We evaluated racial/ethnic differences in the receipt of naloxone distributed by opioid overdose prevention programs (OOPPs) in New York City (NYC).We used naloxone recipient racial/ethnic data collected by OOPPs from April 2018 to March 2019. We aggregated quarterly neighborhood-specific rates of naloxone receipt and other covariates to 42 NYC neighborhoods. We used a multilevel negative binomial regression model to assess the relationship between neighborhood-specific naloxone receipt rates and race/ethnicity. Race/ethnicity was stratified into four mutually exclusive groups: Latino, non-Latino Black, non-Latino White, and non-Latino Other. We also conducted racial/ethnic-specific geospatial analyses to assess whether there was within-group geographic variation in naloxone receipt rates for each racial/ethnic group.Non-Latino Black residents had the highest median quarterly naloxone receipt rate of 41.8 per 100,000 residents, followed by Latino residents , non-Latino White  and non-Latino Other residents . In our multivariable analysis, compared with non-Latino White residents, non-Latino Black residents had a significantly higher receipt rate, and non-Latino Other residents had a significantly lower receipt rate. In the geospatial analyses, both Latino and non-Latino Black residents had the most within-group geographic variation in naloxone receipt rates compared to non-Latino White and Other residents.This study found significant racial/ethnic differences in naloxone receipt from NYC OOPPs. We observed substantial variation in naloxone receipt for non-Latino Black and Latino residents across neighborhoods, indicating relatively poorer access in some neighborhoods and opportunities for new approaches to address geographic and structural barriers in these locations. From 2016 to 2019, the opioid-related overdose death rate was stable in New York City (NYC) . Since f.Naloxone, a highly effective opioid reverse agent, is available through pharmacies and community-based distribution by opioid overdose prevention programs (OOPPs). Inadequately stocked pharmacies and pharmacy deserts in racial minority and low-income communities, especially in metropolitan areas, have led to substantial racial/ethnic inequalities in pharmacy-based naloxone access –10. The .In NYC, OOPPs are any program registered with New York State as a source of naloxone distribution and training, which include a range of community-based organizations, healthcare facilities, syringe service programs, and other organizations. In 2018, NYC began collecting individual-level data to understand who receives naloxone kits from OOPPs and where kits are received . In thisn = 14), correctional health services (n = 8), shelters (n = 7), drug treatment programs (n = 50), healthcare facilities (n = 48), substance use-related community-based organizations (n = 7), DOHMH (n = 5), multi-component priority programs (n = 14) and other types of programs (n = 17). Multi-component priority programs encompass more than one type of program from the previously mentioned types of programs. Data were collected between April 1, 2018, and March 31, 2019. We were unable to include data after March 2019 because January and March 2019 was the last quarter with completed data at the time of the analysis. The quarterly counts of naloxone kits received by NYC residents from OOPPs were stratified by the naloxone recipients’ neighborhood of residence designated by United Hospital Fund (UHF) neighborhoods and racial/ethnic groups [We used data collected by the Bureau of Alcohol and Drug Use Prevention, Care and Treatment (BADUPCT) of the NYC Department of Health and Mental Hygiene (DOHMH). Individual-level naloxone recipient data were collected from 170 OOPPs using a standardized naloxone recipient form, which collected self-reported race/ethnicity, age, and ZIP code of residence. The 170 OOPPs comprised of syringe service programs , including the number of residents who identify as either non-Latino Black, non-Latino White, Latino or non-Latino Other, and the percentages of residents in poverty and residents with a Bachelor’s degree or higher in 2018 . NeighboFirst, we calculated the median quarterly naloxone receipt rate across UHF neighborhoods for each race/ethnicity group. For this measure, we calculated the naloxone receipt rate per race/ethnicity group per UHF neighborhood for each quarter included in the study. Next, we calculated the median naloxone receipt rate per race/ethnicity across quarters. We did not report the median naloxone receipt rate per race/ethnicity group per quarter because there was no significant difference in naloxone receipt rates among the quarters by race/ethnicity.Next, We conducted a multilevel negative binomial regression model nested by UHF neighborhoods to assess the difference in naloxone receipt rates across racial/ethnic categories. The outcome variable, quarterly naloxone receipt rate in a neighborhood, was defined as the number of naloxone kits received by individuals in each racial/ethnic group according to neighborhood and quarter, with racial/ethnic stratified population sizes defined as the offset. The independent variable of interest was categorical, representing the four mutually exclusive racial/ethnic groups noted above. Other covariates included in the multivariate models were neighborhood-level annual opioid-related overdose death rate, incarceration rate, percentage of residents in poverty, and the percentage of residents over 25 years old with a Bachelor’s degree or higher. We also conducted a sensitivity analysis where we replaced the opioid-related overdose death rate with the all-type overdose death rate in the model. Given the similarity in results, the latter results are not shown.Lastly, we used the Getis-Ord Gi* statistic for neighborhood-level geospatial analysis. Using the Gi* statistic and z-scores, we identified geospatial clustering of racial/ethnic-specific naloxone receipt rates. Hot spots were clusters of ≥ 2 adjacent UHF neighborhoods with significantly higher naloxone receipt rates than the expected rate. Cold spots were clusters of ≥ 2 adjacent UHF neighborhoods with statistically significantly lower naloxone receipt rates than the expected. We performed separate geospatial analyses for each racial/ethnic group to evaluate within-group variation in the distribution of naloxone receipt rates across neighborhoods in NYC. This analysis used R version 1.0.143, SAS version 9.4, and ArcGIS version 10. The Brown University School of Public Health and NYC DOHMH Institutional Review Boards approved and considered this study exempt.Across the 42 neighborhoods, the median percentage of non-Latino Black residents was 10.3%, 21.3% of residents were Latino, 12.9% were non-Latino Other, and 31.9% were non-Latino White. The number of naloxone kits distributed between April 2018 and March 2019 across all neighborhoods was 79,555, with 52.7% reporting that the reason the kit was being obtained was just in case they saw someone overdose. Other reasons people received naloxone kits from OOPPs were; [In the bivariate multilevel regression models Table , we founIn the geospatial analysis Fig. , hot spoOur study found differences in naloxone receipt distributed by OOPPs between and within racial/ethnic groups in NYC neighborhoods. Non-Latino Black and Latino residents had higher rates of naloxone receipt from OOPPs than non-Latino White residents when we adjusted for opioid-related overdose death rates. In the geospatial analysis, we identified neighborhoods where naloxone was distributed at higher and lower than expected rates within each racial/ethnic group. We found hot spots in each of the racial/ethnic group analyses; however, cold spots were found in some neighborhoods in the southern and southeastern areas of NYC for non-Latino Black and Latino residents only.Previous studies conducted in Philadelphia, Rhode Island, and Massachusetts have found similar results in which community-based naloxone was distributed at higher rates to neighborhoods with over 50% of residents identifying as Black , 19. HowWhen we conducted the race/ethnicity-specific geospatial analysis, we only found cold spots in the non-Latino Black and Latino analyses. Most cold spots were found in neighborhoods in Queens, which, compared to other NYC boroughs, has the highest concentration of foreign-born residents (47%) . AlthougTo increase access, naloxone distribution by OOPPs should be expanded in these neighborhoods to the extent feasible and/or resources provided to expand naloxone access by other means that address distance and structural barriers. These activities should be implemented alongside additional efforts to educate foreign-born residents about the resources available when seeking social and healthcare services, reduce language and cultural barriers, and eliminate US Immigration and Customs Enforcement (ICE) enforcement in spaces where residents receive naloxone to minimize documentation status fears .Our study is not without limitations. Small event counts by neighborhood meant that we: (a) were only able to include overall fatal overdose rates (as opposed to race/ethnicity-specific rates) at the neighborhood level as a covariate in our study, which may have led to under- or over-estimation of the neighborhood-level overdose burden within each of the racial/ethnic groups; (b) implemented larger periods (quarters) within a one year study period which may have masked temporal trends in OOPP-distributed naloxone receipt over time; and (c) collapsed multiple racial/ethnic groups into one non-Latino Other category. The results indicating that those in the non-Latino Other group received less naloxone from OOPPs than other racial/ethnic groups suggest the importance of future work to better understand naloxone receipt barriers and facilitators for the different members of this group.Our study identified racial/ethnic differences in OOPP-distributed naloxone receipt in NYC. We found that non-Latino Black residents had a higher rate of naloxone receipt from OOPPs than non-Latino White residents. However, we also identified neighborhoods with within-group differences in naloxone receipt from OOPPs among non-Latino Black and Latino residents. The next steps in this line of inquiry are to assess naloxone distribution approaches that can address reasons for geographic and population variation in naloxone receipt, and identify geographic and structural barriers contributing to disparities in naloxone receipt.


People living with HIV (PLWH) are prone to mental health problems and evidence indicates that HIV-related stigma can negatively impact mental health-related quality of life. This study explored potential mechanisms between HIV-related stigma and mental health-related quality of life, specifically whether anxiety or depression mediates, and whether social support moderates, the relationship.A total of 1197 Chinese PLWH participated in the study. The Berger HIV Stigma Scale, the Hospital Anxiety and Depression Scale (HADS), the 12-item Brief Health Survey (SF-12), and the Multidimensional Scale of Perceived Social Support (MSPSS) were employed.HIV-related stigma was negatively associated with mental health-related quality of life. Anxiety and depression partially mediated the relationship between HIV-related stigma and mental health-related quality of life; social support played a moderating role.The mental health-related quality of life in PLWH was shown to be indirectly affected by HIV-related stigma through anxiety and depression in China. The negative impact of HIV-related stigma decreased with increased social support. AIDS remains a global public health problem, with 38.4 million people living with HIV (PLWH) worldwide as of 2021 , anxietySince the beginning of the AIDS epidemic, HIV-related stigma has posed significant challenges for PLWH, not only leading to mental health and substance abuse issues , but alsAnxiety and depression are critical factors affecting MHRQoL. It is common for PLWH to have anxiety and depression, with prevalence rates higher than 40% and 60%, respectively, in China . StudiesSocial support may help PLWH cope with HIV. Developing multiple social support resources may protect HIV-positive adolescents from experiencing poor mental health and may have a direct protective effect on depressive symptoms . MoreoveHIV-related stigma has been recognized as having a direct impact on MHRQoL of PLWH; anxiety, depression, and social support are also significantly associated. Yet, how these psychosocial factors interact with MHRQoL has rarely been explored fully. Therefore, in order to better understand the possible mechanisms between these factors, this study hypothesized that: (1) HIV-related stigma is negatively associated with MHRQoL; (2) HIV-related stigma is positively associated with anxiety and depression; (3) anxiety and depression are negatively associated with MHRQoL; (4) anxiety and depression mediate the relationship between HIV-related stigma and MHRQoL; (5) social support moderates the relationship between anxiety and depression-mediated HIV-related stigma and MHRQoL.The cross-sectional study was conducted from November 2017 to November 2018 in three regions of China: Shanghai, Henan, and Zhejiang. A multi-stage, stratified, random-sampling method based on the duration of antiretroviral treatment, age, and gender was used to select 1331 PLWH. Study eligibility criteria included (a) age ≥ 18 years; (b) diagnosis of HIV and infection for at least one year; (c) at least three months as a highly active antiretroviral therapy (HAART) recipient; (d) voluntary participation in the study after informed consent; (e) clear consciousness and language and ability to cooperate with the instructions and requirements of the study.The study was conducted by local Centers for Disease Control and Prevention (CDC) agencies in the three regions. Relevant questionnaires were completed face-to-face after eligible participants signed informed-consent forms. Following the sampling plan, a total of 1331 eligible PLWH were contacted. After removing participants who were unable to complete the questionnaire for various reasons, a valid sample of 1273 PLWH was generated. In further analysis, the number of participants with some variables missing was 76 and they were removed, resulting in a final sample size of 1197 and a validity rate of 89.93%.Data related to the following sociodemographic variables were collected: age , sex , ethnicity (Han/other), religion (faith/none), marital status (single/married/divorced/widowed), employment status (employed/unemployed), and duration of treatment .Participants’ HIV-related stigma was assessed using the Berger HIV Stigma Scale , which cAnxiety and depression were assessed by the Hospital Anxiety and Depression Scale (HADS) . The HADThe Multidimensional Scale of Perceived Social Support (MSPSS)  was emplParticipant MHRQoL was assessed by the 12-item Brief Health Survey (SF-12) , a scaleAll data analyses were performed using SPSS v. 26 . ResultsFirst, a partial correlation analysis was performed to examine the correlation between the variables of interest. The mediation model and the moderated mediation model were analyzed using process macros in SPSS . All conTable r = -0.207, p < 0.001). Depression  and anxiety  were negatively associated with MHRQoL. They were also negatively associated with social support . HIV-related stigma was significantly associated with depression , anxiety , and social support .The means, standard deviations, and partial correlations for all variables are presented in Table p < 0.001) as well as positively associated with anxiety . The indirect effect shown in the relationship between HIV-related stigma and MHRQoL via anxiety was also significant , indicating that anxiety also partially mediated the relationship between HIV-related stigma and MHRQoL.As shown in Table p = 0.002) and negatively associated with MHRQoL . Depression partially mediated the relationship between HIV-related stigma on MHRQoL ; the total effect of HIV-related stigma on MHRQoL was -0.134 .Table 2 = 0.345, F = 69.342, p < 0.001). Furthermore, the interaction between social support and anxiety was statistically significant for MHRQoL . The simple slope in Fig. As indicated in Table 2 = 0.250, F = 43.999, p < 0.001). Also as shown, the interaction with depression was statistically significant for MHRQoL . Figure The results presented in Table The negative impact of HIV-related stigma is pronounced in the Chinese cultural environment , but fewThe present study demonstrated that anxiety was positively associated with HIV-related stigma and negatively associated with MHRQoL, mediating between the two variables. This finding is consistent with previous findings, for example, found that anxiety has a strong psychological impact on patients  and thatAs hypothesized, depression was shown to mediate the relationship between HIV-related stigma and MHRQoL, with a direct positive association with HIV-related stigma but a direct negative relationship with MHRQoL. In a study of incarcerated Malaysian men with AIDS, depression mediated the relationship between AIDS-related stigma and health-related quality of life . These pExtending the findings of previous studies, the study demonstrated a moderating effect of social support. Specifically, the negative impact of HIV-related stigma mediated through depression on MHRQoL was stronger in PLWH with less social support. This result is consistent with the finding that social support helps buffer the negative impact of depression on health-related quality of life . HoweverSimilarly, the presence of anxiety reinforced the negative impact of HIV-related stigma on MHRQoL but was mitigated in PLWH with high social support. Social support buffered the impact of anxiety-mediated HIV-related stigma on MHRQoL. This study also found that social support can directly affect anxiety and depression. This result is consistent with previous studies that found that social support may be important in improving depressive symptoms . HoweverThus, study findings highlighted the importance of considering the effects of stigma, anxiety, depression, and social support in efforts to improve MHRQoL in PLWH. Related psychotherapy research has found that stigma has a relatively powerful and long-lasting effect on well-being ; ReducinAlthough this study advances the understanding of how psychosocial factors  interact and influence MHRQoL, some research limitations must be considered. First, the study was a cross-sectional study with several drawbacks, including the inability to measure incidence, difficulty in making causal inferences , and theDespite these limitations, the present study had some advantages. The study had a large sample size, which enhanced the statistical effects of the exploration. The study went beyond the exploration of the HIV-related stigma mechanisms affecting MHRQoL to consider the moderating role of social support in the model. Finally, the study provides a reference for follow-up studies and further investigation of MHRQoL of people living with HIV.The current study established that the partial mediating role of anxiety and depression in the relationship between stigma and MHRQoL among people living with HIV in China was moderated by social support. The results suggested that depression, anxiety, and social support play important roles in MHRQoL of PLWH. Future interventions that aim to improve MHRQoL of PLWH should have primary goals that include reducing stigma, anxiety, and depressive symptoms, as well as increasing social support.


The COVID-19 pandemic has had profound impacts on people worldwide. Previous studies have shown that fear learning, extinction, recall, and contextual information processing involve the activation of emotion and sensory brain systems, which can be modified. However, it remains unclear whether brain functions associated with these processes have been altered over the pandemic period.n = 16), who completed a baseline FALT before the pandemic and repeated the task during the pandemic, and the non-pandemic group (n = 77), who completed both sessions before the pandemic began.We compared pre- and peri-pandemic brain activation during a fear-associated learning task (FALT) using previously collected data. The participants were divided into two groups: the pandemic group (Compared with the non-pandemic group, the pandemic group exhibited significant decreases in brain activation from baseline to follow-up assessments, including activation in the brainstem during early fear learning, the posterior thalamus/hippocampus during late extinction, and the occipital pole during late recall phases for contextual processing. Furthermore, activations associated with retrieving safety cues were reduced in the posterior cingulate, premotor, and calcarine cortices during the early recall phase, and activations associated with retrieving dangerous cues decreased in the occipital pole during the late recall phase. Additionally, correlations between decreased activation and elevated posttraumatic stress symptoms were observed.These findings suggest that activations associated with processing low arousal contextual information, safety cues, and extinguished fear cues decreased during the pandemic. These changes in brain activation may have contributed to the increase in mental health disturbances observed during this time. COVID-19 introduced the worst global pandemic in a century, with more than 622 million cases and 6.55 million confirmed deaths  and thatIn addition to these initial behavioral findings that link fear learning functions with mental health symptoms caused by the pandemic, the study of brain activation to fear learning and memory during the pandemic may provide a greater understanding of the neurobiological mechanisms that underlie pandemic-associated increases in psychiatric disturbances in the general public. Previous fMRI studies that used the fear-associated learning task (FALT) have successfully examined brain activation connected to the learning, extinction, and retention of fear-evoking and safety cues, as well as the contextual information associated with these cues . Using FGiven the unexpected onset of the COVID-19 pandemic, it was not feasible to plan well-designed studies comparing pre- and peri-pandemic mental health symptoms and FALT-related brain function in the same individuals. Therefore, we leveraged data collected from a longitudinal study, which included participants who completed 1-year follow-up assessments after experiencing life-threatening traumatic events before the pandemic, as well as another group of participants who experienced the pandemic during the 1-year follow-up period. In this case–control longitudinal analysis, we aimed to investigate changes in brain activation related to fear-associated learning functions between the two groups and their associations with posttraumatic stress symptoms. It should be noted that our focus is not on the impacts of COVID-19 infection on brain function, but rather on considering COVID-19 as a unique, stress-inducing event for the participants in this investigation.Data from 93 participants  who completed both initial and follow-up assessments from 2017 to 2021 were included in the present study. The original longitudinal study aimed to investigate early and longitudinal brain changes associated with PTSD development after trauma exposure . In brieParticipants' mental health conditions were evaluated with surveys and interviews during the study period. The Mini-International Neuropsychiatric Interview (MINI)  and the *-weighted, echo-planar/gradient echo pulse sequence . A T1-weighted gradient echo overlay  and a high-resolution 3D FSPGR structural MRI image  were collected for registration  and a cluster-wise p threshold of 0.05, for both the positive and negative contrast directions. The FEAT-related activations were then extracted from the contrast of parameter estimate (COPE) images of tasks. FEAT query was used to extract mean percentage COPE values.All fMRI images were processed with FSL version 5.0.10 using standard procedures . All scaFear-associated learning task (FALT). During MRI scans, participants performed FALT modified from Garfinkel et al.  over 2 dFear acquisition was evaluated with psychophysiological reactions using skin conductance reactions (SCRs) derived from event-related electrodermal responses (EDRs). EDR was recorded and processed using BIOPAC AcqKnowledge software and established procedures . An SCR t-tests or chi-square tests as appropriate. A factorial ANOVA was used to assess the impact of time , phase , stimulus type, and group affiliation on both SCR and US expectancy. Partial correlation analyses were conducted to investigate the relationships between changes in FALT-related brain activations and in PCL-5 total scores over time, while controlling for variables such as age, sex, and scan interval. Moderation analysis further explored whether group status influenced the relationship between FALT-related brain activation and PCL-5 total scores. In our dataset, all variables adhered to the assumption of normal distribution, with the exceptions of SCR and US expectancy. Preliminary analyses, including the Shapiro–Wilk test and visual inspections of Q-Q plots, confirmed these deviations from normality. Despite these violations, the statistical methods employed in this study pertinent to SCR and US expectancy data were robust to such deviations. Corrections for multiple comparisons were made using the false discovery rate (FDR) method. All statistical analyses were performed using SPSS version 27.0 . The level of statistical significance was set at α = 0.05, a two-tailed test.The demographic characteristics and mental health conditions of participants were analyzed across the pandemic and non-pandemic groups using independent p > 0.05). However, the interval between the two MRI sessions was significantly longer in the pandemic group (549.63 ± 128.48) than in the non-pandemic group (372.92 ± 10.17). Based on this finding and previous research . No other statistically significant findings were detected in either the Extinction or Recall phases . It is important to note that a substantial portion of the SCR data was missing due to technical difficulties.In the ANOVA, evidence of successful fear acquisition was observed in both the baseline and follow-up sessions. This was indicated by elevated skin conductance responses (SCRs) associated with the conditioned stimulus plus (CS+) when compared to the conditioned stimulus minus (CS–) during the Acquisition phase  = 6.17, p < 0.05, partial η2 = 0.07, where the pandemic group showed higher US expectancy than the non-pandemic group. Furthermore, in the late recall phase, a three-way interaction between time, stimulus, and group was statistically significant, F = 3.65, p < 0.05, partial η2 = 0.4. Follow-up analyses indicated that this three-way interaction was due to the finding that the US expectancy of CS + U decreased over time in the pandemic group but not in the non-pandemic group.Changes over time in activation associated with processing a dangerous contextual environment during the early acquisition phase differed significantly between the pandemic and non-pandemic groups in a cluster of the brainstem . ContextChanges over time in activation associated with processing a safe contextual environment were significantly different between the pandemic and non-pandemic groups in the bilateral superior lateral occipital cortex (sLOC) and a cluster spanning the left posterior thalamus and posterior hippocampus during the late extinction phase . ContextChanges over time in activation associated with CS- during the early recall phase were significantly different between the pandemic and non-pandemic groups in the right posterior cingulate cortex (PCC), left calcarine cortex, and right premotor cortex . The actAdditionally, during the late recall phase, changes over time in activation associated with CS+ (combining CS+E and CS+U) were significantly different between the pandemic and non-pandemic groups in the right occipital pole . Activation associated with CS+ decreased in the pandemic group, but only had trivial changes in the non-pandemic group over the study period  left. Inr = −0.30, p < 0.05, FDR-corrected). A similar negative correlation was found between changes in activation associated with safe context during the late extinction phase in the right sLOC and changes in PCL-5 scores . Additionally, moderation analyses indicated that the associations of these two changes of activation with changes in PCL-5 scores were not contingent upon group status (p < 0.05 for both interaction terms). These two correlations are visualized in After controlling for age, sex, and scan interval, changes in activation associated with safe context during the late extinction phase in the left sLOC were negatively correlated with changes in PCL-5 scores  associated with contextual processing in the brainstem during early acquisition, in the bilateral sLOC and thalamus during late extinction, and in the occipital pole during late recall phases, (b) associated with CS- in the PCC, premotor cortex, and calcarine cortex during the early recall phase, and (c) associated with CS+ in the occipital pole during late recall phase, compared with the non-pandemic group. These reductions in fear-associated brain activation over time suggest the COVID-19 pandemic had specific influences on brain emotion-related functions. In addition, negative correlations over time of bilateral sLOC activation with PTSD symptom severity were also found. While we acknowledge that interpretations of the findings carry a degree of speculation due to the absence of direct measures of pandemic experiences, we also recognize the strength of our study design. The inclusion of a unique non-pandemic control group and the utilization of pre-pandemic baselines from the same participants lend a specific context to the observed differences between groups. This specificity pertains to the distinctive nature of the pandemic period, even considering the potential variations in individual pandemic experiences .The findings of longitudinal reductions in activations in the subcortical regions and primary visual cortex suggest that pandemic experiences reduced contextual encoding and retrieval at early levels of the visual sensory processing pathway. First, the brainstem plays roles in sensorimotor and visceral integration between the cerebrum and the body . The retThe current study also observed decreased activation that was associated with processing CS– in the PCC, premotor area (BA 6), and calcarine cortex during early recall in the pandemic group. The PCC and calcarine cortex have been considered as parts of the safety signal processing network that is more active during learning CS– than CS+. In addition, the PCC is part of the limbic system and plays an important role in emotion processing, particularly internal feeling/sense of self and reward . On the The pandemic group also showed decreased primary visual cortex activation associated with CS+ during the late recall phase. This finding is in line with an fMRI investigation of fear-associated learning in individuals with anxiety disorder, whereas the authors reported anxiety symptoms associated with hippocampal, frontal, visual, insular, and ACC activation, which involve contextual processing, safety cue learning and renewal, and fear cue learning and retrieval .Overall, we observed that the pandemic group showed a reduction over time of brain activations associated with FALT across different phases and brain regions. While we acknowledge that the brain regions identified in our current study may not conventionally align with fear learning tasks, it is important to note that a number of studies have highlighted instances of modified context and sensory processing within fear-related fMRI tasks [see , 43 for Regarding behavioral US expectancy, we found significant main effects of conditioned stimuli on unconditioned stimulus (US) expectancy across all phases, and notably, higher US expectancy in the pandemic group during the early extinction phase. A unique three-way interaction in the late recall phase further revealed dynamic changes in the pandemic group's US expectancy over time. In contrast, Harnett et al. (48) found no such main effects but identified a significant stimulus-by-group interaction, suggesting that trauma exposure could modulate US expectancy. The divergent findings between the two studies highlight the complex interplay of variables, including pandemic-related stress and study duration, that shape US expectancy. These discrepancies warrant further research to reconcile the observed differences.The current study reported preliminary findings of FALT-related brain activation reductions over the pandemic period and their associations with posttraumatic stress symptoms. Yet, caution should be taken when interpreting these findings because the original longitudinal study was not specifically designed to investigate the effects of pandemic experiences. For example, although we did not observe differences between the pandemic and non-pandemic groups on their mental health conditions, it should be noted that all study participants were recruited due to trauma exposure and some subjects had developed PTSD prior to the pandemic. In addition, the difference that occurred in scan interval comparing the two groups was not desired and can only be adjusted statistically. Furthermore, we did not collect information regarding pandemic experiences directly but only administered instruments that assess psychological symptoms in general or in relationship to trauma exposure. This had the effect of limiting our understanding of the influence of pandemic experiences on changes in FALT-related brain function, behavioral symptoms, and their associations. However, it should be noted that the observed activation changes most likely did not directly result from virus infection because no participants reported active or residual COVID symptoms during the scan period although long-term physiological or neural effects of infection remain largely unknown.In summary, the current longitudinal analysis reported initial evidence of pandemic experience-related changes in brain emotion-related functions, where such changes reduced fear-associated learning activation in the PCC, premotor, hippocampus, visual cortices, brainstem, and thalamus regions. The effects of the pandemic experience were seen mainly in activation associated with low arousal stimulation, that is, context, safety cues, and extinguished fear cues. These findings may help identify brain mechanisms that underlie increased mental health disturbances caused by the pandemic. Yet, limited information about personal pandemic experiences impedes the identification of specific factors that contributed to these activation changes. While we speculate that the COVID-19 pandemic contributed to emotion activation changes, further comprehensive assessments of specific pandemic-related factors would be needed to further characterize changes in brain emotion functions.The original contributions presented in the study are included in the article/The studies involving humans were approved by the University of Toledo Institutional Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.XW and HX: conceptualized the study, funding acquisition, and supervised the study. CP, AG, AZ, and C-HS: contributed to formal analysis. CP and AG: were involved in writing the original draft of the manuscript. CP, AG, C-HS, NC, AZ, XW, and HX: contributed to writing, reviewing, and editing of the manuscript. All authors contributed to the article and approved the submitted version.


Automatic hand gesture recognition in video sequences has widespread applications, ranging from home automation to sign language interpretation and clinical operations. The primary challenge lies in achieving real-time recognition while managing temporal dependencies that can impact performance. Existing methods employ 3D convolutional or Transformer-based architectures with hand skeleton estimation, but both have limitations. To address these challenges, a hybrid approach that combines 3D Convolutional Neural Networks (3D-CNNs) and Transformers is proposed. The method involves using a 3D-CNN to compute high-level semantic skeleton embeddings, capturing local spatial and temporal characteristics of hand gestures. A Transformer network with a self-attention mechanism is then employed to efficiently capture long-range temporal dependencies in the skeleton sequence. Evaluation of the Briareo and Multimodal Hand Gesture datasets resulted in accuracy scores of 95.49% and 97.25%, respectively. Notably, this approach achieves real-time performance using a standard CPU, distinguishing it from methods that require specialized GPUs. The hybrid approach’s real-time efficiency and high accuracy demonstrate its superiority over existing state-of-the-art methods. In summary, the hybrid 3D-CNN and Transformer approach effectively addresses real-time recognition challenges and efficient handling of temporal dependencies, outperforming existing methods in both accuracy and speed. In recent years, there has been a significant increase in research on novel devices and techniques for human–computer interactions (HCIs). In particular, hand gesture interfaces have gained popularity in various applications, including smart homes, health monitoring, virtual reality, automobile equipment control, and sign language translation ,2,3,4. HExisting technologies for hand gesture recognition can be classified into two main categories: sensor-based and vision-based methods. Sensor-based methods rely on wearable devices to capture motion data and recognize hand gestures. These devices include sensors such as accelerometers , inertiaCamera-based methods offer a more natural way to interact with human–machine interfaces, eliminating the need for on-body devices. Existing studies have utilized various sources of data for hand gesture recognition tasks, such as RGB, depth, infrared, and optical flow, used either independently or in combination with each other ,15. ProcRegardless of the skeleton recognition method used, analyzing the temporal dependencies of a sequence of skeletons offers more advantages than analyzing a sequence of images. However, skeleton sequences can exhibit significant intra-class and inter-class variability, making it challenging to learn discriminative features for accurate hand gesture classification. Additionally, understanding the temporal dynamics of skeleton sequences is crucial for capturing motion patterns and transitions between different hand poses. Modeling long-term dependencies and capturing subtle temporal changes in hand keypoints can also pose challenges.Motivated by this, some authors have applied CNN-based methods to hand skeleton sequences to learn hand posture variations and hand movements in different branches and subsequently fuse the learned features. However, Convolutional Neural Networks (CNNs) may not efficiently address temporal dependencies compared to Transformers. To better model long-range temporal changes, other authors have introduced Transformer-based methods. Most of the employed embedding strategies are not efficient as they directly model the pairwise relations of the entire input skeleton sequence, encompassing both spatial and temporal dimensions. This approach overlooks the importance of spatial interactions among distinctive local joints and the significance of capturing short-term temporal dynamics, which are essential for recognizing hand motion patterns. In summary, the main research gap lies in the fact that existing methods fail to achieve real-time hand gesture recognition without compromising accuracy. This issue can be attributed to the lack of more efficient embedding strategies capable of capturing spatial interactions among joints in short-term temporal dynamics, as well as the absence of more effective methods to analyze long-term temporal dependencies in skeleton sequences.The main objective of the proposed work is to achieve real-time hand gesture recognition and address the identified research gaps by introducing an efficient hybrid neural network. This network comprises a Local Spatio-Temporal Embedding module implemented using 3D-CNN and a Long-Term Embedding module implemented using a Transformer. The Local Spatio-Temporal Embedding module is designed to analyze both the spatial relationships between subgroups of hand joints and the temporal evolution of those relationships over time. This local feature extraction proves beneficial for recognizing fine-grained hand gestures. Implementing the Long-Term Embedding module using the Transformer architecture, the network aims to better capture long-range dependencies, thereby refining the learned representations by attending to the global temporal dynamics and interactions between subgroups of hand joints. The experimental results demonstrate that the proposed hybrid neural network achieves high recognition accuracies of 95.49% and 97.25% after evaluation on Briareo and Multimodal Hand Gesture datasets, respectively. This work contributes to the advancement of skeleton-based hand gesture recognition by effectively addressing temporal dependencies and efficiently capturing spatial interactions, leading to improved performance and potential applications in various fields, such as VR, AR, and human–robot interactions.The novelty of this research lies in its innovative hybrid neural network architecture that combines 3D-CNN and Transformer models. By fusing these two techniques, the system can leverage both spatial and temporal information contained in the embeddings, addressing the limitations of traditional methods. Previous sensor-based approaches often suffered from high computational requirements, time-consuming preprocessing, and restricted practicality due to the need for on-body devices. On the other hand, vision-based methods face challenges in efficiently modeling temporal dependencies. However, the proposed hybrid neural network overcomes these limitations.Hybrid Neural Network Design: The proposed approach presents a novel hybrid neural network for skeleton-based hand gesture recognition, efficiently combining a 3D-CNN to infer high-level semantic skeleton embeddings with a Transformer-based model that utilizes a self-attention mechanism to capture long-term dependencies of the previous sequence of skeleton embeddings. It should be noted that the skeleton embeddings contain partial information in the spatial and time domain; that is, an embedding includes information from a subset of skeleton nodes along a limited time span.Real-time Efficiency: A major breakthrough achieved in this research is the real-time capability of the system. Thanks to the carefully designed neural network architecture, the entire system can efficiently run on a Computer Processing Unit (CPU), eliminating the need for specialized and resource-intensive hardware. This achievement is a crucial step toward practical applications of hand gesture recognition in real-world scenarios.Competitive Performance: The proposed method demonstrates competitive performance on the main hand gesture recognition benchmarks. By effectively capturing local spatial relationships and long-term temporal dependencies, the hybrid neural network outperforms the state-of-the-art accuracy by 4.19% on the Briareo dataset.The main contributions can be summarized as follows:Existing skeleton-based action recognition works ,20,21,22Methods based on 1D Convolutional Neural Networks (CNNs). These methods take sequences of skeleton joint coordinates as input and perform 1D convolution along the temporal dimension. For instance, Devineau et al. [u et al.  proposedLong Short-Term Memory (LSTM). This is a type of Recurrent Neural Network (RNN) that is well suited for modeling temporal sequences of joint coordinates. However, LSTMs do not inherently handle spatial information or inter-joint coordinates. This limitation can be addressed by integrating CNNs before LSTMs or RNNs. Previous works often converted skeleton coordinates into pseudo-images and skeleton sequences into video streams to apply convolutional layers to skeleton sequences. However, such input conversions may not fully exploit the locality of convolutional layers [l layers , as skell layers ,28, the Narayan et al.  proposedMethods based on 3D Convolutional Neural Networks (3D-CNNs). These methods are particularly interesting for analyzing dynamic hand gestures, as they can capture both local spatial and temporal characteristics. Liu et al. [u et al.  proposedMohammed et al.  proposedTo the best of the authors’ knowledge, there are fewer studies exploring the full potential of 3D-CNNs for analyzing skeleton data compared to their more common utilization for RGB-image-based hand gesture recognition. For example, Chen et al.  designedGraph Convolutional Networks (GCNs). These have gained significant attention in recent years for recognizing skeleton-based hand gestures [gestures ,37,38,39In a study by Li et al. , they deTransformer-based methods. Transformer architectures excel at capturing long-term dependencies, in contrast to previous methods that mainly focused on short-term information. Therefore, they are particularly well suited for analyzing long skeleton sequences that represent hand gestures. Surprisingly, while there is a substantial body of research on Transformer-based architectures in skeleton-based action recognition [ognition ,42,43,44The main drawback of a pure Transformer architecture is the simplistic modeling of spatial data, specifically the joint relationships inside the skeleton. This is typically accomplished by using a fully connected layer to create an embedding that globally encodes all the joints in the skeleton . As a reDespite the advances made in previous studies, effectively capturing both spatial and temporal information in hand skeleton sequences for gesture recognition remains a challenging task. To address these specific issues, this work proposes a novel method, named 3D-Jointsformer, that combines the power of 3D-CNN and Transformers. The proposed method is specifically designed for skeleton-based gesture recognition, where input sequences consist of hand-skeleton data. By leveraging the strengths of both architectures, the proposed hybrid approach aims to effectively encode spatial and temporal information, achieving real-time hand gesture recognition without compromising accuracy.The proposed system, called 3D-Jointsformer, enables real-time recognition of hand gestures from color video sequences using a standard CPU, in contrast to other approaches that require specialized hardware like Graphical Processing Units (GPUs). The system consists of two modules see : Hand SkT is the length of the sequence of hand keypoints and V is the total number of hand joints, a gesture sample, The Hand Skeleton Detection module is based on  and inteThe hand gesture recognition module estimates the category of the performed gesture from a sequence of 3D skeletons through three different stages. First, the Local Spatio-Temporal Embedding Estimator computes local embeddings that encode subsets of skeleton joints within short time spans, resulting in high-level semantic representations of subparts of a hand skeleton sequence. These embeddings are obtained using a 3D-CNN neural network, which is a modified implementation of SlowFast Networks, initially conceived for Video Recognition . The oriRegarding the nature of the hand skeleton, four modifications were introduced to the original architecture. The first modification is to adopt only the Slow Pathway stream from the original architecture to fulfill the purpose of this local embedding module, which is to compute the Local Spatio-Temporal Embedding from subsets of joints. By using only the Slow Pathway, the overall computational complexity of the model is reduced, facilitating real-time operation. The second modification reduces the depth of the network by using fewer convolutional blocks, considering that the number of joints is much less than the number of pixels in an image. This helps avoid overfitting problems associated with excessive network parameters while reducing computational complexity. The third modification involves reducing the width of the network by using fewer channels for similar purposes. The fourth modification is to use a smaller kernel size, which reduces the receptive field, aiming to focus on capturing more local spatial information. C stands for channel, T stands for temporal, and V stands for spatial dimensions. The kernel size applied is Based on the optimal trade-off between computational efficiency and performance, the final setting of the proposed lightweight Local Spatio-Temporal Embedding Estimator is composed of four CNN blocks. The first block, also known as the stem layer, processes an input tensor of D represents the embedding dimension of the model.For all blocks, batch normalization (BN) and rectified linear unit (ReLU) layers are applied after each convolutional layer, helping to regularize the model. Moreover, residual connections are adopted after each block to alleviate the problem of vanishing gradients and stabilize the network training. Consequently, the output of the 3D-CNN backbone is a feature map of size S, which contains local spatio-temporal joint skeleton information. To achieve this, a Transformer neural network [N Transformer encoder blocks , Key (K), and Value (V), are computed using fully connected layers. Subsequently, the dot product, S, the input sequence. To ensure proper scaling, the dot product is normalized by a factor of Q or K). The resulting values are referred to as attention scores, and they contain information about the affinity of each spatio-temporal joint skeleton embedding with respect to the others in the sequence. These attention scores are further processed using the softmax function, which enables them to represent the importance of each embedding in relation to the others. Finally, the attention scores are used as weights to integrate the most relevant spatio-temporal contextual information into each local joint skeleton embedding. This process is illustrated in Equation  and Feed-Forward Neural Network (FFN) blocks (Equations  and 5)))5)). GivIn the final stage, the classifier head comprises a fully connected layer followed by a softmax layer, which calculates a probability distribution across all hand gesture classes. The predicted gesture is then determined to be the one with the highest probability.This section provides a comprehensive explanation of the experimental setup used to assess the performance of the proposed real-time hand gesture recognition system. It includes the evaluation metrics and provides details about the hardware and software configurations to ensure reproducibility and facilitate further research in the field.c, where g, and c is the threshold for the maximum norm, empirically set to 40. To avoid overfitting, we also applied the “drop attention” strategy introduced by Zehui et al. [The proposed system was implemented using the PyTorch deep learning framework. It was trained and evaluated on a server equipped with an Intel Core i7-4790 with a 3.30 GHz CPU and 32 GB of memory, along with an Nvidia Titan Xp (12 GB) GPU. The proposed model was trained with a batch size of 32 video samples for a total of 100 epochs. The Adam optimizer was chosen to minimize the cross-entropy loss due to its adaptive learning rate property. The initial learning rate was set to 1 i et al.  to regulith sample and ith sample and the jth highest probability score.Regarding metrics, prediction accuracy was used to evaluate the proposed model using the standard top-1 accuracy. Equation  shows thThe Briareo  and MultThe proposed system, 3D-Jointsformer, is compared with other state-of-the-art methods on the Briareo dataset using RGB data in The CNN-based architecture proposed by D’Eusanio  is basedResNet-18 + Transformer  is conceMultiscale 3D-CNN  proposesAdditionally, a fivefold cross-validation was conducted on the Briareo dataset to mitigate the effects of data randomness and ensure the consistency of the proposed 3D-Jointsformer’s performance. The average top-1 accuracy over the five folds of the dataset is 95.21 ± 0.89%, which verifies the model’s performance and robustness. The confusion matrix of 3D-Jointsformer shown in An ablation study is presented to measure the influence of the embedding strategy for the input skeleton sequences on the final system performance. The proposed 3D-CNN strategy is compared to a standard and widely used linear projection composed of two fully connected layers followed by a GELU non-linear layer, which extracts a D-dimensional skeleton feature vector per video frame used as tokens for the Transformer. Another ablation study was carried out to measure the impact of using a Transformer to capture long-term relationships by comparing the strategy adopted in a 3D-Jointsformer with a 3D-CNN network followed by Global Temporal Average Pooling plus a fully connected layer. Regarding computational cost, 3D-Jointsformer is very lightweight and efficient, with only 8.8 M parameters and 0.17 GFLOPs, which achieves a top-1 accuracy of 95.49%, as previously shown. Thus, it can operate in real time for hand gesture recognition applications. Moreover, it can operate not only in GPU real time but also in CPU. More specifically, the 3D-Jointsformer can process 2790 skeletons/s and 2569 skeletons/s for GPU and CPU, respectively. To ensure a consistent comparison, the metrics reported and the evaluation format are calculated, as described in the referenced paper . It is iThe recognition capabilities of the proposed method were also evaluated on the Multimodal Hand Gesture Dataset (MMHGD) . The datThe results, as reported in In conclusion, this work introduces a novel hybrid neural network for skeleton-based hand gesture recognition, effectively capturing both local spatio-temporal information and long-term dependencies. The primary objective was to develop a real-time model using only the CPU without sacrificing accuracy. The proposed architecture combines a Local Spatio-Temporal Embedding Estimator utilizing 3D-CNN to capture local patterns and a Long-Term Embedding Estimator based on a Transformer to model sequential dependencies. One of the model’s notable achievements lies in its real-time operation, which addresses the long-standing challenges of rapid data processing in many real-world scenarios. Through careful network design and computational efficiency optimization, the model achieves real-time performance without compromising accuracy. The experimental results on two publicly available datasets demonstrate the superiority of the proposed hybrid neural network over state-of-the-art methods, achieving accuracy rates of 95.49% on the Briareo dataset and 97.25% on the Multimodal Hand Gesture dataset. For future studies, real-world deployment will be addressed by conducting experiments and evaluations of the model’s performance in real-world settings. Feedback should be gathered to assess the model’s usability and user experience, ensuring it meets the needs of the intended applications. This work lays a strong foundation for advancing skeleton-based hand gesture recognition systems with broad implications in human–computer interactions, gaming, robotics, and assistive technologies.


The postacute sequelae of COVID-19 (PASC) is a syndrome characterized by persistent COVID-19 symptoms or the onset of new symptoms following recovery from the initial or acute phase of the illness. Such symptoms often occur 4 or more weeks after being diagnosed with COVID-19. Although a lot of work has gone into understanding the long-term mental health effects of PASC, many questions related to the etiology and risk of this condition remain.This protocol is for a systematic review assessing the association between PASC and adverse psychiatric outcomes and whether people with PASC are at greater risk of developing an adverse psychiatric outcome than those without PASC.Various medical literature databases  will be searched for eligible articles, using predefined search criteria. Gray literature will also be explored. Epidemiological observational studies and secondary analyses of randomized controlled trials that report a quantitative relationship between PASC and at least one adverse psychiatric outcome will be included. The Population, Exposure of interest, Comparator, and Outcome framework will be used as a standardized framework for the inclusion criteria. The Joanna Briggs Institute critical appraisal tools will be used to assess methodological quality and critically appraise the risk of bias in included studies. A random-effects meta-analysis will be conducted if possible. A formal narrative synthesis will be performed if a meta-analysis is impossible due to substantial heterogeneity across studies. The Grading of Recommendations Assessment, Development and Evaluation approach will be used to rate the cumulative certainty of the evidence for all outcomes. Ethical approval is not required. The study results will be published in a peer-reviewed journal.This study documents and addresses etiology, risk factors, and long-term symptoms of COVID-19 among people with PASC. It focuses on a key priority area for new evidence syntheses on the clinical management of COVID-19 and pandemic-related conditions. It will include evidence on nonhospitalized and hospitalized patients with a history of PASC.Substantial heterogeneity across studies may limit the ability to perform a meta-analysis. Findings will inform disease prevention, decision-making, health care policy, and clinical research (Reviewed by the Plan P #PeerRef Community).PROSPERO CRD42022308737; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=308737 COVID-19 is a contagious illness caused by SARS-CoV-2. Persistent and long-lasting (>4 weeks) symptoms following infection with acute COVID-19 have given rise to a syndrome known as post-acute sequelae of COVID-19 (PASC) or post–COVID-19 condition . IncidenSimilarly, clinical anxiety and depression, as well as other psychiatric sequelae, have been reported following diagnosis with COVID-19 in other studies -8. AlthoAmong people aged 35-49 years, the prevalence of PASC is 26.8% compared with 26.1% and 18% among people aged 50-69 years and 70 years or older, respectively . PersistIn a study where the short-term and long-term sequelae of COVID-19 were systematically evaluated, PASC was categorized as short-term (1 month), intermediate-term (2-5 months), and long-term (≥6 months) following COVID-19 diagnosis . ClinicaTo clarify whether COVID-19 is a risk factor for psychiatric disorders and vice versa, an electronic health record network cohort study of 69 million people consisting of 62,354 people with a COVID-19 diagnosis compared the rates of psychiatric sequelae of health in the initial 4 months of the pandemic (January to April 2020) and subsequently (after April 2020) . The stuP<.001) compared with a matched cohort of people with specific physical risk factors for COVID-19 without a psychiatric diagnosis [Likewise, a diagnosis of psychiatric disorder in the 12 months preceding the COVID-19 pandemic was associated with a 65% increased risk of COVID-19  compared with other health events. A prospective cohort study of patient-reported outcome measures 3 months after initial COVID-19 symptom onset noted impairment with self-care and anxiety or depression as being present in 13% and 22% of its 78 subjects, respectively, with at least 1 Charlson comorbidity at baseline compared to subjects without any Charlson comorbidities (4% and 9% respectively). Among subjects without any Charlson comorbidities, 70% reported an abnormal PROM, and 33% had at least 1 moderate issue in at least 1 EQ-5D assessment . In addiAlthough some studies indicate that most people who acquire COVID-19 are at risk of psychiatric sequelae and their symptoms tend to improve over time, others suggest that symptoms may worsen over time or point to a different disease trajectory ,30. ReseFew epidemiological studies have investigated the short- and long-term impact of COVID-19 and PASC on mental health. Thus, this study will examine the causes of adverse psychiatric outcomes and risk factors in people with PASC. Furthermore, prior studies on this and related topics report internal validity and generalizability  limitations due to evidence derived solely from electronic health records, single networks, or claims data. Because data on the psychiatric sequelae of PASC are conflicting and sparse, it is imperative to systematically summarize the evidence and combine the results of various scientific studies.This study aims to generate a new hypothesis on causality and provide a more precise estimate of the risk factors underlying PASC and adverse psychiatric outcomes. An initial search of peer-reviewed and gray literature found no systematic reviews and meta-analyses on the topic. This protocol is for a systematic review that assesses the literature on PASC duration and risk factors that act as determinants (etiologies) of adverse psychiatric outcomes.The primary objective of this systematic review is to determine whether people with PASC are at greater risk of developing an adverse psychiatric outcome  than those without PASC.Secondary review questions include the following:Does the association between PASC and an adverse psychiatric outcome vary with age, sex, severity of COVID-19 , and duration of PASC ?Is PASC an independent risk factor for an adverse psychiatric outcome?This protocol has been drafted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidance for protocols (PRISMA-P) . The sysThe review will include observational studies, namely, retrospective studies, and prospective longitudinal cohort studies, case-control studies, cross-sectional studies, case series, and case reports. Secondary analyses of randomized controlled trials will also be included. Effect measures of risk factors, including the incidence rate ratio, risk difference, relative risk, odds ratio (OR), and hazard ratio central to the primary outcome, will be included. Risk factors predispose people with PASC to an adverse psychiatric outcome ,33. SuchCOVID-19 diagnosis must have been confirmed on the basis of clinical suspicion or with a positive nucleic acid amplification test such as reverse transcriptase–polymerase chain reaction, an antigen test, or a serologic test  . StudiesStudies with adult participants (aged 18 years or older) will be included. Pediatric and animal studies will not be included. There will be no sex, ethnicity, or race limitations. The search dates will range from December 2019 (date of the first confirmed case of COVID-19) until March 2023 (the anticipated completion date of the review). COVID-19 filters will be used, if necessary, to limit search results to COVID-19– and PASC-related articles.PASC, for this review, is defined as a continuing symptomatic illness or the emergence of new symptomatic illness in people with a confirmed history of COVID-19 after recovery from the acute phase of the illness. PASC will be categorized as short-term (1 month), intermediate-term (2-5 months), and long-term (≥6 months) following a COVID-19 diagnosis or hospital discharge.The severity of COVID-19  will be considered.People with a confirmed history of COVID-19 without PASC will be considered.The severity of COVID-19  will be considered.Primary outcome variables will include adverse psychiatric outcomes such as depression, anxiety, substance use disorder, posttraumatic stress disorder, and psychosis.Secondary outcome variables will include nonsuicidal self-injury (self-harm) and suicide.Information, including titles and abstracts extracted from evidence sources, will be initially screened against the review questions. Information deemed eligible for inclusion will undergo more comprehensive screening. Once an article, study, or review is considered suitable for inclusion, it will be placed in the list of included studies. The steps above will be carried out for each information source, after which duplicates will be removed. The study selection process will be described in a PRISMA flow diagram and reported in the systematic review. Author AE will develop the search strategy in consultation with a medical research librarian. The following databases and evidence sources will be searched: PubMed, Ovid MEDLINE, Embase, JBI EBP Database, CINAHL Plus, UpToDate, APA PsycInfo, Google Scholar, ProQuest Dissertations & Theses Global, Scopus, Web of Science, the University of Toronto COVID-19 Data & Statistical Sources, Centre for Addiction and Mental Health (CAMH) COVID-19 National Survey Dashboard reports, and COVID-END. Gray literature will also be considered where appropriate. Search strategies will be comprehensive and adapted for each information source. See Appendix 1 for a sample of the Embase search strategy.The Covidence or JBI SUMARI software will be used during the systematic review process for screening, appraisal of evidence sources, data extraction, synthesis, and study completion.Ethical approval is not required for this study. The study findings will be disseminated via preprints, peer-reviewed publications, conference abstracts, posters, plain-language summaries, presentations, and infographics.Input on the review questions and outcomes was informally sought from patients and people who had been previously diagnosed with COVID-19 and PASC.Information, including titles and abstracts, extracted from information sources will be initially screened by AE and a second reviewer against the research questions. Information deemed eligible for inclusion will undergo more comprehensive screening. Once an article, study, or review is considered suitable for inclusion, it will be placed in the list of included studies. The steps above will be carried out for each information source, after which, duplicates will be removed. Disagreements on inclusion will be resolved through discussion or arbitration. The study selection process will be described in a PRISMA flow diagram and reported in the systematic review.P values, associated 95% credibility intervals, and associated 95% CIs. RRs for subgroups  will be extracted if reported. The following data will also be extracted: authorship, publication year, journal name, study design, study location, sample size, baseline characteristics of the study participants, demographics , study population characteristics , the definition of PASC, duration of PASC, comorbidities, other risk factors, duration of follow-up, list of adjusted and unadjusted colliders  and a list of adjusted and unadjusted confounders , and propensity methods.Data will be extracted on primary and secondary outcome measures following the PRISMA guideline for systematic reviews . OutcomeTwo reviewers will conduct the data extraction. Discrepancies in data extraction will be resolved through discussion or arbitration.The JBI critical appraisal checklist will be used to determine the methodological quality and to critically appraise the risk of bias for included studies. Assessment will be done at the study and outcome level. Information related to a variable , misclassification, confounders, participant selection, reverse causation, missing data, study power, and generalizability will be appraised. Two reviewers will initially pilot the checklist to enhance consistency, mitigate potential issues with mechanistic scoring, and mitigate performance bias in the overall risk-of-bias assessment. Studies that do not adequately report on statistical analyses or address confounders, biases  and other biases will be deemed lower-quality studies—that is, when they consistently have “no,” “unclear,” and “not applicable” ratings’ across relevant items.I2 and Cochran Q statistics. A Cochran Q test based on a chi-square statistic with a P value of <.05, and greater than the df, will indicate heterogeneity. The I2 statistic will be interpreted as follows: 0%-40%, minimal heterogeneity; 30%-60%, moderate heterogeneity; 50%-90%, substantial heterogeneity; and 75%-100%, considerable heterogeneity. If there is substantial heterogeneity, subgroup analysis (based on the duration of PASC or COVID-19 severity) will be conducted. Subgroup effect sizes (Cohen d or Hedges g) and correlations will be assessed and compared with unadjusted values to interpret for meaningful effects. Observed effects will be considered robust if the effect estimates of the primary outcome remain consistent or there are no large differences in the magnitude of effect across subgroups. Subgroup analyses will not be performed if there is minimal or moderate heterogeneity. A formal narrative synthesis will be performed if meta-analysis is not possible. The reasons for not pooling data  will be reported in the review.Summary treatment effects estimated as continuous outcomes will be converted to ORs, RRs, incidence rate ratios, risk differences, and number needed to treat with a 95% CI or 95% credibility interval  for easier interpretation where possible. A random-effects meta-analysis will be conducted if possible. Statistical heterogeneity across studies will be explored using the Higgins A methodological quality-based sensitivity analysis presented as a summary table will be used to assess the robustness of the findings. Authors of included studies with missing information will be contacted for clarification. The Grading of Recommendations, Assessment, Development, and Evaluations approach will be used to rate the overall certainty of the evidence obtained from the study.


Left ventricle remodeling (LVR) after acute myocardial infarction (MI) leads to impairment of both systolic and diastolic function, a significant contributor to heart failure (HF). Despite extensive research in the field, predicting post-MI LVR and HF is still a challenge. Several circulant microRNAs have been proposed as LVR predictors; however, their clinical value is controversial. Here, we used real-time quantitative PCR to quantify the plasma levels of hsa-miR-101, hsa-miR-150, and hsa-miR-21 on the first day of hospital admission of MI patients with ST-elevation (STEMI). We analyzed their correlation to the patient’s clinical and paraclinical variables and evaluated their ability to discriminate between post-MI LVR and non-LVR. We show that, despite being excellent MI discriminators, none of these microRNAs can distinguish between LVR and non-LVR patients. Furthermore, we found that diabetes mellitus (DM), Hb level, and the number of erythrocytes significantly influence all three plasma microRNA levels. This suggests that plasma microRNAs’ diagnostic and prognostic value in STEMI patients should be reevaluated and interpreted in the context of associated pathologies. Coronary artery disease (CAD) is the leading cause of mortality and morbidity worldwide. The most common type of CAD is myocardial infarction (MI) . AlthougMicroRNAs (miRNAs) are short (21–24 nucleotides) RNA molecules that act as post-transcriptional regulators of gene expression in all aspects of cardiac pathogenesis, including LVR through modulation of both pro- and anti-fibrotic processes ,14. miRNSeveral circulant miRNAs have been proposed as post-MI LVR predictors. miR-101 protects against myocardial remodeling, and miR-150 attenuates cardiac remodeling and predicts post-MI HF ,17. miR-The present study uses real-time quantitative PCR to investigate these circulant miRNAs in the clinical context of MI. It aims to characterize their association with the clinical and paraclinical variables, focusing on the ability of these miRNAs to predict post-MI ventricular remodeling. We show that none of these plasma microRNAs (as assessed on the day of admission) may serve as LVR predictors or correlate with biomarkers known for their variation after MI .This study has been conducted in accordance with the Declaration of the Helsinki Code of Ethics; the local hospital ethics review board has reviewed and approved the entire protocol (3822/31 May 2016). We enrolled (November 2016–October 2018) 105 patients diagnosed with first ST-elevation acute myocardial infarction (STEMI) at The Institute for Cardiovascular Disease in Timisoara, Romania. Each patient provided a signed, written consent. The protocol involved blood sampling and echocardiographic evaluations upon hospital admission. The echocardiographic studies were performed on a Philips machine in the intensive coronary care unit. We performed standard echo assessment from all commonly available views . We assessed wall thickness in diastole, end-diastolic diameter of LV and RV, aortic annulus, left atrium, ascending aorta, systolic and diastolic function of LV  and RV, valvular stenosis or regurgitations, complications of MI , and presence of liquid. The Simpson method is the gold standard technique to evaluate ventricular volumes and ejection fraction and relies on tracing the endocardial border of the LV cavity in systole and diastole. Ejection fraction is calculated with the formula EF(%) = [(EDV − ESV) × 100]/EDV . Only 43-STEMI diagnostic based on the Third Universal Definition of Myocardial Infarction guidelines issued by the European Society of Cardiology (ESC) in 2012 ;-Hospital admission within 12 h from the onset of MI symptoms;-Pre-hospital care according to the ESC guidelines for STEMI ;-Age over 18.The inclusion criteria are:-History of CAD;-Cardiac arrest resuscitated before hospital admission;-Valvular disease (moderate/severe stenosis or regurgitation);-Associated diagnostics of cancer, acute infectious diseases, and liver dysfunction;-Inability to provide signed, written consent.The exclusion criteria are:All patients underwent primary percutaneous coronary intervention (PCI), routine early PCI, or rescue PCI after fibrinolysis, according to the ESC guidelines.Since the measurement of left ventricular longitudinal strain is not yet accessible in our emergency room (ER) unit, LVR was diagnosed when a minimum of a 10% increase from the baseline end-diastolic volume (ΔEDV) was documented at the follow-up evaluation. The follow-up lot was stratified into the LVR group (the patients who developed LV dysfunction and HF symptoms (NYHA class 2 or higher), LVEF < 50%; ΔEDV ≥ 10%, n = 14) and the non-LVR group (the patients who did not develop LV dysfunction and HF symptoms (NYHA class I), LVEF ≥ 50%; ΔEDV < 10%; n = 29).The control group consists of 17 patients without any medical history or clinical signs of MI, for which all the exclusion criteria apply.The blood samples (3 mL) were collected in EDTA-coated tubes and processed within 10 min for plasma separation (10 min centrifugation at 1500 rpm and room temperature). The plasma was collected and stored at −80 °C until further use. Plasma samples showing signs of hemolysis, hyperlipemia, and icterus were discarded.Total RNA was purified from 200 µL of plasma using a miRNeasy Serum/Plasma Kit , according to manufacturer instructions. RNA quality and quantity (A260/A280 and A230/260 ratios) were evaluated spectrophotometrically using a Nanodrop 2000 instrument.Caenorhabditis elegans cel-miR-39. We used the TaqMan® MicroRNA Reverse Transcription Kit  for reverse transcription starting from the equal (10 ng) total RNA input. All qRT-PCR reactions were performed in duplicate, using inventoried TaqMan™ MicroRNA Assays . Fold changes were calculated using the ΔΔCT method of relative quantification with C. elegans miR-39 as a normalizer [All RNA samples were spiked in with rmalizer .t-test  and Mann–Whitney U test  were used to assess the differences between continuous variables. We used the Z-test to compare the binary variables datasets. All correlation analyses were performed using the Spearman test; all ROC/AUC analyses were performed using the Wilson/Brown method for 95% CI calculation. For all tests, the threshold of statistical significance is 0.05. All statistical tests are two-tailed.All statistical analyses were performed on Prism 9 for MacOS, Version 9.3.1. We used basic descriptive statistics to describe the demographic, clinical, functional, and laboratory data of the patients. Data distribution was checked using the Kolmogorov–Smirnov test; the heteroscedastic Student’s Only 43 patients had an echocardiographic evaluation one year after the STEMI and were, thus, included in the follow-up group. Compared to the control group, the plasma levels of all three miRNAs  were significantly increased in patients with STEMI. The receiver operating characteristic (ROC) curve analysis showed areas under the ROC curve (AUC) ranging from 0.78 (miR-150) to 0.885 (miR-21), confirming that these miRNAs could be used as diagnostic biomarkers for STEMI . Of notep = 0.006), creatinine level , lymphocytes , and erythrocyte sedimentation rate . Of note, there is a strong correlation between all three plasma miRNAs, presumably reflecting a common tissue source and/or a common physio-pathological regulatory mechanism.We have systematically evaluated the correlation of the three miRNAs with the clinical and paraclinical parameters accessible through the electronic hospital archive . None ofp-value = 0.014), miR-101 , miR-21  in the Hb 13–15 g/dL group, and only for miR-150  in the Hb > 15 g/dL group. Furthermore, in the Hb 13–15 g/dL group, miR-101  and miR-21  correlate to the time elapsed since the onset of symptoms. The correlations between all three plasma miRNA levels are preserved except for miR-150 and miR-101 in the Hb > 15 g/dL group. None of the STEMI biomarkers  correlate with any of the three miRNAs in any of the three Hb subgroups.Given the well-known impact of Hb on the clinical outcome of MI, we further stratified our cohort according to Hb level: Hb < 13 g/dL, between 13 and 15 g/dL, and over 15 g/dL . All miRThe expression levels of plasma miRNAs are not significantly different in the LVR vs. non-LVR groups, and their AUCs are modest and statistically insignificant . FurtherThe correlation to DM is lost for all miRNAs; none of the miRNAs in either group are associated with Hb levels, erythrocyte numbers, or hematocrit levels.= 0.587; p = 0.034); the time elapsed since the onset of symptoms is negatively correlated to miR-101  and miR-21 .There is a strong correlation between all three plasma miRNAs in the non-LVR group, while in the LVR group, miR-150 becomes disconnected from the other two miRNAs. In the LVR group, all correlations to DM, Hb level, number of erythrocytes, and hematocrit are lost; smoking is correlated to miR-21 (r p = 0.048) and in-hospital death ratios (p = 0.0015) in favor of diabetic patients have been noticed.In our study, demographics, cardiovascular risk factors, and type of myocardial infarction are not significantly different between diabetic and non-diabetic patients . NeverthAll three plasma miRNA levels are significantly downregulated in diabetic vs. non-diabetic MI patients; their ability to discern between diabetic and non-diabetic MI patients is rather modest . Of notep = 0.004) and hematocrit , while miR-150 is positively correlated to obesity . In the diabetic subgroup, except for miR-21, the other two miRNAs have preserved their negative association with Hb levels.The miRNAs’ correlations to erythrocytes, Hb levels, and hematocrit are partially preserved in diabetic and non-diabetic subgroups. In the non-diabetic cohort, miR-101 is negatively correlated to erythrocytes , the timing of blood sampling, and the analytical platforms used. Here, we analyzed the early  plasma expression level of three miRNAs , presumed LVR prognostic biomarkers, with a special focus on diabetes and hemoglobin levels.All three miRNAs negatively correlate to Hb levels, the number of erythrocytes, and the hematocrit value. Of note, to our knowledge, none of the three miRNAs are expressed in erythrocytes; hence, none are expected to increase upon hemolysis.2 to miRNA levels in these patients, one can only speculate that this might be due to better tissue  oxygenation. The relationship of these miRNAs to Hb and the number of erythrocytes and the hematocrit is interesting since both animal and human studies have shown that plasma Hb level impacts the clinical outcome of MI, and anemia is a risk factor in post-MI evolution [Interestingly, the MI discriminative power of all miRNAs is strongly impacted by the Hb level: the lower the Hb, the more modest and less statistically significant the AUC. In the absence of data relating PaOvolution ,32,33,34miR-21 is hypoxia-responsive and shows significant cardioprotective effects against hypoxia lesions upon in vivo and ex vivo overexpression ,36. A siPlasma miR-150 (but not miR-21) was found to be significantly upregulated in aplastic anemia patients; this is in line with our data showing a negative correlation with the level of Hb and the number of erythrocytes. Of note, miR-150 was proposed as a clinical evolution biomarker in aplastic anemia, being (together with miR-1) an excellent responder to immunosuppressive therapy . The negDM strongly influences LV dynamics  in diabetic patients with HF and MI, with a significant impact on life quality and cardiovascular outcome ,45. EarlmiR-21 has long been associated with DM and its complications  . miR-21 Circulant miR-101 level is altered in diabetic patients and has been proposed as a diagnostic biomarker for type I and type II DM ,51. TogeThe relationship of miR-150 to DM has been more thoroughly investigated. Circulant miR-150 is significantly decreased in both type I and II DM patients, being associated with islet autoimmunity and (together with miR-21) diabetic retinopathy ,53,54. TLVR refers to changes in ventricular size, shape, and function. It is a consequence of fibrosis, cardiomyocyte hypertrophy, and apoptosis. Fibrosis is the main component of post-MI remodeling and represents an unrestrained accumulation of extracellular matrix (ECM) components produced mainly by fibroblasts . This leWe showed that none of the investigated miRNAs are differentially expressed and cannot differentiate between LVR and non-LVR patients. Interestingly, all plasma miRNA levels correlate to EF changes in non-LVR patients but not in LVR ones, while the changes in EDV are not correlated to any of the miRNAs. Of note, all three miRNAs are excellent MI discriminators, the performances of which are influenced by DM status and the level of plasma Hb.miR-21 promotes cardiac fibrosis, hypertrophy, and angiogenesis and reduces apoptosis and inflammation ,65,66,67miR-150 protects against MI-induced fibrosis, and its prognostic use has been evaluated in four human trials . SimilarWhile many reports support the role of miR-101 as protective against adverse cardiac remodeling, only one human trial confirmed its prognostic value ,75,76,77Many factors could account for the lack of concordance regarding the role of these miRNAs as LVR predictors, starting with the cohorts’ sample size, time of blood collection, detection and data normalization methods, and LVR definition. Our data indicate that patients’ comorbidities like DM and paraclinical parameters like Hb levels and erythrocyte numbers are potential confounders and could significantly influence the expression levels of all three miRNAs and their statistical association with ventricular remodeling. In this respect, a major limitation of our study  is the lack of an in-depth analysis of the impact of medication (like statins and antiplatelet drugs) due to the rather low adherence of our patients to the physicians’ indications ,79.Plasma miR-21, miR-101, and miR-150 fail as early (day of admission) biomarkers for LVR, despite being excellent discriminators of MI. Our analysis shows that the plasma levels of these miRNAs are strongly influenced by the patient’s diabetic status and hemoglobin levels. Future studies on much larger cohorts are needed to globally and comprehensively evaluate the impact of clinical and paraclinical confounding factors on the statistical association of miRNA to MI in general and to ventricular remodeling phenomena in particular.