be biometric, behavioral, or environmental in nature.The general digital data acquisition scenario anticipates distributed networked devices engaged in autonomous communication with one or more centralized data aggregation agents: i.e., an Internet of Things (IoT) pattern. The Automated acquisition of continuous time-series data  differs from acquisition of discrete data values in several ways. One key difference is the need for data integration agility if inconsistencies are detected in data streams, necessitating tradeoffs between system validation integrity and reaction times. The possible need for on-the-fly data de-identification and clinician access to near real-time summarized data also create differences in the technical approach.Data Engineering incorporates agile and flexible transformation into the Logical Architecture, converting acquired data streams into common data structures with normalized units and appropriately de-identified/anonymized values. It provides access points for adding new analytics or transformation logic into the data flow with low disruption to existing components. It is also supports near real-time data stream access for implementation of HCP appropriate summarized data visualizations.Main-line clinical trials typically perform data analysis as the last step: i.e., during statistical end point analysis. Data flows from the source  to Sponsor statistical computing environments without addition or deletion, except for well-controlled corrections applied in the data review process.eSource introduces data analysis via analytics throughout the trial lifecycle to reduce, interpret and clinically surface the incoming digital signals. This implies the Logical Architecture provide raw digital data persistence and analytics pre-processing prior to data ingestion by existing Sponsor clinical data management systems. The analytics can be exploratory or validated in nature. In the case of exploratory analytics, researchers attempt to discover correlations between digital signals and biological events. In the case of validated analytics Sponsors use known correlations that have been scientifically confirmed and accepted by Regulatory authorities to reduce digital signals into clinically relevant results.Data Security and Integrity. Clinical data, even when de-identified, may retain patient personal information attributes, and these data often are used in analysis to support the safety and efficacy of investigational medical therapies. Patients, HCPs, ethics boards and regulatory authorities must retain trust in the data for clinical research to function. Therefore, solutions must include data security and integrity considerations into their development.Adherence to Regulatory Requirements. Regulatory authorities throughout the world place requirements upon computer information systems used in clinical research. While many of these requirements share common attributes with professional software development expectations, some result in unique constraints upon how clinical research data systems should function. Therefore, solution providers must understand how these requirements affect implementation decisions.Data Lineage and Traceability. A key factor of data quality assurance involves the tracking of changes to data content—i.e., additions or deletions, who made changes and why—with un-alterable audit trails. Tracing the lifecycle of data records from data source through to analysis and reported outcomes is a necessary condition applied to all stages of implementation.The three bottom rows of Diagram all things equal, statements of truth. These principles are not meant as absolutes—organizational, resource or evolving regulatory constraints could alter the relevance of these principles to specific implementations. See Appendix A  for a full discourse on the architectural principles.The Logical Architecture is based upon five guiding principles that provide self-evident, Existing sponsor data flow architecture is not replaced in support of eSource, but rather augmented with new architectural capabilities.Regulatory considerations mandate that trial sites maintain the certified systems of record for trial data , 9. In sData acquisition from multivariate, time-series data sources can generate volumes in the tera-byte to peta-byte range per study. Architectural decisions that improve data access and availability at the point of analysis improve computational cycle efficiency and, indirectly, the speed of clinical insights.self-evident clinical relevance. This leads to a distinction between eSource data with a priori intrinsic clinical relevance  vs. data whose clinical relevance requires algorithmic interpretation .eSource potentially generates data that lacks inferable The velocity of eSource adoption is in part governed by the ease study teams can integrate eSource use into study protocols. Architectures that reduce study team up front effort and implementation risk are favored. The easier the capability is to use, the more likely it will be used.The Logical Architecture asserts a common conceptual framework for sharing Stakeholder perspectives, similar to use of a common coordinate system to share physical locations. The results below leverage the framework to articulate three technology considerations: new technologies, anticipated patterns of use, and implications to existing clinical research practices.Architectures do not mandate specific technologies, but they do prescribe boundary conditions: i.e., the available capabilities that solutions should use for development. In this regard, the Logical Architecture identifies three new technology capabilities for implementation of eSource solutions.IoT represents networks of dedicated physical objects (things) that autonomously communicate their internal states as they sense and interact with external environments. This autonomous communication enables synchronized event capture across patient populations and data systems to derive end-user behavior, take preventive action, or augment business processes. Specifically for eSource, IoT enables at-scale use of network-connected device fleets for the acquisition of trial subject data and continuous patient health monitoring.Big Data is a set of techniques and associated technologies to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex for use in traditional data processing applications. This capability enables eSource to manage and analyze the large data sets resulting from continuous acquisition of time-series digital signals.Predictive analytics encompasses a variety of statistical techniques—e.g. data mining, predictive modeling, machine learning—which analyze current and historical facts across data sets to make predictions about future or otherwise unknown events. In addition to making statistical predictions between two effects, this class of analytics also allows for use of correlations between multiple variables to detect effects between events not present in single relationships. In the case of eSource, predictive analytics enable automated transformation of raw digital signals into clinically relevant data results, potential detection of trial subject health issues, and contextual combination of disparate data sources into merged data sets .patterns of use. A brief discussion of each pattern is provided below. See Appendix B  for a full discussion of the patterns.When mapped to the Logical Architecture, the four eSource Modalities lead to twelve common Direct site EHR to Sponsor clinical data exchange during clinical trials that eliminates double data entry (EHR and EDC) and data transcription errors.Direct EHR access by RWE teams to reduce startup study costs and expand dataset availability.Pre-populating site EHRs with study participant schedule of events to reduce manual effort and reduce transcription errors.Using site EHR data to identify potential study participant pools, using appropriate patient identity and privacy safeguards.Use of electronic Informed consent techniques via mobile devices to lessen patient burden.Use of cloud-based data aggregation points to encapsulate mobile device management and communication, alleviating the need for direct device support.Implementing custom-built mobile device communication capability when pre-existing data aggregation points do not exist .Using automated mobile device setup and direct-to-patient device distribution, removing the need for participants trial site visits to receive and setup mobile devices.Flowing Non-CRF data generated by 3rd parities  into Sponsor data systems via automated data exchange.Collecting CRF data at trial sites using mobile devices to eliminate clinician double data entry  without limiting the ability of clinicians to record and maintain non-protocol mandated information ; for exaDeveloping predictive analytics that discover and, subsequently, encapsulate correlations between digital signals and biological events.Modeling study placebo arms with previously collected information to potentially improve patient care and study efficiency , 14.The Logical Architecture exposes technology-driven implications for clinical research. These implications could affect how Stakeholders utilize eSource technologies, or how clinical research evolves to meet the implications. The topics listed below illustrate some potential implications. A full discourse on the implications can be found in Appendix C .The networked nature of digital data acquisition and persistence creates potential situations where data are gathered from patients or sites in country A from a centralized system physically instantiated in country B.Digital devices can generate constant and voluminous streams of patient health telemetry. If telemetry should indicate a patient Adverse Event (AE) or other health issue, will the system acquiring the telemetry be able to detect it? And if so, how will the acquiring system notify HCPs to intervene?Good Clinical Practice calls for Sponsors to review clinical data for inconsistences and ask trial sites for clarification/correction of unexpected data values. For the Devices and Apps modality, this practice becomes a practical impossibility due to the data volumes and complexities of digital signal inspection.The data volume and complexity necessitate use of automated analytics to summarize and process the acquired data. These analytics occupy a new niche in the clinical data flow from existing statistical techniques, i.e., these analytics can affect trial operations as well as decisions made by HCPs in patient care.The nature of digital data acquisition and transformation introduces complexities into traditional clinical research data quality safeguards. Four specific use cases highlight this new implication to clinical research: differing EHR data conventions, safeguarding site EHR data from unintentional but improper Sponsor access, agile system updates vs. principles of traditional system validation, and trust of eSource data origination.Devices and Apps data streams provide a potentially useful patient monitoring capability; however, these data are generally unsuitable for HCP use in its raw form. Data summarizations via analytics potentially enable HCP data usability, but might also introduce new patient risks if HCPs cannot access or correctly interpret the results.With use of eSource, trial sites do not necessarily acquire the digital data. When data are not directly acquired by trial sites the system of record principle becomes difficult to maintain.In main-line clinical trials that use EDC systems for data acquisition, the process of site Primary Investigator (PI) data approval is a straightforward activity, i.e., the PI reviews and electronically signs each eCRF sent to the Sponsor to assure its validity. As eSource data do not generally flow through an EDC system, the existing process of PI data approval cannot be duplicated.early-adaptor clinical research efforts have leveraged PHR, and PDEs are just now entering widespread use. The Logical Architecture includes consideration of PHR, but the practical implementations needed for at-scale use are not yet understood.Every patient may eventually be their own eSource data provider. Personal Health Records (PHR) and Personal Data Exchange (PDE) mechanisms allow individuals to capture, manage, and share their own health data. Only PHR information content is similar to an EHR, but they are managed directly by patients . In 2006Practical system-to-system clinical data exchange requires wide adoption of data standards and the ability to translate between them. Three standards in particular—HL7 FHIR, CDISC CDASH, and OMOP CDM—are key for at-scale system interoperability.The HL7 Fast Healthcare Interoperability Resources (FHIR) standard promises to revolutionize healthcare data accessibility. FHIR defines common data structures and vocabularies as well as a scalable system-to-system exchange protocol. Healthcare community and IT vendor momentum behind FHIR adoption are strong, especially in the US; however, several important FHIR resource types  lack wide adoption, as does a standard process for trial site-to-Sponsor exchange. Like-minded sites and Sponsors, such as the SCDM eSource Implementation Consortium , have beThe desired target-state of standards interoperability foresees generalized FHIR resource mappings to the Clinical Data Interchange Standards Consortium (CDISC) and Observational Medical Outcomes Partnership (OMOP) family of standards. FHIR, CDISC, and OMOP encapsulate conceptually similar data domain models, but the specifics of mapping between them are an enormous undertaking. A number of initial efforts in this space are in progress , 19. Staconcept that involves using research data for a research activity or purpose other than that for which it was originally intended. ” [The US National Institutes of Health defines Data Reuse as a “ended. ”  eSource Patterns of Use). Each of these uses potentially accelerate trial execution speed, lower study costs and provide improved patient care outcomes. But the advantages come with associated challenges: e.g., informed consent, data quality, patient privacy, data ownership, and intellectual property concerns. [The advantages of clinical data reuse range from predictive analytics development to RWE studies and Phase I/II study synthetic arm construction , and Sponsors. The technology considerations make it clear that Stakeholders must act in unison along several fronts to achieve higher value clinical trials, new medical insights, and personalized health outcomes.The digital paradigm simultaneously disrupts and transforms how Sponsors approach therapeutic development. Using the eSource Logical Architecture as a basis to quantify implications, we find that the disruptions and transformations affect the interests of all Stakeholders—Previous survey results show that Sponsors embrace use of eSource, and Stakeholders want to modernize how the clinical research community develops supporting evidence for new drugs . RegulatAll fundamental ingredients to integrate eSource into clinical research and gain the benefits of at-scale use are currently, or soon to be, available. The significant challenge facing Stakeholders is how and when to combine the ingredients and tackle the resulting regulatory, process, and ethical consequences.Supplementary material 1 (DOCX 17 kb)Supplementary material 2 (DOCX 712 kb)Supplementary material 3 (DOCX 34 kb)Supplementary material 4 (XLSX 13 kb)Below is the link to the electronic supplementary material.


This way, initial model-based expectations are iteratively reaffirmed under uncertainty, even in the absence of prediction errors. Using electroencephalography (EEG), the present study aimed to investigate the interplay of such global uncertainty information and local adjustment cues prompting on-line adjustments of expectations. Within a stream of single digits, participants were to detect ordered sequences  that had a regular length of five digits and were occasionally extended to seven digits. Over time, these extensions were either rare (low irreducible uncertainty) or frequent (high uncertainty) and could be unexpected or indicated by incidental colour cues. Accounting for cue information, an N400 component was revealed as the correlate of locally unexpected (vs expected) outcomes, reflecting effortful integration of incongruous information. As for model-compliant information, multivariate pattern decoding within the P3b time frame demonstrated effective exploitation of local  and global information (high vs low uncertainty regular endings) sampled from probabilistic events. Finally, superior fit of a global model  compared to a local model  in a representational similarity analysis underscored the precedence of global reference frames in hierarchical predictive processing. Overall, results suggest that just like error-induced model adaptation, model evaluation is not limited to either local or global information. Following the hierarchical organisation of predictive processing, model evaluation too can occur at several levels of the processing hierarchy.While prediction errors have been established to instigate learning through model adaptation, recent studies have stressed the role of model-compliant events in predictive processing. Specifically, probabilistic information at critical points in time (so-called  Group-lExtending the narrative of previous studies (see ) on moduZ = 8.86, p < .001), particularly apparent later in the time series (around 400–700 ms). In line with central propositions of predictive processing accounts, the statistical comparison of both model RDMs demonstrates that the global event structure acquired through statistical learning took precedence over on-line information conveyed by intermittent adjustment cues. In other words, statistical learning of event categories  was not cancelled out by temporary shifts in expectations but remained the strongest predictor of overall similarity in EEG activation patterns.We employed searchlight RSA to compare two binary representational dissimilarity matrices (RDMs) modelling representational similarity of event classes in the EEG data. Both the global and the local model were found to be significantly correlated with the neural RDM over the entire time course of  ms . Direct Predictive brain processing vitally relies on top-down hierarchical models continuously matched against incoming sensory information. In case of deficient model assumptions, mismatch signals termed prediction errors (PE) are propagated upstream to instigate model updating for future occasions. Recent fMRI and EEG studies , 8 have Multivariate classification analysis revealed that adjustment cues could be reliably decoded from uninformative events at the same position within the P3b time frame. Moreover, locally unexpected events elicited a mismatch-related N400 component when compared to expected events. Combined with behavioural data, these findings demonstrate that local cueing information was efficiently exploited to adapt to current task requirements. Aside from on-line adjustments prompted by local cues, global uncertainty was found to modulate the functional signature of checkpoints: Consolidating previous evidence from fMRI  and EEG local information was introduced in the form of adjustment cues reliably indicating a specific outcome . This information thus modified the statistically learned contingencies and called for a rapid adjustment of expectation in an ongoing sequence. Effective exploitation of adjustment cues was reflected in behavioural response patterns at regular sequence ends: Participants responded less frequently to unexpected regular endings (REG+) within a time frame of ± 500 . This time frame was used in a previous study to mark anticipatory and particularly quick reactions, indicating behavioural facilitation effects of cue recognition [It was one of the main aims of this study to assess the influence of two separate sources of information in predictive processing. First, in the present experimental context, global task structure and transition probabilities are acquired through statistical learning, starting with the training session. Higher-order information also comprised different levels of irreducible uncertainty, that is, how frequently implicit expectations regarding sequence length  were violated by sequential extensions. Secondly, ognition . In the ognition , 8.On the neural level, two key findings provided critical insight into the role of adjustment cue processing: First, supporting our hypothesis, ERP analysis revealed a significant N400 component for locally unexpected (vs expected) sequential events. Critically, unexpected events comprised REG+ and EXT- trials, thus accounting for adjusted expectations due to cue information. Note that expected  and unexpected  event classes were defined by means of a weighted average of trials, precluding ERP effects to be caused by mere differences in trial numbers. This finding replicates results from an earlier study  in whichTo assess the effect of irreducible uncertainty on checkpoint processing, we once again employed multivariate pattern classification for decoding of CP under high and low uncertainty, respectively. In order to increase comparability between studies, we restricted the present analysis to the precise characteristics defined in earlier work, namely probabilistic events with an expected outcome not preceded by an adjustment cue . Supporting our hypothesis, we found clusters of significant decoding accuracy within the P3b time frame. This finding considerably adds to earlier results from two directions: First, as mentioned above, our previous EEG study revealed P3b as the correlate of excess information gain at checkpoints, effectively separating it from non-informative analogues . Second,Representational similarity analysis of a local and a global model RDM revealed significant correlation with neural data for both models. Pre-eminence of the global model , however, leaves room for discussion and motivates further research. Critically, the two models differed with regard to which events were jointly classified as unexpected: The local model reflected adjusted probabilities as a function of on-line cues, grouping REG+ and EXT- occurrences as locally unexpected events. In contrast, the global model did not account for any adjustments but rather modelled all extensions  as similar and as distinct from regular endings . The differential modelling of unexpectedness in the model RDMs as well as the late timing of the difference in model fit strongly suggest these RSA results to be related to the N400 finding reported above: While locally unexpected events could be reliably decoded from expected events in the preceding classification analysis, the higher fit of the global RDM shows that—across all event classes—late potentials reflected an even higher (dis-)similarity of globally unexpected (vs expected) events. In other words, whereas local information was undoubtedly exploited for on-line adjustments in prediction and behaviour, it did not fully overwrite statistically learned global contingencies. Previous studies have highlighted this multi-level hierarchical organisation by demonstrating distinct error signals for the violation of local probabilities and global rules –45, effepotential information gain at respective sequence positions may be sufficient to single out these events compared to deterministic standard trials. This kind of functional prominence, in turn, conceivably depends on how vital potential information from cue positions would be in the current task context. Conceivably, the more instructive adjustment cues are, the more resources will presumably be allocated to sequential positions from which such information could potentially be gained, irrespective of the actual outcome. To this end, future studies should assess neural correlates not only in response to, but also leading up to, these events. Furthermore, it is not clear to what extent differential motor responses to regular endings vs extensions might have contributed to the respective model fits in the RSA. Conceivably, consistent motor responses might have selectively increased the fit of the global model. To circumvent such limitations, upcoming efforts should assess expectations prior to potentially informative sequential positions by means of pre-stimulus ERPs or time frequency analyses. One promising candidate marker of information expectation is the stimulus-preceding negativity , a slowSPN; see . It remaSPN; see ), assessadjustment cues, we were able to replicate a significant N400 component as an index of mismatch-induced model adaptation. Furthermore, using multivariate pattern classification, sequential positions providing excess local information could be reliably decoded from those that did not within the P3b time frame. Global effects of irreducible uncertainty on checkpoint processing remained, as reflected by significant classification of high vs low uncertainty checkpoints in the same time frame. Representational similarity of a global model was found to fit the neural data better than a local model, suggesting that adjustments reflected by late ERPs  are made to a higher-level model of global contingencies. Overall, our pattern of results suggests that just like error-induced model adaptation, model evaluation is not limited to either local or global information. Instead, the overall hierarchical organisation of predictive processing implies that model evaluation can occur at different levels of the processing hierarchy. Intriguing research questions remain with regard to the precise interplay of local vs global information sampling.Model-compliant events have previously been established as critical reference points informing predictive processing in the absence of prediction errors. A similar functional role on the local level is now suggested for a different class of model-compliant events: Accounting for information from local


Psychotic disorders are characterized by profound social impairment. An accumulation of research has explored the contribution of symptoms, cognitive functioning, and behavioral skills deficits to this social dysfunction. Recent research indicates that sleep disturbance has significant social implications in nonclinical populations—this research suggests that sleep problems may also be relevant to understanding social impairment in psychosis. This study adopted a symptom-oriented dimensional approach to examine how sleep disturbance and sleep-related impairment are related to multiple social domains within a transdiagnostic sample (N = 90). This sample included individuals with a variety of psychotic disorders (n = 75) along with healthy non-clinical participants (n = 15) to ensure sampling across the full range of sleep problems and social functioning. Social domains spanned self-reported perceptions of social relationships, social functioning in the community, and behavioral assessments of social competence. We hypothesized that greater sleep disturbance and sleep-related impairment would be associated with more negative or problematic perceptions of social relationships , poorer social functioning in the community, smaller social networks, and poorer behavioral ratings of social competency. Results supported these hypotheses indicating that sleep disturbance and sleep-related impairment have widespread deleterious impacts on perceptions of social relationships, social functioning, and competence. Sleep disturbance retained associations with perceptions of social relationships, social functioning, and social competence even after controlling for total symptoms or cognitive functioning. These findings indicate that sleep problems may have important implications for fully understanding the causes of social impairment in psychosis. Psychotic disorders, such as schizophrenia and bipolar disorder with psychosis, lead to profound and enduring social impairment –3. This Social impairment in psychosis is determined by multiple factors. Findings in schizophrenia indicate that social impairment is a consequence of the combined effects of negative symptoms , cognitiRecent research suggests the novel idea that sleep may be an important consideration in understanding social impairment in psychosis. Sleep disturbances, such as insomnia, have been found to be related to a variety of mental disorders , 19. As Gordon et al.  have proBeyond cross-sectional findings showing an association between social relationships and sleep, other methods have been used to directly test the causal role that sleep may have in social outcomes. In a 3-year longitudinal study, Tavernier and Willoughby  found biLess is understood regarding the social implication of sleep in psychosis. In schizophrenia, poor sleep quality has been found to be associated with lower self-reported quality of life , 44 inclConsistent with the research in non-clinical populations, the existing literature suggests potential links between sleep disturbance and social impairment in psychosis. However, this sleep research in psychosis is limited in that few studies have examined objective indicators of social functioning  and mosGuided by the NIMH research domain criteria (RDoC) framework –51, we aParticipants were enrolled in a larger ongoing grant-funded project examining social affiliative deficits from an RDoC perspective . The mixed sample (N = 90) included clinical (n = 75) and non-clinical community (n = 15) participants. Participants diagnosed with a psychotic disorder  were recruited from outpatient mental health clinics in the Baltimore and Washington, D.C. metro areas. Demographic and diagnostic characteristics of the study sample are summarized in via online advertisements, and inclusion criteria included (1) aged 18–60, (2) no current clinical disorder or psychiatric medications, (3) no lifetime history of a psychotic or mood disorder, (4) no avoidant, paranoid, schizotypal, or schizoid personality disorder, and (5) fluent in English. Exclusion criteria for all participants included (1) current substance use disorder, (2) neurological conditions , (3) evidence of intellectual disability as determined by medical history or cognitive testing, (4) any history of serious head injury, (5) any MRI contraindications , and (6) unwillingness to be videotaped during study participation.Inclusion criteria for clinical participants included (1) aged 18–60, (2) lifetime history of a psychotic disorder, (3) clinical stability  as indicated by approval of clinician and medical record review, and (4) fluent in English. Community participants were recruited Sleep assessments utilized the National Institutes of Health Patient-Reported Outcomes Measurement Information System (PROMIS™) Sleep Disturbance and Sleep-Related Impairment short-form scales . The SleThe Adult Social Relationships Scales (ASRS)  consiststypical level of functioning” rated on a five-point Likert scale. Higher ratings indicate better functioning.The Specific Levels of Functioning Scale (SLOF)  is a selThe Social Network Index (SNI)  is a 13-The UCSD Performance-Based Skills Assessment Brief Version (UPSA-B)  utilizesThe MATRICS Consensus Cognitive Battery (MCCB)  is a cogBrief Psychiatric Rating Scale, expanded version (BPRS) , is a 24Data were collected from within a larger fMRI study exploring the links between social affiliation and neurological threat response along with social reward processing. Study procedures were approved by the University of Maryland School of Medicine Institutional Review Board. Participants completed a standardized informed consent process with trained recruiters and signed an informed consent document. A brief questionnaire was administered by trained study staff to verify that participants were competent to provide consent and understood the consent document. After the consent process, participants completed in-person clinical interviews and self-report paper-and-pencil questionnaires related to diagnoses, cognitive functioning, social affiliation, and social functioning. Participants were also compensated for their participation.Descriptive statistics for the sleep and social functioning measures are presented in Correlations between sleep variables and social variables are presented in = −.22, p < .05). Finally, greater sleep disturbance and sleep-related impairment were related to poorer social competence as measured by ratings of communication skills  but not financial skills on the UPSA.With regard to social functioning, greater sleep disturbance and sleep-related impairment were associated with poorer functioning across all domains with the exception of activities . Turning to social network size, greater sleep disturbance (but not sleep-related impairment) was related to smaller social networks  and after simultaneously controlling for all seven MATRICS subtests . Results of partial correlations are presented in This study sought to examine the contribution of sleep disturbance and sleep-related impairment to a variety of social domains within a transdiagnostic sample including psychotic disorders. Sleep disturbance and sleep-related impairment were assessed with standardized and validated measures. A strength of the study was that social assessments spanned several domains including self-reported perceptions of social relationships, social functioning in the community, social network size, and behavioral assessments of social competence.Regarding perceptions of social relationships, sleep disturbance and sleep-related impairment were related to reports of lower emotional  support as well as lower reports of friendship and greater loneliness. The results are consistent with findings from non-clinical samples indicating that lower social support and greater loneliness are related to lower sleep quality –35 and gGreater sleep disturbance and sleep-related impairment were associated with social dysfunction across a variety of domains including poorer overall functioning and poorer work functioning. Notably, these correlates reflected medium to large effect sizes. Importantly, these associations between sleep disturbance and poorer social functioning persisted even after controlling for symptoms or cognitive functioning . These results suggest that sleep disturbance may have a unique contribution to social impairment in psychosis above and beyond symptomatology and cognitive impairment. Our findings replicate and extend those of Afonso et al.  indicatiFindings indicated that sleep disturbance and sleep-related impairment were related to behavioral ratings of social competence in the domain of communication (but not finance). Sleep disturbance remained associated with social competency ratings in communication even after controlling for symptoms and cognitive impairment. This is the first demonstration that we are aware of showing that sleep disturbance manifests in behavior with impaired social skill. Given the contribution of social competence to functional impairment , 8, 16, Overall, our results indicate that sleep problems in psychosis are associated with a variety of social problems involving negative social perceptions, poor functioning in the community, and diminished social skill. These results fit with prior work in non-clinical populations indicating the important connections between sleep and social processes , 28. TheThe present findings suggest several paths for future research. An examination of how sleep problems impact social functioning is needed to better understand the mechanisms underlying this association. Prior research suggests that sleep insufficiency may lead to behavioral alterations including a lower ratio of positive to negative affect, lower empathic accuracy, and less conflict resolution . Sleep lBeyond clinical, self-report and behavioral assessments, future research should explore neural mechanisms that might underlie the connection between sleep and social dysfunction in psychosis. Simon and Walker  found thFinally, the current results may have implications for developing interventions to improve social functioning in psychosis. Our findings suggest that sleep disturbance may contribute to social impairment beyond overall symptoms and cognitive impairment. Should this finding be replicated, such an association might indicate that targeting sleep disturbance could enhance functional outcomes in psychotic disorders. Prior research has indicated that sleep interventions can positively impact social functioning in non-psychiatric individuals e.g., –42]. Giv. Giv42)]Several limitations constrain interpretation of the present findings. First, as noted previously, this is a cross-sectional study and we are not able to establish if sleep disturbance and related impairment are contributing to social problems or if social difficulties give rise to sleep problems. Second, while we adopted an RDoC approach –51 to exIn summary, consistent with our hypotheses, results indicated that sleep disturbance and sleep-related impairment have widespread deleterious impacts on perceptions of social relationships, social functioning, and social competence or behavioral skill. Sleep disturbance retained its associations with perceptions of social relationships, social functioning, and social competence even after controlling for total symptoms or cognitive functioning. These findings have implications for understanding the profound social impairment that occurs in psychosis and suggest novel approaches to treating social dysfunction in these disorders.The datasets generated for this study can be found in the NIMH Data Archive (NDA study account in process).The studies involving human participants were reviewed and approved by Institutional Review Board, University of Maryland, Baltimore. The patients/participants provided their written informed consent to participate in this study.JB and MB developed the study design. RO, AJ, and CS assisted with data collection. JB analyzed the data. All authors contributed to writing the manuscript.This work was supported by the National Institutes of Health grant (R01MH110462).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.


Sarcopenia – the accelerated age-related loss of muscle mass and function – is an under-diagnosed condition, and is central to deteriorating mobility, disability and frailty in older age. There is a lack of treatment options for older adults at risk of sarcopenia. Although sarcopenia's pathogenesis is multifactorial, its major phenotypes – muscle mass and muscle strength – are highly heritable. Several genome-wide association studies of muscle-related traits were published recently, providing dozens of candidate genes, many with unknown function. Therefore, animal models are required not only to identify causal mechanisms, but also to clarify the underlying biology and translate this knowledge into new interventions. Over the past several decades, small teleost fishes had emerged as powerful systems for modeling the genetics of human diseases. Owing to their amenability to rapid genetic intervention and the large number of conserved genetic and physiological features, small teleosts – such as zebrafish, medaka and killifish – have become indispensable for skeletal muscle genomic studies. The goal of this Review is to summarize evidence supporting the utility of small fish models for accelerating our understanding of human skeletal muscle in health and disease. We do this by providing a basic foundation of the (zebra)fish skeletal muscle morphology and physiology, and evidence of muscle-related gene homology. We also outline challenges in interpreting zebrafish mutant phenotypes and in translating them to human disease. Finally, we conclude with recommendations on future directions to leverage the large body of tools developed in small fish for the needs of genomic exploration in sarcopenia. Summary: Zebrafish and other small fish have become powerful disease models. Here, we summarize the evidence for the utility of small teleost models for genetic research in sarcopenia – the age-related loss of muscle mass and function. Muscle health is an essential element of healthy life and successful aging. Skeletal muscle represents the largest organ system, comprising 35-50% of the body mass . SarcopeNotably, the degree of muscle loss and decline in physical performance in older people is highly variable and seems to be regulated by intrinsic factors. Muscle loss etiology is likely to be due to tissue-level molecular perturbations in key biological pathways. Understanding these perturbations is essential for developing efficient approaches to more effectively detect, prevent and treat the loss of muscle mass and performance.Traditionally, muscle mass and muscle strength (force) were key indicators for a diagnosis of sarcopenia. Recently, metrics of poor physical functioning were added for defining sarcopenia . A sarcoWith all the progress in the disease definition, its clear framing and operationalization remain challenging; only recently (2016) was sarcopenia recognized as a nosologic entity . FurtherAlthough also involving atrophy, seen particularly at the cellular level, cachexia  usually There are also debates as to whether sarcopenia is a reflection of normal aging or is indeed a stand-alone nosologic entity. Importantly, from the point of view of genomic exploration, there is no difference; the ability to accurately phenotype larger human cohorts is a key for success of genomic studies. When studying sarcopenia, both muscle mass and muscle strength are measurable exophenotypes , while eGiven that the cellular and biochemical factors (the aforementioned endophenotypes) affecting muscle structure and composition in aging are still uncertain, measuring or imaging body composition offers a rough approximation. The two standard techniques available for body composition parameter quantification are dual X-ray absorptiometry (DXA), which estimates fat-free (lean) and fat mass , and bioFatty infiltration and replacement of fast with slow muscle fiber types, together with fibrosis, are probably the most obvious histological phenotypes one needs to consider to define muscle pathology. Yet, objective methods such as biopsy are invasive and thus problematic in large samples of humans, especially those unaffected by muscle loss, and the histological and immunohistochemical parameters for sarcopenia are not standardized . Also, g3) [termed D3-Cr dilution] determined by D3-Cr enrichment in urine, is receiving increasing attention for its potential application to quantify muscle mass  production (ATPmax) in vivo was proposed. It is based on the idea that diminished mitochondrial function may contribute to higher levels of fatigability in older adults .Fast muscle: also referred to as white muscle. Muscle fibers for which functions are fueled by glycolytic metabolism.Linkage disequilibrium: non-random association of polymorphic alleles at different loci in a given population, whereby haplotypes do not occur at the expected frequencies.Muscular dystrophy: a muscle weakness caused by the breakdown of muscle fibers.Myopathy: lack of essential muscle developmental components or a delay of muscle repair after damage.Myotome: a hub for muscle fibers, controlled by a single nerve root.Ohnologs: gene duplicates originating from whole genome duplication (as distinguished from orthologs).Orthologs: genes in different species descending from a common ancestor and encoding proteins with the same function.Slow muscle: also referred to as red muscle. Muscle fibers for which functions are fueled by oxidative metabolism.Exophenotypes measuring various facets of sarcopenia have a strong genetic component, with heritability estimates above 50% . The herAs soon as heritability is ascertained, genome-wide association studies (GWAS) of sarcopenia become justified. In GWAS, millions of variants [usually single nucleotide polymorphisms (SNPs)] are genotyped in thousands of individuals who are also measured for muscle phenotypes. For each SNP, a test is performed to determine whether a phenotype (dichotomous or quantitative) is different in individuals carrying different variants. Large-scale GWAS consortia continue to discover muscle-related loci through amassing data from immense cohorts of consistently phenotyped people. For example, international consortia have published the largest GWAS meta-analyses of lean mass  and handSNPs in close genomic proximity tend to be genetically linked, a phenomenon called linkage disequilibrium . Thus, SThe structure and function of skeletal muscles can be regulated by multiple mechanisms, including anatomical , physiological  or even behavioral . There are known molecular pathways that contribute to muscle mass, including the growth hormone/IGF-1 axis, WNT signaling, sex steroids and the TNF-α /NF-κB  pathway  robustly associated with either total and/or appendicular lean mass , vascular and nervous tissue. Additionally, the biochemical composition of skeletal muscle mostly constitutes of a different proportion of organic and inorganic substances, such as 75% water, 20% protein, 1-10% fat and 1% glycogen. Likewise, 90% of muscle material is occupied by fast (white), slow (red) and intermediate (pink) muscle fibers and the remaining 10% is intramuscular connective tissue. It is clear that the optimal concentration of each chemical, biological and biochemical component of muscle tissue is essential to provide muscle fiber organization and maintain its functional stability throughout an organism's lifetime .Intramuscular fat is observed in humans and small fish models alike. This intramuscular fat is mainly made up of saturated, unsaturated and storage lipids. Studies are suggesting that muscle-fiber-type-specific lipid metabolism could be an emerging research topic, aiming to investigate muscle structural and morphological information for developing new drug target molecules for treating muscle disorders like cachexia and sarcopenia .Genetic, epigenetic and environmental factors significantly alter structural, biochemical and functional properties, indicating that accumulation of intramuscular fat may cause damage and dysfunction of a muscle network system . NeverthMuscle damage is caused both by biological factors, such as elevated inflammation markers, cytokines (e.g. IL-10 and TNF-α), and by physical factors, such as trauma and injury to the muscle tissue. In the muscle regeneration cascade, pro-inflammatory markers play a critical role in controlling active immune response to maintain routine muscle regeneration functions. Similarly, many reports are suggesting that alteration of metabolic regulatory factors associated with lipid metabolism promotes muscle injury, but the mechanism of muscle healing/regeneration remains to be elucidated .Muscle regeneration is mainly carried out by its tissue-specific resident stem cells. Hence, the balance between muscle stem cells (MuSCs) and other MuSC-related cofactors is essential to activate muscle regeneration. MuSCs dynamics are largely controlled by many intrinsic and extrinsic factors. To date, it is unclear whether impaired muscle regeneration and loss of muscle-fiber-type-specific myogenic markers, like Pax3 and Pax7, would lead to sarcopenia. Another unknown is whether the inactivation of muscle quiescent cells at muscle-tendon junctions, which causes a delay in muscle fascicle fusion for regeneration, is due to aging or to accumulating lipids .de novo immature fibers. This in vivo finding – confirming the initial in vitro only results – resolved a long-term debate surrounding the existence of this stem cell self-renewal and muscle repair mechanism in vivo. Improved understanding of this mechanism could help identify therapeutic windows for muscle-wasting conditions such as sarcopenia.Live imaging studies in zebrafish e.g.  showed tHuman and zebrafish muscle-related genes have broad functional conservation in muscle growth and development, implying that zebrafish is an excellent model to study human diseases such as myopathies and muscular dystrophies . To studsapje zebrafish strain carries a mutation in the dystrophin (dmd) gene that is responsible for altered protein-coding sequences with impaired cellular functions, thus causing progressive muscle wasting and loss of muscle integrity, similar to the phenotype of human DMD mutations in Duchenne muscular dystrophy . Consequently, zebrafish mybpc1 knockdown demonstrated abnormal developmental phenotypes such as a decrease in embryo hatching, impaired motor function and reduced number of sarcomeres (We summarize notable zebrafish studies in ystrophy . Similarrcomeres .Table 1.NEB) gene causes muscle weakness and decreased motor functions. The neb zebrafish genetic model that harbors the same mutation manifests similar histological features to humans, including decreased force generation and alterations in the muscle filament organization (COL6A1ama605003) causes Bethlem myopathy . Myostatin is a negative regulator of skeletal muscle growth and development and the most explored member of the TGF-β  superfamily , muscle wasting, motor function, muscle fiber development and sarcomere organization. All these phenotypes are usually assessed at embryonic and larval stages. It would be imperative to assess similar muscle phenotypes in adult fish to better understand sarcopenia . Moreovehttps://www.ncbi.nlm.nih.gov/assembly/GCF_000002035.6/), more than any previously sequenced vertebrate, probably as a consequence of whole-genome duplication (WGD). Several rounds of WGD have occurred early in the evolution of vertebrates, although there has been ongoing debate on the timing of these events and how many occurred (TNNC1) is expressed in both cardiac and skeletal muscle and is associated with certain forms of familial cardiomyopathy   . This tal muscle . Such seAlthough teleosts diverged from lineages leading to humans more than 400 million years ago, a comparison of the zebrafish and human protein-coding genes reveals high genetic similarity, with over 70% of human genes having at least one functional zebrafish ortholog. Furthermore, 82% of all potential disease-related genes listed in the Online Mendelian Inheritance in Man (OMIM) database have at least one ortholog in zebrafish , enablinDespite the teleost genome duplication, zebrafish chromosomes are mosaically orthologous to several human chromosomes . The conAlthough protein-coding DNA is highly conserved among vertebrates, it covers only ∼1.5% of the human genome, while the rest is non-coding. The massive amount of data produced in recent years have confirmed that the majority of the evolutionarily constrained non-coding DNA (∼8% of the human genome) serves as protein-binding sites . These cVertebrate genomes contain numerous non-coding RNA genes (ncRNAs), broadly classified according to the size of their mature transcripts as either small  or long . Unlike the low retention rate for duplicated protein-coding genes, zebrafish microRNA (miRNA) ohnologs were conserved at a considerably higher rate of 39% , consistOverall, improved understanding of both the coding and non-coding genome in teleosts will help researchers to generate relevant models for functional assessment of many GWAS-identified variants, particularly those in the non-coding genome, associated with complex conditions, such as sarcopenia.Sarcopenia is an aging disease characterized both by loss of muscle mass and altered muscle function. Notably, the major sarcopenic phenotypes – muscle mass and muscle strength – are highly heritable. Nevertheless, our knowledge of genetic pathways responsible for regulating muscle-related traits is still far from complete. Owing to the resolution of GWAS, the causative gene at a particular locus often remains unidentified. Thus, to fully harness the power of this genetic approach, the community will need to embrace extensive and often costly functional testing. As of now, many studies are focusing on understanding the mechanisms related to muscle pathophysiology using rodent models. Yet, many researchers have developed reliable zebrafish models to understand muscle degeneration and its pathophysiology .Fig. 2.Despite some anatomical differences, zebrafish muscle fibers are both functionally and ultrastructurally very similar to their amniote counterparts. Phenotypes of teleost muscle structure are well understood; muscle strength less so. Investigating abnormal myogenesis and its molecular mechanisms may help understand why synthesis of new muscle fibers, which is indispensable for (re)building healthy muscle tissue, is delayed in muscle-wasting conditions.In this Review, we have outlined the challenges in interpreting zebrafish mutant phenotypes and translating these findings to human skeletal muscle disease. Other small fishes, like medaka and killifish, are also indispensable for skeletal muscle genomic studies, mostly due to the economy of scale achieved by raising a large number of genetically identical organisms. This knowledge is also important for aquaculture, where genetic and environmental factors can be modulated to achieve fish production efficiency. We thus believe that this Review article provides directions to leverage the large body of tools developed in small teleost for the needs of genomic exploration in sarcopenia and other age-related muscle conditions. Ultimately, the established zebrafish genetic models would provide added information for pharmacological intervention, and for implementing effective treatment for muscle disorders such as cachexia and sarcopenia.


There is increasing evidence of neurological involvement in central and peripheral nervous systems in COVID-19 patients –4 . Neuroimaging involvement of thalamus, basal ganglia and substantia nigra as well as positive CSF viral markers are common findings seen in viral parkinsonism . Given tInterestingly, these reports –4 have nViral parkinsonism or postencephalitic parkinsonism, though rare, has been well recognized for decades and can be caused by a variety of viruses . EncephaIn summary, while the reports of PD following COVID-19 infection are intriguing, more concrete data are needed to support their causal link and the purported direct invasion of COVID-19 into the basal ganglia in the absence of any clinical or neuroimaging evidence of encephalitis.