Our final sample included 24 studies that investigated changes in emotional reactivity in response to negatively and/or positively valenced material compared to neutral material over a period of sleep compared to a matched period of waking. Primary analyses used random effects modeling to investigate whether sleep preferentially modulates reactivity in response to emotional stimuli; secondary analyses examined potential moderators of the effect. Results showed that sleep  did not significantly affect psychophysiological measures of reactivity to negative or neutral stimuli. However, self-reported arousal ratings of negative stimuli were significantly increased post-sleep but not post-waking. Sub-group analyses indicated that (a) sleep-deprived participants, compared to those who slept or who experienced daytime waking, reacted more strongly and negatively in response to positive stimuli; (b) nap-exposed participants, compared to those who remained awake or who slept a full night, rated negative pictures less negatively; and (c) participants who did not obtain substantial REM sleep, compared to those who did and those exposed to waking conditions, had attenuated reactivity to neutral stimuli. We conclude that sleep may affect emotional reactivity, but that studies need more consistency in methodology, commitment to collecting both psychophysiological and self-report measures, and should report REM sleep parameters. Using these methodological principles would promote a better understanding of under which conditions particular effects are observed.Many studies have investigated whether sleep affects cognitively unmodulated reactivity to emotional stimuli. These studies operationalize emotion regulation by using subjective and/or objective measures to compare pre- and post-sleep reactivity to the same emotional stimuli. Findings have been inconsistent: some show that sleep attenuates emotional reactivity, whereas others report enhanced or maintained reactivity. Across-study methodological differences may account for discrepant findings. To resolve the questions of whether sleep leads to the attenuation, enhancement, or maintenance of emotional reactivity, and under which experimental conditions particular effects are observed, we undertook a synthesized narrative and meta-analytic approach. We searched PubMed, PsycINFO, PsycARTICLES, Web of Science, and Cochrane Library databases for relevant articles, using search terms determined  Numerous investigations have examined the role of sleep in emotion regulation . Among these investigations are studies of pre- to post-sleep changes in mood, in cognitively modulated emotion regulation , and in spontaneous reactivity to emotional stimuli . The typical paradigm in this literature operationalizes emotional change by comparing pre- and post-sleep reactivity to the same set of emotional stimuli. This reactivity may be measured using either subjective or objective measures, with the latter including psychophysiological outcomes such as skin conductance levels and heart rate-associated variables .The outcome in these studies is the degree to which participants react differently to the same stimuli after a period of sleep as compared to an equivalent period of waking. An attenuated response (rather than an enhanced or maintained response) is considered more adaptive because in healthy individuals repeated exposure to emotional  stimuli during waking hours and in an unthreatening environment is associated with increasingly attenuated responses to the material  stages  whether sleep leads to attenuation, enhancement, or maintenance of emotional reactivity, and (b) under which experimental conditions particular directions of results are observed. We conducted a systematic review with a narrative synthesis and meta-analysis, reviewing 24 studies that reported emotional reactivity for negatively and positively valenced material compared to neutral material over any period of sleep (whole night or nap) compared to a matched period of waking or sleep deprivation . After a series of primary analyses assessing the general question of whether sleep preferentially modulates emotional reactivity in response to emotional stimuli, secondary analyses examined potential moderators of the effect.https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=271030.The study protocol was submitted and approved for registration on PROSPERO: sleep AND emotion OR affect) NOT ]. These terms were determined a priori, and the search was limited to articles published in English between January 1st 2006 and June 2nd 2021. The search process continued until July 2021. A total of 15,419 articles were retrieved.Two authors  searched PubMed, PsycINFO, PsycARTICLES, Web of Science, and Cochrane Library databases using these terms: ; whether or not IAPS stimuli were used; total number of participants enrolled in the study; total number of participants who completed the protocol; sample age  for each stimulus category , at each of the pre- and post-condition measurement points, for each of the sleep and comparison conditions. Physiological variables included heart rate deceleration (HRD), skin conductance response (SCR) or skin conductance level (SCL), pre-ejection period (PEP), and late positive potential (LPP) of the electroencephalogram. Self-report variables included valence and arousal ratings in response to the presented stimuli.For the 14 studies included in the meta-analysis, we also extracted physiological and/or self-report emotion regulation outcome data  clear definition of the study sample; (b) clear stipulation of study eligibility criteria and clear demonstration of how these were enforced; (c) clear definition of the sampling strategy/ies; (d) matching of groups/conditions on sociodemographic and/or other characteristics; (e) control of confounds  for each sleep and comparison condition; (f) the quality and validity of outcome measures; (g) number of participants completing the study protocols >80%; (h) reporting on and accounting for missing data; (i) reporting of all study parameters; (j) use of WebPlotDigitizer to extract data; (k) statistical adjustment of results for confounders; and (1) other potential sources of bias.All extracted data were entered into a Microsoft Excel spreadsheet, where they were cleaned and prepared for further analysis.n = 10) as well as those that were included in the meta-analysis (n = 14).Because not all study characteristics and outcomes can be described adequately in accompanying tables, we include a narrative account of those articles whose study descriptions precluded incorporation of data into the meta-analysis  is described by values that are larger and positive. In contrast, for SCR larger positive values represent greater emotional reactivity and, therefore, attenuation over the sleep or waking interval is described by values that are larger and negative. We completed a similar process of study-by-study evaluation for self-report data, which also varied depending on the stimulus used  and the subsequent method of emotion measurement related to that stimulus .d standardized effect size.Due to anticipated between-study heterogeneity, we pooled studies appropriate for meta-analysis using a generic inverse variance random effects model. Because different scales were reported across different studies, we report the meta-analysis of standardized mean differences, with associated 95% confidence intervals. The standardized mean difference can be interpreted in a similar manner to a Cohen's Because the studies included in our review sample reported a mix of separate baseline and follow-up measures and collapsed change from baseline to follow-up scores, it was not possible to include all types of outcomes in a standardized mean differences meta-analysis. So, to maximize the data available for analysis, we used change scores only after (where necessary) converting separate baseline and follow-up values using procedures set out in the Cochrane Handbook  over a period of sleep as compared to over a similar period of waking? Hence, at this stage we calculated the standardized mean change (SMC), in terms of emotional reactivity, for (a) valenced material over a period of sleep vs. over a comparison period , and then (b) neutral material over a period of sleep vs. over a comparison period. We ran separate analyses for the psychophysiological  outcomes and the self-report  outcomes. In cases where it appeared there was indeed preferential regulation of emotionally valenced material, we then compared the SMC from the valenced condition(s) in the sleep-vs.-comparison analysis to the SMC from the neutral condition in the sleep-vs.-comparison analysis. The methods used for the calculations were as set out in Morris  and SchäFunnel plots were produced to examine evidence of publication bias in all analytic comparisons that included 10 or more studies. Egger's test was carried out to test for small study effects.We used the meta command in Stata version 17 for all analyses.M and SD/SEM values. The paragraphs below provide more detail regarding our risk-of-bias ratings.Most of the 24 studies in our review sample were rated as having low or unclear risk of bias on most rated dimensions see , 3. As tdefinition of the study sample, we considered all studies to be of low or unclear risk of bias. Although all studies defined their samples adequately, those rated as unclear on this dimension provided few details regarding recruitment.Regarding stipulation of study eligibility criteria and demonstration of how these were enforced, again we considered all studies to be of low or unclear risk of bias. Studies rated unclear on this dimension usually provided a list of exclusion criteria but gave little information on how participants were screened against these criteria or how their exclusion was ensured.Regarding definition of the sampling strategy, again we considered all studies to be of low or unclear risk of bias. Studies rated unclear on this dimension were those that provided insufficient detail to allow clear judgment on how the sampling proceeded.Regarding matching of study groups/conditions on sociodemographic and/or other characteristics, most studies provided a description of these matching processes and of an assessment of their success. Hence, they were rated as being at low risk of bias on this dimension. The remaining studies were rated as unclear.Regarding methodological attempts to control for potential confounding factors, three studies were rated as being at high risk of bias. Ashton et al.  We decided to rate as unclear those studies that made no report, and as low risk those that made distinct statements indicating that more than 80% of enrolled participants had completed the study protocols. Ashton et al.  or did not describe the methods used (and were therefore judged to have an unclear risk of bias).Regarding other potential sources of bias, all studies either provided information on funding and potential conflicts of interest  or did not report this information (and were therefore judged to have an unclear risk of bias).Regarding all studies included in the review .Here, we provide a narrative account of Nine studies reported psychophysiological outcomes related to valenced material in comparison to neutral material after a period of sleep or waking. Two of those studies  rather than waking  evaluated self-reported valence ratings in response to emotional and neutral stimuli. Only three of those studies showed less negative reactivity in response to negative rather than neutral stimuli after a period of either overnight sleep or a daytime nap  evaluated self-reported arousal ratings in response to emotional and neutral stimuli. Of these studies, two reported decreased arousal after a period of overnight sleep  measured psychophysiological outcomes. Overall, this group of studies indicated that neither sleep nor equivalent periods of wakefulness had a statistically significant effect on emotional reactivity to either negatively valenced or neutral stimuli.Five studies , perhaps due to the small sample size  and no statistically significant difference  and no statistically significant difference  to negative, neutral, or positive material over a period of sleep compared to changes over an equivalent comparison period. The three separate analyses (one for each stimulus type) all detected small-to-moderate effects sizes that were not statistically significant.N = 297) evaluated changes in reactivity to negatively valenced material over a period of sleep compared to changes over an equivalent comparison period. Analysis of those data detected a very small effect size of −0.07  that was not statistically significant  evaluated changes in reactivity to neutral material over a period of sleep compared to changes over an equivalent comparison period. Analysis of those data detected a moderate effect size of 0.44  that was not statistically significant  evaluated changes in reactivity to positively valenced material over a period of sleep compared to changes over an equivalent comparison period. Analyses of those data detected a small effect size of 0.11  that was not statistically significant  in reactivity to negative, neutral, or positive material over a period of sleep compared to changes over an equivalent comparison period. Only analyses of data comprising negative material detected a statistically significant effect; analyses of positive and neutral data detected small and non-significant effects.N = 169) evaluated changes in reactivity to negatively valenced material over a period of sleep compared to changes over an equivalent comparison period. Analysis of those data detected a modest effect size of −0.30  that was statistically significant  evaluated changes in reactivity to neutral material over a period of sleep compared to changes over an equivalent comparison period. Analysis of those data detected a small effect size of −0.18  that was not statistically significant  evaluated changes in reactivity to positively valenced material over a period of sleep compared to changes over an equivalent comparison period. Analysis of those data detected a small effect size of −0.18  that was not statistically significant  contributed data to this analysis, which detected a moderate effect size of 0.65  that was statistically significant , type of waking control (sleep deprivation vs. ordinary daytime waking), sleep duration (whole night vs. nap), kind of emotional stimuli (IAPS vs. other) and whether participants obtained REM sleep  influenced self-reported valence and arousal outcomes.Regarding psychophysiological outcomes, there was too little variation in these moderator variables between studies for us to conduct sub-group analyses. For example, Pace-Schott et al.  was the Regarding self-reported valence and arousal outcomes, analyses detected statistically significant subgroup effects for valence outcomes only. Three moderators had an influence on valence ratings: the kind of comparison condition used (daytime waking vs. sleep deprivation), the duration of sleep (whole night vs. nap), and whether participants experienced REM sleep or not  and neutral (p = 0.21) comparisons.Only the negative and neutral valence comparisons had a sufficient number of included studies to explore evidence for possible publication bias. The funnel plots see , 24 suggPreviously published studies investigating whether sleep regulates spontaneous emotional regulation have produced contradictory results: it is unclear whether a period of sleep experienced subsequent to encounters with emotionally valenced stimuli will, upon further exposure to those stimuli, attenuate, enhance, or simply maintain reactivity to them. Hence, we set out to systematically review and meta-analyze published work investigating whether sleep, without cognitive modulation, acts to regulate emotion. We reviewed studies that reported emotional reactivity for negatively and/or positively valenced material compared to neutral material over any period of sleep (whole night or nap) compared to a matched period of waking or sleep deprivation . Given the broader literature indicating that sleep restriction impacts mood negatively, we hypothesized that our review would show that sleep preferentially down-regulates or ameliorates reactivity to emotional stimuli over neutral stimuli, and that this down-regulation or amelioration is greater than what is observed over equivalent periods of waking.Broadly speaking, our results did not confirm this prediction. Sleep  did not have a statistically significant effect on physiological measures of emotional reactivity to either negatively valenced or neutral stimuli. However, sleep did have a statistically significant effect on self-report measures of emotional reactivity, albeit not always in the predicted direction. Specifically, arousal ratings in response to negatively valenced stimuli  were significantly higher after a period of sleep but not after an equivalent period of waking.Sub-group analyses of data regarding self-reported valence and arousal outcomes indicated that several important moderators influenced differences in emotional reactivity between sleep and waking conditions. First, participants who were sleep deprived in comparison to those who slept a full night rated positive stimuli more negatively, but no such between-condition difference was significant when the comparison condition was a period of daytime wakefulness. Second, participants in nap conditions in comparison to those who experienced an equivalent period of waking rated negative pictures less negatively. Participants showed a slight but inconsistent increase in reactivity after a whole night of sleep, in comparison with waking conditions. Third, participants who did not spend a significant amount of time in REM sleep tended to provide attenuated valence ratings in response to neutral stimuli after a period of sleep, rather than waking. This effect was not observed in those who did achieve REM sleep.Our finding that sleep did not have a statistically significant effect on changes in psychophysiological reactivity to negatively valenced or neutral stimuli is consistent with results reported previously in this literature. Several single empirical studies have shown that HRD or SCL responses to valenced and neutral material were not significantly different before and after a period of sleep  does appear relatively more sensitive to sleep-associated changes in emotional reactivity, effects are not strong or consistent and it remains unclear whether they are specific to valenced material.Our finding of the trend toward post-sleep enhanced HRD in response to negative material is mirrored by our findings from the data regarding self-reported arousal ratings. Our analyses indicated that, for negative material to a significantly greater extent than neutral material, these ratings were also higher after a period of sleep compared to a period of waking. Overall, these results suggest that self-reported arousal is a sensitive measure of changes in emotional reactivity across periods of sleep.This finding is consistent with some results reported previously in the literature. For example, Kuriyama et al.  and AlfaThis common trend across the psychophysiological and self-report data  may reflect a type of environmental adaptation. In healthy adults, enhanced arousal after sleep may contribute to an emotional “next-day readiness” that allows the person an increased sensitivity to emotional stimuli in the environment. Future studies could examine sleep-dependent emotional processing in the context of the cortisol awakening response, which is a biological measure of next-day readiness , the duration of sleep (nap or whole night), and the presence/absence of REM sleep all had an impact on self-reported valence ratings.Regarding the use of daytime waking or sleep deprivation as a comparison to the sleep condition, ideally we would have sought to examine the effects of each kind of comparison condition separately – sleep loss may have different effects on emotion regulation than the passage of ordinary wakefulness  tended to decrease valence ratings in response to these stimuli, a full night of sleep  slightly increased valence ratings. These results suggest that responses to previously encountered negative stimuli are modulated differently by a daytime nap and a full night of sleep. One possible mechanism underlying this effect may be the differing proportions of NREM and REM sleep or spindle quality during napping and whole-night sleep  did obtain substantial REM sleep, or (b) were exposed to sleep deprivation or ordinary wakefulness. This result may point to differential effects of REM and NREM sleep stages on sleep-dependent emotional processing, with NREM sleep possibly more responsible for processing neutral information and down-regulating responses to such stimuli.Perhaps the most consequential limitation of the foundational literature, certainly in terms of its influence on our review, is the vast cross-study differences in methodology. These methodological variations include the timing and duration of the sleep condition, the type of waking control used, the kind of emotional stimulus presented, the primary outcome measured, and whether participants obtained REM sleep  or ordinary daytime waking, but because all the psychophysiological studies included in our sample used a waking control group we could not investigate further. Other potential moderators are sleep duration and whether participants in the sleep condition obtained REM sleep, but again only one psychophysiological study in our sample used a nap rather than a whole night condition and only one presented REM-specific data. Given this lack of sub-group analyses, we cannot comment fully on the question of under which specific experimental conditions particular effects might be observed.A third potential limitation is that, in most studies included in our review sample , participants viewed many IAPS pictures. Hence, after a certain point they may have become desensitized to the valenced material and might have begun to evince similar reactions to those images as to neutral images. This consequence of desensitization might explain why the analyses did not consistently detect differences in emotional reactivity to valenced vs. neutral material.A fourth potential limitation is that not all studies in our sample controlled for time-of-day effects. These influence performance on a variety of cognitive tasks, including emotion regulation  does not have a statistically significant effect on psychophysiological measures of emotional reactivity, to either valenced or neutral stimuli. However, self-reported arousal ratings in response to negatively valenced stimuli  were significantly higher after a period of sleep but not after an equivalent period of waking. Sub-group analyses suggested that the kind of waking comparison condition (regular daytime waking or nighttime sleep deprivation), sleep duration (nap or whole night), and the presence/absence of REM sleep all had an impact on self-reported valence ratings.Taken together, these results suggest that sleep may have a larger effect on subjective emotional experience than on objectively measured physiological experiences of emotion. In other words, sleep might impact more on responses that are subject to cognitive control than on those that are generated automatically. A caution here is that this speculation is influenced by the fact that a relatively small number of studies in this literature have taken psychophysiological measures.More consistency in study methodology is needed before the field can gain a better understanding of how sleep impacts reactivity to emotionally valenced information. Future research studies should endeavor to collect both psychophysiological (with an emphasis on HRD) and self-report measures, to report and collect REM sleep parameters, to report all different valence- and condition-specific data independently rather than in aggregate form and control for circadian and time-of-day effects.A number of other important questions remain unanswered in the field. First, underlying mechanisms explaining sleep-dependent modulation of emotional reactivity remain poorly understood. These mechanisms may be numerous and anatomically widespread. For example, sleep-dependent memory consolidation is governed by reactivation, neocortical-thalamic- hippocampal dialogue, and synaptic downscaling  Competitive Support for Unrated Research grant awarded to GL (grant number NRF116229).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Infective endocarditis (IE) is a common cause of embolic strokes. Early diagnosis and treatment are essential to decrease the risk of ischemic stroke and ensure appropriate treatment, particularly given the higher risk of hemorrhagic complications. Treatment is also essential to prevent other complications such as heart failure, perivalvular abscesses, or intracranial abscesses. Transthoracic echocardiogram (TTE) and transesophageal echocardiogram (TEE) may not show endocardial involvement in the early stages of IE and, rarely, some patients may lack systemic signs of infection, making the early detection of IE challenging. Multifocal ischemic strokes may be the initial manifestation of IE and warrant further workup directed at IE even if initial echocardiogram findings are negative. A high index of clinical suspicion and thorough history are of utmost importance. Infective endocarditis (IE) is a common cause of embolic strokes and can affect the acute management of ischemic stroke, as the use of thrombolytics in embolic stroke secondary to infective endocarditis is associated with a higher risk of hemorrhagic complications and lower rates of favorable outcomes . Prompt A 62-year-old female with a past medical history significant for coronary artery disease, aortic regurgitation status post replacement with bioprosthetic aortic valve , and a recent admission for multifocal ischemic strokes with residual left arm weakness was readmitted to the Neurology service with new-onset, right upper extremity numbness and worsening left upper extremity weakness. On examination, she was noted to have left-sided dysmetria, left arm weakness, and decreased peripheral vision in the left eye.She was originally admitted to the Neurology service approximately four weeks prior to this presentation with acute-onset left arm weakness and was found to have multifocal supratentorial and infratentorial infarcts. No dysmetria or visual deficits were noted at that time. Transthoracic echocardiogram (TTE) and transesophageal echocardiogram (TEE) at that time did not show evidence of vegetation or intracardiac thrombus Figure . Compute9/L with a left shift. The electrocardiogram showed a new, first-degree atrioventricular block that was not present on review of telemetry from the previous hospitalization. Magnetic resonance imaging (MRI) head revealed new supratentorial and infratentorial ischemic strokes and hemorrhagic conversion of previously demonstrated right parietal ischemic stroke (Figure On readmission, the patient was noted to have decreased peripheral vision in the left eye, mild weakness in the left upper extremity, and bilateral dysmetria markedly worse on the left side. She was normotensive and afebrile. Labs were remarkable for mild leukocytosis to 13.2 x 10Blood cultures grew Staphylococcus epidermis, prompting the initiation of intravenous antibiotics. Repeat TEE revealed large vegetation involving one of the aortic prosthetic valve leaflets with associated mild aortic regurgitation, as well as vegetation involving the anterior mitral leaflet Figure  and an aRadiographic evidence of endocarditis as demonstrated by transthoracic and transesophageal echocardiogram is one of two major diagnostic criteria of endocarditis . It is wIn our case, we believe that the patient likely had endocarditis on initial presentation despite negative TEE. Multifocal strokes at that time were likely a result of septic embolism given the appearance and distribution of lesions, clinical progression, and lack of an alternative explanation despite extensive workup. It is possible that vegetations were too small to be visualized on the first TEE. Notably, at the time of initial presentation, blood cultures were not obtained as the patient was nontoxic appearing without leukocytosis and had no history of immunocompromise. Interestingly, even on readmission, the patient did not have any clinical signs of IE such as fever, chills, or evidence of embolic phenomena on physical exam. This underlines the importance of pursuing workup for IE with blood cultures and imaging even if systemic signs of infection are absent in patients with multifocal embolic-appearing strokes and risk factors for IE.Infectious endocarditis is a common cause of embolic strokes and should remain on the differential even if initial echocardiogram findings are unremarkable and systemic signs of bacteremia are absent, particularly in those with notable risk factors such as prosthetic valves. Multifocal embolic strokes may be the first clinical manifestation of IE and may necessitate a repeat TEE and blood cultures even if the initial evaluation with an echocardiogram was negative, especially if alternative sources of thromboembolism have been ruled out.


Transarterial chemoembolization (TACE) is a minimally invasive radiological procedure which consists of infusing a chemotherapeutic agent in the main arterial supplier of the liver tumor, usually emulsion-based doxorubicin, followed by the occlusion of the involved vessel with 100–500 micron-sized embolic particles. The direct toxic effect of doxorubicin and the ischemic damage display a dual synergic tumoricidal effect which provokes cell apoptosis and immunogenic changes in the tumor microenvironment, hampering oncogenesis ,3, whichThe therapeutic effect of TACE is assessed one month after the procedure using a liver dynamic imaging technique, mainly magnetic resonance or computed tomography. Patients are then classified according to the Modified Response Evaluation Criteria in Solid Tumors (mRECIST) , which aPredicting radiological response after TACE is of outmost importance as it would allow for individualizing the surveillance strategy. In addition, patients with a priori high risk of incomplete tumor response after TACE could benefit from future strategies including adjuvant systemic agents, which are currently under investigation in randomized controlled trials. There are some clinical characteristics or tumor features associated with poor response to TACE, including number and diameter of the HCC nodules, the Child–Pugh class (standing for liver function), and serum alpha-fetoprotein, either alone or combined in prognostic indexes such as mHAP-II  and SNACIn this Special Issue published in the Journal of Personalized Medicine, A. Granito et al. present the TRANS-TACE study , which iPrevious studies have suggested a relationship between elevated transaminases and worse oncological outcomes in patients with HCC undergoing TACE . HoweverTransient cytolysis after TACE is observed in approximately 30% of patients  and may Future studies with larger sample size and more prolonged surveillance are required to externally validate the findings by Granito et al.  to ascer


The search identified 383 Citations; we screened 220 and excluded 163 . Of the 220 screened articles, we included 35 and disqualified 155 (did not meet the eligibility criteria). We organized eligible articles under two themes: financial benefits/barriers of routine HIV screening in healthcare settings (9 articles); and Cost-effectiveness of routine screening in healthcare settings (26 articles). The review concluded drawing recommendations in three areas: (1) Finance: Incentivize healthcare providers/systems for implementing HIV routine screening and/or separate its reimbursement from bundle payments; (2) Personnel: Encourage nurse-initiated HIV screening programs in primary care settings and educate providers on CDC recommendations; and (3) Approach: Use opt-out approach.The Centers for Disease Control and Prevention recommends everyone between 13–64 years be tested for HIV at least once as a routine procedure. Routine HIV screening is reimbursable by Medicare, Medicaid, expanded Medicaid, and most commercial insurance plans. Yet, scaling-up HIV routine screening remains a challenge. We conducted a scoping review for studies on financial benefits and barriers associated with HIV screening in clinical settings in the U.S. to inform an evidence-based strategy to scale-up routine HIV screening. We searched Ovid MEDLINE In the United States, an estimate of 1.19 million people aged 13 years or older were living with HIV at the end of 2019, of whom approximately 158,500 (13.3%) were undiagnosed  (accessed on 15 October 2022). These recommendations have been proven to be not only successful but also financially feasible as evidenced by a majority of states in the United States have adopted laws to ensure all pregnant women are tested for HIV during the 3rd trimester, during labor and delivery when HIV status is not documented, and of the newborn if the mother’s HIV status remains unknown.This scoping review provides compelling evidence that opt-out routine HIV testing is cost-effective in detecting HIV infections and is accompanied by financial gains. The studies we reviewed consistently conclude that routine HIV screening in healthcare facilities can effectively overcome financial obstacles, resulting in widespread facilitation of care and improved healthcare outcomes. It is worth noting the 2006 CDC revised HIV testing recommendations in health care settings were based on the CDC perinatal HIV testing recommendations to prevent vertical transmission to newborns [We found that the implementation of routine HIV screening is affected by variability in reimbursement structures and provider financial benefits. Reimbursement structures that vary based on insurance status, insurance coverage, the payer, and location pose challenges . VariatiRoutine testing is cost-effective and can be financially attractive with a unified payment structure that is separated from any bundled payments. This will make HIV screening financially beneficial to healthcare systems regardless of the insurance coverage or modality. A new temporary financial mechanism to cover the cost of testing for the uninsured may also enhance the adoption of routine HIV testing in clinical settings. Additionally, routine HIV testing was designated by the USPSTF as a Grade A preventive service in 2013; the addition of the modifier 33 to the CPT code alerts payers that this is a preventive service that is required to be provided to patients and covered at no cost to the patient. It is imperative to emphasize that the main budget impact for HIV is associated with the treatment of people living with HIV and not the testing .Our review found that the opt-out HIV screening approach and nurse-initiated HIV screening programs are more effective than their counterparts ,67,68,69This review presents strong evidence that using an opt-out versus an opt-in approach to HIV testing in clinical settings facilitates the identification of HIV in undiagnosed persons unaware of their HIV status. Routine screening addresses these missed opportunities to diagnose patients and to refer them to HIV medical treatment and other support services to achieve and maintain viral suppression eliminating the potential of new HIV transmissions. Additionally, routine screening eliminates the stigma and bias associated with HIV. If implemented according to the recommendations, screening can be offered to adolescents before they become sexually active with the opportunity to educate them about the risks associated with acquiring HIV and how to prevent or avoid them. Persons with HIV also live longer, and many who were diagnosed during the early days of HIV/AIDS may have fallen out of care. Screening all patients regardless of risk or advanced age, i.e., ages 55 to 75, which was previously identified as controversial , can alsFinancial incentives to patients were found to contribute to the increase in testing acceptability . HoweverAlthough research supports the cost-effectiveness of routine HIV screening in healthcare settings, the lack of knowledge among providers of recommended HIV prevention practices and its cost-effectiveness is a barrier to implementing routine HIV screening programs ,60. RecoOne shortcoming of this review was the lack of clear identification of test technology employed in many of the articles reviewed. When considering the cost-effectiveness of routine HIV screening, it is important to identify the best test technology to implement based on the type of healthcare setting, specifically primary or emergency care. Both POC test kits and lab-based testing in accordance with the CDC/APHL HIV Testing Algorithm are effective tools for diagnosing HIV. When choosing a testing technology, considerations should include the time required of staff to collect the specimen and the time and efficiency to perform, interpret and document the test result. Although POC test kits offer rapid results within one to twenty minutes, they also require hands-on time by staff and may be subject to human error when interpreting and documenting test results. Additionally, a positive POC test must be confirmed with a lab-based conventional blood draw according to the HIV testing algorithm. POC test kits, however, may be more effective in the primary care setting to enable immediate preliminary results delivery, increasing the chance the patient will return for confirmatory results and referral to care.Emergency departments are set up for conventional lab-based testing on random-access analyzers that can produce preliminary results in 30 min to an hour. Cost-effective benefits include that the HIV test can be performed on the same serum specimen submitted for other tests ordered and processed in real-time in the hospital E.D. lab, with expedited results documented automatically in the electronic health record system. Compared to POC test technology, lab-based testing can detect the HIV p24 antigen, which identifies acute or early infection before the presence of antibodies is detected, offering the patient the benefit of access to rapid ART treatment long before the POC test detect HIV.Early oral signs of HIV qualify dental settings to be optimal places for early detection. The adoption of HIV routine screening in dental facilities may significantly expand the coverage of HIV testing . We alsoHIV routine screening is a major step towards ending the HIV endemic in the US, which will save the nation more than twenty-eight billion US Dollars as federal spending on HIV care and treatment per year (based on the estimates of 2022) .We note overall that routine HIV testing is financially feasible and generally cost-saving. Nevertheless, reimbursement varies in structure, and providers often fail to understand the cost implications, discouraging the adoption of routine screening. The effect of undiagnosed HIV on healthcare costs was not included in this review, but there are many anecdotal cases of multiple healthcare visits to primary care and emergency departments due to symptoms related to HIV; diagnosing HIV in these patients provides the opportunity to refer them to HIV medical care and treatment to achieve viral suppression and eliminate many of unnecessary healthcare costs associated with undiagnosed HIV. Implementing routine HIV screening in healthcare settings will go a long way to contributing to the National HIV/AIDS Strategy goals to end the HIV epidemic in the United States by 2030.


Emerging technologies now allow for mass spectrometry-based profiling of thousands of small molecule metabolites (‘metabolomics’) in an increasing number of biosamples. While offering great promise for insight into the pathogenesis of human disease, standard approaches have not yet been established for statistically analyzing increasingly complex, high-dimensional human metabolomics data in relation to clinical phenotypes, including disease outcomes. To determine optimal approaches for analysis, we formally compare traditional and newer statistical learning methods across a range of metabolomics dataset types. In simulated and experimental metabolomics data derived from large population-based human cohorts, we observe that with an increasing number of study subjects, univariate compared to multivariate methods result in an apparently higher false discovery rate as represented by substantial correlation between metabolites directly associated with the outcome and metabolites not associated with the outcome. Although the higher frequency of such associations would not be considered false in the strict statistical sense, it may be considered biologically less informative. In scenarios wherein the number of assayed metabolites increases, as in measures of nontargeted versus targeted metabolomics, multivariate methods performed especially favorably across a range of statistical operating characteristics. In nontargeted metabolomics datasets that included thousands of metabolite measures, sparse multivariate models demonstrated greater selectivity and lower potential for spurious relationships. When the number of metabolites was similar to or exceeded the number of study subjects, as is common with nontargeted metabolomics analysis of relatively small cohorts, sparse multivariate models exhibited the most-robust statistical power with more consistent results. These findings have important implications for metabolomics analysis in human disease. Mass spectrometry-based measurements of small molecule metabolites, also known as metabolomics, has emerged as a powerful tool for phenotyping biochemical variation in health and disease across organisms. Accordingly, there has been a rapidly growing interest in applying metabolomics to clinical studies of human disease traits ,2. RobusInitial clinical studies involving targeted metabolomics approaches have used relatively conservative statistical approaches to analyze up to 200 variables, such as Bonferroni correction of multiple t-tests or the false discovery rate (FDR) . AdditioMetabolomics studies of human samples can vary substantially by sample size, the number of metabolites assayed, and the type and frequency of a clinical outcome of interest, with each of these factors potentially influencing statistical analysis results. To evaluate statistical methods for handling a variety of datasets, metabolomics data were simulated for clinical studies of a varying number of study subjects, number of metabolites, and outcome type (continuous vs. binary). A total of six traditional statistical ), and statistical learning (least absolute shrinkage and selection operator (LASSO), sparse partial least squares (SPLS), and random forest) methods were used to analyze 1000 simulated metabolomics datasets , with eaAn equally important aspect of a statistical procedure is the identification of important metabolites via variable selection or significance testing. Variable selection is not generally possible with PCR and random forest analyses, precluding assessment of these approaches for prioritizing individual metabolites. In either the continuous or binary settings, univariate approaches performed worse as the number of study participants increased . While cAlthough we aggregated over all 10 significant metabolites to calculate measures of method performance, differences in operating characteristics such as power likely correspond with smaller effect sizes. While most methods identify associations with large magnitudes of effect, potentially important discrepancies can become apparent for associations with smaller effect sizes. Thus, we also examined variation in power across a range of effect sizes for different statistical approaches, and these results were concordant with those of aggregated data .We also simulated scenarios that included negative as well as positive between-metabolite correlations in addition to pairs of highly intercorrelated metabolites representing molecular markers putatively derived from the same biological pathway. We observed that the results of these additional simulations were very similar to those produced by the primary simulations and reported herein , suggestAlthough the results reported above put forth a statistical framework for considering analysis of clinical metabolomics based on analyses of simulated data, we sought to compare our findings with those using actual “real world” experimentally derived metabolomics data. For these analyses, we used a nontargeted metabolomics-based panel of 1933 metabolites measured across 2895 individuals (see Methods). We restricted attention to the methods that would easily allow for individual variable  importance selection in the dataset, which precluded random forest and PCR from entering into the analysis. Analyses using the three main statistical approaches  revealed overlap  for onlyWe excluded from the Venn diagram the results from the Bonferroni correction, given it produces a subset of the same metabolites chosen using the less conservative FDR correction. We applied a false discovery rate of 0.1, which suggests that 10% of the metabolites on average should be false discoveries. For both outcomes, use of FDR resulted in a large number of statistically significant results, with >50% of all assayed metabolites  reaching the threshold, suggesting that an FDR correction of 0.1 was in fact detecting nearly all of the signals. The approaches rooted in sparsity, however, obtained solutions with far fewer metabolite “hits”. In both cases, LASSO analysis resulted in far fewer metabolites than FDR correction (206 for sex and 378 for age). By contrast, SPLS provided far fewer metabolite hits than either LASSO or FDR correction (93 for sex and 37 for age). In the case of both age and sex, SPLS did not identify any new metabolites beyond those found in the LASSO or FDR subsets. We found in this study that when implementing cross-validation to estimate tuning parameters of SPLS and SPLSDA, the cross-validation curve is relatively flat, an issue that has been previously encountered . We repeTo provide further context for these results, we used a basic network analysis to visualize correlations between metabolite measures in the Framingham cohort and compared the relative location of metabolites identified in association with age or sex by the univariate and multivariate methods evaluated . We obseThe multiple statistical analysis approaches, when applied to both the simulated and the experimentally derived data, produced relatively comparable results with respect to very large numbers of metabolite markers identified by univariate compared to multivariate methods. Of the multivariate methods evaluated, LASSO appeared to perform slightly better than SPLS across the different types of simulated data structures, and especially those involving larger numbers of metabolites . In compThrough extensive simulation, we investigated the relative merits of traditional statistical and statistical learning approaches for the analysis of human metabolite data. Using a data structure based on real-world metabolite data with varying sample size, metabolite number, and outcome measures, our results offer a framework for considering optimal statistical approaches for a given study. We found that penalized approaches favoring sparsity led to substantially improved inference for a wide range of scenarios. Both LASSO and SPLS (SPLSDA for binary outcomes) provided reasonable results in all simulation scenarios studied, identifying important metabolites without suffering from large numbers of false positives. The only scenario wherein univariate procedures were most reliable was when the sample size was small and the outcome was binary. With a binary outcome, there is relatively little information available to identify associations among a very large number of metabolites; thus, approaches that attempt to model all metabolites at once do not perform as well with smaller sample sizes. Interestingly, we observed the counterintuitive phenomenon that univariate procedures perform worse at identifying significant metabolites as the sample size grows. This appeared to be due to the correlation structure present in the data, which leads to a large number of false positives, and presents a finding with important implications for future analyses of nontargeted metabolomics data.Human metabolomics studies have historically relied on univariate approaches with an FDR procedure, and less frequently a Bonferroni correction procedure, PCA, or PLSDA without penalization, in the absence of any formal evaluation of optimal statistical methods. While such approaches have proved useful in some respects for analyzing metabolite data, our findings indicate that these approaches may suffer major drawbacks in certain situations. Univariate approaches, as discussed above, can lead to misleading results when the data are intercorrelated, as is nearly always the case in metabolomics studies given common biochemical and biological origins. Other approaches such as PCA or PCR do not directly provide measures of statistical significance, although P values can be generated by permutation testing to completement additional ad hoc measures of variable importance. While metabolomics data may offer some unique challenges, including scale in metabolite levels and missingness across a population, as well as biologically driven intercorrelations, related molecular phenomics fields have similarly suggested that newer statistical approaches may be of great value in identifying statistical significance and prioritization of variables for biological follow up ,17,18,19There is, in metabolomics, a strong interest in pathway analyses that might offer some insight regarding the biological mechanisms underlying statistical associations observed between metabolites and a given outcome. These approaches have been established for the vast majority of standard metabolites; for novel metabolites, efforts made towards compound identification can also benefit from pathway mapping, which can offer clues regarding the likely identity of a given unknown molecule . Thus, wIn our study, we found that multivariate approaches that assume some level of sparsity by design  perform with the greatest efficiency for identifying important metabolites. Importantly, this conclusion is based on settings in which the relationship between a metabolite and outcome is linear. In the setting of nonlinearity, it is likely that random forest or other machine learning-based approaches that allow for highly nonlinear relationships may be preferable, although this would require more formal evaluation than provided herein. The value of sparse multivariate analysis may be due to several potential reasons, including the large amount of correlation between metabolites, which requires approaches to examine a metabolite that are conditional to other metabolites. In addition, the fact that many metabolites indeed have little to no association with an outcome of interest favors approaches that enforce sparsity. With these results in mind, we can provide recommendations for future analysis of high dimensional metabolite data. For larger (>1000) samples, multivariate approaches based on sparsity provide a very reasonable strategy to identify important metabolites. In small samples (<200), particularly for binary outcomes, there is no clear-cut ‘best’ method, and the merits of each method depend heavily on the structure of the data. In these cases, utilizing more than one analysis tool in conjunction could help identify key covariates. Importantly, the goal of the study should be taken into account before selecting a statistical approach. If false positives are very undesirable, then we recommend approaches such as LASSO or SPLS that impose sparsity into the model and tend to eliminate presumably less-relevant metabolites.Our findings can be used to guide the design of future studies, particularly those for which investigators may be interested in estimating a minimum amount of statistical power to detect an association of interest. If pre-existing knowledge of the distribution of metabolite values and their correlation structure is available, either through preliminary or previously analyzed data, then simulation studies similar to those described in this manuscript could be performed. Investigators can use an observed correlation structure to simulate a dataset of metabolite measures and then simulate outcomes given a known range of effect sizes, from which power to detect these effects can be estimated for different statistical approaches.There are several limitations of the study that merit consideration. The primary findings were based on simulated data, albeit data constructed based on the known structure of an existing high-dimensional dataset derived from actual values in a human cohort. As such, our results may have been influenced by the nature of the underlying artificially created data structure. For this reason, we conducted parallel analyses in a de novo real-world dataset of metabolomics performed in a community-based cohort, and observed results that were largely consistent with those of the simulated data analyses. The observed substantial difference in performance between traditional statistical and statistical learning methods may well have emerged from the difference between univariate and multivariate methods. Accordingly, investigators have suggested that in situations where intercorrelations among predictor variables are expected, a permutation-based FDR approach to univariate analyses should be considered . The extIn summary, our findings further indicate that statistical learning approaches aimed at modeling a high-dimensional set of metabolites and their associations with a given outcome warrant more attention in the literature. Taken together, our results suggest that metabolomic analyses should shift towards use of multivariate approaches for identifying distinct markers associated with clinical traits. Univariate approaches, while simple to use, will identify large numbers of false positives when the metabolites are highly correlated with each other—a problem ubiquitous in metabolomics research. If interest lies solely in finding large biologic pathways instead of causal markers , then univariate approaches may still be useful.When compared to traditional and frequently employed univariate approaches, statistical learning methods (such as LASSO or SPLS) offer effective and easy to implement options for handling high-dimensional, correlated data of the nature that is commonly seen in metabolomics. In fact, these approaches may well outperform many of the conventionally used methods across a wide variety of scenarios encountered in human metabolomics studies.We developed a series of simulated metabolomics datasets based on characteristic data features seen in both experimental and human studies  using both targeted and nontargeted mass spectrometry. In particular, we designed the datasets to include the range of structural characteristics typically observed in human plasma metabolomics datasets.Structural features with respect to outcomes included: (i) binary outcomes in small studies of up to 200 individuals with an outcome frequency of 50%, representing case-control studies with a 1:1 case/control ratio; (ii) binary outcomes in large studies with an outcome frequency of 20% in cohorts with >200 individuals, representing larger observational cohort studies; (iii) continuous outcomes measured in all patients. Structural features with respect to exposures included: (i) number of metabolites ranging from 200  to 2000 (representative of an nontargeted method); (ii) metabolite values following a normal distribution, which is similar to what is commonly observed after a logarithmic or other transformation of the data; (iii) general positive correlation between metabolites, with pairwise correlations randomly distributed around a mean of +0.40 ; (iv) clustering within the data such that large groups of metabolites are highly correlated; and (v) the number of assigned ‘true’ positive metabolites independently associated with the clinical outcome set to 10, with varying effect sizes. The data structures are summarized in For comparison of analyses, we applied the following statistical methods to the simulated metabolomics datasets: (1) univariate analyses with multiple testing correction using Bonferroni or false discovery rate (FDR) ; (2) priIt is also important to clarify the distinction between variable selection and significance testing. Methods such as LASSO or other sparse models do not perform significance testing in the traditional sense of controlling type I error or testing hypotheses. Rather, they simply identify a set of metabolites that are relatively important for predicting a given outcome. Thus, herein, we will compare approaches aimed at identifying metabolites of greatest interest in relation to a given outcome, wherein some approaches involve traditional hypothesis testing, and others simply involve variable selection.Rv3.2.3 . To visualize the relatedness of metabolites, Spearman correlation coefficients were estimated for all pairs of metabolites, and correlations above +0.75 were isolated, with clusters of these correlations visualized using a D3 visualization framework [To compare the performance between statistical methods in this regard, we evaluated the following metrics: (i) probability of identifying an assigned ‘true’ positive metabolite through variable selection/significance testing as a function of the true effect size ; (ii) probability of identifying an assigned ‘true’ positive metabolite as a “top 10” metabolite based on effect size; (iii) average number of false positive metabolites identified by variable selection/significance testing; (iv) positive predictive value (PPV), the probability that a metabolite identified through variable selection/significance testing is truly related to the clinical outcome; (v) negative predictive value (NPV), the probability that a metabolite not identified is truly unrelated to the clinical outcome. These metrics were evaluated separately for continuous and binary outcomes, with all analyses performed using ramework ; within m/z. Thermo .raw data files were converted to 32-bit centroid .mzXML using Msconvert (Proteowizard software suite), and resulting .mzXML files were analyzed using Mzmine 2.21, as described in [13C isotopes were consolidated under the monoisotopic peak; common adducts  were consolidated, with the most-abundant species being reported; multiple charge states were consolidated with the singly charged state, with the most-abundant being reported; and, common ESI in-source fragments  were removed. In addition, all chromatographic features present in a sample blank subjected to the entire sample preparation protocol (with the exception of water being used instead of plasma) were removed. Finally, all remaining chromatographic features were manually inspected for quality in peak shape, retention time consistency, and signal-to-noise ratio, with features exhibiting subpar characteristics subsequently removed. Known compounds that are typically observed in human plasma when applying this method are listed in As part of the community-based Framingham Heart Study, the offspring cohort participants underwent a standardized evaluation that included fasting blood sample collection at their eighth examination in 2002–2005, as previously described . All parribed in . To elimFrom plasma collected from N = 2895 participants, a total of 1933 distinct metabolites were measured with a non-missing value recorded for every participant. We log-transformed and standardized all metabolites to have mean of 0 and standard deviation of 1 due to the expectedly right-skewed nature of the data. Using the same statistical analytical methods described above, we conducted analyses to identify distinct metabolites demonstrating significant associations with age and sex. These phenotypes were specifically selected given both are basic factors available in almost all biomarker analyses, and they allow for analysis of a continuous and binary outcome, respectively.We examined the results of both traditional statistical and statistical learning methods across a range of metabolomics datasets. We observed that when the number of metabolites was similar to or exceeded the number of study subjects, as is common with nontargeted metabolomics performed in small cohorts, sparse multivariate models demonstrated the most consistent results and the most statistical power. These findings have important implications for the analysis of metabolomic studies of human disease.


Mental disorders represent an increasing source of disability and high costs for societies globally. Molecular imaging techniques such as positron emission tomography (PET) represent powerful tools with the potential to advance knowledge regarding disease mechanisms, allowing the development of new treatment approaches. Thus far, most PET research on pathophysiology in psychiatric disorders has focused on the monoaminergic neurotransmission systems, and although a series of discoveries have been made, the results have not led to any material changes in clinical practice. We outline areas of methodological development that can address some of the important obstacles to fruitful progress. First, we point towards new radioligands and targets that can lead to the identification of processes upstream, or parallel to disturbances in monoaminergic systems. Second, we describe the development of new methods of PET data quantification and PET systems that may facilitate research in psychiatric populations. Third, we review the application of multimodal imaging that can link molecular imaging data to other aspects of brain function, thus deepening our understanding of disease processes. Fourth, we highlight the need to develop imaging study protocols to include longitudinal and interventional paradigms, as well as frameworks to assess dimensional symptoms such that the field can move beyond cross-sectional studies within current diagnostic boundaries. Particular effort should be paid to include also the most severely ill patients. Finally, we discuss the importance of harmonizing data collection and promoting data sharing to reach the desired sample sizes needed to fully capture the phenotype of psychiatric conditions. In a given year, one out of six individuals in Europe will suffer from a mental health problem, and it is estimated that the total cost to European societies for these disorders is 600 billion euros . Recover11C]raclopride was followed by a series of occupancy studies outlining thresholds for treatment efficacy and side effects [18F]DOPA uptake and more pronounced amphetamine-induced decreases in D2-R receptor binding, a small increase in striatal D2-R availability and lower D2-R availability in the thalamus rolipram, a nonselective PDE4 antagonist, was used to show reduced cAMP activity in MDD compared to controls, and an upregulation after selective serotonin reuptake inhibitor (SSRI) treatment PK-11195 showed increased binding in patients, this could not be confirmed in ensuing studies using TSPO radioligands with increased sensitivity such as [11C]PBR28 and [18F]FEPPA. Instead, data has converged towards lower binding across several brain regions deprenyl and [¹¹C]SL25.1188, targeting the enzyme MAO-B, have been considered markers of astroglial activation. The latter radioligand has been used to show increased binding in the prefrontal cortex in patients with MDD UCB-J binds to the synaptic vesicle glycoprotein 2A (SV2A), which has shown high correspondence to the established post-mortem marker synaptophysin in non-human primate studies [11C]UCB-J has been applied in patients with MDD and/or PTSD, showing an inverse correlation between depressive symptoms and SV2A density, as well as an association between SV2A density and functional connectivity in prefrontal cortex as measured using resting-state functional MR (fMRI) UCR (fMRI) . The radR (fMRI) , 34. RecR (fMRI) , a drug R (fMRI) . An impo1CUCR . In oncological PET, the use of labelled antibodies with high specificity and affinity has long been of interest. To enable brain uptake sufficient for imaging, recent developments have shown the possibility of designing bi-specific antibodies that undergo active transport across the BBB by transferrin-receptor-mediated transcytosis visualizing, e.g., amyloid-beta . Althoug cleared . Hence, 11C]raclopride, which has primarily been used to investigate high-density striatal regions, has been suggested also for measurements in low-density cortical brain regions based on test-retest reliability studies and between-sample comparisons. Importantly, competition studies and within-subject comparisons to high-affinity radioligands showed low validity for this approach SynVesT-1, have recently been developed for SV2A FDG and [15O]water) most tracers undergo peripheral metabolism and arterial sampling for metabolite analysis will therefore still be required to assess the amount of intact tracer in blood. The possibility of using venous instead of arterial samples for metabolite analysis would result in a less invasive procedure. Although metabolite analysis based on venous sampling has been published for several tracers, direct comparison to arterial sampling has only been done in few studies with varying results DOPA and a constant infusion design, where task-related deviation from expected linear increase in tracer uptake was calculated to study the dopaminergic underpinnings of reward processing, thereby making the temporal resolution of neurotransmitter release more equal to the level of fMRI Flumazenil PET were performed in patients with MDD compared to healthy controls DOPA examinations in individuals with clinical high risk for psychosis, showing an increase in uptake in those individuals that converted to psychosis, and the conversion was in turn predicted by higher levels of [18F]DOPA at baseline PHNO PET DOPA uptake predicted a positive treatment response to antipsychotics [18F]FDG PET in a sample of patients with the major depressive disorder before being randomized to either CBT or SSRI treatment. For CBT, hypometabolism in the insula (relative to the whole-brain mean) predicted remission and hypermetabolism in the same region predicted poor response, whereas the opposite pattern was noted for the SSRI escitalopram [18F]FDG levels predicted treatment response to vagus nerve stimulation FDG levels, but could not provide support for insula metabolism as a treatment selection biomarker [18F]FDG PET derived cerebral biomarkers of citalopram or placebo response [18F]FDG PET for localization of targets for rTMS have not been successful PBR28 was pooled to show the effects of gender, age and body mass index which had not been conclusively demonstrated before [Another general limitation in psychiatric PET research, as in other areas of molecular imaging, is the use of small samples. Notably, this gives rise not only to low sensitivity for true effects but also increases the risk for type II errors . Given td before . Similard before , 116.In order to examine both within-diagnosis heterogeneity as well as cross-diagnostic research questions, combining samples becomes even more essential. To facilitate this development, it would of great value to collect data on dimensional symptoms, to allow for diagnostic-agnostic investigations of the biological basis for symptoms, across samples. Similarly, it is advised to include in the informed consent that data may be shared as part of international collaborations . Data shAnother challenge when performing molecular imaging in psychiatry is the population of severely ill patients who may not be able to provide informed consent to research participation. An example is catatonia, a striking clinical presentation of a severe psychomotor disturbance, which can occur across diagnostic categories. It has been hypothesized that the symptom may have distinct neurobiological underpinnings, based on the dramatic effects of interventions such as lorazepam injection or ECT . Yet, ouOther areas where ethical issues can be present are studies of paediatric populations or young individuals with prodromal signs of psychotic illness. Studies using molecular imaging early in the development of a brain disorder can be critical for the understanding of key disease mechanisms and how they may be modified or even prevented. An example of this is studies of autism during childhood and adolescence where an aberrant development of the serotonergic system has been revealed, suggesting new possible interventions . Hence, The contribution of mental disorders to the global disease burden has increased from 3.1 to 4.9% between 1990 and 2019, corresponding to 125.3 million disability-adjusted life-years (DALYs) . This diThe discovery and marketing of antipsychotics, acting mainly on the dopamine system, in the 1950s and serotonergic antidepressants in the 1980s, was the main basis for two major disease models in psychiatry – the dopamine hypothesis of schizophrenia, and the serotonin hypothesis of depression. These conceptualizations have been a powerful driver of research into pathophysiology, resulting in a strong focus of psychiatric molecular imaging research on monoaminergic brain neurotransmission systems. The lack of resulting diagnostic tools or new treatment approaches indicate that causal mechanisms may involve other aspects of brain function, hence identifying new targets and radioligands is a critical step to advance psychiatric PET research. A concern is that programs for radioligand development are often driven by the pharmaceutical industry to validate compounds based on existing disease models, such as those above, or aimed towards other disease areas such as neurodegeneration or oncology, rather than being informed by new hypotheses regarding psychiatric disorders. The immune system has been implicated in both genetic and biomolecular studies across several psychiatric diagnoses , and witPsychiatric conditions in general are likely not caused by loss of neurons or tissue damage. Instead, symptoms are thought to reflect changes in dynamic brain function and plasticity, leading to aberrant information processing. Hence, these aspects need to be incorporated in order to fully understand the results of molecular imaging studies. A growing number of studies are combining PET with other imaging modalities such as fMRI as well as neurophysiological measures. These paradigms have mainly targeted monoaminergic brain neurotransmission systems, and should now also be extended to new putative markers of pathophysiology. In addition, there is increasing evidence indicating that psychiatric symptoms may be a result of an interplay between the brain and other organs such as the gut , suggestWhereas some psychiatric conditions are by definition considered neurodevelopmental disorders (autism spectrum disorders and attention-deficit/hyperactivity disorder), an emerging view also for other diagnoses is that genetic and environmental risk factors interact over longer time periods before symptoms present . HExamining individuals with severe mental disorders involves both practical and ethical challenges. In order to gain information regarding these conditions, it is vital to simplify experimental procedures as well as limit the number of research visits. Methods to quantify radioligand binding without the use of an arterial input function, methodology allowing for shorter times of acquisition, as well as simultaneous multimodal measurements are important steps in this direction.The definition of psychiatric diagnoses has essentially remained the same since before the advent of genetic research and modern neuroscience. Despite that psychiatric diagnoses are generally acknowledged to not reflect underlying biology, most research using molecular imaging have aimed to compare specific diagnostic populations to unaffected individuals. Initiatives such as the RDoC  have beeA general constraint in PET research has been small samples, both limiting the inferential value of results and hampering research into diagnostic heterogeneity. In a field with limited resources, systems for grant distribution and publication lead to a focus on novelty rather than broad, systematic research programs. At present, in order to reach desired sample sizes, collaboration and pooling across centres are necessary. Initiatives to harmonize research protocols and data acquisition is an important development towards this goal. This would also require a shift of focus in grant providers, as well as legislators regarding the possibility of sharing information across regional and national boundaries, such that researchers can engage in a concerted action to diminish the global burden of mental disorders.In conclusion, by being the only method that can directly access brain molecular processes we believe that PET will play an increasingly important part in understanding the aetiology, progression, and treatment of the highly impairing psychiatric disorders. However, this requires substantial investments to accelerate the development across several areas, including identifying and validating suitable radioligands, more precise acquisition and quantification methods, multimodal imaging protocols, longitudinal and interventional paradigms in large samples, as well as data sharing and collaboration.


Our musculoskeletal system enables our body’s movement and function. Accordingly, diseases and disorders that inhibit these functions can have an enormous impact on the quality of life for patients suffering from them. For example, osteoarthritis results from the body’s inability to regenerate its joints after injury. Approximately 528 million people world-wide suffers from this disease, with a high prevalence (10%–14%) reported in adult populations across North America, North Africa, Middle East and Australasia by public health agencies. This disabling joint disease generates a significant socioeconomic burden on the healthcare system, with direct costs ranging between 1% and 2.5% of the gross national product, generating $65.5 billion annually in direct medical costs in the US alone (2008–2014). Likewise, the global market for bone implants is estimated to range from $40 to $70 billion, depending on the source of reporting. These implants are used to treat diseased and damaged bones and examples include metal implants as replacements for missing bone tissue or damaged hip and knee joints. Current metallic implants serve as an end-stage solution to treat predominant pain symptoms as it is costly, highly invasive and the available implants does not restore full function of the bones or joint, nor do they last a lifetime. New implant technologies, which can combine a wide range of surface modifications, signaling molecules and cells with biomaterials to instead drive the regeneration of healthy joint tissues and modulate local immune cell activation are thus highly attractive alternatives. These advanced treatment paradigms ultimately seek to fully repair and maintain the native tissue function and longevity after injection or transplantations. This Research Topic contains four research studies and one review article that have examined unique strategies for promoting osteogenesis–the generation of bone tissues.in vivo performance as it dictates cells’ ability to adhere, infiltrate and secrete healthy bone tissue at the implant-host tissue interface (osseointegration). Accordingly, a study from Sun et al. examined how different methods of applying hydroxyapatite coatings on 3D-printed titanium scaffolds affected their performance. They compared using plasma spray and electrochemically deposition to coat these scaffolds with hydroxyapatite and then characterized their properties both in vitro and in vivo. The use of plasma spraying resulted in a more consistent coating, with lower crystallinity and smoother surface, that was cytocompatible and promoted in vitro osteogenic differentiation of rat bone marrow derived stem cells. Further in vivo evaluation revealed an improved performance to repair a bone injury in rabbits both at early and late time-points. This study illustrates how the method of deposition affects the performance of biomaterial coatings on 3D-printed Ti-implants for bone repair by tailoring physicochemical properties of the coatings. Similarly, Wang et al. also looked a how a novel combination of surface coatings could be used to improve the performance of titanium implants both in vitro and in vivo. Here their multifunctional coating fabricated using a layer-by-layer fashion where tannic acid was deposited on hydroxyapatite followed by the application of lysozyme. This combination of materials generated both antibacterial and antioxidant effects in vitro, serving as a cytocompatible substrate for the culture of mouse osteoblasts. Implants containing this novel coating also demonstrated improved performance in an in vivo model of bone injury. Together, this pair of studies illustrate the importance of biomaterials as a tool to improve implant performance by enhancing existing materials and imbuing them with new functionality for improved osseointegration and bone healing.For example, the surface properties of bone implants play a large role in their Nakai et al. The team specifically explored the use of a novel biomaterial substrate to control cell behavior through direct reprogramming, where 1 cell type (fibroblasts) was converted into the desired cell type (osteoblasts). Here, they used a novel nanomaterial called FD-NanoClip gel made up of chemically modified cholesterol-bearing pullulan. Through coating with fibronectin and culture in osteogenic media with a ALK inhibitor II, both cell attachment and cellular reprogramming of human dermal fibroblasts could be achieved. This novel biomaterial could furthermore be freeze-dried and formulated into a sheet or fibrous structures, demonstrating a difference in osteoinductive properties due to change in surface area across the two morphologies. Their results indicated that combination of protein coated biomaterials with reprogrammed cells produced high levels of calcium deposition and that architectural properties of the implant can be adjusted to guide bone healing.Additional strategies using biomaterials to induce desirable cell-material interactions to heal bone was further examined with emerging cell reprogramming strategies by Wang et al. designed and produced a novel recombinant protein that combined the properties of a complement immune system regulator that is unique to cartilage, the N-terminal non-collagenous domain 4 (NC4) of collagen type IX, with a heparin-binding domain to enable it to better target cells in joint tissues. They expressed the protein recombinantly in E. coli and verified its activity using an in vitro hemolytic assay to show that the modification did not affect protein function. Using a surgical-inducing osteoarthritic mouse model, they found that injections of the fusion protein (NC4-heparin) was able to localize to the joints with high retention over time. Through interactions with host cells in the joint, the delivered fusion protein further reduced inflammation and subsequently reduced loss of joint tissue. This study demonstrated how using protein engineering to generate novel biomaterials can provide novel treatments for reducing the onset of osteoarthritis by targeting complement immune system activity following joint injuries. You et al. furthermore provide a comprehensive review of how strontium can be used to functionalize biomaterials when engineering bone tissues by considering the immune response triggered. Strontium, a soft silver-white yellowish metal, is often used to dope metal implants when bone tissue engineering. This review delves into the signaling pathways and the role that this metal plays in modulating the different cells of the immune system, including macrophages and their polarization. It provides an interesting insight in considerations when using this material for tissue engineering applications. It specifically highlights the increasingly important role of understanding how the immune system interacts with bone implants to drive osteogenesis as well as the formation of blood vessels within bone tissues.As our knowledge around biomaterials and bone biology increases, the field is furthermore redirecting resources to develop biomaterials that can modulate the immune microenvironment–a complex response which plays a vital role in regulating both the speed and quality of the repair process. For example, Overall, this Research Topic provides an interesting Research Topic of articles focused on novel biomaterial strategies for osteogenic treatments–ranging from protein therapeutics, surface modifications, direct reprogramming and immunoengineering. It is a timely Research Topic given the significant interest in improving health outcomes for patients suffering from diseases and disorders of the musculoskeletal system.


The success of a particular plant phenotype in any given environment depends on how well the expression of traits allows plants to negotiate interactions with other organisms. Because plants cannot move away from unfavorable conditions, they express traits that attract mutualists and repel antagonists. These interaction-mediating traits have been well studied for plant interactions with herbivores as antagonists and pollinators as mutualists, to an extent that they both became models for understanding biotic interactions in general. Recently, these two schools of thought, plant defense theory and pollinator attraction have started to merge as scientists became aware that many of the plant traits that mediate the respective interaction, are overlapping. So, both fields of research face the same fundamental question: How do plants attract mutualists and repel antagonists with the same suit of basic traits, much relying on secondary metabolites, and within the same information space?This conflict that plants face is most apparent when considering chemical traits mediating biotic interactions. Plants can produce toxic, anti-digestive and anti-nutritive compounds that directly protect them against herbivores and pathogens and/or that are induced following the attack. Moreover, the herbivory-induced production of volatile organic compounds can attract predators and parasitoids facilitating the top-down control of pest organisms (indirect defenses). Thus, plant secondary metabolism allows plants to limit the number of potential antagonistic interactors (constitutive defenses) and to involve the entire interaction community in a plant defense strategy . Despite these findings, the mechanisms as well as the evolutionary consequences underlying the observed ecological consequences of a shared information space between plants, pollinators, and herbivores, remain poorly understood  show that defensive leaf volatiles play a role in host plant selection of the pollinating herbivore Manduca sexta and demonstrate that adults assess information from leaves differently when choosing between foraging and oviposition locations. This raises the interesting question if such ontogenetically differential use of chemical information is common or a specific feature of herbivores that also function as major pollinators on their host plants and what drives the evolution of such differential behavior.Challenges of attracting mutualists while repelling antagonists are likely to be particularly strong with pollinating herbivores, such as many Lepidopteran species with herbivorous larvae and pollinating adults. Davidowitz et al. (2022) offer a special perspective and partial answer to the latter question. They also focused on pollinating herbivores to explore the hypothesis that increased allocation of resources to flight could lead to higher pollination efficiency of Lepidopteran pollinators, and thus, higher plant fitness. Reciprocally, this also means that if the same insect increases resource allocation to reproduction instead of flight, herbivore population size is likely to increase with potentially negative consequences for plant fitness. Davidowitz et al. propose such resource allocation trade-offs between flight and fecundity in insects as potential drivers of differential selection on plant defenses and pollinator, herbivore counter defenses, as well as plant reproductive traits. For example, Villamill et al. (2022) show how herbivory can affect plant sexual expression by teasing apart the effects of herbivory-caused resource limitation and plant responses to herbivory-induced jasmonate signaling on wind-pollinated Mercurialis annua. They found that the jasmonate-mediated induction reverses the effect of tissue loss on male reproductive investment indicating that plant defence expression can have consequences for sex allocation and eventually for the evolution of plant sexual systems.Aranda-Rickert et al. (2021) provide the first evidence of distance-dependent contribution of ants to pollination, but with no effect on plant defence. They show how ants can offset pollen limitation in isolated females of wind-pollinated Ephedra triandra by contributing to targeted delivery of airborne pollen while they consume the sugary pollination drops. Whether other dioecious plants lacking male reward for pollinators share similar mechanisms of ambophily remains to be studied. Pollinators and herbivores can exert selection on plant reproductive and defensive traits suggesting conflicting selection pressures. Here, Wu et al. (2021) demonstrate that herbivore-mediated selection can generate selective pressures for greater flower production on insect-pollinated plants. Their study indicates that the variation in the intensity of plant-antagonistic interactions can drive spatial variation in natural selection on floral traits.Plant-visiting ants are often functional herbivores or predators, only rarely pollinators. However, De-la-Cruz et al. (2022) explains how the traditional experimental and modern methods including next-generation sequencing, metabolomics, and gene-editing technologies can enhance our understanding of the genes and traits involved in mediating complex plant-pollinator-herbivore interactions.Despite recent advances, the evolutionary consequences of the complex ecological interactions remain poorly understood. The Review by We hope that you enjoy this Research Topic and that it gives inspiration for more research uniting the traditionally separately studied fields of plant-pollinator and plant-herbivore interactions.All authors listed have made substantial intellectual contributions to the work and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.